U.S. patent application number 15/469973 was filed with the patent office on 2018-06-14 for nmr-based metabolite screening platform.
The applicant listed for this patent is Children's Medical Center Corporation, President and Fellows of Harvard College. Invention is credited to Judy Lieberman, Elizabeth M. O'Day, Gerhard Wagner.
Application Number | 20180164290 15/469973 |
Document ID | / |
Family ID | 48948107 |
Filed Date | 2018-06-14 |
United States Patent
Application |
20180164290 |
Kind Code |
A1 |
O'Day; Elizabeth M. ; et
al. |
June 14, 2018 |
NMR-BASED METABOLITE SCREENING PLATFORM
Abstract
Methods that enable one to specifically measure the metabolic
product of a particular molecule in relatively few cells, e.g.
primary cells, are described. The methods involve optionally
preloading cells with labeled substrate (e.g. labeled by .sup.13C,
.sup.15N, or .sup.31P). The methods allow for easy identification
of metabolites that are differentially generated in cells of
different phenotypes. The new methods for unbiased
multi-dimensional NMR screening and rapid and efficient analysis of
the NMR screening identify differentially expressed metabolites in
different cell or tissue types. Analysis of the differentially
expressed metabolites can present unique druggable targets to which
small molecule therapeutics can be designed.
Inventors: |
O'Day; Elizabeth M.;
(Braintree, MA) ; Lieberman; Judy; (Brookline,
MA) ; Wagner; Gerhard; (Chestnut Hill, MA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Children's Medical Center Corporation
President and Fellows of Harvard College |
Boston
Cambridge |
MA
MA |
US
US |
|
|
Family ID: |
48948107 |
Appl. No.: |
15/469973 |
Filed: |
March 27, 2017 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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14377257 |
Aug 7, 2014 |
9606106 |
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PCT/US2013/025628 |
Feb 11, 2013 |
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15469973 |
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61597298 |
Feb 10, 2012 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 5/055 20130101;
G01N 2458/15 20130101; G01N 24/08 20130101; G01R 33/465 20130101;
G01N 33/5011 20130101; G01N 2500/10 20130101; G01N 33/5038
20130101; G01N 33/5005 20130101; A61P 43/00 20180101; G01R 33/4633
20130101; G01N 2570/00 20130101; A61P 35/00 20180101 |
International
Class: |
G01N 33/50 20060101
G01N033/50; G01R 33/465 20060101 G01R033/465; G01R 33/46 20060101
G01R033/46; G01N 24/08 20060101 G01N024/08 |
Goverment Interests
STATEMENT AS TO FEDERALLY SPONSORED RESEARCH
[0002] The inventions were made with Government support under R21
AI087431 awarded by the National Institutes of Health. The
government has certain rights in the inventions.
Claims
1. A method for monitoring metabolism of a substrate within a given
type of cell in a sample, the method comprising: a. culturing a
given type of cell of a first sample with a substrate for a
sufficient period of time to allow metabolic breakdown of the
substrate into substrate metabolites, wherein at least a portion of
the substrate is optionally labeled with a nuclear magnetic
resonance (NMR) stable isotope; b. harvesting the substrate
metabolites from the cells of step (a) to obtain a second sample of
substrate metabolites; and c. performing multi-dimensional NMR on
the second sample of step (b) to determine a resonance spectrum of
the metabolized substrate, wherein the resonance spectrum
represents the metabolites of the substrate, and wherein the
multi-dimensional NMR comprises any one of the following
techniques: spectral width folding, random phase sampling,
non-uniform sampling, and data extension for enhanced dynamic range
data reconstruction.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of and claims priority to
U.S. patent application Ser. No. 14/377,257, filed on Aug. 7, 2014,
which claims priority to International Patent Application Serial
No. PCT/US2013/025628, filed on Feb. 11, 2013, which claims benefit
of prior U.S. Provisional Application Ser. No. 61/597,298, filed on
Feb. 10, 2012. The above applications are incorporated herein by
reference in their entirety.
FIELD OF THE INVENTION
[0003] The invention relates to NMR-based screening platforms.
BACKGROUND OF THE INVENTION
[0004] The metabolic output of a cell is the summation of the
functional genomic, transcriptomic and proteomic networks that
define that cell type. Metabolomics is the comprehensive and
simultaneous systematic determination of metabolite levels in the
metabolome and their changes over time as a consequence of stimuli.
While other fields may provide information, for example, regarding
the copy number of a given gene, mRNA or protein; this study of
chemical processes involving metabolites provides the downstream
summation of all aberrant genes, RNAs, and/or proteins. This
`metabolic fingerprint` represents a snapshot of all the
functioning or non-functioning pathways in a particular cell
type.
[0005] Several analytical methods including mass spectrometry,
chromatography, and NMR spectroscopy have been used to quantify
cellular metabolites. Mass spectrometry and chromatography both
require small sample amounts and can be easily adapted for high
throughput analysis; however, both methods typically involve at
least one if not several purification steps. Furthermore, in most
cases the metabolites to be examined must be pre-selected a priori.
Untargeted mass spectrometry approaches are possible but require
several rounds of purification and further identification methods.
In addition, not all metabolites, including nucleotide analogs and
lipids, are easily ionizable and thus cannot be detected via mass
spectrometry. Further, the fragmentation pattern resulting from
mass spectrometry is not always suitable to distinguish between
molecules such as sugars that have equal mass, but different
structures, hence limiting the analysis.
SUMMARY OF THE INVENTION
[0006] Described herein is a rapid, unbiased, ultra-high
resolution, quantitative NMR screening platform that utilizes any
one, two, three, four, or all five, in any combination, of the
following techniques: stable isotope labeling of a substrate,
spectral width folding, random phase sampling, non-uniform
sampling, and data extension for enhanced dynamic range data
reconstruction, to generate custom "NMR Metabolite Arrays" in which
the resonances of all known metabolites of a given cell sample are
categorized and used for comparison for simplified statistical
analysis. This is the first time all these techniques have been
combined to provide a robust, efficient and high throughput NMR
metabolite screening protocol. The combinations of steps allow for
global, unbiased, ultra-high resolution of both water-soluble and
lipid-based metabolites. In addition, the novel "NMR Metabolite
Array" programs described herein provide a new way to analyze large
complex NMR datasets in a simplified manner. The new platform
permits both the rapid identification of differentially expressed
metabolites, quantification of specific metabolites, and the
ability to analyze the metabolic flux of given precursors.
[0007] The new methods enable one to specifically follow the
metabolic breakdown of a particular molecule in relatively few
cells, e.g., about 2-20 million cells. The methods involve
preloading cells with a labeled precursor substrate (e.g., labeled
with .sup.13C, or .sup.15N, or .sup.31P) and using multidimensional
NMR. The methods do not require purification of the individual
metabolites of interest prior to analysis allowing for global,
unbiased identification of metabolites that are differentially
generated in cells with different properties.
[0008] Identification of metabolites differentially expressed in
normal and disease state cells can be a powerful tool in the
clinic. The new methods for monitoring differential expression of
metabolites from cells that are phenotypically different are
particularly useful for identifying therapeutic targets that can be
used to modulate the phenotype. This includes targets that are
present in the biosynthetic pathway of the metabolite, or the
metabolite itself. Further, identifying differentially expressed
metabolites can be used to differentiate cells of a normal versus a
disease state. Hence, they have the potential to serve as a
biomarker for the phenotype with which it is associated, making the
methods described herein useful for identifying diagnostic markers,
e.g., markers for diagnosis of disease.
[0009] For example, described herein we identified
N-acetylneuraminic acid (NANA) as a novel biomarker for breast
cancer tumor initiating cells, and monitoring its expression could
be useful in diagnosing and detecting breast cancer. In addition,
the protein level of CMAS, an enzyme in NANA biosynthesis was shown
to be dramatically over-expressed in breast tumor initiating cells.
Before this work, the role of CMAS in tumor initiation and
metastasis has not been explored. Herein we provide evidence that
CMAS expression is absolutely crucial for tumor formation and
migration and that CMAS is a novel bona fide target for breast
cancer.
[0010] The application of this methodology to an individual
patient's cell analysis will also provide the basis for a
"personalized medicine" approach to patient care.
[0011] Accordingly, this disclosure describes methods for
monitoring the metabolism of a given substrate precursor within a
cell population, e.g., a primary cell population, a tissue cell
population, or cultured cells (e.g., immortalized cells). Using the
methods described herein, the identification of differentially
expressed metabolites between two or more cell populations that
have different phenotypes is described. Also described are methods
for identifying potential therapeutic targets and diagnostic
markers.
[0012] In general, in a first aspect, the disclosure features new
methods of monitoring metabolism of a substrate within a given type
of cell in a sample. The new methods include (a) culturing a given
type of cell of a first sample with a substrate for a sufficient
period of time to allow metabolic breakdown of the substrate into
substrate metabolites, wherein at least a portion of the substrate
is optionally labeled with a nuclear magnetic resonance (NMR)
stable isotope; (b) harvesting the substrate metabolites from the
cells of step (a) to obtain a second sample of substrate
metabolites; and (c) performing multi-dimensional NMR on the second
sample of step (b) to determine a resonance spectrum of the
metabolized substrate, wherein the resonance spectrum represents
the metabolites of the substrate, and wherein the multi-dimensional
NMR comprises any one of the following techniques: spectral width
folding, random phase sampling, non-uniform sampling, and data
extension for enhanced dynamic range data reconstruction.
[0013] In another aspect, the disclosure features methods for
identifying differentially expressed substrate metabolites between
a first population of cells and a second population of cells. These
methods include (a) optionally loading a first and a second
population of cells with a nuclear magnetic resonance (NMR) stable
isotope-labeled substrate; (b) culturing the first and the second
population of cells of step (a) for a sufficient period of time to
allow metabolic breakdown of the substrate into substrate
metabolites; (c) harvesting the substrate metabolites from the
first and the second population cells of step (b) to obtain a
sample of substrate metabolites from each of the first and the
second cell populations; (d) performing multi-dimensional NMR on
the sample of step (c) for each of the first and the second cell
populations to determine a resonance spectrum of the metabolized
substrate of the first population of cells and of the second
population of cells, wherein the resonance spectrum represents the
metabolites of the substrate; and (e) comparing the resonance
spectrum of the first population of cells with the resonance
spectrum of the second population of cells to determine which
resonances are differentially expressed, wherein the differentially
expressed resonances provide a resonance signature that represents
differentially expressed metabolites.
[0014] In any of these methods, the multi-dimensional NMR can
include any two, three, or all four, in any combination, of the
following techniques: spectral width folding, random phase
sampling, non-uniform sampling, and data extension for enhanced
dynamic range data reconstruction. In any of these methods, the
substrate can be labeled with an NMR stable isotope and the
multi-dimensional NMR can include any two, three, or all four, in
any combination, of the following techniques: spectral width
folding, random phase sampling, non-uniform sampling, and data
extension for enhanced dynamic range data reconstruction.
[0015] In some implementations of these methods, the substrate
metabolites that are present in the sample are not purified away
from the other molecules in the sample. In some implementations the
substrate concentration within the population of cells is reduced
for a period of time prior to loading the cells with the
NMR-labeled substrate and the resonances of the metabolites of the
labeled substrate are determined using NMR pulse programs or
filtering techniques, or both, customized to the substrate. The
number of cells within the population of cells can be is less than
2.times.10.sup.6 and the population of cells can be a primary
population of cells.
[0016] Any of these methods can further include comparing the
resonance signature of step (e) with a database of known resonance
signatures to determine the molecular structure that the resonance
signature represents, and thereby determine the substrate
metabolites that are differentially expressed between the first and
the second populations of cells.
[0017] In certain implementations, the methods can further include
identifying a biosynthetic pathway involved in generation of the
substrate metabolites and identifying proteins/enzymes of the
pathway that may be targeted to modulate the differential
expression of the metabolite, to thereby modulate the phenotype of
the cells. In some embodiments the first population of cells and
the second population of cells are isogenic populations and/or the
first population of cells and the second population of cells have
different phenotypes. In some implementations, the first population
of cells is a control population of cells and the second population
of cells has been contacted with a test compound or agent. The
methods can be used to identify metabolic pathways that are
overactive or underactive in a particular cell type. The methods
can further include inhibiting or overexpressing a gene in the
second population of cells and the method is used to identify the
metabolic consequences of over-expressing or inhibiting a gene in a
cell.
[0018] In another aspect, the disclosure features methods for
treating cancer in a subject. The methods are based on results
determined using the new platform methods described herein. The
methods of treating cancer include administering to a subject in
need thereof an effective amount of an inhibitor of
N-acylneuraminate cytidylyltransferase (CMAS), an inhibitor of
N-acetylneuraminic acid synthase (NANS), or a molecule that
decreases the expression of N-acetylneuraminic acid. For example,
the inhibitor can be an enzyme or can be selected from the group
consisting of a small molecule, a ribonucleic acid, a
deoxyribonucleic acid, a protein, a peptide, and an antibody.
[0019] As used herein, the term "isogenic" refers to cells of the
same genetic background (any cell type, e.g., epithelial, or fat,
or stem, or muscle cells etc.) that are isolated from the same
tissue type (any tissue, e.g., tissue of the same organ, skin,
bladder, liver, heart, etc.) and from the same organism type (e.g.,
human, or animal, or fish).
[0020] As used herein, the term "metabolite" refers to the
intermediate or the end products of metabolism. "Metabolites" have
functions comprising energy source, structural, signaling,
stimulatory, and inhibitory effects on enzymes. Metabolites can
also have catalytic activity themselves. A metabolite can be the
end product of a substrate-enzyme reaction.
[0021] As used herein, the term "metabolic precursor" is a compound
that participates in a chemical reaction. The term is meant to
include to a compound that is a starting compound or an
intermediate compound of an enzymatic reaction from which an end
product results.
[0022] The term "substrate" refers to a molecule or compound on
which an enzyme acts and results in the substrate transforming into
one or more end products. The end products are released from the
active site of the enzyme.
[0023] The term "enzyme" refers to a molecule that accepts a
substrate in its active site and transforms the substrate into one
or more end products that are subsequently released from the active
site.
[0024] As used herein, the term "primary cell" or "primary tissue"
refers to cells or tissue taken directly from living tissue of a
normal individual or an individual with an acquired or inherited
disease and established to grow in vitro.
[0025] The term "metastasis" refers to a process by which cancer
spreads from the place at which it first arose as a primary tumor
to distant locations in the body as well as the newly established
tumor itself, which is also referred to as a "metastatic tumor"
that can arise from a multitude of primary tumor types, including
but not limited to those of prostate, colon, lung, breast, bone,
and liver origin. Metastases develop, e.g., when tumor cells shed
from a primary tumor adhere to vascular endothelium, penetrate into
surrounding tissues, and grow to form independent tumors at sites
separate from a primary tumor.
[0026] The term "cancer" refers to cells having the capacity for
autonomous growth. Examples include cells having an abnormal state
or condition characterized by rapidly proliferating cell growth.
The term is meant to include cancerous growths, e.g., tumors (e.g.,
solid tumors); oncogenic processes, metastatic tissues, and
malignantly transformed cells, tissues, or organs, irrespective of
histopathologic type or stage of invasiveness. Also included are
malignancies of the various organ systems, such as respiratory,
cardiovascular, renal, reproductive, hematological, neurological,
hepatic, gastrointestinal, and endocrine systems; as well as
adenocarcinomas which include malignancies such as most colon
cancers, renal-cell carcinoma, prostate cancer and/or testicular
tumors, non-small cell carcinoma of the lung, cancer of the small
intestine, and cancer of the esophagus. Cancer that is "naturally
arising" includes any cancer that is not experimentally induced by
implantation of cancer cells into a subject, and includes, for
example, spontaneously arising cancer, cancer caused by exposure of
a patient to a carcinogen(s), cancer resulting from insertion of a
transgenic oncogene or knockout of a tumor suppressor gene, and
cancer caused by infections, e.g., viral infections. The term
"carcinoma" is art recognized and refers to malignancies of
epithelial or endocrine tissues. The term also includes
carcinosarcomas, which include malignant tumors composed of
carcinomatous and sarcomatous tissues. An "adenocarcinoma" refers
to a carcinoma derived from glandular tissue or in which the tumor
cells form recognizable glandular structures.
[0027] As used herein, the term "treating" or "treatment" refers to
administering one or more of the compounds described herein to a
subject who has an a disorder treatable with such compounds, and/or
a symptom of such a disorder, and/or a predisposition toward such a
disorder, with the purpose to confer a therapeutic effect, e.g., to
cure, relieve, alter, affect, ameliorate, or reduce the disorder,
the symptom of it, or the predisposition.
[0028] As used herein, the term "an effective amount" or "an amount
effective" refers to the amount of an active compound that is
required to confer a therapeutic effect on the treated patient.
Effective doses will vary, as recognized by those skilled in the
art, depending on the types of diseases treated, route of
administration, excipient usage, and the possibility of co-usage
with other therapeutic treatment.
[0029] Dosage, toxicity, and therapeutic efficacy of therapeutic
compounds can be determined by standard pharmaceutical procedures
in cell cultures or experimental animals, e.g., for determining the
LD50 (the dose lethal to 50% of the population) and the ED50 (the
dose therapeutically effective in 50% of the population). The dose
ratio between toxic and therapeutic effects is the therapeutic
index and it can be expressed as the ratio LD50/ED50. Compounds
that exhibit high therapeutic indices are preferred. While
compounds that exhibit toxic side effects may be used, care should
be taken to design a delivery system that targets such compounds to
the site of affected tissue in order to minimize potential damage
to uninfected cells and, thereby, reduce side effects.
[0030] Unless otherwise defined, all technical and scientific terms
used herein have the same meaning as commonly understood by one of
ordinary skill in the art to which this invention belongs. Although
methods and materials similar or equivalent to those described
herein can be used in the practice or testing of the present
invention, suitable methods and materials are described below. All
publications, patent applications, patents, and other references
mentioned herein are incorporated by reference in their entirety.
In case of conflict, the present specification, including
definitions, will control. In addition, the materials, methods, and
examples are illustrative only and not intended to be limiting.
[0031] Other features and advantages of the inventions will be
apparent from the following detailed description, and from the
claims.
BRIEF DESCRIPTION OF THE DRAWINGS
[0032] FIG. 1 is a flow chart summarizing the key steps of the NMR
based metabolite screening platform described herein.
[0033] FIGS. 2A-F detail each step of the acquisition and
processing in the NMR methods that allow for rapid, unbiased,
ultra-high resolution metabolite profiling. FIGS. 2A-2D demonstrate
spectral width (sw) folding strategies, in which decreasing the sw
from 220 pm to 90 ppm led to 2.5 fold increase in resolution. FIG.
2E summarizes a non-uniform sampling (NUS) strategy allowing either
an 8-fold increase in resolution or a 4-fold increase in resolution
in time reduction by 40%. FIG. 2F shows a NUS 13C-1H HSQC spectra
(left) processed using forward maximum entropy reconstruction and
the same NUS 13C-1H HSQC spectra (right) after including a
data-extension step in the reconstruction that increased resolution
by 2-fold.
[0034] FIGS. 3A and 3B are .sup.13C-.sup.1H HSQC spectra indicating
the full metabolic coverage of water-soluble (3A) and lipid-based
(3B) metabolites from the same p53 deficient mouse lung tumor,
respectively.
[0035] FIG. 4 is a flow chart summarizing the custom NMR analysis
program used to create NMR arrays for rapid analysis.
[0036] FIGS. 5A-C are a set of .sup.13C-.sup.1H HSQC resonance
spectra of water-soluble metabolites from 20 million unlabeled (5A)
.sup.13C-gluatmine incubated (5B) and .sup.13C-glucose incubated
(5C) breast tumor initiating cells.
[0037] FIG. 6 highlights the information available in the NMR
arrays described herein. Using the unlabeled, glutamine, and
glucose spectra in FIGS. 5A-C, a master look up was created to
generate metabolite IDs for all possible resonance metabolites that
and these are listed on the X-axis. The Y-axis displays the
relative intensity of each resonance in each condition,
highlighting the differential glucose and glutamine derived
metabolites.
[0038] FIGS. 7A-B are representative examples of .sup.13C-.sup.1H
HSQC resonance spectra of water soluble glucose derived metabolites
in breast tumor initiating BPLER cells (FIG. 7A) and less malignant
isogenic HMLER cells (FIG. 7B).
[0039] FIG. 7C summarizes the NMR arrays for the BPLER and HMLER
.sup.13C-.sup.1H HSQC resonance spectra, showing how the intensity
(Y-axis) of all possible metabolite resonances (X-axis) changes in
each cell type. BPLER and HMLER cells originate from the same
normal breast tissue and were grown into two cell types BPECs
(breast primary epithelial cells) grown in chemically defined WIT
medium and HMECs (human mammary epithelial cells) grown in MEGM
media. BPEC and HMEC cells were transformed with hTERT (L), the
SV40 early region (E), and H-ras (R) to give rise to BPLER and
HMLER cells.
[0040] FIG. 7D is a zoomed in region of the a overlay of BPLER
(red) and HMLER (blue) .sup.13C-.sup.1H HSQC spectra in a region
the NMR array predicted to only have BPLER resonances.
[0041] FIGS. 8A-C summarize various methods used to validate that
the differentially expressed metabolite overexpressed in the BPLER
NMR arrays is N-acetylneuraminic acid (NANA). FIG. 8A is the
.sup.13C-.sup.1H HSQC of pure NANA. FIG. 8B illustrates the results
of custom NMR HCN (hydrogen-carbon-nitrogen) experiment, confirming
BPLER cells have a differentially expressed resonance with similar
connectivity to NANA. FIG. 8C shows the M/S results directly
measuring NANA in HMLER (left) and BPLER (right) cells using liquid
chromatography-mass spectrometry multiple reaction monitoring
(LC/MS MRM) where the number reported is the area under the NANA
peak.
[0042] FIG. 9 is a series of representations of microscope images
of rhodamine-labeled wheat germ agglutinin (WGA) immune-fluorescent
microscopy showing the expression of NANA in HMLER (top row) and
BPLER cells (bottom row), respectively. WGA specifically binds to
NANA.
[0043] FIG. 10 is a schematic diagram depicting the enzymatic steps
to convert glucose to NANA, in which N-acetylneuraminic acid
synthase (NANS) and N-acylneuraminate cytidylyltransferase (CMAS)
are key enzymes.
[0044] FIG. 11 is a bar graph showing the effects on proliferation
by downregulating CMAS, NANS, and PLKI via siRNA on the viability
of HMLER and BPLER cells.
[0045] FIGS. 12A-C are a series of images that provide an overview
of the NANA effect on cell migration of BPLER and HMLER cells.
FIGS. 12A and 12B are each a series of immunohistochemistry images
showing the inhibition of cell migration in the absence of NANS and
CMAS and the rescue of migration with NANA in HMLER and BPLER
cells, respectively. FIG. 12C is a bar graph quantifying the number
of migrating cells in the absence of NANS and CMAS and the
subsequent migration following addition of NANS.
[0046] FIGS. 13A-B are a bar graph depicting the quantitative PCR
(mRNA levels) of CMAS and cMYC (13A) and a Western Blot analysis of
NANS and CMAS expression (13B), respectively, in HMLER and BPLER
cells.
[0047] FIGS. 14A and 14B are immunohistochemistry images of the
effect of CMAS expression on cell migration. FIG. 14A is a series
of immunohistochemistry images of HMLER cell migration following
overexpression of CMAS. FIG. 14B is a series of
immunohistochemistry images of BPLER cells and with stable CMAS
knockdown BPLER cells (BPLER-shCMAS1).
[0048] FIGS. 15A-B summarize the strategy to determine the effect
of CMAS levels on tumor initiation and metastasis in vivo. FIG. 15A
shows the immunization scheme, and FIG. 15B shows the tumor volume
growth per day after in NOD/SCIN mice injected with 500,000 BPLER
(left) or 500,000 BPLER-shCMAS1 cells.
[0049] FIGS. 16A, 16B, 16C, and 16D are the enzyme mechanism of
CMAS, a synthesized substrate based inhibitor of CMAS based on the
structure of NANA, and immunohistochemistry analysis showing the
inhibition of cell migration with the synthesized fluorine-NANA
inhibitor, respectively.
[0050] FIGS. 17A and 17C are the chemical structures of
(2R,3R,4S)-4-guanidino-3-(prop-1-en-2-ylamino)-2-((1R,2R)-1,2,3-trihydrox-
ypropyl)-3,4-dihydro-2H-pyran-6-carboxylic acid (which is
Zanamivir, marketed as Relenza.RTM.)(FIG. 17A) and ethyl
(3R,4R,5S)-5-amino-4-acetamido-3-(pentan-3-yloxy)-cyclohex-1-ene-1-carbox-
ylate (which is oseltamivir, marketed as Tamiflu.RTM.)(FIG.
17C).
[0051] FIGS. 17B and 17D illustrate the immunohistochemistry
analyses of the effects of Relenza.RTM. on BPLER cell migration
(17D) and a control (17B). Both of these drugs are neuraminidase
inhibitors.
[0052] FIGS. 18A and 18B are immune-fluorescent microscopy images
showing the expression of NANA in HMLER and BPLER cells in the
absence and presence of neuraminidase (18A) and
immunohistochemistry images of HMLER and BPLER cell migration in
the presence and absence of neuraminidase (18B).
DETAILED DESCRIPTION
[0053] The present disclosure describes novel methods that provide
benefits over numerous aspects of known NMR data acquisition and
allow for rapid, unbiased, global, quantitative ultra-high
resolution NMR data acquisition and custom NMR analysis utilizing a
novel approach. The present disclosure describes new NMR screening
methods that can be used to identify, follow, and characterize the
metabolic breakdown of a particular molecule and to analyze the
cellular metabolomics essential to a given cell type. The methods
described herein circumvent the hurdles presented by known NMR
protocols, namely a reduction in the sample size necessary to
perform the data acquisition, eliminating the need for purification
of the metabolites to be analyzed, reduced experimental time
required for multi-dimensional NMR and obtaining high resolution
necessary for metabolite identification. The method requires
relatively few cells (2-20 million), allowing the methods to be
used to study metabolites from primary cells and tissues rather
than just from cell cultures. In addition, the disclosed methods do
not require purification of the individual metabolites of interest
from other cellular metabolites prior to analysis. Further, the new
methods allow one to visualize the specific metabolic fate of a
given precursor in any cell type, and thus provide for the
simplified identification of metabolites that are differentially
generated in different types of cells utilizing novel data analysis
methods also described herein.
[0054] To highlight the power and breadth of the new platform
methods, this disclosure describes the identification of
differentially expressed metabolites in triple negative breast
cancer tumor-initiating BPLER cells that are highly aggressive
compared to a less malignant isogenic line HMLER. It is widely
known in the field that cancer cells thrive on glucose consumption.
The glucose metabolite N-acetyl-neuraminic acid (NANA) has been
identified herein as being differentially expressed in breast
cancer tumor-initiating BPLER cells (i.e., increased expression).
The biosynthetic pathway that generates the metabolite has also
been identified and used to identify proteins/enzymes required for
the synthesis of the metabolite as candidate targets within the
pathway to modulate the phenotype of increased tumor initiation and
metastatic potential.
[0055] In addition, the results of knock-down experiments in which
genes that produce key enzymes NANS and CMAS are silenced
demonstrated that the reduction of the normal function of these
enzymes to generate NANA and attach it to proteins had no effect on
the proliferation of BPLER cells, but greatly reduced their
migration. On the other hand, forced over-expression of the same
enzymes in HMLER cells increased their migration. Stable knockdown
of CMAS in BPLER cells completely prevented tumor formation in
mice. Thus, the new methods were successfully used to identify
metabolites (e.g., NANA) important for tumorigencity and to
demonstrate that NANA and the proteins involved in the synthesis of
NANA may serve as targets for therapeutic intervention to reduce
breast tumor formation and the metastasis of tumor-initiating
cells. In addition, NANS, and CMAS were validated as targets for
inhibition of tumor initiation and metastasis in vitro and in vivo.
Thus, small molecule and other inhibitors of these enzymes are new
candidate therapeutic agents that can be used to specifically
target breast tumor-initiating cells. Furthermore, the
differentially expressed metabolites (e.g., NANA) can also serve as
biomarkers for the phenotype with which they are associated,
allowing the methods described herein to be used to identify new
candidate diagnostic markers.
General Methodology
[0056] In general, the new methods described herein for monitoring
metabolites of a given precursor molecule (i.e., substrate) within
a given type of cell include the steps of: (a) optionally loading a
population of cells with a labeled substrate, e.g., a
.sup.13C-labeled substrate, (b) culturing the cells of step (a) for
a sufficient period of time to allow metabolic breakdown of the
substrate into substrate metabolites (e.g., typically 5 minutes to
24 hours depending on the experimental question); (c) harvesting
the substrate metabolites from the cells to obtain a sample of
substrate metabolites, e.g., a water-soluble sample of substrate
metabolites and organic sample of lipid-based metabolites; and (d)
performing multi-dimensional NMR on the sample of step (c) to
determine the resonance spectra of the metabolized substrate,
wherein the resonances represents the resonances of metabolites of
the substrate. How the multi-dimensional NMR is performed is
described in further detail below.
[0057] In these methods, various substrates and various stable
spin-1/2 nuclear isotopes can be used to label those substrates.
For example, glucose, glutamine, fatty acids, amino acids,
pyruvate, drug compounds, and other molecules can be used as
substrates, and stable isotopes such as .sup.13C, .sup.15N,
.sup.29Si, .sup.31P, or others can be used to label the substrates.
Any tissue, primary cells or cultured cell lines can be used for
the analysis, such as cancer cells, muscle cells, fat cells,
endothelial cells, epithelial cells, neuronal cells, cardiac cells,
and many others. In general, one would want to test cells
associated with a particular disease or disorder, such as a cancer
cell, as well as the same type of cells from a healthy subject, to
provide a differential metabolic analysis. One could also test
cells with a single gene mutation (i.e. mutant cells vs. wild-type)
or cells treated or not with a drug, or the same cells incubated
with the precursor for various times. The cell populations of each
sample can be a homogeneous cell population. In alternative
embodiments, the cell population of each sample can be a
heterogeneous cell population (e.g., derived from a tissue sample).
While any number of cells can be used in the methods described
herein, in various embodiments the number of cells within each
population of cells can be less than 1.times.10.sup.8, less than
8.times.10.sup.7, less than 7.times.10.sup.7, less than
6.times.10.sup.7, less than 5.times.10.sup.7, less than
2.5.times.10.sup.7, or less than 2.0.times.10.sup.7, or the cell
number can range from approximately 1.times.10.sup.6 to
1.times.10.sup.8 cells, or 1.times.10.sup.6 to 5.times.10.sup.7, or
1.times.10.sup.6 to 2.5.times.10.sup.7, or 1.times.10.sup.6 to
2.0.times.10.sup.7.
[0058] Methods for loading cells are well known to those of skill
in the art, e.g., the labeled substrate can be added to the cell
culture medium for a period of time (e.g., 5 minutes to 24 hours),
or may be loaded by transfection (e.g., liposome or calcium
phosphate), transduction (e.g., viral delivery of labeled
substrate) or transfusion (e.g. direct injection into a tumor). In
one embodiment, labeled precursor is administered to a subject,
e.g., orally, topically, parenterally, or intravenously. In some
embodiments, the labeled substrate is added to animal feed.
[0059] In various embodiments, prior to adding the culture medium
containing the labeled substrate, the cells are incubated with cell
culture medium that lacks any form of the substrate, e.g., lacks
the substrate in unlabeled or labeled form. The cells may be
incubated in this medium for minutes to hours, essentially starving
the cells of the substrate. This helps reduce background in the
later analysis. The cells are then incubated with cell culture
medium containing only labeled substrate, (e.g., 5 minutes to 24
hours). Any concentration of substrate can be used. In various
embodiments, the concentration ranges from 1 ng/ml to >1 mg/ml.
A skilled artisan can easily determine the best concentration to
use by testing various concentration ranges. After incubation the
cells are washed briefly and immediately harvested to separate the
metabolites. To harvest the metabolites, a simple chloroform
extraction may be performed in order to obtain a water-soluble
sample or a non-water soluble sample of metabolites present in the
organic layer. No additional purification is required.
[0060] Several nuclear magnetic resonance (NMR) techniques may be
used in the methods of the invention, preferably multidimensional
NMR. For example, heteronuclear single quantum correlation (HSQC)
spectroscopy, variations of HSQC, and other multidimensional NMR
techniques can be used. Methods for performing multidimensional NMR
(e.g., 2D NMR and/or .sup.13C-.sup.1H HSQC NMR) are well known to
those of skill in the art.
[0061] The resonance spectra of the metabolites of the labeled
substrate can be determined using NMR pulse programs, which can be
customized to the substrate. In general, NMR uses a static,
homogeneous external magnetic field to polarize the NMR sample.
This primary field is typically called the "B0" field, and it
defines a reference axis for the NMR system. The NMR sample is
magnetized in the direction of the B0 field by placing the sample
in the B0 field for a period of time (e.g., minutes) and allowing
the sample to reach a thermal equilibrium state. The primary B0
field also typically defines the resonance frequencies of the
spin-1/2 nuclear species in the sample. For example, a stronger
primary field generally increases the nuclear spins' resonance
frequencies. The nuclear spins "precess" about the B0 field at
their respective resonance frequencies. In most NMR systems, the
nuclear spins have a resonance frequency in the radio frequency
(if) range.
[0062] In NMR experiments, the nuclear spins in the NMR sample are
manipulated by applying a time-varying magnetic field at the
nuclear spins' resonance frequency. In some instances (e.g., for
low flip-angle pulses), high-intensity radio frequency (RF) pulses
provide fast, precise control of the nuclear spins. High-intensity
RF pulses have the benefit of shorter pulse times, which reduces
the amount of decoherence that occurs during the pulse. In some
instances (e.g., for high-flip angle pulses), high-intensity RF
pulses provide less precision, for example, due to non-uniform
power output over a frequency range of interest, due to spatial
inhomogeneity in the RF field, or due to other considerations.
[0063] In some implementations, adiabatic pulses can provide more
precise control of the nuclear spins. Adiabatic pulses typically
have a lower intensity and require a longer pulse time. In some
cases, adiabatic pulses are used for larger flip-angle pulses
(e.g., 180 degree flip angle) to provide a more uniform flip angle
over the entire frequency range of interest. Adiabatic pulses are
typically implemented shaped pulses (meaning that they have a
time-varying power profile) that can be parameterized for a
particular flip angle, a particular frequency range, etc.
[0064] In various embodiments, the new NMR methods include the use
of any one or more the following methods and techniques to increase
analysis speed and/or resolution, or both: (i) stable isotope
labeling (ii) folding the spectra width and aliasing peaks, (iii)
random phase sampling (iv) non-uniform sampling, and (v) data
extension. In some embodiments the methods include at least two or
three of these methods in any combination. In some embodiments, the
methods include all five methods. Table 1 below summarizes the
effects of each step on the NMR acquisition and data resolution.
These steps are described in more detail below.
TABLE-US-00001 TABLE 1 EFFECT ON NMR ACQUISITION & METHOD DATA
RESOLUTION Stable isotople labeling Increases metabolite signal
detection by 99% Folding spectral width (sw) Increases resolution
by 2.5 fold to 44 Hz (approaching theoretical limit of C-C
decoupling) Random phase sampling Decreases the total acquisition
time by 50% Non-uniform sampling For time equivalent spectra one
can gain 8- fold increase in resolution or one can gain 4- fold
increase in resolution and at the same time cut experimental time
by 40%. Data-extension 2-fold increase in data resolution with no
effect on experimental time
Stable Isotope Labeling
[0065] Traditional 2-D NMR metabolite profiling relies on
.sup.13C-natural abundance, which exists at 1.1%. As such, in order
to observe any signal at all, large amounts of sample are required
(on the average +200 million cells). This limits detection to
cultured cells lines and only the most abundant metabolites are
detected. To decrease the amount of material needed and view
metabolites with a broader concentration range, samples (e.g.,
cells, tissues or tumors) were directly supplemented with
.sup.13C-labeled precursors (glucose, glutamine, pyruvate and amino
acids were used but other substrates and other isotopes are also
possible). Theoretically, this should decrease the sample burden by
.about.99%. If, for example a 1 mM metabolite required 200 million
cells to be detected with .sup.13C natural abundance, using a label
to see the same intensity would require only 2 million cells.
Including this step in our method reduces sample requirements to
those similar for some mass spectrometry (M/S) approaches.
Untargeted M/S requires at least 1 million cells and is followed by
several rounds of chromatography purification and detection. Our
method requires few cell numbers and no purification.
Folding the Spectra Width and Aliasing Peaks
[0066] When an atom is placed in a strong magnetic field (B0), the
electrons in that molecule precess in the direction of the applied
magnetic field. This precession creates a small magnetic field at
the atomic nucleus. The magnetic field at the nucleus (B) is
therefore generally less than the external magnetic field (B0) by
.tau..
B=B0(1-.tau.).
The electron density around each nucleus within a molecule varies
according to the types of nuclei and the bonds in the molecule. The
opposing field and therefore the effective magnetic field at each
nucleus will vary. In pulsed NMR spectroscopy these differences can
be measured by applying a radio frequency pulse that causes the
nuclear magnetization to oscillate inducing an electrical current
in a coil that can be measured. This signal, known as "free
induction decay" (FID) is plotted as current with respect to time.
By applying a discrete Fourier transform, the FID can be converted
to frequency domain and the resonance frequency of each observable
nuclei can be converted to chemical shift (.delta.) by the
equation,
.delta.=(n-n.sub.REF).times.10 .sup.6/n.sub.REF
where n is the resonance frequency of the nucleus and n.sub.REF is
the resonance frequency of a standard.
[0067] Chemical shift is a very precise metric of the chemical
environment around a nucleus. Unlike M/S and chromatography, NMR is
one of the only methods that can distinguish molecules that have
the same mass but different chemical connectivity. However to
utilize this information ultra-high resolution spectroscopy is
needed.
[0068] In NMR, digital resolution is determined by the sweep width
(sw) and the total number of data points (TD), such that
Resolution=sw/TD, measured in Hz/point.
SW is the range of frequencies over which NMR signals are to be
detected. Metabolite mixtures contain diverse molecules, and the
spectral width necessary to cover all potential carbon chemical
shifts spans over -220 ppm. The large .sup.13C-chemical shift
window creates a dilemma, where in order to have maximum resolution
and a broad enough sw to encompass all possible chemical shifts one
would require an incredibly large number of data points. In
practical terms this is time-prohibitive.
[0069] To circumvent this, our method can include folding the sw.
The sw is purposefully set to smaller range and if some peaks occur
outside this range they will appear "folded" at aliased chemical
shifts. Folded spectra can be unfolded by suitable data processing
techniques. For example, resonance frequencies can be dealiased by
expanding the frequency spectrum and shifting the aliased
frequencies by a pre-defined amount, to their actual locations. In
some cases, the data acquisition parameters define the spectral
folding windows in a manner that reduces or minimizes any overlap
between folded spectral peaks and non-folded spectral peaks. As
such, the folded spectral peaks can be de-aliased without affecting
other data in the frequency spectrum, in some cases. Folding
spectra decreases the overall number of points required in order to
achieve the maximum resolution possible. Our custom folding
strategy and de-aliasing program allows ultra-high resolution
spectra with .about.44 Hz/point separation.
Random Phase Sampling
[0070] As described above converting the FID signal to frequency
data requires Fourier transformation. However, for a nuclei
rotating at +x magnetization vector around the Z-axis, the Fourier
transform will give peaks at both +.nu. and -.nu. because the
Fourier transformation cannot distinguish between a +.nu. and -.nu.
rotation of the vector. The most common method to distinguish the
sign of the frequency requires sampling the signal at two different
receiver phases (for example 0.degree. and 90.degree.). For
multidimensional NMR this increases the experiment time by a factor
of two for each dimension. To increase the speed of our analysis we
employed random phase sampling (RPS) where a single phase is used
to detect each point but the phase is randomly alternated for
different points in the signal. This allows us to resolve the phase
of the frequency but cut the acquisition time in half.
Non-Uniform Sampling and Data Extension
[0071] Two-dimensional NMR techniques generate two dimensions of
data in the time domain: a direct domain and an indirect domain.
The direct domain data are generated by running an experiment and
collecting an NMR signal (e.g., an FID, an echo, or a stroboscopic
signal). In other words, the direct domain is the time domain of an
NMR experiment. The indirect domain data are generated by
systematically varying a time parameter of the NMR experiment
(e.g., incrementing a delay time), running the NMR experiment for
each value of the parameter, and combining the NMR signals from all
experiments. In other words, the indirect domain is the time domain
of the parameter that is systematically varied.
[0072] In some cases, non-uniform sampling can be used in
multi-dimensional NMR for the methods described herein. For
example, non-uniform sampling can be used in the indirect domain to
reduce the number of NMR experiments that are needed to obtain a
particular spectral range and frequency resolution.
[0073] Non-uniform sampling (NUS) can be accomplished by
incrementing the indirect domain time parameter systematically and
in a non-uniform manner. In particular, instead of incrementing the
time parameter by the same amount for each successive NMR
experiment, the time parameter can be incremented by an amount that
varies depending on one or more factors. For example, the time
delay parameter can be incremented by an amount that changes (e.g.,
increases or decreases) from experiment to experiment. Varying the
time delay according to a Poissonian distribution or another
nonlinear distribution results in sparsely sampled indirect domain
data. The missing points in the "sparse" data set can be calculated
using reconstruction methods. The forward maximum entropy
reconstruction technique can conserve the measured time-domain data
points and guess the missing data points by an iterative process.
The iterative process can include discrete Fourier transformation
of the sparse time-domain data set, computation of the spectral
entropy, determination of a multidimensional entropy gradient, and
calculation of new values for the missing time-domain data points
with a conjugate gradient approach. Since this procedure does not
alter measured data points, it can reproduce signal intensities
with high fidelity and avoid dynamic range problems. In some cases,
our method indicates with appropriate sampling schedules NUS has
enhanced ability to detect weak peaks. This is extremely important
for metabolite analysis where there is a large dynamic range
between abundant metabolites (milimolar concentration) and rare
metabolites (nanomolar concentration).
[0074] During the reconstruction it is possible to further increase
the resolution of resonances utilizing "data extension." In this
method, during the reconstruction, the total number of points in
the indirect dimension is doubled. In one embodiment, the first
half of the time domain (composed of NUS sampled points) is solved
according to standard forward maximum entropy protocols and the
second half of the data that does not contain any sampled points is
completely built using iterative soft thresholding. In some cases,
this allows a two-fold increase in resolution without affecting
acquisition time.
Optional Enhancements to Reduce Background
[0075] In general, when using the methods described herein there is
no need to purify or isolate the substrate metabolites that are
present in the sample from the other molecules in the sample (e.g.,
by chromatographic column (e.g., Sidelmann, et al. Purification and
1H NMR Spectroscopic Characterization of Phase II Metabolites of
Tolfenamic Acid Drug Metab Dispos Jun. 1, 1997 25:725-731) or by
other means well known to those of skill in the art). Thus, in
typical embodiments, the only purification performed in the method
prior to NMR is the separation of the metabolites into a water
soluble sample or a non-water soluble sample.
[0076] In some embodiments, the concentration of the unlabeled
substrate within the cell can be reduced for a period of time
(e.g., 10 minutes to 4 hours) before adding the labeled substrate.
This technique can help reduce background signal. The appropriate
time frame can be determined by testing a range of conditions and
monitoring background as compared to control cells not loaded with
labeled substrate.
Automated NMR Analysis
[0077] NMR metabolite analysis is tedious and complex. To
circumvent these problems, a custom "NMR Metabolite Arrays" program
was created to automate the process. As shown in FIG. 4, spectra
are automatically first phased, aligned, and normalized with a
spike-in control. For example, a known material, such as
4,4-dimethyl-4-silapentane-1-sulfonic acid (DSS), tetramethylsilane
(TMS), trimethylsilyl propionate (TSP),
4,4-dimethyl-4-silapentane-1-ammonium trifluoracetate (DSA) or
other NMR standard reference compound can be added into each
sample, and its concentration used as a reference to enable
relative quantification comparisons between samples. Next, using a
custom automatic peak picking program, peak lists can be generated
for each sample, where each resonance peak is converted into an X,
Y coordinate with an intensity value. A "MASTER PEAK LIST" program
that generates a "master" look up table for all the resonances in
the spectra under investigation can then be run. This program reads
all X, Y points from the individual peak list files, removes
duplicates within defined tolerances, and writes the resulting set
of peaks to a standard output such that all possible metabolite
resonances under investigation are determined. Depending on the
analysis it is possible to input the entire HSQC data from the
Human Metabolite Database into the master peak-list. However,
taking this approach requires longer computation times, and in most
cases is unnecessary. Thus, creating master look up tables for the
spectra that are specifically being investigated is preferred.
[0078] As further shown in FIG. 4, after creating the master look
up table, "NMR arrays" are next generated for each sample. NMR
arrays consist of a list of all possible metabolites and intensity
values for each resonance under investigation. They are created by
combining the individual test peak list and master peak list to
fill in the intensity for resonances for all possible metabolites.
If a metabolite is expressed in a test sample, the program will
select that intensity value. If it is not present, the intensity is
set at zero or an arbitrary number. The NMR arrays can then be
analyzed via traditional statistical analysis programs to identify
the differentially expressed resonances between spectra. The
resonance frequencies can then be uploaded directly into a
database, such as the Human Metabolome Database, to identify which
metabolites are differentially expressed. Candidate metabolites can
then be confirmed via additional NMR or M/S experiments.
Differential Expression Analysis
[0079] Also provided herein are novel methods for identifying
substrate metabolites differentially expressed by at least two
populations of cells, e.g., a first population of cells and a
second population of cells. One of the populations of cells can be
from a healthy subject or cell line and used as a control. The
methods can use the various features of the various method steps
and techniques described herein and include: (a) loading a first
and a second population of cells with a labeled substrate (e.g., a
.sup.13C, .sup.15N, or .sup.31P-labeled substrate); (b) culturing
the first and the second population of cells of step a) for a
sufficient period of time to allow metabolic breakdown of the
labeled substrate into substrate metabolites; (c) harvesting the
substrate metabolites from the first and the second population
cells of step (b) to obtain a sample of substrate metabolites from
each of the first and the second cell populations, (d) performing
multidimensional NMR on the samples from each of the first and the
second cell populations to determine the resonance spectra of the
metabolized substrate, wherein the resonance spectra represents the
metabolites of the substrate; and (e) processing the resonance
spectra using a custom "NMR arrays" program (f) comparing the
resonance intensity of the first population of cells with the
resonance spectra of the second population of cells to determine
which resonance spectra are differentially expressed, wherein the
differentially expressed resonance spectra represents
differentially expressed metabolites.
[0080] In some embodiments, the methods for identifying
differentially expressed substrate metabolites between at least two
populations of cells can further include comparing the resonance
spectra of step (f) with a data base of known resonance spectra to
determine the molecular structure(s) that the resonance spectra
represents, and thereby determine which specific substrate
metabolites are differentially expressed between the first and
second, different population of cells. Specific metabolites may be
identified in this manner.
[0081] The methods described herein are useful for monitoring
metabolism in any cell type, as well as in any tissue (e.g., the
cell population can contain a heterogeneous population of cells),
and are useful for monitoring metabolism in cells exhibiting any
phenotype as compared to a cell not exhibiting the phenotype.
Diagnostic Applications
[0082] Identified differentially expressed metabolites are
indicative of the different phenotype, and their expression can
therefore be used to diagnose this phenotype, e.g. to diagnose
increased metastatic potential, or to diagnose insulin resistance,
or to diagnose disease, etc.
[0083] We discovered N-acetylneuraminic acid (NANA) is more highly
expressed in breast cancer tumor initiating cells. From a
diagnostic point of view, the presence of excess NANA can serve as
a biomarker for tumorigenic potential. Once such differential
expression is observed, detection methods such as antibodies or
mass spectrometry can be utilized to monitor NANA expression (e.g.,
intracellularly or extracellularly) to help identify aggressive
tumors. In summary, using the methods described herein for breast
cancer cells, the specific breakdown of glucose was followed and
NANA was discovered to be widely upregulated in more malignant
cells. Enzymes were identified in NANA biosynthesis as new
therapeutic targets, and the expression levels of the molecule was
found to correlate with increased migratory potential.
[0084] The novel methods can also be applied to patient biofluids
(e.g., blood, urine, plasma, and tissue samples) to discover
metabolic differences that can serve as novel biomarkers for a
particular disease.
Methods of Determining Candidate Therapeutic Agents
[0085] In various embodiments, the methods for identifying
substrate metabolites differentially expressed by at least two
populations of cells can further include the step of identifying a
biosynthetic pathway involved in the generation of the substrate
metabolites and identifying proteins/enzymes of the pathway that
can be targeted to modulate the differential expression of the
metabolite. In turn, these proteins and enzymes can serve as
candidate targets for modulating the phenotype of the cells, e.g.,
a disease phenotype, metastatic potential, or resistance. Thus, the
new methods provide a means for identifying therapeutic targets.
Once the metabolite is identified and an NMR metabolite array has
been created for a given sample, databases of biosynthetic pathways
can be screened to identify the pathway of synthesis of the
metabolite.
[0086] For example, the NMR metabolite array can be electronically
linked to the Human Metabolome Database and/or ChemPub, to select
possible therapeutic targets within the metabolite biosynthetic
pathways and propose substrate-based inhibitors using the
metabolite itself as a lead scaffold for drug design. A series of
differentially expressed metabolites that can serve as biomarkers
have been identified and described herein. Novel therapeutic
targets within the biosynthetic pathways as well as FDA-approved
drugs that show efficacy in the laboratory that could be rapidly
translated into new applications in the clinic have also been
identified.
[0087] The cell populations used in the various methods described
herein can be from healthy or diseased subjects. The cells can be
from isogenic populations. In some embodiments, the first
population of cells and the second population of cells have
different phenotypes (e.g., differ in metastatic potential, differ
in response to insulin, or differ in expression of disease genes).
Differential metabolites expressed in any phenotype can be assessed
as compared to an isogenic cell that does not exhibit the
phenotype. Phenotypes are easily identified by those skilled in the
art, and include but are not limited to phenotypes associated with
a particular disease or disorder.
[0088] In some embodiments, the first population of cells is a
control population of cells and the second population of cells has
been contacted with a test compound or agent (e.g., after treatment
with the compound or agent). The disappearance of differentially
expressed metabolites that are associated with a particular
phenotype serves as an indicator that the test compound or agent is
capable of inhibiting the phenotype, for example, inhibiting
metastasis, or inhibiting the effects of the expression of a
diseased gene. Alternatively, the appearance of a differentially
expressed metabolite (e.g., one expressed only in normal cells as
opposed to diseased cells) serves as an indicator that the compound
or agent is useful for treatment of the disease. Thus, in various
embodiments, the methods for identifying differentially expressed
metabolites can be used to screen for compounds or agents that
modulate a phenotype, which can be used for treatment of
disease.
[0089] The metabolic consequences of overexpressing or inhibiting a
gene of interest can be identified using the new methods described
herein. In one embodiment, the method further comprises inhibiting
or overexpressing a gene in one of the cell populations (e.g., the
second population of cells). Similarly, the metabolic consequences
of particular compounds or agents, e.g., to assess toxicity, also
can be identified.
[0090] The test compounds or agents can be, for example, a small
molecule, a nucleic acid RNA (e.g., siRNA or microRNA), a nucleic
acid DNA, a protein, a peptide, or an antibody. The inhibitors can
be selected from the group consisting of: a small molecule, a
nucleic acid RNA (e.g., siRNA), a nucleic acid DNA, a protein, a
peptide, and an antibody. In one embodiment, the inhibitor is an
inhibitor of an enzyme (e.g., a neuraminidase inhibitor).
Methods of Treating Disorders with Therapeutic Agents
[0091] As described in the examples below, through the methods
described herein, CMAS, NANS (also known as sialic acid synthase),
and NANA cell-surface expression have been determined to be
therapeutic targets that decrease migration of cancer cells and
prevent tumor initiation in vivo. Thus, in another aspect, the
present disclosure includes new methods for treating disorders such
as cancer (e.g., by inhibiting metastasis and/or blocking tumor
initiation) in a subject by administering an effective amount of an
inhibitor of the targets discovered using the new methods described
herein. In particular, the methods include administering to a
subject in need thereof a therapeutically effective amount of an
inhibitor of CMAS or NANS, or a therapeutically effective amount of
an inhibitor or agent that lowers NANA expression. For example we
have identified candidate CMAS inhibitors including a molecule we
designed and synthesized, termed F-NANA, and already FDA approved
influenza drugs, including Relenza and Tamiflu. The efficacy of
these drugs in in vivo mouse models is being tested and because
Relenza and Tamiflu have already been evaluated for safety in human
subjects accelerated FDA approval for an investigational new drug
is possible.
Application of the New Methods to Personalized Medicine
[0092] As mentioned, the new methods described herein have been
successfully shown to characterize the metabolic differences in
several oncology models using both cell lines and primary tissue.
The methods have the potential to profoundly affect the strategies
for designing novel therapeutic intervention and could be lay the
foundation for a metabolite-based approach for "personalized
medicine."
[0093] According to the National Institutes of Health,
"personalized medicine" is a practice of medicine that uses an
individual's genetic profile to guide customized decisions made
with regard to the prevention, diagnosis and treatment of disease
in that individual. To date, most efforts rely on genomic
information to identify DNA mutations, amplifications, or
deletions. Rarely, however, is a disease the result of a single
genetic lesion, and it is often not obvious how genetic variations
will manifest themselves. However, non-genetic changes, including
epigenetic differences, can also have profound effects on gene
expression and cellular properties. Further, many commonly mutated
genes, such as in cancer, do not have small molecule inhibitors and
are often termed "undruggable targets." Establishing the metabolic
profile of key cells in an individual suffering from a disease,
with the methodology described herein, could provide powerful
information useful in diagnosing, treating and monitoring the
disease state of an individual.
[0094] As noted, diseases are incredibly complex and heterogeneous,
and the effect of a misregulated gene or genes is not always
obvious; making it difficult to design the best therapeutic
strategies for intervention. Metabolism on the other hand is the
end product of the genome. Using the new platforms described
herein, the metabolic differences in a specific patient can quickly
highlight functioning or non-functioning pathways in that
individual. Further, metabolic pathways have been extensively
studied and in many cases inhibitors for metabolic enzymes, such
antimetabolites, substances bearing a close structural resemblance
to the natural metabolite, already exist and are already used in
the clinic. Examples of such potential therapeutic discoveries
based on the differential expression NMR analysis are also
presented in detail below in the Examples. The aforementioned
differential expression, for example of metabolites, can be a
powerful tool for diagnosing, monitoring, and treating disease in a
patient on an individual, customized basis. Examples of analysis
using this protocol to deduce novel metabolism pathways and
differential metabolite expression between wildtype and disease
tissue are described herein.
EXAMPLES
[0095] The invention is further described in the following
examples, which do not limit the scope of the invention described
in the following examples, which do not limit the scope of the
invention described in the claims.
[0096] The following examples discuss the novel protocols and the
subsequent novel analysis methods that are used to efficiently
determine the differential expression of metabolites in normal and
disease state tissue. The results of such differential expression
are described herein and are shown to be utilized in the design and
identification of potential small molecule therapeutics.
Example 1: Methodology for the Screening Platform Utilizing NMR
[0097] Preparation of Biological Sample:
[0098] FIG. 1 provides an overall description of the platform.
Approximately 20 million cells were used for each sample (however
it is possible to use as few as 2 million). Before harvesting,
.sup.13C-labeled precursors (glucose and glutamine in these
examples) were added directly to the media and allowed to incubate
for a user-defined amount of a time and in this case 4 hrs. After
aspirating the media and washing two times with phosphate-buffered
saline (PBS), the cells were again counted and collected. An equal
number of cells with no label were also harvested to serve as a
.sup.13C background control. Cells were lysed by the addition of
ice-cold methanol, and an aqueous extraction was performed by
adding equal parts water and chloroform. After centrifugation the
water soluble and organic metabolites were separately collected,
dried, and stored until ready to be further analyzed. No additional
purification was performed.
[0099] Acquisition of Data with NMR:
[0100] 2D NMR spectroscopy was employed primarily relying on
heteronuclear single quantum correlation (HSQC) to identify
metabolites. HSQC experiments provide one-bond correlation between
a heteronucleus (.sup.13C in the following examples, although other
isotopes are feasible) and a proton. Crosspeaks arise due to
transfer through the relatively large one-bond heteronuclear
coupling, making it possible to identify shifts of directly
attached nuclei. The unique chemical environment of each carbon
atom paired to a proton gives rise to characteristic chemical
shifts specific to a given metabolite. Reference HSQC spectra of
purified metabolites (commonly available) were used for comparison.
For example, at present the Human Metabolome Database (HMDB)
contains information on 40,260 metabolite entries many with HSQC
data.
[0101] To overcome the traditional drawbacks of 2D NMR metabolite
profiling (large sample requirements and long acquisition times),
several additional techniques were used to improve the resolution
and reduce the time required for the analysis. As described above,
in a first step, to decrease the amount of material needed cells
were supplemented with .sup.13C-labeled precursors (glucose,
glutamine, pyruvate and amino acids were used but other substrates
and other isotopes are also possible). Theoretically, this should
decrease the cell number required by a factor of .about.100. Partly
due to varying ionic strengths, this does not always scale
perfectly linearly and if there are no constraints regarding the
sample size (i.e., using cell lines), it is recommended here that
about 2-20 million cells be used for each analysis.
Example 2: Rapid Ultra High Resolution NMR Data Acquisition
[0102] Folding the Spectra:
[0103] While the aforementioned sample preparation alleviated the
physical demands on the amount sample, the long acquisition time
required to record high resolution 2D NMR spectroscopy was still a
concern. To combat this, a multi-prong approach was taken:
"folding" the spectra width, using random phase sampling (RPS),
implementing non-uniform sampling (NUS) techniques and data
extension in the analysis.
[0104] As described above, the spectral width (sw) is the range of
frequencies over which NMR signals are to be detected. Metabolite
mixtures contain diverse molecules, and the spectral width
necessary to cover all potential carbon chemical shifts spans over
-220 ppm. In FIG. 2A, the HSQC spectra for all water soluble
metabolites in KRAS mutant pancreatic cancer cells is shown. In
this instance the 13C-sw spans 220 ppm and a total 1024 points were
collected. By solving the equation, resolution=SW/TD (where TD is
the total data points), we observed the resolution is limited to
.about.107 Hz/point. However, if you examine FIG. 2A closer, the
majority of metabolites run along a diagonal and most the spectrum
is empty. Collecting points along the entire sw greatly diminishes
resolution, and as acquisition time is the reciprocal of
resolution, experimental time is also wasted. As such we gradually
decreased the sw to increase the resolution. FIG. 2B shows an HSQC
of the same sample with 140 ppm sw and as a result resolution of
.about.68 Hz/point, FIG. 2C with sw of 110 ppm and as a result 54
Hz/point resolution and FIG. 2D with 90 ppm sw generates ultra-high
resolution of .about.44 Hz/point. In each of the folded spectra the
aliased peaks are easily identifiable and the true chemical shifts
can be can be back calculated using the following equation
(.delta.obs=.delta.+sw). Of note, the maximum resolution due to
C--C scalar coupling is .about.35 Hz. Using our folding strategy we
are able to obtain ultra-high resolution spectra.
[0105] Of note, to provide optimal flip angles uniformly across the
large carbon spectral width, broad band adiabatic shaped pulses
were utilized for all 180 degree pulses along the carbon channel.
This is especially important for enabling efficient coherence
transfer among scalar coupled spins.
[0106] Non Uniform Sampling and Extension of Data:
[0107] The measured "free-induction decay" (FID) of an NMR sample
is created by the oscillating current generated by the precession
of all magnetized bonds. This signal decays due to nuclei in other
molecules creating spin-spin decoherence. The rate at which this
occurs is known as the transverse relaxation rate (T2). For any NMR
experiment it is widely viewed that to obtain maximum resolution
one should collect points in the indirect dimension close to
1.2*T2. However, metabolites move rapidly with molecular motion
correlation times on the average of 10.sup.-12 to 10.sup.-11 sec.
Due to this rapid movement, for many metabolites there is little
spin-spin decoherence and the T2 rates are almost infinitely long.
Thus collecting ultra-high resolution metabolite data is
theoretically possible but in practice it would require extremely
long measurement times and in most experiments only a subset of
data is collecting sacrificing resolution for speed.
[0108] By employing non-uniform sampling (NUS) techniques that are
outlined in FIG. 2E we were able to not only greatly increase the
resolution of our data but also increase the speed at which we
recorded high resolution spectra. For example using the same sample
we could perform a uniformly sampled experiment with 128 indirect
points in 5 hours with X resolution, using NUS we could collect 10%
of 1024 points and in equivalent time to generate a spectra with
8.times. resolution, alternatively we could sample 10% of 512
points increasing the resolution by 4.times. and decreasing the
acquisition time by .about.40%.
[0109] The Poisson-gap distribution was selected for the sampling
schedule followed by forward maximum (FM) entropy reconstruction.
Metabolite mixtures contain molecules at various concentrations,
and this has been shown to be the most effective method in
detecting weak peaks. In addition, to further enhance our
resolution we created a "data-extension" add-on, in which before
reconstruction the total number of points in the indirect dimension
is artificially doubled. The first half of the NUS data set is
reconstructed using the sparsely sampled data and filling in the
missing points according to FM reconstruction. The second half of
the data is completely built using iterative soft thresholding. As
shown in FIG. 2F this increased our resolution by 2-fold without
affecting acquisition time.
[0110] Analysis of Metabolites in the Water and the Organic
Layer:
[0111] While it is not necessary to follow each step, this method
allows for a full metabolic profile of both water soluble or
organic metabolites. FIG. 3 shows the HSQC of water based
metabolites (FIG. 3A) and organic based metabolites (FIG. 3B) from
the same million p53 deficient lung cancer cells and each
experiment required only 1 hour of acquisition time. Equivalent
spectra using 13C-natural abundance and standard NMR techniques for
the same sample amount would require several days. Both spectra are
extremely well resolved, making it easy to identify metabolite
resonance peaks. Importantly many M/S methods have struggled to
accurately detect lipids, using our method, as shown in FIG. 3B,
the resonances from organic molecules are readily identifiable.
Example 3: Analyzing Metabolite NMR Data
[0112] NMR Analysis: As summarized in FIG. 4, a custom "NMR
Metabolite Array" program was created to automate the NMR analysis
process using the method herein described. First, the spectra are
phased, aligned, and scaled with an internal control. 1 mM
4,4-dimethyl-4-silapentane-1-sulfonic acid (DSS) was added into
each sample, and its concentration was used as a reference to allow
relative quantification comparisons between samples to be made. We
created an automatic peak picking program to generate peak lists
for each sample, where each resonance peak was converted into an X,
Y coordinate with an intensity value. Next we created a "MASTER
PEAK LIST" program that generates a "master" look up table for all
the resonances in the spectra under investigation and was
subsequently run.
[0113] In short, this program reads all X, Y points from the
individual peak list files, removes duplicates within defined
tolerances and writes the resulting set of peaks to a standard
output. Depending on the analysis it is possible to input the
entire HSQC data from the Human Metabolite Database into the master
peak-list. Taking this approach requires longer computation times,
and in most cases is unnecessary. Creating master look up tables
for the spectra that are specifically being investigated is
preferred.
[0114] Next, NMR arrays are generated for each sample in which the
individual peak list and master peak list were combined to fill in
the intensity for resonances for all possible metabolites. If a
metabolite is expressed in a test sample, the program will select
that intensity value. If it is not present, the intensity is set at
zero or an arbitrary number. The NMR arrays can now be analyzed via
traditional statistical analysis programs to identify the
differentially expressed resonances between spectra. The resonance
frequencies can then be uploaded directly into the Human Metabolome
Database to identify which metabolites are differentially
expressed. Candidate metabolites can then be confirmed via
additional NMR or M/S experiments.
Example 4: Analysis of Differentially Expressed Metabolites: A
Novel Approach
[0115] Background Correction:
[0116] To monitor the flux of a given precursor, a separate spectra
with an equal number of cells with no labeled precursor was
recorded. The spectra from the unlabeled cells represent the
.sup.13C background within the cell and can be subtracted from the
test spectra to specifically follow the metabolic breakdown of the
.sup.13C labeled substrate. Glucose and glutamine are two of the
main energy sources within a cell, and the metabolic breakdown of
each precursor is well characterized. To examine the flux of
glucose and glutamine into specific pathways, .sup.13C-.sup.1H HSQC
spectra of equal number of breast tumor initiating cells with no
labeled precursor and either .sup.13C-glutamine or .sup.13C-glucose
added as substrate were recorded. As shown in FIG. 5A with this
limited number of cells, very few signals arise from the .sup.13C
background, and the glutamine (5B) and glucose (5C) spectra are
quite distinct. By creating NMR arrays we were able to convert the
complex NMR data into standard text files. The intensity values for
resonances in the unlabeled sample were subtracting from the
matching signals in the glutamine or glucose arrays. As shown in
FIG. 6, by plotting the intensity value and resonance metabolite ID
from the NMR array it is possible to identify changes specific to
glucose or glutamine flux through a given sample. The X-axis lists
all the resonance metabolite ids for every metabolite identified
from the HSQC spectra of FIG. 6b and FIG. 6c. The Y-axis highlights
how the intensity of each resonance changes in each condition. As
expected, it is clear glutamine and glucose cause flux into
different metabolic pathways. The resonance metabolite IDs
correspond to specific .sup.13C-.sup.1H chemical shifts which can
uploaded into the Human Metabolome Database to identify the
differential metabolites.
Example 5: Identifying Differentially Expressed Metabolites in
Triple Negative Breast Cancer Tumor Initiating Cells
[0117] Protocol:
[0118] Originating from the same normal breast tissue, BPLER and
HMLER cells were transformed with identical genetic factors but
were propagated in different culture media. BPLER are highly
tumorigenic and have an increased metastatic potential over that of
HMLER cells. Less than 50 BPLER cells injected into the mammary fat
pad of a mouse result in the development of a tumor, while more
than 10 .sup.6 HMLER cells are required to form a tumor in vivo
(Table 2, below). BPLER cells are a model cell line for triple
negative breast cancer tumor initiating cells, and BPLER tumors
histologically resemble that of triple negative breast cancer
patients. According to the protocol about 20 million BPLER and
HMLER cells were cultured in the presence of uniformly labeled
.sup.13C-glucose, and subsequently harvested and lysed. The aqueous
layer was then collected, dried, and re-dissolved in ultra-pure D2O
and ready for NMR analysis. The organic layer was stored for future
examination.
TABLE-US-00002 TABLE 2 Tumors Formed Cells BPLER HMLER MCF7 5
.times. 10.sup.4 4/4 0/4 0/4 5 .times. 10.sup.3 4/4 0/4 0/4 5
.times. 10.sup.2 4/4 0/4 0/4 5 .times. 10 4/4 0/4 0/4
[0119] Using the new platform methodology described herein, the
rapid, unbiased, ultra-high resolution NMR metabolite screening was
performed. Examples of resulting .sup.13C-.sup.1H HSQC for BPLER
and HMLER cells are shown in FIGS. 7A and 7B respectively.
[0120] Results:
[0121] Using our custom NMR analysis program, the resonances in
each spectra were converted into NMR arrays. FIG. 7C summarizes the
information. By combining all replicates from both cell lines
approximately .about.2100 resonances were identified. The
metabolite ID of each resonance is listed on the X-axis. The
relative intensity of each resonance is plotted on the y-axis. From
this analysis we observed a high degree of similarity between the
metabolite resonances of each cell line (>75% of the resonances
were present in both cell lines). However several resonance peaks
that were common to both cell lines had varied expression, while
some peaks were unique to HMLER cells and others specific to BPLER.
To confirm the arrays accurately reflected the HSQC data, as shown
in FIG. 7D the region of the HSQC spectra that was predicted to
have resonances specific to BPLER cells in the NMR array was
expanded and indeed resonances were only found in the BPLER
spectra.
[0122] Using the NMR arrays we were able to quickly identify
resonances that were specifically enriched in BPLER tumor
initiating cells. Table 3 highlights the top resonances most
enriched in BPLER tumor initiating cells. Shown are the metabolite
IDs from the array, as well as the corresponding .sup.13C-.sup.1H
data.
TABLE-US-00003 TABLE 3 Metabolite Resonance ID 13C 1H 589 62.993
3.842 1951 65.98 3.823 283 68.633 4.248 1381 69.973 4.002 402
42.028 2.193 1602 72.951 3.739 245 72.849 3.953 1333 42.015 1.814
119 106.501 6.151
[0123] These resonances were input into the Human Metabolome
Database and 6 of the 9 resonances, highlighted in yellow were
predicted to be from N-acetylneuraminic acid (NANA), strongly
suggesting NANA is the metabolite corresponding to the
differentially expressed resonances identified in the NMR
arrays.
[0124] Several additional steps were taken to confirm NANA is
indeed upregulated in BPLER tumor initiating cells. First,
.sup.13C-.sup.1H HSQC of pure NANA shown in FIG. 8A contains cross
peaks at approximately the same location as those found
over-represented in the BPLER spectra. Second, we designed custom
NMR pulse programs to specifically examine NANA. In NANA
biosynthesis the C2 of glucose and a nitrogen atom of glutamine are
joined to form a carbon-nitrogen bond. As such BPLER cells were
incubated with .sup.13C--C2 glucose and .sup.15N-glutamine, and a
HCN experiment was recorded to detect metabolites resonances that
contain a hydrogen, connected to a carbon, that is also connected
to a nitrogen atom (HCN). In this experiment, as shown in FIG. 8B
BPLER cells contain a differentially expressed resonance at the
same position as would be expected in the NANA standard. Lastly,
mass spectrometry experiments shown in FIG. 8C, confirmed NANA is
approximately 7-fold higher in BPLER tumor initiating cells by
performing multiple reaction monitoring LC/MS using electrospray in
the negative mode. The reported values are the area under the curve
for NANA expression in each cell line.
[0125] Using Results of Differentially Expressed Metabolites to
Develop Diagnostics:
[0126] By following glucose flux within BPLER cells (i.e.
subtracting background .sup.13C and tracing specific breakdown of
glucose), the tumor initiating cells were observed to divert part
of their glucose metabolism to NANA production. NANA is 9-carbon
sugar that is often incorporating onto the cell surface of
glycoproteins. Previous reports identified that wheat-germ
agglutinin (WGA) has a strong affinity for NANA-modified proteins.
Using rhodamine labeled WGA, we preformed immune-fluorescent
microscopy shown in FIG. 9 and observed BPLER cells have increased
NANA expression on their cell surface. Thus, NANA itself represents
a novel diagnostic to specifically identify breast tumor initiating
cells, and WGA, and similar molecules that specifically recognize
NANA and NANA modified molecules could provide new tools to detect
& isolate tumor initiating cells or those with increased
malignant potential.
Example 6: Identification of a Target Utilizing the Differential
NMR Data
[0127] NANA is a sugar that is often incorporated onto cell surface
proteins. Shown in FIG. 10 NANA is derived from glucose by about 15
distinct enzymes including key enzymes NANS and CMAS. Using
CelTiterGlo, a well-known assay for cell viability, it was observed
that knockdown of NANS or CMAS had little to no effect on cell
viability or proliferation of the cells (FIG. 11), whereas
knockdown of PLK1 enzyme lead to total cell death.
[0128] However, NANA is incorporated on the cell surface of several
proteins involved in cell adhesion, and loss of NANA was suspected
to affect cell motility. Using a cell migration assay, cells were
cultured in a dual-chamber containing small pores at the bottom of
the top chamber, malignant cells (especially those with metastatic
potential) are able to migrate through the pores and form colonies.
As expected, shown in FIG. 12 BPLER have an increased migration
rate as compared to HMLER cells due to their increased turmorigenic
properties. However, the knockdown of NANS or CMAS completely
abolished the cells' ability to migrate to the lower chamber, while
cells transfected with control siRNAs maintained normal migration.
To confirm NANA expression directly influences motility, a rescue
experiment was performed in which cells transfected with siRNAs
against NANS or CMAS were supplemented NANA. In the presence of
NANA they were able to partially restore the migration phenotype.
These results of several migration studies are quantified in FIG.
12B. These results suggest NANA expression is crucial for cell
migration and could be important for metastasis. Monitoring NANA
expression could predict the metastatic potential of a cell. In
addition both NANS/CMAS are novel key targets to manipulate
migration and metastasis.
Example 7: CMAS Increase Cell Migration
[0129] As mentioned, the knockdown of NANS and CMAS, key enzymes
used to generate and attach NANA to proteins, had no effect on cell
proliferation but greatly reduced the ability of BPLER cells to
migrate (shown in FIG. 12). While the mRNA level of CMAS and NANS
was equivalent in HMLER and BPLER cells, we observed the protein
expression of CMAS is dramatically over expressed in BPLER
tumor-initiating cells (FIG. 13). To determine if CMAS expression
contributes to the malignant phenotype of tumor initiating BPLER
cells, non-aggressive HMLER cells were transfected with a plasmid
to force the expression of CMAS. Using the previously described
cell migration assay, as shown in FIG. 14A HMLER cells with
enhanced CMAS expression dramatically increased their migration
potential by several fold compared to the control. The reciprocal
experiment was also performed and stable CMAS knockdown BPLER cells
(BPLER-shCMAS1) were created. As expected, shown in FIG. 14B these
cells were not able to form colonies in the same migration assay.
These results suggest CMAS protein expression is pivotal in cell
migration and/or metastasis. To date there have been no references
to the role of either NANS or CMAS in cancer. This could be in part
due to most techniques relying on sequencing and/or microarray
experiments that only examine mRNA levels. This highlights the
strength of our method. By probing metabolites, NANA was identified
as being expressed significantly higher in breast tumor-initiating
BPLER cells. By subsequently probing the enzymes in its
biosynthetic pathway, a novel, potent target that could be
important for tumorigenicity was discovered.
Example 8: CMAS Impacts Tumor Formation Initiation
[0130] To determine how loss of CMAS/NANA expression effects the
tumor initiation in vivo we performed the experiment outlined in
FIG. 15A. As described above stable CMAS knockdown BPLER cells
(BPLER-shCMAS1) were created that do not express CMAS protein. In
our experiment 500,000 BPLER cells over expressing an empty vector
(control) and 500,000 BPLER-shCMAS1 cells (which do not express
CMAS) were injected into the mammary fat pad of NOD/SCID mice. The
goal was to analyze differences in tumor size and the number of
metastasis between each group. Every three days for 45 days the
mice were examined for palpable tumors, and if detected the tumor
height, length and width were directly measured to calculate tumor
volume. As shown in FIG. 15B, within 45 days, 4/4 of the control
mice had developed large primary tumors. Amazingly, none (0/5) of
BPLER-shCMAS1 mice had any palpable tumors. Indeed after 90 days
the mice injected with BPLER-shCMAS1 cells remained tumor free.
Taken together the in vitro and in vivo data suggest that CMAS is a
completely novel and bona fide therapeutic target for cancer.
Example 9: Using the NMR Data to Identify and Design Therapeutic
Agents for Breast Cancer Therapy
[0131] Enzymes such as CMAS are ideal candidates for small molecule
drug inhibition. The enzyme mechanism of CMAS is FIG. 16A, where
CMAS activates the hydroxy group of NANA in a divalent cation
dependent manner, so that it can subsequently attack the
alpha-phosphate of an incoming cytidine triphosphate (CTP) molecule
to form a cytidine monophosphate-NANA (CMP-NANA) intermediate.
Using the NANA scaffold, a substrate-based analog replacing the
hydroxyl group with a fluorine was designed and synthesized (FIG.
16B). This substitution should theoretically maintain the ability
of NANA to bind CMAS however, prevent the enzymatic reaction. In
the presence of the CMAS inhibitor, using the previously described
cell migration assay, it was shown that BPLER cells are no longer
able to migrate (FIG. 16C). Using the NANA scaffold, we were able
to rapidly design and synthesize a substrate based inhibitor that
has effect in cell lines.
[0132] The F-NANA derivative synthesized had a slight chemical
likeness to the FDA approved drugs Relenza and Tamiflu (FIG. 17).
Relenza and Tamiflu are both designed to inhibit the influenza
enzyme neuraminidase. Neuraminidase specifically cleaves NANA
molecules on the cell-surface to facilitate viral entry into the
cell. Neuraminidase and CMAS share NANA as a substrate, and hence
these known influenza therapeutics were suspected to be inhibitors
of CMAS. As shown in FIG. 17b, Relenza treatment of BPLER cells
blocked cell migration. Both Relenza and Tamiflu are already FDA
approved, marketed therapeutics, and pending positive results in
mouse models, a rapid entry to clinical trial for cancer
indications.
[0133] Neuraminidase itself is known to remove NANA from the cell
surface. We suspected neuraminidase could be used to remove NANA
from the surface of malignant cells and just like siRNAs against
CMAS exert a similar effect on migration and tumor initiation.
Pre-incubation of BPLER cells with active neuraminidase enzyme
diminished NANA expression as determined by rhodamine labeled wheat
germ agglutinin (WGA) microscopy (FIG. 18A). In addition, BPLER
cells treated with neuraminidase were no longer able to migrate in
the migration assay (FIG. 18B). Neuraminidase and flu-like
molecules, including empty virions, may represent an innovative way
to both detect tumor-initiating cells (influenza virions have a
high affinity for NANA) and inhibit tumor initiation and metastasis
by removing NANA from tumor populations.
OTHER EMBODIMENTS
[0134] It is to be understood that while the inventions have been
described in conjunction with the detailed description thereof, the
foregoing description is intended to illustrate and not limit the
scope of the inventions, which are defined by the scope of the
appended claims. Other aspects, advantages, and modifications are
within the scope of the following claims.
Sequence CWU 1
1
41357PRTHomo sapiens 1Met Pro Leu Glu Leu Glu Leu Cys Pro Gly Arg
Trp Val Gly Gly Gln 1 5 10 15 His Pro Cys Phe Ile Ile Ala Glu Ile
Gly Gln Asn His Gln Gly Asp 20 25 30 Leu Asp Val Ala Lys Arg Met
Ile Arg Met Ala Lys Glu Cys Gly Ala 35 40 45 Asp Cys Ala Lys Phe
Gln Lys Ser Glu Leu Glu Phe Lys Phe Asn Arg 50 55 60 Lys Ala Leu
Glu Arg Pro Tyr Thr Ser Lys His Ser Trp Gly Lys Thr65 70 75 80 Tyr
Gly Glu His Lys Arg His Leu Glu Phe Ser His Asp Gln Tyr Arg 85 90
95 Glu Leu Gln Arg Tyr Ala Glu Glu Val Gly Ile Phe Phe Thr Ala Ser
100 105 110 Gly Met Asp Glu Met Ala Val Glu Phe Leu His Glu Leu Asn
Val Pro 115 120 125 Phe Phe Lys Val Gly Ser Gly Asp Thr Asn Asn Phe
Pro Tyr Leu Glu 130 135 140 Lys Thr Ala Lys Lys Gly Arg Pro Met Val
Ile Ser Ser Gly Met Gln145 150 155 160 Ser Met Asp Thr Met Lys Gln
Val Tyr Gln Ile Val Lys Pro Leu Asn 165 170 175 Pro Asn Phe Cys Gln
Cys Thr Ser Ala Tyr Pro Leu Gln Pro Glu Asp 180 185 190 Val Asn Leu
Arg Val Ile Ser Glu Tyr Gln Lys Leu Phe Pro Asp Ile 195 200 205 Pro
Ile Gly Tyr Ser Gly His Glu Thr Gly Ile Ala Ile Ser Val Ala 210 215
220 Ala Val Ala Leu Gly Ala Lys Val Leu Glu Arg His Ile Thr Leu
Asp225 230 235 240 Lys Thr Trp Lys Gly Ser Asp His Ser Ala Ser Leu
Glu Pro Gly Glu 245 250 255 Leu Ala Glu Leu Val Arg Ser Val Arg Leu
Val Glu Arg Ala Leu Gly 260 265 270 Ser Pro Thr Lys Gln Leu Leu Pro
Cys Glu Met Ala Cys Asn Glu Lys 275 280 285 Leu Gly Lys Ser Val Val
Ala Lys Val Lys Ile Pro Glu Gly Thr Ile 290 295 300 Leu Thr Met Asp
Met Leu Thr Val Lys Val Gly Glu Pro Lys Gly Tyr305 310 315 320 Pro
Pro Glu Asp Ile Phe Asn Leu Val Gly Lys Lys Val Leu Val Thr 325 330
335 Val Glu Glu Asp Asp Thr Ile Met Glu Glu Leu Val Asp Asn His Gly
340 345 350 Lys Lys Ile Lys Ser 355 21253DNAHomo sapiens
2cggcgaccgc gggctgacgt ggcggggctg gcgtgtgggt ctcgcagcgt tgctcacaga
60acagagtaga ggcggcggcg gcggcggccg gacccagact ggtagtgagg ctttggaccc
120cgagccgctg caatgccgct ggagctggag ctgtgtcccg ggcgctgggt
gggcgggcaa 180cacccgtgct tcatcattgc cgagatcggc cagaaccacc
agggcgacct ggacgtagcc 240aagcgcatga tccgcatggc caaggagtgt
ggggctgatt gtgctaagtt ccagaagagt 300gagctagaat tcaagtttaa
tcggaaagcc ttggagaggc catacacctc gaagcattcc 360tgggggaaga
cgtacgggga gcacaaacga catctggagt tcagccatga ccagtacagg
420gagctgcaga ggtacgccga ggaggttggg atcttcttca ctgcctctgg
catggatgag 480atggcagttg aattcctgca tgaactgaat gttccatttt
tcaaagttgg atctggagac 540actaataatt ttccttatct ggaaaagaca
gccaaaaaag gtcgcccaat ggtgatctcc 600agtgggatgc agtcaatgga
caccatgaag caagtttatc agatcgtgaa gcccctcaac 660cccaacttct
gcttcttgca gtgtaccagc gcatacccgc tccagcctga ggacgtcaac
720ctgcgggtca tctcggaata tcagaagctc tttcctgaca ttcccatagg
gtattctggg 780catgaaacag gcatagcgat atctgtggcc gcagtggctc
tgggggccaa ggtgttggaa 840cgtcacataa ctttggacaa gacctggaag
gggagtgacc actcggcctc gctggagcct 900ggagaactgg ccgagctggt
gcggtcagtg cgtcttgtgg agcgtgccct gggctcccca 960accaagcagc
tgctgccctg tgagatggcc tgcaatgaga agctgggcaa gtctgtggtg
1020gccaaagtga aaattccgga aggcaccatt ctaacaatgg acatgctcac
cgtgaaggtg 1080ggtgagccca aaggctatcc tcctgaagac atctttaatc
tagtgggcaa gaaggtcctg 1140gtcactgttg aagaggatga caccatcatg
gaagaattgg tagataatca tggcaaaaaa 1200aagtcttaaa aataaagtgc
cattctctga attctcaaaa aaaaaaaaaa aaa 12533432PRTHomo sapiens 3Met
Asp Ser Val Glu Lys Gly Ala Ala Thr Ser Val Ser Asn Pro Arg 1 5 10
15 Gly Arg Pro Ser Arg Gly Arg Pro Pro Lys Leu Gln Arg Asn Ser Arg
20 25 30 Gly Gly Gln Gly Arg Gly Val Glu Lys Pro Pro His Leu Ala
Ala Leu 35 40 45 Ile Leu Ala Arg Gly Gly Ser Lys Gly Ile Pro Leu
Lys Asn Ile Lys 50 55 60 His Leu Ala Gly Val Pro Leu Ile Gly Trp
Val Leu Arg Ala Ala Leu65 70 75 80 Asp Ser Gly Ala Phe Gln Ser Val
Trp Val Ser Thr Asp His Asp Glu 85 90 95 Ile Glu Asn Val Ala Lys
Gln Phe Gly Ala Gln Val His Arg Arg Ser 100 105 110 Ser Glu Val Ser
Lys Asp Ser Ser Thr Ser Leu Asp Ala Ile Ile Glu 115 120 125 Phe Leu
Asn Tyr His Asn Glu Val Asp Ile Val Gly Asn Ile Gln Ala 130 135 140
Thr Ser Pro Cys Leu His Pro Thr Asp Leu Gln Lys Val Ala Glu Met145
150 155 160 Ile Arg Glu Glu Gly Tyr Asp Ser Val Phe Ser Val Val Arg
Arg His 165 170 175 Gln Phe Arg Trp Ser Glu Ile Gln Lys Gly Val Arg
Glu Val Thr Glu 180 185 190 Pro Leu Asn Leu Asn Pro Ala Lys Arg Pro
Arg Arg Gln Asp Trp Asp 195 200 205 Gly Glu Leu Tyr Glu Asn Gly Ser
Phe Tyr Phe Ala Lys Arg His Leu 210 215 220 Ile Glu Met Gly Tyr Leu
Gln Gly Gly Lys Met Ala Tyr Tyr Glu Met225 230 235 240 Arg Ala Glu
His Ser Val Asp Ile Asp Val Asp Ile Asp Trp Pro Ile 245 250 255 Ala
Glu Gln Arg Val Leu Arg Tyr Gly Tyr Phe Gly Lys Glu Lys Leu 260 265
270 Lys Glu Ile Lys Leu Leu Val Cys Asn Ile Asp Gly Cys Leu Thr Asn
275 280 285 Gly His Ile Tyr Val Ser Gly Asp Gln Lys Glu Ile Ile Ser
Tyr Asp 290 295 300 Val Lys Asp Ala Ile Gly Ile Ser Leu Leu Lys Lys
Ser Gly Ile Glu305 310 315 320 Val Arg Leu Ile Ser Glu Arg Ala Cys
Ser Lys Gln Thr Leu Ser Ser 325 330 335 Leu Lys Leu Asp Cys Lys Met
Glu Val Ser Val Ser Asp Lys Leu Ala 340 345 350 Val Val Asp Glu Trp
Arg Lys Glu Met Gly Leu Cys Trp Lys Glu Val 355 360 365 Ala Tyr Leu
Gly Asn Glu Val Ser Asp Glu Glu Cys Leu Lys Arg Val 370 375 380 Gly
Leu Ser Gly Ala Pro Ala Asp Ala Cys Ser Thr Ala Gln Lys Ala385 390
395 400 Val Gly Tyr Ile Cys Lys Cys Asn Gly Gly Arg Gly Ala Ile Arg
Glu 405 410 415 Phe Ala Glu His Leu Leu Met Glu Lys Val Asn Asn Ser
Cys Gln Lys 420 425 430 41741DNAHomo sapiens 4gatcgggcgg cgccgagctg
aggtggtgag ggactagctc ccggatgtgg agaagctggg 60gagaaggcgt gggaggaaga
tggactcggt ggagaagggg gccgccacct ccgtctccaa 120cccgcggggg
cgaccgtccc ggggccggcc gccgaagctg cagcgcaact ctcgcggcgg
180ccagggccga ggtgtggaga agcccccgca cctggcagcc ctaattctgg
cccggggagg 240cagcaaaggc atccccctga agaacattaa gcacctggcg
ggggtcccgc tcattggctg 300ggtcctgcgt gcggccctgg attcaggggc
cttccagagt gtatgggttt cgacagacca 360tgatgaaatt gagaatgtgg
ccaaacaatt tggtgcacaa gttcatcgaa gaagttctga 420agtttcaaaa
gacagctcta cctcactaga tgccatcata gaatttctta attatcataa
480tgaggttgac attgtaggaa atattcaagc tacttctcca tgtttacatc
ctactgatct 540tcaaaaagtt gcagaaatga ttcgagaaga aggatatgat
tctgttttct ctgttgtgag 600acgccatcag tttcgatgga gtgaaattca
gaaaggagtt cgtgaagtga ccgaacctct 660gaatttaaat ccagctaaac
ggcctcgtcg acaagactgg gatggagaat tatatgaaaa 720tggctcattt
tattttgcta aaagacattt gatagagatg ggttacttgc agggtggaaa
780aatggcatac tacgaaatgc gagctgaaca tagtgtggat atagatgtgg
atattgattg 840gcctattgca gagcaaagag tattaagata tggctatttt
ggcaaagaga agcttaagga 900aataaaactt ttggtttgca atattgatgg
atgtctcacc aatggccaca tttatgtatc 960aggagaccaa aaagaaataa
tatcttatga tgtaaaagat gctattggga taagtttatt 1020aaagaaaagt
ggtattgagg tgaggctaat ctcagaaagg gcctgttcaa agcagacgct
1080gtcttcttta aaactggatt gcaaaatgga agtcagtgta tcagacaagc
tagcagttgt 1140agatgaatgg agaaaagaaa tgggcctgtg ctggaaagaa
gtggcatatc ttggaaatga 1200agtgtctgat gaagagtgct tgaagagagt
gggcctaagt ggcgctcctg ctgatgcctg 1260ttctactgcc cagaaggctg
ttggatacat ttgcaaatgt aatggtggcc gtggtgccat 1320ccgagaattt
gcagagcaca tttgcctact aatggaaaag gttaataatt catgccaaaa
1380atagaaatta gcgtaatatt gagaaaaaaa tgatacagcc ttcttcagcc
agtttgcttt 1440tatttttgat taagtaaatt ccatgttgta atgttacaga
gagtgtgatt tggtttgtga 1500tatatatata ttgtgctcta cttttctctt
tacgcaagat aattatttag agactgatta 1560cagtctttct cagattttta
gtaaatgcaa gtaagaacat catcaaagtt cactttgtat 1620tgtaccctgt
aaaactgtgt gtttgtgtgc tttcaaagat gttgggattt tatttatctg
1680gggacagtgt gtatggtaag acatgccctt ctattaataa aactacattt
ctcaaacttg 1740a 1741
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