U.S. patent application number 13/908791 was filed with the patent office on 2014-01-09 for methods for the survey and genetic analysis of populations.
This patent application is currently assigned to TAXON BIOSCIENCES, INC.. The applicant listed for this patent is TAXON BIOSCIENCES, INC.. Invention is credited to Matthew Ashby.
Application Number | 20140011692 13/908791 |
Document ID | / |
Family ID | 26891630 |
Filed Date | 2014-01-09 |
United States Patent
Application |
20140011692 |
Kind Code |
A1 |
Ashby; Matthew |
January 9, 2014 |
METHODS FOR THE SURVEY AND GENETIC ANALYSIS OF POPULATIONS
Abstract
The present invention relates to methods for performing surveys
of the genetic diversity of a population. The invention also
relates to methods for performing genetic analyses of a population.
The invention further relates to methods for the creation of
databases comprising the survey information and the databases
created by these methods. The invention also relates to methods for
analyzing the information to correlate the presence of nucleic acid
markers with desired parameters in a sample. These methods have
application in the fields of geochemical exploration, agriculture,
bioremediation, environmental analysis, clinical microbiology,
forensic science and medicine.
Inventors: |
Ashby; Matthew; (Mill
Valley, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
TAXON BIOSCIENCES, INC. |
TIBURON |
CA |
US |
|
|
Assignee: |
TAXON BIOSCIENCES, INC.
Tiburon
CA
|
Family ID: |
26891630 |
Appl. No.: |
13/908791 |
Filed: |
June 3, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13019045 |
Feb 1, 2011 |
8476016 |
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13908791 |
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10607077 |
Jun 25, 2003 |
8071295 |
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13019045 |
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09829855 |
Apr 10, 2001 |
6613520 |
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10607077 |
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60196258 |
Apr 11, 2000 |
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60196063 |
Apr 10, 2000 |
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Current U.S.
Class: |
506/9 |
Current CPC
Class: |
C12Q 1/6809 20130101;
C12Q 1/6869 20130101; C12Q 1/6809 20130101; C12Q 2525/151 20130101;
C12Q 2525/191 20130101; C12Q 2521/313 20130101; C12Q 2525/191
20130101; Y10T 436/21 20150115; C12Q 2525/131 20130101; C12Q 1/6888
20130101; C12Q 2600/156 20130101; C12Q 1/683 20130101; C12Q 1/683
20130101; G16B 30/00 20190201; G16B 20/00 20190201 |
Class at
Publication: |
506/9 |
International
Class: |
G06F 19/18 20060101
G06F019/18 |
Goverment Interests
[0002] This Invention was made with Government support under
Contract No. DE-FC36-01GO11016 awarded by the Department of Energy.
The Government has certain rights in this invention.
Claims
1-44. (canceled)
45. A culture-independent method of identifying an indicator for a
sample parameter, said parameter not including bacteria, said
method comprising the steps of: a. providing a plurality of samples
at least some of which samples contain the parameter, wherein each
sample is independently obtained from humans, animals, plants,
parasites, water, soil, air, foodstuffs, cell or tissue cultures;
b. isolating a plurality of genomic DNAs from each of the samples
provided in step a; c. isolating a plurality of rRNA gene segments
from each plurality of genomic DNAs isolated in step b; d.
determining the abundance of each of said rRNA gene segments in
each plurality of rRNA gene segments isolated in step c; e.
determining the abundance of the parameter in each of the samples
provided in step a; f. correlating the abundance of each rRNA gene
segment determined in step d with the abundance of the parameter
determined in step e; g. designating at least one rRNA gene segment
whose abundance correlates to the abundance of said parameter, as
determined in step f, as an indicator for the sample parameter.
46. A culture-independent method of identifying a microbial
indicator for a sample parameter, said parameter not including
bacteria, said method comprising the steps of: a. providing a
plurality of samples at least some of which samples contain the
parameter, wherein each sample is independently obtained from
humans, animals, plants, parasites, water, soil, air, foodstuffs,
cell or tissue cultures; b. isolating a plurality of genomic DNAs
from each of the samples provided in step a; c. isolating a
plurality of rRNA gene segments from each plurality of genomic DNAs
isolated in step b; d. determining the abundance of each of said
rRNA gene segments in each plurality of rRNA gene segments isolated
in step c; e. determining the abundance of the parameter in each of
the samples provided in step a; f. correlating the abundance of
each rRNA gene segment determined in step d with the abundance of
the parameter determined in step e; g. selecting at least one rRNA
gene segment whose abundance correlates to the abundance of said
parameter, as determined in step f; h. designating a microbe that
comprises said rRNA gene segment selected in step g as a microbial
indicator.
47. A culture-independent method of determining the abundance of a
sample parameter in an sample, said parameter not including
bacteria, comprising the steps of: a. providing at least one
sample, said sample being obtained from humans, animals, plants,
parasites, water, soil, air, foodstuffs, cell or tissue cultures;
b. isolating a plurality of genomic DNAs from said sample(s); c.
determining the abundance of an indicator identified in the method
of claim 45 in the plurality of genomic DNAs isolated in step b;
and d. inferring the abundance of the parameter in said sample(s)
based upon the abundance of said indicator in said sample(s).
48. A culture-independent method of determining the abundance of a
sample parameter in an sample, said parameter not including
bacteria, comprising the steps of: a. providing a first plurality
of samples at least some of which samples contain the parameter,
wherein each sample is independently obtained from humans, animals,
plants, parasites, water, soil, air, foodstuffs, cell or tissue
cultures; b. isolating a plurality of genomic DNAs from each of the
samples provided in step a; c. isolating a plurality of rRNA gene
segments from each plurality of genomic DNAs isolated in step b; d.
determining the abundance of each of said rRNA gene segments in
each plurality of rRNA gene segments isolated in step c; e.
determining the abundance of the parameter in each of the samples
provided in step a; f. correlating the abundance of each rRNA gene
segment determined in step d with the abundance of the parameter
determined in step e; g. selecting at least one rRNA gene segment
whose abundance correlates to the abundance of said parameter, as
determined in step f; h. providing a sample set of at least one
sample, said sample being obtained from humans, animals, plants,
parasites, water, soil, air, foodstuffs, cell or tissue cultures;
i. isolating a plurality of genomic DNAs from each sample of the
sample set provided in step h; j. determining the abundance of said
rRNA gene segment selected in step g in each plurality of genomic
DNAs isolated in step i; and k. inferring the abundance of the
parameter in each sample of the sample set provided in step h based
upon the abundance of said rRNA gene determined in step j in each
sample of the sample set provided in step h.
49. The method according to claim 45, wherein said rRNA gene
segment is a 16S rRNA gene segment.
50. The method according to claim 45, wherein the sample(s) is a
human or animal body tissue sample, a human or animal body fluid
sample, a cell culture sample or a tissue culture sample.
51. The method according to claim 50, wherein the parameter is
selected from the group consisting of a medical condition, an acute
disease state, a chronic disease state, a physiological state and
developmental state.
52. The method according to claim 45, wherein the correlation in
step g is expressible by an r-value selected from 1, 0.8 to 0.99,
0.5 to 0.7, or 0.3 to 0.4.
53. The method according to claim 46, wherein said rRNA gene
segment is a 16S rRNA gene segment.
54. The method according to claim 46, wherein the sample(s) is a
human or animal body tissue sample, a human or animal body fluid
sample, a cell culture sample or a tissue culture sample.
55. The method according to claim 54, wherein the parameter is
selected from the group consisting of a medical condition, an acute
disease state, a chronic disease state, a physiological state and
developmental state.
56. The method according to claim 46, wherein the correlation in
step g is expressible by an r-value selected from 1, 0.8 to 0.99,
0.5 to 0.7, or 0.3 to 0.4.
57. The method according to claim 47, wherein said indicator is a
16S rRNA gene segment.
58. The method according to claim 47, wherein the sample(s) is a
human or animal body tissue sample, a human or animal body fluid
sample, a cell culture sample or a tissue culture sample.
59. The method according to claim 58, wherein the parameter is
selected from the group consisting of a medical condition, an acute
disease state, a chronic disease state, a physiological state and
developmental state.
60. The method according to claim 47, wherein the abundance of said
indicator in said plurality of genomic DNAs is determined using PCR
or a hybridization assay.
61. The method according to claim 48, wherein said rRNA gene
segment is a 16S rRNA gene segment.
62. The method according to claim 48, wherein the sample(s) is a
human or animal body tissue sample, a human or animal body fluid
sample, a cell culture sample or a tissue culture sample.
63. The method according to claim 62, wherein the parameter is
selected from the group consisting of a medical condition, an acute
disease state, a chronic disease state, a physiological state and
developmental state.
64. The method according to claim 48, wherein the correlation in
step g is expressible by an r-value selected from 1, 0.8 to 0.99,
0.5 to 0.7, or 0.3 to 0.4.
65. The method according to claim 48, wherein the determination in
step j, of the abundance of said rRNA gene segment, is done using
PCR or a hybridization assay.
Description
[0001] This application is a divisional application of U.S. patent
application Ser. No. 13/019,045, filed Feb. 1, 2011 (now U.S. Pat.
No. 8,476,016), which is a continuation application of U.S. patent
application Ser. No. 10/607,077, filed Jun. 25, 2003 (now U.S. Pat.
No. 8,071,295), which is a divisional application of U.S. patent
application Ser. No. 09/829,855, filed Apr. 10, 2001 (now U.S. Pat.
No. 6,613,520), which claims the benefit of U.S. provisional
application 60/196,063, filed Apr. 10, 2000 (now expired) and U.S.
provisional application 60/196,258, filed Apr. 11, 2000 (now
expired), the entire disclosures of each of which applications are
incorporated by reference herein.
TECHNICAL FIELD OF THE INVENTION
[0003] The present invention relates to methods for performing
surveys of the genetic diversity of a population. The invention
also relates to methods for performing genetic analyses of a
population. The invention further relates to methods for the
creation of databases comprising the survey information and the
databases created by these methods. The invention also relates to
methods for analyzing the information to correlate the presence of
nucleic acid markers with desired parameters in a sample. These
methods have application in the fields of geochemical exploration,
agriculture, bioremediation, environmental analysis, clinical
microbiology, forensic science and medicine.
BACKGROUND OF THE INVENTION
[0004] Microbes have been used previously as biosensors to identify
chemicals in the environment. For instance, microbes have been
utilized as biosensors for the presence of nitrates (Larsen, L. H.
et al., 1997, A microscale NO.sub.3.sup.- biosensor for
environmental applications. Anal. Chem. 69:3527-3531), metals
(Virta M. et al., 1998, Bioluminescence-based metal detectors.
Methods Mol. Biol. 102:219-229), and a variety of hydrocarbons
(Sticher P. et al., 1997, Development and characterization of a
whole-cell bioluminescent sensor for bioavailable middle-chain
alkanes in contaminated groundwater samples. Appl. Environ.
Microbiol. 63(10):4053-4060). In these examples however, the
indicator microbes are not native species, but rather, the product
of recombinant manipulations designed for specific applications.
These modifications involve coupling the nutrient sensing machinery
of well-characterized bacterial strains with reporter genes to aid
identification. This approach is limited, however, by the metabolic
diversity of a few well-characterized bacterial strains. In
contrast, the large and diverse pool of microbes in the environment
represents a source of biosensors for a much larger range of
applications than currently exists. Thus, there is a need to
identify and use other microbes, especially those found in situ, as
biosensors.
[0005] Microbes also have an important impact on health and
medicine. Estimates have been made there may ten times the number
of microbial cells associated with the human body as there are
human cells. Many microbial cell populations that are associated
with the human body play a beneficial role in maintaining health.
For instance, gut microflora is important for proper digestion and
absorption of nutrients and for production of certain factors,
including some vitamins. In general, the human immune system is
able to keep the bacterial populations of the human body in check
and prevent the overgrowth of beneficial microbial populations and
infection by detrimental microbial populations. Nevertheless, the
list of human diseases that are now attributed to microbial
pathogens is growing. However, nearly all of the information
regarding the relationships between microbes and human disease have
been gained from approaches that require culture of microbial
species.
[0006] Two examples of diseases where the causative agents were
identified through molecular methods include bacillary angiomatosis
(Relman, D. A. et al., 1990, New Engl. J. Med. 323: 1573) and
Whipple's disease (Wilson, K. H. et al., 1991, Lancet 338: 474).
Further, the central aspects of atherosclerosis are consistent with
the inflammation that results from infection. DNA sequences from
Chlamydia have been identified from atherosclerotic lesions and has
led to suggestions that this organism plays a role in the
disease.
[0007] In addition, bacterial infections have become an increasing
health problem because of the advent of antibiotic-resistant
strains of bacteria. Further, microbial infections caused by
bacteria or fungi that do not usually infect humans may be a
problem in immunocompromised individuals. Further, individuals in
developing countries who may be malnourished or lack adequate
sanitary facilities may also support a large load of opportunistic
bacteria, many of which may cause sickness and disease. In
veterinary medicine, livestock living in close quarters also may be
prey to infections caused by a variety of different types of
microbes. Thus, there is a need to develop sensitive methods of
identifying many different types of microbes without having to
cultivate them first in order to treat or prevent microbial
infections in humans and other animals.
[0008] Assays for microbial contamination is an important component
of food testing as well. A large number of different types of
microbes may contaminate food for humans or animals. Thus, an
ability to test food for contamination quickly and effectively is
critical for maintaining food safety. However, many of the microbes
responsible for causing sickness in humans and animals are
difficult to isolate or identify.
[0009] Assays for microbial populations also has use in fields such
as forensic science. Over the past ten to twenty years, scientists
have determined that microbial populations change when bodies begin
to decay, and have begun to identify certain microbial species that
are indicative of decomposition (Lawrence Osborne, Crime-Scene
Forensics; Dead Men Talking, New York Times, Dec. 3, 2000).
However, only a few microbial species that may be useful in these
analyses has been identified.
[0010] The problem of determining genetic diversity is not confined
to microbial populations. Antibody diversity is critical for a
proper immune response. During B cell differentiation, antibody
diversity is generated in the heavy and light chains of the
immunoglobulin by mechanisms including multiple germ line variable
(V) genes, recombination of V gene segments with joining (J) gene
segments (V-J recombination) and recombination of V gene segments
with D gene segments and J gene segments (V-D-J recombination) as
well as recombinational inaccuracies. Furthermore, somatic point
mutations that occur during the lifetime of the individual also
lead to antibody diversity. Thus, a huge number of different
antibody genes coding for antibodies with exquisite specificity can
be generated. T cell receptor (TCR) diversity is generated in a
similar fashion through recombination between numerous V, D and J
segments and recombinational inaccuracies. It has been estimated
that 10.sup.14 V.delta. chains, more than 10.sup.13 .beta. chains
and more than 10.sup.12 forms of V.alpha. chains can be made
(Roitt, I. et al., Immunology, 3rd Ed., 1993, pages 5.1-5.14). A
knowledge of the antibody or TCR diversity in a particular
individual would be useful for diagnosis of disease, such as
autoimmune disease, or for potential treatment.
[0011] The identification of microbes, especially soil microbes,
has traditionally relied upon culture-dependent methods, whereby
the detection of a microbial species depends upon the ability to
find laboratory conditions that support its growth. To this end,
96-well plates have been commercially developed to identify
microbes with different metabolic requirements. For instance,
BioLog plates incorporate 96 different media formulations into the
wells of a 96-well plate. Despite these efforts, it is now accepted
that far fewer than 1% of microbes can propagate under laboratory
conditions (Amann, R. I. et al., 1995. Phylogenetic identification
and in situ detection of individual microbial cells without
cultivation. Microbiol. Rev. 59:143-169).
[0012] The widespread interest in genomics has created many
exciting new technologies for the parallel quantitation of
thousands of distinct nucleic acid sequences simultaneously. While
still in their infancy, these technologies have provided
unprecedented insight into biology. To date, these technologies
have predominately been utilized in pharmaceutical and agricultural
applications. Genome expression profiling has gained general
acceptance in biology and is likely to become commonplace in all
academic, biotechnology and pharmaceutical institutions in the
21.sup.st. century. For instance, Serial Analysis of Gene
Expression (SAGE) is a hybridization-independent method designed to
quantitate changes in gene expression (Velculescu, V. E. et al.,
1995, Serial analysis of gene expression. Science 270:484-487 and
U.S. Pat. No. 5,866,330). However, SAGE only measures RNA levels
from tissues or organisms, and is not suitable for examining
genetic diversity.
[0013] The widespread interest in genomics has also led to the
development of many technologies for the rapid analysis of tens of
thousands of nucleic acid sequences. One such technology is the DNA
chip. Although this approach had been used as a diagnostic for
distinguishing between several species of the genus Mycobacterium
(Troesch, A., et al., 1999, Mycobacterium species identification
and rifampin resistance testing with high-density DNA probe arrays.
J. Clin. Microbiol. 37:49-55), it has limited utility for an
environmental microbial survey for two reasons. First, the sequence
of the target DNAs to be analyzed must be known in order to
synthesize the complementary probes on the chip. However, the vast
majority of environmental microbes have not been characterized.
Second, DNA chips rely on hybridization of nucleic acids which is
subject to cross hybridization from DNA molecules with similar
sequence. However, the resolving power of a hybridization-based
approach is limited because one must identify regions of DNA that
do not cross-hybridize, which may be difficult for related
microbial species.
[0014] Genomic technologies and bioinformatics hold much untapped
potential for application in other areas of biology, especially in
the field of microbiology. However, to date there has not been a
method to rapidly and easily determine the genomic diversity of a
population, such as a microbial or viral population. Further, there
has not been a method to easily determine the antibody or TCR
diversity of a population of B or T cells, respectively. Thus,
there remains a need to develop such methods in these areas.
BRIEF SUMMARY OF THE INVENTION
[0015] The present invention solves this problem by providing
methods for rapidly determining the diversity of a microbial or
viral population and for determining the antibody or TCR diversity
of a population of B or T cells. The present invention relies on
hybridization-independent genomic technology to quickly "capture" a
portion of a designated polymorphic region from a given DNA
molecule present in a population of organisms or cells. This
portion of the DNA molecule, a "marker," is characteristic of a
particular genome in the population of interest. The marker can be
easily manipulated by standard molecular biological techniques and
sequenced. The sequence of a multitude of markers provides a
measure of the diversity and/or identity of a population. In one
aspect, the invention provides a method, Serial Analysis of
Ribosomal DNA (SARD), that can be used to distinguish different
members of a microbial population of interest.
[0016] In another aspect, the invention provides a method for
analyzing a designated polymorphic region from a population of
related viruses using method steps similar to those described for
SARD. In a further aspect, the invention provides a method for
analyzing the variable regions from the immunoglobulins or TCR
genes of a population of immune cells using methods steps similar
to those described for SARD.
[0017] In another aspect of the invention, a method is provided for
analyzing a population based upon an array of the masses of
peptides that are encoded by polymorphic sequences of particular
DNA molecules in a region of interest. In a preferred embodiment,
the region of interest is a designated polymorphic region from an
rDNA gene from each member of a microbial population.
[0018] In another aspect of the invention, a method is provided for
analyzing the information provided by the above-described methods.
The method enables the creation of a diversity profile for a given
population. A collection of diversity profiles provides an accurate
representation of the members present in a population. These
diversity profiles can be entered into a database along with other
information about the population. The diversity profiles can be
used with various correlation analyses to identify individual, or
sets of individuals that correlate with each other. The correlation
analyses can be used for diagnostic or other purposes. In another
aspect, the invention provides databases comprising various
diversity profiles. In a preferred embodiment, the diversity
profile is obtained by SARD.
[0019] In yet another aspect of the invention, a method is provided
for identifying a diversity profile, as described above, that
correlates with a parameter of interest. In a preferred embodiment,
the diversity profile is a profile of the microbial populations
that correlate with the presence of mineral deposits and/or
petroleum reserves. In another preferred embodiment, the diversity
profile is a profile of populations of different antibodies or TCR
that correlate with a specific disease state, such as an autoimmune
disorder.
[0020] In a still further aspect, the invention provides a method
for locating mineral deposits or petroleum reserves comprising
identifying one or more nucleic acid markers that correlate with
the presence or mineral deposits or petroleum reserves, isolating
nucleic acid molecules from an environmental sample, determining
whether the nucleic acid markers are present in the environmental
sample, wherein if the nucleic acid markers are present, then the
area from which the environmental sample was obtained is likely to
have mineral deposits or petroleum reserves.
BRIEF DESCRIPTION OF THE DRAWINGS
[0021] FIG. 1 shows a representation of a 16S rDNA gene from
Bacteria illustrating polymorphic regions (shown as dark bands) and
constant regions (shown as light bands).
[0022] FIG. 2 shows a schematic representation of the SARD method
for isolating polymorphic sequence tags from Bacterial rDNA.
[0023] FIG. 3 shows a number of representative members of the
domain Bacteria with their taxonomic relationships.
[0024] FIG. 4 shows a number of representative members of the
domain Archaea with their taxonomic relationships.
[0025] FIGS. 5A and 5B show examples of Marker-Marker correlation
scatter plots. Each point represents a single sample population.
FIG. 5A shows a positive correlation and FIG. 5B shows zero
correlation.
[0026] FIGS. 6A-6F show examples of various Marker-Parameter
scatter plots. Each point represents a single sample population.
FIG. 6A shows zero correlation; FIG. 6B shows a curvilinear
relation; FIG. 6C shows a positive linear correlation; FIG. 6D
shows a negative linear correlation; FIG. 6E shows a nonlinear
relation; and FIG. 6F shows a nonlinear relation.
[0027] FIGS. 7A, 7B and 7C shows scatter plots comparing Marker
Diversity Profile (MDP) profiles. Each point represents one marker.
FIG. 7A: Significant correlation with all markers within the MDP.
FIG. 7B: No correlation found using all the markers within the MDP.
FIG. 7C: Same plot as B except that significant correlation is
found using a subset of markers.
[0028] FIG. 8 shows a schematic for generating a marker diversity
profile matrix database. The first step 100 involves assigning N an
integer value of 1 corresponding to the first MDP data structure.
The second step 105 involves assembling polymorphic markers from a
sample. Examples of methods for assembling such markers are
described in this application and include, but are not limited to,
SARD (FIG. 2, Tables I and II) or mass tag compilation (Table III).
The next step 110 involves detecting the polymorphic markers.
Examples of methods to detect polymorphic markers described above
include DNA sequence analysis of SARD tags and MALDI-TOF analysis
of mass tags, respectively. This detection step includes detecting
the presence and abundance of each marker in the sample. The next
step 115 involves the conversion or transduction of the MDP into an
electrical signal output. Generally, this process is a linear
electronic conversion of the data into a digital signal. Step 120
involves detecting parameters that are associated with the sample
N. Step 125 involves transducing the sample parameter data, which
may include, without limitation, such parameters as pH, grain size,
elemental analysis and/or organic analysis, into an electrical
signal output in the form of a digital signal. Step 130 involves
storing into the memory of a computer the output signal from each
MDP into a matrix data structure and associating it with sample
parameters. The next step is a decision block 135 where if all the
data structures have not been completed the routine advances to
step 140 where n is incremented and the data structure generating
steps 105-130 are repeated for the n+1.sup.th marker diversity
profile. Once all the data structures are completed the routine
proceed to step 145 to form the MDP database from n data
structures. Each marker is assigned a unique identifier along with
its relative abundance in the population. This information is also
optionally indexed with other known parameters that are associated
with the sample including, for instance, the time, date, elevation
and geographical location. These signals are digitized and stored
in the memory of a computer.
[0029] FIG. 9 shows a schematic of the steps involved in
determining parameters associated with an MDP. A marker diversity
profile 200 is created for a sample. The marker diversity profile
is subject to a comparison function 205 which compares the profile
with resident marker diversity profiles in the database. Step 210
is a decision block where a query is made whether the new marker
diversity profile equals a resident marker diversity profile in the
database. If the query returns a "Yes" the new marker diversity
profile is deduced 215 to share the same parameters as the resident
marker diversity profile. Since the parameters associated with the
resident marker diversity profile are characterized, the parameters
associate with the new marker diversity profile are identified.
[0030] If the query in decision block 210 returns a "No" the
routine proceeds to decision block 220 which queries whether the
new marker diversity profile is a subset of a resident profile in
the database. If the query returns a "No" the parameters remain
undefined 225. If the query returns a "Yes" the routine proceeds to
step 230. Step 230 is an optional step to determine the correlation
between members of the common subset of markers and may either be
performed for each new profile or may be queried from a matrix
table of pre-calculated values from existing profiles. Such values
generally would be maintained in a relational database. If this
step is not performed all common markers are parsed into groups of
individual markers and treated as correlated groups 255. If the
marker-marker correlation is performed between the common subset of
markers, the routine proceeds to decision block 235 which queries
whether all of the common markers are correlated. If the query
returns a "Yes" the markers are correlated with the parameters 240
resident in the database. If none of the markers are correlated
with a parameter, the parameter(s) remain undefined 245 whereas if
the markers are correlated with a parameter, the parameter is
deduced to be associated with the marker diversity profile 250. If
the decision block query 235 returns a "No", the common markers are
sorted into groups of correlated markers 255. The first correlated
marker group N 260 is subject to a decision block 265 that queries
whether the markers in this group are correlated with a parameter.
A "No" determines that the parameters remain undefined. If the
marker(s) are correlated with a parameter, the parameter is deduced
to be associated with the marker diversity profile. Steps 260-275
are repeated in steps 280-295 for each correlated group of markers.
The groups of correlated markers may be comprised of a single or
multiple markers. The confidence level in deducing that a parameter
is associated with a marker diversity profile is determined by the
level of correlation between the marker(s) and the parameter.
Therefore, sets of correlated markers are expected to be more
robust indicators of any given parameter.
[0031] FIG. 10 shows a schematic for generating a marker diversity
profile matrix database. The first step 300 involves assigning N an
integer value of 1 corresponding to the first MDP data structure.
The second step 305 involves assembling polymorphic markers from a
sample. Examples of methods for assembling such markers are
described in this application and include, but are not limited to,
SARD (FIG. 2, Tables I and II) or mass tag compilation (Table III).
The next step 310 involves detecting the polymorphic markers.
Examples of methods to detect polymorphic markers described above
include DNA sequence analysis of SARD tags and MALDI-TOF analysis
of mass tags, respectively. The next step 315 involves the
conversion or transduction of the MDP into an electrical signal
output. Generally, this process is a linear electronic conversion
of the data into a digital signal. Step 320 involves storing into
the memory of a computer the output signal from each MDP into a
matrix data structure associating each MDP with a geographic
coordinates such as longitude and latitude. The next step is a
decision block 325 where if all the data structures have not been
completed the routine advances to step 330 where n is incremented
and the data structure generating steps 305-320 are repeated for
the n+1.sup.th marker diversity profile. Once all the data
structures are completed the routine proceed to step 335 to form
the MDP database from n data structures. Each marker is assigned a
unique identifier and indexed with its relative abundance in the
population. These signals are digitized and stored in the memory of
a computer.
[0032] FIG. 11 shows a schematic for mapping applications using
marker diversity profiles. Marker diversity profile data 400 can be
processed in several ways to create maps that provides significant
environmental information. In one example 405, each marker
diversity profile in a database can be correlated with every other
marker diversity profile in a pairwise manner to create a
correlation matrix. By appending this data to the geographical
coordinates of each sample 410, a map can be constructed that
reflects the correlation values of physically neighboring sample
sites. Preferably the correlation values will be color coded to
reflect the level of correlation. The color is chosen from a
reference color spectrum that is indexed to correlation values
between 0-1.
[0033] Marker diversity profiles 400 can also be processed into
maps at the individual marker or correlated marker group level.
This approach is preferable since subsets of markers are likely to
correlate to fewer number of sample associated parameters. Each
marker in a marker diversity profile database is correlated with
every other marker in the database in a pairwise manner to create a
correlation matrix 415. The source database can either be composed
of marker diversity profiles from a single geographic area or
several distinct areas. In step 420, the markers from one
geographic area are sorted into groups based upon their level of
correlation. In step 425, the relative representation of the
correlated marker group N is determined along with its geographical
coordinates for each marker diversity profile in a geographic area.
A map is constructed 430 where the relative abundance of each
correlated marker group is color-coded with its geographical
coordinates. Steps 425 and 430 are repeated as in 435 and 440 for
each correlated group of markers.
[0034] FIG. 12 shows oligonucleotides useful for amplifying nucleic
acid molecules for SARD.
[0035] FIGS. 13A and 13B show the use of the SARD strategy for
Eubacteria. The double-underlined sequence and the wavy-underlined
sequence represent the sequence tags for the two pools and the
single-underlined sequence delineates the BpmI recognition
site.
[0036] FIG. 14 is a graphical representation of a SARD analysis of
a defined population.
[0037] FIG. 15 shows the sequence of SARD tags identified from Wy-1
sample. The number in parentheses indicates the number of tags
having that sequence.
[0038] FIG. 16 shows SARD tags identified from Wy-2 sample. The
number in parentheses indicates the number of tags having that
sequence.
[0039] FIGS. 17A and 17B are graphical representations of the
number and abundance of SARD tags. FIG. 17A shows the Wy-1 SARD Tag
Diversity Profile and FIG. 17B shows the Wy-2 SARD Tag Diversity
Profile.
DETAILED DESCRIPTION OF THE INVENTION
[0040] The extent of the diversity of microbes in our environment
has only recently been recognized. With the advent of the
polymerase chain reaction (PCR) and small subunit ribosomal DNA
(rDNA) sequence analysis, researchers have been able to detect and
perform phylogenetic analyses on individual microbes without first
cultivating the microbes of interest. This molecular phylogenetic
approach has significantly changed our view of microbial evolution
and diversity (Woese, C. R., 1987, Bacterial evolution. Microbiol.
Rev. 51(2):221-71; Pace, N. R., 1997, A molecular view of microbial
diversity and the biosphere. Science. 276(5313):734-40). For
instance, the earliest life forms are now thought to have utilized
inorganic compounds for nutrition rather than compounds based upon
organic carbon. In addition, the vast proportion of biological
diversity is now known to be due to microbial species. Estimates
have been made that there may be more than ten thousand distinct
species of microbes in a single gram of soil. FIGS. 3 and 4 show
some of the representative members of the domains Bacteria and
Archaea, respectively, that may be found in environmental
samples.
[0041] Microbes inhabit virtually all niches including extreme
environments with temperatures between 20.degree. and 250.degree.
F. Microbes have even been isolated from deep petroleum reservoirs
more than a mile beneath the earth's surface (Jeanthon, C. et al.,
1995, Thermotoga subterranea sp. nov., a new thermophilic bacterium
isolated from a continental oil reservoir. Arch. Microbiol.
164:91-97). In order to prevail under such diverse conditions,
microbes have made remarkable adaptations and have attained the
ability to utilize unusual carbon and mineral resources that are
immediately available. These physiological and metabolic
adaptations that enable some microbes to inhabit a particular niche
may also restrict their distribution to such areas. Numerous
examples of environmental parameters that lead to restrictions of
microbial distribution are well known and are usually dictated by a
species' specific metabolic program (e.g. obligate nature of the
carbon, nitrogen and energy source).
[0042] Microbes that have highly defined nutrient requirements are
likely to have a restricted distribution in the environment. Thus,
the microbes' dependence on the presence of a particular resource
to proliferate can serve as the basis for an assay to identify the
presence, and characterize the distribution, of various features in
the environment, such as biological, chemical and geochemical
features. In other words, microbes can function as environmental
biosensors.
[0043] In one aspect of this invention, the ability of microbes to
function as environmental biosensors is used to identify particular
environmental states. In a preferred embodiment, a profile of a
microbial population is used to identify one or more parameters of
a particular environmental state. In a more preferred embodiment, a
microbial population profile is used to identify areas that are
likely to have mineral deposits and/or petroleum reserves. In
another preferred embodiment, a microbial population profile is
used in forensic science to identify decomposition of a body or to
associate an individual to another individual, to an object or to a
location. In yet another preferred embodiment, a microbial
population profile is used to identify microbial contamination of
human and animal foodstocks. In yet another preferred embodiment,
the profile is used to diagnose human or animal disease.
DEFINITIONS
[0044] Unless otherwise defined, all technical and scientific terms
used herein have the meaning as commonly understood by one of
ordinary skill in the art to which this invention belongs. The
practice of the present invention employs, unless otherwise
indicated, conventional techniques of chemistry, molecular biology,
microbiology, recombinant DNA, genetics and immunology. See, e.g.,
Maniatis et al., 1982; Sambrook et al., 1989; Ausubel et al., 1992;
Glover, 1985; Anand, 1992; Guthrie and Fink, 1991 (which are
incorporated herein by reference).
[0045] A microbe is defined as any microorganism that is of the
domains Bacteria, Eukarya or Archaea. Microbes include, without
limitation, bacteria, fungi, nematodes, protozoans, archaebacteria,
algae, dinoflagellates, molds, bacteriophages, mycoplasma, viruses
and viroids.
[0046] A marker is a DNA sequence that can be used to distinguish
or identify a particular gene, genome or organism from another. In
one embodiment, a marker may be generated by one of the methods
described herein. A marker represents one or a limited number of
taxonomic species or genes. In a preferred embodiment, a marker
represents a single taxonomic species or gene. In one embodiment,
the marker represents a single microbial species. In another
embodiment, the marker represents a single viral species or type.
In another embodiment, the marker represents a single
immunoglobulin or TCR variable domain.
[0047] A marker diversity profile (MDP) is a data set that is
obtained from each population sample and that contains a collection
of markers. In a preferred embodiment, the MDP also comprises other
information, including all known parameters associated with a
particular population sample. Such parameters that relate to
environmental samples may include inorganic components (obtained
through atomic adsorption analysis), organic components (obtained
through GC-MS or LC-MS), grain size analysis, pH, and salinity.
Parameters that relate to medical samples would include, but are
not limited to, a complete medical history of the donor. In a
preferred embodiment, the markers are obtained by SARD.
Methods for the Genetic Analysis of Populations
[0048] Ribosomes, which are comprised of numerous ribosomal
proteins and three ribosomal RNA (rRNA) molecules, are a key
component of protein synthesis. The 16S subunit rRNA, which is
encoded by the 16S rDNA gene, has been the focus of much attention
in microbial phylogenetic studies. The 16S rDNA sequence is highly
conserved between taxonomic groups, yet also possesses regions that
are highly polymorphic (FIG. 1). Moreover, the rate of change in
the RNA sequence is thought to have been relatively constant over
evolutionary time, enabling scientists to determine the relative
relatedness of different organisms.
[0049] Typical molecular microbial analyses involve utilizing the
highly conserved regions of the 16S rDNA to amplify the roughly
1,500 bp gene. The sequence of the PCR-amplified product is
determined and compared with other known rDNA sequences. Although
this approach is highly informative, it is not amenable to a rapid
survey of an environmental microbial community.
[0050] The instant invention provides methods for quickly and
easily calculating the genetic diversity of a population. The
methods uses hybridization-independent genomic technologies to
overcome the previously-identified problems of determining genetic
diversity. This method may be used for any population of cells,
viruses or organisms which comprise at least one DNA molecule that
comprises regions of high sequence conservation interspersed with
polymorphic sequences, wherein the polymorphic sequences can be
used to distinguish different members of the population of
interest. One aspect of the present invention describes a method
(SARD) that can capture a designated polymorphic region from a
given DNA molecule present in the members of a microbial community.
In a preferred embodiment, the DNA molecule is a 16S rDNA molecule.
In another embodiment, the DNA molecule is the intergenic region
between the 16S and 23S rDNA genes. In another embodiment, the
method is used to identify the genetic diversity of a population of
viral samples or of cells or organisms infected with a population
of viruses. In another embodiment, the method is used to identify
the diversity of immunoglobulin and/TAR genes in a population of B
and/or T cells.
[0051] The method may be performed as follows (see FIG. 2):
Step I. Sample Preparation and DNA Amplification by PCR
[0052] Samples may be obtained from any organism or region desired.
For environmental microbial analyses, samples may be obtained from,
without limitation, buildings, roadways, soil, rock, plants,
animals, cell or tissue culture, organic debris, air or water. For
medical microbial analyses, samples may be obtained from, without
limitation, humans, animals, parasites, water, soil, air and
foodstuffs. For viral analyses, samples may be obtained from,
without limitation, viral culture stocks, humans, animals, plants,
cell or tissue culture and microbes. For immunoglobulin or TCR
analyses, samples may be obtained from, without limitation, humans,
animals or cell or tissue cultures. DNA molecules from the sample
of interest may be isolated by any method known in the art. See,
e.g., Sambrook et al., 1989 and Ausubel et al., 1992. In a
preferred embodiment, DNA is obtained as described by Yeates et
al., "Methods for Microbiological DNA Extraction from Soil for PCR
Amplification," Biological Procedures Online, Volume 1, May 14,
1998, available through the Internet; Liu et al., Applied and
Environmental Microbiology (1997) 63: 4516-4522; and Tsai et al.,
Applied and Environmental Microbiology (1992) 58: 2292-2295. The
DNA molecules do not have to be completely purified but only need
be isolated to the point at which PCR may be performed.
[0053] Environmental microbes often exist in biofilms (Costerton,
J. W., et al., 1999, Bacterial biofilms: a common cause of
persistent infections. Science 284(5418):1318-1322) or in tight
association with solid surfaces. Microbial DNA from a sample of
interest is isolated by one of several methods that are widely
known to those skilled in the art and are described in the
literature (Gillan, D. C. et al., 1998, Genetic diversity of the
biofilm covering Montacuta ferruginosa (Mollusca, bivalvia) as
evaluated by denaturing gradient gel electrophoresis analysis and
cloning of PCR-amplified gene fragments coding for 16S rRNA. Appl.
Environ. Microbiol. 64(9):3464-72).
[0054] The samples may be selectively enriched before they are
isolated by any method known in the art. In one embodiment, for a
population of microbes that are known or suspected to feed on a
hydrocarbon source such as propane, the hydrocarbon may be added to
the environment in which the microbes live for a period of time
before the microbes are harvested. In another embodiment, a viral
population may be cultivated in cells before they are isolated. In
a further embodiment, B and T cells may be expanded in culture
before isolation. It may be easier to obtain sufficient sample
amounts if a population is expanded before isolation. However, this
must be weighed against the possibility that expansion will alter
the ratio of different members of the population to each other.
[0055] In general, the primers used for amplification are designed
to hybridize to a region of the DNA that is highly conserved
between members of the population. Further, the primers should
flank a polymorphic region the partial sequence of which should
provide diagnostic information regarding the genetic diversity of
the population. For instance, for the 16S rDNA gene, primers are
designed to hybridize to a highly conserved region of the 16S rDNA
gene flanking a polymorphic region (see FIG. 2). In an
immunoglobulin gene, the primers are designed to hybridize to a
region of the B cell DNA flanking the V-J recombination site.
Alternatively, primers may be designed that bind to the relatively
constant regions within certain regions of the V-J gene that flank
a polymorphic region. See Roitt et al., Immunology, 3rd Ed., 1993,
pp. 5.2-5.14, herein incorporated by reference, which shows regions
of variability and conservation within immunoglobulin and TCR
genes. One having ordinary skill in the art following the teachings
of the specification will recognize that other genes that have
regions that are highly conserved between members of the population
and that flank polymorphic regions may be used in the design of
primers.
[0056] The primers should also be designed to flank a region of DNA
that comprises a restriction site for a restriction enzyme. In a
preferred embodiment, the restriction enzyme is a cuts at a
four-basepair recognition site, such as AluI (see FIG. 2).
Furthermore, the restriction site should be near but not in the
polymorphic region of the gene of interest. In a preferred
embodiment, the restriction site should be one that is present in
the gene of interest in a majority of the known species or
genes.
[0057] A single set of primers may be used or multiple sets of
primers may be used. A single set of primers may be used if it is
known that the region of DNA to which the primers will bind is very
highly conserved. Alternatively, if it is known that there is some
variation in the conserved region, multiple sets of primers may be
used to bind to the conserved DNA region. Using multiple sets of
primers may be useful to identify more members of a population,
especially those members of the population that exhibit less
sequence identity in the conserved areas of a nucleic acid
sequence. In one embodiment, four to ten sets of primers may be
used to identify members of a population. Alternatively, the
primers used may be degenerate, such that different molecules
within a primer population will include a different base at one or
more specific sites in the primer. For instance, a primer may have
a site that has either cytosine or thymidine. The purpose of making
primers degenerate is to increase the number of different DNA
molecules that will hybridize to a particular primer. Methods of
making degenerate primers are well-known in the art.
[0058] The primers used in this and in subsequent steps are
generally of a length sufficient to promote specific hybridization
to a DNA sequence. The primers generally have a length of at least
12 bases, more preferably at least 15 bases, even more preferably
at least 18 bases. The primers may have a length of up to 60 bases,
although usually most are under 40 bases in length. Primers may
include both bases that are naturally found in DNA, such as
adenine, guanine, cytosine and thymidine, and may also include
nucleotides that are not usually found in DNA, such as inosine.
[0059] One of the primers (the "upstream" primer) should be
modified to incorporate a moiety that can be used to bind the PCR
product to a solid support. The upstream primer is defined as the
primer that is located on the opposite side of the polymorphic
region of interest relative to the flanking four-base restriction
site. A number of different binding moieties are known in the art.
In a preferred embodiment, the moiety is biotin. In another
preferred embodiment, the moiety is digoxigenin or six
histidines.
[0060] PCR is performed using the primers to amplify a subregion
that contains a polymorphic site of interest. Methods for
performing PCR are well-known in the art. In one embodiment, the
PCR products are normalized or subtracted by methods known in the
art to lower the representation of the dominant sequences.
Exemplary methods are described in Sambrook et al., 1989, Ausubel
et al., 1992; Glover, 1985; Anand, 1992 (which are incorporated
herein by reference).
Step II. Digestion of the Amplified Fragment and Binding to Solid
Support
[0061] The amplified fragment is cut with the restriction enzyme as
discussed in Step I. Any restriction enzyme may be used in this
step so long as it is cuts at a site immediately adjacent to the
polymorphic sequence. In a preferred embodiment, the enzyme is a
four-base restriction enzyme. Examples of four-base restriction
enzymes are well-known in the art and include many that are
commercially available. See, e.g., New England Biolabs Catalog
2000, herein incorporated by reference. Examples of four-base
restriction enzymes include, without limitation, AluI, Bsh1236I,
DpnI, HpaII, MboI, MspI, PalI (an isoschizomer of HaeIII), RsaI,
Sau3AI and TaqI. After restriction, the DNA fragment is bound to a
solid support. Numerous solid supports to immobilize DNA are known
in the art. Examples include, without limitation, streptavidin
beads, which would bind to a PCR product labeled with biotin, and
anti-digoxigenin beads, which would bind to a PCR product labeled
with digoxigenin, and beads conjugated to nickel, which would bind
to a six-histidine labeled product. In a preferred embodiment,
streptavidin beads are used (FIG. 2).
[0062] Since the SARD tag position is dictated by the first
restriction enzyme recognition site distal to the biotinylated
primer used in the initial PCR reaction, there may be cases in
which the first restriction enzyme recognition site is located
within a conserved region of the gene of interest. In general, this
will not be a problem because even though the tags from the
conserved region may not be informative, most tags derived by SARD
will be from a polymorphic region and will be informative. However,
if one desires to decrease the number of tags that contain
information from a conserved region of a gene rather than from a
polymorphic region, one may purify the desired PCR products after
restriction. In a preferred embodiment, one may do this by gel
purifying those PCR products that have the expected size.
Step III. Digestion of the Amplification Product and Ligation to
Linkers
[0063] The immobilized products are split into two pools and
linkers are attached to the immobilized products of each pool. Each
linker is a double-stranded synthetic DNA molecule comprising a
specific DNA sequence. Both linkers incorporate a Type IIS
restriction enzyme site. In a preferred embodiment, the two linkers
incorporate the same Type IIS restriction enzyme site. Each of the
two linkers also comprises a DNA sequence that specifically
hybridizes to a primer. In one embodiment, the linkers are
identical to one another and hybridize to the same primer. In a
preferred embodiment, the linkers are different from each other
such that each hybridizes to a different primer.
[0064] The double-stranded linker is ligated to the immobilized PCR
product. The linker may incorporate the Type IIS restriction enzyme
site or it may incorporate only a portion of the site. In this
case, the linker will be designed such that ligation of the linker
to the restricted DNA will reconstitute the Type IIS restriction
site. In a preferred embodiment, the linker incorporates a BpmI
site. Linker ligation is well-known and may be accomplished by any
method known in the art. After ligation, the immobilized PCR
product is isolated from the free linkers by any method known in
the art. See, e.g., Velcelescu et al., Science 270: 484-487, 1995;
Powell, Nucleic Acids Research 14: 3445-3446, 1998; Sambrook et
al., pp. F.8-F.10, 1989.
[0065] Type IIS restriction enzymes cleave at a defined distance up
to 20 basepairs away from their asymmetric recognition sites. Type
IIS restriction enzymes that are commercially available include
enzymes that leave 5' overhangs and those that leave 3' overhangs
as double-stranded DNA products. Some enzymes of the former class
include: BsmFI (10/14), Bst71I (8/12), and FokI (9/13), where the
number in parentheses indicate the cleavage position on the same
DNA strand as the recognition sequence/cleavage position on the
complementary DNA strand. Enzymes of the latter class include: BpmI
(16/14), BsgI (16/14), Eco57I (16/14) and GsuI (16/14). The 3'
overhang left by these enzymes must be removed for a blunt ligation
(Step IV). Therefore, enzymes that cleave at positions 16/14 result
in a 14 base-pair tag. Other enzymes that cut at a more distal
position could create a larger tag. For instance, MmeI (20/18)
leaves a 3' overhang, but is not commercially available (Tucholski,
J. et al., 1995, MmeI, a class-IIS restriction endonuclease:
purification and characterization. Gene 157: 87-92).
Step IV. Digestion of the Product with Type IIS Restriction
Enzyme
[0066] The product is digested with the appropriate Type IIS
restriction enzyme to release a DNA fragment from the anchoring
bead and produce a short hybrid DNA fragment containing a portion
of the polymorphic region of the DNA of interest (the tag) and the
linker DNA. After digestion, the DNA must be either filled in or
digested to create blunt ends. If the Type IIS restriction enzyme
produces a 3' overhang, the fragment is digested with T4 DNA
polymerase to remove the 3' overhang. If the Type IIS restriction
enzyme produces a 5' overhang, the overhang must be filled in using
the appropriate deoxynucleotides and the Klenow fragment of DNA
polymerase I. The DNA fragment is separated from the rest of the
immobilized PCR product. In a preferred embodiment, the two pools
of immobilized PCR products are digested with BpmI to release the
polymorphic markers and digested with T4 DNA polymerase to create
blunt ends (FIG. 2).
Step V. Ligation of Tags and PCR Amplification of Resulting
Ditags
[0067] The tags are blunt-end ligated to one another using methods
well-known in the art to form ditags. See, e.g., Sambrook et al.,
1989; Velcelescu et al., Science 270: 484-487, 1995. The ditags are
subsequently amplified by PCR using primers that are unique to the
linkers used in Step III. In a preferred embodiment, the primers
are different from one another if the linkers used in Step III were
different from one another. Alternatively, the primers may be the
same if the linkers used in Step III were identical to one another.
The number of PCR amplification reactions will vary depending upon
the amount of DNA present in the starting material. If there is a
large amount of DNA, then only one PCR amplification reaction,
wherein each reaction comprises from approximately 15-30 cycles,
will be required at this step. If the starting amount of DNA is
low, then more than one PCR amplification reaction may be required
at this step.
Step VI. Cleavage of the Ditags and Ligation to Form Ditag
Concatemers
[0068] The ditags are cleaved with the four-base restriction enzyme
used in Step II. The products are then ligated to create ditag
concatemers. In one embodiment, the ditag concatemers range from 2
to 200 ditags. In a more preferred embodiment, the concatemer
comprises 20-50 polymorphic tags. The concatemer may be sequenced
directly, or may be cloned into a sequencing vector. Using a
96-channel capillary DNA sequencer, about 12,000 tags could be
easily analyzed in one day. Alternatively, the concatemers may be
sequenced manually.
Methods to Analyze Marker Data
[0069] The invention is directed toward methods of analyzing the
genetic diversity of a population in a sample. Each population that
is analyzed will have its own unique set of different organisms or
genes. The data set that is captured from each sample should
recapitulate the genetic structure in a survey format to include a
marker for each gene or organism and the relative abundance of each
gene or organism in the population as a whole. The markers for a
particular population form a marker diversity profiles (MDPs), that
may be entered into a database. See, e.g., FIG. 8 which shows one
schematic for generating such a database. The method by which the
data are captured is not critical as long as it produces an
accurate representation of each population.
[0070] In one aspect of the method, MDPs are entered into a
database. In a preferred embodiment, the database is kept in a
computer-readable form, such as on a diskette, on a non-removable
disk, on a network server, or on the World Wide Web. However, the
method by which the data are captured is not critical as long as it
produces an accurate representation of each microbial
community.
[0071] Artificial intelligence (AI) systems can perform many data
management and analysis functions. Examples of AI systems include
expert systems and neural networks. Expert systems analyze data
according to a knowledge base in conjunction with a resident
database. Neural networks are comprised of interconnected
processing units that can interpret multiple input signals and
generate a single output signal. AI systems are ideally suited for
analyzing complex biological systems including populations through
the use of deduction protocols.
[0072] A marker may be correlated with a particular condition or
with another marker. See, e.g., FIG. 9 for a schematic of the steps
involved in determining particular parameters associated with an
MDP and FIG. 10, which shows a schematic for generating a marker
diversity matrix database. A condition or state may be an
environmental condition such as pH, temperature, salinity, or the
presence or absence of an organic or inorganic compound such as
hydrocarbons, nitrates or mineral deposits. A condition may be a
physiological or medical condition such as an acute or chronic
disease state, physiological state, developmental stage or
associated with a particular body tissue or fluid. Information
regarding all known parameters associated with the samples will
also be saved together with the MDPs.
[0073] Each MDP is composed of markers which represent a small
number, more preferably one, species or gene. For instance, in the
case of Example 1, each marker would be comprised of a 12 base-pair
polymorphic 16S rDNA sequence. Such parameters that relate to
environmental samples may include inorganic components (obtained
through atomic adsorption analysis), organic components (obtained
through GC-MS or LC-MS), grain size analysis, pH, and salinity.
Parameters that relate to medical samples would include, but are
not limited to, a complete medical history of the donor. See, e.g.,
FIG. 11, which shows a schematic for mapping applications using
marker diversity profiles.
[0074] In another aspect of the invention, MDPs are collected for a
time course, and each time point is one of the parameters included.
Time courses may be useful for tracking changes over time for a
wide variety of indication. For example, time courses may be useful
for tracking the progression of a disease, during environmental
remediation efforts, and during oilfield production.
[0075] In another aspect of the invention, MDPs are collected in
various distinct locations, such as in various geographical
locations or in various tissues of the body. Comparison of MDPs
compiled from various distinct locations are useful for
distinguishing changes between these various locations, which may
be indicative of particular environmental conditions or disease
states.
[0076] Comparison of marker diversity profiles can reveal trends in
populations either relative to time or to geographical location. In
the latter case, comparisons of microbial populations can resolve
special information about the environment that would otherwise be
undetected. Examples of such information include migration patterns
of water, organic compounds and minerals. For instance, placer
deposits of minerals are caused by the action of both water and
wind causing the minerals to migrate from a lode deposit at one
location only to deposited at another location. The migration of
such minerals may leave a detectable trace upon the microbial
populations in the path of migration. Physical attributes of the
environment could also be detected such as structures, formations
and fault lines. It is commonly understood that faults offer a
significant vertical migration route for gases such as methane
which is known to be differentially utilized by microbes. By
combining MDP data with geographical coordinates such as elevation,
longitude and latitude that can easily be obtained with global
positioning system devices, it is possible to create maps
delineating the distribution of various microbes in the
environment.
[0077] Correlation analyses between one marker and all the other
markers in the database will reveal pairs of markers that have a
propensity to coincide. This process can be repeated in an
iterative manner for all markers to produce a matrix of correlation
coefficients between all observed markers. FIG. 5 shows a scatter
plot for two pairs of markers with one of the pairs exhibiting a
high degree of correlation. This approach can also be used to
create a dendrogram that reflects the relative level of correlation
between each marker. Therefore, at any chosen level of correlation,
all of the observed markers can be divided into groups where the
markers in each group share the same level of correlation with each
other member of the group. If a high correlation coefficient value
is chosen (e.g. 0.8), the markers of each group would, more often
than not, be found in the same sample. Thus, this exercise will
divide a given population into groups of genes or organisms that
have a propensity to co-localize with each other. In one preferred
embodiment, the exercise will divide a microbial community into
groups of microbes that have a propensity to co-localize.
[0078] Correlation analysis between a marker (variable 1) and a
sample parameter (variable 2) will identify markers whose presence
often, or invariably, coincides with a component present in the
samples. Some types of relationships between markers and sample
components (or parameters) are shown in FIG. 6. A strong
correlation value between a marker and sample parameter would allow
predictions to be made about the abundance of either variable
(marker or sample parameter) as long as one of the variables is
known.
[0079] In some cases, a marker will not be specific for a single
species or gene. For example, the tag sequence that would be
identified by the approach depicted in Example 1 would be identical
for Denitrobacter permanens and Legionella anisa. In the cases
where a significant correlation is found between a marker and
sample parameter of interest, the preferred action is to use the
tag sequence information to identify the complete gene sequence.
The sequence can then be used to identify the species and to
identify species-specific probes to verify the correlation. One may
do this using methods known in the art, such as by PCR or by
hybridization to DNA molecules isolated from the sample of
interest, followed by sequencing or other method of analysis.
[0080] Species-specific probes that are identified from markers
with a robust correlation to a sample parameter of interest can
then be utilized as a diagnostic, or to prospect for the parameter
of interest. Such assays would preferably be PCR-based and would be
highly sensitive, rapid and inexpensive. In a preferred embodiment,
a marker identified by these methods may be used as a hybridization
probe to identify a larger piece of the DNA from which the marker
is derived. The sequence of the larger DNA molecule can then be
used to design primers that will specifically hybridize to the DNA
molecule of interest and which can be used to specifically amplify
the DNA molecule by PCR. Alternatively, one may use a
hybridization-based assay using a probe that binds specifically to
the DNA molecule of interest. Using specific primers or probes are
especially useful for quickly determining whether a large number of
samples contains the DNA molecule that correlates to the parameter
of interest.
[0081] In a preferred embodiment, a marker that correlates with a
desired parameter is identified. The marker may be identified using
SARD, or may be identified using another method, such as
restriction fragment length polymorphisms (RFLP) or terminal
restriction fragment length polymorphisms (T-RFLP; Liu et al.,
Applied and Environmental Microbiology 63: 4516-4522, 1997). A
method such as denaturing gradient gel electrophoresis (DGGE) may
be used to identify size differences. In a preferred embodiment,
SARD is used to identify the marker. Other samples are screened to
determine whether they have the marker of interest. In a preferred
embodiment, the screen used is PCR or hybridization, more
preferably PCR. In an even more preferred embodiment, the marker
correlates with the presence of mineral deposits or petroleum
reserves.
[0082] Correlation analysis between MDPs (MDPn, variable 1; MDPn+1,
variable 2) can reveal the relative similarities between samples.
Samples taken from the same individual or from proximal
environmental sites that have similar composition, are expected to
show a robust correlation coefficient (FIG. 7A). However, samples
that share only one or a few parameters in common are expected not
to show a significant correlation when all of the markers are
considered (FIG. 7B). By incorporating the knowledge learned from
the Marker-Marker and Marker-Parameter correlations, MDPs can be
compared using either individual markers or preferably, subsets of
correlated markers in the analysis (FIG. 7C). This approach can
eliminate much of the noise and enable one to identify hidden
relationships.
[0083] Correlation analyses may be performed by any method or
calculation known in the art. Correlation analyses for r and
r.sup.2 may be performed as described by M. J. Schmidt in
Understanding and Using Statistics, 1975 (D.C. Health and Company),
pages 131-147. The degree of correlation for r may be defined as
follows:
TABLE-US-00001 1.0 Perfect 0.8-0.99 High 0.5-0.7 Moderate 0.3-0.4
Low 0.1-0.2 Negligible
[0084] In one embodiment, the correlation between two markers or
between a marker and a parameter is at least low (r is 0.3-0.4). In
a preferred embodiment, the correlation is at least moderate (r is
0.5 to 0.7). In a more preferred embodiment, the correlation is
high (r is 0.8 to 0.99).
[0085] With the development of numerous genomic technologies for
analyzing complex sets of nucleic acids, we have the opportunity to
begin to catalog the reservoir of microbial, and hence, metabolic
diversity. Since the proliferation of a microbe in a given location
will depend upon the presence of the requisite metabolic nutrients,
information as to the abundance of that microbe can serve as a
biosensor for a given set of parameters. When viewed as a whole,
the microbial community structure in a given location will hold
intrinsic biosensor potential for a wide range of parameters. The
predictive reliability of the data from a complete microbial
community will also be significantly increased. For example, if a
given microbe were present in 50% of soil samples taken above
petroleum reservoirs and were found nowhere else, then the presence
of ten such microbes would create a predictive value with 99.9%
accuracy.
Applications of the Invention
Geochemical and Mineral Exploration
[0086] The methods described in this invention have several
benefits over existing technologies. For instance, in the area of
geochemical exploration, genomic rDNA-based assays potentially will
be able to resolve an extensive set of geochemical parameters of
interest to the petroleum and mining industries. Currently, many
different technologies are required to measure these parameters.
Because this invention is based upon a universal measure, nucleic
acid detection, it can greatly reduce instrumentation and sample
outsourcing costs.
[0087] Oil and gas reservoirs are located well beneath the earth's
surface at depths from a few hundred feet to more than 10,000 feet.
When oil is formed, it undergoes a migration in which one of two
things take place. The oil may continue to migrate until it
ultimately reaches the surface, where it evaporates over time.
Alternatively, its migration may be blocked by an impermeable
structure, a so-called "trap". Geophysical methods (such as
three-dimensional seismic methods) for petroleum exploration relies
on finding these trap structures with the hope that they contain
oil.
[0088] Crude oil is made up of a variety of hydrocarbon chain
lengths. The lightest hydrocarbons (namely methane, ethane, propane
and butane) are often able to diffuse through the trap structures
and, as a result of pressure gradients, undergo a vertical
migration to the surface. Certain microbes present at the surface
or in the surface layer are able to utilize these migrating
hydrocarbons, which occasionally results in mineralogical changes
that are detectable at the surface. Thus, these migrating
hydrocarbons would be expected to affect microbial populations,
such that the ability to determine the genetic diversity of a
microbial population may reveal microbial signatures that are
indicative of the presence of oil.
[0089] Recent advances in microfluidics in the genomics industry
have resulted in the development of instruments that can detect
specific nucleic acids within a few minutes. Utilizing such
instruments will enable measurements to be made in the field for a
variety of parameters. In contrast, conventional chemical assays
require laboratory analysis and interpretation.
[0090] Biosensors have been created that are able to detect
hydrocarbons present at, or below, the level of detection of
sophisticated GC-MS analytical instrumentation (Sticher, P. et al.,
1997, Development and characterization of a whole-cell
bioluminescent sensor for bioavailable middle-chain alkanes in
contaminated groundwater samples. Appl Environ Microbiol.
63:4053-4060). Sticher et al. demonstrated that by using single
reporter gene in a genetically engineered microbe comprising a
reporter gene was able to sense extremely small changes in their
environment in response to an acute treatment with a particular
hydrocarbon. The instant invention could document the effect upon a
population comprising thousands of microbes over geologic time and
thus, has the potential of being more sensitive than current
analytical instruments.
[0091] This invention may also be used to create a survey of
biological entities that is limited only by the prerequisite that
these entities contain nucleic acids that are arranged in regions
that are conserved and regions that are polymorphic when compared
to sequences from related organisms. Some additional examples of
the application of this invention are described below.
Oil and Gas Reservoir Development
[0092] In addition to the application of this invention in
petroleum exploration, this invention could also be useful in the
development of oil and gas reservoirs. Several properties of oil
reservoirs that directly affect the commercial viability of the
reservoir are modulated at some level by microbes. Hydrogen sulfide
is sometimes present in crude oil and can render otherwise `sweet`
oil into `sour` oil. In addition to its corrosive effect on
oilfield equipment, H.sub.2S also poses risk to the workers and
significantly reduces the value of an oil reservoir since a washing
plant must be installed to remove the gas. The levels of H.sub.2S
can change during the development of a reservoir and is now thought
to be the result of sulfate-reducing bacteria (Leu, J.-Y. et al.,
1999, The same species of sulphate-reducing Desulfomicrobium occur
in different oil field environments in the north sea. Lett. Appl.
Microbiol. 29(4):246-252). By identifying the presence of microbes
that could lead to H.sub.2S production, the valuation of new
reservoirs and the resulting developmental strategies could be made
more effective.
[0093] Crude oil and natural gas are composed of a complex mixture
of hydrocarbons including straight chain hydrocarbons of lengths
generally between 2-40 carbon atoms. The shorter chain-length
hydrocarbons are more valuable (e.g. gasoline, C.sub.4-C.sub.10).
In some oil reservoirs, the lighter hydrocarbons are selectively
removed either during or prior to development of the reservoir.
Microbes have been suspected to play a role in this process since
the shorter chain-length hydrocarbons are more bioavailable. This
invention could identify microbes that are involved in this process
and therefore make predictions as to the susceptibility of certain
reservoirs to the depletion of short chain hydrocarbons. This
invention may also be able to identify microbes capable of
shortening long chain hydrocarbons thereby increasing the value of
existing reservoirs.
Insect and Parasite Detection
[0094] The significant negative impact insects can have on
agriculture is widely known. Insects can also serve as vectors for
the transmission of many disease causing microbes. Numerous
microbe-insect relationships have been described. For example, the
bacterial genus Wolbachia is found associated with many species of
ants and has been shown to alter sex determination and fecundity in
the host (Wenseleers, T. et al., 1998, Widespread occurrence of the
micro-organism Wolbachia in ants. Proc. R. Soc. Lond. B. Biol. Sci.
265(1404):1447-52). In addition, many intracellular endosymbiotic
bacterial species have been identified in ants (Schroder, D. et
al., 1996, Intracellular endosymbiotic bacteria of Camponotus
species (carpenter ants): systematics, evolution and
ultrastructural characterization. Mol. Microbiol. 21:479-89). Most,
if not all, insects probably have species-specific intimate
relationships with microbes which could represent could represent
an Achilles heel for the control of insect populations. The
invention described in this application could provide a means to
identify microbes that modulate the well-being of a given insect
species.
[0095] The identification of microbes that are specifically
associated with a given insect could also potentially serve as a
the basis for a highly sensitive test for the presence of the
insect. For instance, current methods to identify the presence of
termites in wooden structures is based on visual inspection and is
largely inadequate. A test for the presence of a termite-associated
microbe that is based on PCR-amplification would be both
non-invasive and highly sensitive.
[0096] Further, the ability to create comprehensive inventories of
microbial diversity has several applications that relate to
microbial ecology that would have utility in the agricultural
industry. For instance, the agriculture industry utilizes enormous
amounts of pesticides prophylactically to prevent loss of crops.
This invention provides the ability to perform comprehensive
surveys of microbial populations and could lead to predictions as
to the susceptibility of a given field to particular plant
pathogens. This knowledge could lead to a better strategy of
pesticide applications.
[0097] Further, surveying microbial diversity in an environment
such as a agricultural field at various times of the year would
reveal the dynamic changes in microbial populations that occur as a
result of seasonal fluctuations (temperature and moisture),
pesticide application and the proliferation of certain
organisms.
[0098] Comparison of the diversity profiles taken from the same or
similar site at different times would reveal interactions between
species in a population. These productive interactions may manifest
themselves either in the increase or decrease in the representation
of one marker relative to the decrease or increase in the
representation of a second marker, respectively.
[0099] Such information could provide an early warning of the
proliferation of a pest species as well as the identification of
species that are pathogenic to a pest species. These pathogenic
organisms may have value either as a biological agent to control
proliferation of pest species and/or as a source for genes or
compounds that would act as pesticides. This phenomenon is not be
limited to microbe-microbe interactions. The eggs as well as larval
stages of insects likely interact with soil microbes and create a
detectable impact upon microbial populations. An example of such an
organism is Bacillus thuringensis, which itself is commonly used in
organic farming as an insecticidal agent. The gene responsible for
this insecticidal activity (Bt toxin) has been widely used to
create transgenic plants with resistance to insect attack.
Bioremediation
[0100] A considerable amount of effort has gone into the
development of methods for microbe-based removal of chemicals from
the environment. Such chemicals include heavy metals, polynuclear
aromatics (PNAs), halogenated aromatics, crude oil, and a variety
of other organic compounds such as MTBE. Regulatory considerations
for the release of micro-organisms into the environment have
re-directed efforts towards identifying and augmenting the growth
of endemic organisms that have the capability to metabolize or
remove compounds of interest from the environment.
[0101] The present invention can facilitate bioremediation efforts
in two ways. First, organisms can be identified at a given site
that have either been previously shown to be capable of removing
compounds of interest, or that have significant likelihood of
having the capacity metabolize the relevant compounds based upon
its coincidence in the environment with the chemical in numerous
geological settings. Secondly, this invention can identify trends
of soil types and particular microbial species. In this case, the
correlations that are drawn from the database between microbial
distribution and soil types can be coupled with the existing
knowledge base of geochemistry. For instance, the USGS provides
many publically available maps describing numerous geochemical and
geophysical parameters. Extrapolations of the distribution of
microbial species can be made to the regional, and possibly
worldwide, level. These extrapolated microbial distributions could
serve as the basis for site-specific treatment regimens to augment
the growth of certain relevant species without first performing a
microbial survey.
Immunological Applications
[0102] Methods could be applied to map immunoglobulin and TCR gene
rearrangements, which occur normally during B and T cell
differentiation. These rearrangements could provide a profile of an
individual's immunoglobulin and TCR diversity that could be
correlated with medical history. This type of analysis might lead
to the early diagnosis of certain conditions or diseases and allow
for a more proactive and early treatment. Further, samples of
immune cells could be isolated from individuals or certain body
fluids or tissues to identify potential immunoglobulin or TCR
profiles that may be correlated with particular diseases,
particularly autoimmune diseases. For instance, it has been
demonstrated that T cells expressing particular TCR subtypes are
found in higher levels in the synovial fluid of individuals
suffering from rheumatoid arthritis. The methods of the instant
invention may be used to identify other correlative immunoglobulin
or TCR profiles in autoimmune and other diseases.
Virus Detection
[0103] The human genome contains thousands of copies of human
endogenous retroviruses (HERVs) that make up as many as 1% of the
human genome (Sverdlov, E. D. 1998, Perpetually mobile footprints
of ancient infections in human genome. FEBS Lett. 428:1-6). These
sequences are thought to be remnants of infections that occurred
millions of years ago. These sequences can transpose to other
locations in the human genome and may be responsible for
disease-susceptibility in certain human populations (Dawkins, R. et
al., 1999, Genomics of the major histocompatibility complex:
haplotypes, duplication, retroviruses and disease. Immunol. Rev.
167:275-304). This invention could be used to survey HERV
polymorphic sequences and determine whether they correlate with a
variety of clinical parameters.
Forensic Science
[0104] This invention, particularly SARD analysis, can be used in
forensic applications as well. Studies over the past ten to twenty
years have focused upon the changes that occur in a person's or
animal's body after death. Many of these changes involve changes in
microbial populations that occur during decomposition of the body.
Changes in microbial populations have been correlated with length
of time that a person has been dead and the conditions that the
body experienced after death, e.g., heat, sun exposure, partial or
complete burial, rain, etc. SARD analyses would permit forensic
scientists to quickly and accurately determine the size and type of
microbial populations, which in turn may be used to determine more
accurate times of death as well as conditions that the body may
have been exposed to.
Other Applications
[0105] This approach can be utilized for any polymorphic region in
a genome, whether microbial, viral or eukaryotic that is flanked by
conserved DNA sequences. This method also need not be restricted to
genes. The DNA sequence of intergenic regions of genomes are not
under as high a level of selective pressure and thus, represent
highly polymorphic DNA sequence. One example of such a region is
the intergenic region between the large (23S) and small (16S) rDNA
subunits coding regions. The above-described methods may be used to
distinguish members of a population based upon size differences of
the intergenic region between the 16S-23S or 23S-5S rDNA genes. The
spacer region between these genes has been found to be
hypervariable in microbial populations. In the case of the 16S-23S
intergenic region, the spacer size ranges between about 200-1500
base-pairs depending the presence or absence of various tRNA genes.
(Noor M. 1998, 16S-23S and 23S-5S intergenic spacer regions of
lactobacilli: nucleotide sequence, secondary structure and
comparative analysis, Res. Microbiol. 149(6):433-448; Berthier F.
et al., 1998, Rapid species identification within two groups of
closely related lactobacilli using PCR primers that target the
16S/23S rRNA spacer region, FEMS Microbiol Lett. 161(1):97-106;
Tilsala-Timisjarvi A. et al., 1997, Development of oligonucleotide
primers from the 16S-23S rRNA intergenic sequences for identifying
different dairy and probiotic lactic acid bacteria by PCR Int. J.
Food Microbiol. 35(1):49-56). Both of these rDNA genes are
transcribed on the same operon. Therefore, the conserved regions of
the rDNA coding sequence of these subunits can be utilized to
amplify the intergenic regions.
[0106] In order that this invention may be better understood, the
following examples are set forth. These examples are for purposes
of illustration only and are not to be construed as limiting the
scope of the invention in any manner.
Example 1
Serial Analysis of rDNA Polymorphic Tags from the Domain
Eubacteria
[0107] A sample comprising environmental bacteria was obtained and
total DNA was extracted from the sample. To amplify the DNA, PCR
was performed by mixing 5 .mu.L 10.times. Advantage2 reaction
buffer (Clontech), 2.5 .mu.L dNTPs, 5 .mu.L 8 .mu.M TX9/TX16
primers, 50 ng of sample DNA, 0.5 .mu.L Advantage2 Taq polymerase
(Clontech) and water to 50 .mu.L. Primer TX9 was biotinylated. The
primers are shown in FIG. 12 and a general strategy of this example
is shown in FIG. 13. The reaction mixture was then subjected to PCR
under the following conditions:
[0108] a) 94.degree. C. for 5 minutes;
[0109] b) 94.degree. C. for 1 minute, 10 seconds;
[0110] c) 55.degree. C. for 50 seconds;
[0111] d) 68.degree. C. for 1 minute;
[0112] e) repeat steps (b) through (d) 20 times; and
[0113] f) 68.degree. C. for 2 minutes.
[0114] The approximately 600 basepair PCR product was gel purified
using 1% agarose and a Qiaquick kit (Qiagen) according to
manufacturer's instructions. The DNA was eluted with 50 .mu.L
Tris-EDTA (TE; 10 mM Tris pH 8; 1 mM EDTA). To restrict the
amplified DNA, 10.times. New England Biolabs Buffer #2 (NEB#2) was
added to bring the concentration of the buffer to 1.times. and 25
units of AluI was added. The reaction mixture was incubated for 2
hours at 37.degree. C.; 25 additional units of AluI was added and
the reaction mixture was incubated for a further one hour followed
by inactivation of the enzyme at 65.degree. C. for 20 minutes.
[0115] In order to immobilize the restricted DNA fragment to a
bead, an equal volume of 2.times.BW buffer (2.times.BW=10 mM Tris
pH 7.5; 1 mM EDTA; 2 M NaCl) was added to the reaction mixture,
followed by addition of 50 .mu.L washed M-280 Streptavidin (SA)
beads (Dynal). The reaction mixture was incubated for 20 minutes at
room temperature. The beads were then washed twice in 1.times.BW
and twice in wash buffer (10 mM Tris pH 8; 10 mM MgSO.sub.4; 50 mM
NaCl). During the last wash step, the beads were split into two
pools.
[0116] To add the linkers to the immobilized DNA, one pool of beads
were resuspended in 4 .mu.L of 10 .mu.M linker TX-12/13 (a
double-stranded DNA molecule comprising primers TX-012 and TX-013)
to form pool A, while the other pool (pool B) were resuspended in 4
.mu.L of 10 .mu.M linker TX-14/15 (a double-stranded DNA molecule
comprising primers TX-014 and TX-015) was added to the other pool.
36 .mu.L of T4 ligase mix (4 .mu.L 10.times. ligase buffer; 32
.mu.L water; 0.2 .mu.L T4 ligase [400 U]) were added to the
linker/bead mixture and the mixture was incubated overnight at
16.degree. C.
[0117] The DNA molecule attached to the streptavidin beads was then
incubated with BpmI, which recognizes its specific restriction
sequence (GAGGTC) in the DNA molecule. BpmI cleaves the DNA such
that it releases the streptavidin bead from the DNA molecule and
incorporates a part of the polymorphic region of the DNA molecule
first amplified. The beads were washed twice with 0.5 mL each
1.times.BW and twice with wash buffer. The beads were resuspended
in 10 .mu.L BpmI mix (1 .mu.L 10.times.NEB#3 [New England Biolabs];
1 .mu.L 1 .mu.g/.mu.L bovine serum albumin; 8 .mu.L water and 1
.mu.L BpmI [New England Biolabs]). The reaction mixture was
incubated at 37.degree. C. for 2 hours and then at 65.degree. C. to
inactivate the enzyme. The supernatant containing the DNA tags was
then isolated.
[0118] In order to remove the 3' overhang on the DNA tags and make
them blunt ended, to the supernatant (10 .mu.L) on ice was added 10
.mu.L T4 polymerase mix (1 .mu.l, 10.times.NEB#2; 0.5 .mu.L 4 mM
dNTPs; 8.5 .mu.L water and 0.33 .mu.L T4 polymerase [1 U; New
England Biolabs]). The reaction was incubated at 12.degree. C. for
20 minutes and at 65.degree. C. for 20 minutes to inactivate the
polymerase. In order to form ditags, pool A and pool B were
recombined to give a total volume of 40 .mu.L. Four .mu.L 10 mM
rATP and 0.2 .mu.L T4 DNA ligase (400 U) was added and the reaction
was incubated 4 hours to overnight at 16.degree. C.
[0119] As an intermediate amplification step, the ditags were then
amplified in a 300 .mu.L PCR reaction (30 .mu.L 10.times.
Advantage2 Taq buffer; 15 .mu.L 4 mM dNTPs; 30 .mu.L 8 .mu.M
TX111/TX121 primer mix; 3 .mu.L Advantage2 Taq polymerase; 3 .mu.L
ditag template; 219 .mu.L water). The 300 .mu.L reaction mix was
split into three 100 .mu.L reactions and amplified using the
following conditions:
[0120] a) 94.degree. C. for 5 minutes;
[0121] b) 94.degree. C. for 30 seconds;
[0122] c) 56.degree. C. for 30 seconds;
[0123] d) 68.degree. C. for 40 seconds;
[0124] e) repeat steps (b) through (d) for 15 cycles; and
[0125] f) 68.degree. for 2 minutes.
After amplification, three volumes (900 .mu.L) QG buffer (Qiagen)
and one volume (300 .mu.L) isopropanol was added, the mixture was
bound to a Gel Extraction Spin Column (Qiagen) and the DNA was
eluted with 50 .mu.L TE.
[0126] The amplified DNA was then subjected to non-denaturing
polyacrylamide gel electrophoresis (PAGE; Novex 1 mm 10% TBS-PAGE
gel). The gel was stained with 5 .mu.g/mL ethidium bromide, the
approximately 106 basepair ditags were excised from the gel, the
excised gel was fragmented and 0.3 mL of TE was added to the gel
and incubated at 65.degree. C. for 30 minutes. The gel/TE mixture
was transferred to a miniprep spin column (Qiagen) and the eluate
containing the amplified ditags was collected (the ditag template).
This amplified DNA was called the P300 ditag template.
[0127] In order to determine the optimal number of PCR cycles for
large-scale amplification (PCR cycle titration), 6.4 .mu.L P300
ditag template was mixed with 8 .mu.L 10.times. Advantage2 Taq
buffer, 4 .mu.L 4 mM dNTPS, 8 .mu.L TX111/TX121 primers, 0.8 .mu.L
Advantage2 Taq polymerase and 52.8 .mu.L water. The reaction
mixture was split into three 25 .mu.L reactions and amplified for
6, 8 or 10 cycles using the PCR conditions described above for the
intermediate amplification step. After amplification, the DNA
products were desalted and purified using a Qiagen Gel Extraction
Spin Column as described above except that only 75 .mu.L QG buffer
and 25 .mu.L isopropanol were used, and the DNA products were
eluted with 20 .mu.L TE. The cycle number that produced the largest
106 basepair ditag yield without any detectable vertical smearing
by 10% TBS-PAGE analysis was used for large-scale
amplification.
[0128] For large-scale ditag amplification, 180 .mu.L 10.times.
Advantage2 Taq buffer, 90 .mu.L 4 mM dNTPs, 180 .mu.L 8 .mu.M
TX111/TX121 mixture, 18 .mu.L Advantage2 Taq polymerase, 144 .mu.L
P300 ditag template and 1188 .mu.L water was mixed together and
then split into 18 100 .mu.L reactions. PCR was performed as
described immediately above using the number of cycles that were
determined from the previous titration step. The PCR reactions were
desalted by adding three volumes QG buffer and one volume
isopropanol to the reactions and passed through a total of four
Qiagen Gel Extraction Spin Columns. Each column was eluted with 50
.mu.L TE. The samples were subjected to non-denaturing preparative
PAGE (Novex 1.5 mm 10% TBS-PAGE). The DNA bands were stained and
excised as above and split into three tubes. The polyacrylamide was
fragmented and eluted using TE as described above and the DNA was
purified by passing the contents of the gel/TE mixture through a
Qiagen Plasmid Spin Miniprep column as described above.
[0129] To precipitate the DNA, 1 .mu.L glycogen, 150 .mu.L 10M
ammonium acetate and 1125 .mu.L ethanol was added to each eluate of
approximately 300 .mu.L. The mixture was incubated at -80.degree.
C. for 20 minutes and microfuged at 13,000 rpm at 4.degree. C. for
15 minutes. The pellet was washed with 1 mL 70% ethanol and dried
at 37.degree. C. for 5 minutes.
[0130] The DNA pellets were resuspended in 150 .mu.L AluI mix (15
.mu.L NEB#2, 135 .mu.L water and 15 .mu.L AluI [150 U]). The
reaction mixture was incubated at 37.degree. C. for 2 hours. 150
.mu.L of 2.times.BW buffer was added and the sample was transferred
to a new tube containing 150 packed SA beads. The beads were
incubated with the DNA mixture for 30 minutes at room temperature
and the beads were pelleted magnetically. The supernatant was
removed, extracted with 250 .mu.L phenol/chloroform/isoamyl
alcohol, and the aqueous phase was precipitated with 150 .mu.L 10M
ammonium acetate and 1125 .mu.L ethanol overnight at -80.degree. C.
The DNA was centrifuged at 13,000 rpm at 4.degree. C. for 15
minutes, the pellet washed with 70% ethanol and dried. This step
removes any free linkers that were present as well as any
incompletely digested DNA products that were still bound to the
biotinylated linkers.
[0131] In order to concatenate the ditags, the DNA pellet was
resuspended in 10 .mu.L ligase mix (1 .mu.L 10.times. ligase
buffer; 9 .mu.L water and 1 .mu.L T4 ligase [100 U]) and incubated
at 16.degree. C. for 30 minutes. The DNA was precipitated by added
40 .mu.L TE, 25 .mu.L 10M ammonium acetate and 180 .mu.L ethanol.
The DNA was precipitated for 15 minutes at -80.degree. C. and
centrifuged, washed and dried as described above.
[0132] The concatenated ditags were purified by resuspending the
pellet in 1.times.TBE loading buffer and subjecting the sample to
non-denaturing PAGE (8% TBE-PAGE). The area between 0.5 to 1.2 kb
was excised and the DNA was eluted from the gel into 300 .mu.L TE
as described above. The DNA was precipitated using 1 .mu.L
glycogen, 150 .mu.L 10M ammonium acetate and 1125 .mu.L ethanol.
The DNA was precipitated, centrifuged, washed and dried as
before.
[0133] The concatenated ditags were cloned in pUC19 by resuspending
the DNA pellet in 3 .mu.L SmaI-cut pUC 19 (approximately 100 ng
plasmid DNA) and adding 7 .mu.L T4 ligase mix (1 .mu.L 10.times.
ligase buffer, 6 .mu.L water and 0.2 .mu.L T4 ligase [400 U]). The
plasmid/ditag mixture was incubated for 2 hours at 16.degree. C.
and 1 .mu.L of the mixture was used to transform 100 .mu.L
chemically-competent DH10B. Amp-resistant transformants were
screened by PCR using pUC/m13 forward/reverse 17mers as PCR
primers. Transformants containing concatemerized ditags were then
sequenced.
Example 2
SARD Analysis of a Defined Population
[0134] SARD was performed essentially as described in Example 1. In
this example, commercially available bacterial genomic DNA samples
were mixed at an equal concentration (weight/volume). The bacterial
DNA samples used included Bacillus subtilis, Clostridium
perfringens, Escherchia coli, Lactococcus lactis and Streptomyces
coelicolor. Equal volumes of the DNA samples (total genomic DNA)
were mixed at a concentration of 50 ng/.mu.L each. Chart I shows
the size of the genomes of each bacterial species, the number of
16S rDNA copies per genome and the molar percentage of 16S copies
for each bacterial species in the total DNA sample.
Chart I
TABLE-US-00002 [0135] Bacteria Genome (Mb) 16S Copies/Genome Molar
% B. subtilis 4.2 10 17.2 C. perfringens 4.4 10 16.4 E. coli 4.6 7
11.0 L. lactis 2.4 10 30.7 S. coelicolor 8.0 6 5.4
[0136] After SARD,120 tags were sequenced. Chart II shows the
expected number and percentage of tags compared to the observed
number and percentage of tags for the population. See also FIG.
14.
Chart II
TABLE-US-00003 [0137] Expected Observed Tag Observed Tag Expected
Tag Tag Bacteria Number Number Percentage Percentage B. subtilis 21
44 17.2% 36.7% C. perfringens 20 18 16.4% 15.0% E. coli 13 8 11.0%
6.7% L. lactis 37 35 30.7% 29.2% S. coelicolor 6 6 5.4% 5.0%
[0138] Each of the rDNA genes from these species produced a SARD
tag that was distinguishable from the other members of the set. As
can be observed, approximately twice as many tags that corresponded
to 16S rDNA from B. subtilis were found than was expected based
upon the molar percentage of 16S rDNA from B. subtilis. The
observation that B. subtilis appeared to be twice as abundant as
was expected has been reported previously. Farrelly et al.
(Farrelly, V. F. et al., 1995, Effect of genome size and rrn gene
copy number on PCR amplification of 16S rRNA genes from a mixture
of bacterial species. Appl. Environ. Microbiol. 61(7):2798-2801)
described similar results when PCR amplifying the 16S rDNA gene
from mixed populations of genomic DNA. They concluded that this
phenomenon was the result of the tandem organization of the rrn
operons in the B. subtilis genome where multiple rDNA genes may be
amplified as a single product. The remaining tags were found at
abundances close (<40% deviation) to their expected values.
Example 3
SARD Analysis of Environmental Bacterial Diversity
[0139] In order to demonstrate that the SARD method could be used
to survey environmental bacterial diversity, total DNA was
extracted from two soil samples (Wy-1 and Wy-2) taken from the
Rocky Mountain Oilfield Testing Center (RMOTC, Casper, Wyo.) in
October, 2000. The samples were collected about 0.5 miles apart and
from a depth of 14-18 inches. The environmental DNA samples were
subjected to SARD analysis as described in Example 1. In a
preliminary analysis, 148 tags were identified from Wy-1 and 234
tags were identified from Wy-2 (FIGS. 15 and 16, respectively).
[0140] In the Wy-1 sample, 58 distinct tags were identified and the
abundance of each tag varied. The most abundant tag
(ATGGCTGTCGTCAGCT) (SEQ ID NO: 6) made up about 34% of the
population. This tag sequence is identical to many bacterial
sequences in GenBank and its position within the 16S rDNA gene
indicates that it is located in a conserved region located distal
to the targeted AluI restriction site. In other words, the
contributing 16S gene(s) for this tag did not contain the conserved
AluI site. Since the SARD tag position is dictated by the first
AluI site distal to the biotinylated primer used in the initial PCR
reaction, it is likely that the first AluI site in the contributing
16S gene(s) was located downstream within a conserved region. In
order to decrease the number of tags that do not contain the
conserved AluI site next to the polymorphic region, one may gel
purify the approximately 100 basepair PCR products after the first
AluI restriction step. However, this may result in losing some
information. Nevertheless, 39% of the tags (58/148) in this set
were different from each other. See FIGS. 15 and 17.
[0141] The Wy-2 sample was found to contain 79 different tags out
of a total of 234 tags that were examined Thus, 34% of the tags
(79/234) in this set were different from each other. See FIGS. 16
and 17. As in the case with Wy-1, the tag ATGGCTGTCGTCAGCT (SEQ ID
NO: 6), which represents a conserved sequence in a 16S rDNA gene,
was most abundant and made up about 30% of the population.
[0142] Combining the tags from the two sets reveals a total of 105
different tags from a total of 382 tags. Thus, 26 of 58 different
tags (45%) in Wy-1 were not present in Wy-2. Likewise, 47 of 79
different tags (59%) in Wy-2 were not present in Wy-1. The tags
that were only found in one of the samples are candidates for
bacteria with indicator value for various parameters associated
with each sample. However, there was no attempt in this preliminary
analysis to obtain all of the tags present in the two samples, so
it cannot be concluded that some or most of the tags found in one
sample are not present in the other sample. Thus, one cannot
conclude that there are tags in these two samples that are
indicators for various parameters associated with each sample.
Nonetheless, a full-fledged analysis of these samples may provide
such indicators.
Example 4
Serial Analysis of rDNA Polymorphic Tags from the Domain
Archaea
[0143] The method described in Example 1 can also be applied to the
domain Archaea. The domain Archaea is made up of two known
kingdoms, Euryarchaeota and Crenarchaeota (Pace, N. R. 1997, A
molecular view of microbial diversity and the biosphere. Science
276:734-740). One set of oligonucleotides and restriction enzymes
can be used to survey both of these domains.
[0144] In this example, the following oligonucleotides are
designed: 5'
biotin-TA(CT)T(CT)CCCA(GA)GCGG(CT)(GCT)(GC)(GA)CTT(AGCT)-3' (SEQ ID
NO: 155) corresponding to position 817-838 of the Methanococcus
jannaschii 16S rDNA gene (GenBank Accession number M59126), and
(5'-GGTG(TGC)CA(GC)C(CA) GCCGCGGTAA(TC)ACC(AGCT)-3' (SEQ ID NO:
156) corresponding to position 457-481 of the Methanococcus
jannaschii 16S rDNA gene.
[0145] In this example, a BfaI site (CTAG) is utilized that
corresponds to position 768-771 of the Methanococcus jannaschii 16S
rDNA gene. This site is immediately flanking a polymorphic
region.
[0146] The SARD method was tested in silico using 17
representatives from the domain Archaea (FIG. 4). 15 of the SARD
tags that would be identified were all unique to this set (Table
II). Two species of the genus Methanobacterium did not possess any
BfaI sites in the region that would be amplified and therefore,
would not produce any tags.
Example 5
Surveying PCR-Amplified Polymorphic rDNA Regions
[0147] Oligonucleotide primers that are complementary to conserved
regions immediately flanking a polymorphic region could be used to
amplify the polymorphic DNA sequences. The oligonucleotide primer
sequences could include existing or introduced restriction sites to
enable subsequence cloning into a bacterial vector. By utilizing or
introducing different restriction sites in each primer, the
restriction digested PCR products could be concatemerized in a
unidirectional fashion prior to cloning. This step would allow a
serial sequence analysis of multiple polymorphic regions from a
single recombinant product.
Example 6
Surveying Translation Products of 16S rDNA Polymorphic Regions
[0148] The polymorphic regions of 16S rDNA could also be surveyed
by the parallel identification of the translation products of a
given polymorphic region. Oligonucleotides could be designed such
that they can serve to amplify a polymorphic region. The 5' primer
would also include a T7 polymerase binding site or other polymerase
binding site, a Kozak consensus sequence, an initiator ATG codon
and an epitope to facilitate purification. Examples of epitopes
include hemagglutinin (HA), myc, Flag or polyhistidine. Following
amplification, the products are subjected to in vitro
transcription/translation to produce the peptide products. These
peptides are purified from the cell extract and analyzed by mass
spectrometry. This type of an approach has been applied to the
identification of mutations in the BRCA1 gene (Garvin, A. M. et
al., 2000 MALDI-TOF based mutation detection using tagged in vitro
synthesized peptides. Nature Biotechnol. 18:95-97).
[0149] Although this process would not readily provide DNA sequence
information from which to deduce taxonomy, it would allow for the
creation of microbial diversity profiles comprised of `mass tags`.
These mass tags could be used to identify correlations between
specific tags and various sample parameters. To test the
information content of this approach, a polymorphic region of the
16S rDNA genes from the species in FIG. 3 was translated in silico.
Of the 34 polymorphic regions examined, 32 produced a tag with a
unique mass in this set (Table III).
[0150] Another approach to translating the amplified polymorphic
regions would be to clone and express the sequences in whole cells.
For instance, oligonucleotides could be designed to amplify
polymorphic regions that include sequences at the 5' ends that
would allow for cloning by homologous recombination in yeast. These
sequences could be cloned into an expression cassette to create a
fusion between a secreted protein, such as alpha factor or
invertase, and an epitope to facilitate purification and the
translated amino acid sequence of the rDNA polymorphic region.
Homologous recombination in yeast is quite robust and could easily
enable the isolation of 10.sup.3-10.sup.4 independent recombinants
on a single transformation plate. The secreted products could be
isolated from the medium and identified by mass spectrometry.
Example 7
Hybridization of Microbial rDNA to Immobilized Oligonucleotides
[0151] A complication with using short oligonucleotide probes in
DNA microarrays is the instability of short oligonucleotides
duplexes. A possible solution to this problem is to synthesize
probes that include degenerate sequences to accommodate unknown
sequences. The length of the oligonucleotide is dictated by the
number of degeneracies, or sequence permutations, that are to be
accommodated. For instance, a fully degenerate 9mer oligonucleotide
requires 4.sup.9 or 262,144 different oligonucleotide sequences.
The efficient hybridization of 9mer oligonucleotides is not
possible using standard conditions. One solution has been to
incorporate a ligation step in the hybridization of a target
sequence to a 9mer oligonucleotide probe (Gunderson, K. L. et al.,
1998, Mutation detection by ligation to complete n-mer DNA arrays.
Genome Res. 8:1142-1153).
[0152] Another solution could be to effectively increase the length
of the oligonucleotide by including nucleotides in the primer that
are not degenerate. This approach could be applied to a survey of a
microbial community by constructing oligonucleotide probes that are
composed of a constant region that corresponds to a well conserved
region of a 16S rDNA gene together with a degenerate sequence that
corresponds to the flanking polymorphic region.
[0153] An example of such a collection of degenerate
oligonucleotides for the domain Bacteria could include permutations
of the following primer: 5'-AACGAGCGCAACC-3' (SEQ ID NO: 157),
where N indicates any nucleotide at that position. This sequence
corresponds to position 1101-1122 of the E. coli 16S rDNA gene
(GenBank Accession number E05133). Alternatively, the primers could
be designed such that they are composed of a mixture of constant
sequence, semi-degenerate positions (e.g. A or G) and degenerate
positions (e.g. A, G, C or T).
[0154] All publications and patent applications cited in this
specification are herein incorporated by reference as if each
individual publication or patent application were specifically and
individually indicated to be incorporated by reference. Although
the foregoing invention has been described in some detail by way of
illustration and example for purposes of clarity of understanding,
it will be readily apparent to those of ordinary skill in the art
in light of the teachings of this invention that certain changes
and modifications may be made thereto without departing from the
spirit or scope of the appended claims.
TABLE-US-00004 TABLE I* Species GenBank Acc # Tag Sequence Position
SEQ ID NO: Desulfurobacterium thermolithotrophum AJ001049
GTCAGTTGCCGAAGCT 814-829 158 Uncultured Aquificales OPS132 AF027104
GTCCGTGCCGTAAGCT 810-825 159 Bacteroides caccae X83951 ##STR00001##
1021-1036 160 Actinomyces bovis X81061 TTTCCGCGCCGTAGCT 834-849 161
Actinomyces meyeri X82451 TTTCTGCGCCGTAGCT 828-843 162
Denitrobacterium detoxificans AF079507 CCTCCGCGCCGCAGCT 788-803 163
Uncultured GNS bacteria BPC110 AF154084 CCCGGTAGTCCTAGCT 765-780
164 Uncultured GNS bacteria GCA004 AF154104 CATCGGTGCCGCAGCT
824-839 165 Uncultured GNS bacteria GCA112 AF154100
CGGCGGTGCCGTAGCT 826-841 166 Acetobacter aceti AF127399
ACTCAGTGTCGTAGCT 782-797 167 Gluconobacter asaii AB024492
ACTCAGTGTCGAAGCT 783-798 168 Burkholderia sp. JB1 X92188
CCTTAGTAACGAAGCT 837-852 169 Denitrobacter permanens Y12639
##STR00002## 789-804 170 Desulfobacter curvatus M34413
CTGCTGTGCCNAAGCT 861-876 171 Desulfobulbus sp. BG25 U85473
CCTCTGTGTCGCAGCT 854-869 172 Legionella anisa X73394 ##STR00003##
790-805 173 Benzene mineralizing clone SB-1 AF029039 ##STR00004##
1029-1044 174 Escherichia coli E05133 CGTGGCTTCCGGAGCT 848-863 175
Uncultured Acidobacterium Sub. Div-1 X68464 CCGCCGTGCCGAAGCT
813-828 176 Uncultured Acidobacterium Sub. Div-1 Z73363
CGGCTGTGCCGAAGCT 521-536 177 Uncultured Acidobacterium Sub. Div-1
Z73365 CCACTGTGCCGTAGCT 521-536 178 Uncultured Acidobacterium Sub.
Div-1 Z73368 CTGCTGTGCCGCAGCT 521-536 179 Uncultured Acidobacterium
Sub. Div-1 Z73364 CTGCCGTGCCGGAGCT 521-536 180 Uncultured
Acidobacterium Sub. Div-1 U68659 CCAATGTGCCGGAGCT 319-334 181
Uncultured Acidobacterium Sub. Div-1 D26171 CCGTCGTGCCGTAGCT
779-794 182 Uncultured Acidobacterium Sub. Div-1 X97101
CCGTCGTGTCGTAGCT 687-702 183 Uncultured Acidobacterium Sub. Div-1
X97098 CTGCCGTGTCGAAGCT 798-813 184 Uncultured Acidobacterium Sub.
Div-1 AF047646 CTCCCGTGTCGAAGCT 779-794 185 Uncultured
Acidobacterium Sub. Div-1 AF050548 CCGCCGTGCCGGAGCT 316-331 186
Uncultured Acidobacterium Sub. Div-2 U68612 CTGAGGAACGAAAGCT
226-241 187 Uncultured Acidobacterium Sub. Div-2 Y07646
GTGTCGTCCCGGAGCT 830-845 188 Uncultured Acidobacterium Sub. Div-3
X97097 GGGCTGTGCCGAAGCT 804-819 189 Uncultured Acidobacterium Sub.
Div-3 X68466 GGTCGGTGCCGGAGCT 796-811 190 Uncultured Acidobacterium
Sub. Div-3 X68468 GGTCGGTGCCAGAGCT 796-811 191 Uncultured
Acidobacterium Sub. Div-3 U68648 GGTTCGTGCCGGAGCT 317-332 192
Uncultured Acidobacterium Sub. Div-3 X68467 TGTCTGTGCCGGAGCT
796-811 193 Uncultured Acidobacterium Sub. Div-3 AF013515
TATCCGTGCCGGAGCT 799-814 194 Uncultured Acidobacterium Sub. Div-3
AF027004 GGTCCGTGCCGGAGCT 778-793 195 *Sequences shown in bold with
shadow indicates they are not unique to this set.
TABLE-US-00005 TABLE II GenBank SEQ ID Species Acc# Tag Sequence
Position NO: Crenarchaeota Aeropyrum pernix D83259 CTAGGGGGCGGGAG
614-627 196 Desulfurococcus mobilis M36474 CTAGGTGTTGGGTG 856-869
197 Staphylothermus marinus X99560 CTAGGTGTTGGGCG 770-783 198
Metallosphaera sedula X90481 CTAGGTGTCGCGTA 756-769 199 Sulfolobus
acidocaldarius D14053 CTAGGTGTCGAGTA 785-798 200 Sulfolobus
metallicus D85519 CTAGGTGTCACGTG 744-757 201 Caldivirga
maquilingensis AB013926 CTAGCTGTTGGGTG 773-786 202 Pyrobaculum
islandicum L07511 CTAGCTGTCGGCCG 781-794 203 Euryarchaeota
Archaeoglobus fulgidus X05567 CTAGGTGTCACCGA 780-793 204
Archaeoglobus veneficus Y10011 CTAGGTGTCACCGG 758-771 205
Haloarcula japonica D28872 CTAGGTGTGGCGTA 762-775 206 Halococcus
morrhuae D11106 CTAGGTGTGGCGTT 765-778 207 Nlethanococcus
jannaschii M59126 CTAGGTGTCGCGTC 768-781 208 Methanobacterium
bryantii AF028688 None Methanobacterium subterraneum X99045 None
Pyrococcus abyssi Z70246 CTAGGTGTCGGGCG 767-780 209 Picrophilus
oshimae X84901 CTAGCTGTAAACTC 742-755 210
TABLE-US-00006 TABLE III GenBank SEQ ID Species Acc# Tag Sequence
M.W. Position NO: Desulfurobacterium thermolithotrophum AJ001049
RAQPLSLVASG* 1097.40 1079-1136 211 Uncultured Aquificales OP S132
AF027104 RAQPLSCVTSG* 1117.40 1074-1131 212 Bacteroides caccae
X83951 RAQPLSSVTNRSC* 1417.70 1069-1126 213 Actinomyces Bovis
X81061 RAQPLSRVASTLWWGLAGD 2083.60 1088-1145 214 Actinomyces meyeri
X82451 RAQPLPYVASTLWWGLVGD 2128.60 1082-1139 215 Denitrobacterium
detoxificans AF079507 RAQPLPHVASIRLGTHGG 1866.50 1039-1094 216
Uncultured GNS bacteria BPC110 AF154084 RAQPLLYVIRVIPD 1652.10
1074-1116 217 Uncultured GNS bacteria GCA004 AF154104
RAQPSLYVTRIIRD 1687.10 1080-1122 218 Uncultured GNS bacteria GCA112
AF154100 RAQPSPYVIRVIRD 1669.00 1082-1124 219 Acetobacter aceti
AF127399 RAQPLSLVASMFGWAL* 1746.30 1038-1095 220 Gluconobacter
asaii AB024492 RAQPLSLVASTFRWAL* 1815.30 1034-1092 221 Burkholderia
sp. JB1 X92188 RAQPLSLVATQEHSRET 1922.20 1094-1144 222
Denitrobacter permanens Y12639 RAQPLPLVATFSWAL* 1669.10 1077-1131
223 Desulfobacter curvatus M34413 RAQPLSLVASTLCGNSNET 1960.40
1116-1172 224 Desulfobulbus sp. BG25 U85473 RAQPLPLVASSSAGHSKGT
1863.40 1114-1170 225 Legionella anisa X73394 RAQPLSLVAST* 1141.40
1078-1135 226 Benzene mineralizing clone SB-1 AF029039
RAQPLPLVANRSSWGL* 1764.20 1077-1134 227 Escherichia coli E05133
RAQPLSFVASGPAGNSKET 1916.30 1103-1159 228 Uncultured Acidobacterium
Sub. Div-1 273363 RAQPLSLVASGSSRAL* 1612.10 775-832 229 Uncultured
Acidobacterium Sub. Div-1 273365 RAQPLSSVAIGSSRATLAK 1912.50
777-835 230 Uncultured Acidobacterium Sub. Div-1 273368
RAQPLFASCHH* 1933.50 779-835 231 Uncultured Acidobacterium Sub.
Div-1 273364 RAQPLFAQLPSFSWALCRN 2204.80 778-835 232 Uncultured
Acidobacterium Sub. Div-1 U68659 RAQPLLPXAII* 1218.70 573-630 233
Uncultured Acidobacterium Sub. Div-1 D26171 RAQPLLPVATI* 1177.50
1035-1090 234 Uncultured Acidobacterium Sub. Div-1 X97101
RAQPLSPVAII* 1163.50 943-998 235 Uncultured Acidobacterium Sub.
Div-1 X97098 RAQPLSSVATI* 1141.40 1054-1109 236 Uncultured
Acidobacterium Sub. Div-1 AF047646 RAQPLFLVATI* 1227.60 1035-1090
237 Uncultured Acidobacterium Sub. Div-1 AF050548 RAQPSSLVANTLW*
1441.70 572-629 238 Uncultured Acidobacterium Sub. Div-2 U68612
RAQPLHVVATRKRELYVD* 2150.60 577-630 239 Uncultured Acidobacterium
Sub. Div-2 Y07646 RAQPLHVVATPQGGTLRG 1857.30 1085-1140 240
Uncultured Acidobacterium Sub. Div-3 X97097 RAQPSSLVANPQGKHPKGT
1972.40 1060-1116 241 Uncultured Acidobacterium Sub. Div-3 U68648
RARPLSCVAII* 1197.70 574-629 242 Uncultured Acidobacterium Sub.
Div-3 AF013515 RAQPLSCVANPQGCTLRR 1969.50 1057-1112 243 Uncultured
Acidobacterium Sub. Div-3 AF027004 RAQPSPCVATPPRAGALSGD 1950.40
1036-1096 244 *Indicates an in-frame stop codon was encountered
within the polymorphic sequence.
Sequence CWU 1
1
244116DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
1acgatgagca ctagct 16216DNAUnknownDescription of Unknown ribosomal
DNA sequence tag isolated from microbes in soil sample collected in
Wyoming, USA 2acgatgagta ctagct 16316DNAUnknownDescription of
Unknown ribosomal DNA sequence tag isolated from microbes in soil
sample collected in Wyoming, USA 3acgatgatga ctagct
16416DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
4acgatggatg ctagct 16516DNAUnknownDescription of Unknown ribosomal
DNA sequence tag isolated from microbes in soil sample collected in
Wyoming, USA 5atgctagtct ggagct 16616DNAUnknownDescription of
Unknown ribosomal DNA sequence tag isolated from microbes in soil
sample collected in Wyoming, USA 6atggctgtcg tcagct
16716DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
7atggttgtcg tcagct 16816DNAUnknownDescription of Unknown ribosomal
DNA sequence tag isolated from microbes in soil sample collected in
Wyoming, USA 8attccgtgcc gtagct 16916DNAUnknownDescription of
Unknown ribosomal DNA sequence tag isolated from microbes in soil
sample collected in Wyoming, USA 9cactagtggc gcagct
161016DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
10cccccgtgcc gaagct 161116DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 11cccccgtgcc gcagct
161216DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
12cccccttcct ccagct 161316DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 13ccccggtgcc gcagct
161416DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
14ccgggtagtc ccagct 161516DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 15cctccgtgcc gaagct
161616DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
16cctccgtgcc gcagct 161716DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 17cctccgtgct gcagct
161816DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
18cctcggcgcc gcagct 161916DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 19cctcggtgcc gcagct
162016DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
20cctcggtgtc gcagct 162116DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 21cctgggtgcc gcagct
162216DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
22cctgtgtgac gaagct 162316DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 23ccttggtaac gaagct
162416DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
24ccttggtacc gaagct 162516DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 25cgccagtgcc gtagct
162616DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
26cgcctgtgcc gtagct 162716DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 27cgtccgtgcc gaagct
162816DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
28cgtccgtgcc gcagct 162916DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 29cgtcggtgcc gcagct
163016DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
30ctcccgtgcc gcagct 163116DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 31ctcccgtgcc ggagct
163216DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
32ctccggtgcc gcagct 163316DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 33ctcctgtgcc gaagct
163416DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
34ctcctgtgcc gcagct 163516DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 35ctgccgtgcc gaagct
163616DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
36ctgctgtgcc gaagct 163716DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 37ctgtcgtgcc gaagct
163816DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
38cttcagtatc gaagct 163916DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 39cttccgcgcc ggagct
164016DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
40cttccgtgcc gcagct 164116DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 41cttccgtgcc ggagct
164216DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
42cttcggtgcc gcagct 164316DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 43cttctgtggc gaagct
164416DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
44gatccgtgcc gtagct 164516DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 45gctctgtgcc gaagct
164616DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
46gctgggtgcc caagct 164716DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 47ggtccgtgcc gcagct
164816DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
48ggtgctcttc ggagct 164916DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 49gtaaacgatg gaagct
165016DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
50gtggctgtcg tcagct 165116DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 51gttccgtgcc gaagct
165216DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
52gttccgtgcc gcagct 165316DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 53tatcagtggc gcagct
165416DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
54tctccgtgcc gcagct 165516DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 55tctctgtgcc gcagct
165616DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
56tctctgtgcc gtagct 165716DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 57tgtccgtgcc gtagct
165816DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
58tttccgtgcc gcagct 165916DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 59acgatgataa ctagct
166016DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
60acgatgatga ctagct 166116DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 61acgatgggca ctagct
166216DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
62acggctgtcg tcagct 166316DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 63actacgagcg caagct
166416DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
64acttaatgcg ttagct 166516DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 65atgctagtct ggagct
166616DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
66atggctctcg tcagct 166716DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 67atggctgtcg ccagct
166816DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
68atggctgtcg tcagct 166916DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 69atggttgtcg tcagct
167016DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
70atgtagacct ggagct 167116DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 71attccgtgcc gcagct
167216DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
72attccgtgcc gtagct 167316DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 73cacaagcggt ggagct
167416DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
74cactagtggc gcagct 167516DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 75catccgtgcc gaagct
167616DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
76ccccagggcc caagct 167716DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 77ccccggtgcc gcagct
167816DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
78cccgcgtgcc ggagct 167916DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 79ccgcggtgcc gtagct
168016DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
80ccgggtagtc ccagct 168116DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 81ccgggtagtc ctagct
168216DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
82cctccgtgcc gaagct 168316DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 83cctccgtgcc gcagct
168416DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
84cctccgtgcc ggagct 168516DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 85cctcgtaagg ggagct
168616DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
86cctgggtgcc gcagct 168716DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 87cctggtagtc ccagct
168816DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
88cctggtagtc ctagct 168916DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 89ccttagtaac gcagct
169016DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
90ccttggtaac gaagct 169116DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 91cgccagtgcc gaagct
169216DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
92cgccggtgcc gcagct 169316DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 93cgcctgtgcc gtagct
169416DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
94cgctcgtggc gaagct 169516DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 95cggaggcgtc gtagct
169616DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
96cgtcagtgtc gcagct 169716DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 97cgtccgtgcc gaagct
169816DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
98cgtccgtgcc gcagct 169916DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 99cgtccgtgcc ggagct
1610016DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
100cgtcggtgcc gcagct 1610116DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 101ctccagtgcc gcagct
1610216DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
102ctcccgtgcc acagct 1610316DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 103ctcccgtgcc gcagct
1610416DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
104ctcccgtgcc ggagct 1610516DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 105ctccggtgcc gcagct
1610616DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
106ctcctgtgcc gcagct 1610716DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 107ctgccgcgcc ggagct
1610816DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
108ctgccgtgcc gaagct 1610916DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 109ctgccgtgcc taagct
1611016DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
110ctgcggtgcc gcagct 1611116DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 111ctgctgtgcc gaagct
1611216DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
112ctgtcgtgcc gaagct 1611316DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 113cttccgcgcc ggagct
1611416DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
114cttccgtgcc gaagct 1611516DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 115cttccgtgcc gcagct
1611616DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
116cttccgtgcc ggagct 1611716DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 117cttcggtgcc gcagct
1611816DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
118cttcggtgtc gcagct 1611916DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 119cttgggtgcc gcagct
1612016DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
120ctttagtaac gcagct 1612116DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 121gacccgcaag ggagct
1612216DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
122gatccgtgcc gcagct 1612316DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 123gctccgtgcc gaagct
1612416DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
124gctccgtgcc gtagct 1612516DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 125gctctgtgcc gaagct
1612616DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
126gctctgtgcc gtagct 1612716DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 127gggcttgtcg tcagct
1612816DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
128gtaaacgatg gaagct 1612916DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 129gttccgtgcc gcagct
1613016DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
130gttccgtgcc gtagct 1613116DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 131gttctgtgcc gcagct
1613216DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
132tctcacgaca cgagct 1613316DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 133tctcagtaac gtagct
1613416DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
134tctccgtgcc gcagct 1613516DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 135tctctgtgcc gcagct
1613616DNAUnknownDescription of Unknown ribosomal DNA sequence tag
isolated from microbes in soil sample collected in Wyoming, USA
136tggacgttgc ggagct 1613716DNAUnknownDescription of Unknown
ribosomal DNA sequence tag isolated from microbes in soil sample
collected in Wyoming, USA 137tttccgtgcc ggagct 1613820DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide 138gtgtaghrgt gaaatdcdya 2013921DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide 139ytcacgrcay gagctgacga c 2114028DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide 140gctccaggtc tacatcctag tcaggacc
2814124DNAArtificial SequenceDescription of Artificial Sequence
Synthetic oligonucleotide 141ataggtcctg actaggatgt agac
2414228DNAArtificial SequenceDescription of Artificial Sequence
Synthetic oligonucleotide 142gctccagact agcatccgct gacttgac
2814324DNAArtificial SequenceDescription of Artificial Sequence
Synthetic oligonucleotide 143aatgtcaagt cagcggatgc tagt
2414421DNAArtificial SequenceDescription of Artificial Sequence
Synthetic oligonucleotide 144ggtcctgact aggatgtaga c
2114521DNAArtificial SequenceDescription of Artificial Sequence
Synthetic oligonucleotide 145gtcaagtcag cggatgctag t
2114621DNAArtificial SequenceDescription of Artificial Sequence
Synthetic oligonucleotide 146ggattagawa cccbggtagt c
2114730DNAArtificial SequenceDescription of Artificial Sequence
Synthetic oligonucleotide 147ataggtcctg actaggatgt agacctggag
3014830DNAArtificial SequenceDescription of Artificial Sequence
Synthetic oligonucleotide 148aatgtcaagt cagcggatgc tagtctggag
3014927DNAArtificial SequenceDescription of Artificial Sequence
Synthetic oligonucleotide 149ctccaggtct acatcctagt caggacc
2715030DNAArtificial SequenceDescription of Artificial Sequence
Synthetic oligonucleotide 150ataggtcctg actaggatgt agacctggag
3015127DNAArtificial SequenceDescription of Artificial Sequence
Synthetic oligonucleotide 151ctccagacta gcatccgctg acttgac
2715230DNAArtificial SequenceDescription of Artificial Sequence
Synthetic oligonucleotide 152aatgtcaagt cagcggatgc tagtctggag
3015333DNAArtificial SequenceDescription of Artificial Sequence
Synthetic oligonucleotide 153ctcgatagtc acggtcctga ctaggatgta gac
3315433DNAArtificial SequenceDescription of Artificial Sequence
Synthetic oligonucleotide 154caggttgcaa cggtcaagtc agcggatgct agt
3315522DNAArtificial SequenceDescription of Artificial Sequence
Synthetic oligonucleotide 155taytycccar gcggybsrct tn
2215625DNAArtificial SequenceDescription of Artificial Sequence
Synthetic oligonucleotide 156ggtgbcascm gccgcggtaa yaccn
2515722DNAArtificial SequenceDescription of Artificial Sequence
Synthetic oligonucleotide 157aacgagcgca accnnnnnnn nn
2215816DNADesulfurobacterium thermolithotrophum 158gtcagttgcc
gaagct 1615916DNAUnknownDescription of Unknown Uncultured
Aquificales OPS132 159gtccgtgccg taagct 1616016DNABacteroides
caccae 160atggttgtcg tcagct 1616116DNAActinomyces bovis
161tttccgcgcc gtagct 1616216DNAActinomyces meyeri 162tttctgcgcc
gtagct 1616316DNADenitrobacterium detoxificans 163cctccgcgcc gcagct
1616416DNAUnknownDescription of Unknown Uncultured GNS bacteria
BPC110 164cccggtagtc ctagct 1616516DNAUnknownDescription of Unknown
Uncultured GNS bacteria GCA004 165catcggtgcc gcagct
1616616DNAUnknownDescription of Unknown Uncultured GNS bacteria
GCA112 166cggcggtgcc gtagct 1616716DNAAcetobacter aceti
167actcagtgtc gtagct 1616816DNAGluconobacter asaii 168actcagtgtc
gaagct 1616916DNABurkholderia sp.JB1 169ccttagtaac gaagct
1617016DNADenitrobacter permanens 170acgatgtcaa ctagct
1617116DNADesulfobacter curvatusmodified_base(11)..(11)a, c, t or g
171ctgctgtgcc naagct 1617216DNADesulfobulbus sp.BG25 172cctctgtgtc
gcagct 1617316DNALegionella anisa 173acgatgtcaa ctagct
1617416DNAUnknownDescription of Unknown Benzene mineralizing clone
SB-1 174atggttgtcg tcagct 1617516DNAEscherichia coli 175cgtggcttcc
ggagct 1617616DNAUnknownDescription of Unknown Uncultured
Acidobacterium Sub.Div-1 176ccgccgtgcc gaagct
1617716DNAUnknownDescription of Unknown Uncultured Acidobacterium
Sub.Div-1 177cggctgtgcc gaagct 1617816DNAUnknownDescription of
Unknown Uncultured Acidobacterium Sub.Div-1 178ccactgtgcc gtagct
1617916DNAUnknownDescription of Unknown Uncultured Acidobacterium
Sub.Div-1 179ctgctgtgcc gcagct 1618016DNAUnknownDescription of
Unknown Uncultured Acidobacterium Sub.Div-1 180ctgccgtgcc
ggagct
1618116DNAUnknownDescription of Unknown Uncultured Acidobacterium
Sub.Div-1 181ccaatgtgcc ggagct 1618216DNAUnknownDescription of
Unknown Uncultured Acidobacterium Sub.Div-1 182ccgtcgtgcc gtagct
1618316DNAUnknownDescription of Unknown Uncultured Acidobacterium
Sub.Div-1 183ccgtcgtgtc gtagct 1618416DNAUnknownDescription of
Unknown Uncultured Acidobacterium Sub.Div-1 184ctgccgtgtc gaagct
1618516DNAUnknownDescription of Unknown Uncultured Acidobacterium
Sub.Div-1 185ctcccgtgtc gaagct 1618616DNAUnknownDescription of
Unknown Uncultured Acidobacterium Sub.Div-1 186ccgccgtgcc ggagct
1618716DNAUnknownDescription of Unknown Uncultured Acidobacterium
Sub.Div-2 187ctgaggaacg aaagct 1618816DNAUnknownDescription of
Unknown Uncultured Acidobacterium Sub.Div-2 188gtgtcgtccc ggagct
1618916DNAUnknownDescription of Unknown Uncultured Acidobacterium
Sub.Div-3 189gggctgtgcc gaagct 1619016DNAUnknownDescription of
Unknown Uncultured Acidobacterium Sub.Div-3 190ggtcggtgcc ggagct
1619116DNAUnknownDescription of Unknown Uncultured Acidobacterium
Sub.Div-3 191ggtcggtgcc agagct 1619216DNAUnknownDescription of
Unknown Uncultured Acidobacterium Sub.Div-3 192ggttcgtgcc ggagct
1619316DNAUnknownDescription of Unknown Uncultured Acidobacterium
Sub.Div-3 193tgtctgtgcc ggagct 1619416DNAUnknownDescription of
Unknown Uncultured Acidobacterium Sub.Div-3 194tatccgtgcc ggagct
1619516DNAUnknownDescription of Unknown Uncultured Acidobacterium
Sub.Div-3 195ggtccgtgcc ggagct 1619614DNAAeropyrum pernix
196ctagggggcg ggag 1419714DNADesulfurococcus mobilis 197ctaggtgttg
ggtg 1419814DNAStaphylothermus marinus 198ctaggtgttg ggcg
1419914DNAMetallosphaera sedula 199ctaggtgtcg cgta
1420014DNASulfolobus acidocaldarius 200ctaggtgtcg agta
1420114DNASulfolobus metallicus 201ctaggtgtca cgtg
1420214DNACaldivirga maquilingensis 202ctagctgttg ggtg
1420314DNAPyrobaculum islandicum 203ctagctgtcg gccg
1420414DNAArchaeoglobus fulgidus 204ctaggtgtca ccga
1420514DNAArchaeoglobus veneficus 205ctaggtgtca ccgg
1420614DNAHaloarcula japonica 206ctaggtgtgg cgta
1420714DNAHalococcus morrhuae 207ctaggtgtgg cgtt
1420814DNAMethanococcus jannaschii 208ctaggtgtcg cgtc
1420914DNAPyrococcus abyssi 209ctaggtgtcg ggcg
1421014DNAPicrophilus oshimae 210ctagctgtaa actc
1421111PRTDesulfurobacterium thermolithotrophum 211Arg Ala Gln Pro
Leu Ser Leu Val Ala Ser Gly 1 5 10 21211PRTUnknownDescription of
Unknown Uncultured Aquificales OPS132 212Arg Ala Gln Pro Leu Ser
Cys Val Thr Ser Gly 1 5 10 21313PRTBacteroides caccae 213Arg Ala
Gln Pro Leu Ser Ser Val Thr Asn Arg Ser Cys 1 5 10
21419PRTActinomyces bovis 214Arg Ala Gln Pro Leu Ser Arg Val Ala
Ser Thr Leu Trp Trp Gly Leu 1 5 10 15 Ala Gly Asp
21519PRTActinomyces meyeri 215Arg Ala Gln Pro Leu Pro Tyr Val Ala
Ser Thr Leu Trp Trp Gly Leu 1 5 10 15 Val Gly Asp
21618PRTDenitrobacterium detoxificans 216Arg Ala Gln Pro Leu Pro
His Val Ala Ser Ile Arg Leu Gly Thr His 1 5 10 15 Gly Gly
21714PRTUnknownDescription of Unknown Uncultured GNS bacteria
BPC110 217Arg Ala Gln Pro Leu Leu Tyr Val Ile Arg Val Ile Pro Asp 1
5 10 21814PRTUnknownDescription of Unknown Uncultured GNS bacteria
GCA004 218Arg Ala Gln Pro Ser Leu Tyr Val Thr Arg Ile Ile Arg Asp 1
5 10 21914PRTUnknownDescription of Unknown Uncultured GNS bacteria
GCA112 219Arg Ala Gln Pro Ser Pro Tyr Val Ile Arg Val Ile Arg Asp 1
5 10 22016PRTAcetobacter aceti 220Arg Ala Gln Pro Leu Ser Leu Val
Ala Ser Met Phe Gly Trp Ala Leu 1 5 10 15 22116PRTGluconobacter
asaii 221Arg Ala Gln Pro Leu Ser Leu Val Ala Ser Thr Phe Arg Trp
Ala Leu 1 5 10 15 22217PRTBurkholderia sp.JB1 222Arg Ala Gln Pro
Leu Ser Leu Val Ala Thr Gln Glu His Ser Arg Glu 1 5 10 15 Thr
22315PRTDenitrobacter permanens 223Arg Ala Gln Pro Leu Pro Leu Val
Ala Thr Phe Ser Trp Ala Leu 1 5 10 15 22419PRTDesulfobacter
curvatus 224Arg Ala Gln Pro Leu Ser Leu Val Ala Ser Thr Leu Cys Gly
Asn Ser 1 5 10 15 Asn Glu Thr 22519PRTDesulfobulbus sp.BG25 225Arg
Ala Gln Pro Leu Pro Leu Val Ala Ser Ser Ser Ala Gly His Ser 1 5 10
15 Lys Gly Thr 22611PRTLegionella anisa 226Arg Ala Gln Pro Leu Ser
Leu Val Ala Ser Thr 1 5 10 22716PRTUnknownDescription of Unknown
Benzene mineralizing clone SB-1 227Arg Ala Gln Pro Leu Pro Leu Val
Ala Asn Arg Ser Ser Trp Gly Leu 1 5 10 15 22819PRTEscherichia coli
228Arg Ala Gln Pro Leu Ser Phe Val Ala Ser Gly Pro Ala Gly Asn Ser
1 5 10 15 Lys Glu Thr 22916PRTUnknownDescription of Unknown
Uncultured Acidobacterium Sub.Div-1 229Arg Ala Gln Pro Leu Ser Leu
Val Ala Ser Gly Ser Ser Arg Ala Leu 1 5 10 15
23019PRTUnknownDescription of Unknown Uncultured Acidobacterium
Sub.Div-1 230Arg Ala Gln Pro Leu Ser Ser Val Ala Ile Gly Ser Ser
Arg Ala Thr 1 5 10 15 Leu Ala Lys 23111PRTUnknownDescription of
Unknown Uncultured Acidobacterium Sub.Div-1 231Arg Ala Gln Pro Leu
Phe Ala Ser Cys His His 1 5 10 23219PRTUnknownDescription of
Unknown Uncultured Acidobacterium Sub.Div-1 232Arg Ala Gln Pro Leu
Phe Ala Gln Leu Pro Ser Phe Ser Trp Ala Leu 1 5 10 15 Cys Arg Asn
23311PRTUnknownDescription of Unknown Uncultured Acidobacterium
Sub.Div-1 233Arg Ala Gln Pro Leu Leu Pro Xaa Ala Ile Ile 1 5 10
23411PRTUnknownDescription of Unknown Uncultured Acidobacterium
Sub.Div-1 234Arg Ala Gln Pro Leu Leu Pro Val Ala Thr Ile 1 5 10
23511PRTUnknownDescription of Unknown Uncultured Acidobacterium
Sub.Div-1 235Arg Ala Gln Pro Leu Ser Pro Val Ala Ile Ile 1 5 10
23611PRTUnknownDescription of Unknown Uncultured Acidobacterium
Sub.Div-1 236Arg Ala Gln Pro Leu Ser Ser Val Ala Thr Ile 1 5 10
23711PRTUnknownDescription of Unknown Uncultured Acidobacterium
Sub.Div-1 237Arg Ala Gln Pro Leu Phe Leu Val Ala Thr Ile 1 5 10
23813PRTUnknownDescription of Unknown Uncultured Acidobacterium
Sub.Div-1 238Arg Ala Gln Pro Ser Ser Leu Val Ala Asn Thr Leu Trp 1
5 10 23918PRTUnknownDescription of Unknown Uncultured
Acidobacterium Sub.Div-2 239Arg Ala Gln Pro Leu His Val Val Ala Thr
Arg Lys Arg Glu Leu Tyr 1 5 10 15 Val Asp
24018PRTUnknownDescription of Unknown Uncultured Acidobacterium
Sub.Div-2 240Arg Ala Gln Pro Leu His Val Val Ala Thr Pro Gln Gly
Gly Thr Leu 1 5 10 15 Arg Gly 24119PRTUnknownDescription of Unknown
Uncultured Acidobacterium Sub.Div-3 241Arg Ala Gln Pro Ser Ser Leu
Val Ala Asn Pro Gln Gly Lys His Pro 1 5 10 15 Lys Gly Thr
24211PRTUnknownDescription of Unknown Uncultured Acidobacterium
Sub.Div-3 242Arg Ala Arg Pro Leu Ser Cys Val Ala Ile Ile 1 5 10
24318PRTUnknownDescription of Unknown Uncultured Acidobacterium
Sub.Div-3 243Arg Ala Gln Pro Leu Ser Cys Val Ala Asn Pro Gln Gly
Cys Thr Leu 1 5 10 15 Arg Arg 24420PRTUnknownDescription of Unknown
Uncultured Acidobacterium Sub.Div-3 244Arg Ala Gln Pro Ser Pro Cys
Val Ala Thr Pro Pro Arg Ala Gly Ala 1 5 10 15 Leu Ser Gly Asp
20
* * * * *