U.S. patent application number 14/800501 was filed with the patent office on 2015-11-05 for ratiometric pre-rrna analysis.
This patent application is currently assigned to University of Washington. The applicant listed for this patent is University of Washington. Invention is credited to Gerard A. Cangelosi, John Scott Meschke, Kris Weigel.
Application Number | 20150315632 14/800501 |
Document ID | / |
Family ID | 42243079 |
Filed Date | 2015-11-05 |
United States Patent
Application |
20150315632 |
Kind Code |
A1 |
Cangelosi; Gerard A. ; et
al. |
November 5, 2015 |
RATIOMETRIC PRE-rRNA ANALYSIS
Abstract
Disclosed are compositions and methods for detecting the
presence of viable cells in a sample. Included are compositions and
methods for increasing the sensitivity of a nucleic acid
amplification test for determining the presence of at least one
target microorganism in a sample. Also disclosed are compositions
and methods for detecting ribosomal RNA precursors (pre-rRNA) as
dynamic indicators of viable microorganisms in a sample.
Inventors: |
Cangelosi; Gerard A.;
(Seattle, WA) ; Meschke; John Scott; (Bothell,
WA) ; Weigel; Kris; (Seattle, WA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
University of Washington |
Seattle |
WA |
US |
|
|
Assignee: |
University of Washington
Seattle
WA
Seattle Biomedical Research Institute
Seattle
WA
|
Family ID: |
42243079 |
Appl. No.: |
14/800501 |
Filed: |
July 15, 2015 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13133889 |
Nov 1, 2011 |
9115407 |
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PCT/US2009/067565 |
Dec 10, 2009 |
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14800501 |
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61121485 |
Dec 10, 2008 |
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Current U.S.
Class: |
506/9 ; 435/6.11;
435/6.12 |
Current CPC
Class: |
Y02A 50/30 20180101;
C12Q 1/02 20130101; C12Q 1/689 20130101; Y02A 50/59 20180101 |
International
Class: |
C12Q 1/68 20060101
C12Q001/68 |
Goverment Interests
FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT
[0001] The compositions and methods disclosed herein were developed
under STAR Research Assistance Agreements #FP91698201-0 and
#R833011 awarded by the Environmental Protection Agency. As such,
the government may have certain rights in the invention.
Claims
1-29. (canceled)
30. A method of detecting the viability of at least one target
prokaryotic microorganism in a collected sample, the method
comprising: quantifying the level of at least one target pre-rRNA
of the at least one target prokaryotic microorganism in the
collected sample; nutritionally stimulating and incubating a
portion of the collected sample for a time period of less than one
generation time of the at least one target prokaryotic
microorganism, thereby producing a nutritionally stimulated sample;
quantifying the level of the at least one target pre-rRNA of the at
least one target prokaryotic microorganism in the nutritionally
stimulated sample; comparing the level of the at least one target
pre-rRNA of the at least one prokaryotic microorganism in the
nutritionally stimulated sample with the level of the at least one
target pre-rRNA of the at least one target prokaryotic
microorganism in the collected sample, wherein when the ratio of
the level of the at least one target pre-rRNA in the nutritionally
stimulated sample to the level of the at least one target pre-rRNA
in the collected sample is greater than 1, the at least one target
prokaryotic microorganism in the collected sample is viable, and
when the ratio is not greater than 1, no viable target prokaryotic
microorganism is detected in the collected sample.
31. The method of claim 30, wherein the percentage of the at least
one target prokaryotic microorganism that is viable in the
collected sample is at least one of approximately 0.01%, 0.02%,
0.03%, 0.04%, 0.05%, 0.1%, 0.5%, 1.0%, 2.0%, 3.0%, 4.0%, 5.0%, 10%,
15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%,
80%, 85%, 90%, and 95%.
32. The method of claim 30, wherein at least one of the quantifying
the level of the at least one target pre-rRNA in the collected
sample and the quantifying the level of the at least one target
pre-rRNA in the nutritionally stimulated sample comprises the use
of a microfluidic device.
33. The method of claim 30, further comprising an immunoseparation
process wherein the collected sample is screened for the presence
of a particular isolate of the at least one target prokaryotic
microorganism.
34. The method of claim 30, wherein the at least one target
prokaryotic microorganism is a member of a genera of microorganisms
selected from the group consisting of Acinetobacter,
Actinobacillus, Aeromonas, Arcobacter, Bacteroides, Bordetella,
Borrelia, Brucella, Burkholderia, Campylobacter, Citrobacter,
Cronobacter, Edwardsiella, Enterobacter, Escherichia, Eubacterium,
Francisella, Fusobacterium, Haemophilus, Helicobacter, Klebsiella,
Legionella, Leptospira, Moraxella, Morganella, Neisseria,
Pasteurella, Plesiomonas, Porphyromonas, Prevotella, Proteus,
Providencia, Pseudomonas, Salmonella, Serratia, Shigella,
Stenotrophomonas, Treponema, Veillonella, Vibrio, Yersinia,
Actinomyces, Bacillus, Bifidobacterium, Clostridium,
Corynebacterium, Enterococcus, Lactobacillus, Listeria,
Micrococcus, Mobiluncus, Mycobacterium, Nocardia,
Peptostreptococcus, Propionibacterium, Rhodococcus, Staphylococcus,
Streptococcus, and Streptomyces.
35. The method of claim 30, wherein the at least one target
prokaryotic microorganism is at least one of Chlamydia trachomatis,
Legionella pneumonia, Listeria monocytogenes, Campylobacter jejuni,
Clostridium difficile, Bacillus anthracis, Francisella tularensis,
Rickettsia prowasekii, Rickettsia typhi, and Helicobacter
pylori.
36. The method of claim 33, wherein the immunoseparation process
comprises screening the collected sample for the presence of E.
coli 0157.
37. The method of claim 30, wherein the at least one target
prokaryotic microorganism is a Mycobacterium species.
38. The method of claim 30, wherein the nutritionally stimulating
and incubating a portion of the collected sample comprises
enriching the collected sample with at least one nutrient to
encourage an upshift in the production of the at least one target
pre-rRNA of the at least one target prokaryotic microorganism.
39. The method of claim 30, wherein the collected sample is derived
from tissues or bodily fluids.
40. The method of claim 30, wherein the collected sample is from
the tissue, blood, or sputum of a human or animal subject.
41. A method of detecting viable microorganisms in a sample, the
method comprising: collecting a sample; nutritionally stimulating a
first aliquot of the sample; maintaining a second aliquot of the
sample under non-nutritionally stimulating control conditions;
incubating the first aliquot and the second aliquot; comparing the
level of at least one target pre-rRNA from at least one target
microorganism from the first aliquot with the level of the at least
one target pre-rRNA from at least one target microorganism in the
second aliquot; wherein the ratio of the level of the at least one
target pre-rRNA in the first aliquot and the level of the at least
one target pre-rRNA in the second aliquot is indicative of viable
microorganisms in the sample.
42. The method of claim 41, further comprising extracting RNA from
the first aliquot and extracting RNA from the second aliquot; and
quantifying the level of the at least one target pre-rRNA in the
first aliquot and quantifying the level of the at least one target
pre-rRNA the second aliquot.
43. The method of claim 41, wherein the percentage of the at least
one target microorganism that are viable in the sample is at least
one of approximately 0.01%, 0.02%, 0.03%, 0.04%, 0.05%, 0.1%, 0.5%,
1.0%, 2.0%, 3.0% 4.0%, 5.0%, 10%, 15%, 20%, 25%, 30%, 35%, 40%,
45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, and 95%.
44. The method of claim 42, wherein extracting the RNA from the
first aliquot and the second aliquot and quantifying the at least
one pre-rRNA from the first aliquot and the second aliquot
comprises the use of a microfluidic device.
45. The method of claim 41, further comprising an immunoseparation
process wherein the sample is screened for the presence of a
particular isolate of the at least one target microorganism in the
sample.
46. The method of claim 41, wherein the at least one target
microorganism is a member of a genera of microorganisms selected
from the group consisting of Acinetobacter, Actinobacillus,
Aeromonas, Arcobacter, Bacteroides, Bordetella, Borrelia, Brucella,
Burkholderia, Campylobacter, Citrobacter, Cronobacter,
Edwardsiella, Enterobacter, Escherichia, Eubacterium, Francisella,
Fusobacterium, Haemophilus, Helicobacter, Klebsiella, Legionella,
Leptospira, Moraxella, Morganella, Neisseria, Pasteurella,
Plesiomonas, Porphyromonas, Prevotella, Proteus, Providencia,
Pseudomonas, Salmonella, Serratia, Shigella, Stenotrophomonas,
Treponema, Veillonella, Vibrio, Yersinia, Actinomyces, Bacillus,
Bifidobacterium, Clostridium, Corynebacterium, Enterococcus,
Lactobacillus, Listeria, Micrococcus, Mobiluncus, Mycobacterium,
Nocardia, Peptostreptococcus, Propionibacterium, Rhodococcus,
Staphylococcus, Streptococcus, and Streptomyces.
47. The method of claim 41, wherein the at least one target
microorganism is at least one of Chlamydia trachomatis, Legionella
pneumonia, Listeria monocytogenes, Campylobacter jejuni,
Clostridium difficile, Bacillus anthracis, Francisella tularensis,
Rickettsia prowasekii, Rickettsia typhi, and Helicobacter
pylori.
48. The method of claim 41, wherein the first aliquot is incubated
for a time period that is less than the doubling time of the at
least one target microorganism.
49. The method of claim 41, wherein the collected sample is derived
from tissues or bodily fluids.
Description
TECHNICAL FIELD
[0002] The invention relates to detecting and determining the
presence of viable cells in a sample. More specifically, the
invention relates to detecting viable cells present in very small
numbers in a sample. Included are compositions and methods for
detecting ribosomal RNA precursors (pre-rRNA) as dynamic indicators
of viable microorganisms in a sample.
BACKGROUND
[0003] Microorganisms such as bacterial pathogens can be difficult
to cultivate from complex clinical and environmental samples. They
may be present in small numbers or in injured and aged
physiological states with poor plating efficiency. Samples often
have competing microbial flora that overgrow pathogens on
non-selective media, while selective media can reduce yield and
select against some strains. Most culture-based detection methods
require 1-3 days to yield results, too slow for many circumstances,
especially life-threatening ones.
[0004] An alternative to bacteriological culture is nucleic acid
amplification testing (NAAT). The most common type of NAAT, the
polymerase chain reaction (PCR), is rapid and sensitive. A
limitation of PCR is its inability to distinguish viable pathogen
cells from non-viable cells, from free nucleic acids in samples,
and from contaminating nucleic acids introduced during the testing
process. PCR is also mechanistically complex and susceptible to
inhibition by substances in samples. These limitations are
especially problematic when PCR is used to assess the efficacy of
antimicrobial treatment, disinfection (e.g. water treatment), and
clean-up processes.
[0005] In order to improve the sensitivity and specificity of NAAT
for viable microorganisms it would be valuable to reduce or
eliminate the false-positive detection of non-viable microorganisms
and free DNA. One approach is the detection of microbial RNA rather
than DNA. RNA is considered less stable than DNA in solution and in
dead cells. Species-specific probes for ribosomal RNA (rRNA) or
messenger RNA (mRNA) are known. However, microbial mRNA is
difficult to detect due to its instability and low abundance
(Gedalanga and Olson. 2009. Development of a quantitative PCR
method to differentiate between viable and nonviable bacteria in
environmental water samples. Appl Microbiol Biotechnol.
82:587-596). Conversely, mature rRNA is fairly stable and can
persist within dead bacterial cells for long periods of time.
SUMMARY
[0006] To improve sensitivity and specificity for viable cells,
assays for microbial rRNA precursors (pre-rRNA) may be used.
Pre-rRNAs are intermediates in rRNA synthesis generated by rapid
nucleolytic cleavage of the polycistronic rrs-rrl-rrf operon
transcript. Leader and tail fragments are subsequently removed in
slower reactions tied to ribosome assembly, yielding the mature
rRNA subunits. In growing bacterial cells, pre-rRNAs account for a
large fraction of total rRNA. Pre-rRNAs are significantly more
abundant and easier to detect than even the most strongly-expressed
mRNA molecules in bacteria. Moreover, their intracellular copy
numbers rapidly increase upon nutritional stimulation, a dynamic
property that facilitates the interpretation of borderline results,
thereby improving the functional sensitivity of tests for cells
present in very small numbers in samples. Additionally, they
frequently have species-specific sequences that facilitate their
detection in complex samples by NAAT.
[0007] As disclosed herein, NAATs have been developed that detect
species-specific pre-rRNA molecules. Pre-rRNAs are intermediates in
the synthesis of mature rRNA. They are abundant cellular components
with highly species-specific nucleotide sequences. This makes them
good targets for detecting microbial pathogens in complex samples.
Pre-rRNA copy number increases by orders of magnitude when
microbial cells undergo nutritional stimulation. This response is
very rapid (<1 generation time) and easy to detect due to
pre-rRNA abundance in stimulated cells. Quantitative PCR
measurement of pre-rRNA in stimulated and control samples yields
numerical ratios. If positive, these ratios confirm the presence of
intact, viable pathogen cells in samples. When quantitative PCR
signals are very weak, positive ratios increase confidence that
assay results represent true positive results. This improves the
functional sensitivity of assays.
[0008] Additional aspects and advantages will be apparent from the
following detailed description of preferred embodiments, which
proceeds with reference to the accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0009] FIG. 1 shows pre-16S rRNA and mature 16S rRNA pools during
outgrowth from stationary phase on LB broth.
[0010] FIG. 2 shows pre-16S rRNA pool upon nutritional stimulation
of stationary phase M. bovis BCG.
[0011] FIG. 3 shows the timecourse of nutritional stimulation of
pre-rRNA in water-starved A. hydrophila (A) and M. avium strain 104
(B) cells.
[0012] FIG. 4 shows the correlation between the presence of viable
A. hydrophila cells and pre-rRNA stimulation ratio (A) or genomic
DNA quantified by qPCR (B) in hypochlorite treated laboratory
suspensions.
[0013] FIG. 5 shows the results of multiple RT-qPCR reactions
conducted on paired stimulated and control aliquots derived from a
single fresh water lake sample.
[0014] FIG. 6 lists example genera and species of target
microorganisms that may be targeted by the methods of RPA disclosed
herein.
[0015] FIG. 7 shows examples of qPCR primers, including alternative
forward, reverse, and reverse transcriptase primers for the
referenced organisms.
DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
[0016] Disclosed are materials, compositions, and components that
can be used for, can be used in conjunction with, can be used in
preparation for, or are products of the disclosed compositions and
methods. These and other materials are disclosed herein, and it is
understood that when combinations, subsets, interactions, groups,
etc. of these materials are disclosed that, while specific
reference of each various individual and collective combinations
and permutation of these compounds may not be explicitly disclosed,
each is specifically contemplated and described herein. For
example, if an oligonucleotide is disclosed and discussed and a
number of modifications that can be made to a number of molecules
including the oligonucleotide are discussed, each and every
combination and permutation of oligonucleotide and the
modifications that are possible are specifically contemplated,
unless specifically indicated to the contrary. This concept applies
to all aspects of this application including, but not limited to,
steps in methods of making and using the disclosed compositions.
Thus, if there are a variety of additional steps that can be
performed it is understood that each of these additional steps can
be performed with any specific embodiment or combination of
embodiments of the disclosed methods, and that each such
combination is specifically contemplated and should be considered
disclosed.
[0017] Those skilled in the art will recognize, or be able to
ascertain using no more than routine experimentation, equivalents
to the specific embodiments of the method and compositions
described herein. Such equivalents are intended to be encompassed
by the included claims.
[0018] It is also to be understood that the terminology used herein
is for the purpose of describing particular embodiments only, and
is not intended to limit the scope of the present invention which
will be limited only by the appended claims.
[0019] Unless defined otherwise, all technical and scientific terms
used herein have the meanings that would be commonly understood by
one of skill in the art in the context of the present
specification.
[0020] It must be noted that as used herein and in the appended
claims, the singular forms "a," "an," and "the" include plural
reference unless the context clearly dictates otherwise. Thus, for
example, reference to "a microorganism" includes a plurality of
such microorganisms, reference to "the microorganism" is a
reference to one or more microorganisms and equivalents thereof
known to those skilled in the art, and so forth.
[0021] Compounds and methods are described herein that exploit
pre-rRNA replenishment as the basis for NAATs specific to viable
microbial cells. When microorganism growth slows or stops, pre-rRNA
synthesis decreases but its processing continues, resulting in
active and substantial drainage of pre-rRNA pools. Pre-rRNA pools
are rapidly replenished when growth-limited cells are given fresh
nutrients. Such fluctuations occur consistently in intact, viable
microbial cells. They are not seen in dead cells, with free nucleic
acids, or with other types of background assay "noise".
[0022] Pre-rRNA sequences have specificity comparable to the most
hypervariable regions of mature rRNA. Therefore, viable microbial
cells of a given species can be distinguished from other species by
pre-rRNA detection. Moreover, viable microbial cells can be
distinguished from dead cells of the same species by measuring
their pre-rRNA in samples that have been briefly stimulated with
nutrients. The level of pre-rRNA present in the stimulated sample
is compared to a non-stimulated control sample, and when
species-specific pre-rRNA in the stimulated sample exceeds that of
the control sample, the presence of viable cells is indicated. This
ratiometric approach is referred to herein as Ratiometric Pre-rRNA
Analysis (RPA).
[0023] As disclosed herein, RPA may be conducted by dividing a
sample into two or more aliquots wherein at least one aliquot is
nutritionally stimulated and at least one aliquot is treated as a
non-stimulated control. The pre-rRNA levels in the nutritionally
stimulated sample are compared with the pre-rRNA levels in the
control sample wherein, the replenishment of pre-rRNA in the
stimulated sample is indicative of viable cells in the sample.
[0024] In one embodiment, RPA may include the use of two equal
aliquots of a sample, wherein one aliquot is nutritionally
stimulated while the other is held in a non-nutritionally
stimulated control. After nutritional stimulation for <1
generation time, species-specific pre-rRNA is quantified
ratiometrically to determine the pre-rRNA stimulation ratio values.
In one embodiment, nutritional stimulation may last for a period of
<1 generation, <1/2 generation, <1/3 generation, <1/4
generation, and <1/8 generation time of a target microorganism.
The nutritional stimulation step is not of sufficient duration for
even modest amplification of microbial numbers. As such, RPA is not
a culture enrichment. In one such embodiment, pre-rRNA stimulation
ratio values are the ratios of pre-rRNA levels in stimulated
samples relative to control samples. In particular embodiments,
pre-rRNA stimulation values are used to determine the presence of
viable microbial of cells in a sample. For example, the presence of
viable microorganisms is indicated when the pre-rRNA values in a
nutritionally stimulated aliquot are greater than the pre-rRNA
values in a non-stimulated control aliquot.
[0025] It has been found that, in specific embodiments, the methods
of RPA disclosed herein may be used to detect viable target
microorganisms that are substantially outnumbered by inactivated or
dead microorganisms of the same species. In one embodiment, RPA may
be conducted to detect viable microorganisms in a sample wherein
approximately 0.01% to 99% of the target microorganisms are viable
microorganisms. In one such embodiment, RPA may be used to detect
the presence of viable target microorganisms that are present in a
sample at a level of approximately 0.01%, 0.02%, 0.03%, 0.04%,
0.05%, 0.1%, 0.5%, 1.0%, 2.0%, 3.0% 4.0%, 5.0%, 10%, 15, 20%, 25%,
30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%,
and 95% of the total population of target microorganisms
(live+dead). As used herein, the percentage of viable target
microorganisms in a sample is the number of viable target
microorganisms relative to the total number of target
microorganisms, both viable and inactivated.
[0026] RPA as described herein can be conducted on various
different types of specific samples. In one such embodiment, the
sample as used herein can be a sample collected from any desired
source or location that may potentially comprise cells of interest.
In one embodiment, the sample may be taken from a liquid, a solid,
a gas, a composite, a tissue, or any other desired substrate. The
sample may be taken from an outdoor environment or an indoor
environment, or in other embodiments, the sample may be a tissue,
fluid, or swab sample taken from a subject. In one such embodiment,
a tissue sample may be a blood, saliva, sputum, stool, urine, hair,
skin, or any other sample taken from the body of a subject. For
purposes of the present description, the term "subject" refers to a
human, animal, or plant subject. Even further, a sample for
analysis using the RPA methods described herein may be collected
from a natural environment, industrial environment, health care
environment, residential environment, agricultural environment,
water distribution environment, wastewater treatment environment,
food production or distribution environment, recreational
environment, or any desired environment or combinations thereof. A
sample may comprise inorganic and/or organic materials, and may be
collected from a marine environment or fresh water environment, and
may comprise dirt, rocks, soil, vegetation, air, and combinations
thereof.
[0027] A sample suitable for RPA as described herein may include a
cell of interest, which can be a prokaryotic cell or a eukaryotic
cell. In particular embodiments, the microorganism may be a gram
negative bacterium, a gram positive bacterium, or another type of
bacterium. Therefore, the methods for RPA described herein may be
applied for the detection of one or more microorganisms that have
significance in one or more contexts including human and veterinary
clinical settings. For instance, in one embodiment the methods of
RPA as described herein may be used for the detection of foodborne
and waterborne microorganisms. In another embodiment, RPA as
disclosed herein may be used for biodefense and the detection of
microorganisms used for bioweapons. In another embodiment, the
methods of RPA as described herein may be used for infectious
disease diagnosis or treatment monitoring. In another embodiment,
the methods of RPA as described herein may be used for quality
assurance of manufacturing processes including but not limited to
food, drinks, or medical devices. In another embodiment, the
methods of RPA as described herein may be used for assuring the
effective sterilization, or maintenance of sterility, of devices
and materials used in health care.
[0028] As shown in FIG. 6, RPA may be conducted with samples
containing one or more microorganisms of interest including many
species of microorganisms from many different genera. The methods
of RPA disclosed herein are suited to the detection of
species-specific pre-rRNAs, and in specific embodiments, RPA as
disclosed herein may detect species-specific pre-rRNAs of
microorganisms from one or more genera selected from Acinetobacter,
Actinobacillus, Aeromonas, Arcobacter, Bacteroides, Bordetella,
Borrelia, Brucella, Burkholderia, Campylobacter, Citrobacter,
Cronobacter, Edwardsiella, Enterobacter, Escherichia, Eubacterium,
Francisella, Fusobacterium, Haemophilus, Helicobacter, Klebsiella,
Legionella, Leptospira, Moraxella, Morganella, Neisseria,
Pasteurella, Plesiomonas, Porphyromonas, Prevotella, Proteus,
Providencia, Pseudomonas, Salmonella, Serratia, Shigella,
Stenotrophomonas, Treponema, Veillonella, Vibrio, Yersinia,
Actinomyces, Bacillus, Bifidobacterium, Clostridium,
Corynebacterium, Enterococcus, Lactobacillus, Listeria,
Micrococcus, Mobiluncus, Mycobacterium, Nocardia,
Peptostreptococcus, Propionibacterium, Rhodococcus, Staphylococcus,
Streptococcus, and Streptomyces.
[0029] The methods for RPA comprise dividing a sample into two or
more aliquots wherein at least one aliquot is nutritionally
stimulated and at least one aliquot is treated as a non-stimulated
control. In one embodiment, the material present in the
nutritionally stimulated aliquot is pelleted, washed, and placed
under desired microbial culture conditions. Microbial culture
conditions as disclosed herein are the environmental and nutrient
conditions generally known by those of skill in the art appropriate
for a desired growth of a target organism. Generally, optimized
microbial culture conditions may include nutrient media,
temperature, humidity, oxygen tension, the presence of specific
micro- or macro-nutrients, absence of inhibitors, and pressure,
appropriate for a target microorganism. In one such embodiment, the
nutritionally stimulated aliquot is incubated or cultured for a
desired period of time under conditions wherein the aliquot is
supplemented with culture media appropriate for a target
microorganism. For example, where the target organism is a gram
negative bacillus Aeromonas hydrophila the sample may be incubated
with a culture media comprising a nutrient broth culture media. In
another example, the target organism is Mycobacterium avium and the
microbial culture conditions may include incubating the sample with
Middlebrook 7H9 medium. In another example, the target organism is
an anaerobe and the nutritional stimulation conditions may include
low oxygen tension. In another example, the target organism is
pathogen such as Listeria that lives in an intracellular
environment with limited iron availability, and the nutritional
stimulation conditions may include the provision of iron. In one
embodiment, the non-nutritionally stimulated control aliquot is
incubated under control conditions designed to maintain the status
of a target microorganism. In one such embodiment, the control
aliquot is incubated in water or buffer. In another embodiment, the
control aliquot is maintained in an unfavorable atmosphere, such as
atmospheric oxygen concentration in the case of anaerobe
detection.
[0030] The methods of RPA described herein typically include the
quantification of one or more pre-rRNA molecules that have been
isolated from target microorganisms in a sample. In one such
embodiment, pre-rRNA are isolated from a sample according to
nucleic acid extraction techniques know by those of skill in the
art. For example, the cells in the sample may be lysed and the
nucleic acids extracted according to standard methods such as a
phenol-chloroform extraction method. Exemplary methods of nucleic
acid extraction, including pre-rRNA extraction and quantification,
are disclosed in U.S. Pat. No. 5,712,095, Cangelosi et al. 1997,
and Cangelosi et al. 1996 (Cangelosi, G. A. and W. H. Brabant.
1997. Depletion of pre-16S rRNA in starved Escherichia coli cells.
J. Bacteriol. 179:4457-4463; Cangelosi, G. A., W. H. Brabant, T. B.
Britschgi, and C. K. Wallis. 1996. Detection of rifampin- and
ciprofloxacin-resistant Mycobacterium tuberculosis by using
species-specific assays for precursor rRNA. Antimicrob. Agents
Chemother. 40:1790-1795) each of which are incorporated herein by
reference.
[0031] The quantification of pre-rRNA molecules includes the use of
nucleic acid amplification technologies. In one such embodiment,
the nucleic acid amplification technology may be a PCR-based
technology. In another such embodiment, nucleic acids may be
amplified by a non-PCR based method such as an isothermal
amplification method such as, for example, Nucleic Acid Sequence
Based Amplification (NASBA). Examples of methods of nucleic acid
amplification are disclosed by Gill and Ghaemi, 2008 (Pooria Gill
and Amir Ghaemi. 2008. Nucleic acid isothermal amplification
technologies--a review. Nucleosides, Nucleotides, and Nucleic
Acids. 27:224-243), incorporated by reference herein. In one
embodiment of RPA described herein, RT-qPCR may be used to quantify
species-specific pre-rRNA from a sample to determine the pre-rRNA
stimulation values. RT-qPCR uses reverse transcriptase to convert
RNA to cDNA, which is then measured by standard quantitative PCR
(qPCR).
[0032] The methods of RPA disclosed herein may use oligonucleotide
primers designed to target pre-rRNA sequences of a target
microorganism, and primers for use with RPA may target any mature
rRNA sequence or any pre-rRNA sequence. In one embodiment, primers
may target the 5' pre-rRNA leader regions. In one such embodiment,
primers for methods of RPA disclosed herein may target the
sequences immediately upstream of the mature 5' 16S rRNA terminus
because these promoter-proximal regions would be abundant in cells
that are actively transcribing pre-rRNA. In another such
embodiment, primers for use with RPA may target a spacer sequence
downstream of the 16S rRNA gene. In further embodiments, primer
pairs may straddle the 5' or 3' mature rRNA terminus, such that
amplification requires intact pre-rRNA as templates. Reverse
primers for use in the methods for RPA described herein may be
designed to recognize semi-conserved regions within the mature
rRNA, and forward primers may be designed to recognize
species-specific sequences within the pre-rRNA. Alternatively,
reverse primers for use in the methods for RPA described herein may
be designed to recognize species-specific sequences within the
pre-rRNA, and forward primers may be designed to recognize
semi-conserved regions within the mature rRNA. Length and
composition of primers are not important to the invention, as long
as they are designed to specifically amplify pre-rRNA and not
mature rRNA or DNA.
[0033] In one particular embodiment, primers may be designed to
quantify pre-rRNA molecules of M. avium. In one such embodiment,
with reference to FIG. 7, forward and reverse primers can be
designed to generate an amplification product that straddles the 5'
mature 16S rRNA terminus, such that successful amplification
requires intact pre-16S rRNA as a template. For example, the cDNA
synthesis for RT-qPCR may be primed by the mature rRNA sequence
5'-GCCCGCACGCTCACAGTTAAG-3' (SEQ ID NO: 3). Forward and reverse PCR
primers may be 5'-TTGGCCATACCTAGCACTCC-3' (SEQ ID NO: 1) and
5'-GATTGCCCACGTGTTACTCA-3' (SEQ ID NO: 2), respectively. The
reverse primer may be within the mature rRNA sequence, whereas the
forward primer may recognize a site in external transcribed
spacer-1 (ETS-1). Examples of primers for use with the methods of
RPA are shown in FIG. 7 including sets of forward, reverse, and
reverse transcriptase primers, and alternative primers, which may
be used with the referenced target microorganisms.
[0034] For the methods disclosed herein, the pre-rRNA stimulation
ratio values are the ratios of pre-rRNA levels in stimulated
samples relative to pre-rRNA levels in control samples. In
particular embodiments, methods of RPA disclosed herein include the
step of ratiometrically quantifying species-specific pre-rRNA in a
sample. In one embodiment, species-specific pre-rRNA is quantified
ratiometrically to determine the pre-rRNA stimulation ratio values.
In one such embodiment, pre-rRNA stimulation ratio values are the
ratios of pre-rRNA levels in a nutritionally stimulated sample
relative to a non-nutritionally stimulated control sample, and the
pre-rRNA stimulation values are used to determine the presence of
viable target cells in a sample. For example, the presence of
viable cells is indicated when the pre-rRNA stimulation ratio value
is approximately equal to, or greater than, a viability threshold
value. In one such embodiment, the viability of targeted cells is
indicated by a viability threshold value when the pre-rRNA levels
in a nutritionally stimulated aliquot are greater than the pre-rRNA
values in non-stimulated control aliquot.
[0035] As used herein, the term "viability threshold value" is the
calculated ratio of pre-rRNA levels in stimulated samples relative
to pre-rRNA levels in control samples that indicates the presences
of viable cells in a sample. As disclosed herein, the viability
threshold value for a given sample may depend on the target
organism, the type of sample, the resolving power of the NAAT, and
other conditions that may affect the quantification of pre-rRNA in
the sample. In specific embodiments, the viability threshold value
for a sample may range from approximately 1 to 100. The choice of a
threshold value might depend upon specific assay requirements. For
example, a test that requires the highest possible sensitivity for
the presence of a pathogen (such as medical device quality control)
might use a threshold value of 1. Alternatively, a test that
requires specificity for viable cells but not a high degree of
analytical sensitivity, such as wastewater treatment monitoring,
might use a higher threshold value to minimize the frequency of
costly false-positive results.
[0036] In one embodiment, RPA may be conducted on samples derived
from natural or in vivo sources. For example, the methods for RPA
described herein may be conducted on samples derived from tissues
or bodily fluids. In one embodiment, a sample may be collected from
the tissue, blood or sputum of a human or animal subject. In
comparison to tap or lake water, blood and sputum may be
nutrient-rich environments. Microorganisms in such natural samples
may replicate actively and maintain large pre-rRNA pools. However,
the balanced and optimized nutritional conditions of laboratory
media are very rare in nature. In natural environments microbial
growth is usually limited by the availability of specific
nutrients. For example, humans have innate immune mechanisms that
limit iron availability in tissues. Because specific nutrients are
limiting, microorganisms may divide poorly, if at all, in natural
samples such as sputum or whole blood. In this sense natural
environments are similar to spent culture media, which contain
large amounts of some nutrients but are depleted for others
(usually carbon or nitrogen). A natural sample containing
microorganisms that are limited for a specific nutrient, be it
carbon, nitrogen, oxygen, or a trace element, can undergo a
measurable burst of pre-rRNA synthesis when provided with the
limiting nutrient under nutritional stimulation.
[0037] In one embodiment, samples collected for RPA as disclosed
herein may include natural samples comprising spatial variations
with regard to nutrient availability and the presence of growth
inhibitors and host defenses. For example, tuberculosis bacilli in
freshly infected macrophages may replicate at top speed, while
growth is likely to be slow in the extracellular matrix or in host
cells with very large bacillary burdens. In one such embodiment,
for natural samples collected during acute infection in a subject
it may be unlikely that all potential target organisms in a sample
are provided the optimum nutrient mix to ensure maximum cell
growth. As such, target microorganisms in natural samples can be
expected to synthesize pre-rRNA and show pre-rRNA upshift when
incubated under nutritional stimulation conditions. In one
embodiment, methods of RPA as disclosed herein may comprise
collecting a natural sample comprising a target microorganism
living in a nutrient limited environment. In one such embodiment,
the methods of RPA as described herein may include determining the
limiting nutrient in the natural sample and then nutritionally
stimulating an aliquot of the natural sample with an enriched
nutrient media comprising the limiting nutrient. Accordingly, the
enriched nutrient media may cause an upshift in the pre-rRNA levels
in the target microorganism by providing the limiting nutrient to
the target microorganism.
[0038] In one particular embodiment, RPA may be conducted for a
target organism such as M. tuberculosis in a sample derived from a
human or animal subject. In one such embodiment, sputum may be
collected from a subject suspected of being infected with M.
tuberculosis or undergoing treatment for M. tuberculosis. The
sputum samples may be divided into 2 aliquots, one of which may be
nutritionally stimulated with enrichment media, such as Middlebrook
7H9 broth, while the other can be held in PBS or water as a
control. In one embodiment, nutritional stimulation may proceed for
approximately 3-5 hours at 37.degree. C. The bacteria in the
stimulated and control aliquots may then be lysed and RT-qPCR may
be used to quantify pre-rRNA and calculate pre-rRNA stimulation
ratio values. Pre-rRNA stimulation values may be used to determine
the presence of viable M. tuberculosis in the natural sample. In
one such embodiment, the presence of viable M. tuberculosis cells
is indicated when the pre-rRNA values in the nutritionally
stimulated aliquot are greater than the pre-rRNA values in the
non-stimulated control aliquot. In similar embodiments,
intracellular pathogens of the genera Chlamydia, Listeria,
Legionella, or others may be detected by RPA using nutritional
stimulation with limiting nutrients. Target pathogens do not need
to be "culturable" in vitro. For example, an obligate intracellular
pathogen such as Chlamydia trachomatis can be detected in a vaginal
swab by using RPA in which a specific nutrient is provided that was
limiting in its natural intracellular environment. The pathogen may
not be able to replicate under these conditions, but it can sense
the presence of the limiting nutrient and synthesize pre-rRNA in an
abortive attempt to replicate, because pre-rRNA synthesis is a very
early step in cell growth. Such synthesis would be detectable by
RPA.
[0039] Well-known manual or automated methods for nucleic acid
extraction and quantification may be applied in carrying out the
methods of RPA as disclosed herein. In one such embodiment, RPA may
include nucleic acid extraction and/or quantification that uses one
or more technologies such as nucleic acid chip technology,
microarrays, multiplex technology, lab-on-a-chip, lab-on-a-card,
microfluidic devices, and other nucleic acid extraction and
quantification technologies known by those of skill in the art. As
used herein, the term "microfluidic device" is a device that may be
used to conduct RPA and may include nucleic acid chip technology,
microarrays, multiplex technology, lab-on-a-chip, lab-on-a-card,
and related technologies. For example, methods of nucleic acid
extraction and analysis are disclosed in U.S. Pat. No. 7,608,399
and U.S. patent application Ser. No. 11/880,790, both of which are
incorporated by reference herein.
[0040] In one embodiment, the methods of RPA disclosed herein may
include using a Nucleic Acid Card for RNA extraction and
amplification, followed by ratiometric analysis. An aliquot taken
from a sample is nutritionally stimulated while a control aliquot
is held in buffer (step A). After brief nutritional stimulation,
the cells are lysed (step B) and then loaded onto paired Nucleic
Acid Cards. Upon completion of the RNA extraction (step C), eluates
are subjected to qPCR. When applied to slow-growing mycobacteria,
the total process including nutritional stimulation may take from 6
to 24 hours. Comparatively, Mycobacterium culture requires 5-14
days.
[0041] In one embodiment, RPA as disclosed herein may include
nucleic acid extraction and quantification using a flat-glass or
composite card capable of quickly, easily, and reliably isolating
DNA and RNA from blood and a variety of other biological samples.
In one embodiment, RPA as disclosed herein may be conducted using a
device that combines cellular lysis, nucleic acid extraction and
purification, and measurement of extracted nucleic acids. In one
such embodiment, the device may be a vessel for receiving and
processing a biological sample as described herein. In one such
embodiment, the methods of RPA disclosed herein may comprise the
use of a flow-through glass walled nucleic acid card for extraction
of nucleic acids from a sample. In such an embodiment, the card may
be used for nucleic acid quantification, DNA or RNA extraction and
concentration determination. In an alternative embodiment, the
extraction and/or quantification of nucleic acids may be done
manually by using pipettes inserted into loading and elution ports
located on a nucleic acid card. Alternatively, the extraction
and/or quantification of nucleic acids as disclosed herein may be
automated by using a fluid handling device or other appropriate
devices known by those of skill in the art.
[0042] The methods of RPA as disclosed herein may include a
pre-screening process, such as an immunoseparation or
immunoscreening process, to improve the specificity of RPA. In one
embodiment, RPA may comprise a step in which one or more target
microorganisms of interest may be identified or captured on beads
or other particles that are coated with antibodies or other probes
or peptides that bind specifically to the target microorganisms. In
one such embodiment, target microorganisms identified by antibodies
or probes may be subjected to RPA as disclosed herein. For example,
target microorganisms identified by a preliminary immunoseparation
process may be divided into two or more separate aliquots wherein
one aliquot is nutritionally stimulated and another aliquot is
reserved as a non-nutritionally stimulated control aliquot.
Pre-rRNA replenishment in nutritionally stimulated samples relative
to control samples can then be quantified as described herein. In
one embodiment, an immunoseparation process as described herein may
be used to isolate a specific strain or population of a target
microorganism. For example, a method of RPA as disclosed herein may
comprise an immunoseparation step wherein a specific strain or
isolate within a population of target microorganisms may be
identified. In one particular example, a method of RPA as disclosed
herein may comprise an immunoseparation step wherein a specific E.
coli strain such as E. coli O157 is separated from other
microorganisms of the same species.
[0043] In one embodiment, RPA as disclosed herein may be used to
improve the sensitivity of detection of target microorganisms. For
example, RPA may be used to confirm the presence of viable target
microorganisms in conjunction with another test. As a dynamic
measurement of a cellular activity, RPA as disclosed herein offers
greater confidence in borderline signals than does static DNA
detection. This may improve the overall sensitivity, reliability,
and robustness of nucleic acid amplification tests for
microorganisms in a sample. The improved biological sensitivity
stems from the dynamic nature of RPA. An analogy would be the
observation of animals in a forest, in that a moving animal is much
easier to spot than a stationary one. The pre-rRNA synthesis seen
in RPA is a type of bacterial "movement" that is reliably induced
by nutritional stimulation.
[0044] Furthermore, RPA has additional advantages over traditional
NAATs. Most reverse transcriptase quantitative-PCR (RT-qPCR)
protocols have 3 steps: DNAse digestion to remove genomic DNA that
might interfere with RNA quantification; reverse transcriptase (RT)
to convert RNA to cDNA; and finally qPCR to quantify the cDNA. In
RPA the DNAse digestion step is not necessary, because genomic DNA
in bacterial cells is outnumbered by pre-rRNA by 1-3 orders of
magnitude. Genomic DNA is also expected to be found in similar
quantities in stimulated and control aliquots. As a result, genomic
DNA causes very little background signal and does not interfere
with the ratiometric analysis.
[0045] In one embodiment, RPA may be used to improve the confidence
in the results of a primary analysis when the primary analysis
gives results that are inconclusive, borderline, or difficult to
interpret. For example, generally, RT-qPCR with cycle threshold
(Ct) values of <30 (i.e. positive results after fewer than
.about.30 amplification cycles) are unambiguously positive.
However, Ct values >30 are borderline and can be difficult to
interpret. Such signals can result from sample contamination or
even from background noise. In one embodiment, RPA may be used to
confirm the results of a RT-qPCR test for the presence of microbial
cells when Ct values are >30. When repeated measurements are
made and nutritionally stimulated aliquots exhibit RT-qPCR signal
that are consistently stronger than control aliquots, then this
result most likely reflects the presence of viable cells.
Background noise, DNA contamination, or other causes of borderline
positive results would be highly unlikely to cause such results.
Therefore, in addition to improving specificity for viable
microbial cells, RPA can significantly improve the functional
sensitivity of NAAT for microbial cells. Other examples of NAATs
may include non-ratiometric rRNA amplification (mature or
precursor) and non-ratiometric rRNA detection by direct
hybridization.
[0046] In addition to Ct values, other quantitative or
semi-quantitative NAAT test read-outs can be used with RPA.
Examples include gel electrophoresis results, fluorescent or
colorimetric signals, thermal read-outs, melt curves, and nucleic
acid probe hybridization-based read-outs such as line probe assays
or nucleic acid lateral flow (NALF). In all cases, RPA can improve
specificity for viable cells as well as functional sensitivity for
detection of microbial cells present in small numbers.
[0047] In one embodiment, RPA may be used to increase the
sensitivity of a primary NAAT, such as a DNA detection assay
designed to identify the presence of microorganisms in a sample. In
one such embodiment, a genomic DNA detection assay of a sample may
be performed concurrently with RPA, or followed by RPA, for the
same sample to detect the presence of viable target microorganisms.
In another embodiment, RPA may be used to overcome background noise
or environmental DNA contamination that may make it difficult to
interpret borderline results generated by methods for detecting
target microorganisms.
EXAMPLES
[0048] The Examples that follow are offered for illustrative
purposes only and are not intended to limit the scope of the
compositions and methods described herein in any way. It is to be
understood that the disclosed compositions and methods are not
limited to the particular methodologies, protocols, and reagents
described herein. In each instance, unless otherwise specified,
standard materials and methods were used in carrying out the work
described in the Examples provided. All patent and literature
references cited in the present specification are hereby
incorporated by reference in their entirety.
[0049] The practice of the present invention employs, unless
otherwise indicated, conventional techniques of chemistry,
molecular biology, microbiology, recombinant DNA, genetics,
immunology, cell biology, cell culture and transgenic biology,
which are within the skill of the art. (See, e.g., Real-Time PCR in
Microbiology: From Diagnosis to Characterization (I. M. Makay ed.
2007); Nolan, T., et al. (2006) Quantification of mRNA using
real-time RT-PCR. Nature Protocols 1, 1559-1582; Maniatis, T., et
al. (1982) Molecular Cloning: A Laboratory Manual (Cold Spring
Harbor Laboratory, Cold Spring Harbor, N.Y.); Sambrook, J., et al.
(2001) Molecular Cloning: A Laboratory Manual, 2.sup.nd Ed. (Cold
Spring Harbor Laboratory, Cold Spring Harbor, N.Y.); Ausubel, F.
M., et al. (1992) Current Protocols in Molecular Biology, (J. Wiley
and Sons, NY); Glover, D. (1985) DNA Cloning, I and II (Oxford
Press); Anand, R. (1992) Techniques for the Analysis of Complex
Genomes, (Academic Press); Guthrie, G. and Fink, G. R. (1991) Guide
to Yeast Genetics and Molecular Biology (Academic Press); Harlow
and Lane (1988) Antibodies: A Laboratory Manual (Cold Spring Harbor
Laboratory, Cold Spring Harbor, N.Y.); Jakoby, W. B. and Pastan, I.
H. (eds.) (1979) Cell Culture. Methods in Enzymology, Vol. 58
(Academic Press, Inc., Harcourt Brace Jovanovich (NY); Nucleic Acid
Hybridization (B. D. Hames & S. J. Higgins eds. 1984);
Transcription And Translation (B. D. Hames & S. J. Higgins eds.
1984); Culture Of Animal Cells (R. I. Freshney, Alan R. Liss, Inc.,
1987); Immobilized Cells And Enzymes (IRL Press, 1986); B. Perbal,
A Practical Guide To Molecular Cloning (1984); the treatise,
Methods In Enzymology (Academic Press, Inc., N.Y.); Gene Transfer
Vectors For Mammalian Cells (J. H. Miller and M. P. Calos eds.,
1987, Cold Spring Harbor Laboratory); Methods In Enzymology, Vols.
154 and 155 (Wu et al. eds.); Immunochemical Methods In Cell And
Molecular Biology (Mayer and Walker, eds., Academic Press, London,
1987); Handbook Of Experimental Immunology, Volumes I-IV (D. M.
Weir and C. C. Blackwell, eds., 1986); Hogan et al. (eds) (1994)
Manipulating the Mouse Embryo; A Laboratory Manual, 2.sup.nd
Edition, Cold Spring Harbor Laboratory Press, Cold Spring Harbor,
N.Y.). 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.,
2001; Ausubel et al., 1992; Glover, 1985; Anand, 1992; Guthrie and
Fink, 1991).
[0050] Nothing herein is to be construed as an admission that the
subject matter taught herein is not entitled to antedate such
disclosure by virtue of prior invention. No admission is made that
any reference constitutes prior art. The discussion of references
states what their authors assert, and applicants reserve the right
to challenge the accuracy and pertinency of the cited documents. It
will be clearly understood that, although a number of publications
are referred to herein, such reference does not constitute an
admission that any of these documents forms part of the common
general knowledge in the art.
Example 1
Design of Pre-rRNA-Based Cell Viability Tests
[0051] Pre-rRNA pools are rapidly replenished by bacteria that
sense new nutrients in their environments. As shown in FIG. 1,
pre-16S rRNA pools are replenished in E. coli after a nutritional
upshift of stationary phase cells. With continued reference to FIG.
1, overnight cultures of E. coli were diluted 20-fold into fresh LB
broth at time zero (arrow). At time points before and after
dilution, optical densities were recorded, and samples were
analyzed for pre-rRNA and mature 16S rRNA content by
chemiluminescent sandwich hybridization assays. Open circles,
culture OD600 (right axis); open triangles, pre-16S rRNA per OD600
(left axis); filled triangles, mature 16S rRNA per OD600. Means and
standard deviations of three parallel cultures are shown in FIG.
1.
[0052] In contrast to E. coli, which divides and doubles every 30
min and has high rRNA copy number, Mycobacterium bovis BCG doubles
every .about.24 hours and has fewer rRNA copies. Nonetheless,
pre-rRNA replenishment is clearly visible within one doubling time
of nutritional upshift in this organism. FIG. 2 illustrates this in
an experiment conducted on M. bovis BCG in which a slot blot
hybridization assay was used to detect pre-rRNA (closed circles).
With continued reference to FIG. 2, stationary phase M. bovis BCG
cells were diluted into fresh 7H10 broth at the time indicated by
the arrow. Pre-rRNA copy number and cell density were tracked
before and after nutritional stimulation. Closed circles show the
pre-rRNA to genomic DNA ratio. Open triangles show the OD600 of the
M. bovis culture. Direct detection without amplification was
possible because pre-rRNAs are abundant in bacterial cells,
accounting for 4%-20% of total rRNA. As a result, the sensitivity
of pre-rRNA detection exceeds that of genomic DNA detection.
Example 2
Ratiometric Pre-rRNA Analysis
[0053] RPA tests were developed for two bacterial pathogens
suspected of causing human disease acquired from drinking water.
The model species were the rapidly growing gram negative bacillus
Aeromonas hydrophila and the slowly growing actinomycete
Mycobacterium avium. For both species, 5' pre-rRNA leader regions
(the sequences immediately upstream of the mature 5' 16S rRNA
terminus) were targeted on the assumption that these
promoter-proximal regions would be abundant in cells that are
actively transcribing pre-rRNA. Primer pairs straddled the 5'
mature rRNA terminus, such that amplification required intact
pre-rRNA as templates. Reverse primers recognized semi-conserved
regions within the mature rRNA. Forward primers recognized
species-specific sequences within the 5' leader.
[0054] M. avium forward and reverse primers were designed to
generate a predicted 237 bp amplification product that straddled
the 5' mature 16S rRNA terminus, such that successful amplification
required intact pre-16S rRNA as a template. The cDNA synthesis was
primed by the mature rRNA sequence 5'-GCCCGCACGCTCACAGTTAAG-3' (SEQ
ID NO: 3). Forward and reverse PCR primers were
5'-TTGGCCATACCTAGCACTCC-3' (SEQ ID NO: 1) and
5'-GATTGCCCACGTGTTACTCA-3' (SEQ ID NO: 2), respectively. The
reverse primer was within the mature rRNA sequence, whereas the
forward primer recognized a site in ETS-1. Consistent with the
species specificity predicted from BLAST analysis, PCR with gel
electrophoresis consistently yielded products of the expected sizes
when applied to nucleic acid from 15 clinical isolates of M. avium
and 4 clinical isolates of M. intracellulare. These two
closely-related species comprise the clinically relevant grouping
known as the M. avium complex (MAC). No products were observed when
the reactions were applied to M. tuberculosis, M. smegmatis, M.
terrae, M. gastri, M. nonchromogenicum, M. phlei, and M. vaccae
(data not shown). These observations illustrate the useful
phylogenetic specificity of pre-rRNA analysis.
[0055] A. hydrophila forward and reverse primers generated a
predicted 189 bp amplification product. The cDNA synthesis was
primed by the mature rRNA sequence 5'-CTACAAGACTCTAGCTGGACAGT-3'
(SEQ ID NO: 6). Forward and reverse PCR primers were
5'-ATTGAGCCGCCTTAACAGG-3' (SEQ ID NO: 4) and
5'-AACTGTTATCCCCCTCGAC-3' (SEQ ID NO: 5), respectively. BLAST
analysis conducted against the NCBI non-redundant database found no
matches with the forward primer other than A. hydrophilia. The
closely related species A. salmonicida A449 did not have a
homologous sequence.
[0056] To assess the time course of pre-rRNA replenishment upon
nutritional stimulation, early stationary-phase A. hydrophila ATCC
7966 cells were washed, resuspended in autoclaved tap water (ATW),
and incubated for 7 days with aeration at 28.degree. C. Early
stationary-phase cells of MAH strain HMC02 were washed, resuspended
in ATW, and then incubated for 14 days with aeration at 37.degree.
C. These conditions were designed to drain pre-rRNA pools in
simulated water supply environments. To conduct RPA, water-starved
bacteria were divided into two aliquots and centrifuged. One pellet
was resuspended in culture media (nutritional stimulation), and the
other in ATW (control). Final cell densities were approximately
10.sup.6 cfu/mL. Nutrient Broth was used for nutritional
stimulation of A. hydrophila, and Middlebrook 7H9 medium with 10%
ADC supplement was used for MAH. After incubation for varying
periods of time, cells were lysed by high-energy bead beating, RNA
was isolated by acidified phenol-chloroform, and pre-rRNA was
measured by RT-qPCR. The ratios of RT-qPCR values in nutritionally
stimulated and control samples were calculated following
normalization to genomic DNA standard curves. Pre-rRNA stimulation
was very rapid in both organisms. Approximately 15 minutes of
nutritional stimulation was adequate for consistent pre-rRNA
upshift in A. hydrophila. Approximately 4 hours was required for
maximal pre-rRNA stimulation in M. avium, a slow-growing organism
with a generation time of >20 hours. For both organisms these
time periods are <1 generation time.
[0057] FIG. 3 shows the timecourse of nutritional stimulation of
pre-rRNA in water-starved A. hydrophila (A) and M. avium strain 104
(B) cells. Pre-rRNA stimulation ratio values are the ratios of
pre-rRNA in stimulated samples relative to control samples,
measured by RT-qPCR. Values are means and SD of >2 experiments
per time point. To conduct RT-qPCR on extracted RNA, complementary
DNA (cDNA) was first generated using the Superscript III system
(Invitrogen Corp., Carlsbad, Calif.) and cleaned using a Qiagen PCR
purification kit (Cat#28104, Qiagen Inc., Valencia, Calif.).
Amplification of cDNA was performed using the Applied Biosystems
(ABI) Power SYBR Green mix (Applied Biosystems Inc., Foster City,
Calif.). Reactions were conducted in triplicate at two different
dilutions to assure quantitative read-outs. Amplifications were run
in 96-well plates on an ABI Prism RT-7500 as follows: 10 minutes
95.degree. C., 40 cycles of (15 s 95.degree. C., 30 s 60.degree.
C., 30 s 72.degree. C.) using `9600 emulation.` ABI's SDS software
was used to set Ct threshold values.
Example 3
Correlation Between Pre-rRNA Stimulation Ratio and Cell
Viability
[0058] To assess the specificity of RPA for viable cells, sodium
hypochlorite treatment was used to generate A. hydrophila cell
suspensions with varying ratios of viable and inactivated cells.
The ratios were quantified by viable plating after chlorine
exposure, and were expressed as percent viability relative to the
input density of approximately 1.times.10.sup.6 cfu/mL. To conduct
RPA on the chlorine-treated and -untreated cell suspensions, paired
aliquots were centrifuged and cell pellets were resuspended in
water (control sample) or nutrient broth (stimulated sample). After
1 hour of nutritional stimulation, pre-rRNA stimulation ratios were
determined. In some experiments, genomic DNA in stimulated and
control samples was also quantified by qPCR. This allowed
assessment of the specificity of RPA to viable cells, in comparison
to the specificity seen with traditional qPCR of DNA.
[0059] Table 1 shows results of two experiments in which genomic
DNA as well as pre-rRNA were measured. In the first experiment,
samples with percent viabilities of 96.3%, 26.9%, and 0.02%
exhibited pre-rRNA stimulation ratios values of >3.+-.1 SD.
Samples with no detectable viable cells (0% viability) exhibited
pre-rRNA stimulation ratios that were not statistically greater
than 1.0. Therefore, RPA showed significant pre-rRNA stimulation
ration values in a sample where up to approximately 99.98% of the
target microorganisms were dead. In contrast, qPCR detection of A.
hydrophila genomic DNA was strongly positive in all samples,
regardless of cell viability. Moreover, there was no difference
between DNA signals in nutritionally stimulated and control
aliquots (not shown). Similar results were seen in the second
experiment (Table 1). This example illustrates the remarkable
sensitivity of RPA for viable cells, even when outnumbered by
inactivated cells by factors of 5000-fold or more, as in the sample
in Experiment 1 that was treated with 2 mg/l hypochlorite.
TABLE-US-00001 TABLE 1 Pre-rRNA Genomic Hypochlorite Final Percent
stimulation DNA copies (mg/l) cfu/mL viability.sup.1 ratio.sup.2
(millions) Experiment 1 0 963000 96.3 3.0 .+-. 0.2 1.4 .+-. 0.4 1
279000 27.9 17.2 .+-. 3.6 3.8 .+-. 0.5 2 190 0.02 73.0 .+-. 54.2
4.0 .+-. 0 3 0 0 0.6 .+-. 1.0 3.8 .+-. 0.5 4 0 0 0.04 .+-. 0.1 5.4
.+-. 0.6 Experiment 2 0 774000 77.4 39.4 .+-. 17.0 0.5 .+-. 0.1 1
846000 84.6 17.44 .+-. 5.4 2.2 .+-. 0.5 1.5 186000 18.6 16.47 .+-.
6.6 2.7 .+-. 0.1 2 0 0 1.28 .+-. 0.4 2.2 .+-. 0.04 .sup.1Normalized
to estimated 1 .times. 10.sup.6 input bacteria. .sup.2Mean .+-. SD
of 3 replicate samples.
[0060] In four experiments using the protocol in Table 1, RPA was
applied to a total of 18 chlorine-treated and untreated samples
with varying percent viabilities. Pre-rRNA stimulation ratios
observed in samples with no detectable colony forming units were
significantly lower (p=0.0026 by the Mann-Whitney U test) than
those observed in samples with detectable colony forming units
(FIG. 4A). There was some overlap between the two groups, however
the overlap was significantly less than that seen when genomic DNA
was quantified by qPCR in either stimulated or non-stimulated
samples (FIG. 4B). There was no significant correlation between
viability and DNA stimulation ratios. More specifically, FIG. 4
shows the correlation between the presence of viable A. hydrophila
cells and pre-rRNA stimulation ratio (A) or genomic DNA quantified
by qPCR (B) in hypochlorite treated laboratory suspensions.
Pre-rRNA stimulation ratio values (A) are the ratios of pre-rRNA in
stimulated samples relative to control samples, measured by
RT-qPCR. Values are means of 3 measurements per sample. Genomic DNA
copies (B) were quantified by qPCR normalized to a genomic DNA
standard curve. DNA was measured in nutritionally stimulated
samples (open squares) as well as non-stimulated samples (open
triangles).
Example 4
Field Testing of an RPA Assay
[0061] As a common inhabitant of surface waters, A. hydrophila was
a convenient model for field testing RPA. Samples were collected
from fresh and salt water sites in Seattle, Wash. A portion of each
sample was autoclaved to generate an inactivated control.
Autoclaved and non-autoclaved samples (300 mL each) were
concentrated by filtration. After re-suspension, aliquots were
diluted two-fold in 2.times. nutrient broth (stimulated sample) or
water (control). After 1 hour of incubation, bacteria and
particulates were concentrated by centrifugation and then A.
hydrophila pre-rRNA in the pellets was measured by RT-qPCR. Viable
counts of A. hydrophila in the samples were determined by viable
plating following standard methods.
TABLE-US-00002 TABLE 2 A. hydrophila viable Pre-rRNA (mean ratio
counts (mean cfu stimulated/ Site Description per mL .+-. SD)
control .+-. SD).sup.1 A1 Fresh water 798 4.8 .+-. 1.4 A2 Fresh
water 280 9.5 .+-. 5.9 B Salt water 6 No pre-rRNA detected C Fresh
water 760 39.8 .+-. 12.8 .sup.1Means and standard deviations of
.gtoreq.4 measurements per sample.
[0062] In total, 3 fresh water samples and 1 salt water sample were
analyzed. The fresh water samples yielded viable counts of A.
hydrophila ranging from 280 to 798 cfu/mL. All of them exhibited
positive RPA signals (Table 2). All autoclaved samples yielded no
cfu and no A. hydrophila pre-rRNA was detected in these samples.
The salt water sample had 6 cfu/mL A. hydrophila, however no A.
hydrophila pre-rRNA was detected in either stimulated or
non-stimulated samples, with or without autoclaving.
[0063] The results support the use of RPA as a means to
specifically detect viable microorganisms in environmental samples.
The RPA methods may be used to eliminate false positive results
seen in samples containing only dead bacterial cells and DNA. The
use of RPA can also reduce false positives caused by laboratory
contamination of samples or PCR reagents. Furthermore, RPA is
robust and built upon a physiological feature of all bacteria and
is useful in food and water safety analysis, either by itself or as
an adjunct to other tools.
Example 5
Biological Sensitivity of RPA
[0064] RPA may be used to improve assay sensitivity relative to
genomic DNA detection. FIG. 5 shows the results of multiple RT-qPCR
reactions conducted on paired stimulated and control aliquots
derived from a single fresh water lake sample (sample A2 from Table
2) that contained 280 cfu/mL viable A. hydrophila. With continued
reference to FIG. 5, a sample from Lake Union, Seattle, Wash. was
divided into two aliquots, one of which was stimulated with
nutrient broth (dark bars) and the other resuspended in ATW (light
bars) as a control. The results shown in FIG. 5 are expressed as
approximate pre-rRNA copies per mL of sample calculated by
comparing cycle threshold (Ct) values to a genomic DNA standard
curve. In each of these technical replicates, pre-rRNA signals in
stimulated samples exceeded those of control samples by substantial
margins.
[0065] The Ct values in Table 2 were all in the range of 32 to 43,
i.e. signals were borderline and weak. This was most likely due to
PCR inhibitors that are common in concentrated surface water
samples. Despite these limitations, the results were unambiguously
positive, because the consistent upshift in pre-rRNA signal in
stimulated samples lent confidence to the conclusion that viable A.
hydrophila cells were present.
Sequence CWU 1
1
110120DNAMycobacterium avium 1ttggccatac ctagcactcc
20220DNAMycobacterium avium 2gattgcccac gtgttactca
20321DNAMycobacterium avium 3gcccgcacgc tcacagttaa g
21419DNAAeromonas hydrophila 4attgagccgc cttaacagg
19519DNAAeromonas hydrophila 5aactgttatc cccctcgac
19623DNAAeromonas hydrophila 6ctacaagact ctagctggac agt
23722DNABacillus anthracis 7acaaacaacg tgaaacgtca at
22820DNABacillus anthracis 8gtccgccgct aacttcataa 20925DNABacillus
anthracis 9aactttattg gagagtttga tcctg 251020DNABacillus anthracis
10cccggagtta tcccagtctt 201120DNABacillus anthracis 11cagtttccaa
tgaccctcca 201221DNABacillus anthracis 12tgcactcaag tctcccagtt t
211320DNABacillus anthracis 13gagccgttac ctcaccaact
201420DNABordetella pertussis 14gatcagggtc cacacacaga
201520DNABordetella pertussis 15ccacgctttc gcgtagttat
201620DNABordetella pertussis 16aagcgatacg gatcctggtt
201720DNABordetella pertussis 17ccgacttgca tgtgtaaagc
201821DNABordetella pertussis 18gcccggtagt taaaaatgca g
211921DNABordetella pertussis 19aaggttaagc cctgggattt c
212020DNABordetella pertussis 20tcctctcaaa ccagctacgg
202123DNABorrelia burgdorferi 21gccaaaagaa taaacaaaac ctg
232220DNABorrelia burgdorferi 22ccgtttgact tgcatgctta
202322DNABorrelia burgdorferi 23ttggaagatg agagaaggga ag
222420DNABorrelia burgdorferi 24ttcgccactg aatgtattgc
202521DNABorrelia burgdorferi 25agtttccaac ataggtccac a
212623DNABorrelia burgdorferi 26agttgagctg tggtatttta tgc
232720DNABorrelia burgdorferi 27tgccttggta ggcatttacc
202827DNACampylobacter jejuni 28tttaggcata agcaattatg taaaatc
272920DNACampylobacter jejuni 29cgttcactct gagccaggat
203026DNACampylobacter jejuni 30gatttaggca taagcaatta tgtaaa
263120DNACampylobacter jejuni 31agccaggatc aaactctcca
203222DNACampylobacter jejuni 32gagacttgat aatccgccta cg
223317DNACampylobacter jejuni 33cctacgcgcc ctttacg
173420DNACampylobacter jejuni 34tcgtttccaa ctgttgtcct
203523DNAClostridium difficile 35aagaaacaaa ccataaagcc aga
233620DNAClostridium difficile 36tcgctcaact tgcatgtgtt
203727DNAClostridium difficile 37tttgataaca atagtatctg agcctga
273820DNAClostridium difficile 38ggtaggttac ccacgcgtta
203920DNAClostridium difficile 39tccactctcc tctcctgcac
204021DNAClostridium difficile 40tgcactcaag tctcccagtt t
214120DNAClostridium difficile 41cgtaggagtt tggaccgtgt
204220DNAEscherichia coli 42gtcgcaagac gaaaaatgaa
204320DNAEscherichia coli 43tcgacttgca tgtgttaggc
204426DNAEscherichia coli 44tctttgagca tcaaactttt aaattg
264520DNAEscherichia coli 45caggcagttt cccagacatt
204620DNAEscherichia coli 46tcagatgcag ttcccaggtt
204718DNAEscherichia coli 47cccggggatt tcacatct
184820DNAEscherichia coli 48ctcagaccag ctagggatcg
204925DNAHaemophilus influenzae 49cgattgaact tgaattgaag agttt
255020DNAHaemophilus influenzae 50cactcgtcag caagaaagca
205127DNAHaemophilus influenzae 51ttgaagtctt aataggtgct taactga
275220DNAHaemophilus influenzae 52ctttctcctg ctaccgttcg
205320DNAHaemophilus influenzae 53ctgaaatgca attcccaggt
205419DNAHaemophilus influenzae 54ggggctttca cacctcact
195520DNAHaemophilus influenzae 55cagtcccgca ctttcatctt
205618DNAHelicobacter pylori 56gttgttagga ataacaac
185720DNAHelicobacter pylori 57agcttcatcg ttcgacttgc
205822DNAHelicobacter pylori 58cgagttcttg tgatacgcta aa
225920DNAHelicobacter pylori 59ttccaatggc tatcccaaac
206023DNAHelicobacter pylori 60ctcccacact ctagaatagt agt
236126DNAHelicobacter pylori 61acactctaga atagtagttt caaatg
266220DNALegionella pneumophila 62agagctagtg ccggaattga
206320DNALegionella pneumophila 63tgagtttccc caagttgtcc
206421DNALegionella pneumophila 64gacaaactgt gtgggcactt t
216520DNALegionella pneumophila 65gctagacaat gctgccgttc
206621DNALegionella pneumophila 66aggttaagcc caggaatttc a
216721DNALegionella pneumophila 67attatctgac cgtcccaggt t
216818DNALegionella pneumophila 68gtccccagct ttcgtcct
186924DNAListeria monocytogenes 69agctgttttc aacaaaacaa acta
247020DNAListeria monocytogenes 70cctgagccag gatcaaactc
207127DNAListeria monocytogenes 71gctgttttca acaaaacaaa ctagtaa
277220DNAListeria monocytogenes 72agcaagctct tcctccgttc
207320DNAListeria monocytogenes 73ggggctttca catcagactt
207418DNAListeria monocytogenes 74aatgaccctc cccggtta
187520DNAListeria monocytogenes 75aggttgccca cgtgttactc
207620DNAMycobacterium tuberculosis 76tttccaaagg gagtgtttgg
207720DNAMycobacterium tuberculosis 77acccagtttc ccaggcttat
207820DNAMycobacterium tuberculosis 78tacctttggc tcccttttcc
207920DNAMycobacterium tuberculosis 79tcacccacgt gttactcacc
208021DNAMycobacterium tuberculosis 80gcccgcacgc tcacagttaa g
218120DNAMycobacterium tuberculosis 81gtctgggccg tatctcagtc
208218DNAMycobacterium tuberculosis 82cgtcacccca ccaacaag
188320DNANeisseria gonorrhoeae 83tgtcggtttc tttgaagcag
208420DNANeisseria gonorrhoeae 84ccggtacgtt ccgatatgtt
208527DNANeisseria gonorrhoeae 85gcagaccaga agttaaaaag ttagaga
278620DNANeisseria gonorrhoeae 86atcagttatc ccccgctacc
208719DNANeisseria gonorrhoeae 87ggggatttca catcctgct
198820DNANeisseria gonorrhoeae 88actcgagtca cccagttcag
208920DNANeisseria gonorrhoeae 89ctactgatcg tcgccttggt
209020DNAPorphyromonas gingivalis 90gggtaataat cggcgtctga
209120DNAPorphyromonas gingivalis 91ccctcgactt gcatgtgtta
209224DNAPorphyromonas gingivalis 92cgaggtgtac tacctgataa atcg
249320DNAPorphyromonas gingivalis 93cctatcgcta gcgttcatcc
209418DNAPorphyromonas gingivalis 94gtttcaacgg caggctga
189518DNAPorphyromonas gingivalis 95gagcgctcag gtttcacc
189620DNAPorphyromonas gingivalis 96gtccgtcttt caacgggtta
209720DNATreponema pallidum 97gatcctggct cagaacgaac
209820DNATreponema pallidum 98gcagattacc cacgcgttac
209919DNATreponema pallidum 99cctggaaacg gggtttaga
1910020DNATreponema pallidum 100ttactcacca gtccgccact
2010120DNATreponema pallidum 101gattccaccc ctacacttgg
2010220DNATreponema pallidum 102gtttcccctc cgtgattcta
2010320DNATreponema pallidum 103tctcaggtcg gatacccatc
2010421DNAVibrio cholerae 104gcaatcattc agcacagtca a
2110520DNAVibrio cholerae 105tcgacttgca tgtgttaggc 2010621DNAVibrio
cholerae 106cagtattcat tgagccgaag c 2110720DNAVibrio cholerae
107atggttatcc ccctctaccg 2010821DNAVibrio cholerae 108gtcagtttca
aatgcgattc c 2110919DNAVibrio cholerae 109tgcgattcct aggttgagc
1911020DNAVibrio cholerae 110cagaccagct agggatcgtc 20
* * * * *