Monitoring the microbiome for food safety and quality using deep
Running Title: Monitoring the microbiome for food safety and quality
Kristen L. Beck1,8*+, Niina Haiminen2,8+, David Chambliss1,8, Stefan Edlund1,8, Mark Kunitomi1,8,
B. Carol Huang3,8, Nguyet Kong3,8, Balasubramanian Ganesan4,5,8, Robert Baker4,8, Peter
Markwell4,8, Ban Kawas1,8, Matthew Davis1,8, Robert J. Prill1,8, Harsha Krishnareddy1,8, Ed
Seabolt1,8, Carl H. Marlowe6,8, Sophie Pierre7,8, André Quintanar7,8, Laxmi Parida2,8, Geraud
Dubois1,8, James Kaufman1,8, and Bart C. Weimer3,8*
Contact information: Kristen L. Beck, IBM Almaden Research Center, 650 Harry Road, San Jose
CA, 95120 USA, email@example.com, +1 408-927-1963
1IBM Almaden Research Center, San Jose CA
2IBM T.J. Watson Research Center, Yorktown Heights, NY
3University of California Davis, School of Veterinary Medicine, 100K Pathogen Genome Project,
Davis, CA 95616
4Mars Global Food Safety Center, Beijing, China
5Wisdom Health, A Division of Mars Petcare, Vancouver WA.
was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (whichthis version posted May 19, 2020. . https://doi.org/10.1101/2020.05.18.102574doi: bioRxiv preprint
6Bio-Rad Laboratories, Hercules CA
7Bio-Rad, Food Science Division, MArnes-La-Coquette, France
8Consortium for Sequencing the Food Supply Chain, San Jose, CA
In this work, we hypothesized that shifts in the food microbiome can be used as an indicator of
unexpected contaminants or environmental changes. To test this hypothesis, we sequenced total
RNA of 31 high protein powder (HPP) samples of poultry meal pet food ingredients. We
developed a microbiome analysis pipeline employing a key eukaryotic matrix filtering step that
improved microbe detection specificity to >99.96% during in silico validation. The pipeline
identified 119 microbial genera per HPP sample on average with 65 genera present in all
samples. The most abundant of these were Bacteroides, Clostridium, Lactococcus, Aeromonas,
and Citrobacter. We also observed shifts in the microbial community corresponding to
ingredient composition differences. When comparing culture-based results for Salmonella with
total RNA sequencing, we found that Salmonella growth did not correlate with multiple
sequence analyses. We conclude that microbiome sequencing is useful to characterize complex
food microbial communities, while additional work is required for predicting specific species’
viability from total RNA sequencing.
microbiome, food safety, bioinformatics, shotgun sequencing, microbial ecology, pathogens
was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (whichthis version posted May 19, 2020. . https://doi.org/10.1101/2020.05.18.102574doi: bioRxiv preprint
Sequencing the microbiome of food may reveal characteristics about the associated
microbial content that culturing or targeted whole genome sequencing alone cannot. However, to
meet the various needs of food safety and quality, next generation sequencing (NGS) and analysis
techniques require additional development1 with specific consideration for accuracy, speed, and
applicability across the supply chain.2 Microbial communities and their characteristics have been
studied in relation to flavor and quality in fermented foods,3–5 agricultural processes in grape6 and
apple fruit7, and manufacturing processes and production batches in Cheddar cheese.8 However,
the advantage of using the microbiome specifically for food safety and quality has yet to be
Currently, food safety regulatory agencies including the Food and Drug Administration
(FDA), Centers for Disease Control and Prevention (CDC), United States Department of
Agriculture (USDA), and European Food Safety Authority (EFSA) are converging on the use of
whole genome sequencing (WGS) for pathogen detection and outbreak investigation. Large scale
WGS of food-associated bacteria was first initiated via the 100K Pathogen Genome Project9 with
the goal of expanding the diversity of bacterial reference genomes— a crucial need for foodborne
illness outbreak investigation, traceability, and microbiome studies.10,11 However, since WGS
relies on culturing a microbial isolate prior to sequencing, there are inherent biases and limitations
in its ability to describe the microorganisms and their interactions in a food sample. Such
information would be very valuable for food safety and quality applications.
High throughput sequencing of total DNA and total RNA are promising approaches to
characterize microbial niches in their native state without introducing bias due to culturing.12–14
Additionally, total RNA sequencing has the potential to provide evidence of live and biologically
active components of the sample.14,15 It also provides accurate microbial naming, relative
was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (whichthis version posted May 19, 2020. . https://doi.org/10.1101/2020.05.18.102574doi: bioRxiv preprint
microbial abundance, and better reproducibility than total DNA or amplicon sequencing.14 Total
RNA sequencing minimizes PCR amplification bias that occurs in single gene amplicon
sequencing and overcomes the decreased detection sensitivity from using DNA sequencing in
metagenomics.14 Total RNA metatranscriptome sequencing, however, is yet to be examined in raw
food ingredients as a method to provide robust characterization of the microbial communities and
the interacting population dynamics.
From a single sequenced food microbiome, numerous dimensions of the sample can be
characterized that may yield important indicators of safety and quality. Using total DNA or RNA,
evidence for the eukaryotic food matrix can be examined. In Haiminen et al.,16 we quantitatively
demonstrated the utility of metagenome sequencing to authenticate the composition of complex
food matrices. In addition, from total DNA or RNA, one can observe signatures from commensal
microbes, pathogenic microbes, and genetic information for functional potential (from DNA) or
biologically active function (from RNA).14,15 Detecting active transcription from live microbes in
food is very important to avoid spurious microbial observations that may instead be false positives
due to quiescent DNA in the sample. Use of RNA in food analytics also offers the opportunity to
examine expression of metabolic processes that are related to antibiotic resistance,17,18 virulence
factors, or replication genes, among others. Additionally, it has the potential to define viable
microbes that are capable of replication in the food and even microorganisms that stop replicating
but continue to produce metabolic activity that changes food quality and safety.19–24
Microorganisms are sensitive to changes in temperature, salinity, pH, oxygen content, and
many other physicochemical factors that alter their ability to grow, persist, and cause disease. They
exist in dynamic communities that change in response to environmental perturbation – just as the
gut microbiome shifts in response to diet.25–28 Shifts in microbiome composition or activity can be
leveraged in the application of microbiome characterization to monitor the food supply chain. For
example, Noyes et al. followed the microbiome of cattle from the feed lot to the food packaging,
concluding that the microbial community and antibiotic resistance characteristics change based on
the processing stage.17,18,29 We hypothesize that observable shifts in microbial communities of
food can serve as an indicator of food quality and safety.
In this work, we examined 31 high protein powder samples (HPP; derived from poultry meal).
HPP are commonly used raw materials in pet foods. They are subject to microbial growth prior to
preparation and continued survival in powder form.30 We subjected the HPP samples to deep total
RNA sequencing with ~300 million reads per sample. In order to process the 31 samples collected
over ~1.5 years from two suppliers at a single location, we defined and calibrated the appropriate
methods– from sample preparation to bioinformatic analysis– needed to taxonomically identify
the community members present and to detect key features of microbial growth. First, we removed
the HPP’s food matrix RNA content as eukaryotic background with an important bioinformatic
filtering step designed specifically for food analysis. The remaining sequences were used for
relative quantification of microbiome members and for identifying shifts based on food matrix
content, production source, and Salmonella culturability. This work demonstrates that total RNA
sequencing is a robust approach for monitoring the food microbiome for use in food safety and
quality applications, while additional work is required for predicting pathogen viability.
2.1 Evaluation of microbial identification capability in total RNA and DNA sequencing
Microbial identification in microbiomes often leverages shotgun DNA sequencing; however,
total RNA sequencing can provide additional information about viable bacterial activity in a
community via transcriptional activity. Since using total RNA to study food microbiomes is novel,
each step of the analysis workflow (Figure 1) was carefully designed and scrutinized for accuracy.
For all analyses done in this study, we report relative abundance in reads per million (RPM)
(Equation 1) as recommended by Gloor et al31,32 and apply the conservative threshold of RPM >
0.1 to indicate presence as previously described by Langelier et al and Illot et al.33,34 Numerically,
this threshold translates to ~30 reads per genus per sample considering a sequencing depth of ~300
million reads per sample (Methods Section 4.4). First, we examined the effectiveness of RNA for
taxonomic identification and relative quantification of microbes in the presence of food matrix
reads. We observed that RNA sequencing results correlated (R2 = 0.93) with the genus relative
quantification provided by DNA sequencing (Supplementary Figure S1). RNA sequencing also
detected more genera demonstrated by a higher a-diversity than the use of DNA (Supplementary
Figure S2). Additionally, from the same starting material, total RNA sequencing resulted in 2.4-
fold more reads classified to microbial genera compared to total DNA sequencing (after
normalizing for sequencing depth). This increase is substantial as microbial reads are such a small
fraction of the total sequenced reads. Considering these results, we further examined the microbial
content from total RNA extracted from 31 high protein powder (HPP) samples (Supplementary
Table 1) that resulted in an average of ~300 million paired end 150 bp sequencing reads per sample
in this study.
2.2 Evaluation and application of in silico filtering of eukaryotic food matrix reads
Sequenced reads from the eukaryotic host or food matrix may lead to false positives for microbial
identification in microbiome studies.35 This may occur partly due to reads originating from low
complexity regions of eukaryotic genomes, e.g. telomeric and centromeric repeats, being
misclassified as spurious microbial hits.36 In total DNA or RNA sequencing of clinical or animal
or even plant microbiomes, eukaryotic content may often comprise > 90% of the total sequencing
reads. This presents an important bioinformatic challenge that we addressed by filtering matrix
content using a custom-built reference database of 31 common food ingredient and contaminant
genomes (Supplementary Table 2) using the k-mer classification tool Kraken.37 This step allows
for rapidly classifying all sequenced reads (~300 million reads for each of 31 samples) as matrix
or non-matrix. The matrix filtering process yielded an estimate of the total percent matrix content
for a sample. See our work in Haiminen et al.38 on quantifying the eukaryotic food matrix
components with further precision.
To validate the matrix filtering step, we constructed in silico mock food microbiomes with
a high proportion of complex food matrix content and low microbial content (Supplementary Table
3). We then computed the true positive, false positive, and false negative rates of observed
microbial genera and sequenced reads (Table 1). False positive viral, archaeal, and eukaryotic
microbial genera (as well as bacteria) were observed without matrix filtering, although bacteria
were the only microbes included in the simulated mixtures. Introducing a matrix filtering step to
the pipeline improved read classification specificity to >99.96% (from 78–93% without filtering)
in both simulated food mixtures, while maintaining zero false negatives. With this level of
demonstrated accuracy, we used bioinformatic matrix filtering prior to further microbiome
2.3 High protein powder microbiome ecology
After filtering eukaryotic matrix sequences, we applied the remaining steps in the
bioinformatic workflow (Figure 1) to examine the shift in the high protein powder (HPP)
microbiome membership and to quantify the relative abundance of microbes at the genus level.
Genus is the first informative taxonomic rank for food pathogen identification that can be
considered accurate given current incompleteness of reference databases11,39–42 and was therefore
used in subsequent analyses. Overall, between 98 and 195 microbial genera (avg. 119) were
identified (RPM > 0.1) per HPP sample (Supplementary Table 4). When analyzing a-diversity
i.e. the number of microbes detected per sample, inter-sample comparisons may become skewed
unless a common number of reads is considered since deeper sequenced samples may contain more
observed genera merely due to a greater sampling depth.43,44 Thus, we utilized bioinformatic
rarefaction i.e. subsampling analysis to showcase how microbial diversity was altered by
sequencing depth. Examination of a-diversity across a range of in silico subsampled sequencing
depths showed that the community diversity varied across samples (Figure 2A). One sample
(MFMB-04) had 1.7 times more genera (195) than the average across other samples (avg. 116,
range 98–143) and exhibited higher a-diversity than any other sample at each in silico sampled
sequencing depth (Figure 2A). Rarefaction analysis further demonstrated that when considering
fewer than ~67 million sequenced reads, the observable microbial population was not saturated
(median elbow calculated as indicated in Satopää, et al.45). This observation suggests that deeper
sequencing or more selective sequencing of the HPP microbiomes will reveal more microbial
Notably, between 2%–4% (approximately 5,000,000–14,000,000) of reads per sample
remained unclassified as either eukaryotic matrix or microbe (Supplementary Figure S3).
However, the unclassified reads exhibited a GC (guanine plus cytosine) distribution similar to
reads classified as microbial (Supplementary Figure S4) indicating these reads may represent
microbial content that is absent or sufficiently divergent from existing references.
We calculated b-diversity to study inter-sample microbiome differences and to identify any
potential outliers among the sample collection. The Aitchison distances46 of microbial relative
abundances were calculated between samples (as recommended for compositional microbiome
data31,32), and the samples were hierarchically clustered based on the resulting distances (Figure
2B). The two primary clades were mostly defined by supplier (except for MFMB-17). In Haiminen
et al.,38 we reported that three of the HPP samples contained unexpected eukaryotic species. We
hypothesized that the presence of these contaminating matrix components (beef identifiable as Bos
taurus and pork identifiable as Sus scrofa) would alter the microbiome as compared to chicken
(identifiable as Gallus gallus) alone. Clustering HPP samples using their microbiome membership
led to a distinctly different group of the matrix-contaminated samples, supporting this hypothesis
(Figure 2B). These observations indicate that samples can be discriminated based on their
microbiome content for originating source and supplier, which is necessary for source tracking
potential hazards in food.
2.4 Comparative analysis of high protein powder microbiome membership and
We identified 65 genera present in all HPP samples (Figure 3A), whose combined
abundance accounted for between 88-99% of the total abundances of detected genera per sample.
Bacteroides, Clostridium, Lactococcus, Aeromonas, and Citrobacter were the five most abundant
of these microbial genera. The identified microbial genera also included viruses, the most abundant
of which was Gyrovirus (< 10 RPM per sample). Gyrovirus represents a genus of non-enveloped
DNA viruses responsible for chicken anemia which is ubiquitous in poultry. While there were only
65 microbial genera identified in all 31 HPP samples, the a-diversity per sample was on average
two-fold greater as previously indicated.
Beyond the collection of 65 microbes observed in all samples, there were an additional 164
microbes present in various HPP samples. Together, we identified a total of 229 genera among the
31 HPP samples tested (Figure 3B and 4, Supplementary Table 4). In order to identify genera that
were most variable between samples, we computed the median absolute deviation (MAD)47 using
the normalized relative abundance of each microbe (Figure 5A). The abundance of Bacteroides
was the most variable among samples (median = 148.1 RPM, MAD = 30.6) and showed increased
abundance in almost all samples from Supplier A compared to Supplier B (abundance for the 10
most variable genera shown in Figure 5B). Clostridium (median = 37.4 RPM, MAD = 24.2),
Lactococcus (median = 36.8 RPM, MAD = 18.2), and Lactobacillus (median = 24.2, MAD = 7.2)
were also highly variable and 3–4 fold more abundant in samples MFMB-04 and MFMB-20
compared to other samples (Figure 5B). Pseudomonas (median = 11.1 RPM, MAD = 12.2) was
markedly more abundant in MFMB-83 than any other sample (Figure 5B). These genera highlight
variability between microbiomes from a single food source and may provide insights into other
dissimilarities in these samples.
2.5. Microbiome shifts in response to changes in food matrix composition
We tested the hypothesis that the microbiome composition will shift in response to changes
in the food matrix and can be a unique signal to indicate contamination or adulteration. In 28 of
the 31 HPP samples, >99% of the matrix reads were determined in our related work38 to originate
from poultry (Gallus gallus), which was the only ingredient expected based on ingredient
specifications. However, three samples had higher pork and beef content compared to all other
HPP samples: MFMB-04 (7.74% pork, 8.99% beef), MFMB-20 (0.53% pork, 1.00% beef), and
MFMB-38 (0.92% pork, 0.29% beef) compared to the highest pork (0.01%) and beef (0.00%)
content among the other 28 HPP samples (Supplementary Data by Haiminen et al.38). The
microbiomes of these matrix contaminated samples also clustered into a separate sub-cluster
(Figure 2B). This demonstrated that a shift in the food matrix composition was associated with an
observable shift in the food microbiome.
MFMB-04 and MFMB-20 had the highest percentage of microbial reads compared to other
samples (Supplementary Figure S3). They also exhibited an increase in Lactococcus,
Lactobacillus, and Streptococcus relative abundances compared to other samples (Figure 5B), also
reflected at respective higher taxonomic levels above genus (Supplementary Figure S5).
There were 53 genera identified uniquely in MFMB-04 and/or MFMB-20, but not present
in any other sample. (MFMB-38 had a very low microbial load and contributed no uniquely
identified genera above the abundance threshold.) MFMB-04 contained 44 unique genera (Figure
4) with the most abundant being Macrococcus (35.8 RPM), Psychrobacter (23.8 RPM), and
Brevibacterium (18.1 RPM). Additionally, Paenalcaligenes was present only in MFMB-04 and
MFMB-20 with an RPM of 6.4 and 0.3, respectively, compared to a median RPM of 0.004 among
other samples. Notable differences in the matrix-contaminated samples’ unique microbial
community membership compared to other samples may provide microbial indicators associated
with unanticipated pork or beef presence.
2.6. Genus level identification of foodborne microbes
We evaluated the ability of total RNA sequencing to identify genera of commonly known
foodborne pathogens within the microbiome. We focused on fourteen pathogen-containing genera
including Aeromonas, Bacillus, Campylobacter, Clostridium, Corynebacterium, Cronobacter,
Escherichia, Helicobacter, Listeria, Salmonella, Shigella, Staphylococcus, Vibrio, and Yersinia
that were found to be present in the HPP samples with varying relative abundances. Of these
genera, Aeromonas, Bacillus, Campylobacter, Clostridium, Corynebacterium, Escherichia,
Salmonella, and Staphylococcus were detected in every HPP with median abundance values
between 0.58–48.31 RPM (Figure 6A). This indicated that a baseline fraction of reads can be
attributed to foodborne microbes when using NGS. Of those genera appearing in all samples, there
was observed sample-to-sample variation in their abundance with some genera exhibiting longer
tails of high abundance, e.g. Staphylococcus and Salmonella, whereas others exhibit very low
abundance barely above the threshold of detection, e.g. Bacillus and Yersinia (Figure 6A). None
of the pathogen-containing genera were consistent with higher relative abundances due to
differences in food matrix composition. Bacillus and Corynebacterium exhibited slightly higher
relative abundances in sample MFMB-04 which contained 7.7% pork and 9.0% beef (Figure 6B).
Yet while MFMB-04 contained higher cumulative levels of these foodborne microbes, the next
highest sample was MFMB-93 which was not associated with altered matrix composition, and
both MFMB-04 and MFMB-93 contained higher levels of Staphylococcus (Figure 6B). Thus,
matrix composition alone did not explain variations of these pathogen-containing genera.
Interestingly, low to moderate levels of Salmonella were detected within all 31 HPP
microbiomes (Figure 6A). The presence of Salmonella in HPP is expected but the viability of
Salmonella is an important indicator of safety and quality. Thus, we further sought to delineate
Salmonella growth capability within these microbiomes by comparing culturability with multiple
established bioinformatic NGS methods for Salmonella relative abundances in the samples.
2.7 Assessment of Salmonella culturability and total RNA sequencing
Total RNA sequencing of food microbiomes has the potential to provide additional
sensitivity beyond standard culture-based food safety testing to confirm or reject the presence of
potentially pathogenic microbes. In all of the examined HPP samples, some portion of the
sequenced reads were classified as belonging to pathogen-containing genera (Figure 6); however,
the presence of RNA transcripts does not necessarily indicate current growth of the organism itself.
We further inspected one pathogen of interest, Salmonella, to determine the congruence between
sequencing-based and culturability results. Of the 31 samples examined with total RNA
sequencing, Salmonella culture testing was applied to 27 samples, of which four were culture-
positive. Surprisingly, Salmonella culture-positive samples were not among those with the highest
relative abundance of Salmonella from sequencing (Figure 7A). When ranking the samples by
decreasing Salmonella abundance, the culture-positive samples were not enriched for higher ranks
(p=0.86 from Wilcoxon rank sum test indicating that the distributions are not significantly
different, Table 2). To confirm that the microbiome analysis pipeline did not miss Salmonella reads
present, we completed two orthogonal analyses on the same data set used in the microbial
identification step. The reference genomes relevant to these additional analyses were publicly
available and closed high quality genomes available from the sources indicated below.
First, for a targeted analysis, we aligned the sequenced reads using a different tool, Bowtie
2,48 to an augmented Salmonella-only reference database. This reference was comprised of the 264
Salmonella genomes extracted from NCBI RefSeq Complete (used in our previous microbial
identification step) as well as an additional 1,183 public Salmonella genomes which represent
global diversity within the genus.49 The number of reads that aligned to the Salmonella-only
reference was on average 370-fold higher than identified as Salmonella by Kraken using the multi-
microbe NCBI RefSeq Complete. In this additional analysis, the culture-positive samples had
overall higher ranks compared to culture-negative samples (p=0.06, Table 2) indicating that
additional Salmonella genomic data in the reference significantly improved discriminatory
identification power. Salmonella culture-positive samples were still not the most abundant (Figure
7B), but with an enriched database, sequencing positioned all four culturable samples within the
top 10 ranking.
The second additional analysis examined alignment of the reads to a specific gene
required50 for replication and protein production in actively dividing Salmonella— elongation
factor Tu (ef-Tu). This was done by aligning the reads to 4,846 gene sequences for ef-Tu extracted
for a larger corpus of Salmonella genomes from OMXWare.51 The relative abundances of this
transcript in culture-positive samples were still comparable to culture-negative samples (Figure
7C). Culture-positive samples did not exhibit higher ranks compared to culture-negative samples
(p=0.56, Table 2), indicating that ef-Tu relative abundance alone was not sufficient to improve the
lack of concordance in culturability vs sequencing. These two orthogonal analyses demonstrated
that results from carefully developed culture-based testing and those from current high-throughput
sequencing technologies, whether assessed at overall reads aligned or specific gene abundances,
were not conclusively in agreement when detecting active Salmonella in food samples (Figure 7
and Table 2). However, the use of a reference database enriched in whole genome sequences of
the specific organism of interested was found appropriate for food safety applications.
Since microbes compete for available resources within an environmental niche and
therefore impact one another,52 we investigated Salmonella culture results in conjunction with co-
occurrence patterns of other microbes in the total RNA sequencing data (Figure 8). Point-biserial
correlation coefficients (rpb) were calculated between Salmonella culturability results (presence or
absence which were available for 27 of the 31 samples) and microbiome relative abundance. We
observed 31 genera that positively correlated and with Salmonella presence (rpb > 0.5).
Erysipelothrix, Lactobacillus, Anaerococcus, Brachyspira, and Jeotgalibaca exhibited the largest
positive correlations. Gyrovirus was negatively correlated with Salmonella growth (rpb = -0.54).
In three of the four Salmonella-positive samples (MFMB-04, MFMB-20, and MFMB-38), food
matrix contamination was also observed (Supplementary Data in Haiminen et al.38). The
concurrency of Salmonella growth and matrix contamination was affirmed by the microbial co-
occurrence (specifically Erysipelothrix, Brachyspira, and Gyrovirus). This highlights the complex
dynamic and community co-dependency of food microbiomes, yet shows that multiple dimensions
of the data (microbiome composition, culture-based methods, and microbial load) will signal
anomalies from typical samples when there is an issue in the supply chain.
Accurate and appropriate tests for detecting potential hazards in the food supply chain are key to
ensuring consumer safety and food quality. Monitoring and regular testing of raw ingredients can
reveal fluctuations within the supply chain that may be an indicator of an ingredient’s quality or
of a potential hazard. Such quality is assessed by standardized tests for chemical and microbial
composition to meet legal requirements and specifications from government agencies throughout
the world. For raw materials or finished products to meet these bounds of safety and quality, their
composition must usually have a low microbiological load (except in fermented foods) and be
chemically identical in macro-components such as carbohydrate, protein, and fat. Methods in this
space must avoid false negative results which could endanger consumers, while also minimizing
false positives which could lead to unnecessary recalls and food loss.
Existing microbial detection technologies used in food safety today such as pulse field gel
electrophoresis (PFGE) and whole genome sequencing (WGS) require microbial isolation. This
provides biased outcomes as it removes microbes from their native environment where other biotic
members also subsist, and selects microbes by culturability alone. Amplicon sequencing, while a
low-cost alternative to metagenome or metatranscriptome sequencing for bacteria, also imparts
PCR amplification bias and reduces detection sensitivity due to reliance on a single gene (16S
ribosomal RNA).14,53,54 We therefore investigated the utility of total RNA sequencing of food
microbiomes and demonstrated that from this single test, we are able to yield several pertinent
results about food safety and quality.
For this evaluation, we developed a pipeline to characterize the microbiome of typical food
ingredient samples and to detect potentially hazardous outliers. Special considerations for food
samples were made as computational pipelines for human or other microbiome analyses are not
sufficient for applications in food safety without modification. In food, the eukaryotic matrix needs
to be confirmed, may be mixed, and, as we and others have shown, affects the identification
accuracy of microbes that are present.35,36 By filtering food matrix sequence data properly, we
avoid incorrect microbial identification and characterization of the microbiome36 while also
increasing the computational efficiency for downstream processing. The addition of this filtering
step in the pipeline removed approximately 90% of false positive genera and provided results at
99.96% specificity when evaluating simulated mixtures of food matrix and microbes (Table 1).
Through the analysis of 31 high protein powder total RNA sequencing samples, we
demonstrated the pipeline’s ability to characterize food microbiomes and indicate outliers. In this
sample collection, we identified a core catalog of 65 microbial genera found in all samples where
Bacteroides, Clostridium, and Lactococcus were the most abundant (Supplementary Table 4). We
also demonstrated that in these food microbiomes the overall diversity was 2-fold greater than the
core microbe set. Fluctuations in the microbiome can indicate important differences between
samples as observed here, as well as in the literature for grape berry6 and apple fruit microbiomes
(pertaining to organic versus conventional farming)7 or indicate inherent variability between
production batches or suppliers as observed here and during cheddar cheese manufacturing.8
Specifically, we observed a shift in the microbial composition (Figure 2B) and the microbial load
(Supplementary Figure S3) in high protein powder samples (derived from poultry meal) where
unexpected pork and beef were observed. Matrix-contaminated samples were marked by increased
relative abundances of specific microbes including Lactococcus, Lactobacillus, and Streptococcus
(Figure 5B). This work shows that the microbiome shifts with observed food matrix contamination
from sources with similar macronutrient content and thus, the microbiome alone is a likely signal
of compositional change in food.
Beyond shifts in the microbiome, we focused on a set of well-defined foodborne-pathogen
containing genera and explored their relative abundances observed from total RNA sequencing.
Of these genera, Aeromonas, Bacillus, Campylobacter, Clostridium, Corynebacterium,
Escherichia, Salmonella, and Staphylococcus were detected in every HPP sample. This highlights
that when using NGS there may be an observable baseline of sequences assigned to potentially
pathogenic microbes. For this ingredient type, this result lends a range of normalcy of relative
abundance generated by NGS. Further work is needed to establish a definitive and quantitative
range of typical variation in samples of a particular food source and the degree of anomaly for a
new sample or genus abundance. However, preliminary studies of this nature can inform the
development of guidelines when working with increasingly sensitive shotgun metagenomic or
Furthermore, sequenced DNA or RNA alone does not imply microbial viability. Therefore,
we investigated the relatedness of culture-based tests and total RNA sequencing for the pathogenic
bacterium Salmonella in the high protein powder samples. As has been reported for human gut55
and deep sea56 microbiomes, we also did not dretect a correlation between Salmonella read
abundance and culturability (Figure 7 and Table 2). Sequence reads matching Salmonella
references were observed for all samples (both culture-positive and culture-negative) as
determined by multiple analysis techniques: microbiome classification, alignment to Salmonella
genomes, and targeted growth gene analysis. When ranking the high protein powder samples based
on Salmonella abundance from whole genome alignments, the culture-positive samples were
enriched for higher ranks (p = 0.06). However, the culture-positive samples were still intermixed
in ranking with culture-negative samples. This indicated that there was no clear minimum
threshold of sequence data as evidence for culturability and that this analysis alone is not predictive
of pathogen growth. One possible reason for this is that the culture-positive variant of Salmonella
is missing from existing reference data sets. Potentially, Salmonella attained a nonculturable state
wherein it was detected by sequencing techniques yet remained nonculturable from the HPP
sources. Successful isolation of total RNA and DNA and gene expression analysis from
experimentally known nonculturable bacteria has been demonstrated by Ganesan et al. in multiple
studies in other genera.19,22 Physiological state should thus be taken under consideration when
benchmarking sequencing technologies in comparison with culture-based methods. Thus, total
RNA sequencing of food samples may identify shifts that standard food testing does not, but the
incongruity between sequencing read data and culture-based results highlights the need to perform
more benchmarking in food microbiome analysis for pathogen detection.
The characterization of HPP food microbiomes leveraged current accepted public reference
databases, yet it is known that these databases are still inadequate.1,2,11,57,58 Furthermore, when
considering congruence between Salmonella culturability and NGS read mapping techniques, the
genetic breadth and depth of multi-genome reference sequences is essential. For example, focusing
on ef-Tu, a known marker gene for Salmonella growth, was not sufficient to mirror viability of in
vitro culture tests. This highlights the limitations of single gene approaches for identification.
When the sequenced reads were examined in the context of an augmented reference collection of
Salmonella genomes, we observed improved ranking and read mapping rate for culture-positive
samples (yet we did not achieve complete concordance). This improvement underlined the
increased analytical robustness yielded from a multi-genome reference. We also recognize that the
read mapping rate may be exaggerated as reads from non-Salmonella genomes could map to
Salmonella in the absence of any other reference genomes. Overall for robust analysis and
applicability to food safety and quality, microbial references must be expanded to include more
genetically diverse representatives of pathogenic and spoilage organisms. Description of food
microbiomes will only improve as additional public sequence data is collected and leveraged.
In our sample collection, 2-4% (effectively 5 to 14 million) of reads remain unclassified. The
GC content distribution of unclassified reads matched microbial GC content distribution
(Supplementary Figure S4) suggesting that these reads may have been derived from microbes
missing from the current reference database that have not yet been isolated or sequenced. By
sequencing the microbiome, we sampled environmental niches in their native state in a culture-
independent manner and therefore collected data from diverse and potentially never-before seen
microbes. Tracking unclassified reads will also be essential for monitoring food microbiomes. The
inability to provide a name from existing references does not eliminate the possibility that the
sequence is from an unwanted microbe or indicates a hazard. In addition to tracking known
microbes, quantitative or qualitative shifts in the unclassified sequences might be used to detect
when a sample is different from its peers.
We demonstrated the potential utility of analyzing food microbiomes for food safety using raw
ingredients. This study resulted in the detection of shifts in the microbiome composition
corresponding to unexpected matrix contaminants. This signifies that the microbiome is likely an
important and effective hazard indicator in the food supply chain. While we have used total RNA
sequencing for detection of microbiome membership, the technology has future applicability for
detection of antimicrobial resistance, virulence, and biological function for multiple food sources,
and for other sample types. Notably, while this pipeline was developed for food monitoring, with
applicable modifications and identification of material-specific indicators, it can be applied to
other microbiomes including human and environmental.
4.1 Sample Collection, Preparation, and Sequencing
High protein powder (HPP, 2.5 kg) samples were each collected from a train car in Reno, NV,
USA between April 2015 and February 2016 in four batches from two suppliers and shipped to
the Weimer lab at the University of California, Davis (Davis, CA). Each HPP sample was
composed of five sub-samples from random locations within the train car prior to shipment.
Sample preparation, total RNA extraction and integrity confirmation, cDNA construction, and
library construction for these samples was previously described by Haiminen et al.38
Sequencing was performed by BGI@UC Davis (Sacramento, CA) using Illumina HiSeq
4000 (San Diego, CA) with 150 paired end chemistry for each sample except the following: HiSeq
3000 with 150 paired end chemistry was used for MFMB-04 and MFMB-17. All total RNA
sequencing data are available via the 100K Pathogen Genome Project BioProject (PRJNA186441)
at NCBI (Supplementary Table 1).
For evaluation of total RNA sequencing for microbial classification in paired processing
steps, total RNA and total DNA were extracted from the same sample and denoted as MFMB-03
and MFMB-08, respectively. Total RNA was extracted and sequenced as described above. Total
DNA was extracted and sequenced as described previously.10,59–64 The Illumina HiSeq 2000 with
100 paired end chemistry was used for MFMB-03 and MFMB-08.
4.2 Sequence Data Quality Control
Illumina Universal adapters were removed and reads were trimmed using Trim Galore65
with a minimum read length parameter 50 bp. The resulting reads were filtered using Kraken37, as
described below in Section 4.3, with a custom database built from the PhiX genome (NCBI
Reference Sequence: NC_001422.1). Removal of PhiX content is suggested as it is a common
contaminant in Illumina sequencing data.66 Trimmed non-PhiX reads were used in subsequent
matrix filtering and microbial identification steps.
4.3 Matrix Filtering Process and Validation
Kraken37 with a k-mer size of 31 bp (optimal size described in the Kraken reference
publication) was used to identify and remove reads that matched a pre-determined list of 31
common food matrix and potential contaminant eukaryotic genomes (Supplementary Table 2).
These food matrix organisms were chosen based on preliminary eukaryotic read alignment
experiments of the HPP samples as well as high-volume food components in the supply chain. Due
to the large size of eukaryotic genomes in the custom Kraken37 database, a random k-mer reduction
was applied to reduce the size of the database by 58% using kraken-build with option --max-db-
size, in order to fit the database in 188 GB for in-memory processing. A conservative Kraken score
threshold of 0.1 was applied to avoid filtering microbial reads. The matrix filtering database
includes low complexity and repeat regions of eukaryotic genomes to capture all possible matrix
reads. This filtering database with the score threshold was also used in the matrix filtering in silico
testing as described below.
Matrix filtering was validated by constructing synthetic paired end reads (150 bp) using
DWGSIM67 with mutations from reference sequences using the following parameters: base error
rate (e) = 0.005, outer distance between the two ends of a read pair (d) = 500, rate of mutations (r)
= 0.001, fraction of indels (R) = 0.15, probability an indel is extended (X) = 0.3. Reference
sequences are detailed in Supplementary Table 3. We constructed two in silico mixtures of
sequencing reads by randomly sampling reads from eukaryotic reference genomes. Simulated
Food Mixture 1 was comprised of nine species with the following number of reads per genome:
2M cattle, 2M salmon, 1M goat, 1M lamb, 1M tilapia (transcriptome), 962K chicken
(transcriptome), 10K duck, 1K horse, and 1K rat totaling 7.974M matrix reads. Simulated Food
Mixture 2 contained 5M soybean, 4M rice, 3M potato, 2M corn, 200K rat, and 10K drain fly reads,
totaling 14.210M matrix reads. Both simulated food mixtures included 1,000 microbial sequence
reads generated from 15 different microbial species for a total of 15K sequence reads
(Supplementary Table 3).
4.4 Microbial Identification
Remaining reads after quality control and matrix filtering were classified using Kraken37
against a microbial database with a k-mer size of 31 bp to determine the microbial composition
within each sample. NCBI RefSeq Complete68 genomes were obtained for bacterial, archaeal,
viral, and eukaryotic microorganisms (~7,800 genomes retrieved April 2017). Low complexity
regions of the genomes were masked using Dustmasker69 with default parameters. A threshold of
0.05 was applied to the Kraken score in an effort to maximize the F-score of the result (as
demonstrated in Kraken’s operating manual.70 Taxa-specific sequence reads were used to calculate
a relative abundance in reads per million (RPM; Equation 1) where
represents the reads
classified per microbial entity (e.g. the genus Salmonella) and
represents the number of
sequenced reads remaining after quality control (trimming and PhiX removal) for an individual
sample, including any reads classified as eukaryotic:
!$%& ' & !"
This value provides a relative abundance of the microbial entity of interest and was used in
comparisons of taxa among samples. Genera with a conservative threshold of RPM > 0.1 were
defined as present, as previously applied by others in the contexts of human infectious disease and
gut microbiome studies.33,34 Pearson correlation of resulting microbial genus counts was
4.5 Community Ecology Analysis
Rarefaction analysis at multiple subsampled read depths RD was performed by multiplying
the microbial genus read counts with RD/RQ and rounding the results down to the nearest integer
to represent observed read counts. Here RQ is the total number of reads in the sample after quality
control (including microbial, matrix, and unclassified reads). Resulting a-diversity at read depth
RD was computed as the number of genera with resulting RPM > 0.1 and plotted at five million
read intervals: RD = 5M, 10M, 15M, …, RQ. If, due to random sampling and rounding effects, the
computed a-diversity was lower than the diversity computed at any previous depth, the previous
higher a-diversity was used for plotting. The median elbow was calculated as previously
described45 using the R package kneed.45
In compositional data analysis,31 non-zero values are required when computing b-diversity
based on Aitchison distance.46 Therefore, reads counts assigned to each genus were pseudo-
counted by adding one in advance of computation of RPM (Eq. 1) prior to calculating the Aitchison
distance for the microbial table. b-diversity was calculated using the R package robCompositions71
and hierarchical clustering was performed using base R function hclust using the “ward.D2”
method as recommended for compositional data analysis.31
4.6 Unclassified Read Analysis
The GC percent distributions of matrix (from matrix filtering), microbial, and remaining
unclassified reads per sample were computed using FastQC72 and collated across samples with
4.7 Analysis of Salmonella Culturability
Growth of Salmonella was determined using a real-time quantitative PCR method for the
confirmation of Salmonella isolates for presumptive generic identification of foodborne
Salmonella. Testing was performed fully in concordance with the Bacteriological Analytical
Manual (BAM) for Salmonella74,75 for this approach that is also AOAC-approved. All samples
with positive results for Salmonella were classified as containing actively growing Salmonella. To
compare culture results with those from total RNA sequencing, Salmonella RPM values were
parsed from the genus-level microbe table (described in Section 4.4).
Two additional approaches were employed to examine Salmonella read mapping with a
more sensitive tool and broader reference databases. Quality controlled matrix-filtered reads were
aligned using Bowtie248 with very-sensitive-local-mode to 1. an expanded collection of whole
Salmonella genomes and 2. to a curated growth gene reference for elongation factor Tu (ef-Tu).
For results from both complete genome and ef-Tu gene alignments, the relative abundance (RPM)
was computed as shown in Equation 1.
For whole genome alignments, a reference was constructed from 1,183 recently published
Salmonella genomes49 in addition to the 264 Salmonella genomes extracted from the
aforementioned NCBI RefSeq Complete collection (see Methods Section 4.4).
To construct a curated growth gene (ef-Tu) reference, gene sequences annotated in
Salmonella genomes as “elongation factor Tu”, “EF-Tu” or “eftu” (case insensitive) were retrieved
from OMXWare51 using its Python package. This query yielded 4,846 unique gene sequences from
a total of 36,242 Salmonella genomes which were assembled or retrieved from the NCBI Sequence
Read Archive or RefSeq Complete Sequences as previously described.51 The retrieved ef-Tu gene
sequences were subsequently used to build a custom Bowtie248 reference. Read alignment was
completed with very-sensitive-local-mode.
The read counts for each sample were ranked and Wilcoxon rank sum test was computed
between the rank vectors of 4 Salmonella-positive and 23 Salmonella-negative samples. The 4
samples with unknown Salmonella status were excluded from the rankings.
Point-biserial correlation coefficients (rpb) were calculated between Salmonella growth
indicated by culture results (+1 and -1 for presence and absence, respectively) and observed
relative abundance from total RNA sequencing results using the R package ltm.76 The point-
biserial correlation is a special case of the Pearson correlation that is better suited for a binary
variable e.g. when Salmonella is reported as present or absent (a sample’s Salmonella status).
All high protein powder (HPP) poultry meal sequences are available through the 100K
Pathogen Genome Project (PRJNA186441) in the NCBI BioProject (see Supplementary Table 1
for a complete list of accession numbers).
The pipeline and microbial or matrix references were constructed from publicly available
tools and reference sequences as described in the Methods section. Automated usability of this
pipeline is available through membership in the Consortium for Sequencing the Food Supply
We’d like to acknowledge the IBM Research OMXWare team for their data management
support and availability for the retrieval and processing of microbial genomes. This research
project was financially supported by the Consortium for Sequencing the Food Supply Chain.
Funding for the total RNA sequencing of high protein powder factory ingredients was provided by
Mars, Incorporated to B.C.W. with specific interest in metagenomics of the food microbiome.
KLB and NH conceived of the experimental design, developed the approach, completed
and oversaw the experiments, performed analyses, and wrote the paper; DC, SE, MK, BK, MD,
RP, HK, ES developed the approach, analyzed data, and revised the manuscript; BCH completed
nucleic acid extraction method development and sequencing library construction, and contributed
to data analysis and writing; NK coordinated sample collection and processing, nucleic acid
extraction and contributed to writing; RB and PM conceived of the experimental design, developed
the approach, and reviewed the paper; BG contributed to the experimental design, developed the
approach, and wrote the paper; GD, CHM, SP, AQ participated to the conception of the
experimental design and to the review of the manuscript; LP conceived of the experiment,
contributed to the data analysis, and wrote the paper; JHK conceived of the experiment, developed
the approach, and wrote the paper; BCW conceived of the experimental design, developed the
approach, oversaw the experiments, performed analyses, and wrote the paper
The authors were employed by private or academic organizations as described in the author
affiliations at the time this work was completed. IBM Corporation, Mars Incorporated, and Bio-
Rad Laboratories are members of the Consortium for Sequencing the Food Supply Chain. The
authors declare no other competing interests
Supplementary information is available at npj Science of Food’s website
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FIGURE and TABLE LEGENDS: (corresponding to their order at end of merged document)
Figure 1: Pipeline description of bioinformatic steps applied to high protein powder
metatranscriptome samples. Black arrows indicate data flow and blue boxes describe outputs
from the pipeline.
Table 1: Accuracy of microbial identification using in silico constructed Simulated Food
Mixtures with expected food matrix and microbial sequences.
Figure 2A: Alpha diversity (number of genera) for all (n = 31) high protein powder
metatranscriptomes is compared to total number of sequenced reads for a range of in silico
subsampled sequencing depths. The dashed vertical line indicates the median elbow (at approx.
67 million reads).
Figure 2B: Hierarchical clustering of Aitchison distance values of poultry meal samples based
on microbial composition. Samples were received from Supplier A (blue and red) and Supplier B
(green). Matrix-contaminated samples are additionally marked in red.
Figure 3A: Phylogram of the 65 microbial genera present in all samples with RPM > 0.1
Figure 3B: Phylogram of all microbes observed in any sample. Log of the median RPM value
across samples is indicated. Grey indicating a median RPM value of 0.
Figure 4: Heatmap (log10-scale) of high protein powder microbial composition and relative
abundance (RPM) where absence (RPM < 0.1) is indicated in grey. Genera are ordered by
summed abundance across samples. Samples were received from Supplier A (blue) and Supplier
B (green). Red stars indicate matrix-contaminated samples (from Supplier A).
Figure 5A: All identified microbial general are plotted with median value and median absolute
deviation (MAD) of RPM abundance. Genera with MAD > 5 are labeled with the genus name.
Figure 5B: Heatmap (log10-scale) of ten microbial genera with the largest median absolute
deviation (MAD) across samples. Genera are ordered by decreasing MAD from top to bottom.
Samples were received from Supplier A (blue) and Supplier B (green). Red stars indicate matrix
contaminated samples (from Supplier A).
Figure 6A: Relative abundance of microbes with high relevance to food safety and quality from
high protein powder total RNA sequenced microbiomes. Width of violin plot indicates density of
samples with relative abundance at that value. Observation threshold of RPM = 0.1 is indicated
with the horizontal black line.
Figure 6B: Foodborne microbe relative abundances are shown across samples of high protein
powder total RNA sequenced samples.
Figure 7: Salmonella culturability status and high-throughput sequencing read abundance
(RPM) from k-mer classification to NCBI Microbial RefSeq Complete (A), from alignments to
1,447 Salmonella genomes (B), and from alignments to 4,846 EF-Tu gene sequences (C).
Salmonella presence (red) indicates culture-positive result, absence (green) indicates culture-
negative result, and no record (black) indicates samples for which no culture test was completed.
Table 2: The ranks for Salmonella-positive samples and the associated p-values from Wilcoxon
rank sum test are shown for high-throughput sequencing read abundance (RPM) for multiple
analyses: k-mer classification to NCBI Microbial RefSeq Complete (left), alignments to 1,447
Salmonella genomes (middle), and alignments to 4,846 ef-Tu gene sequences (right). The
corresponding Salmonella relative abundances are shown in Figure 7A–C.
Figure 8: Salmonella status correlations with genus relative abundances. Only those genera with
absolute value of the correlation coefficient > 0.5 are shown. Positive and negative correlations
are indicated in grey and blue, respectively.
Supplemental Figures (pdf): Supplemental Figures S1–S5
Supplemental Table 1 (.xlsx) - Sample Descriptions
Supplemental Table 2 (.xlsx) - Matrix Filtering Genomes
Supplemental Table 3 (.xlsx) - Simulated Food Mixtures
Supplemental Table 4 (.xlsx) - Microbial Genera
Classificati on with
abundance (Eq. 1)
Classificati on with
Table 1: Microbial Identification Accuracy from Simulated Food Microbiome Mixtures
Simulated Mixture 1 Simulated Mixture 2
With Matrix Filtering No Matrix Filtering With Matrix Filtering No Matrix Filtering
# GENERA GENUS
READS # GENERA GENUS
READS # GENERA GENUS
READS # GENERA GENUS
Bacteria in Simulated Mixture
Observed Microbial Content
(as a % of total observed)
(as a % of total observed)
False Positives Removed
with Matrix Filtering
(as a % of false positives
Salmonella-positive sample k-mer Classification Whole Genome
ef-Tu Alig nment
MFMB-04 8th 10th 1st
MFMB-20 9th 9th 4th
MFMB-38 20th 3rd 21st
MFMB-41 30th 6th 28th
Rank sum test p-value
p=0.86 p=0.06 p=0.56