U.S. patent application number 13/478662 was filed with the patent office on 2012-11-15 for methods and products related to the improved analysis of carbohydrates.
This patent application is currently assigned to MOMENTA PHARMACEUTICALS, INC.. Invention is credited to Carlos Bosques, Pankaj Gandhe, Nishla Keiser, Sasi Raguram, Rahul Raman, Ram Sasisekharan, Aravind Srinivasan, Karthik Viswanathan.
Application Number | 20120289415 13/478662 |
Document ID | / |
Family ID | 37943379 |
Filed Date | 2012-11-15 |
United States Patent
Application |
20120289415 |
Kind Code |
A1 |
Bosques; Carlos ; et
al. |
November 15, 2012 |
METHODS AND PRODUCTS RELATED TO THE IMPROVED ANALYSIS OF
CARBOHYDRATES
Abstract
The invention relates, in part, to the improved analysis of
carbohydrates. In particular, the invention relates to the analysis
of carbohydrates, such as N-glycans and O-glycans found on proteins
and saccharides attached to lipids. Improved methods, therefore,
for the study of glycosylation patterns on cells, tissue and body
fluids are also provided. Information from the analysis of glycans,
such as the glycosylation patterns on cells, tissues and in body
fluids, can be used in diagnostic and treatment methods as well as
for facilitating the study of the effects of glycosylation/altered
glycosylation. Such methods are also provided. Methods are further
provided to assess production processes, to assess the purity of
samples containing glycoconjugates, and to select glycoconjugates
with the desired glycosylation.
Inventors: |
Bosques; Carlos; (Cambridge,
MA) ; Keiser; Nishla; (Cambridge, MA) ;
Srinivasan; Aravind; (Cambridge, MA) ; Raman;
Rahul; (Waltham, MA) ; Viswanathan; Karthik;
(Arlington, MA) ; Sasisekharan; Ram; (Bedford,
MA) ; Gandhe; Pankaj; (Sayreville, NJ) ;
Raguram; Sasi; (Hillsborough, NJ) |
Assignee: |
MOMENTA PHARMACEUTICALS,
INC.
Cambridge
MA
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
Cambridge
MA
|
Family ID: |
37943379 |
Appl. No.: |
13/478662 |
Filed: |
May 23, 2012 |
Related U.S. Patent Documents
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Application
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Filing Date |
Patent Number |
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12623070 |
Nov 20, 2009 |
8209132 |
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13478662 |
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11244826 |
Oct 6, 2005 |
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12623070 |
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11107982 |
Apr 15, 2005 |
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11244826 |
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60562874 |
Apr 15, 2004 |
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Current U.S.
Class: |
506/6 |
Current CPC
Class: |
G01N 33/68 20130101;
G01N 33/6851 20130101; G01N 33/6842 20130101; G01N 2400/10
20130101; G01N 33/6848 20130101; G16C 20/20 20190201; G01N 33/66
20130101 |
Class at
Publication: |
506/6 |
International
Class: |
C40B 20/08 20060101
C40B020/08 |
Goverment Interests
GOVERNMENT SUPPORT
[0002] Aspects of the invention may have been made using funding
from National Institutes of Health Grant number GM 57073.
Accordingly, the Government may have rights in the invention.
Claims
1-328. (canceled)
329. A method comprising steps of: a) providing a sample comprising
a plurality of different glycans, such that the sample has a
glycome which is all of the carbohydrates of the sample, which
sample has a characteristic selected from the group consisting of
function, cellular state, pathological condition, membership in a
population, and combinations thereof; b) analyzing the sample such
that its glycome pattern is determined that is associated with the
certain function, which analyzing comprises both: i) performing one
or more analytical analyses of the sample, wherein the analytical
comprise one or more of mass spectrometry, nuclear magnetic
resonance, electrophoresis, chromatography, and combinations
thereof; and ii) performing one or more computational analyses of
the sample, wherein the computational analyses include one or more
of establishing and solving a mathematical relationship.
330. The method of claim 329, wherein the step of providing
comprises providing a sample obtained through one or more
separating steps performed on a culture of a mammalian cell that
expresses a glycoprotein of interest.
331. The method of claim 329, wherein the at least some glycans of
the plurality of glycans are part of a glycoconjugate.
332. The method of claim 329, wherein at least some glycans of the
plurality of glycans are in free form.
333. The method of claim 329, further comprising steps of:
comparing the determined glycome pattern with a reference glycome
pattern; based on the comparing, concluding that the sample shares
a characteristic with the reference glycome pattern; and in light
of the conclusion, selecting a specific therapy, selecting a
specific production method, or selecting production conditions that
control generation of a specific glycoform.
334. The method of claim 329, wherein the glycans in the sample are
quantified.
335. The method of claim 329, wherein the sample comprising a
plurality of different glycans is a sample of a cell, group of
cells, tissue or body fluid.
336. The method of claim 335, wherein the body fluid is selected
from the group consisting of serum, plasma, blood, urine, saliva,
sputum, tears, CSF, seminal fluid, and feces.
337. The method of claim 329, wherein the sample comprising a
plurality of different glycans is a sample of a glycoprotein
therapeutic.
338. The method of claim 329, wherein performing one or more
analytical analyses of the sample includes obtaining more than one
glycoprofile spectra.
339. The method of claim 338, wherein at least one of the spectra
is of acidic glycans.
340. The method of claim 338, wherein at least one of the spectra
is of neutral glycans.
341. The method of claim 338, wherein at least one of the spectra
is of acidic glycans and at least one of the spectra is from
neutral glycans.
342. The method of claim 329, wherein the analyzing the sample
includes determining one or more features selected from the group
consisting of the presence or absence of a specific glycan, an
amount of a specific glycan; a ratio between two or more glycans; a
range of amounts of one or more glycans; and a range of ratios
between two or more glycans.
343. A method comprising steps of: providing a sample comprising a
plurality of glycoconjugates; determining one or more glycosylation
sites of the glycoconjugates and identifying glycans that may be
present at the one or more glycosylation sites; and characterizing
the glycans at each glycosylation site, thereby characterizing the
glycosylation site occupancy of the one or more
glycoconjugates.
344. The method of claim 343, wherein the identifying glycans that
may be present at the one or more glycosylation sites includes
comparing unlabeled glycoconjugates to labeled glycoconjugates.
345. The method of claim 343, wherein the identifying glycans that
may be present at the one or more glycosylation sites includes
comparing unlabeled deglycosylated fragments of the glycoconjugates
to labeled deglycosylated fragments of the glycoconjugates.
346. A method comprising steps of: a) providing a sample comprising
a plurality of different glycans, such that the sample has a
glycome which is all of the carbohydrates of the sample; b)
analyzing the sample such that the glycome is determined, which
analyzing comprises both: i) performing one or more analytical
analyses of the sample, wherein the analytical comprise one or more
of mass spectrometry, nuclear magnetic resonance, electrophoresis,
chromatography, and combinations thereof; and ii) performing one or
more computational analyses of the sample, wherein the
computational analyses include one or more of establishing and
solving a mathematical relationship.
347. The method of claim 346, further comprising steps of:
comparing the determined glycome with a glycome profile that has
been associated with a certain function, cellular state,
pathological condition, sample identity or population membership;
based on the comparing, concluding that the sample is obtained from
a source characterized by the certain function, cellular state,
pathological condition, sample identity or population membership;
and in light of the conclusion, selecting a specific therapy,
selecting a specific production method, or selecting production
conditions that control generation of a specific glycoform.
Description
RELATED APPLICATIONS
[0001] This application is a continuation of U.S. patent
application Ser. No. 12/623,070, filed Nov. 20, 2009, which is a
continuation of U.S. patent application Ser. No. 11/244,826, filed
Oct. 6, 2005, which is a continuation-in-part of U.S. patent
application Ser. No. 11/107,982, filed Apr. 15, 2005, which claims
the benefit under 35 U.S.C. .sctn.119 from U.S. provisional
application Ser. No. 60/562,874, filed Apr. 15, 2004, the entire
contents of each of which are herein incorporated by reference.
FIELD OF THE INVENTION
[0003] The invention relates to the improved analysis of
carbohydrates. In particular, the invention relates to the analysis
of carbohydrates, such as N-glycans and O-glycans found on proteins
and saccharides attached to lipids. The invention also relates to
the analysis of glycoconjugates, such as glycoproteins, glycolipids
and proteoglycans. Methods for the study of glycosylation patterns
on cells, tissues and in body fluids, such as serum, are also
provided. Information regarding the glycosylation patterns on
cells, tissues and in body fluids can be used in diagnostic and
treatment methods as well as for facilitating the study of the
effects of glycosylation/altered glycosylation on diseases, protein
or lipid function as well as on the function of medical treatments.
Information regarding the glycosylation of glycoconjugates can also
be used in the quality control analysis of the production of
glycoconjugates and therapeutics.
SEQUENCE LISTING
[0004] In accordance with 37 CFR 1.52(e)(5), a Sequence Listing in
the form of a text file (entitled "Sequence Listing.txt," created
on May 21, 2012, and 4 kilobytes in size) is incorporated herein by
reference in its entirety.
BACKGROUND OF THE INVENTION
[0005] Protein glycosylation, the attachment of carbohydrates to
proteins, is one of the most common modifications found in
eukaryotics. Glycosylation falls into three categories: N-linked
modification of the asparagine (Asn) side chain, O-linked
modification of serine (Ser) or threonine (Thr) and the
modification of the protein C-carboxyl terminus by
glycosylphosphatidyl inositol (GPI) derivatization. O-linked
glycosylation and GPI anchor derivatization are post-translational
modifications that take place in the Golgi. On the other hand,
N-linked glycosylation is a co-translational modification. As
proteins are synthesized, the polypeptide enters the endoplasmic
reticulum, where oligosaccharyl transferase (OT) attaches a
branched carbohydrate (N-glycan) to the side chain of certain
asparagine residues (Hirschberg, C. B., Snider, M. D. 1987. Annu
Rev Biochem. 56, 63-87.) This process requires an Asn-X-Ser/Thr
consensus sequence in the peptide substrate, where X is any amino
acid except proline (Bause, E. 1983. Biochem J. 209, 331-6;
Marshall, R. D. 1972. Annu Rev Biochem. 41, 673-702.) The attached
glycans, are subsequently modified by a complex array of
glycosidases and glycosyl transferases in the endoplasmic reticulum
(ER) and Golgi apparatus. The attached glycans play an important
role in protein folding, as well as directing the protein to the
appropriate location within the cell (Dwek, R. A. 1996. Chem. Rev.
96, 683-720; O'Connor, S. E., Imperiali, B. 1996. Chem. Biol. 3,
803-12.) Outside the cell, the sugars aid in protein-protein
interactions, often modulating the activity of the protein to which
they are attached. Depending on the glycan composition, they can
also protect against or facilitate protein degradation in
circulation, as well as target the protein to a specific organ
(Crocker, P. R., Varki, A. 2001. Immunology. 103, 137-45; Helenius,
A., Aebi, M. 2001. Science. 291; 2364-9; Imperiali, B., O'Connor,
S. E. 1999. Curr Opin Chem. Biol. 3, 643-9.)
[0006] Glycans also have an important role in normal biology, as
evidenced by the high lethality in cases of defective
glycosylation. In mouse knockout models, disrupting even one of the
biosynthetic enzymes can lead to enormous multisystemic disorders,
and several result in embryonic lethality (Furukawa, K., et al.
2001. Biochim Biophys Acta. 1525, 1-12.) There are currently six
recognized human congenital disorders of glycosylation (CDGs), all
resulting in patients with multiple organ abnormalities,
developmental delay and immune problems, among others (Jaeken, J.,
Matthijs, G. 2001. Annu Rev Genomics Hum Genet. 2, 129-51; Freeze,
H. H., Aebi, M. 1999. Biochim Biophys Acta. 1455, 167-78; Carchon,
H., et al. 1999. Biochim Biophys Acta. 1455, 155-65.) In fact, the
immune system is one of the most commonly studied systems where
glycans, such as N-glycans, have been shown to play an important
physiological role. For example, specific carbohydrate structures
are recognized by selectins, a family of proteins expressed on
endothelial cells or lymphocytes that can trigger the immune system
upon activation (Powell, L. D., et al. J Biol. Chem. 268, 7019-27;
Sgroi, D., et al. 1993. J Biol. Chem. 268, 7011-8.) The same class
of structures that are necessary for proper immune function can
also provide a binding site for certain viruses, bacteria or tumor
cells in the body (Karlsson, K. A. 1998. Mol. Microbiol. 29, 1-11;
Pritchett, T. J., et al. 1987. Virology. 160, 502-6.)
[0007] Viral infection is mediated by the interaction of viral
proteins with glycans on the cell surfaces of the host (Van Eijk,
M., et al. 2003. Am J Respir Cell Mol. Biol. 6, 871-9.) Despite the
increasing evidence associating glycans to different pathogenic
conditions, in multiple instances it is unclear whether changes in
glycan structure are a cause or a symptom of the disorder. In
cystic fibrosis, increased antennary fucosylation (.alpha.1-3
linked to GlcNAc) is observed on surface membrane glycoproteins of
airway epithelial cells (Glick, M. C., et al. 2001. Biochimie. 83,
743-7; Scanlin, T. F., Glick, M. C. 2000. Glycoconj. J 17,
617-26.)
[0008] There have also been many reports of alterations in glycan
composition on cancer cell proteins. For example, there are
indications that prostate cancer cells produce prostate specific
antigen (PSA) with more glycan branching than non-cancer cells
(Peracaula, R., et al. 2003. Glycobiology. 13, 457-70; Belanger,
A., et al. 1995. Prostate. 27, 187-97; Prakash, S., Robbins, P. W.
2000. Glycobiology. 10, 173-6.) Melanoma and bladder cancer cells
produce proteins with highly branched glycans due to an
overexpression of the biosynthetic enzyme
.beta.1,6-N-acetyl-glucosaminyltransferase V (GnT-V) (Chakraborty,
A. K., et al. 2001. Cell Growth Differ. 12, 623-30; Przybylo, M.,
et al. 2002. Cancer Cell Int. 2, 6.) Increased sialylation and
additional branching have also been observed in cells from human
breast and colon neoplasia (Lin, S., et al. 2002. Exp Cell Res.
276, 101-10; Nemoto-Sasaki, Y., et al. 2001. Glycoconj J. 18,
895-906; Dennis, J. W., et al. 1999. Biochim Biophys Acta. 1473,
21-34; Fernandes, B., et al. 1991. Cancer Res. 51, 718-23.)
SUMMARY OF THE INVENTION
[0009] This invention provides, in part, methods related to the
analysis of carbohydrates. In particular, the invention relates to
the analysis of carbohydrates, such as N-glycans and O-glycans
found on proteins and saccharides attached to lipids. The invention
also relates to the analysis of glycoconjugates, such as
glycoproteins, glycolipids and proteoglycans.
[0010] In one aspect of the invention, therefore, a method of
analyzing a sample containing one or more glycoconjugates, which
comprise one or more carbohydrates (e.g., glycans) conjugated to a
non-saccharide component is provided. The method, in one
embodiment, includes the steps of analyzing the glycoconjugates to
characterize the glycoconjugates, analyzing the non-saccharide
components of the glycoconjugates to characterize the
non-saccharide components, separating the carbohydrates (e.g.,
glycans) from the sample containing one or more glycoconjugates,
analyzing the carbohydrates (e.g., glycans) to characterize the
carbohydrates (e.g., glycans), and determining the identity and
quantity of all of the glycoforms of the glycoconjugates in the
sample with the results obtained from one or more of the analysis
steps and a computational method. In another embodiment the
computational method comprises generating constraints from the
results obtained from one or more of the analysis steps and solving
them.
[0011] In one embodiment the methods provided can include
determining the glycosylation sites and glycosylation site
occupancy of glycoconjugates. In another embodiment the
determination of the glycosylation sites and glycosylation site
occupancy includes the steps of cleaving the non-saccharide
components of the glycoconjugates, cleaving the carbohydrates
(e.g., glycans) from the non-saccharide components and labeling the
non-saccharide components of a first portion of the sample at the
glycosylation sites, cleaving the carbohydrates (e.g., glycans)
from the non-saccharide components of a second portion of the
sample, analyzing the first and second portions of the sample
containing the non-saccharide components, and comparing the
results.
[0012] In another embodiment the methods are also directed to
matching one or more carbohydrates (e.g., glycans) (e.g., of a
glycome) to a glycoconjugate. The method includes, in some
embodiments, determining the glycosylation sites and glycosylation
site occupancy of one or more glycoconjugates and determining the
possible carbohydrates (e.g., glycans) at each site. The method can
also include characterizing the glycome (i.e., characterizing the
carbohydrates (e.g., glycans), glycoconjugates and/or components
thereof). The method can also include the use of a computational
method to match the carbohydrates (e.g., glycans) to the
glycoconjugates. In one embodiment determining all possible
carbohydrates (e.g., glycans) at each site includes comparing
unlabeled to labeled glycoconjugates and unlabeled to labeled
deglyosylated fragments of the glycoconjugates. In another
embodiment the computational method includes generating constraints
from the results of the characterization of the glycome and/or
other information (i.e., one or more sets of data).
[0013] In one embodiment non-saccharide components of a first
portion of a sample are labeled with a labeling agent. In another
embodiment the labeling agent is an isotope of C, N, H, S or O. In
still another embodiment the labeling agent is .sup.18O. In yet
another embodiment the labeling agent is .sup.2H. In still another
embodiment non-saccharide components of a second portion of a
sample are unlabeled. In a further embodiment non-saccharide
components of a second portion of a sample are labeled.
[0014] In one embodiment glycosylation site occupancy is quantified
from ratios of the masses of the non-saccharide components of a
first and second portion of a sample. In another embodiment a first
and second portion of a sample are analyzed with a mass
spectrometric method. In a further embodiment a first and second
portion of a sample are analyzed separately. In yet another
embodiment a first and second portion of a sample are analyzed as a
mixture.
[0015] In another aspect of the invention a method of analyzing a
sample containing one or more glycoconjugates, which comprise one
or more carbohydrates (e.g., glycans) conjugated to a
non-saccharide component, which includes analyzing the
glycoconjugates to determine the glycosylation sites and
glycosylation site occupancy, separating the carbohydrates (e.g.,
glycans) from the sample containing one or more glycoconjugates,
analyzing the carbohydrates (e.g., glycans) to characterize the
carbohydrates (e.g., glycans), and determining the identity and
quantity of all of the glycoforms of the glycoconjugates in the
sample is provided. In one embodiment determining the glycosylation
sites and glycosylation site occupancy comprises cleaving the
carbohydrates (e.g., glycans) from the non-saccharide components
and labeling the non-saccharide components at their glycosylation
sites of a first portion of the sample, cleaving the carbohydrates
(e.g., glycans) from the non-saccharide components of a second
portion of the sample, analyzing the first and second portions of
the sample of glycoconjugates and comparing the results. In another
embodiment determining the glycosylation sites comprises analyzing
the non-saccharide components to characterize the non-saccharide
components. In still another embodiment determining the identity
and quantity of all of the glycoforms of the glycoconjugates in the
sample comprises generating constraints from the results of one or
more of the analysis steps and solving the constraints.
[0016] In yet another aspect of the invention a method of analyzing
a sample containing one or more carbohydrates is provided. In one
embodiment the method includes analyzing the carbohydrates with
MALDI-MS to determine the monomer composition and relative
abundance of the carbohydrates, analyzing the carbohydrates with
NMR to determine the monomer composition and linkage abundance of
the carbohydrates, and generating constraints from the results of
one or both of the analysis steps and solving the constraints with
a computational method. In one embodiment the NMR is used to
determine the relative abundance of one or more monomers or ratios
of monomers. In still another embodiment the method further
comprises analyzing the non-saccharide components of one or more
glycoconjugates or a combination thereof, when the carbohydrates
are part of one or more glycoconjugates.
[0017] In still another aspect of the invention a method of
analyzing a sample containing carbohydrates, is provided, which
includes separating neutral from charged carbohydrates, and
analyzing the neutral and charged carbohydrates separately to
characterize the carbohydrates. In another embodiment the method,
when the carbohydrates are part of one or more glycoconjugates,
includes denaturing the glycoconjugates, separating the
carbohydrates (e.g., glycans) from the non-saccharide components,
and analyzing the carbohydrates (e.g., glycans).
[0018] In still another aspect of the invention a method of
analyzing a sample containing one or more carbohydrates, which
includes analyzing the carbohydrates in the presence of a thymine
derivative and an ion exchange resin is provided.
[0019] The methods provided herein, in one embodiment, when the
carbohydrates are part of glycoconjugates, can also includes
denaturing the glycoconjugates, separating the carbohydrates (e.g.,
glycans) from the non-saccharide components or analyzing the
carbohydrates (e.g., glycans) or some combination thereof.
[0020] In still another aspect of the invention a method of
analyzing a sample containing carbohydrates (e.g., glycans) is
provided, which includes analyzing a first portion of the sample,
wherein the carbohydrates (e.g., glycans) have been removed,
analyzing a second portion of the sample, wherein the second
portion of the sample contains intact glycoconjugates, which
comprise one or more carbohydrates (e.g., glycans) conjugated to a
non-saccharide component, and analyzing a third portion of the
sample, wherein the third portion of the sample contains
carbohydrates (e.g., glycans).
[0021] In one embodiment the second portion of the sample
containing intact glycoconjugates is analyzed with a method, which
includes analyzing the glycoconjugates to characterize the
glycoconjugates, analyzing the non-saccharide components of the
glycoconjugates to characterize the non-saccharide components,
separating the carbohydrates (e.g., glycans) from the sample
containing one or more glycoconjugates, analyzing the carbohydrates
(e.g., glycans) to characterize the carbohydrates (e.g., glycans),
and determining the identity and quantity of all of the glycoforms
of the glycoconjugates in the sample with the results obtained from
one or more of the analysis steps and a computational method. In
another embodiment the second portion of the sample containing
intact glycoconjugates is analyzed with a method, which includes
analyzing the glycoconjugates to determine the glycosylation sites
and glycosylation site occupancy, separating the carbohydrates
(e.g., glycans) from the sample containing one or more
glycoconjugates, analyzing the carbohydrates (e.g., glycans) to
characterize the carbohydrates (e.g., glycans), and determining the
identity and quantity of all of the glycoforms of the
glycoconjugates in the sample. In one embodiment determining the
glycosylation sites and glycosylation site occupancy comprises
cleaving the carbohydrates (e.g., glycans) from the non-saccharide
components and labeling the non-saccharide components at their
glycosylation sites of a first portion of the sample, cleaving the
carbohydrates (e.g., glycans) from the non-saccharide components of
a second portion of the sample, analyzing the first and second
portions of the sample of glycoconjugates and comparing the
results. In another embodiment the second portion of the sample
containing intact glycoconjugates is analyzed with a method, which
includes denaturing the glycoconjugates, separating the
carbohydrates (e.g., glycans) from the non-saccharide components of
the glycoconjugates, analyzing the carbohydrates (e.g., glycans)
with MALDI-MS to determine the monomer composition and relative
abundance of the carbohydrates (e.g., glycans), analyzing the
carbohydrates (e.g., glycans) with NMR to determine the monomer
composition and linkage abundance of the carbohydrates (e.g.,
glycans), and generating constraints from the results of the
analysis steps and solving the constraints with a computational
method. In one embodiment the NMR is used to determine the relative
abundance of one or more monomers or ratios of monomers. In still
another embodiment the second portion of the sample containing
intact glycoconjugates is analyzed with a method, which comprises
denaturing the glycoconjugates, separating the carbohydrates (e.g.,
glycans) from the non-saccharide components of the glycoconjugates,
separating neutral from charged carbohydrates (e.g., glycans), and
analyzing the neutral and charged carbohydrates (e.g., glycans)
separately to characterize the carbohydrates (e.g., glycans). In
yet another embodiment the second portion of the sample containing
intact glycoconjugates is analyzed with a method, which comprises
denaturing the glycoconjugates, separating the carbohydrates (e.g.,
glycans) from the non-saccharide components of the glycoconjugates,
and analyzing the carbohydrates (e.g., glycans) in the presence of
a thymine derivative and an ion exchange resin.
[0022] In yet a further embodiment the third portion of the sample
containing carbohydrates (e.g., glycans) is analyzed with a method,
which includes analyzing the carbohydrates (e.g., glycans) with
MALDI-MS to determine the monomer composition and relative
abundance of the carbohydrates (e.g., glycans), analyzing the
carbohydrates (e.g., glycans) with NMR to determine the monomer
composition and linkage abundance of the carbohydrates (e.g.,
glycans), and generating constraints from the results of one or
more of the analysis steps and solving the constraints with a
computational method. In one embodiment the NMR is used to
determine the relative abundance of one or more monomers or ratios
of monomers. In yet a further embodiment the third portion of the
sample containing carbohydrates (e.g., glycans) is analyzed with a
method, which comprises separating neutral from charged
carbohydrates (e.g., glycans), and analyzing the neutral and
charged carbohydrates (e.g., glycans) separately to characterize
the carbohydrates (e.g., glycans). In yet a further embodiment the
third portion of the sample containing carbohydrates (e.g.,
glycans) is analyzed with a method, which comprises analyzing the
carbohydrates (e.g., glycans) in the presence of a thymine
derivative and an ion exchange resin. In still another embodiment
the carbohydrates (e.g., glycans) of the third portion of the
sample are not part of intact glycoconjugates. In another
embodiment the carbohydrates (e.g., glycans) of the third portion
of the sample are part of intact glycoconjugates, which comprise
one or more carbohydrates (e.g., glycans) conjugated to a
non-saccharide component, and the method further includes
denaturing the glycoconjugates, and separating the carbohydrates
(e.g., glycans) from the non-saccharide components of the
glycoconjugates.
[0023] In another embodiment the methods provided can include
determining the sequence of one or more non-saccharide components.
In one embodiment the non-saccharide components are peptides and a
peptide sequence is determined. In yet another embodiment the
sequence of one or more non-saccharide components is determined
prior to or subsequent to analysis of one or more
glycoconjugates.
[0024] The methods provided can also include the generation of
constraints. Constraints can be generated with results from an
analysis of a carbohydrate, glycoconjugate or a component thereof
(i.e., glycan or non-saccharide component) with an analytical (or
experimental) method and/or by using other information. In one
embodiment constraints are generated from databases containing
information about carbohydrates (e.g., glycans), non-saccharide
components, glycoconjugates or a combination thereof. In another
embodiment constraints are generated from biosynthetic rules. In
still another embodiment constraints are generated from information
about the sample origin. In one embodiment the information about
the sample origin comprises information regarding the expression
system or expression conditions for the synthesis of carbohydrates,
glycoconjugates or components thereof, the species from which
carbohydrates, glycoconjugates or components thereof are derived,
the expression levels of glycosidases and glycosyltransferases from
the source from which carbohydrates, glycoconjugates or components
thereof are obtained, or the state of the source from which the
carbohydrates, glycoconjugates or components thereof are obtained.
In another embodiment constraints can also be generated from the
results of another experimental method. In one embodiment the other
experimental method is a mass spectrometric method, an
electrophoretic method, a NMR method, a chromatographic method or
some combination thereof. In another embodiment the other
experimental method is different from the first experimental
method. In one embodiment, when the first experimental method is
MALDI-MS, the other experimental method is not MALDI-MS. In another
embodiment, where the first experimental method is NMR, the other
experimental method is a different NMR method.
[0025] In one embodiment the constraints are one or more
mathematical equations. Preferably, in another embodiment, the
constraints are more than one mathematical equation. Even more
preferably, in still another embodiment, the constraints are more
than two mathematical equations. In one embodiment, therefore, the
constraints are three or more mathematical equations. In another
embodiment the constraints are five or more mathematical equations.
In another embodiment the constraints are solved by determining the
solution of the one or more mathematical equations. In still
another embodiment the constraints are solved with a computer
program.
[0026] The samples or portions thereof and carbohydrates,
glycoconjugates or components thereof can be analyzed using any of
a number or combination of experimental methods. In one embodiment
two or more experimental methods can be used for analysis. In one
embodiment the experimental method is a mass spectrometric method,
an electrophoretic method, NMR, a chromatographic method or a
combination thereof. In another embodiment the mass spectrometric
method is LC-MS, LC-MS/-MS, MALDI-MS, MALDI-TOF-MS, MALDI-TOF
PSD-MS, MALDI-TOF/TOF-MS, MALDI-TOF/TOF-MS/MS, MALDI-TOF/TOF
PSD-MS, MALDI-FTMS, LC-MALDI-TOF/TOF-MS, Nano-LC MALDI-TOF/TOF-MS,
Nano-LC MALDI-TOF/TOF PSD-MS, Nano-LC MALDI-TOF/TOF-MS/MS or
TANDEM-MS. In yet another embodiment the mass spectrometric method
is ESI-MS, LC-MS, LC-MS/-MS, MALDI-MS, MALDI-MS/MS, MALDI-TOF-MS,
MALDI-TOF PSD-MS, MALDI-TOF/TOF-MS, MALDI-TOF/TOF-MS/MS,
MALDI-TOF/TOF PSD-MS, MALDI-FTMS, LC-MALDI-TOF/TOF-MS, Nano-LC
MALDI-TOF/TOF-MS, Nano-LC MALDI-TOF/TOF PSD-MS, Nano-LC
MALDI-TOF/TOF-MS/MS or TANDEM-MS. In still another embodiment the
mass spectrometric method is LC-MS, LC-MS/-MS, LC-FTMS, TANDEM-MS,
MALDI-MS, MADLI-TOF-TOF-MS, MALDI-FTMS or MALDI/PSD-MS. In yet
another embodiment the mass spectrometric method is a quantitative
MALDI-MS, MALDI-TOF-TOF-MS or MALDI-FTMS using optimized
conditions. In still another embodiment the experimental method is
MALDI-MS. In one embodiment the MALDI-MS provides monomer (e.g.,
monosaccharide) composition and relative abundance information. In
another embodiment MALDI-MS is used with another experimental
method.
[0027] In still another embodiment the experimental method is
nuclear magnetic resonance (NMR). In one embodiment the results
from the NMR provide monomer (e.g., monosaccharide) composition and
linkage information. In one embodiment the NMR is used to determine
the relative abundance of one or more monomers or ratios of
monomers.
[0028] In another embodiment the experimental method is NMR or
MALDI-MS. In still a further embodiment both NMR and MALDI-MS is
used for analysis.
[0029] In another embodiment the electrophoretic method is
capillary electrophoresis (CE) or CE-LIF. In yet another embodiment
the chromatographic method is HPLC.
[0030] The analysis in the methods provided can include the use of
a mass spectrometric method, such as MALDI-MS, in the presence of a
thymine derivative and an ion exchange resin. In one embodiment the
thymine derivative is thiothymine, 2-thiothymine, 4-thiothymine,
5-aza-2-thiothymine or 6-aza-2-thiothymine (ATT). In another
embodiment the ion exchange resin is an ammonium resin, a cationic
exchange resin, a cationic exchange resin in pyridinium form, an
anionic exchange resin or a perfluorinated ion exchange resin. In
still another embodiment the perfluorinated ion exchange resin is
Nafion.TM..
[0031] The methods provided can also include contacting the
carbohydrates with one or more carbohydrate-degrading enzymes. In
one embodiment the one or more carbohydrate-degrading enzymes are
glycan-degrading enzymes, which include, for example, sialidase,
galactosidase, mannosidase, N-acetylhexosaminidase or a combination
thereof. In another embodiment the methods provided can also
include contacting the carbohydrates with strong acidic or basic
conditions.
[0032] The methods provided can also include quantifying the
carbohydrates using calibration curves of known carbohydrate
standards.
[0033] The methods provided can also include purification steps. In
one embodiment the carbohydrates (e.g., glycans) are purified with
solid phase extraction cartridges or ion exchange resins. In
another embodiment the solid phase extraction cartridges are
graphitic carbon columns, non-graphitic carbon columns or C-18
columns.
[0034] The methods provided can also include cleavage steps, which
include the use of PNGase F, Endo H, Endo F, hydrazinolysis or
alkaline borohydride.
[0035] The methods provided can also include denaturation with a
denaturing agent. In one embodiment the denaturing agent comprises
a detergent, urea, high salt concentration, guanidium hydrochloride
or heat.
[0036] In another embodiment the methods provided can also include
reduction with a reducing agent following denaturation. In one
embodiment the reducing agent comprises dithiothreitol (DTT),
.beta.-mercaptoethanol or Tris(2-carboxyethyl)phosphine (TCEP).
[0037] In still another embodiment the methods provided can include
alkylation with an alkylating agent following reduction. In one
embodiment the alkylating agent is iodoacetic acid or
iodoacetamide.
[0038] The carbohydrates analyzed by the methods provided herein
can be any carbohydrate or combination of carbohydrates. In one
embodiment the carbohydrates are polysaccharides. In still another
embodiment the carbohydrates are glycans. In a further embodiment
the carbohydrate is a glycosaminoglycan. In yet another embodiment
the carbohydrate is hyaluronic acid. In another embodiment the
carbohydrates are branched. In yet another embodiment they are
unbranched. In still another embodiment the carbohydrates are a
mixture of branched and unbranched carbohydrates. In a further
embodiment the carbohydrates are a mixture of a number of different
carbohydrates. In another embodiment the carbohydrates are
conjugated to a non-saccharide component and form one or more
glycoconjugates. In one embodiment the glycoconjugate is a
peptide-based glycoconjugate. In another embodiment the
glycoconjugate is a lipid-based glycoconjugate. In another
embodiment the carbohydrates are modified or unmodified or a
mixture thereof. In one embodiment the carbohydrates are glycans
that are modified by permethylation or conjugation to a
peptide.
[0039] The methods provided can also include generating a list of
all possible carbohydrates (e.g., glycans) and/or glycoforms. In
one embodiment the list is based on the results of the analysis of
the carbohydrates (e.g., glycans), glycoconjugates, database
information or from biosynthetic rules or a combination
thereof.
[0040] The methods provided can also include removing abundant or
nonglycosylated lipids and/or proteins from a sample. In one
embodiment the abundant or nonglycosylated lipids or proteins are
albumins or immunoglobulins.
[0041] The methods provided in one embodiment have a detection
limit of less than about 1000, 500, 100, 75, 50, 25, 20, 18, 16,
15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2 or 1
femtomole(s).
[0042] The methods provided in another embodiment can be used to
detect low abundance species. In one embodiment the low abundance
species include, but are not limited to, glycans that contain
fucoses, sialic acids, galactoses, mannoses or sulfate groups.
[0043] The methods provided can be performed in a high-throughput
manner. Therefore, in one embodiment a method or a portion thereof
is performed in a 96-well plate or with a protein-binding membrane.
In one embodiment the 96-well plate comprises a protein-binding
membrane. In another embodiment the protein-binding membrane is a
polyvinylidine difluoride (PVDF) membrane, C-18 membrane or a
nitrocellulose membrane.
[0044] The methods provided can also be performed on a sample (or
more than one sample) of carbohydrates, glycoconjugates or
components thereof that are in solution or are immobilized on a
solid support. In one embodiment the solid support is in a 96-well
plate format or comprises a membrane. In another embodiment the
membrane is a protein-binding membrane. In one embodiment the
membrane is a polyvinylidene difluoride (PVDF) membrane, C-18
membrane or a nitrocellulose membrane.
[0045] The methods provided can include the use of robotics. In one
embodiment one or more steps of the methods provided or one or more
portions thereof are performed with the use of robotics.
[0046] In another embodiment neutral and charged carbohydrates are
analyzed separately in the methods provided.
[0047] The methods provided can be performed on any sample
containing one or more carbohydrates. In one embodiment the sample
is a sample comprising one or more glycoconjugates, one or more
cells, a tissue or body fluid from a subject. In another embodiment
the sample is a batch of glycoconjugates. In yet another embodiment
the sample is a sample of serum, plasma, blood, urine, saliva,
sputum, tears, CSF, seminal fluid, feces, tissues or cells. In
still another embodiment the sample of body fluid is from a subject
with a disease or condition. In yet another embodiment the sample
of body fluid is from a subject that is undergoing treatment for a
disease. In still another embodiment the sample of body fluid is
from a healthy subject. In another embodiment the sample of body
fluid is from a pregnant woman. In another embodiment the sample is
a sample comprising one or more glycoconjugates. In still another
embodiment the sample is a batch of glycoconjugates. In yet another
embodiment more than one sample is analyzed. In one embodiment the
more than one sample are two or more batches of glycoconjugates. In
another embodiment the more than one sample are two or more samples
containing carbohydrates (e.g., glycans). In still another
embodiment the more than one samples are contained in a 96-well
plate or on a protein-binding membrane. In a further embodiment the
one or more sample are in solution. In another embodiment the one
or more samples are analyzed as a mixture.
[0048] The methods provided can be used to analyze an entire sample
or one or more portions or fractions thereof. In one embodiment an
entire sample is analyzed. In another embodiment a fraction of a
sample is analyzed.
[0049] Therefore, the methods provided can also include the
fractionation of a sample or portion thereof. In one embodiment the
fractionation is based on charge, size, molecular weight, binding
properties, acidity, basicity, pI, hydrophobicity or
hydrophilicity. In another embodiment the fractionation is
performed using solid supports with immobilized proteins, organic
molecules, inorganic molecules, lipids, carbohydrates or nucleic
acids; filters or resins. In another embodiment carbohydrates of a
fractionated part of a sample are the carbohydrates that are
analyzed. In still another embodiment a fractionated part of a
sample is isolated. In yet another embodiment a fraction of a
sample is removed and it is the remaining fraction that is
analyzed. In one embodiment the fraction of a sample removed
contains acidic carbohydrates (e.g., glycans). In another
embodiment the fraction of a sample removed contains neutral
carbohydrates (e.g., glycans). In still another embodiment the
fraction of a sample removed contains high abundance proteins. In
one embodiment the high abundance proteins are albumins or
immunoglobulins. In another embodiment the high abundance proteins
are immunoglobulins. In still another embodiment the fraction of a
sample removed does not contain high abundance proteins.
[0050] Any of the methods provided herein can be used as a method
of analyzing the purity of a sample.
[0051] Any of the methods provided herein can be used as a method
for diagnosis or assessing prognosis.
[0052] Any of the methods provided herein can be used as a method
of assessing the effectiveness of a treatment of a subject.
[0053] In still another aspect of the invention a method of
generating a glycoconjugate library, wherein the glycoconjugate
comprises one or more carbohydrates (e.g., glycans) conjugated to a
non-saccharide component, is provided. In one embodiment the method
includes cleaving the non-saccharide components of one or more
glycoconjugates in a sample and labeling the non-saccharide
component fragments generated with a labeling agent in order to
generate a glycoconjugate library, and cleaving the carbohydrates
(e.g., glycans) from the non-saccharide components and labeling the
non-saccharide components in the sample at the glycosylation sites.
In one embodiment the labeling agent is an isotope of C, N, H, S or
O. In still another embodiment the labeling agent is .sup.18O. In
still a further embodiment the labeling agent is .sup.2H. In yet
another embodiment the method further comprises analyzing the
fragments generated from the cleavage of the glycoconjugates.
[0054] In one embodiment the analysis is performed on any sample
containing one or more glycoconjugates. In another embodiment the
glycoconjugate is a lipid-based glycoconugate. In yet another
embodiment the glycoconjugate is a peptide-based glycoconjugate. In
still another embodiment the analyzing results in the
characterization of the glycosylation sites, the peptides and the
carbohydrates (e.g., glycans) of the peptide-based
glycoconjugate.
[0055] In yet another aspect of the invention a library generated
with the methods provided herein is also provided.
[0056] In still another aspect of the invention a method of
analyzing a sample containing glycoconjugates, is provided, which
includes modifying the glycoconjugates in the sample, and comparing
the modified glycoconjugates with the library. In one embodiment
the glycoconjugates are modified by cleavage, labeling or both. In
another embodiment the step of modifying the glycoconjugates
comprises cleaving the glycoconjugates to generate glycoconjugate
fragments. In another embodiment the glycoconjugates are cleaved by
cleaving the non-saccharide components of the glycoconjugates,
cleaving the carbohydrates (e.g., glycans) of the glycoconjugates,
cleaving the carbohydrates (e.g., glycans) from the non-saccharide
components of the glycoconjugates or a combination thereof to
generate fragments. In yet another embodiment the fragments
generated are labeled. In one embodiment the non-saccharide
component fragments are labeled. In a further embodiment the
non-saccharide component fragments are labeled at the sites of
cleavage. In another embodiment the non-saccharide component
fragments are labeled at their glycosylation sites. In still
another embodiment the step of comparing includes mixing the
glycoconjugate fragments with the library and determining the
ratios of the glycoconjugate fragments to the library. In one
embodiment known proportions of samples of the glycoconjugate
fragments are mixed with the library.
[0057] In yet a further aspect of the invention a method of
generating a list of properties is provided. In one embodiment the
method includes determining one or more properties of a sample with
a method as provided herein, and recording a value for the one or
more properties to generate a list, wherein the value of the one or
more properties is recorded in a computer-generated data structure.
In one embodiment the one or more properties comprise the number of
one or more types of monomers of a carbohydrate in the sample. In
another embodiment the one or more properties comprise the mass of
a carbohydrate or portion thereof in the sample. In still another
embodiment the one or more properties comprises the quantity of a
carbohydrate or portion thereof in the sample. In one embodiment
the carbohydrate is conjugated to a glycoconjugate. In another
embodiment the glycoconjugate is a peptide-based glycoconjugate,
and the one or more properties comprises the mass of the peptide of
the peptide-based glycoconjugate. In another embodiment the
glycoconjugate is a lipid-based glycoconjugate, and the one or more
properties comprises the mass of the lipid of the lipid-based
glycoconjugate. In still another embodiment the one or more
properties comprises the mass of the glycoconjugate.
[0058] Also provided in another aspect of the invention is a
database, tangibly embodied in a computer-readable medium, for
storing information descriptive of one or more carbohydrates, the
database, which includes one or more data units corresponding to
the one or more carbohydrates, each of the data units including an
identifier that includes one or more fields, each field for storing
a value corresponding to one or more properties of the
carbohydrates, wherein the value corresponding to one or more
properties of the carbohydrates is determined with a method as
provided herein. In one embodiment the method includes analyzing
the carbohydrates with MALDI-MS to determine the monomer
composition and relative abundance of the carbohydrates, and
analyzing the carbohydrates with NMR to determine the monomer
composition and linkage abundance of the carbohydrates. In one
embodiment the NMR is used to determine the relative abundance of
one or more monomers or ratios of monomers. In another embodiment
the method also includes generating constraints from the results of
one or more of the analysis steps and solving the constraints with
a computational method. In yet another embodiment the methods
include separating neutral from charged carbohydrates, and
analyzing the neutral and charged carbohydrates separately to
characterize the carbohydrates. In still another embodiment the
method includes analyzing the carbohydrates in the presence of a
thymine derivative and an ion exchange resin.
[0059] In one embodiment where the carbohydrates are part of intact
glycoconjugates, which comprise one or more carbohydrates (e.g.,
glycans) conjugated to a non-saccharide component, the method
further includes denaturing the glycoconjugates, and separating the
carbohydrates (e.g., glycans) from the non-saccharide components of
the glycoconjugates.
[0060] In another aspect of the invention a database, tangibly
embodied in a computer-readable medium, for storing information
descriptive of one or more glycoconjugates, the database, which
includes one or more data units corresponding to the one or more
glycoconjugates, each of the data units including an identifier
that includes one or more fields, each field for storing a value
corresponding to one or more properties of the glycoconjugates,
wherein the value corresponding to one or more properties of the
glycoconjugates is determined with a method as provided herein is
provided. In one embodiment the method includes analyzing the
glycoconjugates to characterize the glycoconjugates, analyzing the
non-saccharide components of the glycoconjugates to characterize
the non-saccharide components, separating the carbohydrates (e.g.,
glycans) from the sample containing one or more glycoconjugates,
and analyzing the carbohydrates (e.g., glycans) to characterize the
carbohydrates (e.g., glycans). In another embodiment the method
also includes determining the identity and quantity of all of the
glycoforms of the glycoconjugates in the sample with the results
obtained from one or more analysis steps and a computational
method. In another embodiment the method includes analyzing the
glycoconjugates to determine the glycosylation sites and
glycosylation site occupancy, separating the carbohydrates (e.g.,
glycans) from the sample containing one or more glycoconjugates,
and analyzing the carbohydrates (e.g., glycans) to characterize the
carbohydrates (e.g., glycans). In one embodiment determining the
glycosylation sites and glycosylation site occupancy comprises
cleaving the carbohydrates (e.g., glycans) from the non-saccharide
components and labeling the non-saccharide components at their
glycosylation sites of a first portion of the sample, cleaving the
carbohydrates (e.g., glycans) from the non-saccharide components of
a second portion of the sample, analyzing the first and second
portions of the sample of glycoconjugates and comparing the
results. In still another embodiment the method also includes
determining the identity and quantity of all of the glycoforms of
the glycoconjugates in the sample. In yet another embodiment the
method includes denaturing the glycoconjugates, separating the
carbohydrates (e.g., glycans) from the non-saccharide components of
the glycoconjugates, analyzing the carbohydrates (e.g., glycans)
with MALDI-MS to determine the monomer composition and relative
abundance of the carbohydrates (e.g., glycans), and analyzing the
carbohydrates (e.g., glycans) with NMR to determine the monomer
composition and linkage abundance of the carbohydrates (e.g.,
glycans). In one embodiment the NMR is used to determine the
relative abundance of one or more monomers or ratios of monomers.
In another embodiment the method also includes generating
constraints from the results of one or more of the analysis steps
and solving the constraints with a computational method. In still
another embodiment the method includes denaturing the
glycoconjugates, separating the carbohydrates (e.g., glycans) from
the non-saccharide components of the glycoconjugates, separating
neutral from charged carbohydrates (e.g., glycans), and analyzing
the neutral and charged carbohydrates (e.g., glycans) separately to
characterize the carbohydrates (e.g., glycans). In a further
embodiment the method includes denaturing the glycoconjugates,
separating the carbohydrates (e.g., glycans) from the
non-saccharide components of the glycoconjugates, and analyzing the
carbohydrates (e.g., glycans) in the presence of a thymine
derivative and an ion exchange resin.
[0061] In another aspect of the invention a method of analyzing the
total glycome of a sample is provided. In one embodiment the method
includes analyzing all of the carbohydrates (e.g., glycans) of the
sample, and determining a profile of the carbohydrates (e.g.,
glycans) of the sample. In another embodiment the composition of
the carbohydrates (e.g., glycans) in the sample is determined. In
still another embodiment the structures of the carbohydrates (e.g.,
glycans) in the sample are determined. In another embodiment the
analysis of all of the carbohydrates (e.g., glycans) includes
quantifying the carbohydrates (e.g., glycans) using calibration
curves based on known carbohydrate (e.g., glycan) standards. In
another embodiment the profile of the carbohydrates (e.g., glycans)
is a spectrum of monomer composition and relative abundance of the
carbohydrates (e.g., glycans).
[0062] In one embodiment the carbohydrates (e.g., glycans) of the
sample are analyzed with a mass spectrometric method, an
electrophoretic method, NMR, a chromatographic method or a
combination thereof. In another embodiment the analysis is
performed, for example, with ESI-MS, LC-MS, LC-MS/-MS,
MALDI-TOF-MS, MALDI-MS/MS, MALDI-FTMS, TANDEM-MS, NMR, HPLC or CE.
In a further embodiment the analysis is performed with MALDI-MS or
MALDI-FTMS. In yet another embodiment the analysis is performed
with electrophoresis, microfluidic devices or nanofluidic devices.
In still another embodiment the carbohydrates (e.g., glycans) are
further analyzed with another experimental method. In one
embodiment the other experimental method provides linkage
information. In another embodiment the other experimental method is
LC-MS, LC-MS/-MS, CE-LIF or NMR. In still another embodiment the
other experimental method comprises the use of one or more
carbohydrate-degrading enzymes (e.g., glycan-degrading enzymes). In
still another embodiment the carbohydrates (e.g., glycans) of the
sample are analyzed with a method, which includes analyzing the
carbohydrates (e.g., glycans) with MALDI-MS to determine the
monomer composition and relative abundance of the carbohydrates
(e.g., glycans), and analyzing the carbohydrates (e.g., glycans)
with NMR to determine the monomer composition and linkage abundance
of the carbohydrates (e.g., glycans). In one embodiment the NMR is
used to determine the relative abundance of one or more monomers or
ratios of monomers. In another embodiment the method also includes
generating constraints from the results of the analysis with
MALDI-MS and NMR and solving the constraints with a computational
method. In a further embodiment the carbohydrates (e.g., glycans)
of the sample are analyzed with a method, which includes separating
neutral from charged carbohydrates (e.g., glycans), and analyzing
the neutral and charged carbohydrates (e.g., glycans) separately to
characterize the carbohydrates (e.g., glycans). In still another
embodiment the carbohydrates (e.g., glycans) of the sample are
analyzed with a method, which includes analyzing the carbohydrates
(e.g., glycans) in the presence of a thymine derivative and an ion
exchange resin.
[0063] In one embodiment the carbohydrates are part of intact
glycoconjugates, which comprise one or more carbohydrates (e.g.,
glycans) conjugated to non-saccharide components. In another
embodiment the method also includes separating the carbohydrates
(e.g., glycans) from the non-saccharide components of the
glycoconjugates. In one embodiment the carbohydrates (e.g.,
glycans) are separated by cleavage with an enzymatic method or a
chemical method. In one embodiment the enzymatic method includes
the use of PNGase F, Endo H or Endo F. In another embodiment the
chemical method includes hydrazinolysis or alkaline borohydride. In
still another embodiment the cleavage is performed in a 96-well
plate (e.g., with the glycoconjugates immobilized on a membrane),
on a protein-binding membrane or in solution. In still a further
embodiment the cleavage is performed with the use of robotics or
manually. In another embodiment the method further comprises
purification of the carbohydrates (e.g., glycans). In one
embodiment the purification is performed in a 96-well plate. In
another embodiment the purification is performed using individual
purification columns or cartridges. In yet another embodiment the
purification is performed with solid phase extraction cartridges.
In still another embodiment the purification is performed with the
use of robotics or manually.
[0064] In yet another embodiment the method includes analyzing the
glycoconjugates to characterize the glycoconjugates, analyzing the
non-saccharide components of the glycoconjugates to characterize
the non-saccharide components, separating the carbohydrates (e.g.,
glycans) from the sample, and analyzing the carbohydrates (e.g.,
glycans) to characterize the carbohydrates (e.g., glycans). In
still another embodiment the method also includes determining the
identity and quantity of all of the glycoforms of the
glycoconjugates in the sample with the results obtained from the
analysis and a computational method. In still another embodiment
the method includes analyzing the glycoconjugates to determine the
glycosylation sites and glycosylation site occupancy, separating
the carbohydrates (e.g., glycans) from the sample, and analyzing
the carbohydrates (e.g., glycans) to characterize the carbohydrates
(e.g., glycans). In one embodiment determining the glycosylation
sites and glycosylation site occupancy comprises cleaving the
carbohydrates (e.g., glycans) from the non-saccharide components
and labeling the non-saccharide components at their glycosylation
sites of a first portion of the sample, cleaving the carbohydrates
(e.g., glycans) from the non-saccharide components of a second
portion of the sample, analyzing the first and second portions of
the sample and comparing the results. In another embodiment the
method also includes determining the identity and quantity of all
of the glycoforms of the glycoconjugates in the sample.
[0065] In still another embodiment the method also includes
identifying a pattern by performing a pattern analysis on the
results using a computational method. In one embodiment the
computational method is an iterative computational method. In
another embodiment the iterative computational method determines
the glycoforms in the sample. In still a further embodiment the
method also includes recording one or more values representing the
pattern in a computer-generated data structure. In still another
embodiment the method also includes associating the pattern with
one or more samples of known origin (e.g., a sample from a diseased
patient, a sample for one or more persons with one or more specific
characteristics, a sample of a batch of glycoconjugates, etc.) In
one embodiment, therefore, the pattern is associated with a
population (e.g., healthy subjects, subjects with a specific
disease, pregnant women, subject the have specific demographic
characteristics, etc.) In another embodiment the pattern is
associated with a disease. In still another embodiment the pattern
is associated with patterns of one or more samples of known origin.
In one embodiment the pattern is associated by comparing the
pattern with one or more patterns of one or more samples of known
origin. In another embodiment the pattern is associated by
extracting features of the pattern and comparing the features with
information available for the one or more samples of known
origin.
[0066] In one embodiment the pattern provides diagnostic or
prognostic information. In another embodiment the pattern provides
information about a sample, a person or population from which the
sample was derived or sample origin.
[0067] In one embodiment the sample is from a subject and the
pattern provides information about the subject's state. In another
embodiment the subject's state is a diseased state.
[0068] In another embodiment the identified pattern is compared to
the pattern of at least one other sample. In one embodiment the at
least one other sample is a batch of glycoconjugates. In another
embodiment the at least one other sample is a sample from a healthy
or diseased individual.
[0069] In one embodiment the identified pattern is compared to
another pattern. In another embodiment the other pattern is a known
pattern. In still another embodiment the other pattern is an
unknown pattern. In yet another embodiment the other pattern is a
pattern that represents a batch of glycoconjugates. In one
embodiment the method is a method to assess the purity of a batch
of glycoconjugates and the known pattern represents a batch of
glycoconjugates of known purity.
[0070] In still another embodiment the other pattern is a pattern
that represents a diseased or healthy state. In one embodiment the
diseased state is associated with cancer. In another embodiment the
cancer is prostate cancer, melanoma, bladder cancer, breast cancer,
lymphoma, ovarian cancer, lung cancer, colorectal cancer or head
and neck cancer. In another embodiment the diseased state is
associated with an immunological disorder. In still another
embodiment the diseased state is associated with a
neurodegenerative disease. In one embodiment the neurodegenerative
disease is a transmissible spongiform encephalopathy, Alzheimer's
disease or neuropathy. In another embodiment the diseased state is
associated with inflammation. In still another embodiment the
diseased state is associated with rheumatoid arthritis. In yet
another embodiment the diseased state is associated with cystic
fibrosis. In a further embodiment the diseased state is associated
with an infection. In one embodiment the infection is viral or
bacterial. In another embodiment the diseased state is associated
with a congenital disorder.
[0071] In another embodiment the method is a method of monitoring
prognosis and the known pattern is associated with the prognosis of
a disease.
[0072] In still another embodiment the method is a method of
monitoring drug treatment and the known pattern is associated with
a drug treatment.
[0073] In another embodiment the method also includes validating
the association of the pattern. In one embodiment the association
of the pattern is validated with one or more patterns of one or
more samples of known origin. In another embodiment the pattern is
validated by comparing with one or more patterns of one or more
samples of known origin.
[0074] In another aspect of the invention a method of generating a
glycoprofile with a method provided herein is also provided.
[0075] In still another aspect of the invention a method of
creating a database of glycoprofiles, which includes generating a
glycoprofile of a sample according to a method provided, and
recording one or more values corresponding to the glycoprofile in a
computer-generated data structure is provided. In yet another
aspect of the invention the database so created is also
provided.
[0076] In another aspect of the invention a method of determining a
glycome pattern, which includes obtaining a glycoprofile of total
carbohydrates (e.g., glycans) of a sample with a method provided
herein, identifying features of the glycoprofile, generating data
sets based on the features of the glycoprofile, identifying a
pattern in the data sets, and determining whether or not the
pattern is associated with a known sample or diseased state is
provided. In one embodiment the sample is obtained from a subject.
In another embodiment the subject has a disease or condition. In
another embodiment determining the glycoprofile includes obtaining
more than one glycoprofile spectra. In one embodiment one of the
spectra is of acidic carbohydrates (e.g., glycans). In another
embodiment one of the spectra is of neutral carbohydrates (e.g.,
glycans). In still another embodiment one spectra is of acidic
carbohydrates (e.g., glycans) and another spectra is of neutral
carbohydrates (e.g., glycans).
[0077] In yet another embodiment, when the analysis, includes the
generation of one or more glycoprofile spectra, the methods
provided can also include assigning all of the possible
carbohydrates (e.g., glycans) to the peaks of the one or more
spectra.
[0078] In one embodiment the feature identified is the presence of
one or more carbohydrates (e.g., glycans), the absence of one or
more carbohydrates (e.g., glycans), the relative amount of one or
more carbohydrates (e.g., glycans), the combination of two or more
classes of carbohydrates (e.g., glycans), the presence of a
specific carbohydrate (e.g., glycan) motif (i.e., a specific set of
one or more monomers (e.g., monosaccharides)), the absence of a
specific carbohydrate (e.g., glycan) motif, the relative amount of
a specific carbohydrate (e.g., glycan) motif, the presence of one
or more monomers in a carbohydrate (e.g., glycan), the absence of
one or more monomers in a carbohydrate (e.g., glycan), the relative
amount of one or more monomers in a carbohydrate (e.g., glycan) or
the bond between monomers of a carbohyrdate.
[0079] In one embodiment the pattern is identified by linear
discriminant, nearest neighbor, statistical classifier, neutral
net, decision tree, decision rules or association rules
analysis.
[0080] In another embodiment the glycoprofile is generated from
determining the glycosylation site occupancy of glycoconjugates in
a sample. In still another embodiment the glycoprofile is
determined by identifying and quantifying all of the carbohydrates
(e.g., glycans) and/or glycoconjugates of the sample. In still
another embodiment all of the carbohydrates (e.g., glycans) are
identified and quantified by solving constraints with a
computational method.
[0081] In another aspect of the invention a method of determining a
glycome pattern of a sample, which includes determining the
glycoprofile of the sample according to a method as provided
herein, extracting one or more features of the glycoprofile,
analyzing the one or more features, and validating the glycome
pattern is provided. In one embodiment the one or more features is
the presence or absence of a specific carbohydrate (e.g., glycan),
an amount of a specific carbohydrate (e.g., glycan), a combination
of specific carbohydrates (e.g., glycans), etc. In another
embodiment the one or more features is a ratio between two or more
carbohydrates (e.g., glycans) or monomers or motifs thereof. In
still another embodiment the one or more features is the range of
amounts of one or more carbohydrates (e.g., glycans). In yet
another embodiment the one or more features is the range of ratios
between two or more carbohydrates (e.g., glycans).
[0082] In one embodiment the glycoprofile is generated from
determining the glycosylation site occupancy of glycoconjugates in
a sample.
[0083] In another embodiment the glycoprofile is determined by
identifying and quantifying all of the carbohydrates (e.g.,
glycans) and/or a monomer or motif thereof in the sample. In one
embodiment the carbohydrates (e.g., glycans) are identified and
quantified by solving constraints with a computational method.
[0084] In another aspect of the invention a method of generating a
glycome pattern with a method as provided herein is also
provided.
[0085] In still another aspect of the invention a method of
creating a database of glycome patterns generated by a method
provided is provided herein. In another embodiment the method also
includes recording one or more values representing the glycome
pattern in a computer-generated data structure. The database so
created is also provided.
[0086] Each of the limitations of the invention can encompass
various embodiments of the invention. It is, therefore, anticipated
that each of the limitations of the invention involving any one
element or combinations of elements can be included in each aspect
of the invention.
BRIEF DESCRIPTION OF THE FIGURES
[0087] FIG. 1 shows the conserved N-glycan pentasaccharide
core.
[0088] FIG. 2 illustrates classes of N-linked glycans. High-mannose
structures contain up to nine mannose residues (FIG. 2A). Complex
type glycans are modified with hexosamines, galactoses, sialic
acids and/or fucose, among other residues (FIG. 2B). Complex type
chains can occur as mono-, bi-, tri-, and tetra-antennary
structures. Also, the amount and type of sialylation differs.
Hybrid structures contain characteristics of both high-mannose and
complex types (FIG. 2C).
[0089] FIG. 3 provides the detailed pathway of N-glycan
biosynthesis
(http://www.genome.ad.jp/kegg/pathway/map/map00510.html).
[0090] FIG. 4 shows the cleavage sites of Endo H, Endo F and PNGase
F. Endo H can only act on high mannose and hybrid structures, while
Endo F is effective at cleaving all classes of N-glycans. PNGase F
also cleaves all mammalian N-glycan structures.
[0091] FIG. 5 provides the MALDI-MS spectra of N-glycans from
RNaseB samples prepared by various methods. Glycans prepared using
a GlycoClean S column (Table 2, Sample 12), showed the expected
high mannose peaks and significant contamination of unknown
identity (FIG. 5A). A small amount of sample (10 .mu.g) was
prepared using a 25 mg GlycoClean H column (Table 2, Sample 17),
which showed only detergent peaks (FIG. 5B). A larger amount of
protein (50 .mu.g) was prepared (Table 2, Sample 18), yielding the
expected glycan peaks but still containing detergent contamination
(FIG. 5C). Using a 200 mg GlycoClean H column to purify N-glycans
from 150 .mu.g of RNaseB (Table 2, Sample 20), only the high
mannose saccharides were observed (FIG. 5D).
[0092] FIG. 6 shows the spectrum from MALDI-MS of N-glycans from
ovalbumin. Each labeled peak corresponds to a previously reported
structure listed in Table 3.
[0093] FIG. 7 provides results from a study of N-glycans from
antibody samples. FIGS. 7A and 7B are for samples from Applikon
bioreactors, with DO=50%, pH=7 and DO=90%, pH uncontrolled,
respectively. FIGS. 7C-7E are for samples from Wave reactors. FIG.
7C represents the results for DO controlled, pH uncontrolled, and
NaOH in the media, while FIG. 7D represents the results with
NaHCO.sub.3 in the media instead of NaOH. The results shown in FIG.
7E are for DO uncontrolled with pH=7.
[0094] FIG. 8 shows the structures and theoretical masses of
N-glycans released from antibodies.
[0095] FIG. 9 provides the MALDI-MS spectra of glycans released
from serum proteins using PNGase F and Endo F. Serum samples were
treated with PNGase F (FIG. 9A) or Endo F (FIG. 9B) and purified.
While glycans were observed, the samples did not produce clean
results. The peak cluster indicated by an arrow represents
detergent contamination.
[0096] FIG. 10 shows a separation of neutral and acidic glycans
using a GlycoClean H cartridge. FIG. 10A provides the results of an
original mix of standards in positive mode. A3 and SC1840 are
highly charged and do not ionize well. FIG. 10B shows that neutral
glycans eluted off the GlycoClean H cartridge ionize well in
positive mode, while only the charged sugars are present in the
results provided by FIG. 10C, allowing them to be observed in
negative mode. The multiple peaks in FIG. 10C arise from sodium
adducts, typically one adduct per sialic acid residue.
[0097] FIG. 11 provides the results from MALDI-MS of N-glycans from
human serum in neutral (left) and acidic (right) fractions. FIGS.
11A and 11B show the results with neutral glycans prepared from two
different IMPATH normal male human serum samples, while FIGS. 11D
and 11E show the results with the acidic fraction. FIGS. 11C and
11F show the results with the neutral and acidic fractions of a
normal human sample from Biomedical Resources.
[0098] FIG. 12 provides the results of serum glycans separated by
ConA. FIG. 12A provides the results from SDS-PAGE of ConA
flow-through (Lane 2) and elution (Lane 3). Lane 1 shows molecular
weight standards. The results from MALDI-MS of neutral and acidic
sugars obtained from ConA elution are shown in FIGS. 12B and
12C.
[0099] FIG. 13 provides the results from Protein A separation of
IgG from serum. A glycoblot of Protein A flow-through (Lane 3) and
elution (Lane 4) is shown in FIG. 13A. Lane 1 contains protein
standards, while Lane 2 (negative control) contains human serum
albumin (* marks where albumin would run on an SDS-PAGE gel). Only
glycosylated proteins are observed in the glycoblot, so the albumin
does not stain. FIGS. 13B (neutral) and 13C (acidic) show the
results of MALDI-MS of glycans harvested from the elution fraction.
Total serum glycans are pictured in FIGS. 13D (neutral) and 13E
(acidic).
[0100] FIG. 14 shows the permethylation of N-glycans. All OH and NH
groups can be permethylated. For a complete reaction, it is
important that the reaction vessel is free of air and water.
[0101] FIG. 15 shows the results of MALDI-MS of permethylated
glycan standards. FIG. 15A shows that unmodified standards ionized
unevenly. FIG. 15B shows that permethylated standards were more
uniformly ionized, but generally did not have higher
signal-to-noise ratios.
[0102] FIG. 16 shows the aminooxyacetyl peptide and its conjugation
to N-glycans. The aminooxyacetate end of the synthetic peptide
(top) reacts with the open form of the reducing end GlcNAc of
N-glycans (bottom).
[0103] FIG. 17 shows the results of MALDI-MS of peptide-conjugated
N-linked standards. FIG. 17A shows that unmodified glycans ionize
unevenly, especially charged glycans (f and g). After conjugation
with aminooxyacetyl peptide, ionization is much more uniform (FIG.
17B).
[0104] FIG. 18 shows the identification of serum N-glycans from
MALDI-MS spectra. FIG. 18A shows the results of neutral glycans,
while FIG. 18B shows the results of acidic glycans. Labeled peak
numbers correspond to entries in Table 7.
[0105] FIG. 19 shows the results of neutral N-glycans from PVDF
digest. Only the most abundant glycans are observed.
[0106] FIG. 20 provides MALDI spectra of glycans before and after
applying new recipe with optimized conditions. DHB-Spermine
Neutrals, 1 pmol (FIG. 20A); DHB Spermine Acidics, 1 pmol (FIG.
20B); 5-MSA/DHB Neutrals, 25 fmol (FIG. 20C); and Nafion.TM.+ATT
Acidics, 25 fmol (FIG. 20D).
[0107] FIG. 21 provides results from glycan quantification using an
optimized matrix recipe for MALDI-MS.
[0108] FIG. 22 provides a schematic of an example of a methodology
for analysis.
[0109] FIG. 23 provides a flowchart illustration of one example of
a combined analytical-computational method for glycan analysis.
[0110] FIG. 24 provides a scheme for an exemplary method for
glycoprotein analysis-glycan site occupancy analysis.
[0111] FIG. 25 provides results from a glycan site occupancy
analysis for ribonuclease B. MS data for a peptide eluting at 7.8
minutes for unlabeled sample (FIG. 25A) and for the
.sup.16O/.sup.18O labeled 1:1 mixture (FIG. 25B) are provided. The
expected [M+H]+ for the unlabeled peptide fragment is 476.29
Da.
[0112] FIG. 26 provides a MALDI-MS spectra of N-glycans from RNaseB
with the expected high mannose structures.
[0113] FIG. 27 provides results from MALDI-MS of N-glycans from
ovalbumin. Each labeled peak corresponds to a previously reported
structure.
[0114] FIG. 28 provides structures and theoretical masses of
N-glycans released from antibodies.
[0115] FIG. 29 shows the results from an analysis of depletion of
serum albumin and IgGs from serum. FIG. 29A provides the results
from a SDS gel stained with Simply Blue before (Lane 7 and 14) and
after removal of serum albumin and IgG using different conditions
(Lanes 1-6 and 8-13). FIG. 29B provides the results from a Western
blot (using Protein A-HRP detection) used for quantifying the
removal of IgGs. Lanes 7 and 14 are without depletion, and Lanes
1-6 and 8-13 are using different conditions for the removal. FIG.
29C provides the quantification of IgG removal.
[0116] FIG. 30A shows the results of Protein A separation of IgG
from serum. FIG. 30A provides the results from a glycoblot of
Protein A flow-through (Lane 3) and elution (Lane 4). Lane 1
contains protein standards, while Lane 2 (negative control)
contains human serum albumin (* marks where album would run on an
SDS-PAGE gel). Only glycosylated proteins are observed in the
glycoblot, so the albumin does not stain.
[0117] FIG. 31 shows the identification of serum N-glycans from
MALDI-MS spectra. FIG. 31A shows the results of the neutral
glycans, while FIG. 31B shows the results of the acidic
glycans.
[0118] FIG. 32 provides the results from LC-MS (FIG. 32A) and
CE-LIF (FIG. 32B) analysis of neutral glycome from serum.
[0119] FIG. 33 provides a MALDI-MS acidic glycome profile of saliva
(FIG. 33A) and urine (FIG. 33B).
[0120] FIG. 34 provides a quantitative neutral glycome profile for
serum with normal (FIG. 34A) and low (FIG. 34B) IgG levels.
[0121] FIG. 35 provides alterations in serum glycomic patterns
between matched healthy (FIG. 35A) and cancer (FIG. 35B)
patients.
[0122] FIG. 36 provides a schematic representation of an example of
the computational strategy for the analysis of glycoprofile
patterns.
[0123] FIG. 37 provides the results from a matrix comparison of
MALDI-MS analysis of a hyaluronic acid fragment. DHB matrix (FIG.
37A); ATT-Nafion.TM. matrix (FIG. 37B). Expected [M-H]=4170.6.
[0124] FIG. 38 provides a schematic illustrating an exemplary
analytic method using NMR, MALDI-MS and a computational
approach.
[0125] FIG. 39 provides the structures satisfying experimental
constraints. Mol. Wt. 1990; Man3Gal2Fuc1GlcNAc5-25% (FIG. 39A);
Mol. Wt. 1990; Man3Gal2GlcNAc4GalNac1Fuc1-50% (FIG. 39B); Mol. Wt.
2047; Man3Gal2GalNAc1GlcNAc5-25% (FIG. 39C).
[0126] FIG. 40 provides a schematic illustrating an exemplary
method of glycoconjugate characterization.
DETAILED DESCRIPTION
[0127] It has been recognized that carbohydrates play a significant
role in a variety of biological and pathological processes.
However, information regarding which carbohydrates are important
and how they affect biological functions is limited. Additional
methods for analyzing carbohydrates are desirable. Such methods are
provided herein and can be used for a number of purposes as
described below.
[0128] Improved methods of analyzing carbohydrates are provided
herein. Carbohydrates include, for example, starches, celluloses,
gums and saccharides. Although, for illustration, the term
"saccharide" or "glycan" is used below, this use is not intended to
be limiting. It is intended that the methods provided herein can be
directed to any carbohydrate, and the use of a specific kind of
carbohydrate is merely exemplary.
[0129] As used herein, the term "saccharide" refers to a molecule
comprising one or more monosaccharide groups. Saccharides,
therefore, include mono-, di-, tri- and polysaccharides. A
"polysaccharide", as used herein, is any polymer made up of two or
more monosaccharides consecutively linked through glycosidic
linkages. Polysaccharides include those that are isolated from
plant, animal and microbial (e.g., bacterial, viral) sources. The
term "polysaccharide" as used herein, therefore, includes mucins,
alginates, pectins, chitin, pentosan, dextran sulfate, amylose,
cellulose, etc. Polysaccharides also include glycosaminoglycans
(GAGs), a family of complex polysaccharides that include dermatan
sulfate (DS), chondroitin sulfate (CS), heparin, heparan sulfate
(HS), keratan sulfate and hyaluronic acid (HA).
[0130] Polysaccharides further include glycans. Glycans, as used
herein, are polysaccharides found on cells, proteins, lipids and in
body fluids that are, generally, composed of hexoses,
N-acetylhexosamines (HexNAcs), fucoses, sialic acids, etc. Each of
these in turn can correspond to a single or multiple explicit
monosaccharides, such as glucose (Glc), galactose (Gal), mannose
(Man), N-acetylglucosamine (GlcNAc), N-acetylgalactosamine
(GalNAc), fucose (Fuc), N-acetylneuraminic acid (NeuAc),
N-glycolylneuraminic acid (NeuGc), etc. Glycans can be branched or
unbranched. The term "glycan" includes glycans that are intact
(i.e., as they were originally found in nature or in a sample) or
glycans that have been digested (i.e., fragment(s) of a glycan
produced from chemical or enzymatic treatment.) The term is also
intended to include charged and uncharged glycans and, therefore,
neutral, acidic and basic glycans.
[0131] Glycans can be found linked to non-saccharide components,
such as lipids or proteins. Glycans linked to non-saccharide
components are herein referred to as glycoconjugates. The term
"glycoconjugate" refers to a conjugate of one or more glycans
attached to a non-saccharide component. Generally, the attachment
of the one or more glycans occurs through covalent linkage.
Glycoconjugates include glycoproteins, glycopeptides,
peptidoglycans, proteoglycans, glycolipids and lipopolysaccharides.
The exemplary use of any one of these terms is also not intended to
be limiting. As used herein, a "peptide-based glycoconjugate" is
meant to refer to glycoproteins, glycopeptides, peptidoglycans and
proteoglycans, while a "lipid-based glycoconjugate" is meant to
refer to glycolipids and lipopolysaccharides. As used herein,
"peptides" are intended to refer to proteins and polypeptides.
Peptides, therefore, include short and long polypeptides as well as
complete proteins.
[0132] Peptide-based glycoconjugates contain N- and O-glycans (also
referred to herein as N- and O-linked glycans.) For illustration,
but not intended to be limiting, N-glycans are generally classified
into three types based on their structure: high mannose, hybrid and
complex (Sears, P., Wong, C. H. 1998. Cell Mol Life Sci. 54,
223-52.) All N-glycans contain a conserved pentasaccharide core
composed of two N-acetylglucosamine residues followed by three
mannose saccharides (FIG. 1). High mannose structures contain up to
six more mannoses on both branches without further hexosamine,
galactose or sialic acid residues (FIG. 2A), while complex
structures have no additional mannoses on either arm (FIG. 2B).
Instead, they are composed of additional hexosamines and/or
galactoses and/or sialic acids. Hybrid structures are mixes of both
high mannose and complex structures (FIG. 2C). Additionally, branch
termini can be capped with sialic acid (a charged monosaccharide),
and the core or branches can be fucosylated. In addition, rare
modifications exist, including sulfates, phosphates and xyloses,
although these are typically not found in humans. The term "glycan"
is intended to encompass these and other modified forms. O-glycans,
on the other hand are assembled by series of reactions catalyzed by
glycosyltransferases and sulfotransferases in the Golgi. In the
O-glycan pathways, every sugar is transferred from a specific
nucleotide sugar donor by the action of specific membrane-bound
glycosyltransferases. In cancer cells, many of the enzymes involved
in O-glycan biosynthesis are up- or down-regulated.
[0133] In addition to being found as part of a glycoconjugate,
glycans can be found attached to the surface of a cell or they can
be found in free form (i.e., separate from and not associated with
a cell or other component.) In some instances, the glycans attached
to the surface of a cell are one or more glycans that are part of a
glycoconjugate, wherein the glycoconjugate is attached to or forms
part of the cell's surface. Therefore, the methods provided herein
can be used to analyze glycans that are part of glyconjugates,
attached to the surface of a cell, found in free form or some
combination thereof A "sample containing glycans" is meant to
embrace a sample containing one or more glycans in any of these
aforementioned forms. A "sample containing carbohydrates" is
likewise meant to embrace a sample containing one or more
carbohydrates in free form or as part of a complex or conjugate. As
used herein, the "glycome" of a sample is all of the carbohydrates
of the sample. The carbohydrates can be part of glycoconjugates but
are not necessarily so.
[0134] It has been found that the analysis of carbohydrates (e.g.,
glycans), with an analytical (i.e., experimental) method in
combination with a computational method results in improved
analysis. Therefore, methods for analyzing samples containing
carbohydrates (e.g., glycans) are provided, which include a
combined analytical-computational platform. Non-limiting examples
of such methods are illustrated in detail in the Examples.
[0135] In the methods provided herein, any analytical (or
experimental) method for analyzing samples containing carbohydrates
(e.g., glycans) so as to characterize them can be performed. As
used herein, to "characterize" means to obtain data that can be
used to determine the identity, structure, composition or quantity
of a carbohydrate (e.g., glycan) or a glycoconjugate. The term also
means to determine a property of the carbohydrates (e.g., glycans)
or glycoconjugate. A "property" as used herein is a characteristic
(e.g., structural characteristic) of the carbohydrates (e.g.,
glycans) or glycoconjugate that provides information about the
carbohydrate (e.g., glycan) or glycoconjugate. Examples of
properties include charge, chirality, nature of substituents (or
components), quantity of substituents, molecular weight, molecular
length, compositional ratios of substituents or units, type of
basic building blocks (e.g., saccharide, amino acid, lipid
constituents), hydrophobicity, enzymatic sensitivity,
hydrophilicity, secondary structure and conformation, ratio of one
set of modifications to another set of modifications, etc. When the
term is used in reference to a glycoconjugate, it can also include
determining the glycosylation sites, the glycosylation site
occupancy, the identity, structure, composition or quantity of the
carbohydrate (e.g., glycan) and/or non-saccharide component of the
glycoconjugate as well as the identity and quantity of a specific
glycoform.
[0136] As used herein, "glycosylation" is meant to include the
pattern or a subset or even one particular carbohydrate (e.g.,
glycan), while "glycosylation pattern" refers to a pattern (or
signature) that characterizes or distinguishes a sample with
respect to the carbohydrates (e.g., glycans) present in the sample.
A glycosylation pattern can be determined for a sample even if all
of the details of the carbohydrate (e.g., glycan) structures are
not known. A glycosylation pattern can provide, but is not required
to, for example, the absolute or relative number, identity, etc. of
the carbohydrates or components thereof in a sample. As used
herein, a "component of a carbohydrate" is a monomer, set of
monomers or a motif of the carbohydrate but is not the complete
carbohydrate. As used herein, a motif of a carbohydrate is a
specific set of monomers or subsequence of a carbohydrate.
Generally, the motif includes 3, 4, 5 or more monomers of the
carbohydrate. A "component of a glycoconjugate" includes the
carbohydrate or portion thereof and the non-saccharide moiety or
portion thereof.
[0137] In a population of glycoconjugates, each glycosylation site
may (or may not) be occupied by a specific carbohydrate (e.g.,
glycan) all the time. Therefore, as used herein, a "glycoform" is a
specific form of a glycoconjugate, which contains a particular
carbohydrate (e.g., glycan) or set of carbohydrates (e.g., glycans)
conjugated to a particular non-saccharide component at particular
glycosylation site(s). As used herein, the term "glycosylation site
occupancy" refers to the frequency (percentage) in which one or
more specific glycosylation sites on a lipid or peptide is occupied
by a carbohydrate (e.g., glycan). In one embodiment the
glycosylation site occupancy is the "total glycosylation site
occupancy", which refers to the frequencies in which all of the
specific glycosylation sites on a lipid or peptide are occupied by
a carbohydrate (e.g., glycan). Analyzing glycoconjugates,
therefore, can include analyzing the carbohydrates (e.g., glycans),
the non-saccharide moieties, the complete glycoconjugate or some
combination thereof. In some instances, the analysis of a
glycoconjugate allows for the identification of the one or more
carbohydrates (e.g., glycans) and the non-saccharide component of
the glycoconjugate. The identification can, therefore, include
determining the sequence of the one or more carbohydrates (e.g.,
glycans) and/or the non-saccharide component of the glycoconjugate.
As an example, the characterization with an analytic method can
include determining the peptide (or lipid) sequence, composition or
structure of a peptide-based glycoconjugate (or lipid-based
glycoconjugate). The analysis of a glycoconjugate can also include
the determination of the glycosylation sites and/or the
glycosylation site occupancy of the glycoconjugate. The specific
carbohydrates (e.g., glycans) that occupy each specific
glycosylation site can also be characterized using one or more
analytic (or experimental) techniques. Analyses of glycoconjugates
can, therefore, also include the identification and quantification
of all of the glycoforms in a sample containing glycoconjugates.
The method can further include the determination of the
glycosylation sites and glycosylation site occupancy of the
glycoconjugates.
[0138] In one aspect of the invention, therefore, a method is
provided for the determination of the glycosylation sites and
glycosylation site occupancy. In one embodiment this method
includes cleaving the non-saccharide component of the
glycoconjugates, cleaving the carbohydrates (e.g., glycans) from
the non-saccharide components, labeling the non-saccharide
components of a first portion of the sample at the glycosylation
sites, cleaving the carbohydrates (e.g., glycans) from the
non-saccharide components of a second portion of the sample,
analyzing the first and second portions of the sample containing
the non-saccharide components and comparing the results. The
glycosylation site occupancy can be quantified from ratios of the
masses of the non-saccharide component of the first and second
portions of the sample. The non-saccharide components of the first
portion of the sample can be labeled with a labeling agent, and the
non-saccharide components of the second portion of the sample can
be labeled or unlabeled.
[0139] Labeling agents as used herein include isotopes of C, N, H,
S or O. In one particular example the labeling agent is an isotope
of O, such as .sup.18O. In another example the labeling agent is
.sup.2H.
[0140] Carbohydrates (e.g., glycans), glycoconjugates and
non-saccharide components can be analyzed using any of a number of
analytical methods. Analytical methods include, for example, mass
spectrometric methods, nuclear magnetic resonance (NMR),
electrophoretic methods and chromatographic methods. Examples of
mass spectrometric methods include ESI-MS, LC-MS, LC-MS/-MS,
MALDI-MS, MALDI-MS/MS, MALDI-TOF-MS, MALDI/PSD-MS,
MALDI-TOF/TOF-MS, MALDI-FTMS, LC-MALDI-MS, LC-MALDI-TOF-TOF-MS,
Nano-LC MALDI-TOF-TOF-MS, TANDEM-MS, etc.
[0141] Analysis of a sample containing carbohydrates (e.g.,
glycans) (e.g., with a mass spectrometric method) can, for example,
provide information regarding the monomer composition and/or their
relative abundance. As used herein, "monomer composition" refers to
the identity and/or quantity of the monomers that make up a
carbohydrate. When the carbohydrate is a polysaccharide, such as a
glycan, the term is "monosaccharide composition" (e.g., the number
of hexoses, N-acetyl hexosamines, fucoses, sialic acids, etc.)
"Relative abundance of the monomers" refers to the ratios of the
relative amounts of particular monomers to other monomers.
[0142] In some embodiments, the analytical method includes the use
of MALDI-MS. Matrix-assisted laser desorption ionization mass
spectrometry (MALDI-MS) techniques for the analysis of
oligosaccharides have been described (Juhasz, P. & Biemann, K.
(1995) Carbohydr Res 270, 131-47 and Juhasz, P. & Biemann, K.
(1994) Proc Natl Acad Sci USA 91, 4333-7; Venkataraman, G.,
Shriver, Z., Raman, R. & Sasisekharan, R. (1999) Science 286,
537-42; Rhomberg, A. J., Shriver, Z., Biemann, K. &
Sasisekharan, R. (1998) Proc Natl Acad Sci USA 95, 12232-7; Ernst,
S., Rhomberg, A. J., Biemann, K. & Sasisekharan, R. (1998) Proc
Natl Acad Sci USA 95, 4182-7; and Rhomberg, A. J., Ernst, S.,
Sasisekharan, R. & Biemann, K. (1998) Proc Natl Acad Sci USA
95, 4176-81). Optimized MALDI-MS analytic methods are also provided
herein.
[0143] NMR methods include, for example, simple 1D, 2D, COSY,
gCOSY, TOCSY, NOESY, etc. When NMR is used to analyze a sample
containing carbohydrates (e.g., glycans), the results from the NMR
analysis can provide information regarding the monomer composition
and/or linkage for the carbohydrates (e.g., glycans). "Linkage
information" refers to the type and/or abundance of particular
linkages between monomers (or monosaccharides in the case of
polysaccharides). "Linkage abundance" is used to refer to the
absolute or relative amounts (i.e., as ratios) of particular
linkages. Types of linkages present in glycans include, for
example, NeuNAc.alpha.2-3Gal, NeuNAc.alpha.2-6Gal,
GlcNAc.beta.1-2Man, GlcNAc.beta.1-4Man, Man.alpha.1-6Man,
Man.alpha.1-3 Man, Man.alpha.1-2Man, Gal.beta.1-3GalNAc,
GlcNAc.beta.1-6GalNAc, GlcNAc.beta.1-3GalNAc, etc. NMR analysis can
also provide information regarding the ratios of the monomers of
the carbohydrates (e.g., glycans). NMR can also be used to
determine the glycosylation site occupancy of a glycoconjugate. NMR
can further be used to determine monomer composition as well as
relative amount (i.e., ratios of particular sets of monomers).
[0144] As an example, 2D-NMR can be used for the identification of
N-linked and O-linked glycan site occupancy. A combination of COSY,
TOCSY, NOESY experiments can be first conducted on a specific
quantity of a peptide-based glycoconjugate. Using COSY and TOCSY
data, all the spin systems (amino acids) can be assigned. NOESY
experiments can also be used to determine the specific amino acid
sequence. This information allows the specific identification of
all the asparagines (Asn) and serine (Ser) or threonine (Thr)
residues in the sample. NOEs between the protons of the Asn, Ser or
Thr side chains and proximal carbohydrate residues can be easily
monitored, which allow the monitoring and quantification of
carbohydrate (e.g., glycan) occupancy at each glycosylation site.
This is particularly useful for high abundance glycosylation
sites.
[0145] Incorporating NMR data as constraints to further refine mass
spectrometric information (e.g., MALDI-MS information) enables the
elimination of explicit compositions that do not satisfy the
monomer (e.g., monosaccharide) composition data and a more
quantitative determination of the abundance of monomers (e.g.,
monosaccharide) and linkage distributions. In addition,
biosynthetic rules and database look-ups (e.g.,
http://www.functionalglycomics.org/glycomics/molecule/jsp/carbohydrate/ca-
rbMoleculeHome.jsp) can help in further convergence of the solution
to obtain an accurate picture of the number and relative abundance
of the species in the sample as well as the best characterization
of the individual structures corresponding to these species.
Important NMR information that can be used as constraints to refine
the carbohydrate (e.g., glycan) structures are, for example, the
linkage abundance between certain monomers (e.g., monosaccharides)
and/or the specific ratios between them. Examples of these include
Man.alpha.1-6Man, Man.beta.1-4GlcNAc, GlcNAc.beta.1-4GlcNAc,
Man.alpha.1-3Man, GlcNAc.beta.1-6Man, GlcNAc.beta.1-4Man, GlcNac
.beta.1-2Man, Gal.beta.1-4GlcNAc, Gal.alpha.1-3Gal,
Fuc.alpha.1-6GlcNAc, GalNAc.beta.1-4GlcNAc, NeuNAc.alpha.2-3Gal,
NeuNAc.alpha.2-6Gal, Man.alpha.1-2Man, Gal.beta.1-3GalNAc,
GlcNAc.beta.1-6GalNAc and GlcNAc.beta.1-3GalNAc. Methods using such
information are also provided herein.
[0146] Electrophoretic methods include, for example, gel
electrophoresis, capillary electrophoresis (CE) and capillary
electrophoresis-laser induced fluorescence (CE-LIF), etc. Some of
the electrophoretic methods can further include the labeling of the
carbohydrates (e.g., glycans) with fluorophores, such as, for
example, fluorescence-assisted carbohydrate electrophoresis (FACE)
and CE-LIF. A method for the compositional analysis of
oligosaccharides using CE has been described (Rhomberg, A. J.,
Ernst, S., Sasisekharan, R. & Biemann, K. (1998) Proc Natl Acad
Sci USA 95, 4176-81).
[0147] Chromatographic methods include high performance liquid
chromatography (HPLC).
[0148] Samples containing carbohydrates (e.g., glycans) can be
analyzed with any of the analytical methods provided herein. The
methods can further include a step of contacting a sample
containing carbohydrates (e.g., glycans) with acidic or basic
conditions in order to cleave the carbohydrates (e.g., glycans) or
monomers (e.g., monosaccharides) from the carbohydrates (e.g.,
glycans). For example, glycans can be cleaved by treating a sample
with hydrazine or basic borohydride. Sialic acid residues can be
cleaved using acidic conditions and high temperature (for example,
sulfuric acid at 80.degree. C.).
[0149] Carbohydrates (e.g., glycans) can also be quantified using
calibration curves of known carbohydrate (e.g., glycan) standards.
More detailed examples of these methods are provide below in the
Examples.
[0150] Any of the analytical methods provided herein can further
comprise the use of carbohydrate- or glycan-degrading enzymes, such
as by contacting a sample containing glycans with a
glycan-degrading enzyme. As used herein "carbohydrate-degrading
enzymes" or "glycan-degrading enzymes" are enzymes that modify a
carbohydrate or glycan in some way. As one example, the
modification can be the cleavage of the carbohydrate or glycan.
Following enzymatic degradation, a sample of degraded carbohydrates
(e.g., glycans) can be analyzed with a method as described herein.
Examples of glycan-degrading enzymes are known in the art and
include sialidase, galactosidase, mannosidase,
N-acetylhexosaminidase or a combination thereof.
[0151] The information gathered from the analytical methods can be
used to generate constraints. As an example, a method of analyzing
a sample containing carbohydrates (e.g., glycans) with analytical
and computational methods can include the steps of analyzing the
sample with an analytical method (e.g., performing an experiment on
the sample), generating constraints and solving the constraints. As
used herein, a "computational method" is any method that involves
establishing and/or solving a mathematical relationship.
"Constraints", as used herein, are relationships of one or more
values, results or information about a sample containing
carbohydrates (e.g., glycans) can be compared or evaluated as part
of a computational method. The relationships can be mathematical
equations and/or equalities (e.g., equal to, at most, at least,
including, etc.) The constraints can, for example, be one or more
mathematical equations generated with the data obtained from an
analysis of a sample containing carbohydrates (e.g., glycans)
and/or other information obtained from other sources, such as
databases or with other analytical methods. As part of a
computational method, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 15 or more
mathematical equations can be generated.
[0152] As described above, constraints can be generated with data
obtained from the results of an analytical method. Therefore,
constraints can be generated from the results of an analysis of a
glycoconjugate, a non-saccharide component of a glycoconjugate, a
carbohydrate (e.g., glycan) or some combination thereof.
Constraints can also be generated from information regarding a
component of a carbohydrate (e.g., glycan) and/or a glycoconjugate.
Constraints can also be generated with other information, such as
information from databases that contain information about
carbohydrates (e.g., glycans), glycoconjugates and/or
non-saccharide components, as well as information regarding mass,
enzyme action and/or biosynthesis. The databases referred to herein
can be those described herein in the Examples, known in the art or
can be generated with the methods provided. Constraints can also be
generated from information regarding the origin of a sample, the
expression system or expression conditions for the synthesis of a
carbohydrate (e.g., glycan) or glycoconjugate, the species from
which a carbohydrate (e.g., glycan) or glycoconjugate is derived,
the expression levels of glycosidases and glycosyltransferases from
the source of a carbohydrate (e.g., glycan) or glycoconjugate or
the state of the source of a carbohydrate (e.g., glycan) or
glycoconjugate.
[0153] As mentioned above, the constraints can be generated using,
for instance, what is known of the biosynthetic pathway of glycan
synthesis. Unlike DNA or protein synthesis, which are
template-driven processes, glycan biosynthesis is a complex process
involving a multitude of enzymes. A detailed scheme of N-glycan
biosynthesis is shown in FIG. 3 and the biosynthetic enzymes and
their EC numbers are listed in Table 1. The process is initiated in
the cytoplasm, with the nascent sugar attached to the endoplasmic
reticulum (ER) membrane through a lipid anchor. After a glycan core
of two glucosamines followed by five mannose residues is
constructed, the orientation of the growing glycan is flipped to
face the lumen of the ER. There, four more mannose residues are
added by a-mannosyltransferase, and one branch is capped with three
glucoses. At this point, oligosaccharyl transferase catalyzes the
removal of the nave glycan from its lipid anchor and attaches it to
a glycosylation site on a protein undergoing synthesis in the ER
(Varki, A. (1999) Essentials of glycobiology. Cold Spring Harbor
Laboratory Press, Cold Spring Harbor, N.Y.)
[0154] To ensure that the glycan can play its proper role in
protein folding and transport, the three terminal glucose residues
and one mannose are removed. This trimming is required for the
glycan to interact with the chaperone proteins calnexin and
calreticulin (Helenius, A., Aebi, M. (2001) Science 291, 2364-9;
Parodi, A. J. (2000) Annu Rev Biochem 69, 69-93.) As the correctly
folded protein passes through the Golgi on its way either to
secretion or the cell membrane, further glycan modifications can
take place. Specifically, mannosidases can trim more mannoses off
the core sugar, while a host of glycosyltransferases can add
further GlcNAc, fucose, galactose and sialic acid moieties, among
others (Sears, P., Wong, C. H. (1998) Cell Mol Life Sci 54,
223-52.)
TABLE-US-00001 TABLE 1 Common Enzymes Involved in N-glycan
Biosynthesis EC# Enzyme name 2.4.1.-- Hexosyltransferases (ALG 6,
8, 10, 11) 2.4.1.38 Glycoprotein .beta.-galactosyltransferase
2.4.1.68 Glycoprotein 6-.alpha.-L-fucosyltransferase 2.4.1.83
Dolichyl phosphate mannose transferase 2.4.1.101
.alpha.-1,3-mannosyl-glycoprotein 2-.beta.-N-acetylglucosaminyl
transferase 2.4.1.117 Dolichyl phosphate .beta.-glucosyltransferase
2.4.1.119 Oligomannosyl transferase 2.4.1.130 Oligomannosyl
synthase (ALG 3, 9, 12) 2.4.1.132 Glycolipid
3-.alpha.-mannosyltransferase 2.4.1.141 N,N'-diacetylchitobiosyl
pyrophosphoryldolichol synthase 2.4.1.142
Chitobiosyldiphosphodolichol .beta.-mannosyltransferase 2.4.1.143
.alpha.-1,6-mannosyl-glycoprotein 2-.beta.-N-acetylglucosaminyl
transferase 2.4.1.144 .beta.-1,4-mannosyl-glycoprotein
4-.beta.-N-acetylglucosaminyl transferase 2.4.1.145
.alpha.-1,3-mannosyl-glycoprotein 4-.beta.-N-acetylglucosaminyl
transferase 2.4.1.155 .alpha.-1,6-mannosyl-glycoprotein
6-.beta.-N-acetylglucosaminyl transferase 2.4.1.201
.beta.-1,6-mannosyl-glycoprotein 4-.beta.-N-acetylglucosaminyl
transferase 2.4.99.1 .beta.-galactoside
.alpha.-2,6-sialyltransferase 2.5.1.-- Transferring alkyl or aryl
groups, other than methyl groups 2.7.1.108 Dolichol kinase 2.7.8.15
Chitobiosylpyrophosphoryl dolichol synthase 3.1.3.51
Dolichyl-phosphatase 3.1.4.48 dolichylphosphate-glucose
phosphodiesterase 3.2.1.-- Hydrolyzing O- and S-glycosyl compounds
3.2.1.106 Mannosyl-oligosaccharide glucosidase 3.2.1.113
mannosyl-oligosaccharide 1,2-.alpha.-mannosidase 3.2.1.114
mannosyl-oligosaccharide 1,3-1,6-.alpha.-mannosidase 3.6.1.43
Dolichol diphosphatase
[0155] Constraints can be solved using mathematical and heuristic
approaches known in the art based on the specific constraints
generated for a specific problem. The approaches can range from
standard numerical methods, such as Gaussian Elimination to more
complex methods, such as linear programming and simulated
annealing. Other approaches that may be used to solve the
constraints include parameter estimation approaches, such as least
squares and non-linear methods. Yet another class of approaches are
those based on search techniques that generate optimal solutions.
Many of these mathematical and heuristic methods are available as
computer programs and mathematical software.
[0156] A detailed example of an analytical and computational method
is provided in FIG. 23. One of skill in the art will appreciate,
however, that there are a number of ways in which analytical
methods can be combined with computational methods to achieve the
desired characterization of a sample containing carbohydrates
(e.g., glycans). In some embodiments it is the combination which
provides more efficient analysis. The examples provided are not
intended to be limiting.
[0157] It has also been found that the combination of mass
spectrometry (MS), such as MALDI-MS, and NMR also provides for the
improved analysis of carbohydrates. When a sample containing
carbohydrates (e.g., glycans) is analyzed, methods using MS and
NMR, in one embodiment, can allow for the simultaneous assignment
of monomer composition, linkages between monomers and detailed
information about the chemical structure of the carbohydrates
(e.g., glycans) in the sample.
[0158] In one example, MALDI-MS can provide the molecular weight of
each glycan in a sample in one single profile of mass to charge
ratio vs. intensity of the peak. Typically this gives the molecular
mass since the charge observed in MALDI-MS is 1. Furthermore,
depending on the mode of operation of the MALDI-MS instrumentation,
negatively charged glycans can be analyzed distinctly from neutral
glycans. Based on the molecular weight, the specific composition of
the glycan in terms of number of hexoses, N-acetyl hexosamines,
fucoses and sialic acids can be obtained. The data can, therefore,
provide a first set of constraints for a computational method. In
addition to providing the distinct mass signature for each glycan
in the mixture, the MALDI-MS technique can also be optimized to
provide quantitative information on the relative abundance of the
glycans. This information can also be used as a constraint that
provides a boundary for a computational method to assign the exact
glycan structures for each mass peak.
[0159] Since the hexoses can include galactoses, mannoses and
glucoses, and the HexNAcs can include N-acetylglucosamines and
N-acetylgalactosamines, NMR can be used in combination with the
above-described MALDI-MS analysis to provide additional information
to further characterize the glycans of the sample. The anomeric
proton and carbon of each monosaccharide in a glycan has a distinct
chemical shift and thus provides a signature for quantifying each
monosaccharide in a glycan mixture. Thus, the 1D proton of a glycan
mixture along with the coupling constants, which can further be
determined using gCOSY and TOCSY, can provide quantitative
information about distinct monosaccharides in the mixture. For
example, the ratios in the abundance (ratios of the absolute or
relative amounts) of glucose to galactose to mannose can be
obtained. These parameters can be lumped into hexose abundance in
the MALDI-MS data. Thus, using the explicit monosaccharide
composition based on NMR can provide another constraint for a
computational framework to assign the glycan structures in the
mixture.
[0160] In addition to the monosaccharide composition, NMR
spectroscopy can also provide, for example, quantitative
information on the linkages between monosaccharides. This
information is important, for example, for terminal sialic acids
which can be .alpha.2-3 or .alpha.2-6 linked tope penultimate
monosaccharide. This linkage cannot be explicitly assigned using
MALDI-MS data. The anomeric chemical shifts of the monosaccharides
can further be classified based on the neighboring monosaccharide
(at the reducing end), which can provide the abundance of the
specific linkage between the two monosaccharides. The linkage
abundance is important, since it is required to completely assign
the glycan structure. While sample amounts have been a limiting
factor for complete structure assignment of glycans using NMR
spectroscopy, simple 1D proton and 2D gCOSY experiments do not
require as much sample but can provide much information about a
sample containing glycans. These experiments can also be more
sensitive with low sample amounts compared to NOESY experiments,
therefore, in some embodiments, 1D and 2D gCOSY analytical methods
may be preferred.
[0161] In addition to the MALDI-MS and NMR analysis, a
computational method can be used to incorporate the MALDI-MS and
NMR data as constraints. There are multiple ways to develop the
computational method for incorporating the analytical data as
constraints and in searching for a solution of the constraints
(i.e., obtaining the most accurate chemical structure information
for the carbohydrates (e.g., glycans) in the sample.) In the case
of N-linked glycans, for example, although the biosynthesis is
complex, it is well known in terms of an ordered set of events
which lead to the diversity of glycans. Thus, this knowledge of
biosynthesis can be encoded as rules to construct the entire
solution space of theoretically possible glycan structures based on
the mass and composition information obtained from the MALDI-MS
data set. This large solution space can be narrowed during each
step of applying other information such as relative abundance
between two glycans, explicit monosaccharide composition, linkage
abundance, etc. as constraints to give the final best solution in
terms of the chemical structures of glycans in the sample. In the
case of O-linked glycans, the biosynthesis rules are less defined,
thus starting from a theoretical solution space of all
possibilities might be cumbersome. For O-linked glycans, therefore,
although not required, a heuristic approach of constructing the
most appropriate solution space based on monosaccharide composition
and linkage abundance can be used to provide a rapid way of
identifying the solution.
[0162] In some embodiments, the methods provided herein also
include generating a list of the possible compositions of
carbohydrates (e.g., glycans), glycoconjugates or components
thereof and their theoretical masses. The list can be based on the
biosynthetic pathways for glycosylation (FIG. 3). The list can be
generated with other means, such as with the results from the use
of any of the methods provided herein or known in the art. The
methods provided can also comprise or consist of the steps of
generating a list of carbohydrate (e.g., glycan) or glycoconjugate
properties. One example of such a method includes measuring 1, 2,
3, 4, 5, 6, 7, 8, 9, 10 or more properties of a carbohydrate (e.g.,
glycan) or glycoconjugate and recording a value for the one or more
properties to generate a list of carbohydrate (e.g., glycan) or
glycoconjugate properties. In one embodiment the list comprises the
number of one or more types of monomers (e.g., monosaccharides).
The list can also include the total mass of a carbohydrate (e.g.,
glycan) or glycoconjugate, the mass of a non-saccharide component
of a glycoconjugate, the mass of a carbohydrate (e.g., glycan),
etc.
[0163] The list in one embodiment can be a data structure tangibly
embodied in a computer-readable medium, such as computer hard
drive, floppy disk, CD-ROM, etc. Table 6 represents an example of
such a list. The list of Table 6 has a plurality of entries, where
each entry encodes a value of a property. The values encoded can be
any kind of value, such as, for example, single-bit values,
single-digit hexadecimal values or decimal values, etc.
[0164] Therefore, also provided herein is a database, tangibly
embodied in a computer-readable medium, wherein the database stores
information descriptive of one or more carbohydrates (e.g.,
glycans) and/or glycoconjugates. The database comprises data units
that correspond to the carbohydrate (e.g., glycan) and/or
glycoconjugate. The data units include an identifier that includes
one or more fields, each field storing a value corresponding to one
or more properties of the carbohydrates (e.g., glycans) and/or
glycoconjugates. In one embodiment the identifier includes 2, 3, 4,
5, 6, 7, 8, 9, 10 or more fields. The database, for example, can be
a database of all possible glycoconjugates and/or carbohydrates
(e.g., glycans) or can be a database of values representing a
glycome profile or pattern for one or more samples. Methods of
analyzing and/or determining a glycome profile or pattern is
described further below.
[0165] Carbohydrates (e.g., glycans) can be charged or uncharged.
They can be acidic, basic or neutral. It has also been found that
separately analyzing charged and uncharged carbohydrates (e.g.,
glycans) of a sample can provide an improvement in the analysis of
carbohydrates (e.g., glycans). Therefore, the charged and uncharged
carbohydrates (e.g., glycans) can be separated prior to the
analysis of the carbohydrates (e.g., glycans), such as with an
analytic method or other method as provided herein. As described
further in the Examples provided below, such a method has been
found to discriminate the carbohydrates (e.g., glycans) present in
the sample. Therefore, any of the methods provided herein can
include a step of separating neutral and charged carbohydrates
(e.g., glycans), such as acidic carbohydrates (e.g., glycans).
[0166] Such separation can be achieved using purification methods.
For instance, in a preferred embodiment, the separation is
accomplished with a graphitic carbon purification cartridge by
eluting glycan pools with different concentrations of acetonitrile.
Other methods will be known to those of skill in the art. Analysis
of these separate glycan pools can then be undertaken. For
instance, when using MALDI-MS, acidic glycans can be analyzed in
negative ion mode, while neutral glycans can be analyzed in
positive ion mode.
[0167] In some analytical methods, such as in the analysis with
MALDI-MS, the matrix in which the sample containing carbohydrates
(e.g., glycans) is suspended can affect the quality of the
analysis. It has been found that analysis of a sample containing
carbohydrates (e.g., glycans) is improved when the sample is
analyzed in the presence of a thymine derivative and an ion
exchange resin. The thymine derivative can be thiothymine,
2-thiothymine, 4-thiothymine, 5-aza-2-thiothymine or
6-aza-2-thiothymine (ATT). The ion exchange resin can be an
ammonium resin, a cationic exchange resin, a cationic exchange
resin in pyridinium form, an anionic exchange resin or a
perfluorinated ion exchange resin. The perfluorinated ion exchange
resin can be, for example, Nafion.TM..
[0168] Other matrices can also result in improved analysis. In some
embodiments the matrix preparation is caffeic acid with or without
spermine. In other embodiments, the matrix preparation is
dihydroxybenzoic acid (DHB) with or without spermine. In still
other embodiments the matrix preparation is spermine with DHB. The
spermine, for example, can be in the matrix preparation at a
concentration of 300 mM. The matrix preparation can also be a
combination of DHB, spermine and acetonitrile. Additionally, the
matrix can be a mixture of 5-methoxysalicylic acid (5-MSA) and
DHB.
[0169] Additionally, instrument parameters can also be modified.
These parameters may include guide wire voltage, accelerating
voltage, grid values and negative versus positive polarity. In
other embodiments, spot morphology can be employed to improve
signal intensity.
[0170] In general, when the carbohydrates (e.g., glycans) in a
sample are part of a glycoconjugate, the sample of glycoconjugates
can be first denatured with a denaturing agent. A "denaturing
agent" is an agent that alters the structure of a molecule, such as
a protein. Denaturing agents, therefore, include agents that cause
a molecule, such as a protein to unfold. Denaturing agents include
those that comprise detergents, urea, high salt concentration,
guanidium hydrochloride or heat. The denaturation can be followed
by reduction, which can be followed by carboxymethylation (or
alkylation), etc. Reduction can be accomplished with a reducing
agent, such as, dithiothreitol (DTT), .beta.-mercapto ethanol (BME)
or tris(2-carboxyethyl)phosphine (TCEP). Carboxymethylation or
alkylation can be accomplished with, for example, iodoacetic acid
or iodoacetamide. In some methods, therefore, following
denaturation the sample of glycoconjugates is reduced with a
reducing agent. In other embodiments the sample of glycoconjugates
is alkylated after being reduced.
[0171] The methods provided herein can also include cleaving
carbohydrates (e.g., glycans) from a non-saccharide component using
any chemical or enzymatic method or combination thereof known in
the art. In one embodiment this occurs prior to analysis. An
example of a chemical method for cleaving is treatment of
glycoconjugates with hydrazine or alkali borohydride. Enzymatic
methods include the use of enzymes specific to N- or O-linked
sugars. These enzymatic methods, therefore, include the use of
endoglycosidase H (Endo H), Endo F, N-Glycanase F (PNGase F) or a
combination thereof. In some embodiments, PNGase F is used when the
release of N-glycans is desired. When PNGase F is used for glycan
release from a peptide-based glycoconjugate, the protein is, in
some embodiments, unfolded prior to the use of the enzyme. The
unfolding of the protein can be accomplished with denaturation as
provided above.
[0172] After the release of the carbohydrate (e.g., glycan) from
the non-saccharide component, or when the carbohydrates (e.g.,
glycans) of a sample are in free form (not part of a
glycoconjugate), the sample containing carbohydrates (e.g.,
glycans) can be purified, for instance, by precipitating the
proteins with ethanol and removing the supernatant containing the
carbohydrates (e.g., glycans). Other experimental methods for
removing the proteins, detergent (from a denaturing step) and salts
include methods known in the art. These methods include dialysis,
chromatographic methods, etc. In one example, the purification is
accomplished with a solid phase extraction cartridge or ion
exchange resin. The solid phase extraction column can be, for
example, a graphitic carbon column, non-graphitic carbon column or
C-18 column.
[0173] Samples can also be purified with commercially available
resins and, cartridges for clean-up after chemical cleavage or
enzymatic digestion used to separate carbohydrates (e.g., glycans)
from the non-saccharide components. Such resins and cartridges
include ion exchange resins and purification columns, such as
GlycoClean H, S, and R (Glyco H, Glyco S and Glyco R, respectively)
cartridges. In some embodiments GlycoClean H is used for
purification. In still other embodiments, everything but the
carbohydrates (e.g., glycans) are removed from the sample.
[0174] Purification can also include the removal of high abundance
proteins, such as the removal of albumin and/or antibodies, from a
sample containing carbohydrates (e.g., glycans). In some methods
the purification can also include the removal of unglycosylated
molecules, such as unglycosylated proteins. Removal of high
abundance proteins can be a desirable step in some methods, such as
some high-throughput methods (described elsewhere herein). In some
embodiments, abundant proteins, such as albumin and/or antibodies,
can be removed from the samples prior to the final analysis of a
sample containing carbohydrates (e.g., glycans).
[0175] Prior to the analysis of a sample containing carbohydrates
(e.g., glycans), the sample can be fractionated. The sample can be
fractionated in order to obtain a sample of carbohydrates (e.g.,
glycans) that are a specific subgroup of molecules. "Subgroups of
molecules" include molecules of specific properties, such as
charge, molecular weight, size, binding properties to other
molecules or materials, acidity, basicity, pI, hydrophobicity,
hydrophilicity, etc. In one embodiment the subgroup of molecules is
the low abundance glycan species, and it is the low abundance
glycan species that are analyzed with the methods provided. The low
abundance glycan species include, but are not limited to, glycans
that can contain fucoses, sialic acids, galactoses, mannoses or
sulfate groups. In another embodiment the subgroup of molecules is
a group of high abundance proteins. In one embodiment the subgroup
of molecules is, therefore, the antibodies of a sample. Therefore,
the methods provided herein can be used for the analysis of the
carbohydrates (e.g., glycans) of a subgroup of molecules.
[0176] A sample can be fractionated based on properties of the
carbohydrates (e.g., glycans) and/or glycoconjugates, such as but
not limited to, charge, size, molecular weight, binding properties
to other molecules or materials, acidity, basicity, pI,
hydrophobicity and hydrophilicity. As an example, the fractionation
can be performed using solid supports with immobilized proteins,
organic molecules, inorganic molecules, lipids, carbohydrates,
nucleic acids, etc. As a further example, the fractionation can be
performed using filters, such as molecular weight cutoff (MWCO)
filters. The fractionation can also be performed using resins, such
as, cationic or anionic exchange resins, etc. Any method of
fractionation known in the art can be used. In one embodiment,
however, the sample is not fractionated before it is analyzed by an
analytical method as provided herein.
[0177] In other embodiments, carbohydrates (e.g., glycans) can be
modified to improve their ionization, such as when MALDI-MS is used
for analysis. Such modifications include permethylation and
conjugation of a glycan to a peptide or derivitization with an
organic molecule such as a chromophore. In other embodiments, the
carbohydrates (e.g., glycans) are not modified prior to their
analysis.
[0178] Samples of carbohydrates (e.g., glycans) can be analyzed
separately, or they can be analyzed as a mixture. Therefore,
samples containing carbohydrates (e.g., glycans) can be analyzed by
first separation of a sample into portions of the sample and
analyzing the portions separately or in some combination. The
methods provided include methods for the analysis of glycosylation
of a single glycoconjugate or a mixture of glycoconjugates in a
sample. Such mixtures can contain glycosylated and non-glycosylated
peptides and/or lipids.
[0179] The methods provided herein can have a limit of detection of
less than 1000, 500, 100, 75, 50, 25, 20, 18, 16, 15, 14, 13, 12,
11, 10, 9, 8, 7, 6, 5, 4, 3, 2 or 1 femtomole.
[0180] The carbohydrates (e.g., glycans) or glycoconjugates that
are analyzed with the methods provided herein can be analyzed in
solution or when immobilized on a solid support. In one embodiment
the solid support is in a 96-well plate format. In another
embodiment the solid support is an individual membrane. In yet
another embodiment the solid support can be in a 96-well plate
format that comprises a membrane. Membranes, as used herein,
include protein-binding membranes, such as polyvinylidine
difluoride (PVDF) membranes, C-18 membranes and nitrocellulose
membranes.
[0181] The methods provided herein can be performed in a
high-throughput manner. "High-throughput" methods refer to the
ability to process and/or analyze multiple samples at one time.
High-throughput methods provided herein can include the use of a
membrane-based method, such as a protein-binding membrane.
High-throughput methods can also be performed in a 96-well plate
format. In one embodiment the 96-well plate contains a
protein-binding membrane. The methods provided, therefore, can
include high-throughput sample processing steps (i.e.,
purification, digestion and/or denaturation steps, etc. performed
in a high-throughput manner). Any step or steps of any of the
methods provided herein can be performed in a high-throughput
manner. In some embodiments protein-binding membrane based
high-throughput methods can also include the removal of abundant
proteins such as albumin.
[0182] The methods provided can also include the use of robotics.
Robotics can be used in, for example, denaturation, reduction,
alkylation, purification and fractionation steps.
[0183] Building on the description above, also provided in one
aspect of the invention is a method for determining glycosylation
site occupancy of a glycoconjugate. For the determination of the
glycan site occupancy, such as for lower abundance glycoforms,
concepts from phosphoproteomics were adapted. FIG. 24 provides one
embodiment of a method for determining glycosylation site
occupancy. Briefly, a well-characterized batch of the glycoprotein
under study is used to generate a library of labeled peptides and
glycopeptides by protease digest. In order to order to facilitate
the determination of the glycosylation sites, each glycosylated
amino acid is differentially labeled. The labels that can be used
include isotopes of C, N, H, S or O. In one embodiment the
glycosylated amino acids are labeled with .sup.18O or unlabeled
(.sup.16O) using methods known in the art (Kaji, 2003). After the
labeling, the samples can be further analyzed. In one embodiment
the glycan site occupancy is quantified from the ratios of the
masses of the labeled and unlabeled fragments. In one example,
determining the glycosylation site and its occupancy can include
cleaving and labeling with a first label the glycoconjugates at the
glycosylation sites of a portion of the sample, cleaving the
glycoconjugates at the glycosylation sites of another portion of
the sample and analyzing the portion of the sample. In one
embodiment the glycosylation sites of both portions of the sample
are labeled. The portions of the sample can be analyzed separately
or as a mixture in any ratio. First instance, when there are two
portions of the sample, the two portions can be mixed in a 1:1,
1:2, 1:3, 1:4 or 1:5 ratio. The glycosylation site occupancy method
can be used to determine the identity and number of glycoforms in
the sample. Therefore, a method of determining the identity and
number of glycoforms in a sample comprising determining the
glycosylation site occupancy of a glycoconjugate and analysis to
characterize the glycoconjugates so as to determine the identity
and number of glycoforms is also provided.
[0184] As illustrated in the Examples below, the fragment
containing the partner peak with a molecular weight 2 Da heavier is
identified as the peptide containing the glycosylation site. By
comparing the data between the glycosylated and deglycosylated
samples, a preliminary identification all the peptides (or lipids
when the glycoconjugate is lipid-based) and glycopeptides (or
glycolipids) are identified, and a preliminary identification of
the carbohydrates (e.g., glycans) is obtained. This quantitative
information can be combined with an analytical method and used as
constraints in a computational method to arrive at the complete
characterization of the glycoconjugate.
[0185] Also provided is a method of generating a library. The
library consists of labeled glycoconjugates and fragments of the
glycoconjugates, the fragments being the non-saccharide components
of the glycoconjugates. In one example, a library is generated by
cleaving the backbone of the glycoconjugate and labeling the
non-saccharide components of the glycoconjugates that result with a
labeling agent. This example also includes the step of cleaving the
carbohydrates (e.g., glycans) from a glycoconjugate. The
carbohydrates (e.g., glycans) can then be removed from the sample.
The libraries so produced can be analyzed with the methods provided
herein. The libraries can also be used as a standard once
characterized and methods of using such libraries are also
provided. In one example, a method of analyzing a sample containing
glycoconjugates includes cleaving the glycoconjugates,
enzymatically removing the carbohydrates (e.g., glycans) from the
glycoconjugates and mixing the sample with a standard. The sample
mixed with the standard can then be analyzed. In one embodiment the
amounts of the glycoconjugates and non-saccharide components of the
sample and standard are compared. In one aspect of the invention
methods of producing such standards are also provided.
[0186] Protein glycosylation can affect the function of proteins or
be indicative of a cause or symptom of a disease state. For many
proteins, N- and O-linked glycans are important factors for
determining proper folding, stability and resistance to degradation
(which affects the half-life of the protein). In some proteins, N-
and O-linked glycans play a role in the activity and/or function of
the protein. In some proteins, N- and O-linked glycans are
indicative of a normal or disease state.
[0187] The methods provided, therefore, are also directed to the
analysis of the total glycome of a sample. Such methods include the
steps of analyzing the carbohydrates (e.g., glycans) of the sample
and determining the profile of the carbohydrates (e.g., glycans).
The carbohydrates (e.g., glycans) can be analyzed with any of the
methods provided herein. The "total glycome" refers to all of the
carbohydrates (e.g., glycans) that can found in a sample. For
instance, the carbohydrates (e.g., glycans) of the total glycome
can be in free form and/or they can be part of one or more
glycoconjugates. The total glycome, therefore, represents all of
the carbohydrates (e.g., glycans) in the sample. The representation
of the total glycome can be the number and identity of all of the
carbohydrates (e.g., glycans) in the sample but is not necessary
so. The total glycome, however, does provide information regarding
all of the carbohydrates (e.g., glycans) in the sample, such
information can be one or more properties of the carbohydrates
(e.g., glycans).
[0188] A "glycome profile" or "glycoprofile." refers to the number
and kind of carbohydrates (e.g., glycans) and/or components thereof
found in a sample. The glycome profile can provide, for example,
the number and kind of a specific type of carbohydrate (e.g.,
glycan) (e.g., N-glycan, O-glycan, etc.). Each part of a glycome
profile can correspond to a carbohydrate (e.g., glycan) or
component thereof or a glycoconjugate or component thereof. The
number refers to the amount and can be an actual or a relative
amount. The "total glycome profile" or "total glycoprofile", as
used herein, refers to a profile that provides information
regarding one or more properties of all of the carbohydrates (e.g.,
glycans) in a sample. The total glycoprofile, therefore, provides
the absolute or relative number and kind of all carbohydrates
(e.g., glycans) and/or components thereof in a sample. The
glycoprofile, in some embodiments, also provides information about
carbohydrates (e.g., glycans) as part of a glycoconjugate.
[0189] To assess the glycome profile of a sample any analytical
method can be used. Some of these methods are described above;
others are known in the art. For example, the analytic method can
be MS, NMR, HPLC, electrophoresis, capillary electrophoresis or
analysis with microfluidic or nanofluidic devices. In a preferred
embodiment the glycome profile is determined using a quantitative
MALDI-MS or MALDI-FTMS in the presence of ATT and Nafion.TM.
coating. To quantify the glycans, in one example, calibration
curves of known carbohydrate (e.g., glycan) standards can be
used.
[0190] Once a glycome profile is determined, a glycome pattern can
be identified. As used herein "glycome pattern" refers to a glycome
profile or subset of the profile that has been associated with a
certain function (of a lipid or protein), cellular state,
pathological condition (i.e., a disease condition), sample,
population, etc. A glycome pattern is also intended to refer to a
pattern that characterizes or distinguishes a sample containing
carbohydrates (e.g., glycans) from other samples. A glycome pattern
can be identified using a computational method. A glycome pattern,
like the profile, can be represented by the relative or absolute
amounts of components of the pattern or ratios between the
components of the pattern. The glycome pattern can also be
represented by combinations of different components or ratios
between the components of the pattern. The pattern can also be any
combination of representations, such as those provided herein. As
used herein, "a component of the pattern" refers to the
carbohydrates (e.g., glycans) or portions thereof that are
represented by the pattern. When the carbohydrates (e.g., glycans)
are part of a glycoconjugate, a component of the pattern can also
be the glycoconjugate.
[0191] The pattern can be determined using a computational method.
Examples of such computational methods are provided below and in
the Examples. The computational method can, for example,
incorporate one or more of the following to determine a glycome
pattern: experimental data from analytical methods of glycome
and/or carbohydrate (e.g., glycan) analysis; theoretical
carbohydrate (e.g., glycan) structures; carbohydrate (e.g., glycan)
composition, structure or property information from databases;
carbohydrate (e.g., glycan) biosynthetic pathway information; and
patient or sample origin information, such as patient history,
demographics, etc. In one embodiment a method is provided whereby
features from experimental data sets can be extracted and used to
generate all possible data sets. The data sets can be generated
from analysis as provided herein and/or from databases and other
tools. Such databases and tools include databases of observed
carbohydrate (e.g., glycan) structures, tools to calculate mass
under different conditions, tools to calculate composition, monomer
(e.g., monosaccharide) content, linkage content, motif content,
tools to generate theoretical structures or some combination
thereof. Databases also include data regarding patient history and
related information. The method can also include submitting the
combined information to a data mining analysis, establishing the
relationship rules and validating the pattern. The computational
method can be an iterative process. One example of a method for
determining a glycome profile and pattern is provided below.
Further detailed examples are provided in FIG. 3 and in the
Examples below.
[0192] Glycoprofiling data such as mass spectra can be generated
from samples from subjects belonging to different categories.
Features can be extracted from the glycoprofiling spectra. These
features can be the presence or absence of one or more
carbohydrates (e.g., glycans) in the profile, the relative amount
of different carbohydrates (e.g., glycans) in the profile,
combinations of different carbohydrates (e.g., glycans) found in
the profile and/or other carbohydrate (e.g., glycan)-related
properties. These carbohydrates (e.g., glycans) can be identified
in the glycoprofile spectra and can be corroborated with other
methods, for instance, by using associated glycomics-based
bioinformatics tools and/or a carbohydrate (e.g., glycan) database
(http://www.functionalglycomics.org/glycomics/molecule/jsp/carbohydrate/c-
arbMoleculeHome.jsp).
[0193] The appropriate subjects can be selected for a study (e.g.,
based on their history in a patient database), such that the
subjects chosen have the same distribution when it comes to other
properties such as age, ethnicity, behavioral factors, etc. This
ensures that the variation in the glycoprofiles can be attributed
to the disease condition rather than other factors. The
carbohydrate (e.g., glycan)-related features extracted for a
population via the previous step can be run through a dataset
generator to create the datasets needed for pattern analysis.
Different types of pattern analysis can be performed to identify
the patterns in this dataset. Types of pattern analysis are known
to those of ordinary skill in the art and can be found in Weiss, S.
& Indurkhya, N. 1998. Predictive data mining--A practical
guide. Morgan Kaufmann, San Francisco. Three examples of patterns,
rules or relationships include linear discriminant, neural network
and decision rules analysis.
[0194] Once a pattern is identified using the decision set rules
above, the patterns, rules or relationships can be validated. The
validation can be made based on a variety of statistical methods
that are used in biomarker validation as well as scientific methods
to verify that the carbohydrates (e.g., glycans) found in the
patterns do accurately reflect the disease state. If the patterns
cannot be validated, the process described above can be repeated to
look for other carbohydrate (e.g., glycan)-based patterns in the
glycoprofiles.
[0195] The patterns that are ultimately validated can be recorded
in a computer-generated data structure. A database of validated
glycome patterns is, therefore, also provided herein.
[0196] The patterns determined from the methods provided can
provide information about a sample origin, a subject's state (e.g.,
diseased state), etc. Patterns determined from one sample
containing carbohydrates (e.g., glycans) can also be compared to
patterns from other samples. Patterns that are compared can be
known or unknown patterns. The patterns can represent a diseased
state or a batch of glycoconjugates. Therefore, the total glycome
and/or patterns deduced from the methods provided can be used for
studying the effects of glycosylation on protein activity and/or
function as in the case of glycoprotein therapeutics. The total
glycome and/or patterns deduced can also be used in methods for
diagnosis, assessing prognosis and assessing drug treatment,
etc.
[0197] For example, using optimized methods described above, the
total content of serum, saliva and urine glycome was analyzed, and
it was shown that specific and reproducible MALDI-MS patterns which
are dependent on the source of the sample (e.g., a patient) and
state (e.g., diseased state) could be obtained. Since every signal
inside the pattern corresponds to specific carbohydrates (e.g.,
glycans), the alteration of these patterns are easily determined
and correlated with the expression levels of the carbohydrates.
These alterations can be easily determined manually or more
efficiently with the help of computational methods. Since specific
alterations in these carbohydrate (e.g., glycan) patterns are
associated with disease state, this method serve as reliable
platform for diagnosis, prognosis and the analysis associated with
therapeutics.
[0198] As stated above, the glycosylation of a protein may be
indicative of a normal or a disease state. Therefore, methods are
provided for diagnostic purposes based on the analysis of the total
glycome or glycome pattern. The methods provided herein can be used
for the diagnosis of any disease or condition that is caused by or
results in changes in glycosylation. For example, the methods
provided can be used in the diagnosis of cancer, an immunological
disorder, neurodegenerative disease, inflammatory disease, an
infection or a genetic disorder (e.g., a congenital disorder),
etc.
[0199] The diagnosis can be carried out in a subject with or
thought to have a disease or condition. The diagnosis can also be
carried out in a subject thought to be at risk for a disease or
condition. "A subject at risk" is one that has either a genetic
predisposition to have the disease or condition or is one that has
been exposed to a factor that could increase the risk of developing
the disease or condition.
[0200] Detection of cancers at an early stage is crucial for its
efficient treatment. Despite advances in diagnostic technologies,
many cases of cancer are not diagnosed and treated until the
malignant cells have invaded the surrounding tissue or metastasized
throughout the body. Although current diagnostic approaches have
significantly contributed to the detection of cancer, they still
present problems in sensitivity and specificity. Samples that are
analyzed herein, therefore, can be from a subject with or be
compared to a pattern associated with cancer.
[0201] Cancers or tumors include but are not limited to adrenal
gland cancer, biliary tract cancer; bladder cancer, brain cancer;
breast cancer; cervical cancer; choriocarcinoma; colon cancer;
endometrial cancer; esophageal cancer; extrahepatic bile duct
cancer; gastric cancer; head and neck cancer; intraepithelial
neoplasms; kidney cancer; leukemia; lymphomas; liver cancer; lung
cancer (e.g. small cell and non-small cell); melanoma; multiple
myeloma; neuroblastomas; oral cancer; ovarian cancer; pancreas
cancer; prostate cancer; rectal cancer; sarcomas; skin cancer;
small intestine cancer; testicular cancer; thyroid cancer; uterine
cancer; urethral cancer and renal cancer, as well as other
carcinomas and sarcomas.
[0202] Samples that are analyzed herein can also be from a subject
with or be compared to a pattern associated with a
neurodegenerative disease/disorder. "Neurodegenerative
disease/disorder" is defined herein as a disorder in which
progressive loss of neurons occurs either in the peripheral nervous
system or in the central nervous system. As used herein "central
nervous system disorders" is intended to include neurodegenerative
diseases/disorders, injuries to the central nervous system (e.g.,
spinal cord injury), etc. Examples of neurodegenerative disorders
include: (i) chronic neurodegenerative diseases such as familial
and sporadic amyotrophic lateral sclerosis (FALS and ALS,
respectively), familial and sporadic Parkinson's disease,
Huntington's disease, familial and sporadic Alzheimer's disease,
multiple sclerosis, olivopontocerebellar atrophy, multiple system
atrophy, progressive supranuclear palsy, diffuse Lewy body disease,
corticodentatonigral degeneration, progressive familial myoclonic
epilepsy, strionigral degeneration, torsion dystonia, familial
tremor, Down's Syndrome, Gilles de la Tourette syndrome,
Hallervorden-Spatz disease, diabetic peripheral neuropathy,
dementia pugilistica, AIDS Dementia, age related dementia, age
associated memory impairment, and amyloidosis-related
neurodegenerative diseases such as those caused by the prion
protein (PrP) which is associated with transmissible spongiform
encephalopathy (Creutzfeldt-Jakob disease,
Gerstmann-Straussler-Scheinker syndrome, scrapic, and kuru), and
those caused by excess cystatin C accumulation (hereditary cystatin
C angiopathy); and (ii) acute neurodegenerative disorders such as
traumatic brain injury (e.g., surgery-related brain injury),
cerebral edema, peripheral nerve damage, spinal cord injury,
Leigh's disease, Guillain-Barre syndrome, lysosomal storage
disorders such as lipofuscinosis, Alper's disease, vertigo as
result of CNS degeneration; pathologies arising with chronic
alcohol or drug abuse including, for example, the degeneration of
neurons in locus coeruleus and cerebellum; pathologies arising with
aging including degeneration of cerebellar neurons and cortical
neurons leading to cognitive and motor impairments; and pathologies
arising with chronic amphetamine abuse including degeneration of
basal ganglia neurons leading to motor impairments; pathological
changes resulting from focal trauma such as stroke, focal ischemia,
vascular insufficiency, hypoxic-ischemic encephalopathy,
hyperglycemia, hypoglycemia or direct trauma; pathologies arising
as a negative side-effect of therapeutic drugs and treatments
(e.g., degeneration of cingulate and entorhinal cortex neurons in
response to anticonvulsant doses of antagonists of the NMDA class
of glutamate receptor) and Wernicke-Korsakoff s related dementia.
Neurodegenerative diseases affecting sensory neurons include
Friedreich's ataxia, diabetes, peripheral neuropathy, and retinal
neuronal degeneration. Neurodegenerative diseases of limbic and
cortical systems include cerebral amyloidosis, Pick's atrophy, and
Retts syndrome. The foregoing examples are not meant to be
comprehensive but serve merely as an illustration of the term
"neurodegenerative disease/disorder."
[0203] Samples that are analyzed herein can also be from a subject
with or be compared with a pattern associated with an immunological
disorder. In one embodiment the immunologic disorder is lupus. In
another embodiment the immunologic disorder is primary immune
deficiency disease or an autoimmune disease or disorder. In yet
another embodiment the autoimmune disease or disorder is autoimmune
deficiency syndrome (AIDS), systemic lupus erythematosus (SLE),
rheumatic fever, rheumatoid arthritis, systemic sclerosis,
autoimmune Addison's disease, Anklosing spondylitis or
sarcoidosis.
[0204] Samples that are analyzed herein can further be from a
subject with or be compared to a pattern associated with
inflammation or an inflammatory disorder. In some embodiments the
inflammatory disorder is non-autoimmune inflammatory bowel,
disease, post-surgical adhesions, coronary artery disease, hepatic
fibrosis, acute respiratory distress syndrome, acute inflammatory
pancreatitis, endoscopic retrograde
cholangiopancreatography-induced pancreatitis, burns, atherogenesis
of coronary, cerebral and peripheral arteries, appendicitis,
cholecystitis, diverticulitis, visceral fibrotic disorders, wound
healing, skin scarring disorders (keloids, hidradenitis
suppurativa), granulomatous disorders (sarcoidosis, primary biliary
cirrhosis), asthma, pyoderma gandrenosum, Sweet's syndrome,
Behcet's disease, primary sclerosing cholangitis or an abscess. In
still another embodiment the inflammatory disorder is an autoimmune
condition. The autoimmune condition in some embodiments is
rheumatoid arthritis, rheumatic fever, ulcerative colitis, Crohn's
disease, autoimmune inflammatory bowel disease, insulin-dependent
diabetes mellitus, diabetes mellitus, juvenile diabetes,
spontaneous autoimmune diabetes, gastritis, autoimmune atrophic
gastritis, autoimmune hepatitis, thyroiditis, Hashimoto's
thyroiditis, insulitis, oophoritis, orchitis, uveitis, phacogenic
uveitis, multiple sclerosis, myasthenia gravis, primary myxoedema,
thyrotoxicosis, pernicious anemia, autoimmune haemolytic anemia,
Addison's disease, scleroderma, Goodpasture's syndrome,
Guillain-Barre syndrome, Graves' disease, glomerulonephritis,
psoriasis, pemphigus vulgaris, pemphigoid, sympathetic opthalmia,
idiopathic thrombocylopenic purpura, idiopathic feucopenia,
Siogren's syndrome, Wegener's granulomatosis, poly/dermatomyositis
or systemic lupus erythematosus.
[0205] Samples that are analyzed herein can also be from a subject
with or be compared to a pattern associated with infection (e.g.,
pseudomonas infection or S. aureus infection) or an infection
related disorder. In some embodiments the infection is a viral
infection, a bacterial infection or a fungal infection.
[0206] Samples that are analyzed herein can also be from a subject
with or be compared to a pattern associated with a genetic
disorder. As used herein, a "genetic disorder" is any disorder in
which its onset or progression has a genetic basis. In some
embodiments the genetic disorder is a congenital disorder, which is
a condition that is genetic and is present at birth or shortly
thereafter.
[0207] The methods provided herein also include methods for
determining the glycosylation of a protein and its effects on the
protein's activity and/or function. The protein glycosylation can
be studied with the methods provided to determine the proper
folding of the protein or to determined the influence of the
protein's glycosylation on the stability/and or degradation
resistance of the protein (indicative of the protein's half-life).
Changing the composition or the degree of glycosylation of a
protein can greatly influence its half-life in circulation, as well
as its activity (Chang, G. D., et al. (2003) J Biotechnol 102,
61-71; Perlman, S., et al. (2003) J Clin Endocrinol Metab 88,
3227-35.) For example, erythropoietin (EPO) is a glycoprotein that
has been developed as a therapeutic due to its ability to stimulate
red blood cell production in the bone marrow. It has been
determined that increased sialylation of EPO greatly increases its
half-life in circulation (Darling, R. J., et al. (2002)
Biochemistry 41, 14524-31.) Thus, by understanding the role of EPO
glycosylation, it is possible to manufacture a more potent
drug.
[0208] Similarly, methods are provided for identifying glycosylated
proteins with a desired activity and/or function. For example, in
immunoglobulins, glycosylation plays an important role in the
structure of the Fc region, which is important for activation of
leukocytes expressing Fc receptors. When glycans on the IgG Fc
region are truncated, the resulting conformational changes reduce
the ability of the IgG to bind to the Fc receptor (Krapp, S., et
al. (2003) J Mol Biol 325, 979-89.) In addition, IgG glycosylation
is species specific, making it essential to choose the appropriate
production method for protein therapeutics (Raju, T. S., et al.
(2000) Glycobiology 10, 477-86.) For example, a human protein
produced in a mouse cell line may not have the necessary
carbohydrates (e.g., glycans) for optimal function in human
patients. Therefore, the immune recognition of an antibody can be
assessed with the methods of analysis provided herein.
[0209] Carbohydrates (e.g., glycan) patterns can also be used for
determining the purity of a sample or assessing the production of a
glycoconjugate.
[0210] The methods provided, where the amount or type of
carbohydrates (e.g., glycans) on proteins or lipids can be
determined, can be used to analyze the purity of a sample. As used
herein the term "purity" refers to the proportion of a sample that
contains a particular carbohydrate (e.g., glycan) or a particular
glycosylation pattern. In some embodiments, the sample is
determined to be at least 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%,
90% or more pure. In some embodiments the method is used to assess
the amount of a particular carbohydrate (e.g., glycan) in a sample.
In some instances, it may be desired that the proteins or lipids
are selected depending on the particular glycosylation pattern they
exhibit. In other aspects of the invention the methods provided
herein can be used to evaluate a process of producing proteins or
lipids and/or compare a process with another to evaluate the types
of proteins or lipids produced. The "types of proteins or lipids
produced" includes not only the protein or lipid itself but also
its glycosylation pattern.
[0211] One of the major challenges during the production of
glycoprotein therapeutics is to control the generation of a
specific glycoform and the subsequent characterization for quality
control of the product. Therefore, methods that can efficiently
characterize new batches of glycoprotein therapeutics are of great
value to the pharmaceutical industry. For a complete
characterization of glycoprotein therapeutics, information such as
glycan site occupancy, carbohydrate composition and structure at
each site and quantity of each carbohydrate is required.
[0212] As described herein, the methods for analyzing a sample
containing carbohydrates (e.g., glycans) can be used to assess the
quality and variability of protein or lipid production. With the
recently increased focus on protein-based therapeutics by
pharmaceutical companies and research laboratories, it has become
important to understand how glycosylation composition is influenced
by production methods. In the field of bioprocess engineering,
there are many different types of bioreactors available for
production, e.g., protein production. Depending on the model,
parameters such as pH and dissolved oxygen (DO) can be controlled
in several ways, and agitation methods can result in wide
variations in shear stress. In addition, the cell-feeding process
during fermentation can be altered to change the cell growth
profile. All of these variables can affect glycosylation--even
using identical conditions in two different bioreactors causes
changes in carbohydrate (e.g., glycan) patterns (Kunkel, J. P., et
al. (2000) Biotechnol Prog 16, 462-70; Zhang, F., et al. (2002)
Biotechnol Bioeng 77, 219-24; Senger, R. S., Karim, M. N. (2003)
Biotechnol Prog 19, 1199-209; Muthing, J., et al. (2003) Biotechnol
Bioeng 83, 321-34.) Therefore, provided herein are methods for
analyzing the glycosylation of proteins or lipids to assess
production methods and to determine the purity or homogeneity of
glycosylated products produced. One example is as follows.
[0213] A batch of the glycoproteins can be used to generate a
library of backbone-labeled peptides and glycopeptides by enzymatic
digestion using methods provided herein or known in the art
(Gehrmann, 2004; Yao, 2003; Reynolds, 2002; Yao, 2001). Trypsin
proteolytic digest cleavage can be employed before or after
carbohydrate (e.g., glycan) cleavage in order to expand the peptide
library. Peptide labeling can be performed, and each characterized
and quantified peptide and glycopeptide can be used to generate
calibration curves using LC-MS or LC-MS/MS techniques. These
peptides and glycopeptides can then be mixed (in known
concentrations) with the peptide/glycopeptide mixture resulting
from the trypsin proteolytic cleavage digest of a sample batch
under study. The co-elution of the labeled peptides with the
unknown peptides followed by the co-detection (the ratio between
labeled and unlabeled peptides) using mass spectrometry allows the
quantification of each peptide (and therefore the different
glycoforms) in the sample. In addition to the peptide/glycopeptide
analysis, by splitting the flow from a LC column (before entering
the electrospray source) to a collection plate, the respective
carbohydrates (e.g., glycans) from the eluted glycopeptides can be
analyzed using the methods described herein. The use of other known
methods for the determination of glycan site occupancy can also be
used (Cointe, 2000; Hui, 2002; An, 2003).
[0214] The methods provided herein can also be used to determine
whether or not cells are undergoing dramatic change or are
"stressed cells". Stressed cells are cells that are undergoing a
stress response that alters the cell's protein production. The
stress response can be any change that causes altered protein
production or causes the cell to deviate from its normal state.
Stressed cells can be identified by analyzing the carbohydrates
(e.g., glycans) exhibited by the proteins on the cell's surface.
Such carbohydrates (e.g., glycans) can be found in, for example, a
peptide-based glycoconjugate.
[0215] In other embodiments the methods provided are used to detect
changes in glycosylation that occur under growth conditions or
inflammation.
[0216] The samples for use in the methods provided can be any
sample that contains one or more carbohydrates (e.g., glycans). The
sample can be, for example, a sample of a cell, group of cells,
tissue or body fluid, etc. Body fluids include serum, plasma,
blood, urine, saliva, sputum, tears, CSF, seminal fluid, feces,
etc. The samples can be from a subject, such as a healthy subject
or diseased subject. The samples can also be from a subject
undergoing a treatment for a disease. The sample can also be from a
subject that is a healthy or non-diseased subject. Additionally, a
sample can be from a pregnant woman. The sample can further be a
sample of glycoconjugates, wherein the glycoconjugates are a
produced therapeutic. The sample, therefore, can be a batch of
glycoconjugates that have been produced.
[0217] Therefore, in other aspects of the invention methods are
provided for assessing treatment regimens and/or to select specific
therapies. In other aspects of the invention methods for analyzing
blood type antigens are also provided.
[0218] A subject, as used herein, is any human or non-human
vertebrate, e.g., dog, cat, horse, cow, pig. A sample includes any
sample obtained from any of these subjects.
[0219] The present invention is further illustrated by the
following Examples, which in no way should be construed as further
limiting. The entire contents of all of the references (including
literature references, issued patents, published patent
applications, and co-pending patent applications) cited throughout
this application are hereby expressly incorporated by
reference.
EXAMPLES
Example 1
N-Glycan Analysis
Materials and Methods
[0220] PNGaseF Digest of N-glycans from Protein Cores
[0221] Between 10 and 100 .mu.g of protein were denatured for 10
minutes at 90.degree. C. with 0.5% SDS and 1%
.beta.-mercaptoethanol. Since SDS (and other ionic detergents)
inhibits enzyme activity, 1% NP-40 was added to counteract these
effects. The enzyme reaction was performed overnight with 2 .mu.l
of PNGaseF at 37.degree. C. in a 50 mM sodium phosphate buffer, pH
7.5.
Purification of Released N-glycans
[0222] Proteins were precipitated with a 3.times. volume of 100%
ethanol on ice for 1 hour. After centrifugation to remove the
proteins, the supernatant containing the N-glycans was evaporated
by vacuum (SpeedVac, TeleChem International, Inc., Sunnyvale,
Calif.). Dried glycans were resuspended in 50 .mu.l of water.
[0223] Samples were desalted using 1 ml ion exchange column of
AG50W X-8 beads (Bio-Rad, Hercules, Calif.). The resin was charged
with 150 mM acetic acid and washed with water. Glycan samples were
loaded onto the column in water and washed through with 3 ml
H.sub.2O. This flow-through was collected and lyophilized to obtain
the desalted sugars.
[0224] GlycoClean R and S cartridges were purchased from Prozyme
(San Leandro, Calif.; formerly Glyko). GlycoClean R cartridges were
primed with 3 ml of 5% acetic acid, and the samples were loaded in
water. Sugars were eluted with 3 ml of water passed through the
column. For GlycoClean S, the membrane was primed with 1 ml water
and 1 ml 30% acetic acid, followed by 1 ml acetonitrile. The glycan
sample was loaded (in a maximum volume of 10 .mu.l) onto the disc,
and the glycans were allowed to adsorb for 15 minutes. After
washing the disc with 1 ml of 100% acetonitrile and 5.times.1 ml of
96% acetonitrile, glycans were eluted with 3.times.0.5 ml
water.
[0225] GlycoClean H cartridges were purchased from Prozyme (200 mg
bed) or ThermoHypersil (Thermo Electron Corporation, Somerset,
N.J.) (25 mg bed). To prepare the GlycoClean H cartridge, the
column (containing 200 mg of matrix) was washed with 3 ml of 1M
NaOH, 3 ml H.sub.2O, 3 ml 30% acetic acid, and 3 ml H.sub.2O to
remove impurities. The matrix was primed with 3 ml 50% acetonitrile
with 0.1% trifluoroacetic acid (TFA) (Solvent A) followed by 3 ml
5% acetonitrile with 0.1% TFA (Solvent B). After loading the sample
in water, the column was washed with 3 ml H.sub.2O and 3 ml Solvent
B. Finally, the sugars were eluted using 4.times.0.5 ml of Solvent
A. GlycoClean H cartridges can be reused after washing with 100%
acetonitrile and re-priming with 3 ml of Solvent A followed by 3 ml
of Solvent B. For the 25 mg cartridge, wash volumes were reduced to
0.5 ml. Eluted fractions were lyophilized and the isolated glycans
were resuspended in 10-40 .mu.l H.sub.2O.
MALDI-MS of N-glycans
[0226] Several MALDI-MS matrix compounds were tested in this study.
First, caffeic acid was added to 30% acetonitrile to make a
saturated solution, with or without 300 mM spermine. Alternatively,
a saturated solution of DHB in water was used with or without 300
mM spermine. To prepare the sample spots, three methods were used.
For the crushed spot method, 1 .mu.l of matrix was spotted on the
stainless steel MALDI-MS sample plate and allowed to dry. After
crushing the spot with a glass slide, 1 .mu.l of matrix mixed 1:1
with sample was spotted on the seed crystals and allowed to dry.
Alternatively, 1 .mu.l of matrix was applied followed by 1 .mu.l of
sample, or vice versa. All spectra were taken with the following
instrument parameters: accelerating voltage 22000V, grid voltage
93%, guide wire 0.15% and extraction delay time of 150 nsec (unless
otherwise noted). All N-glycans were detected in linear mode with
delayed type extraction and positive polarity.
Results
[0227] With an IgG-producing mouse hybridoma cell line, the effects
of DO and pH control on cell metabolism and growth kinetics using
two different reactor types was investigated. It was determined
whether the IgG glycan profile was altered by the different reactor
conditions. For a complete glycan analysis, the procedure for
glycan isolation was optimized. The purification and analysis was
performed using two known N-glycosylated standards with different
properties, ribonuclease B (RNaseB), a glycoprotein that only
contains high mannose structures (Joao, H. C., Dwek, R. A. 1993.
Eur J Biochem. 218, 239-44), and ovalbumin, which contains both
hybrid and complex glycan structures at just one glycosylation site
(Harvey, D. J., et al. 2000. J Am Soc Mass Spectrom. 11, 564-71.)
After finding the best methods for glycan analysis, the procedure
was applied to samples (Hamel laboratory, MIT Bioprocess
Engineering Center, Cambridge, Mass.) produced under various
conditions.
[0228] There are several required steps for N-glycan analysis from
proteins. While it is possible to study both the intact
glycoprotein and glycopeptides from digested proteins, these types
of analysis make it difficult to determine the exact composition of
the glycan structures. Therefore, the intact glycan was removed
from the core protein. Then, the sugar structures were separated
from the protein core, purified and analyzed using methods that can
provide specific saccharide compositions in an accurate manner.
Release and Purification of N-glycans from Protein Standards
[0229] There are several methods, both enzymatic and chemical, to
separate glycans from their protein cores. Of the chemical methods,
hydrazinolysis provides the most efficient release of glycans
(Patel, T., et al. 1993. Biochemistry 32, 679-93.) However, both N-
and O-linked glycans are released using this method, and must be
separated afterwards. The sample must be very clean, with no
residual salts, and the reaction does not proceed efficiently in
air or water, making hydrazinolysis somewhat undesirable as a quick
measure of quality control.
[0230] Several enzymatic methods are available that are specific to
N-linked sugars. EndoH and EndoF cleave between the two interior
GlcNAc residues of the glycan core, while PNGaseF cleaves between
the interior GlcNAc and the asparagine side chain of the protein
core (Tarentino, A. L., et al. 1974. J Biol Chem. 249, 818-24;
Tarentino, A. L., Maley, F. 1974. J Biol Chem. 249, 811-7;
Tarentino, A. L., et al. 1985. Biochemistry 24, 4665-71.)
[0231] EndoH only acts on high mannose or hybrid structures, while
EndoF can cleave complex glycans. With EndoH and EndoF, information
about fucosylation on the reducing end GlcNAc is lost since this
residue remains attached to the protein core. On the other hand,
PNGaseF releases the entire glycans and can cleave all classes of
N-glycans, making it a tool of choice for N-glycan release.
[0232] For optimal enzyme activity, proteins should be unfolded
prior to digestion with PNGaseF. Typically, a protein sample can be
denatured by heating in the presence of .beta.-mercaptoethanol
and/or SDS. After PNGaseF cleavage, samples contain a mixture of
free glycans, protein, detergent (from the denaturing step) and
salts. In some instances it is preferred that everything except the
glycans are removed from the sample. To achieve this, the proteins
were first precipitated with ethanol, and the supernatant
containing the glycans was then dried under vacuum (SpeedVac) and
resuspended in water. At this point, the most difficult component
to get rid of was the detergent, which interferes with some types
of analytical techniques.
[0233] There are several commercially available resins and
cartridges for N-glycan clean-up after PNGaseF digest. In addition
to an ion exchange resin (AG50W X-8 from Bio-Rad), three types of
purification columns from Prozyme were tested-Glyco H, S and R.
Glyco R contains a reverse phase material that allows glycans to
flow through, while retaining peptides and detergents. Glyco S is a
small membrane that adsorbs the sugars in >90% acetonitrile,
while hydrophobic molecules are washed away. The glycans can then
be eluted with water. Glyco H, on the other hand, is a porous
graphitic carbon matrix which retains both neutral and charged
sugars, while allowing salts to be washed away with a low
concentration of acetonitrile. The sugars can then be eluted with
higher acetonitrile concentrations. Proteins and detergents
typically remain on the Glyco H column. Overall, the Glyco H
cartridge yielded the best results in these studies (Table 2).
Glycan Analysis by MALDI-MS
[0234] Numerous analytical techniques have been applied to study
N-glycans, including mass spectrometry, NMR, electrophoresis, and
chromatographic methods. NMR, for instance, can provide detailed
structural information in a single experiment. Due to the lack of
natural chromophores in N-linked carbohydrates, many of the
procedures require the labeling of saccharides with chemical tags
or fluorescent labels to facilitate detection. In fluorescence
assisted carbohydrate electrophoresis (FACE), glycans are
fluorescently labeled and run on a polyacrylamide gel (Hu, G. F.
1995. J Chromatogr A. 705, 89-103.) Glycan bands can then be
excised for further structural analysis. Similar methods use HPLC
or CE for greater sensitivity and better separation. However, these
techniques merely yield migration times of a sample's components,
giving limited structural information.
[0235] One of the simplest and most sensitive glycan analysis
methods is MALDI-MS, which has detection limits in the femtomole to
picomole range. In addition, many samples can be analyzed in a
single experiment within minutes. MALDI-MS is a soft ionization
technique that utilizes an organic matrix to absorb and transfer
the ionizing energy from the laser. This technique is useful for
many applications, from small molecules to large proteins over 100
kDa. However, sample ionization is sensitive to instrument
conditions as well as sample preparation.
[0236] In particular, the matrix used to suspend the sample is
important for good ionization. The efficiency of a particular
matrix can vary widely, depending on the nature of the sample.
Multiple matrix preparations were tested, namely caffeic acid
(saturated solution in 30% acetonitrile) with or without spermine,
and DHB (saturated solution in water) with or without spermine. In
addition, several spotting methods were evaluated: spotting 1 .mu.l
of sample followed by 1 .mu.l of matrix, spotting matrix followed
by sample, or mixing the two before spotting. Whether using the
crushed spot method to promote matrix crystallization would improve
signal intensity (Rhomberg, A. J., et al. 1998. Proc Natl Acad Sci
USA 95, 4176-81) was also investigated. When acquiring the MALDI-MS
data, the data collection was optimized by varying instrument
parameters such as guide wire voltage, accelerating voltage, grid
values, as well as negative vs. positive mode.
[0237] To evaluate the MALDI-MS conditions and calibrate the
masses, commercially available N-glycan standards (NGA2 and NGA3)
were used. In addition, RNaseB and ovalbumin were used as model
glycoproteins to determine the effects of sample preparation on
spectra quality and to optimize glycan release.
[0238] MALDI conditions for N-glycan analysis were optimized using
the matrix and sample preparation conditions shown in Table 2.
Among the matrix preparations used, DHB with spermine displayed the
best results. Typically, spermine is used to allow glycans to be
detected in negative mode, but it enhanced the glycan signals even
in positive mode. The neutral glycans had poor signals in negative
mode. Using the crushed spot method did not make a significant
difference in signal intensity.
TABLE-US-00002 TABLE 2 Optimization of Conditions for MALDI-MS and
N-glycan Clean-up Sample Matrix Sample info Results and comments 1.
NGA2, DHB, saturated 1 .mu.l matrix on Signal okay. Not too much
noise. NGA3 solution in H.sub.2O, plate, add 1 .mu.l 300 mM
spermine sample 2. NGA2, DHB, saturated 1 .mu.l sample on Better
signal than Sample 1 or 3. This NGA3 solution in H.sub.2O, plate,
add 1 .mu.l method used in all subsequent samples 300 mM spermine
matrix unless otherwise noted. 3. NGA2, DHB, saturated Mix sample
and Lower signal than Sample 1. NGA3 solution in H.sub.2O, matrix,
spot 1 .mu.l 300 mM spermine 4. NGA3 Caffeic acid, 30% Good signal
intensity but significant ACN, saturated unidentified adduct.
solution 5. NGA3 Caffeic acid, 30% Large unidentified contamination
peak. ACN, 300 mM spermine 6. NGA3 Caffeic acid, 30% Crushed spot
Good signal intensity but also more noise ACN method than Sample 2.
7. NGA3 DHB, saturated Low signal intensity compared to Sample 2
solution in H.sub.2O or 5. 8. NGA3 DHB, saturated ACC voltage
Comparable to Sample 7. solution in H.sub.2O 18000, guide wire 0.1%
9. NGA3 DHB, saturated Good signal. solution in H.sub.2O, 300 mM
spermine 10. NGA3 DHB, saturated ACC voltage solution in H.sub.2O,
18000, guide 300 mM spermine wire 0.1% 11. NGA3 DHB, saturated
Negative mode Very low signal, almost undetectable. solution in
H.sub.2O, 300 mM spermine 12. 500 ug DHB, saturated Glyco S Some
high mannose peaks, many RNaseB solution in H.sub.2O, unidentified
peaks. 300 mM spermine 13. 500 ug DHB, saturated AG50W X-8 Both
spots spread a lot, no signal. RNaseB solution in H.sub.2O, column
and 300 mM spermine batch mode 14. 500 ug DHB, saturated Glyco R
Spot does not dry properly. RNaseB solution in H.sub.2O, 300 mM
spermine 15. 500 ug DHB, saturated Glyco H Good signal, Man-5
through Man-9. RNaseB solution in H.sub.2O, (200 mg) 300 mM
spermine 16. 500 ug DHB, saturated Glyco H Good signal, 30 peaks
that match published Ovalbumin solution in H.sub.2O, (200 mg)
reports. 300 mM spermine 17. 10 ug DHB, saturated Glyco H Spots
spread, mostly contamination peaks RNaseB or solution in H.sub.2O,
(25 mg) in 1000-1300 Da range. Probably detergent. ovalbumin 300 mM
spermine 18. 50 ug DHB, saturated Glyco H Spot spreads a lot,
significant detergent RNaseB solution in H.sub.2O, (25 mg)
contamination peaks. 300 mM spermine 19. 15 ug DHB, saturated Glyco
H Good signal, very slight contamination that RNaseB solution in
H.sub.2O, (200 mg) does not interfere with signal. Glyco H 300 mM
spermine column used for future experiments. 20. 150 ug DHB,
saturated Glyco H Good signal, very clean. RNaseB solution in
H.sub.2O, (200 mg) 300 mM spermine
[0239] Using commercially available glycans and known protein
standards, it was determined that the optimal method for purifying
glycans after PNGaseF release was to use GlycoClean H cartridges
containing 200 mg of the stationary material. The use of this
method resulted in MALDI-MS spectra of N-glycans from RNaseB and
ovalbumin that were consistent with published reports. The ion
exchange resin did not remove all of the detergents from the
sample, causing the sample spots to spread on the MALDI-MS target
and not crystallize properly. GlycoClean R, on the other hand,
removed detergents but did not completely remove salt, which
subsequently interfered with matrix crystallization and spectra
quality. GlycoClean S yielded acceptable sample spots on the
MALDI-MS target, but failed to remove all contamination.
[0240] FIG. 5 shows spectra of some of the representative RNaseB
samples from Table 2, with glycans purified under different
conditions. In the cleanest samples, all glycan masses correspond
with high mannose structures (Man-5 through Man-9).
[0241] To validate the reproducibility of the method, ovalbumin was
used as a protein standard with complex type N-glycans. Optimized
purification and MALDI-MS conditions were used (Glyco H 200 mg, DHB
matrix with spermine). The MALDI-MS data displayed results
comparable to previously published reports (Harvey, D. J., et al.
2000. J Am Soc Mass Spectrom 11, 564-71.) FIG. 6 shows the MALDI-MS
spectrum of ovalbumin glycans, and Table 3 lists the observed peaks
and their structures.
TABLE-US-00003 TABLE 3 N-glycan Structures from Ovalbumin (Harvey
et al, 2000) Theoretical Peak Structure Mass 1 ##STR00001## 1136.4
2 ##STR00002## 1298.5 3a ##STR00003## 1339.5 3b ##STR00004## 1339.5
4 ##STR00005## 1460.5 5 ##STR00006## 1501.5 6a ##STR00007## 1542.6
6b ##STR00008## 1542.6 7 ##STR00009## 1663.6 8 ##STR00010## 1704.6
9a ##STR00011## 1745.6 9b ##STR00012## 1745.6 10a ##STR00013##
1866.7 10b ##STR00014## 1866.7 11a ##STR00015## 1907.7 11b
##STR00016## 1907.7 12a ##STR00017## 1948.7 12b ##STR00018## 1948.7
13 ##STR00019## 2028.7 14a ##STR00020## 2069.7 14b ##STR00021##
2069.7 15a ##STR00022## 2110.8 15b ##STR00023## 2110.8 16
##STR00024## 2151.8 17 ##STR00025## 2272.8 18 ##STR00026## 2313.9
19 ##STR00027## 2475.9 20 ##STR00028## 2638.0
MALDI-MS Analysis of N-glycans from Antibodies Produced in Applikon
and Wave Reactors
[0242] Two antibody samples produced by mouse-mouse hybridoma cells
(Biokit SA, Barcelona, Spain) grown in an Applikon stirred tank
reactor (STR) (Applikon Biotechnology, Dover, N.J.) were analyzed,
along with three samples produced in Wave reactors (Wave Biotech,
Bridgewater, N.J.). The reactor conditions used are shown in Table
4.
TABLE-US-00004 TABLE 4 Reactor Conditions Used to Produce Antibody
Samples Sample Reactor Type DO pH Other 1 Applikon STR 50% 7 2
Applikon STR 90% Not controlled 3 Wave Controlled Not controlled 4
Wave Controlled 7 NaHCO.sub.3 for pH control 5 Wave Not 7 Fresh
media controlled for pH control
[0243] Sample Reactor Type DO pH Other 1 Applikon STR 50% 7 2
Applikon STR 90% Not controlled 3 Wave Controlled Not controlled 4
Wave Controlled 7 NaHCO.sub.3 for pH control 5 Wave Not 7 Fresh
media controlled for pH control
[0244] In the Applikon STR, pH can be controlled automatically by
the instrument, which dispenses CO.sub.2, NaHCO.sub.3 and O.sub.2
as needed. In the Wave reactor, however, measurements must be taken
manually and pH adjusted by hand. The pH in this reactor can be
controlled by either adding fresh media as the cells grow, or
adding NaHCO.sub.3 for increased buffering capacity, and CO.sub.2
as needed. The main difference between the reactor types is the
mode of agitation. In the Applikon STR, a blade stirrer keeps the
cell suspension in motion, while a sparger introduces oxygen to the
system in a controlled manner. In the Wave reactor, a rocking
motion generates waves that mix the components of the system and
aids the transfer of oxygen and other gases into the system.
[0245] The purified antibodies were processed according to the
optimized method described above. For each sample, 100 .mu.g of
protein was used as the starting material. Both positive and
negative ion modes were used in the MALDI-MS to determine whether
there were charged sugars present. No signal was observed in the
negative mode, indicating that only neutral sugars were obtained
from the antibodies. The positive ion mode MALDI-MS data of the
five antibody samples are shown in FIG. 7. Glycoproteins were
produced using the different conditions shown in Table 4. All
fractions contained the same six glycans at 1317 Da, 1463 Da, 1478
Da, 1625 Da, 1641 Da and 1787 Da. The structures corresponding to
these peaks are shown in FIG. 8 with their theoretical masses.
[0246] These results indicate that the production method did not
significantly alter the occurrence of the glycans; rather, the
ratios between glycans seemed to be affected. Notably, samples
prepared in the Wave reactor had a lower amount of the 1625.4 Da
glycan with respect to the other glycans, as well as significant
reductions in the relative peak heights at 1640.9 and 1787.7 Da.
Altering the culture conditions within a reactor type did not
affect the relative abundance of the N-glycans.
[0247] While the exact mechanisms for producing these changes are
not known, it is interesting that the largest changes occurred due
to reactor type, not reactor conditions such as pH, DO or media
composition. In previous studies, pH above 7.2 was shown to affect
glycosylation composition (Muthing, J., et al. 2003. Biotechnol
Bioeng 83, 321-34). However, for the two samples in this study with
pH uncontrolled, the pH was between 6.8 and 7.2 throughout the
culture period. Studies of DO effects on glycosylation demonstrated
the largest differences at extremes (10% or 190%) (Zhang, F., et
al. 2002. Biotechnol Bioeng 77, 219-24), while the samples studied
here were produced under moderate DO conditions (between 50% and
90%). Because the Applikon STR and the Wave reactors differ most in
their method of agitation, reactor configuration is therefore the
most likely source of glycan variation.
[0248] Differences in protein glycosylation have been linked to
shear stress, as can be generated by the stirring blade or the gas
sparger in an STR. However, the turbulence created in the Wave
reactor also generates shear stress. One hypothesis for the shear
stress effect is that cells must increase their overall protein
production in response to membrane and/or cytoskeletal damage. As a
consequence, the biosynthetic enzymes for glycosylation are
diverted away from the protein of interest (Senger, R. S., Karim,
M. N. 2003. Biotechnol Prog 19, 1199-209).
[0249] Although most observed parameters, including total antibody
production, were similar in Applikon STR and Wave cultures, cells
from the Wave reactor had slight increases in metabolic rates.
Changes in cell metabolism may yield effects similar to those
caused by shear stress, as all glycoproteins synthesized in the
cell must compete for the same machinery in the ER and Golgi.
Example 2
Profiling of N-glycans from Human Serum
Materials and Methods
[0250] Cleavage of N-glycans from Serum Glycoproteins
(Reduction/Carboxymethylation Method)
[0251] Human male normal serum samples were obtained from IMPATH
(Franklin, Mass.) and Biomedical Resources (Hatboro, Pa.), and
stored at -85.degree. C. For each experiment, 50 .mu.l of serum was
used to harvest N-glycans. Serum samples were first diluted 1:4
with water, then DTT was added to a final concentration of 80 mM.
After incubation for 30 minutes at 37.degree. C., iodoacetic acid
was added to a final concentration of 400 mM and incubated for 1
hour more at 37.degree. C. The sample was dialyzed against 10 mM
Tris acetate pH 8.3 overnight and concentrated to .about.200 .mu.l
in a spin column with a 3000 Da Molecular Weight Cut off (MWCO)
filter (VivaScience, Hannover, Germany). To cleave the sugars from
the protein, 5 .mu.l (1,000 U) of PNGaseF (New England Biolabs,
Beverly, Mass.) was added and allowed to react overnight at
37.degree. C.
Purification of N-glycans
[0252] After glycans were cleaved from the protein, the sample was
dialyzed against water to remove excess salts and glycerol (from
PNGaseF formulation). Samples were then spun for 5 minutes at
6000.times.g to remove most proteins and the supernatant
lyophilized to <500 .mu.l. A C18 cartridge (Waters Corporation,
Milford, Mass.) was primed with 3 ml methanol, then 3 ml water, and
3 ml 5% acetonitrile with 0.1% TFA. The supernatant from the spun
down sample was applied to the cartridge, and 3 ml of 5%
acetonitrile with 0.1% TFA was added to elute the glycans, while
unwanted proteins were retained on the column.
[0253] GlycoClean H cartridges (Prozyme) were first primed with 3
ml 1M NaOH, 3 ml H.sub.2O, 3 ml 30% acetic acid, and 3 ml H.sub.2O
to clean the column of any impurities. Then the cartridges were
washed with 3 ml Solution A (50% acetonitrile, 0.1% TFA), 6 ml
Solution B (5% acetonitrile, 0.1% TFA), and 3 ml of water. Glycan
samples were loaded in a minimal volume of water (<100 .mu.l),
and the column was washed with 3 ml H.sub.2O followed by 3 ml
Solution B. Neutral glycans were eluted with 3 ml of 15%
acetonitrile, 0.1% TFA, and acidic glycans were eluted with 3 ml of
Solution A. For the trials with glycan standards, the six glycans
listed in Table 5 were mixed in equimolar amounts (1 .mu.l of 100
.mu.M each), and the mixture was applied to the GlycoClean H
cartridge and processed as described above. Each fraction was
dried, then redissolved in 40 .mu.l H.sub.2O for MALDI-MS analysis.
All MALDI-MS spectra were calibrated using the six glycan standards
in Table 5. Separate calibration files were used for positive and
negative modes.
Fractionation of Serum Proteins
[0254] Concanavalin A (ConA)-agarose beads were purchased from
Vector Laboratories (Burlingame, Calif.). To prepare the column, 3
ml ConA-agarose slurry was washed with ConA buffer (20 mM Tris, 1
mM MgCl.sub.2, 1 mM CaCl.sub.2, 500 mM NaCl, pH 7.4). Before
loading, 500 .mu.l of serum was mixed with 150 .mu.l of
5.times.ConA buffer. After washing with 3 ml ConA buffer,
glycoproteins were eluted with 2 ml of 500 mM
.alpha.-methyl-mannoside and dialyzed against 10 mM phosphate pH
7.2 overnight at 4.degree. C.
[0255] Protein A-agarose beads were purchased from Calbiochem (La
Jolla, Calif.). Before use, 1 ml beads were washed 3.times. with
phosphate buffered saline (PBS). To separate IgG from other serum
proteins, samples were diluted 1:4 with PBS and incubated with
Protein A-agarose overnight at 4.degree. C. Non-IgGs were collected
by loading the slurry into a column and washing with 2 ml PBS. IgGs
were eluted with 2 ml of 0.2M glycine, pH 2.5 and neutralized in
200 .mu.l Tris-HCl, pH 6.3.
SDS-PAGE and Glycoblotting of Serum Samples
[0256] Protein samples were prepared for SDS-PAGE by diluting 1:1
with 2.times. denaturing buffer (40 .mu.g/ml SDS, 20% glycerol, 30
.mu.g/ml DTT and 10 .mu.g/ml bromophenol blue in 125 mM Tris, pH
6.8) and boiling for 2 min. Pre-cast Nu-PAGE 10% Bis-Tris protein
gels were obtained from Invitrogen (Carlsbad, Calif.). Each lane
was loaded with a maximum of 10 .mu.l of sample, and run for 50 min
at 200V. After electrophoresis was complete, the gel was stained
with Invitrogen SafeStain (1 hour in staining solution, then washed
overnight with water).
[0257] The GlycoTrack glycoprotein detection kit was obtained from
Prozyme. All reagents except buffers were supplied with the kit.
Two methods were attempted-either biotinylating glycoproteins after
blotting (a) or before blotting (b). For both methods, samples were
first diluted 1:1 with 200 mM sodium acetate buffer, pH 5.5. The
membrane was blocked by incubating overnight at 4.degree. C. with
blocking reagent, then washed 3.times.10 minutes with (Tris
buffered saline (TBS).
[0258] For method (a), samples were denatured with SDS sample
buffer, and subjected to SDS-PAGE and blotting to nitrocellulose.
After washing the membrane with PBS, the proteins were oxidized
with 10 ml of 10 mM sodium periodate in the dark at room
temperature for 20 minutes. The membrane was washed 3 times with
PBS, and 2 .mu.l of biotin-hydrazide reagent was added in 10 ml of
100 mM sodium acetate, 2 mg/ml (ethylenediamine tetra-acetic acid)
EDTA for 60 minutes at room temperature. After 3 washes with TBS,
the membrane was blocked overnight at 4.degree. C. with blocking
reagent. Before adding 5 .mu.l of streptavidin-alkaline phosphatase
(S-AP) conjugate, the membrane was washed again with TBS. The S-AP
was allowed to incubate for 60 minutes at room temperature, and
excess was washed off with TBS. To develop the blot, 50 .mu.l of
nitro blue tetrazolium (50 mg/ml) and 37.5 .mu.l of
5-bromo-4-chloro-3-indolyl phosphate p-toluidine (50 mg/ml) were
added in 10 ml TBS, 10 mg/ml MgCl.sub.2. After 60 minutes, the blot
was washed with distilled water and allowed to air dry.
[0259] In method (b), 20 .mu.l of sample was mixed with 10 .mu.l of
10 mM periodate in 100 mM sodium acetate, 2 mg/ml EDTA and
incubated in the dark at room temperature for 20 minutes. To
destroy excess periodate 100 of a 12.5 mg/ml sodium bisulfite
solution in 200 mM NaOAc, pH 5.5 was added for 5 minutes at room
temperature. Biotinylation was performed by adding 5 .mu.l of
biotin amidocaproyl hydrazide solution in dimethylformamide (DMF).
After incubating at room temperature for 60 minutes, the sample was
mixed with SDS denaturing buffer and boiled for 2 minutes. Samples
were run on SDS-PAGE gels as described above, then transferred to a
nitrocellulose membrane (2 hrs, 30V). At this point, blocking and
developing steps were identical to method (a).
Chemical Modification of N-glycans
[0260] For permethylation, glycans in water were placed in a
round-bottomed flask and lyophilized overnight. A slurry of NaOH in
dimethyl sulfoxide (DMSO) (0.5 ml) was added to the glycan sample,
along with 0.5 ml methyl iodide and incubated for 15 minutes. The
sample was then diluted with water and extracted 2.times. with
CHCl.sub.3, collecting the organic phase. After drying the organic
phase with MgSO.sub.4, it was filtered through glass wool and dried
under vacuum. Samples were then redissolved in methanol for
MALDI-MS analysis.
[0261] To conjugate N-glycans to synthetic aminooxyacetyl peptide,
glycans were dried and resuspended in aqueous peptide solution (240
.mu.M). After adding 1 .mu.l of 500 mM NaOAc pH 5.5 and 20 .mu.l of
acetonitrile, the sample was incubated overnight at 40.degree. C.
Before MALDI-MS analysis, glycopeptides were purified by C18, 0.6
.mu.l bed ZipTip (Millipore, Billerica, Mass.). Specifically, the
tip was washed with 5 .mu.l of 100% acetonitrile, followed by water
and 5% acetonitrile, 0.1% TFA. To load the sample, 2 .mu.l of
sample was drawn into the tip, and discarded after 5 seconds. After
washing 3.times. with 5 .mu.l H.sub.2O, glycopeptides were eluted
with 10% acetonitrile.
PNGaseF Digestion on PVDF Membrane
[0262] PVDF-coated wells in a 96-well plate were washed with 200
.mu.l MeOH, 3.times.200 .mu.l H.sub.2O and 200 .mu.l reduction and
carboxymethylation (RCM) buffer (8M urea, 360 mM Tris, 3.2 mM EDTA,
pH 8.3). The protein samples (50 .mu.l) were then loaded in the
wells along with 300 .mu.l RCM buffer. After washing the wells two
times with fresh RCM buffer, 500 .mu.l of 0.1M DTT in RCM buffer
was added for 1 hr at 37.degree. C. To remove the excess DTT, the
wells were washed three times with H.sub.2O. For the
carboxymethylation, 500 .mu.l of 0.1M iodoacetic acid in RCM buffer
was added for 30 minutes at 37.degree. C. in the dark. The wells
were washed again with water, then the membrane was blocked with 1
ml polyvinylpyrrolidone (360,000 average molecular weight (AMW), 1%
solution in H.sub.2O) for 1 hr at room temperature. Before adding
the PNGaseF, the wells were washed again with water. To release the
glycans, 4 .mu.l of PNGaseF was added in 300 .mu.l of 50 mM Tris,
pH 7.5 and incubated overnight at 37.degree. C. Released glycans
were pipetted from the wells and purified by C18 and GlycoClean H
as described above.
Results
[0263] Building on the work with single protein systems, the
purpose of this study was to isolate and purify N-glycans from
human serum, generating a total N-glycan profile. Serum was chosen
as the diagnostic medium because many disease markers are released
into circulation (Pujol, J. L., et al. 2003. Lung Cancer. 39,
131-8; Gadducci, A., et al. 2004. Biomed Pharmacother. 58, 24-38),
and obtaining serum is a relatively simple procedure.
[0264] Before being able to develop a method to study N-glycans
from serum, it was important to understand the types of molecules
present. Proteins comprise an enormous portion of serum,
approximately 7% of the total wet weight (Vander, A. J., et al.
2001. Human physiology: the mechanisms of body function.
McGraw-Hill, Boston, Mass.) Of this amount, over half is albumin
(.about.50 mg/ml), a protein that can be non-enzymatically
glycosylated, but not N- or O-glycosylated (Rohovec, J., et al.
2003. Chemistry. 9, 2193-9.) Although the overwhelming amounts of
albumin can obscure analysis for proteomics, it may not interfere
with N-glycan profiling. There are also large amounts of
glycosylated antibodies, which have a number of glycan structures
(Bihoreau, N., et al. 1997. J Chromatogr B Biomed Sci Appl. 697,
123-33; Watt, G. M., et al. 2003. Chem. Biol. 10, 807-14.) However,
simple methods exist to separate these abundant antibodies from the
less abundant glycoproteins.
[0265] When working with serum, there are several issues to
consider that are not relevant for single protein systems. Because
the proteins in the sample are so concentrated, they can easily
precipitate out of solution. Also, even though albumin does not
have N-linked sugars, the sheer quantity present may interfere with
glycan release or purification. There are several other major
proteins in serum (i.e. immunoglobulins) that are N-glycosylated,
which may overshadow the signals from less abundant proteins.
However, alterations in immunoglobulin glycosylation may also be
correlated with changes in physiological state. To determine the
contributions and/or interference from major serum proteins,
several options for separating serum proteins into fractions before
analysis were explored.
[0266] There are both neutral and charged sugars on serum
glycoproteins. Acidic glycans generally do not ionize well in the
positive ion mode of MALDI-MS, and also suffer loss of sialic
acids. On the other hand, neutral sugars ionize extremely poorly in
negative mode, which is commonly used for charged glycans.
Therefore, a method where the neutral and acidic structures were
assayed separately was compared to two chemical modification
methods that allow the glycans to ionize more uniformly.
[0267] Identifying glycan structures with complex protein mixtures
can be rather difficult. By generating a master list of all
possible compositions and their theoretical masses based on
biosynthetic pathways for glycosylation, all possible
monosaccharide composition can be assigned to each peak observed in
a MALDI-MS spectrum. In most cases, each mass peak corresponds
uniquely to a monosaccharide assignment. However, in some instances
there can be more than one potential composition. If necessary, the
correct composition can be determined by using commercially
available exoenzymes that cleave the glycans only at particular
linkages.
Sample Preparation
[0268] Serum samples generally contained upwards of 120 mg/ml of
protein, making heat denaturing less ideal. Even when diluted, the
proteins in these samples precipitated rapidly, giving the sample a
gel-like consistency. This could prevent the PNGaseF from accessing
all the N-glycan sites on the proteins. One set of samples was
processed using the traditional heat-denaturation method after
diluting the serum samples 1:10 in water (FIG. 9A). A number of
glycan peaks were observed in the MALDI-MS spectrum, but there
clearly was residual detergent contamination from the denaturing
step. On a separate sample, EndoF was used since the enzyme can act
on folded proteins (FIG. 9B).
[0269] However, EndoF cleaves between the first and second GlcNAc
on the glycan core, causing a loss of information on core
fucosylation. After EndoF digestion, the samples were purified as
usual. As shown in FIG. 9, glycans could indeed be obtained using
both methods. EndoF spectra had a relatively high level of baseline
noise, and signal intensities were relatively low (.about.1000),
leaving room for improvement.
[0270] As an alternative to heat denaturation, the proteins were
reduced with DTT followed by carboxymethylation with iodoacetic
acid to denature the proteins (Lacko, A. G., et al. 1998. J Lipid
Res. 39, 807-20.) Reduction disrupts the disulfide bonds in
proteins, while carboxymethylation prevents the proteins from
re-folding. After dialysis to remove excess iodoacetic acid and
DTT, PNGaseF was added to the denatured proteins for overnight
cleavage. An additional advantage to this method over the regular
SDS/.beta.-mercaptoethanol heat-denaturation method was that the
absence of detergents facilitated purification. After the glycans
were cleaved from the core protein, the sample was dialyzed against
water overnight and lyophilized. Exchanging the sample into water
prepared it to be passed through a C18 cartridge (Waters
Corporation) to remove remaining protein. At this stage, both
neutral and acidic sugars were present in the same sample,
potentially complicating the assignment of glycan peaks in the
MALDI-MS spectrum.
[0271] Because serum contains proteins with a wide variety of
neutral and charged N-glycans, analysis was facilitated by
separating the neutral sugars from the acidic carbohydrates. This
allowed each pool to be analyzed using methods particularly suited
to the chemical properties of neutral vs. charged molecules. The
GlycoClean H purification cartridge (Prozyme) was used for this
purpose by eluting glycan pools with different concentrations of
acetonitrile. Neutral sugars were eluted with 15% acetonitrile,
0.1% TFA, while acidic sugars were eluted with 50% acetonitrile,
0.1% TFA. To test the separation of neutral and acidic sugars, six
known glycan standards were used (Table 5).
TABLE-US-00005 TABLE 5 Glycan Standards Used to Test GlycoClean H
Separation of Neutral and Acidic Sugars Commercial Charge state
name Structure description Mass (# sialic acids) NGA2 Asialo,
agalacto biantennary 1317.2 0 NA2 Asialo, galactosylated, 1641.5 0
biantennary NA3 Asialo, galactosylated, 2006.0 0 triantennary
SC1223 Disialylated, galactosylated, 2370.2 2 fucosylated
biantennary A3 Trisialylated, galactosylated, 2879.9 3 triantennary
SC1840 Tetrasialylated, galactosylated, 3683.4 4 tetrantennary
[0272] The neutral sugars were analyzed in positive ion mode in the
MALDI-MS, while acidic sugars were examined in negative mode (FIG.
10). To confirm that no neutral sugars were present in the acidic
glycan sample, the positive mode spectrum was checked for charged
glycans. When this method was applied to human serum N-glycans, the
spectra appeared much cleaner than those obtained from a mixed
sample, since each group of sugars could be analyzed under optimal
conditions (FIG. 11).
[0273] This process was repeated multiple times with the same serum
sample to ensure reproducibility (three aliquots were purified in
parallel on one day, and another two on two different days). In
addition, multiple normal serum samples were processed by this
method to determine the degree of glycan variation between serum
samples. Five normal male human samples from each of two different
sources (IMPATH tissue bank and Biomedical Resources) were used to
assess whether observed glycan profiles were consistent across
suppliers. As expected, there was some variation in the spectra
from different normal samples in both the neutral and acidic
fraction (FIG. 11), while aliquots from the same serum sample
appeared very similar even when they were purified on different
days. The samples from different serum banks showed similar
profiles and major peak clusters.
Most Abundant Proteins
[0274] Although serum samples can be analyzed with all proteins
present, including non-glycosylated species, it was determined
whether better results could be obtained by removing proteins such
as albumin. ConA is a lectin that binds to .alpha.-linked mannose,
as contained in all N-glycans (Bryce, R. A., et al. 2001. Biophys J
81, 1373-88.) A serum sample was passed through a column of
agarose-bound ConA. Proteins containing N-glycans bound to the
column while non-glycosylated proteins were washed off (this sample
was collected as the ConA flow-through). The glycoproteins were
then eluted with a 500 mM .alpha.-methyl-mannoside solution, which
competes for the ConA binding sites.
[0275] To evaluate the separation of the serum sample into
glycosylated and non-glycosylated proteins, the ConA flow-through
and elution samples were run on an SDS-PAGE gel (FIG. 12A). In the
gel, the albumin fraction is clearly visible in the flow-through
from the ConA column, while multiple bands in the elution lane
represent glycoproteins. In addition, the glycan profiles of both
the flow-through and the elution fraction were analyzed. After
dialyzing the samples against 10 mM phosphate buffer, the samples
were processed with PNGaseF and purified by C18 cartridge and Glyco
H. There were no observable glycans present in the flow-through
fraction, while neutral and acidic sugars from the elution fraction
are shown in FIGS. 12B and 12C. The results from total serum
digests, however, yield MALDI-MS data with signal-to-noise ratio
and signal intensity that are as good as or better than from ConA
elution.
[0276] Therefore, in some cases there will be little to no
advantage to removing non-glycosylated proteins before analysis.
Serum samples were also depleted of antibodies through a Protein A
column to determine how many major peaks in the final spectra came
from IgG. The presence of glycoproteins in both the flow-through
and elution fractions were determined by GlycoTrack glycoprotein
detection kit (Prozyme) (FIG. 13A). The Protein A elution fraction
containing IgGs was treated with PNGaseF and purified as described
above. FIGS. 13B-13E show a comparison of the glycans from IgG
(Protein A elution) to the total glycan profile. Although several
of the major peaks in the spectra indeed come from this antibody
population, they do not appear, at least for these samples, to be
in large enough quantities to interfere with the signals from other
glycans.
MALDI-MS Analysis of Serum N-glycans
[0277] Neutral and acidic sugars require different treatment when
being analyzed by MALDI-MS. In particular, neutral sugars ionize
well in the positive ion mode, but not well in negative mode, while
the opposite is true for charged sugars. Three different matrix
formulations were tested to determine the best one for these
samples. All formulations contained DHB and spermine, as this had
yielded the best results with single-protein studies. The three
matrix preparations were 1) saturated DHB in water with 300 mM
spermine, 2) 20 mg/ml DHB in acetonitrile and 25 mM spermine in
water in a 1:1 ratio and 3) 20 mg/ml DHB in methanol and 25 mM
spermine in water in a 1:1 ratio. Preparation 2 yielded MALDI-MS
spectra with the highest signal-to-noise ratio in both positive and
negative mode, and was used for all experiments.
[0278] There are several reported methods for increasing the
sensitivity and ionization efficiency of mass spectrometry data in
the analysis of glycans. With these methods, it is sometimes
possible to analyze glycan pools as a mixture of neutral and acidic
glycans, as the chemical properties of the glycans are modified to
allow for more uniform ionization. Two types of chemical
modifications were tested to determine whether the MALDI-MS results
could be improved upon.
[0279] N-glycan samples are commonly permethylated to protect each
OH and NH.sub.2 or amide group in the carbohydrate (Fukuda, M.,
Kobata, A. 1993. Glycobiology: a practical approach. IRL Press at
Oxford University Press, Oxford; N.Y.) This is particularly useful
for MS techniques such as fast atom bombardment (FAB-MS), since
permethylated glycans fragment in a much more predictable manner
than underivatized glycans. Permethylation can also increase
sensitivity in electrospray mass spectrometry (ES-MS) and MALDI-MS.
The schematic of the permethylation reaction is shown in FIG. 14.
Some drawbacks to permethylation are that the sample has to be
extremely clean for the reaction to go to completion, and the
sample requires clean-up after the reaction. In the current study,
although this method slightly improved the ionization of N-glycan
standards in MALDI-MS over non-modified glycans, the increase in
signal-to-noise ratio was not significant (FIG. 15).
[0280] A newer method for increasing N-glycan ionization, as well
as allowing the glycans to ionize more uniformly across species is
to conjugate it to a peptide (Zhao, Y., et al. 1997. Proc Natl Acad
Sci USA. 94, 1629-33.) The structure of the peptide and its glycan
conjugation reaction are shown in FIG. 16. Before MALDI-MS, it was
necessary to clean up the reaction mixture using a C18 ZipTip
(Millipore) in order to eliminate the buffer (NaOAc) used in the
reaction. The ZipTip flow-through, water wash and 10% acetonitrile
elution were all spotted on the plate. The glycopeptide conjugates
(in the 10% elution fraction) were readily observed in the
MALDI-MS, and neutral and acidic sugars ionized more evenly in the
positive mode as compared to unmodified glycans (FIG. 17).
[0281] While the glycan-peptide conjugation reaction is simple, the
free peptide is particularly unstable. Specifically, the peptide's
active hydroxylamine group readily reacts with any aldehydes or
ketones present, thus preventing it from conjugating to the
glycans. Although the reaction with glycan standards displayed
promising results, it was difficult to obtain a complete reaction
with serum samples. Even after several attempts to label serum
glycans with varying amounts of peptide, free glycan peaks in the
spectra were observed from flow-through and water wash spots.
Because there may be excess aldehydes or ketones remaining in serum
samples, peptide conjugation was not used, and the samples were
analyzed as separate neutral and acidic fractions.
Identifying Composition of Glycans from MALDI-MS Data
[0282] In a MALDI-MS spectrum, the main information obtained is
mass of the parent ion. With just this data, it was indeed possible
to deduce the monosaccharide composition of each peak (e.g., number
of hexNAc, hexose, fucose and sialic acid residues). Using
knowledge of biosynthetic rules, as well as whether each glycan is
charged or uncharged, the number of possible structures for each
mass peak observed can be significantly limited. A spreadsheet to
use as a lookup table for unknown peaks was created. In addition to
unmodified masses, entries for permethylated masses were included
as well as peptide-conjugated glycans, according to the following
equations:
TABLE-US-00006 s = sialic n = HexNAc h = hexose f = fucose acid
Mol. Wt.-H.sub.2O = 203.1 162.1 146.1 291.3
[0283] Unmodified Glycans [0284]
mass=203.1n+162.1h+146.1f+291.3s+18
[0285] Permethylated Glycans [0286]
perm=mass+51+14[3(n+h)+2f+5s]
[0287] Peptide-Conjugated Glycans [0288] peptide=mass+1527.1
[0289] Using this table, regardless of the analytical methods,
MALDI-MS peaks can be associated with specific monosaccharide
compositions. Sample entries from this database are shown in Table
6.
TABLE-US-00007 TABLE 6 Table of Sample Entries for Identifying
N-glycan Composition from MALDI-MS Data HexNAc Hexose Fucose Sialic
Acid mass perm peptide 2 3 1 0 1056.6 1345.6 2583.7 2 4 0 0 1072.6
1375.6 2599.7 3 3 0 1 1404.9 1777.9 2932 4 3 0 1 1608 2023 3135.1 4
3 2 0 1608.9 2009.9 3136 5 3 1 0 1665.9 2080.9 3193 6 3 0 0 1722.9
2151.9 3250 3 6 1 0 1746 2203 3273.1 4 3 1 1 1754.1 2197.1 3281.2 4
3 2 1 1900.2 2371.2 3427.3
[0290] Using the table almost all the peaks in MALDI-MS serum
profiles could be identified as glycans of known composition (FIG.
18). Many of the unidentified peaks are ammonium or sodium adducts.
The composition and mass of each labeled peak are listed in Table
7. A few peaks in the acidic glycan spectrum correspond to more
than one composition. This is more common in the higher mass range
since there are a larger number of possible monosaccharide
compositions. Many of the glycans observed in these spectra were
also present in other serum samples; there are typically between
25-30 neutral glycans as well as 25-30 acidic glycans present in a
given sample.
TABLE-US-00008 TABLE 7 Composition and Mass of Serum Glycans
Observed in FIG. 18 Peak HexNAc Hexose Fucose Sialic Acid Mass
Neutral glycans (FIG. 18A) 1 2 3 1 0 1056.6 2 2 4 0 0 1072.6 3 2 5
0 0 1234.7 4 3 3 1 0 1259.7 5 3 4 0 0 1275.5 6 4 3 0 0 1316.7 7 2 6
0 0 1396.8 8 3 4 1 0 1421.8 9 3 5 0 0 1437.8 10 4 3 1 0 1642.8 11 4
4 0 0 1478.8 12 5 3 0 0 1519.8 13 2 7 0 0 1558.9 14 3 6 0 0 1599.9
15 4 4 1 0 1624.9 16 4 5 0 0 1640.9 17 5 3 1 0 1665.9 18 5 4 0 0
1681.9 19 4 5 1 0 1787.0 20 4 6 0 0 1803.0 21 5 4 1 0 1828.0 22 5 5
0 0 1844.0 23 2 9 0 0 1883.1 24 5 5 1 0 1990.1 25 5 6 0 0 2006.1 26
5 5 2 0 2136.2 27 5 6 1 0 2152.2 28 5 7 0 0 2168.2 Acidic glycans
(FIG. 18B) 1 4 4 0 1 1770.1 2 3 6 0 1 1891.2 3 4 4 1 1 1916.2 4 4 5
0 1 1932.2 5 5 4 0 1 1973.2 6 3 7 0 1 2053.3 7 4 5 1 1 2078.3 8 5 4
1 1 2119.3 9 5 5 0 1 2135.3 10 4 5 0 2 2223.5 11 4 5 2 1 2224.4 12
5 3 1 2 2248.5 13 5 4 0 2 2264.5 14 5 5 1 1 2281.4 15 5 6 0 1
2297.4 16 4 5 1 2 2369.6 17 4 5 3 1 2370.5 18 5 6 1 1 2443.5 19 5 5
1 2 2572.7 20 5 6 0 2 2588.7 21 5 6 2 1 2589.6 22 6 4 1 2 2613.7 23
5 6 1 2 2734.8 24 5 6 3 1 2735.7 25 5 6 2 2 2880.9 26 5 6 4 1
2881.8
Alternative Sample Preparation Methods
[0291] Besides performing PNGaseF digests in solution, a
membrane-based method was tested with the potential for
high-throughput sample processing. Proteins were adsorbed onto a
PVDF membrane in a 96-well plate, followed by reduction,
carboxymethylation and digestion in the wells (Papac, D. I., et al.
1998. Glycobiology. 8, 445-54.) While this method works well with
single glycoproteins, very few glycans were observed when serum was
used. An explanation for this result is that albumin most likely
saturated the membrane binding capacity, and most of the
glycoproteins were washed away before the PNGaseF was added.
Without removing albumin, only a few glycans were observed in the
neutral fraction, and no acidic glycans were present (FIG. 19).
Although time saved by performing the experiment in a 96-well
format may be negated by the extra steps required to remove
albumin, using the PVDF membrane as a platform for digestion may be
extremely useful for the development of a high-throughput glycomics
methodology for serum samples. All samples in this study were,
however, processed with the PNGaseF digest in solution.
[0292] It has been demonstrated that a complete N-glycan profile
from human serum proteins can be obtained. By separating glycans
into neutral and acidic pools, it was possible to clearly identify
glycans directly from MALDI-MS without chemical modification. In
addition, it was shown that albumin and IgGs do not need to be
removed from serum samples prior to analysis, although their
removal can be beneficial in some contexts (e.g., in a
high-throughput analysis). With the ability to profile all glycans
from serum, it becomes possible to apply bioinformatics approaches
to search for patterns that define normal or disease states.
[0293] Furthermore, a glycomics approach may be even more sensitive
than what can be achieved with proteomics. Even in cases where
protein expression does not change, the types of N-glycans present
on these proteins can indicate a change in physiological condition.
Already, proteomics technologies are being explored as diagnostic
tools. Examining glycosylation patterns may enable more precise
characterization of certain disease states, such as the
differentiation between benign and malignant tumors. Thus, serum
glycan profiling can advance the utility of glycomics data for the
early diagnosis of currently undetectable disease states, such as,
for example, in combination with a bioinformatics/computational
platform.
Example 3
Glycan Analysis
[0294] Release of Glycans from Proteins
[0295] Several methods were used to cleave the carbohydrates from
proteins:
[0296] A) Glycoproteins were denatured with 0.5% SDS and 1%
.beta.-mercaptoethanol. Since SDS (and other ionic detergents)
inhibits enzyme activity, 1% NP-40 was added to counteract these
effects. The enzymatic cleavage was performed overnight with
PNGaseF (New England Biolabs) at 37.degree. C. in sodium phosphate
buffer, pH 7.5 or Tris acetate buffer pH 8.3.
[0297] B) Samples were reduced with DTT followed by alkylation with
either iodoacetic acid or iodoacetamide. The sample was dialyzed
against phosphate buffer, pH 7.5 or Tris acetate, pH 8.3 overnight
and concentrated to .about.200 .mu.l in a spin column with a 3000
Da MWCO filter. To cleave the sugars from the protein between 100
and 2,000 U of PNGaseF (New England Biolabs) were used.
[0298] C) Glycoproteins were denatured using a buffer containing 8M
urea, 3.2 mM EDTA and 360 mM Tris, pH 8.6 (Papac, D. I., et al.
1998. Glycobiology. 8, 445-54.) Reduction and carboxymethylation of
the glycoproteins was then achieved using DTT and iodoacetic acid
(or iodoacetamide), respectively. After removal of denaturing,
reducing and alkylating reagents, N-glycans were selectively
released from the glycoproteins by incubation with PNGase F.
[0299] D) The steps for protein denaturing, protein alkylation and
glycan release were also performed with the proteins bound to a
solid support (Papac, D. I., et al. 1998. Glycobiology. 8, 445-54.)
PVDF-coated wells in a 96-well plate were washed with 200 .mu.l
MeOH, 3.times.200 .mu.l H.sub.2O and 200 .mu.l RCM buffer (8M urea,
360 mM Tris, 3.2 mM EDTA pH 8.3.) The protein samples (10 to 50
.mu.l) were then loaded in the wells along with 300 .mu.l RCM
buffer. After washing the wells two times with fresh RCM buffer,
300 .mu.l of 0.1M DTT in RCM buffer was added for 1 hr at
37.degree. C. To remove the excess DTT, the wells were washed three
times with H.sub.2O. For the carboxymethylation, 300 .mu.l of 0.1M
iodoacetic acid in RCM buffer was added for 30 minutes at
37.degree. C. in the dark. The wells were washed again with water,
and the membrane was then blocked with 1 ml polyvinylpyrrolidone
(360,000 AMW, 1% solution in H.sub.2O) for 1 hr at room
temperature. Before adding the PNGaseF, the wells were washed again
with water. To release the glycans, 100 to 1,000 U of PNGaseF were
added in 300 .mu.l of 50 mM Tris, pH 7.5 and incubated overnight at
37.degree. C. Released glycans were pipetted from the wells and
purified.
[0300] E) Alternatively, after the proteins were denatured, EndoH
or EndoF (instead of PNGaseF) was used to release the glycans.
[0301] F) Chemical methods, such as hydrazinolysis and reductive
(3-elimination were also used.
[0302] G) The denaturing, reduction, alkylation and glycan cleavage
steps were also performed in a semi-high-throughput fashion either
in solution or by binding the proteins to solid supports in plates
with hydrophobic membranes (Papac, D. I., et al. 1998.
Glycobiology. 8, 445-54.)
Purification of Released N-glycans
[0303] Several methods were used to isolate and purify the released
carbohydrates. These methods were used either individually or in
some combination.
[0304] A) Proteins were precipitated with a 3.times. volume of cold
ethanol. After centrifugation to remove the proteins, the
supernatant containing the N-glycans was evaporated by vacuum
(SpeedVac). Dried glycans were resuspended in water.
[0305] B) Concomitant protein and salt removal was achieved using
cation exchange column of AG50W X-8 beads (Bio-Rad). The resin was
charged with 150 mM acetic acid and washed with water. Glycan
samples were loaded onto the column in water, and washed through
with 3 ml H.sub.2O. The flow-through was collected and lyophilized
to obtain the desalted sugars.
[0306] C) GlycoClean R cartridges (Prozyme) were primed with 3 ml
of 5% acetic acid, and the samples were loaded in water. Sugars
were eluted with 3 ml of water passed through the column.
[0307] D) GlycoClean S cartridges (Prozyme) were primed with 1 ml
water and 1 ml 30% acetic acid, followed by 1 ml acetonitrile. The
glycan sample was loaded (in a maximum volume of 10 .mu.l) onto the
disc, and the glycans were allowed to adsorb for 15 minutes. After
washing the disc with 1 ml of 100% acetonitrile and 5.times.1 ml of
96% acetonitrile, glycans were eluted with 3.times.0.5 ml
water.
[0308] E) GlycoClean H cartridges (Prozyme; 200 mg bed) were washed
with 3 ml of 1M NaOH, 3 ml H.sub.2O, 3 ml 30% acetic acid, and 3 ml
H.sub.2O to remove impurities. The matrix was primed with 3 ml 50%
acetonitrile with 0.1% TFA (Solvent A) followed by 3 ml 5%
acetonitrile with 0.1% TFA (Solvent B). After loading the sample in
water, the column was washed with 3 ml H.sub.2O and 3 ml Solvent B.
Finally, the sugars were eluted using 4.times.0.5 ml of Solvent A.
GlycoClean H cartridges can be reused after washing with 100%
acetonitrile and re-priming with 3 ml of Solvent A followed by 3 ml
of Solvent B. For the 25 mg cartridge, wash volumes were reduced to
0.5 ml. Eluted fractions were lyophilized, and the isolated glycans
were resuspended in 10-40 .mu.l H.sub.2O.
[0309] F) Hypercarb SPE cartridges (Thermo Electron Corporation)
were washed with 3 ml of 1M NaOH, 3 ml H.sub.2O, 3 ml 30% acetic
acid and 3 ml H.sub.2O to remove impurities. The matrix was primed
with 3 ml 5% acetonitrile with 0.05% TFA (Solvent B). After loading
the sample in water, the column was washed with 3 ml H.sub.2O and 3
ml Solvent B. Finally, the neutral sugars were eluted using 15%
acetonitrile, 0.05% TFA, and acidic glycans were eluted using 50%
acetonitrile, 0.05% TFA.
[0310] G) Non-porous graphitic carbon SPE cartridges
(Sigma-Aldrich, St. Louis, Mo.) were primed with 3 ml 5%
acetonitrile and 0.05% TFA (Solvent B). After loading the sample in
water, the column was washed with 3 ml H.sub.2O and 3 ml Solvent B.
Finally, the neutral sugars were eluted using 15% acetonitrile,
0.05% TFA, and acidic glycans were eluted using 50% acetonitrile,
0.05% TFA.
[0311] H) The glycan purification step was also performed in a
high-throughput format by using columns in 96-well plates. This
process was facilitated by the use of a Tecan Freedom EVO automated
liquid handling unit (Tecan, Durham, N.C.). This protocol allowed
the processing of more than 90 samples at the same time.
Chemical Modification of N-glycans
[0312] Several derivatization methods are currently used to
increase the sensitivity and ionization efficiency of mass
spectrometry data in the analysis of glycans. With these methods,
it is often possible to analyze glycan pools as a mixture of
neutral and acidic glycans, as the chemical properties of the
glycans are modified to allow for more uniform ionization. N-glycan
samples are commonly permethylated to protect each OH and NH.sub.2
or amide group in the carbohydrate. This is particularly useful for
MS techniques such as FAB-MS, since permethylated glycans fragment
in a more predictable manner than underivatized glycans.
Permethylation can also increase sensitivity in ES-MS and
MALDI-MS.
[0313] For permethylation, glycans in water were placed in a
round-bottomed flask and lyophilized overnight. A slurry of NaOH in
DMSO (0.5 ml) was added to the glycan sample, along with 0.5 ml
methyl iodide and incubated for 15 minutes. The sample was then
diluted with water and extracted 2.times. with CHCl.sub.3,
collecting the organic phase. After drying the organic phase with
MgSO.sub.4, it was filtered through glass wool and dried under
vacuum. Samples were then redissolved in methanol for MALDI-MS
analysis. Some drawbacks to permethylation are that the sample has
to be extremely clean for the reaction to go to completion and
requires additional purification after the reaction. Although this
method slightly improved the ionization of N-glycan standards in
MALDI-MS over unmodified glycans, many species corresponding to
incomplete modification were detected.
[0314] To conjugate N-glycans to the synthetic aminooxyacetyl
peptide, glycans were dried and resuspended in aqueous peptide
solution (240 .mu.M). After adding 1 .mu.l of 500 mM NaOAc, pH 5.5
and 20 .mu.l of acetonitrile, the sample was incubated overnight at
40.degree. C. Before MALDI-MS analysis, glycopeptides were purified
by C18, 0.6 .mu.l bed ZipTip (Millipore). Specifically, the tip was
washed with 5 .mu.l of 100% acetonitrile, followed by water and 5%
acetonitrile, 0.1% TFA. To load the sample, 2 .mu.l of sample was
drawn into the tip, and discarded after 5 seconds. After washing
3.times. with 5 .mu.l H.sub.2O, glycopeptides were eluted with 10%
acetonitrile. Before MALDI-MS, it was necessary to clean up the
reaction mixture using a C18 ZipTip (Millipore) in order to
eliminate the buffer (NaOAc) used in the reaction. The ZipTip
flow-through, water wash and 10% acetonitrile elution were all
spotted on the plate. The glycopeptide conjugates (in the 10%
elution fraction) were readily observed in the MALDI-MS, and
neutral and acidic sugars ionized more evenly in the positive mode
as compared to unmodified glycans.
[0315] While the glycan-peptide conjugation reaction is simple, the
free peptide is unstable. Specifically, the peptide's hydroxylamine
group readily reacts with any aldehydes or ketones present, thus
preventing it from conjugating to the glycans. Other labeling
reagents (i.e. 9-aminopyrene-1,4,6 trisulfonate (APTS),
9-aminonaphtalene-1,4,6 trisulfonate (ANTS), 2-aminoacridone
(AMAC), etc.) have been used, but the analysis of unmodified
glycans, separated into neutral and acidic fractions, was the
method used for these studies.
MALDI-MS Analysis Optimization of Unmodified Glycans
[0316] Neutral and acidic sugar samples can require different
treatment when being analyzed by MALDI-MS. In particular, neutral
sugars ionize well in the positive ion mode, while the ionization
of acidic sugars is optimal using the negative ion mode. For the
analysis of low abundance glycans present in a mixture of
glycoforms or different glycoproteins, a matrix of matrices
containing more than 96 possible recipe combinations was generated.
This study was designed to optimize the MALDI-MS analysis for the
highest sensitivity, spot morphology, reduced peak splitting,
reduced fragmentation and linear response as a function of
concentration.
[0317] As a starting point, DHB was utilized in combination with
spermine (20 mg/ml DHB in acetonitrile and 25 mM spermine in water
in a 1:1 ratio.) This recipe resulted in detection limits of 1 pmol
and 10 pmol for neutral and acidic glycans, respectively.
Significant peak splitting with multiple sodium and potassium ions
were observed. Also, this matrix crystallized as long,
needle-shaped crystals, which makes it difficult to achieve
reproducible quantification of glycans present in a sample and
eliminates the possibility for the automation of data
acquisition.
[0318] Some of the matrices and reagents used in this study were:
caffeic acid, DHB, spermine, 1-hydroxyisoquinoline (HIQ), ATT,
2,4,6-trihydroxyacetophenone (THAP), Nafion.TM., 6-hydroxypicolinic
acid, 3-hydroxypicolinic, 5-methoxysalicylic acid (5-MSA), ammonium
citrate, ammonium tartrate, sodium chloride, ammonium resins, etc.
These reagents were used in combination with different solvents
such as methanol, ethanol, acetonitrile and water. The matrix of
matrices study resulted in new recipes of 2,5-dihydroxybenzoic acid
(5 mg/ml) and 5-MSA (0.25 mg/ml) in acetonitrile, for neutral
glycans, and 6-aza-thiothymine (10 mg/ml in ethanol) spotted on
Nafion.TM. coating, for acidic glycans. These matrices displayed
detection limits, for a mixture of carbohydrates, of 25 fmol and 5
fmol for neutral glycans and acidic glycans, respectively (FIG.
20). The new matrices also showed minimum peak splitting, highly
uniform signal intensity, spot morphology and no detectable
fragmentation.
[0319] A detailed study to correlate between signal intensity,
concentration and molecular weight was also performed. The analysis
covered the entire range of possible molecular weights for
N-glycans (approximately 900-4200 Da). Linear response as a
function of concentration was observed for different glycans. Taken
together, MALDI analysis of glycans using these matrices can be
used to quantify the amount of glycans present in a mixture (FIG.
21). In particular, these data enable the quantification of glycans
at the low femtomole concentration range. Other methods known to
those of ordinary skill in the relevant art can also be used to
quantify glycans at a higher range of concentrations (e.g.,
picomolar) (Harvey, D. J., Rapid Commun Mass Spectrom. 1993 July;
7(7): 614-9). For FIGS. 1 and 2, the assigned peaks and labels
correspond to glycan standards from Dextra Laboratories Ltd.
(Reading, United Kingdom).
[0320] A potential concern with MALDI analysis is that the ion
yield of specific analyte in a mixture drops as the number of
constituents increase. To evaluate this, the effect of the signal
strength on the number of glycans present in a mixture was also
evaluated for both matrices. Interestingly, there was very little
change in the intensity of individual glycan signals even in the
presence of other glycans, thus indicating that the ion yield of a
specific constituent is not affected by the number of analytes
present in the glycan mixture (FIG. 21B). This ensures that even in
a complex mixture of glycans, accurate amounts can be calculated
using the signal intensity. Finally, the dynamic range of these
matrices were in the low femtomole range ensuring that changes in
low abundant glycans can be accurately monitored by using these
matrices.
[0321] To prepare the sample spots, three methods were used. For
the crushed spot method, 1 .mu.l of matrix was spotted on the
stainless steel MALDI-MS sample plate and allowed to dry. After
crushing the spot with a glass slide, 1 .mu.l of matrix mixed 1:1
with sample was spotted on the seed crystals and allowed to dry.
Alternatively, 1 .mu.l of matrix was applied followed by 1 .mu.l of
sample, or vice versa. When resins were used in combination with
the matrices, 1 .mu.l of the resin was applied to the probe and
allowed to dry before applying the sample in a 1:1 mixture with the
matrix. All spectra were taken with the following instrument
parameters: accelerating voltage 22000V, grid voltage 93%, guide
wire 0.15% and extraction delay time of 150 nsec (unless otherwise
noted). All N-glycans were detected in linear mode with delayed
extraction and positive polarity for neutral glycans and negative
polarity for acidic glycans.
LC-MS, LC-MS/MS and Capillary Electrophoresis
[0322] Due to the limitations in isomass characterization using
MALDI-MS, in some instances other techniques such as LC-MS (or
tandem-MS) and CE-LIF can be applied to further characterize the
glycans released from the glycoprotein of interest. For LC-MS (or
tandem-MS), the reducing end of the carbohydrates is reduced using
sodium borohydride and the carbohydrates are separated using a
graphitized carbon column. The column is directly attached to an
ESI-MS, which allows the detection and characterization of the
carbohydrates as they elute from the column. Although the use of
exoglycosidases is often added to this LC-MS analysis, MS/MS
fragmentation is also used for further linkage characterization of
the carbohydrates based on the fragmentation pattern.
[0323] Similarly, CE-LIF can also used for the further separation
and characterization of the glycans. In this case, the
carbohydrates, are first derivatized, in some embodiments, by
reductive amination at their reducing end with a fluorescent
molecule such as APTS, ANTS, AMAC, etc. The fluorescently-modified
(or "labeled") carbohydrates are then separated by capillary
electrophoresis and detected with high sensitivity via
laser-induced fluorescence. Similar to LC-MS, glycosidases can also
used in combination with CE-LIF in order to get further structural
linkage information on the carbohydrates.
Identifying Glycan Composition from MALDI-MS Data
[0324] In a MALDI-MS spectrum, the primary information obtained is
mass of the parent ion. With this data, it was possible to deduce
the monosaccharide composition of each peak (number of hexNAc,
hexose, fucose and sialic acid residues). Using available
information of biosynthetic rules, as well as whether each glycan
is charged or uncharged, the number of possible structures for each
mass peak was significantly limited. A spreadsheet to use as a
lookup table for unknown peaks was created. In addition to
unmodified masses, entries for permethylated masses were included
as well as peptide-conjugated glycans, according to the following
equations:
TABLE-US-00009 s = sialic n = HexNAc h = hexose f = fucose acid
Mol. Wt.-H.sub.2O = 203.1 162.1 146.1 291.3
[0325] Unmodified Glycans [0326]
mass=203.1n+162.1h+146.1f+291.3s+18
[0327] Permethylated Glycans [0328]
perm=mass+51+14[3(n+h)+2f+5s]
[0329] Peptide-Conjugated Glycans [0330] peptide=mass+1527.1
[0331] Using this table, regardless of the analytical methods, mass
spectrometry peaks can be associated with specific monosaccharide
compositions. A table of sample entries is shown in Table 6. Other
methods known to those of ordinary skill in the art can be used to
determine the glycan identity from mass spectrometry data (See, for
example, U.S. Pat. Nos. 5,607,859; 6,597,996; and WO 00/65521).
Computational Tools to Characterize Glycoprotein Mixtures
[0332] The diverse information gathered from different experimental
techniques are incorporated as constraints and used in combination
with a panel of proteomics- and glycomics-based bioinformatics
tools and databases for the efficient characterization
(glycosylation site occupancy, quantification, glycan structure,
etc.) of the glycoprotein mixture of interest (FIGS. 22 and 23).
The following six steps provides one example of how a known or
unknown glycopeptide mixture can be characterized using the
techniques described herein.
Step 1:
[0333] Separate the glycans from the glycopeptide mixture. Isolate
and sequence the resultant peptide(s). In this example, there was
only one peptide chain and that was determined to
be-YCNISQKMMSRNLTKDR. This peptide has two possible N-glycosylation
sites: CNIS and RNLT.
Step 2:
[0334] Digest the glycopeptide using trypsin followed by the
cleavage of the glycans: one sample with .sup.18O labeling and
another without labeling (.sup.16O). Generate LC-MS spectra on both
of the resultant samples. In this example, the following mass peaks
were seen for the sample without labeling (289, 475, 476, 523, 855
and 856). With labeling the following mass peaks were seen (289,
475, 478, 523, 855, and 858).
[0335] By comparing the two spectra, the peptide fragments with
mass 475 and 855 contain the glycoslylation sites-both
glycoslyation sites are glycosylated. Based on a trypsin digest
simulation of the peptide (YCNISQKMMSRNLTKDR) (See, for example,
http://us.expasy.org/tools/peptidecutter/) the different masses
were assigned as the following: 289-DR; 475-NLTK; 523-MMSR;
855-YCNISQK.
[0336] During the deglycosylation step, the Asn residue is
converted to an Asp residue, which results in a total increase in
molecular weight of 1 Da, thus explaining the appearance of the 476
and 856 peaks. The deglycosylation with concomitant
.sup.18O-labeling results in an increase of 2Da in the peptides
that originally had a glycosylation site. This explains the
appearance of the 478 and 858 peaks.
[0337] The quantitative measurement of the peaks via the methods
described above reveals that the glycosylation site at NLTK is 75%
glycosylated. Similarly, the data for YCNISQK reveals that it is
50% glycosylated. Similarly the undigested glycopeptide mixture is
also cleaved of the glycans and label-processed as described above.
The resultant analysis shows that the entire mixture is 75%
glycosylated.
Step 3:
[0338] The glycans are separated and the resultant glycans analyzed
through MALDI-MS. In this example, the resultant masses with
relative abundance (Table 8) were:
TABLE-US-00010 TABLE 8 Masses and Relative Abundance Mass Relative
Abundance 1235 40 1397 44 1559 16
[0339] Thus, there are three different glycans in this glycopeptide
mixture.
Step 4:
[0340] Digest the glycopeptide mixture with trypsin and analyze the
resultant mixture through MALDI-MS. In this example the resultant
masses are 289, 475, 523, 854, 1871, 2033 and 2089.
[0341] Based on comparing the MS results with the trypsin digest
simulation of the peptide, the following observations are made.
Fragment NLTK is glycosylated with glycans with a mass of 1397 or
1559. Fragment YCNISQK is glycosylated with glycans with a mass of
1235.
[0342] Thus there are six possible glycopeptide chains in the
mixture. [0343] Chain A that is not glycosylated. [0344] Chain B in
which the second Asn is glycosylated with Glycan-1397. [0345] Chain
C in which the second Asn is glycosylated with Glycan-1559. [0346]
Chain D in which the first Asn is glycosylated with Glycan 1235.
[0347] Chain E in which the first Asn is glycosylated with 1235 and
the second with 1397. [0348] Chain F in which the first Asn is
glycosylated with 1235 and the second with 1559.
Step 5:
[0349] Generate equations based on the experimental results and/or
other data.
[0350] a, b, c, d, e and f are the relative abundances of chains A,
B, C, D, E and F, respectively, and the following set of equations
were generated based on the experimental results from steps 1
through 4.
TABLE-US-00011 a + b + c + d + e + f = 1 6 possible chains a + b +
c = d + e + f 50% occupancy in first glycosylation site (a + d) * 3
= (b + c + e + f) 75% occupancy in second glycosylation site d + e
+ f = 2.5 * (c + f) Glycan 1235 to Glycan 1559 b + e = 2.75 * (c +
f) Glycan 1397 to Glycan 1559 3 * a = b + c + d + e + f 75% of
glycopeptide chains are glycosylated
[0351] Solving the equations, the results are:
[0352] a=0.25, b=0.25, c=d=0, e=0.3, f=0.2
Step 6:
[0353] The masses from step 3 can be resolved into potential glycan
structures by using a glycan database lookup
(http://www.functionalglycomics.org/glycomics/molecule/jsp/carbohydrate/s-
earchByMw.jsp), and the exact structure of the carbohydrates were
corroborated from the glycosidase digest analysis. By putting
together the results in steps 1 to 6, the unknown glycoprotein
mixture was determined to be (Tables 9 and 10):
TABLE-US-00012 TABLE 9 Glycan Identification Peptide Glycan Site 1
Glycan Site 2 Relative Abundance YCN.sub.1ISQKMMSRN.sub.2LTKDR None
None .25 YCN.sub.1ISQKMMSRN.sub.2LTKDR None HEX.sub.6HEXNAC.sub.2
.25 YCN.sub.1ISQKMMSRN.sub.2LTKDR HEX.sub.6HEXNAC.sub.2
HEX.sub.6HEXNAC.sub.2 .3 YCN.sub.1ISQKMMSRN.sub.2LTKDR
HEX.sub.6HEXNAC.sub.2 HEX.sub.6HEXNAC.sub.2 .2
TABLE-US-00013 TABLE 10 Glycan Structure Glycan Structure
HEX.sub.5HEXNAC.sub.2 ##STR00029## HEX.sub.6HEXNAC.sub.2
##STR00030## HEX.sub.7HEXNAC.sub.2 ##STR00031##
Analysis of Glycosylation of Glycoprotein Standards
[0354] As an example, the optimized procedures were performed using
two known N-glycosylated protein standards with different
properties, RNaseB, a glycoprotein that only contains high mannose
structures, and ovalbumin, which contains both hybrid and complex
glycan structures at one glycosylation site. The procedures
described above were applied to samples obtained from the Hamel
laboratory (MIT Bioprocess Engineering Center, Cambridge, Mass.)
and produced under various conditions.
Determination and Quantification of Glycosylation Site
Occupancy
[0355] Before protease cleavage, the glycoproteins are first
denatured in the presence of urea, reduced with DTT and
carboxymethylated with iodoacetamide. To remove the denaturing
reagents, the samples are concentrated using a centrifugal
concentrator (3,000 MWCO) followed by buffer exchanged into
protease compatible buffer (50 mM ammonium bicarbonate, pH 8.5, for
trypsin digest). The proteins are then cleaved by proteases
followed by denaturation of proteases by boiling the sample in
water and lyophilization. Glycosylation site-specific-labeling is
achieved by reacting the samples with PNGase F in the presence of
.sup.18O-water (FIG. 24). After desalting the glycosylated,
unglycosylated and .sup.18O-labeled unglycosylated peptides through
a C-18 solid phase extraction cartridge, the peptides are used in
LC-MS, LC-tandem-MS, MALDI-MS, MALDI-FTMS or MALDI-TOF-TOF-MS. For
this study, the unlabeled (.sup.16O)- and .sup.18O-labeled samples
were mixed in a 1:1 ratio before injection in order to facilitate
the analysis. Other techniques for peptide sequencing can also be
used at this point. The peptides were analyzed using a capillary
LC-MS using a Vydac C-18 MS 5 .mu.m (250.times.0.3 mm) column
(Grace Vydac, Hesperia, Calif.) coupled to a Mariner
Biospectrometry Workstation (Applied Biosystems, Foster City,
Calif.). The peptides generated from the protease cleavage were
corroborated using the Swiss-Prot database (ribonuclease B, P00656
and ovalbumin, P01012).
[0356] By studying the data obtained from the differentially
labeled peptides after glycan cleavage, the specific glycosylation
site can be determined. The introduction of the .sup.18O at the
glycosylation site is detected as a 2 Da increase for a specific
peptide. This data facilitate the determination of the
glycosylation site and its occupancy. As determined using the
peptide mass calculator from the Protein Data Bank
(http://us.expasy.org/tools/peptide-mass.html), the tryptic digest
of ribonuclease B should yield a peptide fragment with a
[M+H].sup.+ of 475.29 Da containing the glycosylation site (NLTK).
Since the enzyme-mediated glycan cleavage generates an aspartic
acid at the asparagine site, a peptide ion of [M+H].sup.+ of 476.29
Da for the unlabeled peptide and a 478.29 Da for the
.sup.18O-labeled peptide containing the glycosylation site was
expected. As shown in FIG. 25, it was easy to identify the peptide
fragment containing the glycosylation site in ribonuclease B by
comparing the LC-MS data from the 1:1 mixture of
.sup.16O/.sup.18O-labeled peptides against the unlabeled sample.
The presence of the +2 Da species in a 1:1 ratio in the
.sup.16O/.sup.18O-labeled mixture and the absence of species with
[M+H].sup.+ of 475.29 Da indicates that this peptide contains a
glycosylation site and that it is 100% occupied in both samples of
the mixture. By analyzing the differences between the peptide
masses in this batch to the peptide masses from the samples not
exposed to glycan cleavage, a preliminary identification of the
glycans was obtained. This was further validated and quantified by
analyzing the glycans separately as described below.
Release and Purification of N-glycans from Glycoprotein
Standards
[0357] Several enzymatic and chemical methods were used to separate
glycans from their protein cores. Of the chemical methods,
hydrazinolysis provides the efficient release of glycans (Patel,
T., et al. 1993. Biochemistry. 32, 679-93.) However, this approach
requires the sample to be very clean, with no residual salts, and
the reaction does not proceed efficiently in air or water, making
hydrazinolysis somewhat undesirable as a quick measure of quality
control. PNGaseF was chosen among enzymatic methods for the
cleavage of N-linked glycans since the use of other enzymes results
in the loss of information, such as fucosylation at the proximal
GlcNAc.
[0358] For optimal enzyme activity, proteins were unfolded, reduced
and carboxymethylated prior to enzymatic digestion. Typically, the
samples were denatured by heating in the presence of
.beta.-mercaptoethanol and/or SDS or by incubating at room
temperature with urea, followed by reduction with DTT and
carboxymethylation with iodoacetic acid or iodoacetamide. To
isolate the carbohydrates from the sample, the proteins were first
precipitated with ethanol, and the supernatant containing the
glycans was then dried under vacuum and resuspended in water.
Subsequent purification steps were required when detergents were
used. Optimal results were obtained by using porous graphitic
carbon columns. Neutral and charged carbohydrates were separated
using these columns and eluted in mass spectrometry-compatible
buffers. At this point, the most difficult component to get rid of
was the detergent, which interferes with the types of analytical
techniques that were used in this study.
Glycan Analysis
[0359] Different analytical techniques known in the art can be used
for the glycan analysis methods. In this study, MALDI-TOF-MS was
used due to its simplicity and sensitivity (e.g., low femtomole
after optimizations as described herein). The MALDI-MS protocol was
optimized for the detection and quantification of low abundance
carbohydrates (FIGS. 26 and 27). In particular, FIG. 27 shows the
MALDI-MS spectrum of ovalbumin glycans. The observed peaks and
their structures were found. The results are as shown above in
Table 3.
RNAseB Computational Analysis
[0360] The information obtained from the previous analysis was
analyzed using the computational platform that contains the
proteomics- and glycomics-based bioinformatics tools and databases
described herein.
[0361] The sequence of the protein backbone was determined from the
proteomics database to be as follows:
TABLE-US-00014 MALKSLVLLS LLVLVLLLVR VQPSLGKETA AAKFERQHMD
SSTSAASSSN YCNQMMKSRN.sub.1 LTKDRCKPVN TFVHESLADV QAVCSQKNVA
CKNGQTNCYQ SYSTMSITDC RETGSSKYPN CAYKTTQANK HIIVACEGNP
YVPVHFDASV
[0362] The glycosylation site is at SNLT. It is 100% glycosylated,
and five different glycans were observed from the analysis of the
glycans via MALDI-MS. The results of the computational analysis
indicated that there were 5 different chains in the glycoprotein
mixture as shown in Table 11 below:
TABLE-US-00015 TABLE 11 Results from the Computational Analysis
Protein Sequence Glycan Relative Abundance MALKSLVLLS LLVLVLLLVR
VQPSLGKETA HEX.sub.5HEXNAC.sub.2 .41 AAKFERQHMD SSTSAASSSN
YCNQMMKSRN.sub.1 LTKDRCKPVN TFVHESLADV QAVCSQKNVA CKNGQTNCYQ
SYSTMSITDC RETGSSKYFN CAYKTTQANK HIIVACEGNP YVPVHFDASV MALKSLVLLS
LLVLVLLLVR VQPSLGKETA HEX.sub.6HEXNAC.sub.2 .29 AAKFERQHMD
SSTSAASSSN YCNQMMKSRN.sub.1 LTKDRCKPVN TFVHESLADV QAVCSQKNVA
CKNGQTNCYQ SYSTMSITDC RETGSSKYFN CAYKTTQANK HIIVACEGNP YVPVHFDASV
MALKSLVLLS LLVLVLLLVR VQPSLGKETA HEX.sub.7HEXNAC.sub.2 .1
AAKFERQHMD SSTSAASSSN YCNQMMKSRN.sub.1 LTKDRCKPVN TFVHESLADV
QAVCSQKNVA CKNGQTNCYQ SYSTMSITDC RETGSSKYFN CAYKTTQANK HIIVACEGNP
YVPVHFDASV MALKSLVLLS LLVLVLLLVR VQPSLGKETA HEX.sub.8HEXNAC.sub.2
.14 AAKFERQHMD SSTSAASSSN YCNQMMKSRN.sub.1 LTKDRCKPVN TFVHESLADV
QAVCSQKNVA CKNGQTNCYQ SYSTMSITDC RETGSSKYFN CAYKTTQANK HIIVACEGNP
YVPVHFDASV MALKSLVLLS LLVLVLLLVR VQPSLGKETA HEX.sub.9HEXNAC.sub.2
.06 AAKFERQHMD SSTSAASSSN YCNQMMKSRN.sub.1 LTKDRCKPVN TFVHESLADV
QAVCSQKNVA CKNGQTNCYQ SYSTMSITDC RETGSSKYFN CAYKTTQANK HIIVACEGNP
YVPVHFDASV
MALDI-MS Analysis of N-glycans from Antibodies Produced in Applikon
and Wave Reactors
[0363] Two antibody samples produced by mouse-mouse hybridoma cells
(Biokit SA) grown in an Applikon STR (Applikon Biotechnology) were
analyzed, along with three samples produced in Wave reactors (Wave
Biotech). The reactor conditions used are shown in Table 12.
TABLE-US-00016 TABLE 12 Reactor Conditions Used to Produce Antibody
Samples Sample Reactor Type DO pH Other 1 Applikon STR 50% 7 2
Applikon STR 90% Not controlled 3 Wave Controlled Not controlled 4
Wave Controlled 7 NaHCO.sub.3 for pH control 5 Wave Not 7 Fresh
media controlled for pH control
[0364] In the Applikon STR reactor, pH can be controlled
automatically by the instrument, which dispenses CO.sub.2,
NaHCO.sub.3 and O.sub.2 as needed. In the Wave reactor, however,
measurements must be taken manually and pH adjusted by hand. The pH
in this reactor can be controlled by either adding fresh media as
the cells grow, or adding NaHCO.sub.3 for increased buffering
capacity, and CO.sub.2 as needed. The main difference between the
reactor types is the mode of agitation. In the Applikon STR, a
blade stirrer keeps the cell suspension in motion, while a sparger
introduces oxygen to the system in a controlled manner. In the Wave
reactor, a rocking motion generates waves that mix the components
of the system and aids the transfer of oxygen and other gases into
the system.
[0365] The purified antibodies were processed according to the
optimized method described above. For each sample, 100 .mu.g of
protein was used as the starting material. Both positive and
negative ion modes were used in the MALDI-MS to determine whether
there were charged sugars present. No acidic glycans were observed
from the analysis; which indicated neutral sugars were obtained
from the antibodies. The MALDI-MS data of the five antibody samples
produced using different conditions contained the same six glycans
with molecular weights of 1317 Da, 1463 Da, 1478 Da, 1625 Da, 1641
Da and 1787 Da. The corresponding structures to these glycans were
determined using the methods described above and are shown in FIG.
28 with their theoretical masses.
[0366] These results indicate that the production method did not
alter the nature of the glycans present in the samples, rather, the
quantities of some glycans were affected. Notably, samples prepared
in the Wave reactor displayed a 40% decrease in the 1625.4 Da
glycan, as well as, a 20% reduction in the 1787.7 Da glycan with
respect to samples prepared in the Applikon reactor. The other
glycans remained equal.
[0367] While the exact mechanisms for these changes are not known,
it is interesting that the largest changes occurred due to reactor
type, not reactor conditions such as pH, DO or media composition.
Because the Applikon STR and the Wave reactors differ most in their
method of agitation, reactor configuration is therefore the most
likely source of glycan variation.
[0368] Differences in protein glycosylation have been linked to
shear stress, such as by the stirring blade or the gas sparger in
an STR reactor. However, the turbulence created in the Wave reactor
also generates shear stress. One hypothesis for the shear stress
effect is that cells must increase their overall protein production
in response to membrane and/or cytoskeletal damage. As a
consequence, the biosynthetic enzymes for glycosylation are
diverted away from the protein of interest (Senger, R. S., Karim,
M. N. 2003. Biotechnol Prog. 19, 1199-209.)
[0369] Although most observed parameters, including total antibody
production, were similar in Applikon STR and Wave cultures, cells
from the Wave reactor had slight increases in metabolic rates.
Changes in cell metabolism may yield effects similar to those
caused by shear stress, as all glycoproteins synthesized in the
cell must compete for the same machinery in the ER and Golgi.
Example 4
Glycome Profiling
Sample Preparation and Carbohydrate Purification
[0370] Samples (usually 60 .mu.l) from different body fluids (e.g.,
serum, saliva, urine, tears, etc.) were processed in a similar
manner as described below. Although in most cases the entire
glycoproteome from the sample was analyzed, in some cases, the
samples were further fractionated in order to analyze a
"sub-glycome" from a specific body fluid. For example, a specific
subset of proteins (such as antibodies, serum albumins, and other
high abundance proteins) were removed from the original serum
sample in order to analyze a more specific subset of glycoproteins
in more detail. For fractionation, the sample proteome was divided
into "high abundance" and "low abundance" using solid supports
containing antibodies, proteins and synthetic molecules specific
for the desired proteins to be removed or concentrated. For
example, IgGs were removed using Protein A agarose (Bio-Rad), beads
and serum albumin was removed using Affi-Blue gel (Bio-Rad). Other
fractionations included the separation into acidic and basic
proteome using cation and anion exchange chromatography or the
separation between glycosylated and unglycosylated proteins using
ConA columns. The removal of specific proteins was quantified by
Western blots.
[0371] Proteins in the samples (either fractionated or
unfractionated) were then denatured using a buffer containing 8M
urea, 3.2 mM EDTA and 360 mM Tris, pH 8.6 (Papac, D. I., et al.
1998. Glycobiology. 8, 445-454.) Reduction and carboxymethylation
of the sample proteome was then achieved using DTT and
iodoacetamide, respectively. Although iodoacetic acid is often used
as the alkylating agent for carboxymethylation, it is not optimal
when used to analyze body fluids containing a wide range of
glycoproteins, since it generally causes precipitation of most
proteins. After removal of denaturing, reducing and alkylating
reagents, N-glycans were selectively released from the
glycoproteins by incubation with PNGaseF. The steps for protein
denaturing, protein alkylation and glycan release were also
performed with the proteins bound to a solid support. The released
carbohydrates were then purified from the proteins and separated
into neutral and acidic glycans in one step using a graphitized
carbon columns. The glycan purification step was also performed in
a high-throughput format by using columns in 96-well plates. This
process was facilitated by the use of a Tecan Freedom EVO automated
liquid handling unit (Tecan). This protocol allowed for the
processing of more than 90 samples at the same time.
Fractionation of Serum Proteins
[0372] As an example, to remove serum albumin and IgGs, Affi-Blue
gel (Bio-Rad, 200 .mu.L) and Prot A (Bio-Rad, 200 .mu.L) were mixed
in a 1:1 ratio and placed in a serum protein column. The column was
washed with 1 mL of compatible serum protein-binding buffer (20 mM
phosphate, 100 mM NaCl, pH 7.2) using gravity flow. The column was
placed in an empty 2 mL collection tube and centrifuged at 10,000 G
for 20 seconds at 4.degree. C. The flow was stopped during the
sample preparation. Serum (60 .mu.L) was mixed with compatible
serum protein-binding buffer (180 .mu.L), and 200 .mu.L of diluted
serum was added to the top of the resin bed and allowed to mix with
the column for 15 minutes. The column was then centrifuged at
10,000 G for 20 seconds at 4.degree. C. Using the same collection
tube, the column was washed with 200 .mu.L of compatible serum
protein-binding buffer and centrifuged again at 10,000 G for 20
seconds at 4.degree. C. For the removal of IgGs alone, only Protein
A agarose beads were used and the binding buffer was modified to 10
mM phosphate, 150 mM NaCl, pH 8.2.
[0373] To separate glycosylated (mainly high-mannose) from
unglycosylated proteins, ConA-agarose beads (Vector Laboratories)
were used. To prepare the column, 3 ml ConA-agarose slurry was
washed with ConA buffer (20 mM Tris, 1 mM MgCl.sub.2, 1 mM
CaCl.sub.2, 500 mM NaCl, pH 7.4). Before loading, 500 .mu.l of
serum was mixed with 150 .mu.l of 5.times.ConA buffer. After
washing with 3 ml ConA buffer, glycoproteins were eluted with 2 ml
of 500 mM a-methyl-mannoside and dialyzed against 10 mM phosphate,
pH 7.2 overnight at 4.degree. C.
Analysis of IgG and Serum Albumin Depletion
[0374] Samples were prepared for SDS-PAGE electrophoresis by
diluting 1:1 with 2.times. sample buffer (120 mM Tris base, 280 mM
SDS, 20% glycerol, 10% .beta.-mercapto ethanol (BME), 20 ng/ml
bromphenol blue (BPB)), boiled for 5 minutes, and 10 ul was loaded
per lane in a 4-12% Bis-Tris precast gel (NPO323BOX, Invitrogen).
Lane 1 contained 5 .mu.l of a standard (Precision All Blue
Standard, 161-0373, Bio-Rad). The gel was run for 70 minutes at
200V. The gel was stained with SimplyBlue (LC6060, Invitrogen)
according to the manufacturer. Imaging was performed on a Kodak
Image Station 2000R (Kodak, Rochester, N.Y.). Another set of
duplicate depleted samples were run as before. One gel was for
SimplyBlue staining, and the other was transferred to a 0.20 .mu.m
nitrocellulose membrane (LC2000, Invitrogen) employing an X Cell
Blot Module (E19051, Invitrogen) for 70 minutes at 30V. The
membrane was then blocked overnight at 4.degree. C. in 5% Blotto
(sc-2325, Santa Cruz Biotechnology, Santa Cruz, Calif.) and then
probed with 1:1000 Protein A-HRP (10-1023, Zymed, San Francisco,
Calif.) for 1 hour at 4.degree. C. and washed 4 times with washing
buffer (1.times.TBS: 200 mM Tris base, 1.5M NaCl, pH7.5). The blot
was developed with 4 ml of substrate (ECL plus Western Blotting
Detection System, Amersham Biosciences, Piscataway, N.J.) for 2
minutes and then exposed. The bands corresponding to the treatments
were manually captured as region of interest (ROI) employing the
Kodak 1D Image Analysis Software (Kodak), and the mean intensity
was normalized to the controls.
[0375] The same blot was then washed again and re-probed with
1:1000 sheep anti-human albumin-HRP (AHP102P, Serotec, Raleigh,
N.C.) for 1 hour at 4.degree. C. The blot was washed again,
developed and imaged (FIG. 29).
Glycoblotting of Serum Samples
[0376] Protein samples were prepared for SDS-PAGE by diluting 1:1
with 2.times. denaturing buffer (40 .mu.g/ml SDS, 20% glycerol, 30
.mu.g/mM DTT and 10 .mu.g/ml bromophenol blue in 125 mM Tris, pH
6.8) and boiling for 2 min. Pre-cast Nu-PAGE 10% Bis-Tris protein
gels were obtained from Invitrogen. Each lane was loaded with a
maximum of 10 .mu.l of sample and run for 50 min at 200V. After
electrophoresis was complete, the gel was stained with Invitrogen
SafeStain (1 hour in staining solution, then washed overnight with
water).
[0377] The GlycoTrack glycoprotein detection kit was obtained from
Prozyme. All reagents except buffers were supplied with the kit.
Two methods were attempted-either biotinylating glycoproteins after
blotting (a) or before blotting (b). For both methods, samples were
first diluted 1:1 with 200 mM sodium acetate buffer, pH 5.5. The
membrane was blocked by incubating overnight at 4.degree. C. with
blocking reagent, then washed 3.times.10 minutes with TBS.
[0378] For method (a), samples were denatured with SDS sample
buffer, subjected to SDS-PAGE and blotted to nitrocellulose. After
washing the membrane with PBS, the proteins were oxidized with 10
ml of 10 mM sodium periodate in the dark at room temperature for 20
minutes. The membrane was washed 3 times with PBS, and 2 .mu.l of
biotin-hydrazide reagent was added in 10 ml of 100 mM sodium
acetate, 2 mg/ml EDTA for 60 minutes at room temperature. After 3
washes with TBS, the membrane was blocked overnight at 4.degree. C.
with blocking reagent. Before adding 5 .mu.l of S-AP conjugate, the
membrane was washed again with TBS. The S-AP was allowed to
incubate for 60 minutes at room temperature, and excess was washed
off with TBS. To develop the blot, 50 .mu.l of nitro blue
tetrazolium (50 mg/ml) and 37.5 .mu.l of 5-bromo-4-chloro-3-indolyl
phosphate p-toluidine (50 mg/ml) were added in 10 ml TBS, 10 mg/ml
MgCl.sub.2. After 60 minutes, the blot was washed with distilled
water and allowed to air dry.
[0379] In method (b), 20 .mu.l of sample was mixed with 10 .mu.l of
10 mM periodate in 100 mM sodium acetate, 2 mg/ml EDTA and
incubated in the dark at room temperature for 20 minutes. To
destroy excess periodate 10 .mu.l of a 12.5 mg/ml sodium bisulfite
solution in 200 mM NaOAc, pH 5.5 was added for 5 minutes at room
temperature. Biotinylation was performed by adding 5 .mu.l of
biotin amidocaproyl hydrazide solution in DMF. After incubating at
room temperature for 60 minutes, the sample was mixed with SDS
denaturing buffer and boiled for 2 minutes. Samples were run on
SDS-PAGE gels and transferred to a nitrocellulose membrane (2 hrs,
30V). After this point, blocking and developing steps were
identical to method (a) (FIG. 30).
Glycan Release Using Solid Supports: PNGaseF Digestion on PVDF
Membrane
[0380] Glycans were also released using PVDF membranes as described
in Papac, D. I., et al. 1998. Glycobiology. 8, 445-454. However,
high abundance proteins were first removed before using this
method, since it resulted in low recoveries when processing entire
body fluids. PVDF-coated wells in a 96-well plate were washed with
200 .mu.l MeOH, 3.times.200 .mu.l H.sub.2O and 200 .mu.l RCM buffer
(8M urea, 360 mM Tris, 3.2 mM EDTA, pH 8.3). The protein samples
(50 .mu.l) were then loaded in the wells along with 300 .mu.l RCM
buffer. After washing the wells two times with fresh RCM buffer,
500 .mu.l of 0.1M DTT in RCM buffer were added for 1 hr at
37.degree. C. To remove the excess DTT, the wells were washed three
times with H.sub.2O. For the carboxymethylation, 500 .mu.l of 0.1M
iodoacetic acid in RCM buffer was added for 30 minutes at
37.degree. C. in the dark. The wells were washed again with water,
then the membrane was blocked with 1 ml polyvinylpyrrolidone
(360,000 AMW, 1% solution in H.sub.2O) for 1 hr at room
temperature. Before adding the PNGaseF, the wells were washed again
with water. To release the glycans, 4 .mu.l of PNGaseF was added in
300 .mu.l of 50 mM Tris, pH 7.5 and incubated overnight at
37.degree. C. Released glycans were pipetted from the wells and
purified through a graphitized carbon column. Similar to protocols
used for the purification of glycans after performing the cleavage
in solution, the purification of glycans after their release using
PVDF membranes was also performed in a high-throughput format using
columns in 96-well plates. This process was facilitated by the use
of a Tecan Freedom EVO automated liquid handling unit (Tecan).
Glycome Analysis Using Mass Spectrometry
[0381] Glycan analysis was applied to the total body fluid glycome.
Using the methods provided above, more than 90 samples were
analyzed. Optimized MALDI-MS methods, which did not require
additional labeling and purification steps and also displayed great
reproducibility and sensitivity for the carbohydrate analysis, was
used. As shown in FIG. 31, total serum glycome profiles typically
displayed between 25-30 neutral glycans as well as 25-30 acidic
glycans.
[0382] Using the look-up table described previously, almost all of
the peaks in MALDI-MS serum profiles could be identified as glycans
of known composition. Many of the unidentified peaks are sodium
adducts. The composition and mass of each labeled peak are as
listed above in Table 7. However, a few peaks in the acidic glycan
spectrum correspond to more than one composition. This is more
common in the higher mass range since there are a larger number of
possible monosaccharide compositions.
Validation of Biomarker Structures
[0383] MALDI-MS analysis can be used to analyze the entire glycome
profile in a sample and compare the changes in glycome composition
between samples in a rapid and efficient manner. Due to the
limitations in isomass characterization, in some instances, other
techniques known to the art can be used to further characterize and
validate the biomarkers determined from the total profile found
using MALDI-MS techniques. For example, LC-MS and CE-LIF can be
used in combination with a panel of exoglycosidases in order to
obtain further linkage characterization of the carbohydrates (FIG.
32). After a specific pattern is established based on MALDI-MS
results, and the possible species are determined, matched samples
displaying the differences in patterns are analyzed by these
techniques in order to come up with defined structures of the
biomarkers of interest. LC-MS/MS is also used to obtain linkage
information based on fragmentation patterns.
Other Body Fluids
[0384] Similar to the serum glycome analysis, the entire glycome
from other body fluids such as saliva and urine have been studied
(FIG. 33). For these, similar protocols to those employed for serum
were used. In some instances, additional fractionation was used
(e.g., if a fraction of the glycome or glycoproteome was to be
studied.) The methods proved to be equally reproducible and
sensitive for these other body fluids.
Glycome Analysis of Cell Surface Glycoproteins
[0385] The methods provided herein can also be applied to the
glycoprofiling of cell surfaces. All cell surface glycoproteins are
cleaved using methods know to the art. Briefly, to harvest glycans
using protease extraction, cells are washed 3.times. with PBS and
incubated for 20-45 minutes with trypsin/EDTA at 37.degree. C. for
protease extraction. The samples are centrifuged for 10 minutes at
3000.times.g to pellet the cells, and the supernatant containing
glycopeptides is collected and processed using methods described
herein.
Glycomic Pattern Analysis
[0386] The emerging field of clinical proteomics has set new
avenues for the identification of potential cancer-related
biomarkers. In particular, the recent introduction of proteomic
pattern diagnostics (Petricoin III, E. F. et al. 2002. Lancet. 359,
572-577; Wulfkuhle, J. D., et al. 2003. Nature Rev Cancer. 3,
267-275; Conrads, T. P., et al. 2003. Expert Rev Mol Diagn. 3,
411-420) provides a promising platform for the high-throughput
discovery of new and important biomarkers. Since alterations to the
normal function of the glycosylation machinery have been
increasingly recognized as a consistent indication of malignant
transformation and tumorigenesis (Orntoft, T. F. & Vestergaard,
E. M. 1999. Electrophoresis. 20, 362-371; Burchell, J. M., et al.
2001. J Mam Gland Biol Neoplasia. 6, 355-364; Brockhausen, I. 1999.
Biochim Byophis Acta. 1473, 67-95; Dennis, J. W., et al. 1999.
Biochim Byophis Acta. 1473, 21-34), the final glycoproteins
(specifically their carbohydrate moieties) can serve as sensitive
and reliable biochemical markers to numerous diseases including
cancer.
[0387] Methods for glycomic pattern analysis where the total
profile of carbohydrates from body fluids or tissues can be
examined in a rapid format are provided herein. This approach
provides an efficient overview of the total changes in carbohydrate
composition of a tissue or body fluid as a result of pathological
alterations and should be reliable in sensing susceptible
physiological changes to the body's natural homeostasis. The
methods not only serve as fast diagnostic/prognostic tools but can
also help to understand the function of specific carbohydrate
modifications in some diseases. The methods also provides a
reliable system to efficiently monitor the effects of
therapeutics.
[0388] For instance, the optimization of MALDI-MS analysis allows
reliable reproducibility that enables the fast evaluation of
alterations to glycomic patterns and their subsequent association
to pathological/physiological changes to a sample donor. The
optimized detection limits for this method (low femtomole) allows
the detection of low abundance species associated with diseases.
Every signal in the pattern is rapidly correlated to the glycan
identity and can be further validated using a panel of glycosidases
and/or other techniques. This prevents erroneous identification, as
has sometimes been the case in the field of proteomic pattern
diagnostics. The pattern alterations can be easily determined
manually or more efficiently with the aid of bioinformatics tools.
In some cases the decreasing levels of circulating glycoproteins in
serum are easily matched to the analyzed glycans. As shown in FIG.
34, the glycome profile from serum with low IgG levels, reflects
the specific decrease in the respective IgG glycans with molecular
weights of 1463, 1626, 1666, 1788, 1829, 2102, and 1844. These
glycans have been previously shown to be attached to IgG molecules
in serum (Butler, M. et al. 2003. Glycobiology. 13, 601-622.)
[0389] By applying a "glycomic pattern diagnostics" platform to
different body fluids from patients with well-defined demographics,
specific alterations in the glycomic pattern that can be correlated
to the pathological state of the donor can be determined. For
example, glycomic patterns have now been associated to prostate
cancer by studying the serum from prostate cancer patients (FIG.
35). Glycomic patterns from the saliva of patients with viral
infections have also been established. Since every signal inside
the pattern corresponds to specific glycans, the alteration of
these patterns are easily determined and correlated with the
expression levels of the carbohydrates, such as with the methods
provided herein. The specific alterations in these glycan patterns
are associated with a disease state. Therefore, the methods
provided serve as a reliable platform for diagnosis, prognosis and
monitoring the effects of therapeutics.
Computational Pattern Analysis of Glycoprofile
[0390] The following is an example of a computational approach for
identifying glycan-based biomarkers for specific diseases using
data from glycoprofiling. The different steps of the process are
illustrated in FIG. 36.
[0391] Using prostate cancer as an example, the goal is to identify
glycan-based markers for individuals with prostate cancer. In this
example, there are three possible categories of
individuals-individuals with prostate cancer, benign prostatic
hyperplasia (BPH) and individuals that are normal (i.e., they have
a healthy, non-diseased prostate.)
[0392] Glycoprofiling data such as mass spectra are generated from
samples from patients belonging to the different categories.
Features are extracted from the glycoprofiling spectra. These
features can be the presence or absence of one or more glycans in
the profile, the relative amount of different glycans in the
profile, combinations of different glycans found in the profile
and/or other glycan-related properties. These glycans are
identified in the glycoprofile spectra and can be corroborated with
other methods, for instance, by using associated glycomics-based
bioinformatics tools and/or a glycan database
(http://www.functionalglycomics.org/glycomics/molecule/jsp/carbohydrate/c-
-arbMoleculeHomesp).
[0393] The appropriate patient population is selected for the study
(based on their history in a patient database), such that the
subjects chosen in the different categories of prostate cancer, BPH
and normal have the same distribution when it comes to other
properties such as age, ethnicity, behavioral factors, etc. This
ensures that the variation in the glycan profiles can be attributed
to the disease condition rather than other factors. The glycan
related features extracted for this population via the previous
step is run through a dataset generator to create the datasets
needed for pattern analysis. Different types of pattern analysis
are performed to identify the patterns in this dataset. Types of
pattern analysis are known to those of ordinary skill in the art
and can be found in Weiss, S. & Indurkhya, N. 1998. Predictive
data mining--A practical guide. Morgan Kaufmann, San Francisco.
Three examples of patterns, rules or relationships that can be
identified are as follows: [0394] Linear Discriminant: The pattern
identified is in the form of weights (w.sub.11, w.sub.12, etc.) for
the different glycan related features (G.sub.1, G.sub.2, etc.) as
they are related to property or class of interest (prostate cancer,
BPH or normal). [0395] w.sub.11G.sub.1+w.sub.12G.sub.2+ . . .
+w.sub.1mG.sub.m+w.sub.1=prostate cancer [0396]
w.sub.21G.sub.1+w.sub.22G.sub.2+ . . . +w.sub.2mG.sub.m+w.sub.2=BPH
[0397] Neural Network: The neural network identifies non linear
relationships or patterns between the different features and the
property or class of interest. [0398]
net.sub.j=.SIGMA.W.sub.ij*f.sub.i+C.sub.j [0399]
d.sub.j=1/(1+e.sup.-netj), where d.sub.j can be prostate cancer,
BPH or normal [0400] Decision Rules: The pattern identified is in
the form of IF-THEN rules, for example [0401] (IF G.sub.11 is
present and G.sub.7 is not present) or (IF G.sub.8 is present and
G.sub.9 is present) THEN Class=prostate cancer [0402] (IF G.sub.1
is present and G.sub.2 is present and G.sub.3 is not present) THEN
Class=BPH [0403] Otherwise Class=normal
[0404] Once a pattern is identified using the decision set rules
above, the patterns, rules or relationships are validated. The
validation can be made based on a variety of statistical methods
that are used in biomarker validation as well as scientific methods
to verify that the glycans found in the patterns do accurately
reflect the disease state. If the patterns cannot be validated, the
process described above can be repeated to look for other
glycan-based patterns in the glycoprofiles.
Use of Human Glycome for the Profiling of Populations:
Population-Tailored Treatments
[0405] It is documented that different people react differently to
certain drugs. In many instances this might be a result of drug
interference with other inherent molecular components. Also, the
down-regulation of enzymes may prevent the metabolism of some drugs
or their byproducts. The recent emphasis in the development of new
carbohydrate-based therapeutics will face major challenges (in
comparison to protein-based drugs) due to limited availability of
glycomic information and understanding in the field.
[0406] More information of all human molecular components will
significantly facilitate the design and prescription of medications
to specific populations (personalized medicine). The efficient
analysis of the entire glycome from body fluids not only serves as
a reliable diagnosis/prognosis platform but can be valuable for the
profiling of populations. The generation of a human glycome
databank from different ethnic groups, gender, ages, diseases, etc.
will become of enormous value for current and future development
and applications of drugs that might interfere with carbohydrate
function. For example, the overexpression of some carbohydrates in
a specific population will aid in the design and prescription of
therapeutics that might interact with these carbohydrates. This
information will also aid in prospective studies for the selection
of dosing, activity monitoring and efficacy endpoints.
Example 5
Use of Optimized MALDI-MS Conditions for the Improved Analysis of
Highly Acidic Polysaccharides
[0407] Mass spectrometry has been used as a major tool in the
analysis of highly acidic polysaccharides, such as GAGs. MALDI-MS,
in particular, has been a key component in the characterization of
these biopolymers. However, major experimental disadvantages still
exist with the current methods. Due the complex nature of these
polysaccharides, MALDI-MS characterization usually reveals multiple
species. Mass spectra are usually complicated by the multiple ions
complexed with the biopolymers due to their highly acidic nature.
Therefore, the multiple peaks arising from the different
carbohydrate-ions complexes hamper the correct assignment of the
polysaccharide identity. Additionally, the splitting of one species
into multiple ionic complexes decreases the effective concentration
of each species into all the possible complexes resulting in
decreased sensitivity. In order to test the improved matrix
conditions on a highly acidic polysaccharide, hyaluronic acid was
digested with hyaluronidase, fractionated via size exclusion and
anion exchange chromatography, and the fragments were analyzed
using MALDI-TOF-MS. Applying the optimized conditions for MALDI-MS
analysis of the HA fragments, eliminated the multiple
carbohydrate-ion complexes and significantly increased the
sensitivity of the method (FIG. 37).
Example 6
Computational Method Combining NMR Spectroscopy and MALDI-MS for
Characterization of Glycans in a Mixture
[0408] MALDI-MS analysis of a mixture provides exact molecular
weights of the glycans in the mixture. Each mass peak corresponds
to a single or multiple unique monosaccharide compositions in terms
of hexoses, HexNAcs, fucoses, sialic acids, etc. Each of these
compositions in turn correspond to a single or multiple explicit
monosaccharide compositions, such as Ole, Gal, Man, GlcNAc, GalNAc,
Fuc, NeuAc and NeuGc, etc.
[0409] While MALDI-MS is not fully quantitative, the methodology
has been optimized to provide a reasonably accurate quantification
of the relative amounts of each of the mass peaks. Incorporating
NMR data as constraints to further refine information from MALDI-MS
enables the elimination of explicit compositions that do not
satisfy the monosaccharide composition data from NMR and a more
quantitative determination of the abundance of monosaccharides and
linkage distributions. In addition, biosynthetic rules and database
look-ups (e.g.,
http://www.functionalglycomics.org/glycomics/molecule/jsp/carbohydrate/ca-
rbMoleculeHome.jsp) can help in further convergence of the solution
to obtain an accurate picture of the number and relative abundance
of the species in the sample as well as the best characterization
of the individual structures corresponding to these species. A
schematic of an example of this methodology is provided in FIG. 38.
In this example, the starting sample was prepared by mixing 3
N-glycan standards in different proportions to obtain the specific
relative abundance. As described above, MALDI-MS analysis was
performed by mixing the glycan sample with the 5-MSA/DHB matrix for
positive polarity and with ATT (on a Nafion.TM.-coated plate) for
negative polarity. For NMR experiments, the glycan mixture was
dissolved in D.sub.2O and lyophilized 3 times. The sample was
finally dissolved in 500 uL of D2O and 2D-COSY and 1-D 1H-spectra
were recorded using a Bruker 600 MHz NMR (Massachusetts Institute
of Technology NMR Facility, National Institutes of Health Grant
1S10RR133886-01) using sodium
3-(trimethylsilyl)propionate-2,2,3,3-d.sub.4 (TMSP) as internal
standard.
TABLE-US-00017 MALDI-MS Data Mass Relative Intensity 1990 75% 2047
25%
TABLE-US-00018 Monosaccharide Composition Obtained from NMR Data
Monosaccharide Relative Abundance GlcNAc 40.9% Man 27.3% Gal 18.2%
Fuc 6.8% GalNAc 6.8%
TABLE-US-00019 Linkage Abundance Obtained from NMR Data Linkage
Relative Intensity Man.alpha.6Man 10% Man.beta.4GlcNAc 10%
GlcNAc.beta.4GlcNAc 10% Man.alpha.3Man 10% GlcNAc.beta.6Man 2.5%
GlcNAc.beta.4Man 2.5% GlcNAc.beta.2Man 20% Gal.beta.4GlcNAc 15%
Gal.alpha.3Gal 5% Fuc.alpha.6GlcNAc 7.5% GalNAc.beta.4GlcNAc
7.5%
Steps Involved in the Computational Method
[0410] 1. From the masses, possible compositions were obtained:
[0411] a. Mol. Wt. 1990: Hex3Fuc2HexNAc3NeuAc2 or Hex5Fuc1HexNAc5
but Hex3Fuc2HexNAc3NeuAc2 is not possible because of the
monosaccharide data and negative polarity MALDI-MS. [0412] b. Mol.
Wt. 2046: Hex5HexNAc6 or Hex10HexNAc2 [0413] 2. From the
composition, NMR monosaccharide information and biosynthetic rules
and structures found in a carbohydrate data bank, the following
glycans are possible: [0414] a. Hex5Fuc1HexNAc5:
Man3Gal2Fuc1GlcNAc4GalNAc1, Man3Gal2Fuc1GlcNAc5 [0415] b.
Hex5HexNAc6: Man3Gal2GlcNAc5GalNAc1, Man3Gal2GlcNAc6, Man5GlcNAc6
[0416] c. Hex10HexNAc2: Man10GlcNAc2 [0417] 3. Let, [0418] a=the
relative amount of Man3Gal2Fuc1GlcNAc4GalNAc1 [0419] b=the relative
amount of Man3Gal2Fuc1GlcNAc5 [0420] c=the relative amount of
Man3Gal2GlcNAc5GalNAc1 [0421] d=the relative amount of
Man3Gal2GlcNAc6 [0422] e=the relative amount of Man5GlcNAc6 [0423]
f=the relative amount of Man10GlcNAc2 Equations to Match Relative
Abundance Information from NMR and MALDI [0424] 1.
a+b=3*(c+d+e+f)--from MALDI [0425] 2.
(3/11)*(a+b+c+d)+(5/11)*e+(10/12)*f=0.273--from Man composition
(NMR) [0426] 3. (2/11)*(a+b+c+d)=182--from Gal composition (NMR)
[0427] 4. (1/11)*(a+b)=0.068--from Fuc composition (NMR) [0428] 5.
(4/11)*a.+-.(5/11)*(b+c)+(6/11)*(d+e)+(2/11)*f=0.409--from GalNAc
composition (NMR) [0429] 6. (1/11)*(a+c)=0.068 Solving the set of 6
equations leads to the following result:
[0430] a=50%, b=25%, c=25%, d=e=f=0.
Final Convergence Based on Linkages from NMR, Biosynthesis Rules
and Database Look-Up
[0431] Of the following explicit compositions,
Man3Gal2Fuc1GlcNAc4GalNAc1, Man3Gal2Fuc1GlcNAc5 and
Man3Gal2GlcNAc5GalNAc1, structures that contain only the links in
the NMR linkage table and satisfy biosynthetic rules are shown in
FIGS. 39A-C. These results show an exact correlation with the
initial composition of the sample.
[0432] Each of the foregoing patents, patent applications and
references that are recited in this application are herein
incorporated in their entirety by reference. Having described the
presently preferred embodiments, and in accordance with the present
invention, it is believed that other modifications, variations and
changes will be suggested to those skilled in the art in view of
the teachings set forth herein. It is, therefore, to be understood
that all such variations, modifications, and changes are believed
to fall within the scope of the present invention as defined by the
appended claims.
Sequence CWU 1
1
8117PRTArtificial sequenceSynthetic peptide 1Tyr Cys Asn Ile Ser
Gln Lys Met Met Ser Arg Asn Leu Thr Lys Asp1 5 10
15Arg24PRTArtificial sequenceSynthetic peptide 2Cys Asn Ile
Ser134PRTArtificial sequenceSynthetic peptide 3Arg Asn Leu
Thr144PRTArtificial sequenceSynthetic peptide 4Asn Leu Thr
Lys154PRTArtificial sequenceSynthetic peptide 5Met Met Ser
Arg167PRTArtificial sequenceSynthetic peptide 6Tyr Cys Asn Ile Ser
Gln Lys1 57150PRTBos taurus 7Met Ala Leu Lys Ser Leu Val Leu Leu
Ser Leu Leu Val Leu Val Leu1 5 10 15Leu Leu Val Arg Val Gln Pro Ser
Leu Gly Lys Glu Thr Ala Ala Ala 20 25 30Lys Phe Glu Arg Gln His Met
Asp Ser Ser Thr Ser Ala Ala Ser Ser 35 40 45Ser Asn Tyr Cys Asn Gln
Met Met Lys Ser Arg Asn Leu Thr Lys Asp 50 55 60Arg Cys Lys Pro Val
Asn Thr Phe Val His Glu Ser Leu Ala Asp Val65 70 75 80Gln Ala Val
Cys Ser Gln Lys Asn Val Ala Cys Lys Asn Gly Gln Thr 85 90 95Asn Cys
Tyr Gln Ser Tyr Ser Thr Met Ser Ile Thr Asp Cys Arg Glu 100 105
110Thr Gly Ser Ser Lys Tyr Pro Asn Cys Ala Tyr Lys Thr Thr Gln Ala
115 120 125Asn Lys His Ile Ile Val Ala Cys Glu Gly Asn Pro Tyr Val
Pro Val 130 135 140His Phe Asp Ala Ser Val145 15084PRTArtificial
sequenceSynthetic peptide 8Ser Asn Leu Thr1
* * * * *
References