U.S. patent application number 17/000209 was filed with the patent office on 2021-05-20 for trend analysis and statistical process control using multitargeted screening assays.
The applicant listed for this patent is Institute for Environmental Health, Inc.. Invention is credited to Mansour Samadpour.
Application Number | 20210147913 17/000209 |
Document ID | / |
Family ID | 1000005373979 |
Filed Date | 2021-05-20 |
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United States Patent
Application |
20210147913 |
Kind Code |
A1 |
Samadpour; Mansour |
May 20, 2021 |
TREND ANALYSIS AND STATISTICAL PROCESS CONTROL USING MULTITARGETED
SCREENING ASSAYS
Abstract
Aspects of the present invention provide novel multi-targeted
microbiological screening and monitoring methods having substantial
utility for monitoring and control of microbial growth and
contaminants, microbiological processes, predictive microbiology,
and for exposure and risk assessment. Microbial markers shared by
both target and index microbes are used in novel methods for
microbial monitoring, monitoring of microbial performance
potential, trend analysis, and statistical process control (SPC) in
processes or systems that are receptive to a plurality of
genetically distinct microbes.
Inventors: |
Samadpour; Mansour; (Lake
Forest Park, WA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Institute for Environmental Health, Inc. |
Lake Forest Park |
WA |
US |
|
|
Family ID: |
1000005373979 |
Appl. No.: |
17/000209 |
Filed: |
August 21, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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14597733 |
Jan 15, 2015 |
10752959 |
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17000209 |
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11107458 |
Apr 15, 2005 |
8956826 |
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14597733 |
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60562302 |
Apr 15, 2004 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
C12Q 1/06 20130101; C12Q
1/706 20130101; C12Q 2600/158 20130101; C12Q 1/04 20130101; G01N
33/569 20130101; C12Q 1/689 20130101; C12Q 1/701 20130101 |
International
Class: |
C12Q 1/689 20060101
C12Q001/689; C12Q 1/04 20060101 C12Q001/04; G01N 33/569 20060101
G01N033/569; C12Q 1/06 20060101 C12Q001/06; C12Q 1/70 20060101
C12Q001/70 |
Claims
1. A method for process control with respect to a target microbe,
comprising: a) receiving at least one test sample obtained during
each of a plurality of time periods from a process or system
receptive to a plurality of genetically distinct microbes; b)
testing the at least one test sample from each of the plurality of
time periods for a presence or absence for each of a plurality of
suitable markers of at least one target microbe to provide a set of
test results for each time period, wherein at least one of the
markers also detects at least one index microbe, other than the
target microbe, potentially present in the sample and genetically
distinct from the target microbe but positive for a subset of the
plurality of tested markers; c) calculating, for each set of test
results. at least one mathematical index value relating the number
of markers for which positive results are obtained to the total
number of tested markers, wherein the mathematical index value is
predictive of process failure, the presence or absence of the
target microbe, or predictive of both process failure and presence
or absence of the target microbe; and d) initiating pre-emptive
and/or remedial action in the process or system relating to the
target microbe at a threshold index value indicative of process
failure, presence of the target microbe, or indicative of both
process failure and presence of the target microbe, wherein a
method for process control with respect to the target microbe is
afforded.
2-3. (canceled)
4. The method of claim 1, wherein the index microbe is a microbe
that is genetically distinct from the at least one target microbe,
but is otherwise correlatable with the target microbe by virtue of
at least one common property selected from the group consisting of:
coordinate source association; coordinate growth condition
response; indicator organism relationship; same family taxon; same
genus taxon; same species taxon; same biotype; same serotype; same
virulence group; common functional genes; common virulence factors;
common enzymes and enzymatic pathway(s); common engineered genes or
traits; common metabolites or by-products; coordinate sensitivity
to antimicrobial agents or conditions, and same strain
attribution.
5. The method of claim 1, wherein the samples are enriched prior to
testing in b).
6. The method of claim 1, wherein the at least one mathematical
index values determined in c) allows for normalizing the number of
observed marker presence events over the number of samples
taken.
7. (canceled)
8. The method of claim 1, wherein determining the at least one
mathematical index value in c) further comprises weighting, for
purposes of calculating the index value, the value of the presence
of at least one of the markers relative to another.
9. The method of claim 8, wherein the weighting is based on at
least one common property between target and index microbes,
wherein the index microbe is a microbe that is genetically distinct
from the at least one target microbe, but is otherwise correlatable
with the target microbe by virtue of at least one common property
selected from the group consisting of: coordinate source
association; coordinate growth condition response; indicator
organism relationship; same family taxon; same genus taxon; same
species taxon; same biotype; same serotype; same virulence group;
common functional genes; common virulence factors; common enzymes
and enzymatic pathway(s); common engineered genes or traits; common
metabolites or by-products; coordinate sensitivity to antimicrobial
agents or conditions, and same strain attribution.
10. The method of claim 1 wherein the at least one target microbe
is considered present when a specific marker profile is determined
to be present.
11-12. (canceled)
13. The method of claim 1, wherein the at least one threshold
mathematical index value is an upper confidence limit that is
proportional to the standard deviation of the index values over an
investigated time range.
14. (canceled)
15. The method of claim 1, wherein the markers are selected from
the group consisting of genetic markers, antigenic markers,
metabolite and metabolite by-product markers, and combinations
thereof.
16. The method of claim 15, wherein the markers are selected from
the group consisting of DNA markers, virulence factor genes,
virulence factors or putative virulence factors, toxins, enzymes,
proteins, macromolecules, metabolic byproducts, surface antigens,
adhesion proteins, ribosomal gene markers, and combinations
thereof.
17. The method of claim 1, wherein the number of markers tested is
at least 5.
18. The method of claim 17, wherein at least one marker comprises
an antigen of a surface antigen protein of the target microbe, and
at least 4 markers correspond to genetic markers of the target
organism.
19. The method of claim 1, wherein the time periods are separated
by a period selected from the group consisting of seconds, minutes,
hours, days, weeks, months, years, and combinations thereof.
20. The method of claim 1, wherein the at least one target microbe
is selected from the group consisting of pathogens, spoilage
organisms, beneficial organisms, bioremedial organisms, indicator
organisms, fermentation-related organisms, and combinations
thereof.
21. The method of claim 20, wherein the pathogen is characterized
by at least one property selected from the group consisting of
foodborne, waterborne, airborne, bloodborne, sexually transmitted,
vectorborne, and zoonotic organism.
22. The method of claim 20, wherein the pathogen is selected from
the group consisting of bacterial, viral, fungal and parasitic
microorganisms, and by-products of the preceding.
23. The method of claim 20, wherein the pathogen is selected from
the group consisting of bacterial, viral, parasitic, and
fungal.
24. The method of claim 23, wherein the pathogenic organism is E.
coli O157:H7.
25-26. (canceled)
27. The method of claim 1, wherein the tests are selected from the
group consisting of immunoassays, ELISA assays, antigen-antibody
based detection methods, ligand-antigen detection methods, nucleic
acid amplification-based assays, PCR, multiplex PCR, nucleic acid
hybridization-based assays, bio-sensor assays,
immunostaining-microscopy-based assays, nucleic acid-array-based
assays, DNA chip-based assays, dot blots, multi- and single-target
lateral flow devices, bacteriophage-detection-based assays,
microbiology-based assays, and chemical and biochemical assays for
detection of compounds, microbial byproducts, metabolites, organic,
and inorganic molecules associated with the at least one target
microbe.
28. The method of claim 1, wherein the test sample is a composite
sample comprised of a plurality of samples collected from different
sources or locations within the process or system.
29-47. (canceled)
48. The method of claim 1, wherein the at least one threshold index
value corresponds to a particular process interval selected from
the group consisting of daily, weekly, monthly, seasonal, and
process phase based intervals, and is predictive of a status of the
process or system.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. patent
application Ser. No. 14/597,733, filed Jan. 15, 2015, which is a
continuation of U.S. patent application Ser. No. 11/107,458, filed
Apr. 15, 2005, now U.S. Pat. No. 8,956,826, which claims the
benefit of and priority to U.S. Provisional Patent Application No.
60/562,302, filed on 15 Apr. 2004, and entitled "USE OF PATHOGEN
AND INDICATOR ORGANISM PROFILE DATA FOR STATISTICAL PROCESS CONTROL
OF MANUFACTURING PROCESSES AND SANITATION PRACTICES," the
disclosures of which are incorporated by reference herein it their
entirety.
FIELD OF THE INVENTION
[0002] Aspects of the present invention relate generally to the use
of microbial markers shared by both target and index microbes in
novel methods for microbial monitoring, monitoring of microbial
performance potential, trend analysis, and statistical process
control (SPC) in processes or systems that are receptive to a
plurality of genetically distinct microbes.
BACKGROUND
[0003] Attempts to detect a particular (`target`) microbial
presence or contamination thereby are divided in the art into two
broad groups: (i) direct specific detection of the target microbe
by determining a presence or absence status for a presumably
`target microbe-specific` marker or characteristic; and (ii)
indirect detection, based on determining a presence or absence
status for a presumably `indicator microbe-specific` marker, which
if present is deemed to be indicative of the presence of the target
microbe. Such detection schemes, whether direct (target marker) or
indirect (indicator microbe marker), have at least two fundamental
problems by virtue of being premised on isolated presence/absence
tests that yield only an isolated presence/absence signal.
[0004] First, a "presence/absence" test is a hypothesis test for
which only two possibilities exist with respect to the null
hypothesis: either it is true or it is false. In practice,
"presence/absence" tests are thus susceptible to two types of
errors: type-1 errors (false positives), occurring when the test
result is declared positive when the null hypothesis is true (i.e.,
the condition being tested for does not exist); and type-2 errors
(false negatives), occurring when the test result is declared
negative when the null hypothesis is false. These errors are the
result of non-analytic sampling and analysis errors having a
variety of sources including, for example, instances where the test
is not sensitive enough to detect a target-specific marker even if
present, or where errors are introduced during collecting and/or
preparing samples, executing test procedures, or in calculating
results. Additionally, a false positive might occur where a
presumed `target microbe-specific` marker is not absolutely
specific, but is associated with one or more genetically distinct
microbes. Because of Type 1 and 2 errors, therefore, a single test
cannot always be regarded as a definitive measure of whether the
microbial behavior is present or absent.
[0005] Second, prior art detection schemes are not effectively
applicable to statistical process control (SPC). SPC is currently
applied during the manufacture of many materials, and consists of
the systematic monitoring of trends in process control data (e.g.,
corrective actions are applied to bring a process or system back
into control when trends indicate that processes are deviating from
desired ranges. SPC conveys distinct economic advantages to a
manufacturer. By verifying, for example, that conditions during the
manufacturing process fall within a range, SPC helps reassure that
the quality of the finished product will be acceptable.
Additionally, trend information can be used to initiate corrective
actions before product characteristics fall out of acceptable
ranges, thereby increasing yields of acceptable finished
products.
[0006] However, for the majority of samples tested by prior art
presence/absence detection schemes, the particular `target` or
`indicator` microbes are either not present, or are present at
undetectable levels, giving rise to numerous isolated negative
values that cannot be effectively used in SPC to provide early
warning of process failure, exposure and risk assessment, and to
facilitate risk based decision making.
[0007] For example, manufacturing of food, drinking water,
pharmaceuticals and many other materials requires processes and
protocols that result in finished goods with low or no microbial
burden. Unfortunately, as described above, the ability to apply SPC
to microbiological data is often severely limited. A specific case
in point relates to the use of generic E. coli `count` data from
carcasses for SPC of the beef manufacturing processes in abattoirs.
The USDA Food Safety and Inspection Service has encouraged the use
of `count` data in this manner. Practically, however, many E. coli
count data points fall below the limit of detection in
clean/semi-clean environments, and it has become evident that SPC
cannot be applied when the majority of the data points do not allow
identification of trends.
[0008] Equally illustrative are the difficulties faced in
attempting to apply trend analysis and SPC to E. coli O157:H7
presence/absence test results generated from "hold and release"
testing of beef trim products. Application of trend analysis and
SPC to such test results for the purpose of directing meaningful
pre-emptive and preventative remedial action is highly desirable,
because there are severe adverse economic consequences when a
positive (pathogen present) test result is obtained. Practically
speaking, however, the incidence rate of positive test results may
be very low (ca. 1% for E, coli O157:H7 in beet). Again, it has
become evident that SPC cannot be applied when the majority of the
data points do not provide positive tangible results that would
allow for identification of trends.
[0009] Pronounced need in the art. There is, therefore, a
pronounced need in the art for more reliable and robust methods of
determining whether a particular target microbe, or associated
property thereof, is present, or optimally present, in a process or
system that is receptive to a plurality of genetically distinct
microbes. There is also a pronounced need in the art for methods
for predicting a presence of a target microbe, or target microbe
associated condition in such processes or systems, and for
identifying trends for SPC applications to processes or systems
that are receptive to a plurality of genetically distinct microbes
(e.g., manufacturing environments) to help ensure that finished
product meets quality and yield objectives with respect to
microbial burden or distribution.
[0010] There is a pronounced need in the art for methods of
determining microbial performance potential in a process or system
that is receptive to a plurality of genetically distinct microbes
(e.g., bioremediation, fermentation, spoliation). There is a
pronounced need in the art for methods of predicting microbial
performance potential in such processes or systems.
[0011] There is a need, therefore, to extract, derive and/or
generate additional data from microbial test methods that is
suitable for the application in the context of microbial detection,
trend analysis and SPC methodologies.
SUMMARY OF THE INVENTION
[0012] Aspects of the present invention provide novel
multi-targeted microbiological screening and monitoring methods
having substantial utility for monitoring and control of microbial
growth and contaminants, microbiological processes, predictive
microbiology, and for exposure and risk assessment. Microbial
markers shared by both target and index microbes are used in novel
methods for microbial monitoring, monitoring of microbial
performance potential, trend analysis, and statistical process
control (SPC) in processes or systems that are receptive to a
plurality of genetically distinct microbes.
[0013] Particular aspects of the present invention use the results
of multiple independent "presence/absence" tests involving a
plurality of target microbe markers, to determine an aggregate
index value that represents a more accurate, robust and useful
measure of whether a target microbe or target microbe-associated
condition or attribute is present or not. Type 1 and 2 errors
represent only a small percentage of all results, therefore, the
overall effect on the aggregate index value calculation will be
incremental.
[0014] Particular embodiments provide methods for pathogen and
organism profiling, and generating SPC charts for use in any
industrial setting or process, or in any system that requires
microbiological control of production, or microbial balance. Such
applicable processes and systems include, but are not limited to:
food production; manufacturing; processing; storage; transportation
and distribution; with respect to microbial pathogens process
sanitation, environmental contaminants, and spoilage organisms;
with respect to fermentation processes determining purity of the
seed stock and fermentation contaminants; aseptic processing (e.g.,
food and pharmaceutical; with respect to sterility and
environmental control); water treatment (e.g., with respect to
microbiological quality of the raw and treated water, and control
of the organisms throughout the distribution system); wastewater
treatment (e.g., with respect to microbiological quality of the
treated wastewater and biosolids, control of the treatment process,
control of the aerobic and anaerobic digesters, and assessment of
the impact of the discharged wastewater and application of
bio-solids on the receiving environments); control of microbial
contaminants and assessment of their impact in the indoor
environment and indoor air quality assessment studies;
environmental microbiology (e.g., with respect to monitoring the
microbiological quality of shellfish, shellfish beds and cultured
aquatic organisms, assessing the microbiological quality of
recreational waters and swimming beaches, assessing the
microbiological quality of bodies of water, conducting impact
assessment of point and non-point-sources); feed microbiology
(e.g., in determining the microbiological quality and safety of the
feed); soil microbiology in assessing the overall microbiology and
population structure of soil organisms, in assessing target
organisms that can indicate environmental contamination or organic
and inorganic reservoirs (e.g., oil fields)).
BRIEF DESCRIPTION OF THE DRAWINGS
[0015] FIG. 1 shows, according to EXAMPLE 1 herein, exemplary
results for a 4-band multiplex PCR test for E. coli O157:H7. In the
Figure test results are shown as two rows of 9 "lanes" in which
genetic targets amplified by polymerase chain reaction (PCR)
techniques have been separated by agarose gel electrophoresis and
visualized using ethidium bromide dye in a UV transilluminator.
Positive controls with four bands each are shown in the right hand
lanes. Each of the other lanes has bands appearing, indicating the
presence of `index` microbes`; namely, microbes (e.g. organisms)
that are also detected by one or more of the E. coli O157:H7
markers. Note that in this example, none of the lanes shows the
presence of all four bands required to indicate a positive result
for the presence of E. coli O157:H7.
[0016] FIG. 2 shows, according to EXAMPLE 1 herein, a plot of
`Sanitation Index` values over time. Reference lines for mean,
performance target and upper confidence limit (UCL) are included to
facilitate the application of principles of Statistical Process
Control.
[0017] FIG. 3 shows, according to EXAMPLE 2 herein, a plot of the
inventive index value over time (plotted daily and as 7 day moving
average) as applied to harvested beef product (trim), showing
multiple rapid increases in the trim index that can be used for
process control.
[0018] FIG. 4 shows, according to EXAMPLE 2 herein, and for the
same data and period as in FIG. 3, the inventive index values
(plotted as 7 day moving average..) sorted by `shift` (A and B
working shifts). The curves are indicative of mechanical process
failure.
[0019] FIG. 5 shows, according to EXAMPLE 2 herein, and using the
same data set as that used in FIGS. 3 and 4, that over
approximately a month period of time the index values of one shift
"A" significantly varied from those of the other "B." During this
period.sub.; one confirmed case of pathogenic E. coli O157 was
detected on the "A" shift.
[0020] FIG. 6A (upper gel) shows a multiplex PCR analysis of
samples from individual sub-lots (`combos`), showing that the
individual sub lots that tested positive were comprised of `chuck
trim,` while the negative testing sub-lots consisted of various
non-chuck trim products. The data enables the production facility
to efficiently utilize resources in addressing only particlular
anatomicalcarcass areas, and those specific production lines
associated with the production of that anatomical area (e.g., chuck
trim).
[0021] FIG. 6B (lower part) shows a negative multiplex PCR analysis
in which a composite sample initially showed a suspect positive
result, but yielded a negative confirmation. Though the test
yielded a negative result, the individual sub-lot results revealed
that sub lot B was the to source of the suspect result. The data
can be used in the same manner as a positive test result to
identify which common product codes, production lines or lots of
carcasses are carrying index organisms.
[0022] FIG. 7 shows, for establishment `E,` a temporal plot of
"Carcass Index" (i.e., "Carcass Sponge Daily Index") derived from
sponge sampling of carcasses at various time points. Carcass Sponge
Samples are collected from carcasses which are processed to produce
beef trim products, and precede beef trim samples by approximately
24 hours.
[0023] FIG. 8 shows a temporal plot of "Sanitation Index," as
described herein, for the same establishment as in FIG. 7, and
including a corresponding time period (highlighted by dashed
boxes). The data shows that decreasing carcass sanitation index
(FIG. 7) was correlated with a coordinate reduction of trim
sanitation index (FIG. 8).
[0024] FIG. 9 shows, for establishment "B" a temporal plot of
"Carcass Index" in elevated values of the index are observed for
two periods (highlighted by dashed boxes)
[0025] FIG. 10 shows a temporal plot of "Sanitation index" and the
presence of positive results (presumptive) for E. coli O157 for
beef trim produced from the carcasses sampled in FIG. 9.
[0026] The data shows that the increasing carcass sanitation index
(FIG. 9) was correlated with a coordinate increase of trim
sanitation index and the appearance of positive results
(presumptive) for E. coli O157.
[0027] FIG. 11 shows for establishment "E" a temporal plot of
"Carcass index" in which elevated values of the index are observed
for a period (highlighted by dashed box) similar to FIG. 9.
[0028] FIG. 12 shows that similar to FIG. 10 a temporal plot of
"Sanitation Index" and the presence of positive results
(presumptive) for E. coli O157 for beef trim produced from the
carcasses sampled in FIG. 11 was correlated with a coordinate
increase of trim sanitation index and the appearance of positive
results (presumptive) for E. coli O157.
[0029] FIG. 13 shows a temporal plot of "Sanitation Index" and the
presence of positive results (presumptive) for E. coli O157. The
data show that increasing trim sanitation index was correlated with
an increased incidence of positive results (presumptive) for E.
coli O157.
[0030] FIG. 14 shows the same data as in FIG. 13, except plotted as
a 3 day moving average.
[0031] FIG. 15 shows a temporal plot of "Carcass Index" for
establishment "A" in which elevated values of the index are
observed for a period (highlighted by dashed box). In this case the
carcass index was constructed from a 7 factor system in which 2
Salmonella specific markers were added to the 5 E. coli
markers.
[0032] FIG. 16 shows a temporal plot of "Sanitation Index" and the
presence of positive results (presumptive) for Salmonella for beef
trim produced from the carcasses sampled in FIG. 15. The data shows
that the increasing carcass sanitation index (FIG. 15) was
correlated with a coordinate increase of trim sanitation index and
the appearance of positive results (presumptive) for
Salmonella.
[0033] FIG. 17 shows a temporal plot of carcass sanitation index,
showing a period of time where the index was elevated (highlighted
by the dashed box). After corrective actions were taken by the
plant, subsequent values of the carcass sanitation index are
lowered.
[0034] FIG. 18 shows a temporal plot of the sanitation index for
beef trim produced from the carcasses sampled in FIG. 17. The data
show increased values in the index followed by a decline which were
correlated with the carcass sanitation index in FIG. 17.
DETAILED DESCRIPTION OF THE INVENTION
[0035] In prior art microbial detection schemes, samples are tested
by conventional presence/absence detection schemes, and the
particular `target` or `indicator` microbes are either to not
present, or are present at undetectable levels giving rise to
independent, primarily negative values that cannot be effectively
used in monitoring to establish trends or to enable statistical
process control (SPC) applications to provide early warning of
process failure, exposure and risk assessment, and risk based
decision making. Additionally, independent prior art
presence/absence tests for particular target microbes, or for
target microbe-associated conditions or attributes, either cannot
or should not be regarded as a definitive measure of whether a
particular `target` microbe, or target' microbe-associated
condition or attribute, is present or absent. This is because, as
discussed above (under "Background"), conventional presence/absence
test schemes are susceptible to both type-1 (false positives) and
type-2 (false negative) errors.
[0036] These and other problems relating to microbial monitoring
are solved by aspects of the present invention that use microbial
markers shared by both target and index microbes in novel methods
for microbial monitoring, monitoring of microbial performance
potential, trend analysis, and statistical process control (SPC).
The inventive methods allow, inter cilia, for control of microbial
growth, contaminant and management of microbiological processes,
predictive microbiology, and for exposure and risk assessment.
[0037] Particular embodiments provide novel test methods in which
the outcome (herein referred to as an "index value") of a plurality
of presence/absence tests for a corresponding plurality of
particular target microbe markers is based upon an aggregate
analysis scheme applied to the isolated presence/absence marker
test values. The individual presence/absence marker test values
each correspond to a specific property or attribute of a target
microbe being screened for, and according to the present invention,
while the target microbe is considered present only when at least a
plurality (and preferably at least a majority) of the markers
tested are determined to be present, the inventive aggregate
analyses provide for determination of useful index values even
where the sum of the markers present is not great enough to
indicate the presence of the target microbe or the target
microbe-associated condition or attribute.
[0038] The inventive utility of the index value is premised on the
fact that at least one of the target microbe markers also detects
one or more index microbes (or index-microbe-associated condition
or attribute) present in the test sample. Such index microbes are
genetically distinct microbes, and are typically from sources in
common with the target microbe, or typically behave in a fashion
similar to the target microbe or organism when, for example,
microbial interventions are applied or when favorable growth
conditions are encountered. When such index organisms are present,
they give rise to positive signals for a subset of the target
microbe markers, and according to the present invention, while a
sufficient number of such positive signals can support a conclusion
that the target microbe is present, a lesser number (or even one
such marker) also has substantial utility in providing index values
that are predictive of, or indicative of the likelihood of whether
the target microbe or target microbe-associated condition or
attribute will be detected.
[0039] Therefore, aspects of the present invention, in contrast to
prior art methods, extract and use additional data from microbial
test methods in that index microbes (or index-microbe-associated
condition or attribute) are detected, and an index value is
calculated even in instances where the target microbe of interest
is deemed to be "absent," based on the aggregate test results.
Preferably, a mathematical index value is calculated, based on the
number of markers for which positive results are obtained compared
to the total number of possible positive marker results.
[0040] Preferably, this index value is tracked over time, and/or
among different sample source locations within a process or system,
and by applying trend analysis, changes in the index value that
reflect the probability that the target microbe, or target
microbe-associated condition or attribute will be detected can be
tracked. When the trend indicates an increased probability, there
is an opportunity through the application of process control (e.g.,
SPC) to take meaningful pre-emptive and/or remedial actions
relating to the target microbe, or to the target microbe-associated
conditions or attributes.
[0041] Particular aspects comprise use of an aggregate of
presence/absence signals that are either generated by application
of separate test methods, or by using one or more multi-plea test
methods, or a combination of such individual or multiplex methods
to monitor target microbes (e.g., pathogens), a class of target
microbe (e.g., pathogenic), or a target microbe-associated
condition or attribute. Preferably, the analysis is applied to
enriched samples, initially collected from a process or system
(e.g., manufacturing environment including final product,
incomplete product from intermediate process steps, raw
ingredients, treatment materials, equipment swabs, and
environmental samples). Preferably, samples are enriched for the
target microbe or appropriate taxon thereof. Such enrichment
increases detection by, and enhances utility of the test methods,
because of sensitivity considerations and increases the number of
applicable possible test methods. Additionally, some tests result
in several signals (e.g., multiplex PCR), thus many test results
are available which may form the basis for SPC according to
preferred embodiments of the present invention.
[0042] In particular embodiments, the presence/absence microbial
test methods include, but are not limited to multiplex PCR
reaction(s), DNA chips, dot blots, multi- and single-target lateral
flow devices, and combinations thereof. Preferably, the methods
comprise determination of presence/absence microbial tests for
detection of microbe markers including, but not limited to
ribosomal. RNA genes (including those for particular taxons of
microbes or organisms), virulence factors or putative virulence
factors found in target microbes, gene segments which are found in
target and/or index microbes, or in taxons thereof, metabolic
products (e.g., including by-products) associated with microbial
taxons, and antigens which are associated with target and/or index
microbes, or with taxons thereof.
[0043] Preferably, the assay suitable for detection of pathogenic
or microbial contamination is selected from the assay group
consisting of immunoassays, nucleic acid amplification-based
assays, PCR-based assays, nucleic acid hybridization-based assays,
bio-sensor assays, immunostaining-microscopy-based assays, nucleic
acid-array-based assays, DNA chip-based assays,
bacteriophage-detection-based assays, classical microbiology-based
assays, and chemical or biochemical assays based on the detection
of compounds associated with particular target organisms or groups
of target organisms, and combinations thereof.
[0044] Preferably, the microbe or pathogen is selected from the
group consisting of Escherichia coli O157:H7 (E. coli O157:H7),
enterohemorrhagic Escherichia coli (EHEC), enterotoxigenic
Escherichia coli (ETEC), enteroinvasive Escherichia coli (DEC),
enterpathogenic Escherichia coli (EPEC), Salmonella, Listeria,
Yersinis, Campylobacter, Clostridial, species, Staphylococcus spp.;
frank and opportunistic bacterial, fungal, viral, parsitic
pathogens; indicator organisms including heterotrophes, genetic E.
coli, total and fecal coliforms and enterococcus; spoilage
organisms including Pseudomonas; indicator molecules including
glial fibillary acid protein (GFAP), transmissable spongiform
encephalopathy (TSE) agents (prions), including bovine spongiform
encephalopathy (BSE) agents, scrapie, chronic wasting disease; and
combinations thereof. Additional microbe sor pathogensare selected
from the group consisting of Staph. aureus, Bacillus cereus, and
Clostridium botulinum, Clostridium perfringes, Vibrio cholerae and
V. parahemolyticus, Yersinia enterocolitica, Yersinia pestis,
Brucella, Francisella, Aeromonas and Plesiomonas, Citrobacter,
Enierobacter, Klebsiella, Morganella, Proteus, Proridencia,
Serratia, and Shigella.
[0045] Preferably, the pathogen or microbe is Escherichia coli
O157:H7 (E. coli O157:H7).
[0046] In particular embodiments, a point system is applied to the
test signals, wherein each signal is assigned one or more points if
a positive result is observed, and an index value is calculated
based on the ratio of the number of points observed to the
collective total number of possible points. In more specific
aspects, the test methods comprise a combination of a multiplex
(e.g., 4-band)-PCR test and a lateral flow device test (e.g., for
detecting antigen by ELISA) for the detection of a target microbe
(e.g., E. coli O157:H7) and an index value (e.g., `sanitation
index`) is calculated as a %=100.times.TS/(5.times.T) (see herein
below for variable definition).
[0047] In preferred aspects, the index value is plotted over time,
or is compared among samples of differing source within a process
or system, and temporal or spatial changes in the index values are
analyzed to identify trends. Preferably, identified trends relating
to changes in index value are used in making process central
decisions (e.g., consistent with the principles of art recognized
SPC).
Specific Embodiments
[0048] Aspects of the present invention provide a method for
microbial monitoring in a process or system, comprising: (a)
obtaining, at each of a plurality of time points, at least one test
sample from a process or system receptive to a plurality of
genetically distinct microbes; (b) determining for each test
sample, and by using a plurality of suitable tests, a presence or
absence for each of a plurality of markers of at least one target
microbe, wherein at least one of the markers also detects at least
one index microbe present in the sample, and wherein the at least
one target microbe is considered present only when at least a
plurality of all markers tested is determined to be present; and
(c) further determining, for each time point, at least one index
value that is proportional to the number of markers present,
whereby temporal changes in the index value are monitored, and
microbial monitoring in a process or system is, at least in part,
afforded,
[0049] Preferably, the method further comprises the use of the
index values determined in (c) for purposes of trend analysis to
assess a status associated with the process or system. Preferably,
the method further comprises the use of the index values determined
in (c) for purposes of intervention or control of the process or
system.
[0050] Preferably, the index microbe is a microbe that is
genetically distinct from the at least one target microbe, but is
otherwise correlatable with the target microbe by virtue of at
least one common property selected from the group consisting of:
coordinate source association; coordinate growth condition
response; indicator organism relationship; same family taxon; same
genus taxon; same species taxon; same biotype; same serotype; same
virulence group; common functional genes; common virulence factors;
common enzymes and enzymatic pathway(s); common engineered genes or
traits; common metabolites or by-products; coordinate sensitivity
to antimicrobial agents or conditions, and same strain
attribution.
[0051] Preferably, the samples are enriched prior to determining in
(b).
[0052] Preferably, the index values determined in (c) are
calculated by a formula suitable to allow for correlating the
number of observed marker presence events and normalizing them over
the number of samples taken. Preferably, the index values
determined in (c) are proportional to the quotient of the number of
markers present divided by the total number of markers. Preferably,
the determining the index value in (c) further comprises weighting,
for purposes of calculating the index value, the value of the
presence of at least one of the markers relative to another.
Preferably, the weighting is based on at least one common property
between target and index microbes, wherein the index microbe is a
microbe that is genetically distinct from the at least one target
microbe, but is otherwise correlatable with the target microbe by
virtue of at least one common property selected from the group
consisting of: coordinate source association; coordinate growth
condition response; indicator organism relationship; same family
taxon; same genus taxon; same species taxon; same biotype; same
serotype; same virulence group; common functional genes; common
virulence factors; common enzymes and enzymatic pathway(s); common
engineered genes or traits; common metabolites or by-products;
coordinate sensitivity to antimicrobial agents or conditions, and
same strain attribution.
[0053] Preferably, the at least one target microbe is considered
present when a specific marker profile is determined to be present.
Preferably, the at least one target microbe is considered present
when at least a majority of all markers tested are determined to be
present in a particular sample.
[0054] Preferably, the methods further comprise establishment of at
least one threshold index value that is predictive of the presence
of the target microbe in the process or system. Preferably, the at
least one threshold index value is an upper confidence limit, as
defined herein, that is proportional to the standard deviation of
the index values over an investigated time range. Preferably, the
at least one threshold index value corresponds to a particular
process interval selected from the group consisting of daily,
weekly, monthly, seasonal, and process phase based intervals, and
is predictive of a status of the process or system.
[0055] Preferably, the markers are selected from the group
consisting of genetic markers, antigenic markers, metabolite and
metabolite by-product markers, and combinations thereof.
Preferably, the markers are selected from the group consisting of
DNA markers, virulence factor genes, virulence factors or putative
virulence factors, toxins, enzymes, proteins, macromolecules,
metabolic byproducts, surface antigens, adhesion proteins,
ribosomal gene markers, and combinations thereof. Preferably, the
number of markers tested is at least 5. Preferably, at least one
marker comprises an antigen of a surface antigen protein of the
target microbe, and at least 4 markers correspond to genetic
markers of the target organism.
[0056] In particular embodiments, the time points are separated by
a period selected from the group consisting of seconds, minutes,
hours, days, weeks, months, years and combinations thereof.
[0057] Preferably, the at least one target microbe is selected from
the group consisting of a pathogens, spoilage organisms, beneficial
organisms, bioremedial organisms, indicator organisms,
fermentation-related organisms, and combinations thereof.
Preferably, the pathogen is characterized by at least one property
selected from the group consisting of foodborne, waterborne,
airborne, bloodborne, sexually transmitted, vectorborne, and
zoonotic organism. Preferably, the pathogen is selected from the
group consisting of bacterial, viral, fungal and parasitic
microorganisms, and by-products of the preceding. Preferably, the
pathogen is selected from the group consisting of pathogenic
organisms listed in TABLE 2 herein above. Preferably, the
pathogenic organism is E. coli O157:H7.
[0058] In particular embodiments, the spoilage organism is selected
from the group consisting of bacterial., viral, fungal and
parasitic microorganisms, and by-products of the preceding. In
particular embodiments, the fermentation-related organism is
selected from the group consisting of bacterial, viral, fungal and
parasitic microorganisms, and by-products of the preceding.
[0059] In particular embodiments, the tests are selected from the
group consisting of immunoassays, ELISA assays, antigen-antibody
based detection methods, ligand-antigen detection methods, nucleic
acid amplification-based assays, PCR, multiplex PCR, nucleic acid
hybridization-based assays, bio-sensor assays,
immunostaining-microscopy-based assays, nucleic acid-array-based
assays, DNA chip-based assays, dot blots, multi- and single-target
lateral flow devices, bacteriophage-detection-based assays,
microbiology-based assays, and chemical and biochemical assays for
detection of compounds, microbial byproducts, metabolites, organic
and inorganic molecules associated with the at least one target
microbe.
[0060] In particular embodiments, the test sample is a composite
sample comprised of a plurality of samples collected from different
sources or locations within the process or system.
[0061] Additional embodiments of the present invention provide
methods for monitoring of microbial performance or potential
thereof in a process or system, comprising: (a) obtaining, at each
of a plurality of time points, at least one test sample from at
least one source location of a process or system receptive to a
plurality of genetically distinct microbes; (b) determining for
each test sample, and by using a plurality of suitable tests, a
presence or absence for each of a plurality of microbial markers ,
wherein the markers are selected from the group consisting of
genetic markers, antigenic markers, metabolic markers, and
combinations thereof; and (c) further determining, for each time
point, a microbial performance index value that is proportional to
the number of markers present, whereby temporal changes in the
performance index value are observable, and monitoring of microbial
performance or potential thereof in a process or system is, at
least in part, afforded.
[0062] Preferably, the method comprises use of the microbial
performance index value determined in (c) for purposes of trend
analysis to assess a status associated with the process or system.
Preferably, the method comprises use of the microbial performance
index value determined in (c) for purposes of intervention or
control of the process or system.
[0063] Preferably, the samples are enriched prior to determining in
(b).
[0064] Preferably, the microbial performance index values
determined in (c) are calculated by a formula suitable to allow for
correlating the number of observed marker presence events and
normalizing them over the number of samples taken. Preferably, the
microbial performance index values determined in (c) are
proportional to the quotient of the number of markers present
divided by the total number of markers.
[0065] Preferably, determining the microbial performance index
value in (c) further comprises weighting, for purposes of
calculating the microbial performance index value, the value of the
presence of at least one of the markers relative to another.
Preferably, the weighting is based on relevance to a particular
aspect of the microbial performance being monitored.
[0066] Preferably, the method further comprises establishment of at
least one threshold microbial performance index value that is
indicative of a level of microbial performance potential of the
process or system. Preferably, the at least one threshold microbial
performance index value is an upper confidence limit, as defined
herein, that is proportional to the standard deviation of the
microbial performance index values over an investigated time range.
Preferably, the at least one threshold microbial performance index
value corresponds to a particular process interval selected from
the group consisting of daily, weekly, monthly, seasonal, and
process phase based intervals, and is indicative of a status of the
process or system. Preferably, the at least one threshold microbial
performance index value corresponds to a particular process
interval selected from the group consisting of daily, weekly,
monthly, seasonal, and process phase based intervals, and is
predictive of a status of the process or system.
[0067] Preferably, the genetic markers, antigenic markers and
metabolic markers are selected from the group consisting of
microbial DNA markers, ribosomal gene markers, microbial RNA
markers, surface antigens, adhesion proteins, toxins, proteins,
plasmid markers, microbial enzyme markers, microbial enzyme
activity markers, microbial metabolites and metabolic by-products,
and combinations thereof. Preferably, the number of markers tested
is at least 5.
[0068] In particular embodiments, the time points are separated by
a period selected from the group consisting of seconds, minutes,
hours, days, weeks, months, years and combinations thereof.
[0069] Preferably, the tests are selected from the group consisting
of immunoassays, ELISA assays, antigen-antibody based detection
methods, ligand-antigen detection methods, nucleic acid
amplification-based assays, PCR, multiplex PCR, nucleic acid
hybridization-based assays, bio-sensor assays,
immunostaining-microscopy-based assays, nucleic acid-array-based
assays, DNA chip-based assays, dot blots, multi- and single-target
lateral flow devices, bacteriophage-detection-based assays,
microbiology-based assays, and chemical and biochemical assays for
detection of compounds, microbial byproducts, metabolites, organic
and inorganic molecules associated with microbes, and combinations
thereof.
[0070] Preferably, the microbial performance potential is selected
from the group consisting of bioremediation potential, fermentation
potential, spoilage potential, pathogenic potential, beneficial
organism potential, indicator organism potential, and combinations
thereof. Preferably, the microbial performance potential is that of
bioremediation potential, and wherein at least two of the markers
are selected from the group consisting of aromatic oxygenase genes,
catechol 2,3-dioxygenase, nucleic acid marker for dehalococcoides
group organisms, methanotroph markers, pmoA gene (PmoA) markers,
methane monooxygenase (AMMO) markers, Rhodocyclus-like
beta-Proteobacteria markers, phosphate kinase markers,
Thiocyanate-Degrading Bacteria markers and combinations
thereof.
[0071] In particular embodiments, the test sample is a composite
sample comprised of a plurality of samples collected from different
sources or locations within the process or system.
Applications of Inventive Microbial Monitoring Methods
[0072] Generally speaking, the inventive microbial monitoring
aspects of the present invention may be applied to any kind of
sample obtained from an environment where food, drinking water,
pharmaceuticals or any other finished good requiring microbial
monitoring and control (e.g., in manufacturing processes or systems
requiring low or no microbial burden, or in processes where
microbial performance or the potential thereof needs to be
monitored). Such samples include, but are not limited to, final
product, incomplete product from intermediate process steps, raw
ingredients, treatment materials, equipment swabs, and
environmental samples.
[0073] Accordingly, particular embodiments provide methods for
microbial profiling, monitoring, SPC, for use in any industrial
setting or process, or in any system that requires microbiological
control of production, or microbial balance. Such applicable
processes and systems include, but are not limited to: food
production (e.g., food manufacturing; processing; storage,
transportation and distribution); processes or systems susceptible
to microbial pathogens where process sanitation is relevant;
environmental systems and processes susceptible to contaminants;
systems and processes susceptible to spoliation (spoilage
organisms); fermentation processes and systems (e.g., where
monitoring the fermentation process, and the purity of the seed
stock and fermentation contaminants is important); aseptic
processing processes (e.g., with to respect to sterility and
environmental control of food and pharmaceutical processes); water
treatment systems and processes (e.g., with respect to
microbiological quality of the raw and treated water, and control
of the organisms throughout the distribution system); wastewater
treatment (e.g., with respect to microbiological quality of the
treated wastewater and biosolids, control of the treatment process,
control of the aerobic and anaerobic digestors, and assessment of
the impact of the discharged wastewater and application of
bio-solids on the receiving environments); indoor air quality
(e.g., with respect to control of microbial contaminants and
assessment of their impact in the indoor environment and indoor air
quality assessment studies); environmental microbiology (e.g., with
respect to monitoring the microbiological quality of shellfish,
shellfish beds and cultured aquatic organisms, assessing the
microbiological quality of recreational waters and swimming
beaches, assessing the microbiological quality of bodies of water,
conducting impact assessment of point and non-point-sources); feed
microbiology (e.g., in determining the microbiological quality and
safety of the feed); soil microbiology (e.g., in assessing the
overall microbiology and population structure of soil organisms, in
assessing target organisms that can indicate environmental
contamination or organic and inorganic reservoirs (e.g., oil
fields)); and bioremediation.
EXAMPLE
Microbial Monitoring of Processes and Systems Susceptible to
Escherichia Coli O157:H7
[0074] This EXAMPLE describes an exemplary application of
particular aspects of the inventive microbial monitoring methods to
the beef industry,
[0075] Regulatory agencies strive to continually improve the safety
and the quality of food products and related processes, including,
but not limited to, beef products and beef producers. For example,
in response to the recent FSIS directives and Guidelines, many beef
producers have adopted a sampling plan that involves testing of
trim, ground beef or both for the specific microbial pathogen E.
coli O157:H7 after it has been produced, but before it has been
delivered to customers. The sampling plan is a `hold-and-release`
plan which dictates that the trim or ground beef is not released by
the producing facility to a customer until negative laboratory
results are obtained for the presence of E. coli O157:H7. While the
majority of abattoirs (slaughterhouses) conduct daily testing of
final products for E. coli O157:H7. the nature of the data
(presence/absence) and the infrequency of positive results make the
data unsuitable for applying SPC, as discusses in detail herein
above.
[0076] Methods; target microbe detection. The present applicant has
developed a detection method for E. coli O157:117 that is now used
in about 20% of the "hold and release" testing performed in the
United States beef industry. The method is based on a four-band
multiplex method, combined with a method comprising an ELISA-based
lateral flow device. The four bands of the multiplex method target
E. coli O157:117 genes that express: 0157 antigen; intimin
(adhesion protein); and two shiga-like toxins. The lateral flow
device detects the 0157 surface antigen protein itself where it is
being expressed. According to this multi-target assay, a `positive`
result for E. coli O157:117 is indicated by the appearance of all
four bands (markers) associated with respective genes possessed by
E. coli O157:H7, or the appearance of any three such bands, along
with the distinctive band in the lateral flow device assay. Unless
these criteria are met, the sample is declared to be `negative` for
the presence of E. coli O157:H7. However, these genes, or the
surface antigen protein, may be shared by other `index` microbes or
organisms (e.g., those with similarities to E. coli O157:H7), so
that any of these markers may detect an index microbe that is
present in the sample being assayed. Thus, the appearance of any of
the five bands in the absence of a definitive positive for E. coli
O157:H7, is indicative of the presence of one or more index
microbes or organisms.
[0077] An example of a multiplex PCR result is shown in FIG. 1.
Positive controls with four bands each are shown in the right hand
lanes of the figure, which shows a compound gel having two sets of
samples; one above the other. Several of the other lanes have one
or more bands appearing, indicating organisms that are detected by
the respective markers (e.g., that share genes with E. coli
O157:H7). In this instance, none of the lanes has all four bands
required for indicating a positive result for the presence of E.
coli O157:H7. Thus, the figure shows that all field samples in this
instance are negative for the presence of E. coli O157:H7, yet
bands are appearing from which, according to aspects of the present
invention, information can be derived regarding the presence of
index organisms (e.g., those sharing genetic similarities with E.
coli O157:H7).
[0078] The additional information provided by the presence of the
index signals is exploited herein. According to aspects of the
present invention, organisms with similarities to E. coli O157:H7
act as index organisms (i.e. they are detected by E. coli O157:H7
markers, and thus share properties with E. coli O157:H7 (e.g.,
genetically similarity, common sources, display coordinate behavior
when microbial intervention strategies are applied or when
favorable growth conditions are encountered, etc).
[0079] According to this exemplary assay, the appearance of one or
more bands (marker signals) in the combination of the 4-band
multiplex and the lateral flow device analysis methods is
indicative of the presence of one or more index organisms.
Significantly, according to the present invention, tracking of such
index organisms (or index organism-associated condition or
attribute) provides a novel tool for directing meaningful process
and system control (e.g., for directing meaningful preemptive and
preventative remedial action to control E. coli O157:H7)
[0080] Specifically, an aggregate `index value` (referred to in
this particular EXAMPLE as a "Sanitation Index" value) is
determined, based on the appearance of bands in the combination of
the 4-band multiplex and lateral flow device E. coli O157:H7
analysis methods. Thus, in this particular embodiment, there are
five possible index band (marker) signals: four (4) for the
multiplex method; and one (1) for the lateral flow device. Where
any of the band (marker) signals are observed for a given sample, 1
point is recorded. The maximum score that can be recorded for a
given sample is 5, which would correspond to a positive finding for
the presence of E. coli O157:H7. In this exemplary implementation,
a `positive` result for E. coli O157:H7 is indicated by the
appearance of all four multiplex PCR bands (markers) associated
with respective genes possessed by E. coli O157:H7, or the
appearance of any three such bands, along with the distinctive band
in the lateral flow device assay. Significantly, however, anything
with a score greater than 0 (one or more bands appear) but less
than that required for a positive result for the presence of E.
coli O157:H7, is nonetheless positive for the presence of index
microbes (e.g., organisms).
[0081] At a typical beef producing facility, anywhere between 25
and 100 hold and release E. coli O157:H7 `Lot` tests are performed
daily. For such inventive applications, the results of counting
points for individual samples are combined to form a daily
Sanitation Index value according to the following formula:
Sanitation Index, %=100.times.TS/(5.times.T)
Where: TS=Total number of positive bands observed for all samples;
T=Total number of samples; and 5=Number of bands possible per
sample.
[0082] The results can, for example, be plotted daily, and over
time, the patterns of the Sanitation index values can be analyzed
using basic principles of Statistical Process Control. Such an
example is shown in FIG. 2.
[0083] In the graph of FIG. 2, the Sanitation Index has been
plotted over a period of 30 days. The plot also displays `trend
indicators` consistent with the basic principles of Statistical
Process Control which allow meaningful preemptive and preventative
decisions to be made regarding control of E. coli O157:H7. The
trend indicators are: [0084] Monthly mean--a measure of the central
tendency of the Sanitation Index over time; [0085] Upper Confidence
Limit--calculated as 3-times the standard deviation of the
Sanitation to Index values composing the monthly mean, and
represents a measure of the variability of the Sanitation Index,
and a way to identify Sanitation Index values which require
remediation action; [0086] Performance Target--a value based on
experience about what is possible from the theoretical optimum
operation of intervention strategies and what is observed at
facilities known to be run well; and
[0087] Sequences of Sanitation index values--Note the upward trend
from Jan. 5, 2004 to Jan. 14, 2004. The trend is suggestive of the
breakdown of a microbial intervention process and the subsequent
"leakage" of index organisms through to the finished product. The
problem was identified and fixed on Jan. 14, 2004. The Sanitation
Index immediately reflected the change, as shown by the subsequent
values from Jan. 15, 2004 onward.
EXAMPLE 2
Inventive Microbial Monitoring Methods Were Applied to a High
Volume Beef Production Facility to Provide for Process Control
[0088] This EXAMPLE describes another exemplary application of
particular aspects of the inventive microbial monitoring methods to
the beef industry.
[0089] Rationale. Qualitative pathogen testing, by its nature,
typically results in strictly a positive or negative result. As
describe above, in addition to the standard result of positive or
negative for a give pathogen, multiplex PCR (and/or other assays)
allows for determination of negative or positive results for index
organisms (e.g., for related coliform bacteria, or associated
conditions or attributes). PCR bands indicating the presence, for
example, of shiga-like toxin production, or indicating the ability
for an organism to attach and efface do not necessarily indicate a
target profile (e.g., pathogenic profile; e.g., E. coli O157:H7);
that is, frequently, these nonspecific bands are associated with
other index organisms (e.g., coliform bacteria, or associated
conditions or attributes) exhibiting these abilities. Only when
these bands are associated with the E. coli O157 specific rfb PCR
band, for example, does the test indicate a pathogenic profile.
Quantifying a qualitative test may, for example, be accomplished by
dividing the number of observed bands by the total number of
possible bands, where the resulting number is referred to herein as
an index value. The use of such indexing for process control (e.g.,
statistical process control), centers on a critical assumption;
namely, that the presence of one or more index organisms (e.g., a
more common coliform, or associated conditions or attributes) will
serve as a predictive precursor to the appearance of a less common
target microbe (e.g., pathogenic E. coli O157).
[0090] Data for this EXAMPLE was collected from a high volume beef
production facility located in the Midwest United States. The
distribution of band patterns was studied for 25,698 samples
collected from the same beef production facility for a calendar
year. The results of this study show that the most prevalent
occurrence, 74.85 percent of the samples, was the detection of no
multiplex PCR bands (19,236 of 25,698). When Multiplex PCR bands
were detected, the most common result was the detection of the
individual multiplex PCR bands. Significantly, no band combination,
for example the "B" and "C" combination, was more prevalent than
the individual bands making up the combination signal (TABLE 1).
The detection of multiple multiplex PCR bands, therefore, is most
often the result of multiple independent organisms generating
multiple signals, The question of whether the bands of a pathogenic
profile are mutually associated is determined through the
`conformation` process. A conformation indicating a common,
pathogenic, source in this study occurred 50 times out of the
25,698 samples collected. Individual bands and the combination of
individual bands, when quantitatively and temporally tracked as an
index, act as a precursor (i.e., harbinger) to pathogenic
production failure (the presence of more common coliform
effectively act as a precursor to the appearance of the less common
pathogenic E. coli O157).
TABLE-US-00001 TABLE 1 Multiplex PCR Band Distribution Pattern
ASSAY Incidence (O157 O157 EAE SHIGA 2 SHIGA 1 Number of Pattern
(%) antigen) (PCR A) (PCR B) (PCR C) (PCR D) Samples 1 74.85% -- --
-- -- -- 19236 2 22.90% -- -- -- X -- 1480 3 19.62% -- -- X -- --
1268 4 8.45% -- -- X X -- 546 5 8.19% -- -- -- -- X 529 6 7.35% --
-- -- X X 475 7 6.92% -- -- X X X 447 8 6.48% -- X -- -- -- 419 9
4.09% -- X X X X 264 10 2.80% -- -- X -- X 181 11 2.41% -- X -- X
-- 156 12 2.04% -- X X -- -- 132 13 1.55% -- X X X -- 100 14 1.49%
X -- -- -- -- 96 15 1.42% X X -- -- -- 92 16 1.35% -- X -- X X 87
17 0.76% -- X -- -- X 49 18 0.43% X X -- X -- 28 19 0.31% X X X --
-- 20 20 0.31% X X X X X 20 21 0.23% -- X X -- X 15 22 0.20% X X X
X -- 13 23 0.17% X -- X -- -- 11 24 0.12% X -- -- -- X 8 25 0.11% X
X 7 26 0.08% X X -- -- X 5 27 0.06% X X X 4 28 0.06% X X -- X X 4
29 0.05% X -- -- X X 3 30 0.03% X -- X X X 2 31 0.02% X X X -- X
1
[0091] FIG. 3 shows a plot of the index values over time, and
depicts a plurality of rapid increases in the trim index. Through
this period, nine (9) confirmed cases of pathogenic E. coli O157
were detected. According to aspects of the present invention, when
the index is tracked by `shift` (working shift), it provides key
indications regarding the process and the type of failure that is
occurring. Process failures can, for example, be classified as
mechanical process failures or as employee process failures.
[0092] FIG. 4 shows the index separated by `shift` for the same
period as FIG. 3. The fact that the respective index for both
shifts coordinately increased at the same tempo indicates a
mechanical process failure; a failure that is consistent across
both shifts. By contrast, an employee process failure, for example,
would appear as a significant variance between the respective index
values.
[0093] FIG. 5 demonstrates that, over approximately a one month
period of time, the index values of one shift "A" significantly
varied from those of the other "B." During this period, one
confirmed case of pathogenic E. coli O157 was detected. The product
involved in this case was produced on "A" shift. Index values may
also be established on carcasses tested for pathogenic E. coli
O157. This is typically done immediately after the slaughter
operation. Indexing at this point in the process provides another
proactive opportunity to reduce pathogenic bacteria from the
manufacturing process. "Dirty" carcasses proceeding into a
fabrication process will result in "dirty" trim. The reverse is,
however, not always the case. "Clean" carcasses entering into
fabrication do not always result in "clean" trim, because multiple
variables may i dace pathogenic E. coli after the point at which
hot carcasses are tested. Therefore, no strict correlation exists
between the carcass index and the fabrication trim index.
EXAMPLE 3
Inventive Microbial Monitoring Methods Were Applied to Identify
Individual Combos as the Source of a Positive Test
[0094] This EXAMPLE describes yet another exemplary application of
particular aspects of the inventive microbial monitoring methods to
the beef industry.
[0095] When assigning specific combos to a `Lot` (combination of
`combos`; combination bin of trim samples--typically five
combos/Lot), beef production facilities frequently mix and match
combos (sample bins containing trim). Typically, these combos are
produced from different production lines at different times. Where
a respective trim sample (Lot sample) tests positive for a
particular pathogen, it is, therefore, difficult to troubleshoot
the production process to determine which combo of a Lot is
contaminated. Aspects of the present invention (e.g., using
multiplex testing) in conjunction with individual combo testing
provide proactive methods affording reductions of pathogenic
bacteria.
[0096] Samples. The samples for this EXAMPLE were submitted by a
large-scale beef operation located in the Midwest United
States.
[0097] Methods. Samples were collected by the production facility.
Each sample consisted of a 375 gram sample. The 375 gram sample was
evenly split between the combos making up the Lot. For example, a
lot consisting of five combos was submitted to the lab as five bags
each containing approximately 75 gram. Each of the individual bags
was enriched separately and incubated for 8 hours. After
incubation, a wet composite of the five individual bags was
prepared. The composite sample was screened using Multiplex PCR
technology. Suspect samples were further analyzed using
immunomagnetic beads followed by additional analysis utilizing
Multiplex PCR technology. The secondary analysis was performed on
the composite sample as well as each of the individual sub
samples.
[0098] Results. This method has proven to be a beneficial means of
proactively troubleshooting beef production facilities to reduce
the presence of pathogenic bacteria. Samples confirmed as positive
using this method are readily traced to individual combos. Using
this information, production facilities can quickly identify common
product types, production lines, or carcass lots used in the
producing these specific combos.
[0099] With reference to FIG. 6A (showing two rows of gel lanes),
lane tin each row provides a confirmed positive composite sample
for E. coli O157:H7. This particular composite analyzed consisted
of five sub-samples (combo samples), referred to as sub samples A
through E. Lanes 2 through 6 of each row represented these
individual sub-samples. Sub-samples C and D were determined to be
the source of the positive test, based on the analysis. Lane 12 of
each row represents another composite sample that consisted of five
sub-lots referred to as sub-samples A through E. Lanes 13 through
17 of each row represent these individual sub-samples. Sub-lot A
was the source of the positive result, based on the analysis. The
same analysis method can be used on samples that initially display
as being suspect, and are subsequently shown to be negative. For
example, in another exemplary analysis (FIG. 6B), a composite
sample (lanes 1 and 12) was analyzed, consisting of five sub-lots
(lanes 2 through 6, and 13 through 17). The image showed a negative
conformation for E. coli O157:H7. However, it was determined that
sub-lot B was the source of the suspect result. This information
can be used in the same manner as a positive test result to
proactively determine common product codes, production lines, or
lots of carcasses used in producing this specific combo.
EXAMPLE 4
Inventive Multi-Target Microbial Monitoring (e.g., Using Multiplex
PCR) Methods Were Used to Identify Potential Problems in Beef
Production
[0100] This EXAMPLE describes yet another exemplary application of
particular aspects of the inventive microbial monitoring methods to
the beef industry.
[0101] Rationale, Troubleshooting of a beef production facility is
greatly enhanced when an individual combo or combos are identified
as the source of the positive test, According to aspects of the
present invention, and using Multiplex PCR technology as the method
for multi-target analysis, individual combos may be identified as
the source of a positive test result, and such analyses may be
conducted over time. Specific information obtained from this
multi-targeted microbial monitoring method are then utilized to
pinpoint production problems, and afford the opportunity for
proactive efforts in reducing, for example, pathogenic bacteria
from a production process.
[0102] Results. Results were obtained from samples submitted by a
large scale beef operation located in the Midwest United
States.
[0103] Methods. Samples were collected and processed as described
in previous case study of EXAMPLE 3, outlining the identification
of an individual combo or combos as the source of a positive test
result, For each of the described samples, additional information
was collected consisting of production time and product type, and
this additional information was analyzed for commonalities that
could logically direct the investigation.
[0104] Results. As an example of the data collected, and with
further reference to FIG. 6 (show two rows of lanes), lane 1 in
each row shows a confirmed positive composite sample for E. coli
O157:H7. This particular composite consisted of five sub-samples
referred to as sub samples A through E. Lanes 2 through 6 of each
row represent these individual sub-samples. As described above,
sub-samples C and D were determined to be the source of the
positive test.
[0105] Lane 12 of each row represents another composite sample that
consisted of five sub-lots referred to a sub-samples A through E.
Lanes 13 through 17 of each row represent these individual
sub-samples. As described above, sub-lot A was determined to be the
source of the positive result.
Interpretation of Statistical Process Control (SPC) Charts
Generated from "Sanitation indices" Derived from Pathogen
Profiles
[0106] The following exemplary process control "Demonstrations"
were enabled by the inventive methods in view of production time
and product type analyzed for commonalities:
[0107] DEMONSTRATION 1. Multi-targeted microbial Monitoring methods
were used to show that decreasing "carcass sanitation index" was
associated with a coordinate reduction of "trim sanitation
index."
[0108] FIG. 7 shows, for establishment `E,` a temporal plot of
"Carcass Index" (i.e., "Carcass Sponge Daily Index") derived from
sponge sampling of carcasses at various time points. For to
comparison, FIG. 8 shows a temporal plot of trim "Sanitation
Index," as described herein above, for the same establishment, and
corresponding temporal sampling ranges between the analyses of
FIGS. 7 and 8 are highlighted by dashed boxes. The data shows that
decreasing carcass sanitation index was associated with a
coordinate reduction of trim sanitation index.
[0109] DEMONSTRATION 2. Multi-targeted microbial Monitoring methods
were used to show that high "carcass sanitation index" was a
harbinger of increasing "trim sanitation index," as well as of the
presence of E. coli O157:H7 in `trim.`
[0110] FIG. 9 shows, for establishment `B,` a temporal plot of
"Carcass Index" (i.e., "Carcass Sponge Daily Index") derived from
sponge sampling of carcasses at various time points. For
comparison, FIG. 10 shows a temporal plot of trim "Sanitation
Index," as described herein above, for the same establishment, and
corresponding temporal sampling ranges between the analyses of
FIGS. 9 and 10 are highlighted by dashed boxes. The data shows that
high "carcass sanitation index" was a harbinger of increasing "trim
sanitation index," as well as of E. coli O157:H7 in `trim` (one
positive event is shown in each box of FIG. 10).
[0111] DEMONSTRATION 3. Multi-targeted microbial Monitoring methods
were used to show that high "carcass sanitation index" was a
harbinger of increasing "trim sanitation index," as well as of the
presence of E. coli O157:H7 in `trim.`
[0112] FIG. 11 shows, for establishment `E,` a temporal plot of
"Carcass Index" (i.e., "Carcass Sponge Daily Average Index")
derived from sponge sampling of carcasses at various time points.
For comparison, FIG. 12 shows a temporal plot of trim "Sanitation
Index," as described herein above, for the same establishment, and
corresponding temporal sampling ranges between the analyses of
FIGS. 11 and 12 are highlighted by dashed boxes. The data shows
that high "carcass sanitation index" was a harbinger of increasing
"trim sanitation index," as well as of E. coli O157:H7) in `trim`
(one positive event is shown in the box of FIG. 12).
[0113] DEMONSTRATION 4. Multi-targeted microbial Monitoring methods
were used to show that high `trim` "Sanitation Index" was a
harbinger of the presence of E. coli O157:H7 in `trim.`
[0114] FIG. 13 shows, for establishment `B,` a temporal plot of
trim "Sanitation Index" (as defined herein) derived from trim
sampling at various time points, and plotted as `daily index`
values For comparison, FIG. 14 shows a temporal plot of trim
"Sanitation Index" for the same establishment, but plotted as
`3-day moving average` (MA) values. Corresponding temporal sampling
ranges between the analyses of FIGS. 13 and 14 are highlighted by
dashed boxes. The data shows that high `trim` "Sanitation Index"
was a harbinger of the presence of E. coli O157:H7in `trim` (five
positive events are shown in the box of FIG. 14).
[0115] DEMONSTRATION 5. Multi-targeted microbial Monitoring methods
were used to show that continuous high `trim` "Sanitation Index"
correlated with continuous presumptive Salmonella in `trim.`
[0116] FIG. 15 shows, for establishment `A,` a temporal plot of
"Carcass Index" (i.e., "Carcass Sponge Daily Average index")
derived from sponge sampling of carcasses at various time points,
and plotted as `daily index` values For comparison, FIG. 16 shows a
temporal plot of trim "Sanitation Index" for the same
establishment. Corresponding temporal sampling ranges between the
analyses of FIGS. 15 and 16 are highlighted by dashed boxes, The
data shows that continuous high `trim` "Sanitation Index"
correlated with continuous presumptive Salmonella in `trim.`
[0117] DEMONSTRATION 6. Multi-targeted microbial Monitoring methods
were used to show that increasing "carcass sanitation index" was a
harbinger of increasing "trim sanitation index," as well as of the
presumptive presence of E. coli O157:H7 in `trim,` and further show
that corrective actions could be taken in the plant to consequently
reduce both carcass and trim indices.
[0118] FIG. 17 shows, for establishment `E,` a temporal plot of
"Carcass Index" "Carcass Sponge Daily Average Index") derived from
sponge sampling of carcasses at various time points, and plotted as
`daily index` values For comparison, FIG. 18 shows a temporal plot
of trim "Sanitation Index" for the same establishment.
Corresponding temporal sampling ranges between the analyses of
FIGS. 17 and 18 are highlighted by dashed boxes. The data shows
that increasing "carcass sanitation index" was a harbinger of
increasing "trim sanitation index," as well as of the presumptive
presence of E. coli O157:H7 in `trim,` and further show that
corrective actions could be taken in the plant to consequently
reduce both carcass and trim indices.
SUMMARY OF THE ABOVE EXAMPLES AND DEMONSTRATIONS
[0119] Aspects of the present invention solve a long-standing
problem in the art; namely, the inability to apply the results of
prior art microbial detection/monitoring methods for purposes of
process or system control (e.g. statistical process control;
SPC).
[0120] Prior art microbial detection/monitoring methods are
deficient. As stated herein above, prior art methods for microbial
detecting and monitoring are divided into two broad groups: (i)
direct specific detection of the target microbe by determining a
presence or absence status for a presumably `target
microbe-specific` marker or characteristic; and (ii) indirect
detection, based on determining a presence or absence status for a
presumably `indicator microbe-specific` marker, which if present is
deemed to be indicative of the presence of the target microbe. As
further discussed herein, these two approaches have two fundamental
problems by virtue of being premised on isolated presence/absence
tests that yield only an isolated presence/absence signal. First,
because of Type 1 and 2 errors, a single test cannot always be
regarded as a definitive measure of whether the microbial behavior
is present or absent. Second, prior art detection schemes are not
effectively applicable to statistical process control (SPC),
because for the majority of samples tested by such prior art
presence/absence detection schemes, the particular `target` or
`indicator` microbes are either not present, or are present at
undetectable levels, giving rise to numerous isolated negative
values that cannot be effectively used in SPC to Is provide early
warning of process failure, exposure and risk assessment, and to
facilitate risk based decision making.
[0121] Solution to prior art deficiency provided by aspects of the
present invention. As described and disclosed herein, aspects of
the present invention derive, relative to prior art methods,
additional information from microbial marker test results for
samples which have tested negative for the presence of a target
microbe, or microbe-associated property of attribute (e.g.,
pathogens, such as E. coli O157:H7, etc.). Partially-positive
results (i.e., negative results for a particular target microbe or
associated condition or attribute, which are nonetheless positive
for a subset of markers) are indicative of the presence of index
organisms (or index organism-associated conditions or attributes)
that are genetically distinct, but which nonetheless share genetic,
metabolic, behavioral, etc., characteristics with a given target
microbe (e.g., E. coli O157:H7). By preparing an index value (e.g.,
"Sanitation Index value") based on the partially-positive results,
and temporally tracking the Sanitation Index values, trends are
identified, thus affording application of the principles of
Statistical Process Control to direct meaningful preemptive,
preventative and remedial action to control a given microbial
process or system.
[0122] Therefore, the presently disclosed extended analysis of
information derived from the inventive microbial sampling and
monitoring methods overcomes substantial limitations in the prior
art. The inventive methods are pro-active, utilizing index
organisms whose presence is a harbinger of (indicates the
probability of), for example, appearance of a target microbe in a
process or system (e.g., a harbinger of `leakage` of E. coli
O157:H7 into a beef fabrication facility). By providing an early
indicator, preemptive and preventative actions can be taken to
maintain or control the process or system, before imbalance occurs
(e.g., before products become contaminated. The inventive microbial
monitoring methods provide viable economic solutions and
alternatives, whereby a range of controls, remedial actions, etc.,
may be applied when trends, not previously observable using prior
art methods, are observed in the inventive microbial monitoring
indices (e.g., changes in the "Sanitation Index"). Such process
control has substantial utility, because the range of applicable
controls, remedial actions, etc., are far less expensive than loss
of process/system time, and concomitant destruction of product.
[0123] Significantly, the inventive microbial monitoring methods
will finally enable the type of meaningful, preventative monitoring
of process and systems that federal agencies are seeking (e.g.,
USDA). For example, while the USDA Food Safety and Inspection
Service has encouraged the use of `count` data in this manner, most
E. coli count data points fall below the limit of detection in
clean/semi-clean environments. Moreover, such counts lack the
fundamental predictive advantage of monitoring for shared index
markers that the present invention affords and exploits. Thus, it
is evident that SPC cannot be effectively applied using, for
example `count` data, and particularly when the majority of the
data points do not allow identification of meaningful (relevant)
trends. Therefore, the present invention not only solves a
long-standing problem in the art, and is not only economically
highly beneficial, but also, for the first time, allows for
meaningful regulatory oversight and control, and is profoundly in
the broader public interest in view of the health benefits
associated with properly managed processes and systems (e.g.,
abattoirs), and human lives likely to be saved by the present
inventive methods.
Broad Applications
[0124] As stated above, the present inventive microbial monitoring
methods have broad application. Particular embodiments provide
methods for pathogen and organism profiling, and generating SPC
charts for use in any industrial setting or process, or in any
system that requires microbiological control of production, or
microbial balance. Such applicable processes and systems include,
but are not limited to: food production; manufacturing; processing;
storage; transportation and distribution; with respect to microbial
pathogens process sanitation, environmental contaminants, and
spoilage organisms; with respect to fermentation processes
determining purity of the seed stock and fermentation contaminants;
aseptic processing (e.g., food and pharmaceutical; with respect to
sterility and environmental control); water treatment (e.g., with
respect to microbiological quality of the raw and treated water,
and control of the organisms throughout the distribution system);
wastewater treatment (e.g., with respect to microbiological quality
of the treated wastewater and biosolids, control of the treatment
process, control of the aerobic and anaerobic digestors, and
assessment of the impact of the discharged wastewater and
application of bio-solids on the receiving environments); control
of microbial contaminants and assessment of their impact in the
indoor environment and indoor air quality assessment studies;
environmental microbiology (e.g., with respect to monitoring the
microbiological quality of shellfish, shellfish beds and cultured
aquatic organisms, assessing the microbiological quality of
recreational waters and swimming beaches, assessing the
microbiological quality of bodies of water, conducting impact
assessment of point and non-point-sources); feed microbiology
(e.g., in determining the microbiological quality and safety of the
feed); soil microbiology (e.g., in assessing the overall
microbiology and population structure of soil organisms, in
assessing target organisms that can indicate environmental
contamination or organic and inorganic reservoirs (e.g., oil
fields)).
[0125] Application of the inventive microbial monitoring assays
encompasses a broad array of microbes and organisms including, but
not limited to: pathogenic bacterial, viral, parasitic and fungal
organisms (see, e.g., TABLE 2 below); spoilage microbes and
organisms including, but not limited to those implicated in
spoilage and/or fermentation of meat, eggs, seafood, milk,
vegetables, fruits, beer, etc. (see, e.g., TABLE 3 below);
`beneficial organisms` including, but not limited to those
implicated in dairy (fermentation) brewing (fermentation), meat
(fermentation), bacteriocin production, probiotics, antibiotics,
etc. (see, e.g., TABLE 4 below); to microbial contaminants
including, but not limited to bacterial, viral, fungal, etc.
contaminants (see, e.g., TABLE 5 below); indicator organisms,
including but not limited to food-born, airborne, waterborne, etc.
(see, e.g., TABLE 6 below); and bioremediation organisms, including
but not limited to those shown in TABLE 7 below.
TABLE-US-00002 TABLE 2 Examples of Pathogenic Organisms Foodborne
Airborne Waterborne Bacterial Bacillus cereus Bacillus anthracis
Vibreo cholerae Escherichia coli O157:H7 Mycobacterium Salmonella
spp. tuberculosis Listeria monocytogenes Viral Norwalk-like viruses
Influenza Norwalk-like viruses Hepatitis A SARS Hepatitis A virus
Parasitic Cryptosporidium parvum Cysticercosis spp. Cryptosporidium
parvum Cyclospora cayetanensis Cryptosporidium spp. Giardia lamblia
Giardia lamblia Fungal Aspergillus flavus Aspergillosis Aspergillus
spp. Aspergillus parasiticus Cryptococcosis spp. Candida
albicans
TABLE-US-00003 TABLE 3 Examples of Common Spoilage Organisms Food
Product Spoilage Organism(s) Meat Brochothrix thermosphacta
Enterobacteriaceae Lactobacillus sake Eggs Pseudomonas spp. Proteus
vulgaris P. intermedium spp. Serratia spp. Seafood Leuconostoc
gelidum Leuconostoc gasicomitatum Milk Pseudomonas fluorescens
Pseudomonas fragi Vegetables Sclerotinia sclerotiorum Fusarium spp
Colletotrichum lindemuthianum Fruits Colletotrichum musae
Plasmapara viticole Certocystis paradoxa Beer (Fermentation)
Lactobacillus brevis Lactobacillus casey Lactobacillus paracasei
ssp. paracasei Saccharomyces cerevisiae var. diastaticus
TABLE-US-00004 TABLE 4 Examples of Beneficial Organisms Process
Organism(s) Dairy (Fermentation) Lactocooccus lactis Steptococcus
thermophilus Lactobacillus delbruekii Streptococcus thermophilus
Brewery (Fermentation) Saccharomyces cerevisceae Saccharomyces
carlsbergensis Saccharomyces uvarum Meat (Fermentation) Pediococcus
spp. Lactobacillus hordniae Lactobacillus xylosus Lactobacillus
fermentum, Bacteriocin Production Pediococcus spp. Leuconostoc
mesenteroides subsp. Meseteroides Probiotics Lactobacillus
acidophilus Antibiotics Penicillium chlysogenum Cephalosporium
acremonium Penicillium griseofulvum Bacillus subtilis Bacillus
polymyxa
TABLE-US-00005 TABLE 5 Example of Contaminants Food Water Air
Bacteria Acetobacter spp. Coliform Mycobacterium tuberculosis
Acetomonas spp. E. coli O157:H7 M. bovis Vibrio cholerae M. avium
Salmonella typhi Shigella spp. Campylobacter jejuni Escherichia
coli Legionella pneumophila Virus Flavivirus Hepatitis A
Respiratory syncytial virus Fungal Mucor spp. Aspergillus flavus
Penicillium spp. Fusarium spp. Geotrichum spp. Cephalosporium spp.
Cladosporium spp. Stachybotrys spp. Rhizopus spp. Trichoderma spp.
Anisakis simplex Giardia lamblia Cryptosporidium Penicillium spp.
parvum Entamoeba Stachybotrys histolytica chartarum Aspergillus
fumigatus
TABLE-US-00006 TABLE 6 Examples of Indicator Organisms Food Water
Air E. coli E. coli Aspergillus wentii Pseudomonas putrefaciens
Citrobacter spp. Rhizopus stolonifer Enterobacter spp.
Zygosaccharomyces bailii Klebsiella spp. Streptococcus faecalis
Bifidobacterium adolescentis F-specific RNA coliphages
TABLE-US-00007 TABLE 7 Examples of Bioremediation Organisms
Acinetobacter calcoaceticus Agaricus bisporus Klebsiella aerogenes
Leucothrix mucor Lentinus odoides Moraxelha osloensis Phanerochaete
chrysosporium Pseudomonas acidovorans Sphaerotilus natans
Bioremediation
[0126] Hazardous waste sites often contain complex mixtures of
pollutants which include a wide variety of organic contaminants.
Microbial bioremediation of organic pollutants is a promising
method of environmental cleanup. However, the classes of organic
contaminants present can vary widely (comprising, for example,
aliphatic hydrocarbons, aromatic hydrocarbons, and chlorinated
hydrocarbons, In some cases specific contaminants such as
polychlorinated biphenyls are of interest.). It is also a fact that
the conditions present during bioremediation are not well
characterized, both in terms of ability to sustain microbial growth
and in terms of characterizing the microflora present.
[0127] According to additional aspects of the present invention, it
is therefore desirable to prepare and monitor `microbial
performance index` (e.g., bioremediation performance index), based
on the presence or absence of specific microbial markers (e.g.,
genetic markers, antigenic markers, metabolic markers, microbial
behavioral characteristics, etc., and combinations thereof). Such a
performance index can be prepared by an appropriate combination (as
described and disclosed herein) of the +/- (presence/absence)
signals of the microbial markers. Additionally, as in other
applications of the inventive methods described herein, the indices
(e.g., Sanitation Index, or Microbial performance index) may be
modified by weighting the positive (or negative) scores of
different factors to reflect input of environmental assessment data
(e.g., indicating the preponderance of particular chemical
contaminants, conditions, etc., which are is present).
[0128] Representative, exemplary microbial markers for obtaining
microbial/bioremediative performance index data include, but are
not limited to:
[0129] aromatic oxygenase genes (of pollutant bio-degrading
microorganisms; e.g. phenol monooxygenase, identified by PCR
methods for example) (see, e.g., Baldwin et al., Detection and
Enumeration of Aromatic Oxygenase Genes by Multiplex and Real-Time
PCR, Appl. Environ. 69(6): 3350-3358, 2003);
[0130] metabolic intermediates in the degredation of various
aromatic contaminants (e.g., Catechol 2,3-Dioxygenase; using, e,g.,
PCR-based methods for monitoring bioremediation) (see, e,g.,
Mesarch et al., Development of Catechol 2,3-Dioxygenase-Specific
Primers for Monitoring Bioremediation by Competitive Quantitative
PCR, Appl. Environ. Microhiol. 66(2): 678-683, 2000);
[0131] 16S markers(e.g., for dehalococcoides group, which is
capable of degrading chlorinated hydrocarbons in bioremediation
sites) (see, e.g., Hendrickson et al., Molecular Analysis of
Dehalococcoides 16S Ribosomal DNA from Chloroethene-Contaminated
Sites throughout North America and Europe, Appl. Environ.
Microbiol. 68(2): 485-495, 2002);
[0132] markers for methanotrophs (e.g., markers for the pmoA gene,
which encodes the PmoA subunit methane monooxygenase (pMiMO)) (see,
e.g., Hors et al., Detection of Methanotroph Diversity on Roots of
Submerged Rice Plants by Molecular Retrieval of pmoA, mmoX, mxaF,
and 16S rRNA and Ribosomal DNA, Including pmoA-Based Terminal
Restriction Fragment Length Polymorphism Profiling, Appl. Environ,
Microbiol. 67(9): 4177-4185, 2001);
[0133] markers for detection of detect Rhodocyclus-like
beta-Proteobacteria (e.g., for detecting the ability to express
enhanced biological phosphorous removal, e.g., using small subunit
rRNA genes (ppk genes), fluorescent in situ hybridization (FISH),
and dot blot analysis) (see, e.g., McMahon et al., Polyphosphate
Kinase from Activated Sludge Performing Enhanced Biological
Phosphorus Removal, Appl. Environ. Microbiol. 68(10): 4971-4978,
2002);
[0134] markers for detection of thiocyanate-degrading bacteria
(e.g., thiocyanate hydrolase gene markers, detected, for example
using fluorescent immunostaining technique with thiocyanate
hydrolase-specific antibodies) (see, e.g., Yamasaki et al., Genetic
and immunochemical Characterization of Thiocyanate-Degrading
Bacteria in Lake Water, Appl. Environ. Microbiol. 68(2): 942-946,
2002).
Spoilage Organisms
[0135] Beer Spoilage. Identification of brewery isolates has
traditionally been accomplished biochemically by determining the
assimilation and fermentation patterns of a number of carbohydrates
and nitrogen sources..sup.1 Biochemical identification is, however,
not accurate in determining genotypic differences in beer spoilage
microorganisms. For example, in breweries, Lactobacillus brevis is
known as a representative beer-spoilage microorganism, but not all
stains are harmful..sup.2 For quality control in a brewery, it
would be beneficial to develop the means for accurate
identification of beer-spoilage microorganisms and estimation of
their beer spoilage ability. .sup.2
[0136] There is a need to develop a "Spoilage Index" for assessing
the possibility of beer spoilage. Such an index would be
proportional to the presence or absence of specific virulence
factors associated with the microorganisms of concern. This would
be more effective than attempting to detect the organisms directly,
since in many cases the organisms are strains or species which are
members of larger microorganism families. Below is a list of
specific genetic targets, useful to identify possible beer-spoilage
microorganisms from non-harmful strains:
[0137] Open reading frames 5 (ORF5) were found to be useful for
differentiating beer-spoilage ability of Lactobacillus
paracollinoides..sup.3 ORF were detected in the 12 beer-spoilage
strains of L. paracollinoides, and not in the two nonspoilage
variants;
[0138] Lactobacillus spp. LA2 (DSM15502) and related strains (LA2
group) possess strong beer-spoilage ability. The 16S rDNA sequence
of LA2 strain is virtually indistinguishable from that of L.
collinoides, generally considered to be nonbeer-spoilage bacteria.
The 16-23S rDNA intergenic spacer (ITS) regions of Lactobacillus
spp. LA2 and L. collinoides JCM1123T have been sequenced to
identify a genetic marker to distinguish between the two groups.
Sequence comparison analysis between Lactobacillus spp. LA2 and L.
collinoides JCM1123T revealed that the two contiguously located
nucleotides are absent in both ITS regions of Lactobacillus spp.
LA2..sup.4
[0139] The presence or absence of ORFS homologues in Lactobacillus
brevis was found to be highly correlated with the beer-spoilage
ability of L. brevis strains, indicating this ORF is potentially a
useful genetic marker capable of differentiating beer-spoilage
strains among brevis..sup.5
[0140] .sup.1 Riboprinting and 16S rRNA Gene. Barney M, Volgyi A,
Navarro A, Ryder D. Appl Environ Microbiol. 2001. 67 (2),
553-560.
[0141] .sup.2Classification and Identification of Strains of
Lactobacillus brevis Based on Electrophoretic Characterization of
D-lactate dehydrogenase: Relationship Between D-lactate
dehydrogenase and Beer-Spoilage Ability. Takahashi, T., Nakakita,
Y., Sugiyama, H., Shigyo, T., Shinotsuka, K. Journal of Bioscience
and Bioengineering. 1999. 88 (5), 500-506.
[0142] .sup.3Genetic Marker for Differentiating Beer Spoilage
Ability of Lactobacillus paracollinoides Strains Suzuki, K., Ozaki,
K. & Yamashita, H. Journal of AppliedMicrobiology. 2004 97 (4),
712-718.
[0143] .sup.4 Genetic characterization and Specific Detection of
Beer-Spoilage Lactobacillus sp. LA2 and Related Strains. Suzuki,
K., Koyanagi, M. &. Yamashita, H. Journal of Applied
Microbiology 2004 96 (4), 677-683.
[0144] .sup.5 Genetic Characterization of Non Spoilage Variant
Isolated from Beer-Spoilage Lactobacillus brevis
ABBC45.sup.C.Suzuki, K., Koyanagi, M. & Yamashita, H. Journal
of Applied Microbiology. 2004 96 (5), 946-953.
Water Organisms:
[0145] Wastewater. Wastewater contains many nutrients and is drawn
from many different sources. For this reason, wastewater frequently
harbors very high levels of microorganisms. Though many of these
microorganisms are benign, or even beneficial for the degradation
and stabilization of organic matter, others may be pathogenic or
potentially pathogenic. A "pathogenic organism" is defined as one
causing or capable of causing disease. Waterborne and water-related
diseases caused by pathogenic microbes are among the most serious
threats to public health today.
[0146] In order to effectively understand, assess, and control the
potential environmental and human health threats of waterborne
pathogens posed due to changing patterns of water use, increasing
water pollution, aging wastewater treatment systems, and an
inadequate knowledge of the sources and occurrence, there is a
distinct need for surveillance of epidemiological factors
associated with infectious disease outbreaks. The identification
and control of threats posed by waterborne pathogens requires
effective pathogen monitoring procedures.
[0147] Tests which lack specificity (such as coliforms, fecal
conforms or total plate counts) may not be an accurate indicator of
potential pathogenicity. Furthermore, in sonic cases, the same
strain or species may non-pathogenic under some conditions, but may
express its pathogenicity in response to environmental stimuli.
Thus, there is a need to develop a "Virulence Factor Activity
Index" for assessing wastewater quality. Such an index would be
proportional to the presence or absence of specific virulence
factors associated with pathogens of concern. This would be more
effective than attempting to detect the pathogens directly, since
in many cases the pathogens are strains or species within members
of larger non-pathogenic microorganism families.
[0148] The application of such an index will be advantageous from
the following standpoints:
[0149] Water management programs may take a preventative approach
to pathogen pollution and increase source water protection;
[0150] Identification of "hot spot" areas that require targeted
monitoring and intervention;
[0151] Increase understanding of the ecology of pathogens in
aquatic ecosystems; increase understanding of the environmental
risk factors for predicting disease outbreaks; and, evaluate
current emergency response capacity for pathogens.
[0152] A list of the most common waterborne pathogens is provided
in Table 8, below. These pathogens have many characteristics in
common such as their ability to spread by the fecal-oral route with
water as the intermediate medium, and their inability reproduce
outside of a host. Selected members of the group may have other
traits in common. For example, chlorination is an effective
intervention for the bacterial and viral pathogens.
TABLE-US-00008 TABLE 8 Waterborne Fecal-Oral Route Pathogens Type
Illness Detection Issues Molecular Targets Bacteria Salmonella spp.
Salmonellosis S. Typhimurium Typhoid fever Genus Salmonella
IS200-PCR- consists of two amplification of a short species,
bongori and insertion sequence of enterica. Enterica about 708 bp
specific to consists of six Salmonella enterica subspecies. Only
one, subsp. enterica serotype subspecies enterica is
Typhimurium..sup.1 associated with Differential patterns disease,
and even then of acquired virulence mostly the serovar genes
distinguish Typhimurium. Salmonella strains..sup.2 Use of
microarrays to distinguish between strains of serovars..sup.3
Molecular basis for interaction of salmonella with intestinal
mucosa, review article..sup.4 Shigella Shigellosis Numerous
chromosomal and plasmid genes associated with virulence, review
article. Partial list: aerobactin (iucABCCD and iutA), shiga toxin
stx), invasion genes (virB, ipaABCD, ippl).sup.5 Enterotoxigenic E.
coli Diarrhea (indirect reference) (ETEC) List of ninety one
virulence genes including those encoding toxins, adhesion factors,
secretion systems, capsule antigens, somatic antigens, flagellar
antigens, invasins, autotransporters and aerobactin systems studied
for creation of a E. coli Pathotype DNA microarray..sup.6 Review
article, Diarrheagenic E. coli..sup.7 Enterococcus Diarrhea PCR
assay for detection of Enterococci at genus level by targeting tuf
gene..sup.8 Multiplex PCR for detection of asa1, gelE, cylA, esp
and hyl genes in Enterococci..sup.9 Vibrio cholerae Cholera
(indirect reference) Expression of toxT controls the expression of
several virulence factors..sup.10 Review article, Epidemiology,
Genetics and Ecology of Toxigenic Vibrio cholerae..sup.11 Genotypes
associated with virulence in environmental isolates of vibrio
cholerae..sup.12 Slaphlyococcus aureus Single-reaction multiplex
PCR toxin typing assay for enterotoxin genes A-E..sup.13
Campylobacter jejuni Gastroenteritis Random amplified polymorphic
DNA (RAPD) identifies invasion-associated marker (IAM).
Differentiation possible using RAPD and PCR using primers targeting
iam locus..sup.14 Viruses Hepatitis A Hepatitis Member of
Picornaviridae family Norwalk like agents Gastroenteritis
Caliciviruses Virus-like 27 nm Gasteroenteritis particles Rotavirus
Gastroenteritis Reoviridae family and polio Protozoa
Cryptosporidium Cryptosporidiosis Cryptosporidium spp. parvum
Giardia lablia Giardisis ELISA available, otherwise microscopic
exam Entamoeba histolytica Amoebic dystentery .sup.1Evaluation of
IS200-PCR and Comparison with Other Molecular Markers to Trace
Salmonella enterica subsp. enterica Serotype Typhimurium Bovine
Isolates from Farm to Meat. Millemann Y, Gaubert S, Remy D, Colmin
C. J Clin Microbiol. 2000 June; 38(6): 2204-2209.
.sup.2Differential Patterns of Acquired Virulence Genes Distinguish
Salmonella Strains. Conner C P, Heithoff D M, Julio S M, Sinsheimer
R L, Mahan M J, Proc Natl Acad Sci USA. 1998 Apr. 14, 95(8):
4641-4645. .sup.3Characterization of Salmonella enterica Subspecies
I Genovars by Use of Microarrays. Porwollik S, Boyd E F, Choy C,
Cheng P, Florea, L, Proctor E, McClelland M. J Bacteriol. 2004
September; 186(17): 5883-5898. .sup.4Molecular Basis of the
Interaction of Salmonella with the Intestinal Mucosa. Darwin K H,
Miller V L. Clin Microbiol Rev. 1999 July; 12(3): 405-428.
.sup.5Genetic basis of Virulence in Shigella Species. Hale T L.
Microbiol Rev. 1991 June; 55(2): 206-224. .sup.6Rapid
Identification of Escherichia coli Pathotypes by Virulence Gene
Detection with DNA Microarrays. Bekal S, Brousseau R, Masson L,
Prefontaine G, Fairbrother J, Harel J. J Clin Microbiol. 2003 May;
41(5): 2113-2125. .sup.7Diarrheagenic Escherichia coli. Nataro J P,
Kaper J B. Clin Microbiol Rev. 1998 January; 11(0): 142-201.
.sup.8Development of a PCR Assay for Rapid Detection of
Enterococci. Ke D, Picard F J, Martineau F, Menard C, Roy P H,
Ouellette M, Bergeron M G. J Clin Microbiol. 1999 November; 37(11):
3497-3503. .sup.9Development of a Multiplex PCR for the Detection
of asa1, gelE, cylA, esp, and hyl Genes in Enterococci and Survey
for Virulence Determinants Among European Hospital Isolates of
Enterococcus faecium. Vankerckhoven V, Van Autgaerden T, Vael C,
Lammens C, Chapelle S, Rossi R, Jabes D, Goossens H. J Clin
Microbiol. 2004 October; 42(10): 4473-4479. .sup.10pepA, a Gene
Mediating pH Regulation of Virulence Genes in Vibrio cholerae.
Behari J, Stagon L, Calderwood S B. J Bacteriol. 2001 January;
183(1): 178-188. .sup.11Epidemiology, Genetics, and Ecology of
Toxigenic Vibrio cholerae. Faruque S M, Albert M J, Mekalanos J J.
Microbiol Mol Biol Rev. 1998 December; 62(4): 1301-1314.
.sup.12Genotypes Associated with Virulence in Environmental
Isolates of Vibrio cholerae. Rivera I N, Chun J, Huq A, Sack R B,
Colwell R R. Appl Environ Microbiol. 2001 June; 67(6): 2421-2429.
.sup.13Development of a Single-Reaction Multiplex PCR Toxin Typing
Assay for Staphylococcus aureus Strains. Sharma N K, Rees C E, Dodd
C E. Appl Environ Microbiol. 2000 April; 66(4): 1347-1353.
.sup.14Molecular Characterization of Invasive and Noninvasive
Campylobacter jejuni and Campylobacter coli Isolates. Carvalho A C,
Ruiz-Palacios G M, Ramos-Cervantes P, Cervantes L E, Jiang X,
Pickering L K. J Clin Microbiol. 2001 April; 39(4): 1353-1359.
Fermentation:
[0153] Fermentation. During fermentation processes microbial growth
and metabolism leads to the production of a wide range of
metabolites. These metabolites include alcohols, proteins, lipids,
vitamins, antimicrobial compounds (e.g., bacteriocins and
lysozyme); texture-forming agents (e.g., xanthan gum); amino acids;
organic acids (e.g. citric acid, lactic acid), and flavor compounds
(e.g., esters and aldehydes). Many of these microbial metabolites
are commercially valuable (e.g., flavor compounds, amino acids,
organic acids, enzymes, xanthan gums, alcohol etc.) and are
produced through industrial scale fermentation processes.
[0154] The microorganisms which are used in industrial scale
fermentation processes are selected based on their having desirable
attributes. Such attributes include their ability to enhance
sensory qualities (flavor, aroma, visual appearance, texture and
consistency), induce resistance to viruses (bacteriophage) in the
case of dairy fermentations, the ability to produce antimicrobial
compounds (e.g. bacteriocins, hydrogen peroxide) for the inhibition
of undesirable microorganisms, and the ability to degrade or
inactivate natural toxins in food substrates such as cyanogenic
glucosides in cassava, mycotoxins in cereal fermentations and
anti-nutritional factors (e.g. phytates).
[0155] It is therefore desirable to prepare a `Fermentation
Performance index` based on the presence or absence of specific
microbial behavioral characteristics. Such an index can be prepared
by an appropriate combination (in the Sanitation Index type
approach) of presence/absence signals of the following microbial
behavioral characteristic markers:
[0156] Protosymbiosis between Streptococcus thermophilus and
Lactobacillus delbrueckii subsp. bulgaricus (genus specific marker
for both species to show presence of both, and thus a useful yogurt
culture).sup.1
[0157] Gene specific markers for lactic acid, acetylaldehyde,
acetic acid, and diacetyl production in Lactobacilli (allows
detection of `start culture` flavor behavior upon inoculation);
[0158] .sup.1Probiotic Bacteria in Fermented Foods: Product
Characteristics and Starter Organisms Heller, K., American Journal
of Clinical Nutrition. 2001. February; 73(2): 374S-379S.
[0159] These, and many other applications of the inventive
microbial monitoring methods will be recognized by those of skill
in the art, and are encompassed within the present invention.
* * * * *