A Goldilocks Principle for the Gut Microbiome: Taxonomic Resolution Matters for Microbiome-Based Classification of Colorectal Cancer

Armour, Courtney R.; Topçuoğlu, Begüm D.; Garretto, Andrea; Schloss, Patrick D.

doi:10.1128/mbio.03161-21

Cited by 16 publications

(11 citation statements)

References 15 publications

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“…Similarly, the finding that ASVs yield the optimal taxonomical resolution for classifying case status (Figure 2) is interesting. A recent machine learning study determined that OTUs were the optimal taxonomical level for predicting colorectal cancer (34). The preference for use of ASVs or OTUs in microbiome studies remains contested (35,36); however, our study supports the premise that optimal taxonomical resolution is highly dependent on the patient population and outcomes of interest and does not necessarily favor OTUs or ASVs.…”

Section: Discussionsupporting

confidence: 51%

Gut community structure as a risk factor for infection inKlebsiella-colonized patients

Vornhagen

Rao

Bachman

2023

Preprint

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The primary risk factor for infection with members of the Klebsiella pneumoniae species complex is prior gut colonization, and infection is often caused by the colonizing strain. Despite the importance of the gut as a reservoir for infectious Klebsiella, little is known about the association between the gut microbiome and infection. To explore this relationship, we undertook a case-control study comparing the gut community structure of Klebsiella-colonized intensive care and hematology/oncology patients. Cases were Klebsiella-colonized patients infected by their colonizing strain (N = 83). Controls were Klebsiella-colonized patients that remained asymptomatic (N = 149). First, we characterized the gut community structure of Klebsiella-colonized patients agnostic to case status. Next, we determined that gut community data is useful for classifying cases and controls using machine learning models and that the gut community structure differed between cases and controls. Klebsiella relative abundance, a known risk factor for infection, had the greatest feature importance but other gut microbes were also informative. Finally, we show that integration of gut community structure with bacterial genotype or clinical variable data enhanced the ability of machine learning models to discriminate cases and controls. This study demonstrates that including gut community data with patient- and Klebsiella-derived biomarkers improves our ability to predict infection in Klebsiella-colonized patients.

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Section: Discussionsupporting

confidence: 51%

Gut community structure as a risk factor for infection inKlebsiella-colonized patients

Vornhagen

Rao

Bachman

2023

Preprint

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“…S18a). Additionally, when aggregated at the genus level, OTU- and ASV-based abundance profiles had limited differences, suggesting that the choice of sequence variant units has limited impact on our meta-analysis results, as previously indicated [ 66 ] (Additional file 1 : Fig. S18b).…”

Section: Methodsmentioning

confidence: 78%

Population structure discovery in meta-analyzed microbial communities and inflammatory bowel disease using MMUPHin

Shungin

Mallick

et al. 2022

Genome Biol

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Microbiome studies of inflammatory bowel diseases (IBD) have achieved a scale for meta-analysis of dysbioses among populations. To enable microbial community meta-analyses generally, we develop MMUPHin for normalization, statistical meta-analysis, and population structure discovery using microbial taxonomic and functional profiles. Applying it to ten IBD cohorts, we identify consistent associations, including novel taxa such as Acinetobacter and Turicibacter, and additional exposure and interaction effects. A single gradient of dysbiosis severity is favored over discrete types to summarize IBD microbiome population structure. These results provide a benchmark for characterization of IBD and a framework for meta-analysis of any microbial communities.

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“…For all tests involving multiple comparisons, P values were corrected using a Benjamini and Hochberg adjustment for a type I error rate of 0.05 38 . MikropML (v1.6.0) 39 was employed to conduct supervised machine learning for classification of the hive condition of individual honey bee samples using L2 logistic regression and random forest algorithms based on the ASVs annotated to the genus level, as fine resolution ASV level analysis has been found to be too individualized for accurate classification 40 .…”

Section: Methodsmentioning

confidence: 99%

Quantitative microbiome profiling of honey bee (Apis mellifera) guts is predictive of winter colony loss in northern Virginia (USA)

Carlini,

Winslow,

Cloppenborg-Schmidt

et al. 2024

Sci Rep

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For the past 15 years, the proportion of honey bee hives that fail to survive winter has averaged ~ 30% in the United States. Winter hive loss has significant negative impacts on agriculture, the economy, and ecosystems. Compared to other factors, the role of honey bee gut microbial communities in driving winter hive loss has received little attention. We investigate the relationship between winter survival and honey bee gut microbiome composition of 168 honey bees from 23 hives, nine of which failed to survive through winter 2022. We found that there was a substantial difference in the abundance and community composition of honey bee gut microbiomes based on hive condition, i.e., winter survival or failure. The overall microbial abundance, as assessed using Quantitative Microbiome Profiling (QMP), was significantly greater in hives that survived winter 2022 than in those that failed, and the average overall abundance of each of ten bacterial genera was also greater in surviving hives. There were no significant differences in alpha diversity based on hive condition, but there was a highly significant difference in beta diversity. The bacterial genera Commensalibacter and Snodgrassella were positively associated with winter hive survival. Logistic regression and random forest machine learning models on pooled ASV counts for the genus data were highly predictive of winter outcome, although model performance decreased when samples from the location with no hive failures were excluded from analysis. As a whole, our results show that the abundance and community composition of honey bee gut microbiota is associated with winter hive loss, and can potentially be used as a diagnostic tool in evaluating hive health prior to the onset of winter. Future work on the functional characterization of the honey bee gut microbiome’s role in winter survival is warranted.

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A Goldilocks Principle for the Gut Microbiome: Taxonomic Resolution Matters for Microbiome-Based Classification of Colorectal Cancer

Abstract: Despite being highly preventable, colorectal cancer remains a leading cause of cancer-related death in the United States. Low-cost, noninvasive detection methods could greatly improve our ability to identify and treat early stages of disease.

Cited by 16 publications

References 15 publications

Gut community structure as a risk factor for infection inKlebsiella-colonized patients

Gut community structure as a risk factor for infection inKlebsiella-colonized patients

Population structure discovery in meta-analyzed microbial communities and inflammatory bowel disease using MMUPHin

Quantitative microbiome profiling of honey bee (Apis mellifera) guts is predictive of winter colony loss in northern Virginia (USA)

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