Bacteroides Microbial Source Tracking Markers Perform Poorly in Predicting Enterobacteriaceae and Enteric Pathogen Contamination of Cow Milk Products and Milk-Containing Infant Food

Tsai, Kevin; Hoffmann, Vivian; Simiyu, Sheillah; Cumming, Oliver; Borsay, Glorie; Baker, Kelly K.

doi:10.3389/fmicb.2021.778921

Cited by 6 publications

(5 citation statements)

References 51 publications

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“…In this study, Peredibacter was identified as a biomarker for Salmonella Infantis-contaminated samples although Salmonella Infantis could not be detected. Bacteroides is an obligate anaerobic bacterium making up a remarkable portion of fecal bacterial communities, which has been suggested used as fecal indicator organisms for water samples [44]. Our result indicate that Bacteroides could be also applied as pathogen contamination indicator for produce.…”

Section: Integrated Taxonomic Analysis Identifies Generalizable Taxa ...mentioning

confidence: 64%

Alignment-free microbiome-based classification of fresh produce safety and quality

Liao

Wang

Quon

2022

Preprint

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Background: While next generation sequencing has enriched our knowledge about native microbial populations present in fresh produce, the loss of up to 50-70% of data during the alignment and denoising steps of data preprocessing may lead to the missing of important bacterial species information and decrease our ability to identify species associated with poor produce quality and contamination. Microbial studies are also often limited by small sample sizes, making generalization of results beyond individual studies difficult. Results: In this study, we explored separate strategies to mitigate the challenges of data preprocessing and small sample sizes. First, we explored an alignment-free analysis strategy using k-mer hashes to identify DNA signatures predictive of produce safety (contaminated vs. non-contaminated) and produce quality (good-quality vs. decreasing-quality), and compared it against the amplicon sequence variant (ASV) strategy that uses a typical alignment and denoising step. Random forests (RF)-based classifiers were trained on publicly available fresh produce microbiome datasets with data preprocessed using either the k-mer hash or ASV approach. RF-based classifiers for fresh produce safety and quality using 7-mer hash datasets had significantly higher classification accuracy than those using the ASV datasets, supporting the hypothesis that data preprocessing strategies that keep more data (k-mer hash) retain more useful information about bacterial species than approaches that lose data during preprocessing (ASV). We also demonstrated that integrating multiple datasets together also led to higher classification accuracy compared to those trained with individual datasets. Integrated datasets also enabled the identification of more consistent and generalizable biomarkers (ASV, 7-mer hash, or bacterial taxa) associated with fresh produce safety and quality. Conclusions:The proposed combination of integrating multiple datasets and leveraging an alignment-free 7-mer hash strategy substantially mitigates the loss of sequencing data due to the ASV denoising step and leads to better classification performance for fresh produce safety and quality. Results generated from this study lay the foundation for future studies that wish and need to incorporate and/or compare different microbiome sequencing datasets (generated from different studies or different laboratories) for the application of machine learning in the area of microbial safety and quality of food.

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Section: Integrated Taxonomic Analysis Identifies Generalizable Taxa ...mentioning

confidence: 64%

Alignment-free microbiome-based classification of fresh produce safety and quality

Liao

Wang

Quon

2022

Preprint

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“…Although we observed no significant variation based on food types, the prevalence of Enterococcus contamination was considerable in all food types with a slightly higher detection frequency in grains. Our prior work in Kisumu also found a similar prevalence of Enterobacteriaceae detection across food types at the point of infant feeding, regardless of microbial safety of food products used to make the food [ 12 , 17 , 31 ]. Both microbial indicators indicate that multiple types of infant foods are handled under unhygienic conditions in Kisumu.…”

Section: Discussionmentioning

confidence: 74%

“…As result, it is not possible to conclude whether the observed concentration exceeds safety limits. Left-censored distributions with low levels of contaminates is common in environmental studies, including in our prior studies of Enterobacteriaceae and enteric pathogens in Kisumu [12,17,31]. Further studies are required to determine the maximum detection limit of microbial safety indicators in infant foods, their correlation with pathogenic organisms, and the risk of consumption of Enterococcus contaminated foods to include in international and national infant food safety guidelines.…”

Section: Discussionmentioning

confidence: 99%

“…It has been estimated that half of these diarrheal cases are due to foodborne bacterial infections [ 30 ]. Our prior research showed that the type of infant food was significantly associated with the presence and diversity of enteric pathogens in infant weaning foods [ 17 , 31 ]. However, infant food is frequently made in the morning by caregivers and stored over the day at ambient temperatures for re-feeding, suggesting exposure risks from food can change over time and may be heavily influenced by storage conditions.…”

Section: Introductionmentioning

confidence: 99%

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Enterococcus contamination of infant foods and implications for exposure to foodborne pathogens in peri-urban neighbourhoods of Kisumu, Kenya

Gutema,

Cumming,

Mumma

et al. 2024

Epidemiol. Infect.

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We collected infant food samples from 714 households in Kisumu, Kenya, and estimated the prevalence and concentration of Enterococcus, an indicator of food hygiene conditions. In a subset of 212 households, we quantified the change in concentration in stored food between a morning and afternoon feeding time. In addition, household socioeconomic characteristics and hygiene practices of the caregivers were documented. The prevalence of Enterococcus in infant foods was 50% (95% confidence interval: 46.1 -53.4), and the mean log 10 colony-forming units (CFUs) was 1.1 (SD + 1.4). No risk factors were significantly associated with the prevalence and concentration of Enterococcus in infant foods. The mean log 10 CFU of Enterococcus concentration was 0.47 in the morning and 0.73 in the afternoon foods with a 0.64 log 10 mean increase in matched samples during storage. Although no factors were statistically associated with the prevalence and the concentration of Enterococcus in infant foods, household flooring type was significantly associated with an increase in concentration during storage, with finished floors leading to 1.5 times higher odds of concentration increase compared to unfinished floors. Our study revealed high prevalence but low concentration of Enterococcus in infant food in low-income Kisumu households, although concentrations increased during storage implying potential increases in risk of exposure to foodborne pathogens over a day. Further studies aiming at investigating contamination of infant foods with pathogenic organisms and identifying effective mitigation measures are required to ensure infant food safety.

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“…In this study, Peredibacter was identified as a biomarker for Salmonella Infantis-contaminated samples although Salmonella Infantis could not be detected. Bacteroides is an obligate anaerobic bacterium making up a remarkable portion of fecal bacterial communities, which has been suggested used as fecal indicator organisms for water samples 26 . Our result indicate that Bacteroides could be also applied as pathogen contamination indicator for produce.…”

Section: Resultsmentioning

confidence: 99%

Alignment-free microbiome-based classification of fresh produce safety and quality

Liao¹,

Wang²,

Quon

2022

Preprint

View full text Add to dashboard Cite

Small samples sizes and loss of up to 50-70% of sequencing data during the data denoising step of preprocessing can limit the statistical power of fresh produce microbiome analyses and prevent detection of important bacterial species associated with produce contamination or quality reduction. Here, we explored an alignment-free analysis strategy using k-mer hashes to identify DNA signatures predictive of produce safety and produce quality, and compared it against the amplicon sequence variant (ASV) strategy that uses a typical denoising step. Random forests (RF)-based classifiers for fresh produce safety and quality using 7-mer hash datasets had significantly higher classification accuracy than those using the ASV datasets. We also demonstrated that the proposed combination of integrating multiple datasets and leveraging an alignment-free 7-mer hash strategy leads to better classification performance for fresh produce safety and quality. Results generated from this study lay the foundation for future studies that wish and need to incorporate and/or compare different microbiome sequencing datasets for the application of machine learning in the area of microbial safety and quality of food.

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Bacteroides Microbial Source Tracking Markers Perform Poorly in Predicting Enterobacteriaceae and Enteric Pathogen Contamination of Cow Milk Products and Milk-Containing Infant Food

Cited by 6 publications

References 51 publications

Alignment-free microbiome-based classification of fresh produce safety and quality

Alignment-free microbiome-based classification of fresh produce safety and quality

Enterococcus contamination of infant foods and implications for exposure to foodborne pathogens in peri-urban neighbourhoods of Kisumu, Kenya

Alignment-free microbiome-based classification of fresh produce safety and quality

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