Investigating differential abundance methods in microbiome data: A benchmark study

The effect of different sweeteners on the oral microbiome: a randomized clinical exploratory pilot study

Introduction

The aim of the study was to evaluate the modulating effects of five commonly used sweetener (glucose, inulin, isomaltulose, tagatose, trehalose) containing mouth rinses on the oral microbiome.

Methods

A single-centre, double-blind, parallel randomized clinical trial was performed with healthy, 18-55-year-old volunteers (N = 65), who rinsed thrice-daily for two weeks with a 10% solution of one of the allocated sweeteners. Microbiota composition of supragingival dental plaque and the tongue dorsum coating was analysed by 16S RNA gene amplicon sequencing of the V4 hypervariable region (Illumina MiSeq). As secondary outcomes, dental plaque red fluorescence and salivary pH were measured.

Results

Dental plaque microbiota changed significantly for two groups: inulin (F = 2.0239, p = 0.0006 PERMANOVA, Aitchison distance) and isomaltulose (F = 0.67, p = 0.0305). For the tongue microbiota, significant changes were observed for isomaltulose (F = 0.8382, p = 0.0452) and trehalose (F = 1.0119, p = 0.0098). In plaque, 13 species changed significantly for the inulin group, while for tongue coating, three species changed for the trehalose group (ALDEx2, p < 0.1). No significant changes were observed for the secondary outcomes.

Conclusion

The effects on the oral microbiota were sweetener dependant with the most pronounced effect on plaque microbiota. Inulin exhibited the strongest microbial modulating potential of the sweeteners tested. Further full-scale clinical studies are required.

Interpretable machine learning decodes soil microbiome's response to drought stress

BACKGROUND

Extreme weather events induced by climate change, particularly droughts, have detrimental consequences for crop yields and food security. Concurrently, these conditions provoke substantial changes in the soil bacterial microbiota and affect plant health. Early recognition of soil affected by drought enables farmers to implement appropriate agricultural management practices. In this context, interpretable machine learning holds immense potential for drought stress classification of soil based on marker taxa.

RESULTS

This study demonstrates that the 16S rRNA-based metagenomic approach of Differential Abundance Analysis methods and machine learning-based Shapley Additive Explanation values provide similar information. They exhibit their potential as complementary approaches for identifying marker taxa and investigating their enrichment or depletion under drought stress in grass lineages. Additionally, the Random Forest Classifier trained on a diverse range of relative abundance data from the soil bacterial micobiome of various plant species achieves a high accuracy of 92.3 % at the genus rank for drought stress prediction. It demonstrates its generalization capacity for the lineages tested.

CONCLUSIONS

In the detection of drought stress in soil bacterial microbiota, this study emphasizes the potential of an optimized and generalized location-based ML classifier. By identifying marker taxa, this approach holds promising implications for microbe-assisted plant breeding programs and contributes to the development of sustainable agriculture practices. These findings are crucial for preserving global food security in the face of climate change.

Ambient long-term exposure to organophosphorus pesticides and the human gut microbiome: an observational study

BACKGROUND

Organophosphorus pesticides (OP) have been associated with various human health conditions. Animal experiments and in-vitro models suggested that OP may also affect the gut microbiota. We examined associations between ambient chronic exposure to OP and gut microbial changes in humans.

METHODS

We recruited 190 participants from a community-based epidemiologic study of Parkinson's disease living in a region known for heavy agricultural pesticide use in California. Of these, 61% of participants had Parkinson's disease and their mean age was 72 years. Microbiome and predicted metagenome data were generated by 16S rRNA gene sequencing of fecal samples. Ambient long-term OP exposures were assessed using pesticide application records combined with residential addresses in a geographic information system. We examined gut microbiome differences due to OP exposures, specifically differences in microbial diversity based on the Shannon index and Bray-Curtis dissimilarities, and differential taxa abundance and predicted Metacyc pathway expression relying on regression models and adjusting for potential confounders.

RESULTS

OP exposure was not associated with alpha or beta diversity of the gut microbiome. However, the predicted metagenome was sparser and less evenly expressed among those highly exposed to OP (p = 0.04). Additionally, we found that the abundance of two bacterial families, 22 genera, and the predicted expression of 34 Metacyc pathways were associated with long-term OP exposure. These pathways included perturbed processes related to cellular respiration, increased biosynthesis and degradation of compounds related to bacterial wall structure, increased biosynthesis of RNA/DNA precursors, and decreased synthesis of Vitamin B1 and B6.

CONCLUSION

In support of previous animal studies and in-vitro findings, our results suggest that ambient chronic OP pesticide exposure alters gut microbiome composition and its predicted metabolism in humans.

Aerobic digestibility of waste aerobic granular sludge (AGS) assessed by respirometry, physical-chemical analyses, modeling and 16S rRNA gene sequencing

Research has evolved on aerobic granular sludge (AGS) process, but still there are very few studies on the treatment of excess AGS sludge, with almost none considering its aerobic digestion. Here therefore, the aerobic digestibility of typical AGS sludge was assessed. Granules were produced from acetate-based synthetic wastewater (WW) and were subjected to aerobic digestion for 64 d. The stabilization process was monitored over time through physical-chemical parameters, oxygen uptake rates (OUR) and 16S rRNA gene sequencing. The microbial analyses revealed that the cultivated granules were dominated by slow-growing bacteria, mainly ordinary heterotrophic organisms with potential for polyhydroxyalkanoates (PHA) aerobic storage (PHA-OHOs), polyphosphate and glycogen accumulating organisms (PAOs and GAOs), fermentative anaerobes and nitrifiers (AOB and NOB). Differential abundance analysis of the bacterial data (before versus after digestion) discriminated between the most vulnerable microbiome genera and those most resistant to aerobic digestion. Furthermore, modeling of the stabilization process determined that the endogenous decay rate constant (bH) for the heterotrophs present in the granules was notably low; bH = 0.05 d-1 (average), four times less than for common activated sludge (AS), which is rated at 0.2 d-1. For first time, the research reveals another important feature of AGS sludge, i.e. the slow-decaying character of its bacteria (along with their known slow-growing character). This results in slower stabilization, need of bigger digesters and reconsideration of the specific OUR limits in biosolids regulations (SOUR limit of 1.5 mg/gTSS.h), for waste AGS compared to conventional waste AS. The study suggests that aerobic digestion of waste AGS (fully-granulated) could differ from that of conventional AS. Future work is needed on aerobic digestibility of real AGS sludges from municipal and industrial WWs, compared to synthetic WWs.

Examining the relationship between the oral microbiome, alcohol intake and alcohol-comorbid neuropsychological disorders: protocol for a scoping review

INTRODUCTION

Heavy alcohol use and alcohol use disorder (AUD) continues to rise as a public health problem and increases the risk for disease. Elevated rates of anxiety, depression, sleep disruption and stress are associated with alcohol use. Symptoms may progress to diagnosed neurophysiological conditions and increase risk for relapse if abstinence is attempted. Research on mechanisms connecting the gastrointestinal microbiome to neuropsychological disorders through the gut-brain axis is well-established. Less is known how the oral microbiome and oral microbial-associated biomarkers may signal to the brain. Therefore, a synthesis of research studying relationships between alcohol intake, alcohol-associated neurophysiological symptoms and the oral microbiome is needed to understand the state of the current science. In this paper, we outline our protocol to collect, evaluate and synthesise research focused on associations between alcohol intake and AUD-related neuropsychological disorders with the oral microbiome.

METHODS AND ANALYSIS

The search strategy was developed and will be executed in collaboration with a medical research librarian. Studies will be screened by two independent investigators according to the aim of the scoping review, along with the outlined exclusion and inclusion criteria. After screening, data will be extracted and synthesised from the included papers according to predefined demographic, clinical and microbiome methodology metrics.

ETHICS AND DISSEMINATION

A scoping review of primary sources is needed to synthesise the data on relationships between alcohol use, neuropsychological conditions associated with AUD and the oral microbiome. The proposed scoping review is based on the data from publicly available databases and does not require ethical approval. We expect the results of this synthesis will identify gaps in the growing literature and highlight potential mechanisms linking the oral-brain axis to addiction and other associated neuropsychological conditions. The study findings and results will be disseminated through journals and conferences related to psychology, neuroscience, dentistry and the microbiome.

Metagenomic analysis of gut microbiome illuminates the mechanisms and evolution of lignocellulose degradation in mangrove herbivorous crabs

BACKGROUND

Sesarmid crabs dominate mangrove habitats as the major primary consumers, which facilitates the trophic link and nutrient recycling in the ecosystem. Therefore, the adaptations and mechanisms of sesarmid crabs to herbivory are not only crucial to terrestrialization and its evolutionary success, but also to the healthy functioning of mangrove ecosystems. Although endogenous cellulase expressions were reported in crabs, it remains unknown if endogenous enzymes alone can complete the whole lignocellulolytic pathway, or if they also depend on the contribution from the intestinal microbiome. We attempt to investigate the role of gut symbiotic microbes of mangrove-feeding sesarmid crabs in plant digestion using a comparative metagenomic approach.

RESULTS

Metagenomics analyses on 43 crab gut samples from 23 species of mangrove crabs with different dietary preferences revealed a wide coverage of 127 CAZy families and nine KOs targeting lignocellulose and their derivatives in all species analyzed, including predominantly carnivorous species, suggesting the crab gut microbiomes have lignocellulolytic capacity regardless of dietary preference. Microbial cellulase, hemicellulase and pectinase genes in herbivorous and detritivorous crabs were differentially more abundant when compared to omnivorous and carnivorous crabs, indicating the importance of gut symbionts in lignocellulose degradation and the enrichment of lignocellulolytic microbes in response to diet with higher lignocellulose content. Herbivorous and detritivorous crabs showed highly similar CAZyme composition despite dissimilarities in taxonomic profiles observed in both groups, suggesting a stronger selection force on gut microbiota by functional capacity than by taxonomy. The gut microbiota in herbivorous sesarmid crabs were also enriched with nitrogen reduction and fixation genes, implying possible roles of gut microbiota in supplementing nitrogen that is deficient in plant diet.

CONCLUSIONS

Endosymbiotic microbes play an important role in lignocellulose degradation in most crab species. Their abundance is strongly correlated with dietary preference, and they are highly enriched in herbivorous sesarmids, thus enhancing their capacity in digesting mangrove leaves. Dietary preference is a stronger driver in determining the microbial CAZyme composition and taxonomic profile in the crab microbiome, resulting in functional redundancy of endosymbiotic microbes. Our results showed that crabs implement a mixed mode of digestion utilizing both endogenous and microbial enzymes in lignocellulose degradation, as observed in most of the more advanced herbivorous invertebrates.

Tryptophan metabolism, gut microbiota, and carotid artery plaque in women with and without HIV infection

OBJECTIVE

The perturbation of tryptophan (TRP) metabolism has been linked with HIV infection and cardiovascular disease (CVD), but the interrelationship among TRP metabolites, gut microbiota, and atherosclerosis remain unclear in the context of HIV infection.

METHODS

We included 361 women (241 HIV+, 120 HIV-) with carotid artery plaque assessments from the Women's Interagency HIV Study, measured 10 plasma TRP metabolites and profiled fecal gut microbiome. TRP metabolite-related gut bacteria were selected through the Analysis of Compositions of Microbiomes with Bias Correction method. Associations of TRP metabolites and related microbial features with plaque were examined using multivariable logistic regression.

RESULTS

Although plasma kynurenic acid (KYNA) [odds ratio (OR) = 1.93, 95% confidence interval (CI): 1.12-3.32 per one SD increase; P  = 0.02) and KYNA/TRP [OR = 1.83 (95% CI 1.08-3.09), P  = 0.02] were positively associated with plaque, indole-3-propionate (IPA) [OR = 0.62 (95% CI 0.40-0.98), P  = 0.03] and IPA/KYNA [OR = 0.51 (95% CI 0.33-0.80), P  < 0.01] were inversely associated with plaque. Five gut bacterial genera and many affiliated species were positively associated with IPA (FDR-q < 0.25), including Roseburia spp ., Eubacterium spp., Lachnospira spp., and Coprobacter spp.; but no bacterial genera were found to be associated with KYNA. Furthermore, an IPA-associated-bacteria score was inversely associated with plaque [OR = 0.47 (95% CI 0.28-0.79), P  < 0.01]. But no significant effect modification by HIV serostatus was observed in these associations.

CONCLUSION

In a cohort of women living with and without HIV infection, plasma IPA levels and related gut bacteria were inversely associated with carotid artery plaque, suggesting a potential beneficial role of IPA and its gut bacterial producers in atherosclerosis and CVD.

Daily skin-to-skin contact alters microbiota development in healthy full-term infants

The gut microbiota is vital for human body development and function. Its development in early life is influenced by various environmental factors. In this randomized controlled trial, the gut microbiota was obtained as a secondary outcome measure in a study on the effects of one hour of daily skin-to-skin contact (SSC) for five weeks in healthy full-term infants. Specifically, we studied the effects on alpha/beta diversity, volatility, microbiota maturation, and bacterial and gut-brain-axis-related functional abundances in microbiota assessed thrice in the first year. Pregnant Dutch women (n = 116) were randomly assigned to the SSC or care-as-usual groups. The SSC group participants engaged in one hour of daily SSC from birth to five weeks of age. Stool samples were collected at two, five, and 52 weeks and the V4 region was sequenced. We observed significant differences in the microbiota composition, bacterial abundances, and predicted functional pathways between the groups. The SSC group exhibited lower microbiota volatility during early infancy. Microbiota maturation was slower in the SSC group during the first year and our results suggested that breastfeeding duration may have partially mediated this relation. Our findings provide evidence that postpartum SSC may influence microbiota development. Replication is necessary to validate and generalize these results. Future studies should include direct stress measurements and extend microbiota sampling beyond the first year to investigate stress as a mechanism and research SSC's impact on long-term microbiota maturation trajectories.

Selective serotonin reuptake inhibitors during pregnancy and lactation: A scoping review of effects on the maternal and infant gut microbiome

Perinatal mood disorders are a tremendous burden to childbearing families and treatment with selective serotonin reuptake inhibitor (SSRI) antidepressants is increasingly common. Exposure to SSRIs may affect serotonin signaling and ultimately, microbes that live in the gut. Health of the gut microbiome during pregnancy, lactation, and early infancy is critical, yet there is limited evidence to describe the relationship between SSRI exposure and gut microbiome status in this population. The purpose of this Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA)-compliant scoping review is to assess evidence and describe key concepts regarding whether SSRI exposure affects the maternal and infant gut microbiome. Sources were collected from PubMed, Web of Science, and Scopus databases, and an additional gray literature search was performed. Our search criteria returned only three sources, two rodent models and one human subjects research study. Results suggest that fluoxetine (SSRI) exposure may affect maternal gut microbiome dynamics during pregnancy and lactation. There were no available sources to describe the relationship between perinatal SSRI exposure and the infant gut microbiome. There is a significant gap in the literature regarding whether SSRI antidepressants affect the maternal and infant gut microbiome. Future studies are required to better understand how SSRI antidepressant exposure affects perinatal health.

Emergent Functional Organization of Gut Microbiomes in Health and Diseases

Continuous and significant progress in sequencing technologies and bioinformatics pipelines has revolutionized our comprehension of microbial communities, especially for human microbiomes. However, most studies have focused on studying the taxonomic composition of the microbiomes and we are still not able to characterize dysbiosis and unveil the underlying ecological consequences. This study explores the emergent organization of functional abundances and correlations of gut microbiomes in health and disease. Leveraging metagenomic sequences, taxonomic and functional tables are constructed, enabling comparative analysis. First, we show that emergent taxonomic and functional patterns are not useful to characterize dysbiosis. Then, through differential abundance analyses applied to functions, we reveal distinct functional compositions in healthy versus unhealthy microbiomes. In addition, we inquire into the functional correlation structure, revealing significant differences between the healthy and unhealthy groups, which may significantly contribute to understanding dysbiosis. Our study demonstrates that scrutinizing the functional organization in the microbiome provides novel insights into the underlying state of the microbiome. The shared data structure underlying the functional and taxonomic compositions allows for a comprehensive macroecological examination. Our findings not only shed light on dysbiosis, but also underscore the importance of studying functional interrelationships for a nuanced understanding of the dynamics of the microbial community. This research proposes a novel approach, bridging the gap between microbial ecology and functional analyses, promising a deeper understanding of the intricate world of the gut microbiota and its implications for human health.

Uncover a microbiota signature of upper respiratory tract in patients with SARS-CoV-2 +

The outbreak of Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, forced us to face a pandemic with unprecedented social, economic, and public health consequences. Several nations have launched campaigns to immunize millions of people using various vaccines to prevent infections. Meanwhile, therapeutic approaches and discoveries continuously arise; however, identifying infected patients that are going to experience the more severe outcomes of COVID-19 is still a major need, to focus therapeutic efforts, reducing hospitalization and mitigating drug adverse effects. Microbial communities colonizing the respiratory tract exert significant effects on host immune responses, influencing the susceptibility to infectious agents. Through 16S rDNAseq we characterized the upper airways' microbiota of 192 subjects with nasopharyngeal swab positive for SARS-CoV-2. Patients were divided into groups based on the presence of symptoms, pneumonia severity, and need for oxygen therapy or intubation. Indeed, unlike most of the literature, our study focuses on identifying microbial signatures predictive of disease progression rather than on the probability of infection itself, for which a consensus is lacking. Diversity, differential abundance, and network analysis at different taxonomic levels were synergistically adopted, in a robust bioinformatic pipeline, highlighting novel possible taxa correlated with patients' disease progression to intubation.

Different trematode parasites in the same snail host: Species-specific or shared microbiota?

The concept that microbes associated with macroorganisms evolve as a unit has swept evolutionary ecology. However, this idea is controversial due to factors such as imperfect vertical transmission of microbial lineages and high microbiome variability among conspecific individuals of the same population. Here, we tested several predictions regarding the microbiota of four trematodes (Galactosomum otepotiense, Philophthalmus attenuatus, Acanthoparyphium sp. and Maritrema novaezealandense) that parasitize the same snail host population. We predicted that each parasite species would harbour a distinct microbiota, with microbial composition similarity decreasing with increasing phylogenetic distance among parasite species. We also predicted that trematode species co-infecting the same individual host would influence each other's microbiota. We detected significant differences in alpha and beta diversity, as well as differential abundance, in the microbiota of the four trematode species. We found no evidence that phylogenetically closely related trematodes had more similar microbiota. We also uncovered indicator bacterial taxa that were significantly associated with each trematode species. Trematode species sharing the same snail host showed evidence of mostly one-sided bacterial exchanges, with the microbial community of one species approaching that of the other. We hypothesize that natural selection acting on specific microbial lineages may be important to maintain differences in horizontally acquired microbes, with vertical transmission also playing a role. In particular, one trematode species had a more consistent and diverse bacteriota than the others, potentially a result of stronger stabilizing pressures. We conclude that species-specific processes shape microbial community assembly in different trematodes exploiting the same host population.

Critical review of 16S rRNA gene sequencing workflow in microbiome studies: From primer selection to advanced data analysis

The multi-batch reanalysis approach of jointly reevaluating gene/genome sequences from different works has gained particular relevance in the literature in recent years. The large amount of 16S ribosomal ribonucleic acid (rRNA) gene sequence data stored in public repositories and information in taxonomic databases of the same gene far exceeds that related to complete genomes. This review is intended to guide researchers new to studying microbiota, particularly the oral microbiota, using 16S rRNA gene sequencing and those who want to expand and update their knowledge to optimise their decision-making and improve their research results. First, we describe the advantages and disadvantages of using the 16S rRNA gene as a phylogenetic marker and the latest findings on the impact of primer pair selection on diversity and taxonomic assignment outcomes in oral microbiome studies. Strategies for primer selection based on these results are introduced. Second, we identified the key factors to consider in selecting the sequencing technology and platform. The process and particularities of the main steps for processing 16S rRNA gene-derived data are described in detail to enable researchers to choose the most appropriate bioinformatics pipeline and analysis methods based on the available evidence. We then produce an overview of the different types of advanced analyses, both the most widely used in the literature and the most recent approaches. Several indices, metrics and software for studying microbial communities are included, highlighting their advantages and disadvantages. Considering the principles of clinical metagenomics, we conclude that future research should focus on rigorous analytical approaches, such as developing predictive models to identify microbiome-based biomarkers to classify health and disease states. Finally, we address the batch effect concept and the microbiome-specific methods for accounting for or correcting them.

Comprehensive evaluation of methods for differential expression analysis of metatranscriptomics data

Understanding the function of the human microbiome is important but the development of statistical methods specifically for the microbial gene expression (i.e. metatranscriptomics) is in its infancy. Many currently employed differential expression analysis methods have been designed for different data types and have not been evaluated in metatranscriptomics settings. To address this gap, we undertook a comprehensive evaluation and benchmarking of 10 differential analysis methods for metatranscriptomics data. We used a combination of real and simulated data to evaluate performance (i.e. type I error, false discovery rate and sensitivity) of the following methods: log-normal (LN), logistic-beta (LB), MAST, DESeq2, metagenomeSeq, ANCOM-BC, LEfSe, ALDEx2, Kruskal-Wallis and two-part Kruskal-Wallis. The simulation was informed by supragingival biofilm microbiome data from 300 preschool-age children enrolled in a study of childhood dental disease (early childhood caries, ECC), whereas validations were sought in two additional datasets from the ECC study and an inflammatory bowel disease study. The LB test showed the highest sensitivity in both small and large samples and reasonably controlled type I error. Contrarily, MAST was hampered by inflated type I error. Upon application of the LN and LB tests in the ECC study, we found that genes C8PHV7 and C8PEV7, harbored by the lactate-producing Campylobacter gracilis, had the strongest association with childhood dental disease. This comprehensive model evaluation offers practical guidance for selection of appropriate methods for rigorous analyses of differential expression in metatranscriptomics. Selection of an optimal method increases the possibility of detecting true signals while minimizing the chance of claiming false ones.

Dictys: dynamic gene regulatory network dissects developmental continuum with single-cell multiomics

Gene regulatory networks (GRNs) are key determinants of cell function and identity and are dynamically rewired during development and disease. Despite decades of advancement, challenges remain in GRN inference, including dynamic rewiring, causal inference, feedback loop modeling and context specificity. To address these challenges, we develop Dictys, a dynamic GRN inference and analysis method that leverages multiomic single-cell assays of chromatin accessibility and gene expression, context-specific transcription factor footprinting, stochastic process network and efficient probabilistic modeling of single-cell RNA-sequencing read counts. Dictys improves GRN reconstruction accuracy and reproducibility and enables the inference and comparative analysis of context-specific and dynamic GRNs across developmental contexts. Dictys' network analyses recover unique insights in human blood and mouse skin development with cell-type-specific and dynamic GRNs. Its dynamic network visualizations enable time-resolved discovery and investigation of developmental driver transcription factors and their regulated targets. Dictys is available as a free, open-source and user-friendly Python package.

Current concepts, advances, and challenges in deciphering the human microbiota with metatranscriptomics

Metatranscriptomics refers to the analysis of the collective microbial transcriptome of a sample. Its increased utilization for the characterization of human-associated microbial communities has enabled the discovery of many disease-state related microbial activities. Here, we review the principles of metatranscriptomics-based analysis of human-associated microbial samples. We describe strengths and weaknesses of popular sample preparation, sequencing, and bioinformatics approaches and summarize strategies for their use. We then discuss how human-associated microbial communities have recently been examined and how their characterization may change. We conclude that metatranscriptomics insights into human microbiotas under health and disease have not only expanded our knowledge on human health, but also opened avenues for rational antimicrobial drug use and disease management.

Application of Compositional Data Analysis to Study the Relationship between Bacterial Diversity in Human Faeces and Sex, Age, and Weight

This work uses Compositional Data Analysis (CoDA) to examine the typical human faecal bacterial diversity in 39 healthy volunteers from the Andalusian region (Spain). Stool samples were subjected to high-throughput sequencing of the V3 and V4 regions of the 16S ribosomal RNA gene using Illumina MiSeq. The numbers of sequences per sample and their genus-level assignment were carried out using the Phyloseq R package. The alpha diversity indices of the faecal bacterial population were not influenced by the volunteer's sex (male or female), age (19-46 years), and weight (48.6-99.0 kg). To study the relationship between these variables and the faecal bacterial population, the ALDEx2 and coda4microbiome CoDA packages were used. Applying ALDEx2, a trend suggesting a connection between sex and the genera Senegalimassilia and Negatibacillus (slightly more abundant in females) and Desulfovibrio (more abundant in males) was found. Moreover, age was tentatively associated with Streptococcus, Tizzerella, and Ruminococaceae_UCG-003, while weight was linked to Senegalimassilia. The exploratory tool of the coda4microbiome package revealed numerous bacterial log-ratios strongly related to sex and, to a lesser extent, age and weight. Moreover, the cross-sectional analysis identified bacterial signature balances able to assign sex to samples regardless of controlling for volunteers' age or weight. Desulfovibrio, Faecalitalea, and Romboutsia were relevant in the numerator, while Coprococcus, Streptococcus, and Negatibacillus were prominent in the denominator; the greater presence of these could characterise the female sex. Predictions for age included Caproiciproducens, Coprobacter, and Ruminoclostridium in the numerator and Odoribacter, Ezakiella, and Tyzzerella in the denominator. The predictions depend on the relationship between both groups, but the abundance of the first group and scarcity of the second could be related to older individuals. However, the association of the faecal bacterial population with weight did not yield a satisfactory model, indicating scarce influence. These results demonstrate the usefulness of the CoDA methodology for studying metagenomics data and, specifically, human microbiota.

Methods in Nutrition & Gut Microbiome Research: An American Society for Nutrition Satellite Session [13 October 2022]

The microbial cells colonizing the human body form an ecosystem that is integral to the regulation and maintenance of human health. Elucidation of specific associations between the human microbiome and health outcomes is facilitating the development of microbiome-targeted recommendations and treatments (e.g., fecal microbiota transplant; pre-, pro-, and post-biotics) to help prevent and treat disease. However, the potential of such recommendations and treatments to improve human health has yet to be fully realized. Technological advances have led to the development and proliferation of a wide range of tools and methods to collect, store, sequence, and analyze microbiome samples. However, differences in methodology at each step in these analytic processes can lead to variability in results due to the unique biases and limitations of each component. This technical variability hampers the detection and validation of associations with small to medium effect sizes. Therefore, the American Society for Nutrition (ASN) Nutritional Microbiology Group Engaging Members (GEM), sponsored by the Institute for the Advancement of Food and Nutrition Sciences (IAFNS), hosted a satellite session on methods in nutrition and gut microbiome research to review currently available methods for microbiome research, best practices, as well as tools and standards to aid in comparability of methods and results. This manuscript summarizes the topics and research discussed at the session. Consideration of the guidelines and principles reviewed in this session will increase the accuracy, precision, and comparability of microbiome research and ultimately the understanding of the associations between the human microbiome and health.

Protocol for microbiota analysis of a murine stroke model

Investigations on the microbiota in neurological diseases such as stroke are increasingly common; however, stroke researchers may have limited experience with designing such studies. Here, we describe a protocol to conduct a stroke microbiota study in mice, from experimental stroke surgery and sample collection to data analysis. We provide details on sample processing and sequencing and provide a reproducible data analysis pipeline. In doing so, we hope to enable researchers to conduct robust studies and facilitate identification of stroke-associated microbial signatures. For complete details on the use and execution of this protocol, please refer to Sorbie et al. (2022).1.