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Monitoring the microbiome for food safety and quality using deep shotgun sequencing

May 2020

DOI:10.1101/2020.05.18.102574

Authors:

Kristen Beck

Niina Haiminen

DAIN Studios

Stefan Edlund

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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.

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Monitoring the microbiome for food safety and quality using deep

shotgun sequencing

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*

*Co-corresponding authors

+Contributed equally

Contact information: Kristen L. Beck, IBM Almaden Research Center, 650 Harry Road, San Jose

CA, 95120 USA, klbeck@us.ibm.com, +1 408-927-1963

AUTHOR AFFILIATIONS:

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.

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6Bio-Rad Laboratories, Hercules CA

7Bio-Rad, Food Science Division, MArnes-La-Coquette, France

8Consortium for Sequencing the Food Supply Chain, San Jose, CA

ABSTRACT:

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.

KEYWORDS:

microbiome, food safety, bioinformatics, shotgun sequencing, microbial ecology, pathogens

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1. INTRODUCTION:

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

demonstrated.

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

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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

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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.

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In this work, we examined 31 high protein powder samples (HPP; derived from poultry meal).

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HPP are commonly used raw materials in pet foods. They are subject to microbial growth prior to

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preparation and continued survival in powder form.30 We subjected the HPP samples to deep total

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RNA sequencing with ~300 million reads per sample. In order to process the 31 samples collected

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over ~1.5 years from two suppliers at a single location, we defined and calibrated the appropriate

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methods– from sample preparation to bioinformatic analysis– needed to taxonomically identify

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the community members present and to detect key features of microbial growth. First, we removed

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the HPP’s food matrix RNA content as eukaryotic background with an important bioinformatic

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filtering step designed specifically for food analysis. The remaining sequences were used for

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relative quantification of microbiome members and for identifying shifts based on food matrix

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content, production source, and Salmonella culturability. This work demonstrates that total RNA

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sequencing is a robust approach for monitoring the food microbiome for use in food safety and

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quality applications, while additional work is required for predicting pathogen viability.

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2. RESULTS:

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2.1 Evaluation of microbial identification capability in total RNA and DNA sequencing

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Microbial identification in microbiomes often leverages shotgun DNA sequencing; however,

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total RNA sequencing can provide additional information about viable bacterial activity in a

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community via transcriptional activity. Since using total RNA to study food microbiomes is novel,

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each step of the analysis workflow (Figure 1) was carefully designed and scrutinized for accuracy.

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For all analyses done in this study, we report relative abundance in reads per million (RPM)

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(Equation 1) as recommended by Gloor et al31,32 and apply the conservative threshold of RPM >

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0.1 to indicate presence as previously described by Langelier et al and Illot et al.33,34 Numerically,

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this threshold translates to ~30 reads per genus per sample considering a sequencing depth of ~300

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million reads per sample (Methods Section 4.4). First, we examined the effectiveness of RNA for

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taxonomic identification and relative quantification of microbes in the presence of food matrix

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reads. We observed that RNA sequencing results correlated (R2 = 0.93) with the genus relative

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quantification provided by DNA sequencing (Supplementary Figure S1). RNA sequencing also

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detected more genera demonstrated by a higher a-diversity than the use of DNA (Supplementary

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Figure S2). Additionally, from the same starting material, total RNA sequencing resulted in 2.4-

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fold more reads classified to microbial genera compared to total DNA sequencing (after

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normalizing for sequencing depth). This increase is substantial as microbial reads are such a small

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fraction of the total sequenced reads. Considering these results, we further examined the microbial

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content from total RNA extracted from 31 high protein powder (HPP) samples (Supplementary

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Table 1) that resulted in an average of ~300 million paired end 150 bp sequencing reads per sample

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in this study.

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2.2 Evaluation and application of in silico filtering of eukaryotic food matrix reads

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Sequenced reads from the eukaryotic host or food matrix may lead to false positives for microbial

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identification in microbiome studies.35 This may occur partly due to reads originating from low

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complexity regions of eukaryotic genomes, e.g. telomeric and centromeric repeats, being

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misclassified as spurious microbial hits.36 In total DNA or RNA sequencing of clinical or animal

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or even plant microbiomes, eukaryotic content may often comprise > 90% of the total sequencing

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reads. This presents an important bioinformatic challenge that we addressed by filtering matrix

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content using a custom-built reference database of 31 common food ingredient and contaminant

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genomes (Supplementary Table 2) using the k-mer classification tool Kraken.37 This step allows

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for rapidly classifying all sequenced reads (~300 million reads for each of 31 samples) as matrix

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or non-matrix. The matrix filtering process yielded an estimate of the total percent matrix content

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for a sample. See our work in Haiminen et al.38 on quantifying the eukaryotic food matrix

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components with further precision.

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To validate the matrix filtering step, we constructed in silico mock food microbiomes with

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a high proportion of complex food matrix content and low microbial content (Supplementary Table

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3). We then computed the true positive, false positive, and false negative rates of observed

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microbial genera and sequenced reads (Table 1). False positive viral, archaeal, and eukaryotic

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microbial genera (as well as bacteria) were observed without matrix filtering, although bacteria

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were the only microbes included in the simulated mixtures. Introducing a matrix filtering step to

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the pipeline improved read classification specificity to >99.96% (from 78–93% without filtering)

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in both simulated food mixtures, while maintaining zero false negatives. With this level of

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demonstrated accuracy, we used bioinformatic matrix filtering prior to further microbiome

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analysis.

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2.3 High protein powder microbiome ecology

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After filtering eukaryotic matrix sequences, we applied the remaining steps in the

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bioinformatic workflow (Figure 1) to examine the shift in the high protein powder (HPP)

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microbiome membership and to quantify the relative abundance of microbes at the genus level.

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Genus is the first informative taxonomic rank for food pathogen identification that can be

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considered accurate given current incompleteness of reference databases11,39–42 and was therefore

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used in subsequent analyses. Overall, between 98 and 195 microbial genera (avg. 119) were

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identified (RPM > 0.1) per HPP sample (Supplementary Table 4). When analyzing a-diversity

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i.e. the number of microbes detected per sample, inter-sample comparisons may become skewed

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unless a common number of reads is considered since deeper sequenced samples may contain more

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observed genera merely due to a greater sampling depth.43,44 Thus, we utilized bioinformatic

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rarefaction i.e. subsampling analysis to showcase how microbial diversity was altered by

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sequencing depth. Examination of a-diversity across a range of in silico subsampled sequencing

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depths showed that the community diversity varied across samples (Figure 2A). One sample

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(MFMB-04) had 1.7 times more genera (195) than the average across other samples (avg. 116,

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range 98–143) and exhibited higher a-diversity than any other sample at each in silico sampled

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sequencing depth (Figure 2A). Rarefaction analysis further demonstrated that when considering

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fewer than ~67 million sequenced reads, the observable microbial population was not saturated

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(median elbow calculated as indicated in Satopää, et al.45). This observation suggests that deeper

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sequencing or more selective sequencing of the HPP microbiomes will reveal more microbial

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diversity.

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Notably, between 2%–4% (approximately 5,000,000–14,000,000) of reads per sample

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remained unclassified as either eukaryotic matrix or microbe (Supplementary Figure S3).

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However, the unclassified reads exhibited a GC (guanine plus cytosine) distribution similar to

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reads classified as microbial (Supplementary Figure S4) indicating these reads may represent

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microbial content that is absent or sufficiently divergent from existing references.

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We calculated b-diversity to study inter-sample microbiome differences and to identify any

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potential outliers among the sample collection. The Aitchison distances46 of microbial relative

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abundances were calculated between samples (as recommended for compositional microbiome

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data31,32), and the samples were hierarchically clustered based on the resulting distances (Figure

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2B). The two primary clades were mostly defined by supplier (except for MFMB-17). In Haiminen

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et al.,38 we reported that three of the HPP samples contained unexpected eukaryotic species. We

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hypothesized that the presence of these contaminating matrix components (beef identifiable as Bos

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taurus and pork identifiable as Sus scrofa) would alter the microbiome as compared to chicken

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(identifiable as Gallus gallus) alone. Clustering HPP samples using their microbiome membership

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led to a distinctly different group of the matrix-contaminated samples, supporting this hypothesis

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(Figure 2B). These observations indicate that samples can be discriminated based on their

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microbiome content for originating source and supplier, which is necessary for source tracking

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potential hazards in food.

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2.4 Comparative analysis of high protein powder microbiome membership and

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composition

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We identified 65 genera present in all HPP samples (Figure 3A), whose combined

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abundance accounted for between 88-99% of the total abundances of detected genera per sample.

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Bacteroides, Clostridium, Lactococcus, Aeromonas, and Citrobacter were the five most abundant

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of these microbial genera. The identified microbial genera also included viruses, the most abundant

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of which was Gyrovirus (< 10 RPM per sample). Gyrovirus represents a genus of non-enveloped

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DNA viruses responsible for chicken anemia which is ubiquitous in poultry. While there were only

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65 microbial genera identified in all 31 HPP samples, the a-diversity per sample was on average

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two-fold greater as previously indicated.

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Beyond the collection of 65 microbes observed in all samples, there were an additional 164

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microbes present in various HPP samples. Together, we identified a total of 229 genera among the

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31 HPP samples tested (Figure 3B and 4, Supplementary Table 4). In order to identify genera that

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were most variable between samples, we computed the median absolute deviation (MAD)47 using

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the normalized relative abundance of each microbe (Figure 5A). The abundance of Bacteroides

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was the most variable among samples (median = 148.1 RPM, MAD = 30.6) and showed increased

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abundance in almost all samples from Supplier A compared to Supplier B (abundance for the 10

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most variable genera shown in Figure 5B). Clostridium (median = 37.4 RPM, MAD = 24.2),

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Lactococcus (median = 36.8 RPM, MAD = 18.2), and Lactobacillus (median = 24.2, MAD = 7.2)

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were also highly variable and 3–4 fold more abundant in samples MFMB-04 and MFMB-20

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compared to other samples (Figure 5B). Pseudomonas (median = 11.1 RPM, MAD = 12.2) was

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markedly more abundant in MFMB-83 than any other sample (Figure 5B). These genera highlight

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variability between microbiomes from a single food source and may provide insights into other

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dissimilarities in these samples.

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2.5. Microbiome shifts in response to changes in food matrix composition

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We tested the hypothesis that the microbiome composition will shift in response to changes

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in the food matrix and can be a unique signal to indicate contamination or adulteration. In 28 of

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the 31 HPP samples, >99% of the matrix reads were determined in our related work38 to originate

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from poultry (Gallus gallus), which was the only ingredient expected based on ingredient

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specifications. However, three samples had higher pork and beef content compared to all other

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HPP samples: MFMB-04 (7.74% pork, 8.99% beef), MFMB-20 (0.53% pork, 1.00% beef), and

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MFMB-38 (0.92% pork, 0.29% beef) compared to the highest pork (0.01%) and beef (0.00%)

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content among the other 28 HPP samples (Supplementary Data by Haiminen et al.38). The

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microbiomes of these matrix contaminated samples also clustered into a separate sub-cluster

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(Figure 2B). This demonstrated that a shift in the food matrix composition was associated with an

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observable shift in the food microbiome.

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MFMB-04 and MFMB-20 had the highest percentage of microbial reads compared to other

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samples (Supplementary Figure S3). They also exhibited an increase in Lactococcus,

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Lactobacillus, and Streptococcus relative abundances compared to other samples (Figure 5B), also

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reflected at respective higher taxonomic levels above genus (Supplementary Figure S5).

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There were 53 genera identified uniquely in MFMB-04 and/or MFMB-20, but not present

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in any other sample. (MFMB-38 had a very low microbial load and contributed no uniquely

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identified genera above the abundance threshold.) MFMB-04 contained 44 unique genera (Figure

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4) with the most abundant being Macrococcus (35.8 RPM), Psychrobacter (23.8 RPM), and

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Brevibacterium (18.1 RPM). Additionally, Paenalcaligenes was present only in MFMB-04 and

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MFMB-20 with an RPM of 6.4 and 0.3, respectively, compared to a median RPM of 0.004 among

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other samples. Notable differences in the matrix-contaminated samples’ unique microbial

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community membership compared to other samples may provide microbial indicators associated

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with unanticipated pork or beef presence.

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2.6. Genus level identification of foodborne microbes

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We evaluated the ability of total RNA sequencing to identify genera of commonly known

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foodborne pathogens within the microbiome. We focused on fourteen pathogen-containing genera

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including Aeromonas, Bacillus, Campylobacter, Clostridium, Corynebacterium, Cronobacter,

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Escherichia, Helicobacter, Listeria, Salmonella, Shigella, Staphylococcus, Vibrio, and Yersinia

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that were found to be present in the HPP samples with varying relative abundances. Of these

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genera, Aeromonas, Bacillus, Campylobacter, Clostridium, Corynebacterium, Escherichia,

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Salmonella, and Staphylococcus were detected in every HPP with median abundance values

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between 0.58–48.31 RPM (Figure 6A). This indicated that a baseline fraction of reads can be

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attributed to foodborne microbes when using NGS. Of those genera appearing in all samples, there

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was observed sample-to-sample variation in their abundance with some genera exhibiting longer

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tails of high abundance, e.g. Staphylococcus and Salmonella, whereas others exhibit very low

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abundance barely above the threshold of detection, e.g. Bacillus and Yersinia (Figure 6A). None

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of the pathogen-containing genera were consistent with higher relative abundances due to

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differences in food matrix composition. Bacillus and Corynebacterium exhibited slightly higher

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relative abundances in sample MFMB-04 which contained 7.7% pork and 9.0% beef (Figure 6B).

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Yet while MFMB-04 contained higher cumulative levels of these foodborne microbes, the next

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highest sample was MFMB-93 which was not associated with altered matrix composition, and

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both MFMB-04 and MFMB-93 contained higher levels of Staphylococcus (Figure 6B). Thus,

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matrix composition alone did not explain variations of these pathogen-containing genera.

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Interestingly, low to moderate levels of Salmonella were detected within all 31 HPP

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microbiomes (Figure 6A). The presence of Salmonella in HPP is expected but the viability of

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Salmonella is an important indicator of safety and quality. Thus, we further sought to delineate

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Salmonella growth capability within these microbiomes by comparing culturability with multiple

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established bioinformatic NGS methods for Salmonella relative abundances in the samples.

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2.7 Assessment of Salmonella culturability and total RNA sequencing

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Total RNA sequencing of food microbiomes has the potential to provide additional

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sensitivity beyond standard culture-based food safety testing to confirm or reject the presence of

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potentially pathogenic microbes. In all of the examined HPP samples, some portion of the

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sequenced reads were classified as belonging to pathogen-containing genera (Figure 6); however,

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the presence of RNA transcripts does not necessarily indicate current growth of the organism itself.

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We further inspected one pathogen of interest, Salmonella, to determine the congruence between

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sequencing-based and culturability results. Of the 31 samples examined with total RNA

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sequencing, Salmonella culture testing was applied to 27 samples, of which four were culture-

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positive. Surprisingly, Salmonella culture-positive samples were not among those with the highest

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relative abundance of Salmonella from sequencing (Figure 7A). When ranking the samples by

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decreasing Salmonella abundance, the culture-positive samples were not enriched for higher ranks

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(p=0.86 from Wilcoxon rank sum test indicating that the distributions are not significantly

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different, Table 2). To confirm that the microbiome analysis pipeline did not miss Salmonella reads

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present, we completed two orthogonal analyses on the same data set used in the microbial

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identification step. The reference genomes relevant to these additional analyses were publicly

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available and closed high quality genomes available from the sources indicated below.

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First, for a targeted analysis, we aligned the sequenced reads using a different tool, Bowtie

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2,48 to an augmented Salmonella-only reference database. This reference was comprised of the 264

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Salmonella genomes extracted from NCBI RefSeq Complete (used in our previous microbial

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identification step) as well as an additional 1,183 public Salmonella genomes which represent

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global diversity within the genus.49 The number of reads that aligned to the Salmonella-only

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reference was on average 370-fold higher than identified as Salmonella by Kraken using the multi-

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microbe NCBI RefSeq Complete. In this additional analysis, the culture-positive samples had

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overall higher ranks compared to culture-negative samples (p=0.06, Table 2) indicating that

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additional Salmonella genomic data in the reference significantly improved discriminatory

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identification power. Salmonella culture-positive samples were still not the most abundant (Figure

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7B), but with an enriched database, sequencing positioned all four culturable samples within the

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top 10 ranking.

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The second additional analysis examined alignment of the reads to a specific gene

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required50 for replication and protein production in actively dividing Salmonella— elongation

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factor Tu (ef-Tu). This was done by aligning the reads to 4,846 gene sequences for ef-Tu extracted

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for a larger corpus of Salmonella genomes from OMXWare.51 The relative abundances of this

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transcript in culture-positive samples were still comparable to culture-negative samples (Figure

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7C). Culture-positive samples did not exhibit higher ranks compared to culture-negative samples

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(p=0.56, Table 2), indicating that ef-Tu relative abundance alone was not sufficient to improve the

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lack of concordance in culturability vs sequencing. These two orthogonal analyses demonstrated

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that results from carefully developed culture-based testing and those from current high-throughput

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sequencing technologies, whether assessed at overall reads aligned or specific gene abundances,

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were not conclusively in agreement when detecting active Salmonella in food samples (Figure 7

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and Table 2). However, the use of a reference database enriched in whole genome sequences of

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the specific organism of interested was found appropriate for food safety applications.

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Since microbes compete for available resources within an environmental niche and

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therefore impact one another,52 we investigated Salmonella culture results in conjunction with co-

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occurrence patterns of other microbes in the total RNA sequencing data (Figure 8). Point-biserial

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correlation coefficients (rpb) were calculated between Salmonella culturability results (presence or

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absence which were available for 27 of the 31 samples) and microbiome relative abundance. We

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observed 31 genera that positively correlated and with Salmonella presence (rpb > 0.5).

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Erysipelothrix, Lactobacillus, Anaerococcus, Brachyspira, and Jeotgalibaca exhibited the largest

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positive correlations. Gyrovirus was negatively correlated with Salmonella growth (rpb = -0.54).

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In three of the four Salmonella-positive samples (MFMB-04, MFMB-20, and MFMB-38), food

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matrix contamination was also observed (Supplementary Data in Haiminen et al.38). The

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concurrency of Salmonella growth and matrix contamination was affirmed by the microbial co-

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occurrence (specifically Erysipelothrix, Brachyspira, and Gyrovirus). This highlights the complex

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dynamic and community co-dependency of food microbiomes, yet shows that multiple dimensions

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of the data (microbiome composition, culture-based methods, and microbial load) will signal

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anomalies from typical samples when there is an issue in the supply chain.

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3. DISCUSSION:

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Accurate and appropriate tests for detecting potential hazards in the food supply chain are key to

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ensuring consumer safety and food quality. Monitoring and regular testing of raw ingredients can

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reveal fluctuations within the supply chain that may be an indicator of an ingredient’s quality or

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of a potential hazard. Such quality is assessed by standardized tests for chemical and microbial

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composition to meet legal requirements and specifications from government agencies throughout

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the world. For raw materials or finished products to meet these bounds of safety and quality, their

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composition must usually have a low microbiological load (except in fermented foods) and be

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chemically identical in macro-components such as carbohydrate, protein, and fat. Methods in this

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space must avoid false negative results which could endanger consumers, while also minimizing

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false positives which could lead to unnecessary recalls and food loss.

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Existing microbial detection technologies used in food safety today such as pulse field gel

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electrophoresis (PFGE) and whole genome sequencing (WGS) require microbial isolation. This

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provides biased outcomes as it removes microbes from their native environment where other biotic

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members also subsist, and selects microbes by culturability alone. Amplicon sequencing, while a

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low-cost alternative to metagenome or metatranscriptome sequencing for bacteria, also imparts

349

PCR amplification bias and reduces detection sensitivity due to reliance on a single gene (16S

350

ribosomal RNA).14,53,54 We therefore investigated the utility of total RNA sequencing of food

351

microbiomes and demonstrated that from this single test, we are able to yield several pertinent

352

results about food safety and quality.

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For this evaluation, we developed a pipeline to characterize the microbiome of typical food

354

ingredient samples and to detect potentially hazardous outliers. Special considerations for food

355

samples were made as computational pipelines for human or other microbiome analyses are not

356

sufficient for applications in food safety without modification. In food, the eukaryotic matrix needs

357

to be confirmed, may be mixed, and, as we and others have shown, affects the identification

358

accuracy of microbes that are present.35,36 By filtering food matrix sequence data properly, we

359

avoid incorrect microbial identification and characterization of the microbiome36 while also

360

increasing the computational efficiency for downstream processing. The addition of this filtering

361

step in the pipeline removed approximately 90% of false positive genera and provided results at

362

99.96% specificity when evaluating simulated mixtures of food matrix and microbes (Table 1).

363

Through the analysis of 31 high protein powder total RNA sequencing samples, we

364

demonstrated the pipeline’s ability to characterize food microbiomes and indicate outliers. In this

365

sample collection, we identified a core catalog of 65 microbial genera found in all samples where

366

Bacteroides, Clostridium, and Lactococcus were the most abundant (Supplementary Table 4). We

367

also demonstrated that in these food microbiomes the overall diversity was 2-fold greater than the

368

core microbe set. Fluctuations in the microbiome can indicate important differences between

369

samples as observed here, as well as in the literature for grape berry6 and apple fruit microbiomes

370

(pertaining to organic versus conventional farming)7 or indicate inherent variability between

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production batches or suppliers as observed here and during cheddar cheese manufacturing.8

372

Specifically, we observed a shift in the microbial composition (Figure 2B) and the microbial load

373

(Supplementary Figure S3) in high protein powder samples (derived from poultry meal) where

374

unexpected pork and beef were observed. Matrix-contaminated samples were marked by increased

375

relative abundances of specific microbes including Lactococcus, Lactobacillus, and Streptococcus

376

(Figure 5B). This work shows that the microbiome shifts with observed food matrix contamination

377

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from sources with similar macronutrient content and thus, the microbiome alone is a likely signal

378

of compositional change in food.

379

Beyond shifts in the microbiome, we focused on a set of well-defined foodborne-pathogen

380

containing genera and explored their relative abundances observed from total RNA sequencing.

381

Of these genera, Aeromonas, Bacillus, Campylobacter, Clostridium, Corynebacterium,

382

Escherichia, Salmonella, and Staphylococcus were detected in every HPP sample. This highlights

383

that when using NGS there may be an observable baseline of sequences assigned to potentially

384

pathogenic microbes. For this ingredient type, this result lends a range of normalcy of relative

385

abundance generated by NGS. Further work is needed to establish a definitive and quantitative

386

range of typical variation in samples of a particular food source and the degree of anomaly for a

387

new sample or genus abundance. However, preliminary studies of this nature can inform the

388

development of guidelines when working with increasingly sensitive shotgun metagenomic or

389

metatranscriptomic analysis.

390

Furthermore, sequenced DNA or RNA alone does not imply microbial viability. Therefore,

391

we investigated the relatedness of culture-based tests and total RNA sequencing for the pathogenic

392

bacterium Salmonella in the high protein powder samples. As has been reported for human gut55

393

and deep sea56 microbiomes, we also did not dretect a correlation between Salmonella read

394

abundance and culturability (Figure 7 and Table 2). Sequence reads matching Salmonella

395

references were observed for all samples (both culture-positive and culture-negative) as

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determined by multiple analysis techniques: microbiome classification, alignment to Salmonella

397

genomes, and targeted growth gene analysis. When ranking the high protein powder samples based

398

on Salmonella abundance from whole genome alignments, the culture-positive samples were

399

enriched for higher ranks (p = 0.06). However, the culture-positive samples were still intermixed

400

in ranking with culture-negative samples. This indicated that there was no clear minimum

401

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threshold of sequence data as evidence for culturability and that this analysis alone is not predictive

402

of pathogen growth. One possible reason for this is that the culture-positive variant of Salmonella

403

is missing from existing reference data sets. Potentially, Salmonella attained a nonculturable state

404

wherein it was detected by sequencing techniques yet remained nonculturable from the HPP

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sources. Successful isolation of total RNA and DNA and gene expression analysis from

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experimentally known nonculturable bacteria has been demonstrated by Ganesan et al. in multiple

407

studies in other genera.19,22 Physiological state should thus be taken under consideration when

408

benchmarking sequencing technologies in comparison with culture-based methods. Thus, total

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RNA sequencing of food samples may identify shifts that standard food testing does not, but the

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incongruity between sequencing read data and culture-based results highlights the need to perform

411

more benchmarking in food microbiome analysis for pathogen detection.

412

The characterization of HPP food microbiomes leveraged current accepted public reference

413

databases, yet it is known that these databases are still inadequate.1,2,11,57,58 Furthermore, when

414

considering congruence between Salmonella culturability and NGS read mapping techniques, the

415

genetic breadth and depth of multi-genome reference sequences is essential. For example, focusing

416

on ef-Tu, a known marker gene for Salmonella growth, was not sufficient to mirror viability of in

417

vitro culture tests. This highlights the limitations of single gene approaches for identification.

418

When the sequenced reads were examined in the context of an augmented reference collection of

419

Salmonella genomes, we observed improved ranking and read mapping rate for culture-positive

420

samples (yet we did not achieve complete concordance). This improvement underlined the

421

increased analytical robustness yielded from a multi-genome reference. We also recognize that the

422

read mapping rate may be exaggerated as reads from non-Salmonella genomes could map to

423

Salmonella in the absence of any other reference genomes. Overall for robust analysis and

424

applicability to food safety and quality, microbial references must be expanded to include more

425

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genetically diverse representatives of pathogenic and spoilage organisms. Description of food

426

microbiomes will only improve as additional public sequence data is collected and leveraged.

427

In our sample collection, 2-4% (effectively 5 to 14 million) of reads remain unclassified. The

428

GC content distribution of unclassified reads matched microbial GC content distribution

429

(Supplementary Figure S4) suggesting that these reads may have been derived from microbes

430

missing from the current reference database that have not yet been isolated or sequenced. By

431

sequencing the microbiome, we sampled environmental niches in their native state in a culture-

432

independent manner and therefore collected data from diverse and potentially never-before seen

433

microbes. Tracking unclassified reads will also be essential for monitoring food microbiomes. The

434

inability to provide a name from existing references does not eliminate the possibility that the

435

sequence is from an unwanted microbe or indicates a hazard. In addition to tracking known

436

microbes, quantitative or qualitative shifts in the unclassified sequences might be used to detect

437

when a sample is different from its peers.

438

We demonstrated the potential utility of analyzing food microbiomes for food safety using raw

439

ingredients. This study resulted in the detection of shifts in the microbiome composition

440

corresponding to unexpected matrix contaminants. This signifies that the microbiome is likely an

441

important and effective hazard indicator in the food supply chain. While we have used total RNA

442

sequencing for detection of microbiome membership, the technology has future applicability for

443

detection of antimicrobial resistance, virulence, and biological function for multiple food sources,

444

and for other sample types. Notably, while this pipeline was developed for food monitoring, with

445

applicable modifications and identification of material-specific indicators, it can be applied to

446

other microbiomes including human and environmental.

447

448

4. METHODS:

449

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450

4.1 Sample Collection, Preparation, and Sequencing

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High protein powder (HPP, 2.5 kg) samples were each collected from a train car in Reno, NV,

452

USA between April 2015 and February 2016 in four batches from two suppliers and shipped to

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the Weimer lab at the University of California, Davis (Davis, CA). Each HPP sample was

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composed of five sub-samples from random locations within the train car prior to shipment.

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Sample preparation, total RNA extraction and integrity confirmation, cDNA construction, and

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library construction for these samples was previously described by Haiminen et al.38

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Sequencing was performed by BGI@UC Davis (Sacramento, CA) using Illumina HiSeq

458

4000 (San Diego, CA) with 150 paired end chemistry for each sample except the following: HiSeq

459

3000 with 150 paired end chemistry was used for MFMB-04 and MFMB-17. All total RNA

460

sequencing data are available via the 100K Pathogen Genome Project BioProject (PRJNA186441)

461

at NCBI (Supplementary Table 1).

462

For evaluation of total RNA sequencing for microbial classification in paired processing

463

steps, total RNA and total DNA were extracted from the same sample and denoted as MFMB-03

464

and MFMB-08, respectively. Total RNA was extracted and sequenced as described above. Total

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DNA was extracted and sequenced as described previously.10,59–64 The Illumina HiSeq 2000 with

466

100 paired end chemistry was used for MFMB-03 and MFMB-08.

467

468

4.2 Sequence Data Quality Control

469

Illumina Universal adapters were removed and reads were trimmed using Trim Galore65

470

with a minimum read length parameter 50 bp. The resulting reads were filtered using Kraken37, as

471

described below in Section 4.3, with a custom database built from the PhiX genome (NCBI

472

Reference Sequence: NC_001422.1). Removal of PhiX content is suggested as it is a common

473

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contaminant in Illumina sequencing data.66 Trimmed non-PhiX reads were used in subsequent

474

matrix filtering and microbial identification steps.

475

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4.3 Matrix Filtering Process and Validation

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Kraken37 with a k-mer size of 31 bp (optimal size described in the Kraken reference

478

publication) was used to identify and remove reads that matched a pre-determined list of 31

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common food matrix and potential contaminant eukaryotic genomes (Supplementary Table 2).

480

These food matrix organisms were chosen based on preliminary eukaryotic read alignment

481

experiments of the HPP samples as well as high-volume food components in the supply chain. Due

482

to the large size of eukaryotic genomes in the custom Kraken37 database, a random k-mer reduction

483

was applied to reduce the size of the database by 58% using kraken-build with option --max-db-

484

size, in order to fit the database in 188 GB for in-memory processing. A conservative Kraken score

485

threshold of 0.1 was applied to avoid filtering microbial reads. The matrix filtering database

486

includes low complexity and repeat regions of eukaryotic genomes to capture all possible matrix

487

reads. This filtering database with the score threshold was also used in the matrix filtering in silico

488

testing as described below.

489

Matrix filtering was validated by constructing synthetic paired end reads (150 bp) using

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DWGSIM67 with mutations from reference sequences using the following parameters: base error

491

rate (e) = 0.005, outer distance between the two ends of a read pair (d) = 500, rate of mutations (r)

492

= 0.001, fraction of indels (R) = 0.15, probability an indel is extended (X) = 0.3. Reference

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sequences are detailed in Supplementary Table 3. We constructed two in silico mixtures of

494

sequencing reads by randomly sampling reads from eukaryotic reference genomes. Simulated

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Food Mixture 1 was comprised of nine species with the following number of reads per genome:

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2M cattle, 2M salmon, 1M goat, 1M lamb, 1M tilapia (transcriptome), 962K chicken

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(transcriptome), 10K duck, 1K horse, and 1K rat totaling 7.974M matrix reads. Simulated Food

498

Mixture 2 contained 5M soybean, 4M rice, 3M potato, 2M corn, 200K rat, and 10K drain fly reads,

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totaling 14.210M matrix reads. Both simulated food mixtures included 1,000 microbial sequence

500

reads generated from 15 different microbial species for a total of 15K sequence reads

501

(Supplementary Table 3).

502

503

4.4 Microbial Identification

504

Remaining reads after quality control and matrix filtering were classified using Kraken37

505

against a microbial database with a k-mer size of 31 bp to determine the microbial composition

506

within each sample. NCBI RefSeq Complete68 genomes were obtained for bacterial, archaeal,

507

viral, and eukaryotic microorganisms (~7,800 genomes retrieved April 2017). Low complexity

508

regions of the genomes were masked using Dustmasker69 with default parameters. A threshold of

509

0.05 was applied to the Kraken score in an effort to maximize the F-score of the result (as

510

demonstrated in Kraken’s operating manual.70 Taxa-specific sequence reads were used to calculate

511

a relative abundance in reads per million (RPM; Equation 1) where

represents the reads

512

classified per microbial entity (e.g. the genus Salmonella) and

represents the number of

513

sequenced reads remaining after quality control (trimming and PhiX removal) for an individual

514

sample, including any reads classified as eukaryotic:

515

516

!$%& ' & !"

( &)*+++*+++&&&&&&&&,-./0123&)

517

518

This value provides a relative abundance of the microbial entity of interest and was used in

519

comparisons of taxa among samples. Genera with a conservative threshold of RPM > 0.1 were

520

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defined as present, as previously applied by others in the contexts of human infectious disease and

521

gut microbiome studies.33,34 Pearson correlation of resulting microbial genus counts was

522

computed.

523

524

4.5 Community Ecology Analysis

525

Rarefaction analysis at multiple subsampled read depths RD was performed by multiplying

526

the microbial genus read counts with RD/RQ and rounding the results down to the nearest integer

527

to represent observed read counts. Here RQ is the total number of reads in the sample after quality

528

control (including microbial, matrix, and unclassified reads). Resulting a-diversity at read depth

529

RD was computed as the number of genera with resulting RPM > 0.1 and plotted at five million

530

read intervals: RD = 5M, 10M, 15M, …, RQ. If, due to random sampling and rounding effects, the

531

computed a-diversity was lower than the diversity computed at any previous depth, the previous

532

higher a-diversity was used for plotting. The median elbow was calculated as previously

533

described45 using the R package kneed.45

534

In compositional data analysis,31 non-zero values are required when computing b-diversity

535

based on Aitchison distance.46 Therefore, reads counts assigned to each genus were pseudo-

536

counted by adding one in advance of computation of RPM (Eq. 1) prior to calculating the Aitchison

537

distance for the microbial table. b-diversity was calculated using the R package robCompositions71

538

and hierarchical clustering was performed using base R function hclust using the “ward.D2”

539

method as recommended for compositional data analysis.31

540

541

4.6 Unclassified Read Analysis

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The copyright holder for this preprint (whichthis version posted May 19, 2020. . https://doi.org/10.1101/2020.05.18.102574doi: bioRxiv preprint

The GC percent distributions of matrix (from matrix filtering), microbial, and remaining

543

unclassified reads per sample were computed using FastQC72 and collated across samples with

544

MultiQC.73

545

546

4.7 Analysis of Salmonella Culturability

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Growth of Salmonella was determined using a real-time quantitative PCR method for the

548

confirmation of Salmonella isolates for presumptive generic identification of foodborne

549

Salmonella. Testing was performed fully in concordance with the Bacteriological Analytical

550

Manual (BAM) for Salmonella74,75 for this approach that is also AOAC-approved. All samples

551

with positive results for Salmonella were classified as containing actively growing Salmonella. To

552

compare culture results with those from total RNA sequencing, Salmonella RPM values were

553

parsed from the genus-level microbe table (described in Section 4.4).

554

Two additional approaches were employed to examine Salmonella read mapping with a

555

more sensitive tool and broader reference databases. Quality controlled matrix-filtered reads were

556

aligned using Bowtie248 with very-sensitive-local-mode to 1. an expanded collection of whole

557

Salmonella genomes and 2. to a curated growth gene reference for elongation factor Tu (ef-Tu).

558

For results from both complete genome and ef-Tu gene alignments, the relative abundance (RPM)

559

was computed as shown in Equation 1.

560

For whole genome alignments, a reference was constructed from 1,183 recently published

561

Salmonella genomes49 in addition to the 264 Salmonella genomes extracted from the

562

aforementioned NCBI RefSeq Complete collection (see Methods Section 4.4).

563

To construct a curated growth gene (ef-Tu) reference, gene sequences annotated in

564

Salmonella genomes as “elongation factor Tu”, “EF-Tu” or “eftu” (case insensitive) were retrieved

565

from OMXWare51 using its Python package. This query yielded 4,846 unique gene sequences from

566

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a total of 36,242 Salmonella genomes which were assembled or retrieved from the NCBI Sequence

567

Read Archive or RefSeq Complete Sequences as previously described.51 The retrieved ef-Tu gene

568

sequences were subsequently used to build a custom Bowtie248 reference. Read alignment was

569

completed with very-sensitive-local-mode.

570

The read counts for each sample were ranked and Wilcoxon rank sum test was computed

571

between the rank vectors of 4 Salmonella-positive and 23 Salmonella-negative samples. The 4

572

samples with unknown Salmonella status were excluded from the rankings.

573

Point-biserial correlation coefficients (rpb) were calculated between Salmonella growth

574

indicated by culture results (+1 and -1 for presence and absence, respectively) and observed

575

relative abundance from total RNA sequencing results using the R package ltm.76 The point-

576

biserial correlation is a special case of the Pearson correlation that is better suited for a binary

577

variable e.g. when Salmonella is reported as present or absent (a sample’s Salmonella status).

578

579

Data Availability:

580

All high protein powder (HPP) poultry meal sequences are available through the 100K

581

Pathogen Genome Project (PRJNA186441) in the NCBI BioProject (see Supplementary Table 1

582

for a complete list of accession numbers).

583

584

Code Availability:

585

The pipeline and microbial or matrix references were constructed from publicly available

586

tools and reference sequences as described in the Methods section. Automated usability of this

587

pipeline is available through membership in the Consortium for Sequencing the Food Supply

588

Chain.

589

590

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Acknowledgements:

591

We’d like to acknowledge the IBM Research OMXWare team for their data management

592

support and availability for the retrieval and processing of microbial genomes. This research

593

project was financially supported by the Consortium for Sequencing the Food Supply Chain.

594

Funding for the total RNA sequencing of high protein powder factory ingredients was provided by

595

Mars, Incorporated to B.C.W. with specific interest in metagenomics of the food microbiome.

596

597

Contributions:

598

KLB and NH conceived of the experimental design, developed the approach, completed

599

and oversaw the experiments, performed analyses, and wrote the paper; DC, SE, MK, BK, MD,

600

RP, HK, ES developed the approach, analyzed data, and revised the manuscript; BCH completed

601

nucleic acid extraction method development and sequencing library construction, and contributed

602

to data analysis and writing; NK coordinated sample collection and processing, nucleic acid

603

extraction and contributed to writing; RB and PM conceived of the experimental design, developed

604

the approach, and reviewed the paper; BG contributed to the experimental design, developed the

605

approach, and wrote the paper; GD, CHM, SP, AQ participated to the conception of the

606

experimental design and to the review of the manuscript; LP conceived of the experiment,

607

contributed to the data analysis, and wrote the paper; JHK conceived of the experiment, developed

608

the approach, and wrote the paper; BCW conceived of the experimental design, developed the

609

approach, oversaw the experiments, performed analyses, and wrote the paper

610

611

Competing Interests:

612

The authors were employed by private or academic organizations as described in the author

613

affiliations at the time this work was completed. IBM Corporation, Mars Incorporated, and Bio-

614

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Rad Laboratories are members of the Consortium for Sequencing the Food Supply Chain. The

615

authors declare no other competing interests

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Supplementary information is available at npj Science of Food’s website

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REFERENCES:

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1. Kovac, J., Bakker, H. den, Carroll, L. M. & Wiedmann, M. Precision food safety: A

621

systems approach to food safety facilitated by genomics tools. TrAC Trends Anal. Chem.

622

(2017). doi:10.1016/j.trac.2017.06.001

623

2. Weimer, B. C. et al. Defining the food microbiome for authentication, safety, and process

624

management. IBM J. Res. Dev. 60, 1 (2016).

625

3. Walsh, A. M. et al. Microbial Succession and Flavor Production in the Fermented Dairy

626

Beverage Kefir. mSystems 1, (2016).

627

4. Walsh, A. M. et al. Species classifier choice is a key consideration when analysing low-

628

complexity food microbiome data. Microbiome 6, 50 (2018).

629

5. Duru, I. C. et al. Metagenomic and metatranscriptomic analysis of the microbial

630

community in Swiss-type Maasdam cheese during ripening. Int. J. Food Microbiol. 281,

631

10–22 (2018).

632

6. Martins, G. et al. Grape berry bacterial microbiota: Impact of the ripening process and the

633

farming system. Int. J. Food Microbiol. 158, 93–100 (2012).

634

7. Abdelfattah, A., Wisniewski, M., Droby, S. & Schena, L. Spatial and compositional

635

variation in the fungal communities of organic and conventionally grown apple fruit at the

636

consumer point-of-purchase. Hortic. Res. 3, 16047 (2016).

637

8. Williams, A. G., Choi, S.-C. & Banks, J. M. Variability of the species and strain

638

phenotype composition of the non-starter lactic acid bacterial population of cheddar

639

cheese manufactured in a commercial creamery. Food Res. Int. 35, 483–493 (2002).

640

9. Weimer, B. C. 100K Pathogen Genome Project. Genome Announc. 5, e00594-17 (2017).

641

10. Emond-Rheault, J.-G. et al. A Syst-OMICS Approach to Ensuring Food Safety and

642

Reducing the Economic Burden of Salmonellosis. Front. Microbiol. 8, 996 (2017).

643

11. Kaufman, J. H. et al. Insular microbiogeography. (2017).

644

12. Bashiardes, S., Zilberman-Schapira, G. & Elinav, E. Use of Metatranscriptomics in

645

Microbiome Research. Bioinform. Biol. Insights 10, 19–25 (2016).

646

13. McGrath, K. C. et al. Isolation and analysis of mRNA from environmental microbial

647

communities. J. Microbiol. Methods 75, 172–176 (2008).

648

14. Cottier, F. et al. Advantages of meta-total RNA sequencing (MeTRS) over shotgun

649

metagenomics and amplicon-based sequencing in the profiling of complex microbial

650

communities. npj Biofilms Microbiomes 4, 2 (2018).

651

15. Macklaim, J. M. et al. Comparative meta-RNA-seq of the vaginal microbiota and

652

differential expression by Lactobacillus iners in health and dysbiosis. Microbiome 1, 12

653

(2013).

654

16. Haiminen, N. et al. Food authentication from shotgun sequencing reads with an

655

application on high protein powders. npj Sci. Food 3, (2019).

656

The copyright holder for this preprint (whichthis version posted May 19, 2020. . https://doi.org/10.1101/2020.05.18.102574doi: bioRxiv preprint

17. Lakin, S. M. et al. MEGARes: an antimicrobial resistance database for high throughput

657

sequencing.

658

18. Noyes, N. R. et al. Resistome diversity in cattle and the environment decreases during

659

beef production. Elife 5, e13195 (2016).

660

19. Ganesan, B., Dobrowolski, P. & Weimer, B. C. Identification of the Leucine-to-2-

661

Methylbutyric Acid Catabolic Pathway of Lactococcus lactis. Appl. Environ. Microbiol.

662

72, 4264–4273 (2006).

663

20. Ganesan, B., Seefeldt, K., Koka, R. C., Dias, B. & Weimer, B. C. Monocarboxylic acid

664

production by lactococci and lactobacilli. Int. Dairy J. 14, 237–246 (2004).

665

21. Ganesan, B., Seefeldt, K. & Weimer, B. C. Fatty Acid Production from Amino Acids

666

and -Keto Acids by Brevibacterium linens BL2. Appl. Environ. Microbiol. 70, 6385–6393

667

(2004).

668

22. Ganesan, B., Stuart, M. R. & Weimer, B. C. Carbohydrate Starvation Causes a

669

Metabolically Active but Nonculturable State in Lactococcus lactis. Appl. Environ.

670

Microbiol. 73, 2498–2512 (2007).

671

23. Ganesan, B. et al. Probiotic bacteria survive in Cheddar cheese and modify populations of

672

other lactic acid bacteria. J. Appl. Microbiol. 116, 1642–1656 (2014).

673

24. Ganesan, B. & Weimer, B. C. Cheese : chemistry, physics, and microbiology. (Elsevier,

674

2004).

675

25. Sheflin, A. M., Melby, C. L., Carbonero, F. & Weir, T. L. Linking dietary patterns with

676

gut microbial composition and function. Gut Microbes 8, (2017).

677

26. McDonald, D. et al. American Gut: an Open Platform for Citizen Science Microbiome

678

Research. mSystems 3, e00031-18 (2018).

679

27. Clemente, J. C., Ursell, L. K., Parfrey, L. W. & Knight, R. The Impact of the Gut

680

Microbiota on Human Health: An Integrative View. Cell 148, 1258–1270 (2012).

681

28. Richards, J. L., Yap, Y. A., McLeod, K. H., Mackay, C. R. & Mariño, E. Dietary

682

metabolites and the gut microbiota: an alternative approach to control inflammatory and

683

autoimmune diseases. Clin Trans Immunol 5, e82 (2016).

684

29. Yang, X. et al. Use of Metagenomic Shotgun Sequencing Technology To Detect

685

Foodborne Pathogens within the Microbiome of the Beef Production Chain. Appl Env.

686

Microbiol 82, 2433–2443 (2016).

687

30. Hofacre, C. L. et al. Characterization of antibiotic-resistant bacteria in rendered animal

688

products. Avian Dis. 45, 953–61

689

31. Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome

690

Datasets Are Compositional: And This Is Not Optional. Front. Microbiol. 8, 2224 (2017).

691

32. Gloor, G. B. & Reid, G. Compositional analysis: a valid approach to analyze microbiome

692

high-throughput sequencing data. Can. J. Microbiol. 62, 692–703 (2016).

693

33. Langelier, C. et al. Integrating host response and unbiased microbe detection for lower

694

respiratory tract infection diagnosis in critically ill adults. Proc. Natl. Acad. Sci. U. S. A.

695

115, E12353–E12362 (2018).

696

34. Ilott, N. E. et al. Defining the microbial transcriptional response to colitis through

697

integrated host and microbiome profiling. ISME J. 10, 2389–2404 (2016).

698

35. Ripp, F. et al. All-Food-Seq (AFS): a quantifiable screen for species in biological samples

699

by deep DNA sequencing. BMC Genomics 15, 639 (2014).

700

36. Lee, A. Y., Lee, C. S. & Gelder, R. N. Van. Scalable metagenomics alignment research

701

tool (SMART): a scalable, rapid, and complete search heuristic for the classification of

702

metagenomic sequences from complex sequence populations. BMC Bioinformatics 17,

703

292 (2016).

704

The copyright holder for this preprint (whichthis version posted May 19, 2020. . https://doi.org/10.1101/2020.05.18.102574doi: bioRxiv preprint

37. Wood, D. E. & Salzberg, S. L. Kraken: ultrafast metagenomic sequence classification

705

using exact alignments. Genome Biol. 15, R46 (2014).

706

38. Haiminen, N. et al. Food authentication from shotgun sequencing reads with an

707

application on high protein powders. npj Sci. Food in press (2019).

708

39. Hug, L. A. et al. A new view of the tree of life. Nat. Microbiol. 1, 16048 (2016).

709

40. Wu, D. et al. A phylogeny-driven genomic encyclopaedia of Bacteria and Archaea.

710

Nature 462, 1056–1060 (2009).

711

41. Kyrpides, N. C. et al. Genomic Encyclopedia of Bacteria and Archaea: Sequencing a

712

Myriad of Type Strains. PLoS Biol. 12, e1001920 (2014).

713

42. Kyrpides, N. C., Eloe-Fadrosh, E. A. & Ivanova, N. N. Microbiome Data Science:

714

Understanding Our Microbial Planet. Trends Microbiol. 24, 425–427 (2016).

715

43. Thompson, L. R. et al. A communal catalogue reveals Earth’s multiscale microbial

716

diversity. Nature 551, 457 (2017).

717

44. Nayfach, S. & Pollard, K. S. Toward Accurate and Quantitative Comparative

718

Metagenomics. Cell 166, 1103–1116 (2016).

719

45. Satopää, V., Albrecht, J., Irwin, D. & Raghavan, B. Finding a ‘Kneedle’ in a Haystack:

720

Detecting Knee Points in System Behavior.

721

46. Aitchison, J., Barceló-Vidal, C., Martín-Fernández, J. A. & Pawlowsky-Glahn, V.

722

Logratio Analysis and Compositional Distance. Math. Geol. 32, 271–275 (2000).

723

47. Di Palma, M. A. & Gallo, M. A co-median approach to detect compositional outliers. J.

724

Appl. Stat. 43, 2348–2362 (2016).

725

48. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods

726

9, 357–9 (2012).

727

49. Kong, N. et al. Draft Genome Sequences of 1,183 Salmonella Strains from the 100K

728

Pathogen Genome Project. Genome Announc. 5, (2017).

729

50. Tubulekas, I. & Hughes, D. A Single Amino Acid Substitution in Elongation Factor Tu

730

Disrupts Interaction between the Ternary Complex and the Ribosome. J. Bacteriol. 240–

731

250 (1993).

732

51. Seabolt, E. E. et al. OMXWare, A Cloud-Based Platform for Studying Microbial Life at

733

Scale. arXiv 1911.02095, (2019).

734

52. Zelezniak, A. et al. Metabolic dependencies drive species co-occurrence in diverse

735

microbial communities. Proc. Natl. Acad. Sci. 112, 6449–6454 (2015).

736

53. Jones, M. B. et al. Library preparation methodology can influence genomic and functional

737

predictions in human microbiome research. Proc Natl Acad Sci U S A (2015).

738

doi:10.1073/pnas.1519288112

739

54. Pollock, J., Glendinning, L., Wisedchanwet, T. & Watson, M. The madness of

740

microbiome: Attempting to find consensus ‘best practice’ for 16S microbiome studies.

741

Appl. Environ. Microbiol. AEM.02627-17 (2018). doi:10.1128/AEM.02627-17

742

55. Browne, H. P. et al. Culturing of ‘unculturable’ human microbiota reveals novel taxa and

743

extensive sporulation. Nature 533, 543–546 (2016).

744

56. Eilers, H., Pernthaler, J., Glöckner, F. O. & Amann, R. Culturability and In situ abundance

745

of pelagic bacteria from the North Sea. Appl. Environ. Microbiol. 66, 3044–51 (2000).

746

57. Hinchliff, C. E. et al. Synthesis of phylogeny and taxonomy into a comprehensive tree of

747

life. Proc Natl Acad Sci U S A 112, 12764–12769 (2015).

748

58. Knight, R. et al. Best practices for analysing microbiomes. Nat. Rev. Microbiol. 16, 410–

749

422 (2018).

750

59. Weis, A. M. et al. Genomic Comparison of Campylobacter spp. and Their Potential for

751

Zoonotic Transmission between Birds, Primates, and Livestock. Appl. Environ. Microbiol.

752

The copyright holder for this preprint (whichthis version posted May 19, 2020. . https://doi.org/10.1101/2020.05.18.102574doi: bioRxiv preprint

82, 7165 LP – 7175 (2016).

753

60. Miller, B. et al. A novel, single-tube enzymatic fragmentation and library construction

754

method enables fast turnaround times and improved data quality for microbial whole-

755

genome sequencing. Kapa Biosyst. Appl. Note 1–8 (2015).

756

doi:10.13140/RG.2.1.4534.3440

757

61. Lüdeke, C. H. M., Kong, N., Weimer, B. C., Fischer, M. & Jones, J. L. Complete genome

758

sequences of a clinical isolate and an environmental isolate of Vibrio parahaemolyticus.

759

Genome Announc. 3, e00216-15 (2015).

760

62. Jeannotte, R. et al. High-Throughput Analysis of Foodborne Bacterial Genomic DNA

761

Using Agilent 2200 TapeStation and Genomic DNA ScreenTape System. Agil. Appl. Note

762

1–8 (2015). doi:doi:10.6084/m9.figshare.1372504

763

63. Arabyan, N. et al. Salmonella Degrades the Host Glycocalyx Leading to Altered Infection

764

and Glycan Remodeling. Sci. Rep. 6, 1–11 (2016).

765

64. Kong, N. et al. Draft Genome Sequences of 1,183 Salmonella Strains from the 100K

766

Pathogen Genome Project. Genome Announc. 5, e00518-17 (2017).

767

65. Krueger, F. TrimGalore. (2018).

768

66. Mukherjee, S., Huntemann, M., Ivanova, N., Kyrpides, N. C. & Pati, A. Large-scale

769

contamination of microbial isolate genomes by Illumina PhiX control. Stand. Genomic

770

Sci. 10, 18 (2015).

771

67. Homer, N. DWGSIM. (2011).

772

68. O’Leary, N. A. et al. Reference sequence (RefSeq) database at NCBI: current status,

773

taxonomic expansion, and functional annotation. Nucleic Acids Res. 44, D733–D745

774

(2016).

775

69. Morgulis, A., Gertz, E. M., Schäffer, A. A. & Agarwala, R. A fast and symmetric DUST

776

implementation to mask low-complexity DNA sequences. J. Comput. Biol. 13, 1028–1040

777

(2006).

778

70. Wood, D. Kraken’s operating manual.

779

71. Templ, M., Hron, K. & Filzmoser, P. robCompositions: An R-package for Robust

780

Statistical Analysis of Compositional Data. in Compositional Data Analysis 341–355

781

(John Wiley & Sons, Ltd, 2011). doi:10.1002/9781119976462.ch25

782

72. Andrews, S. FastQC.

783

73. Ewels, P., Magnusson, M., Lundin, S. & Käller, M. MultiQC: summarize analysis results

784

for multiple tools and samples in a single report. Bioinformatics 32, 3047–3048 (2016).

785

74. Andrews, W. H., Wang, H., Jacobson, A. & Hammack, T. Bacteriological Analytical

786

Manual (BAM) Chapter 5: Salmonella. in Bacteriological Analytical Manual (U.S. Food

787

and Drug Administration, 2018).

788

75. Grim, C. J. et al. High-Resolution Microbiome Profiling for Detection and Tracking of

789

Salmonella enterica. Front. Microbiol. 8, 1587 (2017).

790

76. Rizopoulos, D. ltm : An R Package for Latent Variable Modeling and Item Response

791

Theory Analyses. J. Stat. Softw. 17, 1–25 (2006).

792

793

794

795

796

797

798

799

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FIGURE and TABLE LEGENDS: (corresponding to their order at end of merged document)

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Figure 1: Pipeline description of bioinformatic steps applied to high protein powder

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metatranscriptome samples. Black arrows indicate data flow and blue boxes describe outputs

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from the pipeline.

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Table 1: Accuracy of microbial identification using in silico constructed Simulated Food

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Mixtures with expected food matrix and microbial sequences.

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Figure 2A: Alpha diversity (number of genera) for all (n = 31) high protein powder

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metatranscriptomes is compared to total number of sequenced reads for a range of in silico

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subsampled sequencing depths. The dashed vertical line indicates the median elbow (at approx.

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67 million reads).

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Figure 2B: Hierarchical clustering of Aitchison distance values of poultry meal samples based

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on microbial composition. Samples were received from Supplier A (blue and red) and Supplier B

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(green). Matrix-contaminated samples are additionally marked in red.

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Figure 3A: Phylogram of the 65 microbial genera present in all samples with RPM > 0.1

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Figure 3B: Phylogram of all microbes observed in any sample. Log of the median RPM value

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across samples is indicated. Grey indicating a median RPM value of 0.

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825

The copyright holder for this preprint (whichthis version posted May 19, 2020. . https://doi.org/10.1101/2020.05.18.102574doi: bioRxiv preprint

826

Figure 4: Heatmap (log10-scale) of high protein powder microbial composition and relative

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abundance (RPM) where absence (RPM < 0.1) is indicated in grey. Genera are ordered by

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summed abundance across samples. Samples were received from Supplier A (blue) and Supplier

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B (green). Red stars indicate matrix-contaminated samples (from Supplier A).

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Figure 5A: All identified microbial general are plotted with median value and median absolute

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deviation (MAD) of RPM abundance. Genera with MAD > 5 are labeled with the genus name.

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Figure 5B: Heatmap (log10-scale) of ten microbial genera with the largest median absolute

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deviation (MAD) across samples. Genera are ordered by decreasing MAD from top to bottom.

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Samples were received from Supplier A (blue) and Supplier B (green). Red stars indicate matrix

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contaminated samples (from Supplier A).

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Figure 6A: Relative abundance of microbes with high relevance to food safety and quality from

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high protein powder total RNA sequenced microbiomes. Width of violin plot indicates density of

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samples with relative abundance at that value. Observation threshold of RPM = 0.1 is indicated

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with the horizontal black line.

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Figure 6B: Foodborne microbe relative abundances are shown across samples of high protein

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powder total RNA sequenced samples.

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Figure 7: Salmonella culturability status and high-throughput sequencing read abundance

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(RPM) from k-mer classification to NCBI Microbial RefSeq Complete (A), from alignments to

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The copyright holder for this preprint (whichthis version posted May 19, 2020. . https://doi.org/10.1101/2020.05.18.102574doi: bioRxiv preprint

1,447 Salmonella genomes (B), and from alignments to 4,846 EF-Tu gene sequences (C).

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Salmonella presence (red) indicates culture-positive result, absence (green) indicates culture-

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negative result, and no record (black) indicates samples for which no culture test was completed.

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Table 2: The ranks for Salmonella-positive samples and the associated p-values from Wilcoxon

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rank sum test are shown for high-throughput sequencing read abundance (RPM) for multiple

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analyses: k-mer classification to NCBI Microbial RefSeq Complete (left), alignments to 1,447

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Salmonella genomes (middle), and alignments to 4,846 ef-Tu gene sequences (right). The

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corresponding Salmonella relative abundances are shown in Figure 7A–C.

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Figure 8: Salmonella status correlations with genus relative abundances. Only those genera with

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absolute value of the correlation coefficient > 0.5 are shown. Positive and negative correlations

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are indicated in grey and blue, respectively.

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SUPPLEMENTAL INFORMATION:

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Supplemental Figures (pdf): Supplemental Figures S1–S5

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Supplemental Table 1 (.xlsx) - Sample Descriptions

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Supplemental Table 2 (.xlsx) - Matrix Filtering Genomes

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Supplemental Table 3 (.xlsx) - Simulated Food Mixtures

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Supplemental Table 4 (.xlsx) - Microbial Genera

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The copyright holder for this preprint (whichthis version posted May 19, 2020. . https://doi.org/10.1101/2020.05.18.102574doi: bioRxiv preprint

RNA

sequencing

Reads

(FASTQ)

Relative

Abund ance

(RPM)

Microbial

Identification

Classificati on with

microbial RefSeq

Complete

Microbial

Quantification

Genus-level relative

abundance (Eq. 1)

Matrix Filtering

Classificati on with

Common Food

Ingredient

Database

Unclassified

Reads

Food Matrix

Reads

Comparative

Statistics

Intrasample

Intersample

Relative change

Sequence

Quality Control

TrimGalore

PhiX Filtering

The copyright holder for this preprint (whichthis version posted May 19, 2020. . https://doi.org/10.1101/2020.05.18.102574doi: bioRxiv preprint

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

Bacteria in Simulated Mixture

(Expected Content)

15,000

Observed Microbial Content

Bacteria

13,517

13,700

13,551

13,999

Viruses

563

328

Archaea

Eukaryota

104

799

Total Observed

13,517

14,368

13,551

15,129

True Positives

(as a % of total observed)

(78%)

13,511

(99.96%)

(29%)

13,571

(94.45%)

(93%)

13,548

(99.98%)

(33%)

13,623

(90.05%)

False Positives

(as a % of total observed)

(22%)

(0.04%)

(71%)

797

(5.55%)

(7%)

(0.02%)

(67%)

1,506

(9.95%)

False Positives Removed

with Matrix Filtering

(as a % of false positives

without filtering)

(88.2%)

791

(99.2%)

(96.4%)

1,503

(99.8%)

The copyright holder for this preprint (whichthis version posted May 19, 2020. . https://doi.org/10.1101/2020.05.18.102574doi: bioRxiv preprint

A. B.

The copyright holder for this preprint (whichthis version posted May 19, 2020. . https://doi.org/10.1101/2020.05.18.102574doi: bioRxiv preprint

Supplier A Supplier B

The copyright holder for this preprint (whichthis version posted May 19, 2020. . https://doi.org/10.1101/2020.05.18.102574doi: bioRxiv preprint

Supplier A Supplier B

The copyright holder for this preprint (whichthis version posted May 19, 2020. . https://doi.org/10.1101/2020.05.18.102574doi: bioRxiv preprint

Salmonella-positive sample k-mer Classification Whole Genome

Alignment

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

The copyright holder for this preprint (whichthis version posted May 19, 2020. . https://doi.org/10.1101/2020.05.18.102574doi: bioRxiv preprint

Application of quasimetagenomics methods to define microbial diversity and subtype Listeria monocytogenes in dairy and seafood production facilities

Article

Full-text available

Oct 2023

Microorganisms frequently colonize surfaces within food production facilities. Detection of Listeria monocytogenes in this setting relies on culture-dependent methods, but the complex dynamics of bacterial interactions within these environments and their impact on pathogen detection remain largely unexplored. To address this challenge, we applied both 16S rRNA and shotgun quasimetagenomic (enriched microbiome) sequencing of swab culture enrichments from five seafood and seven dairy production environments. Utilizing 16S rRNA amplicon sequencing, we observed variability between 355 samples taken from these 12 production facilities and a distinctive microbiome for each environment. With shotgun quasimetagenomic sequencing, we were able to assemble L. monocytogenes metagenome-assembled genomes (MAGs) from 28 of the 32 culture-positive samples. We compared these MAGs to their corresponding whole-genome sequencing assemblies, which resulted in two polyphyletic clades consisting of L. monocytogenes lineages I and II with 13,195 and 25,556 single-nucleotide polymorphism sites, respectively. The remaining four MAGs did not produce sufficient genome coverage. To understand and establish limits for pathogen detection and subtyping using shotgun quasimetagenomics, these same data sets were downsampled in slilico to produce a titration series of abundances of L. monocytogenes and analyzed. Pathogen detection was achieved for all downsampled data sets, even those with only 3× genome coverage. This study contributes to the understanding of microbial diversity within food production environments and presents insights into the level of genome coverage needed in a metagenome sequencing data set to detect, subtype, and source track a foodborne pathogen IMPORTANCE In developed countries, the human diet is predominated by food commodities, which have been manufactured, processed, and stored in a food production facility. Little is known about the application of metagenomic sequencing approaches for detecting foodborne pathogens, such as L. monocytogenes , and characterizing microbial diversity in food production ecosystems. In this work, we investigated the utility of 16S rRNA amplicon and quasimetagenomic sequencing for the taxonomic and phylogenetic classification of Listeria culture enrichments of environmental swabs collected from dairy and seafood production facilities. We demonstrated that single-nucleotide polymorphism (SNP) analyses of L. monocytogenes metagenome-assembled genomes (MAGs) from quasimetagenomic data sets can achieve similar resolution as culture isolate whole-genome sequencing. To further understand the impact of genome coverage on MAG SNP cluster resolution, an in silico downsampling approach was employed to reduce the percentage of target pathogen sequence reads, providing an initial estimate of required MAG coverage for subtyping resolution of L. monocytogenes .

Sustainable genomic research for food security in sub-Saharan Africa

Article

Full-text available

Feb 2021

Although traditional selective breeding has had a tremendous impact on both livestock and crop production in Africa, this has not kept pace with the high demand for animal and plant products on account of increasing urbanization, improved livelihoods and a rapidly growing human population. These challenges justify the need for more innovative and sustainable ways of improving animal and crop production to meet this demand. Over the last two decades the potential of genomics as a key tool to achieve improvement in desirable phenotypic traits has revolutionized the field of applied animal and plant biology. However, well informed use of such techniques has yet to be fully exploited within the context of local resources and capacity-building. The objective of this review is to highlight the rich agricultural biodiversity of sub-Saharan Africa, the potential of genomics in their sustainable use for food security, the challenges of genomic research and the strategies that can be adopted to overcome them in the implementation of agricultural genomic research in Africa. The lack of state-of-the-art laboratory research facilities, skilled human resources, poor funding and non-availability of scientific research resources, paucity of collaborative links between African researchers and a lack of engagement between key stakeholders in the agricultural value chain were identified as major barriers that affect agricultural genomic research in Africa. Finally, the recommended strategies to overcome these challenges will guide policy-makers, researchers, development partners and key stakeholders in the smooth advancement of sustainable agricultural genomic research for improved food security in Africa.

DNA Extraction and Host Depletion Methods Significantly Impact and Potentially Bias Bacterial Detection in a Biological Fluid

Article

Full-text available

Jun 2021

Untargeted sequencing of nucleic acids present in food can inform the detection of food safety and origin, as well as product tampering and mislabeling issues. The application of such technologies to food analysis may reveal valuable insights that are simply unobtainable by targeted testing, leading to the efforts of applying such technologies in the food industry. However, before these approaches can be applied, it is imperative to verify that the most appropriate methods are used at every step of the process: Gathering of primary material, laboratory methods, data analysis, and interpretation. The focus of this study is on gathering the primary material, in this case, DNA. We used bovine milk as a model to (i) evaluate commercially available kits for their ability to extract nucleic acids from inoculated bovine milk, (ii) evaluate host DNA depletion methods for use with milk, and (iii) develop and evaluate a selective lysis-propidium monoazide (PMA)-based protocol for host DNA depletion in milk. Our results suggest that magnetically based nucleic acid extraction methods are best for nucleic acid isolation of bovine milk. Removal of host DNA remains a challenge for untargeted sequencing of milk, highlighting the finding that the individual matrix characteristics should always be considered in food testing. Some reported methods introduce bias against specific types of microbes, which may be particularly problematic in food safety, where the detection of Gram-negative pathogens and hygiene indicators is essential. Continuous efforts are needed to develop and validate new approaches for untargeted metagenomics in samples with large amounts of DNA from a single host.

Food authentication from shotgun sequencing reads with an application on high protein powders

Article

Full-text available

Nov 2019

Here we propose that using shotgun sequencing to examine food leads to accurate authentication of ingredients and detection of contaminants. To demonstrate this, we developed a bioinformatic pipeline, FASER (Food Authentication from SEquencing Reads), designed to resolve the relative composition of mixtures of eukaryotic species using RNA or DNA sequencing. Our comprehensive database includes >6000 plants and animals that may be present in food. FASER accurately identified eukaryotic species with 0.4% median absolute difference between observed and expected proportions on sequence data from various sources including sausage meat, plants, and fish. FASER was applied to 31 high protein powder raw factory ingredient total RNA samples. The samples mostly contained the expected source ingredient, chicken, while three samples unexpectedly contained pork and beef. Our results demonstrate that DNA/RNA sequencing of food ingredients, combined with a robust analysis, can be used to find contaminants and authenticate food ingredients in a single assay.

Integrating host response and unbiased microbe detection for lower respiratory tract infection diagnosis in critically ill adults

Article

Full-text available

Nov 2018

Lower respiratory tract infections (LRTIs) lead to more deaths each year than any other infectious disease category. Despite this, etiologic LRTI pathogens are infrequently identified due to limitations of existing microbiologic tests. In critically ill patients, noninfectious inflammatory syndromes resembling LRTIs further complicate diagnosis. To address the need for improved LRTI diagnostics, we performed metagenomic next-generation sequencing (mNGS) on tracheal aspirates from 92 adults with acute respiratory failure and simultaneously assessed pathogens, the airway microbiome, and the host transcriptome. To differentiate pathogens from respiratory commensals, we developed a rules-based model (RBM) and logistic regression model (LRM) in a derivation cohort of 20 patients with LRTIs or noninfectious acute respiratory illnesses. When tested in an independent validation cohort of 24 patients, both models achieved accuracies of 95.5%. We next developed pathogen, microbiome diversity, and host gene expression metrics to identify LRTI-positive patients and differentiate them from critically ill controls with noninfectious acute respiratory illnesses. When tested in the validation cohort, the pathogen metric performed with an area under the receiver-operating curve (AUC) of 0.96 (95% CI, 0.86–1.00), the diversity metric with an AUC of 0.80 (95% CI, 0.63–0.98), and the host transcriptional classifier with an AUC of 0.88 (95% CI, 0.75–1.00). Combining these achieved a negative predictive value of 100%. This study suggests that a single streamlined protocol offering an integrated genomic portrait of pathogen, microbiome, and host transcriptome may hold promise as a tool for LRTI diagnosis.

Best practices for analysing microbiomes

Article

Full-text available

May 2018

Complex microbial communities shape the dynamics of various environments, ranging from the mammalian gastrointestinal tract to the soil. Advances in DNA sequencing technologies and data analysis have provided drastic improvements in microbiome analyses, for example, in taxonomic resolution, false discovery rate control and other properties, over earlier methods. In this Review, we discuss the best practices for performing a microbiome study, including experimental design, choice of molecular analysis technology, methods for data analysis and the integration of multiple omics data sets. We focus on recent findings that suggest that operational taxonomic unit-based analyses should be replaced with new methods that are based on exact sequence variants, methods for integrating metagenomic and metabolomic data, and issues surrounding compositional data analysis, where advances have been particularly rapid. We note that although some of these approaches are new, it is important to keep sight of the classic issues that arise during experimental design and relate to research reproducibility. We describe how keeping these issues in mind allows researchers to obtain more insight from their microbiome data sets.

Metagenomic and metatranscriptomic analysis of the microbial community in Swiss-type Maasdam cheese during ripening

Article

Full-text available

May 2018
INT J FOOD MICROBIOL

In Swiss-type cheeses, characteristic nut-like and sweet flavor develops during the cheese ripening due to the metabolic activities of cheese microbiota. Temperature changes during warm and cold room ripening, and duration of ripening can significantly change the gene expression of the cheese microbiota, which can affect the flavor formation. In this study, a metagenomic and metatranscriptomic analysis of Swiss-type Maasdam cheese was performed on samples obtained during ripening in the warm and cold rooms. We reconstructed four different bacterial genomes (Lactococcus lactis, Lactobacillus rhamnosus, Lactobacillus helveticus, and Propionibacterium freudenreichii subsp. shermanii strain JS) from the Maasdam cheese to near completeness. Based on the DNA and RNA mean coverage, Lc. lactis strongly dominated (~80-90%) within the cheese microbial community. Genome annotation showed the potential for the presence of several flavor forming pathways in these species, such as production of methanethiol, free fatty acids, acetoin, diacetyl, acetate, ethanol, and propionate. Using the metatranscriptomic data, we showed that, with the exception of Lc. lactis, the central metabolism of the microbiota was downregulated during cold room ripening suggesting that fewer flavor compounds such as acetoin and propionate were produced. In contrast, Lc. lactis genes related to the central metabolism, including the vitamin biosynthesis and homolactic fermentation, were upregulated during cold room ripening.

American Gut: an Open Platform for Citizen Science Microbiome Research

Article

Full-text available

May 2018

We show that a citizen science, self-selected cohort shipping samples through the mail at room temperature recaptures many known microbiome results from clinically collected cohorts and reveals new ones. Of particular interest is integrating n = 1 study data with the population data, showing that the extent of microbiome change after events such as surgery can exceed differences between distinct environmental biomes, and the effect of diverse plants in the diet, which we confirm with untargeted metabolomics on hundreds of samples.Although much work has linked the human microbiome to specific phenotypes and lifestyle variables, data from different projects have been challenging to integrate and the extent of microbial and molecular diversity in human stool remains unknown. Using standardized protocols from the Earth Microbiome Project and sample contributions from over 10,000 citizen-scientists, together with an open research network, we compare human microbiome specimens primarily from the United States, United Kingdom, and Australia to one another and to environmental samples. Our results show an unexpected range of beta-diversity in human stool microbiomes compared to environmental samples; demonstrate the utility of procedures for removing the effects of overgrowth during room-temperature shipping for revealing phenotype correlations; uncover new molecules and kinds of molecular communities in the human stool metabolome; and examine emergent associations among the microbiome, metabolome, and the diversity of plants that are consumed (rather than relying on reductive categorical variables such as veganism, which have little or no explanatory power). We also demonstrate the utility of the living data resource and cross-cohort comparison to confirm existing associations between the microbiome and psychiatric illness and to reveal the extent of microbiome change within one individual during surgery, providing a paradigm for open microbiome research and education.

Species classifier choice is a key consideration when analysing low-complexity food microbiome data

Article

Full-text available

Mar 2018

Background The use of shotgun metagenomics to analyse low-complexity microbial communities in foods has the potential to be of considerable fundamental and applied value. However, there is currently no consensus with respect to choice of species classification tool, platform, or sequencing depth. Here, we benchmarked the performances of three high-throughput short-read sequencing platforms, the Illumina MiSeq, NextSeq 500, and Ion Proton, for shotgun metagenomics of food microbiota. Briefly, we sequenced six kefir DNA samples and a mock community DNA sample, the latter constructed by evenly mixing genomic DNA from 13 food-related bacterial species. A variety of bioinformatic tools were used to analyse the data generated, and the effects of sequencing depth on these analyses were tested by randomly subsampling reads. Results Compositional analysis results were consistent between the platforms at divergent sequencing depths. However, we observed pronounced differences in the predictions from species classification tools. Indeed, PERMANOVA indicated that there was no significant differences between the compositional results generated by the different sequencers (p = 0.693, R² = 0.011), but there was a significant difference between the results predicted by the species classifiers (p = 0.01, R² = 0.127). The relative abundances predicted by the classifiers, apart from MetaPhlAn2, were apparently biased by reference genome sizes. Additionally, we observed varying false-positive rates among the classifiers. MetaPhlAn2 had the lowest false-positive rate, whereas SLIMM had the greatest false-positive rate. Strain-level analysis results were also similar across platforms. Each platform correctly identified the strains present in the mock community, but accuracy was improved slightly with greater sequencing depth. Notably, PanPhlAn detected the dominant strains in each kefir sample above 500,000 reads per sample. Again, the outputs from functional profiling analysis using SUPER-FOCUS were generally accordant between the platforms at different sequencing depths. Finally, and expectedly, metagenome assembly completeness was significantly lower on the MiSeq than either on the NextSeq (p = 0.03) or the Proton (p = 0.011), and it improved with increased sequencing depth. Conclusions Our results demonstrate a remarkable similarity in the results generated by the three sequencing platforms at different sequencing depths, and, in fact, the choice of bioinformatics methodology had a more evident impact on results than the choice of sequencer did.

The Madness of Microbiome: Attempting To Find Consensus "Best Practice" for 16S Microbiome Studies

Article

Full-text available

Mar 2018
APPL ENVIRON MICROB

The development and continuous improvement of high-throughput sequencing platforms has stimulated interest in the study of complex microbial communities. Currently, the most popular sequencing approach to study microbial community composition and dynamics is targeted 16S rRNA gene metabarcoding. To prepare samples for sequencing, there are a variety of processing steps, each with the potential to introduce bias at the data analysis stage. In this short review, key information from the literature pertaining to each processing step is described and consequently, general recommendations for future 16S rRNA gene metabarcoding experiments are made.

Advantages of meta-total RNA sequencing (MeTRS) over shotgun metagenomics and amplicon-based sequencing in the profiling of complex microbial communities

Article

Full-text available

Dec 2018

Sequencing-based microbiome profiling aims at detecting and quantifying individual members of a microbial community in a culture-independent manner. While amplicon-based sequencing (ABS) of bacterial or fungal ribosomal DNA is the most widely used technology due to its low cost, it suffers from PCR amplification biases that hinder accurate representation of microbial population structures. Shotgun metagenomics (SMG) conversely allows unbiased microbiome profiling but requires high sequencing depth. Here we report the development of a meta-total RNA sequencing (MeTRS) method based on shotgun sequencing of total RNA and benchmark it on a human stool sample spiked in with known abundances of bacterial and fungal cells. MeTRS displayed the highest overall sensitivity and linearity for both bacteria and fungi, the greatest reproducibility compared to SMG and ABS, while requiring a ~20-fold lower sequencing depth than SMG. We therefore present MeTRS as a valuable alternative to existing technologies for large-scale profiling of complex microbiomes.

Microbiome Datasets Are Compositional: And This Is Not Optional

Article

Full-text available

Nov 2017

Datasets collected by high-throughput sequencing (HTS) of 16S rRNA gene amplimers, metagenomes or metatranscriptomes are commonplace and being used to study human disease states, ecological differences between sites, and the built environment. There is increasing awareness that microbiome datasets generated by HTS are compositional because they have an arbitrary total imposed by the instrument. However, many investigators are either unaware of this or assume specific properties of the compositional data. The purpose of this review is to alert investigators to the dangers inherent in ignoring the compositional nature of the data, and point out that HTS datasets derived from microbiome studies can and should be treated as compositions at all stages of analysis. We briefly introduce compositional data, illustrate the pathologies that occur when compositional data are analyzed inappropriately, and finally give guidance and point to resources and examples for the analysis of microbiome datasets using compositional data analysis.

Resistome diversity in cattle and the environment decreases during beef production

Article

Full-text available

Mar 2016
eLife

ELife digest When bacteria become resistant to antibiotics, it becomes difficult or impossible to treat infections in both people and animals. Antibiotic resistance is a growing problem, and many fear a “post-antibiotic era” in which common infections become life threatening. In order to slow the spread of antibiotic resistance, it is important to understand how and where this resistance develops. In general, using antibiotics increases the likelihood that bacteria will develop resistance. Therefore, locations where antibiotics are commonly used – such as hospitals, long-term care facilities, livestock facilities (such as feedlots) and crop production areas (such as orchards) – may help antibiotic resistance to develop and spread. However, it is largely unknown how much each location promotes the emergence of antibiotic-resistant bacteria. Until recently, we have only been able to investigate how resistance develops in bacteria grown in a laboratory; or to look for a handful of specific resistance genes in a sample of bacteria collected from people, animals or the environment. Fortunately, a technology called next-generation sequencing now allows us to look at all the resistance genes within all the bacteria in a sample. This may help us to improve our understanding of how and where resistance develops and spreads. Noyes et al. have now used next-generation sequencing to describe the antibiotic resistance potential (known as the “resistome”) found in various types of samples collected from feedlots and slaughterhouses involved in producing beef. This showed that the number of different resistance genes in the samples decreased while cattle were in the feedlot and during the slaughter process. Several groups of resistance genes that were detected when the cattle first arrived in the feedlot were not detected at all at the end of the feedlot period. However, some resistance genes were detected throughout the feedlot period, and these tended to be resistance genes that allow the bacteria to evade the same antibiotics that were used in the cattle. In addition, no resistance genes of any type were detected in the samples collected after the cattle had been slaughtered. As well as providing insights into the resistome of beef production, Noyes et al.’s study also highlights the fact that we need to develop a deeper understanding of the data that come from next-generation sequencing. This may involve developing new laboratory techniques and creating new methods to analyze such data. DOI: http://dx.doi.org/10.7554/eLife.13195.002

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