Investigating differential abundance methods in microbiome data: A benchmark study

The ocular surface microbiome of rhesus macaques

Background

The ocular surface microbiome (OSM) is important for eye health, and variations in OSM composition have been associated with multiple diseases in humans. Studies of OSM-disease dynamics in humans are confounded by lifestyle factors. Animal models provide a complementary approach to understanding biological systems, free from many confounds of human studies. Here, we provide the first study of the OSM of rhesus macaques, a premier animal model for eye health and disease. We describe the taxonomy of the rhesus macaque OSM, and explore compositional correlations with age, sex, and living condition.

Methods

We analyzed eyelid and conjunctival microbiota swabs from 132 individual rhesus macaques (Macaca mulatta) (57 males, 75 females, 1-26 years old) from one captive and one free-ranging group using 16S V3/V4 MiSeq sequencing. We investigated alpha diversity, beta diversity, and differential abundance.

Results

We found several similarities between the top Phyla and Genera of the rhesus macaque OSM and those reported in human literature. Significantly higher alpha diversity, which may reflect age-related ocular surface mucous membrane integrity and immune function, was present in younger individuals compared to older ones. Higher alpha diversity was also present in free-ranging rhesus macaques compared to ones in captivity, possibly related to differences in diet, exercise, and medical exposures between macaques in different living conditions. Beta diversity was most strongly influenced by individual identity, followed by living conditions. Sex did not correlate with any OSM variation.

Conclusions

In this study we describe the taxonomic composition of the rhesus macaque OSM, and identify significant differences in alpha and beta diversity according to individual nonhuman primate host variables and the surrounding environment. Our findings suggest composition of the nonhuman primate OSM is shaped by age-related physiology, individual identity, and external living conditions. Our results offer novel insights into an underexplored region of the primate microbiome and highlight the utility of rhesus macaques as a model system for investigating the links between the OSM, ocular health, and disease.

Elementary methods provide more replicable results in microbial differential abundance analysis

Differential abundance analysis (DAA) is a key component of microbiome studies. Although dozens of methods exist, there is currently no consensus on the preferred methods. While the correctness of results in DAA is an ambiguous concept and cannot be fully evaluated without setting the ground truth and employing simulated data, we argue that a well-performing method should be effective in producing highly reproducible results. We compared the performance of 14 DAA methods by employing datasets from 53 taxonomic profiling studies based on 16S rRNA gene or shotgun metagenomic sequencing. For each method, we examined how the results replicated between random partitions of each dataset and between datasets from separate studies. While certain methods showed good consistency, some widely used methods were observed to produce a substantial number of conflicting findings. Overall, when considering consistency together with sensitivity, the best performance was attained by analyzing relative abundances with a nonparametric method (Wilcoxon test or ordinal regression model) or linear regression/t-test. Moreover, a comparable performance was obtained by analyzing presence/absence of taxa with logistic regression.

Computational Study Protocol: Leveraging Synthetic Data to Validate a Benchmark Study for Differential Abundance Tests for 16S Microbiome Sequencing Data

Background

Synthetic data's utility in benchmark studies depends on its ability to closely mimic real-world conditions and reproduce results obtained from experimental data. Building on Nearing et al.'s study (1), who assessed 14 differential abundance tests using 38 experimental 16S rRNA datasets in a case-control design, we are generating synthetic datasets that mimic the experimental data to verify their findings. We will employ statistical tests to rigorously assess the similarity between synthetic and experimental data and to validate the conclusions on the performance of these tests drawn by Nearing et al. (1). This protocol adheres to the SPIRIT guidelines, demonstrating how established reporting frameworks can support robust, transparent, and unbiased study planning.

Methods

We replicate Nearing et al.'s (1) methodology, incorporating synthetic data simulated using two distinct tools, mirroring the 38 experimental datasets. Equivalence tests will be conducted on a non-redundant subset of 46 data characteristics comparing synthetic and experimental data, complemented by principal component analysis for overall similarity assessment. The 14 differential abundance tests will be applied to synthetic and experimental datasets, evaluating the consistency of significant feature identification and the number of significant features per tool. Correlation analysis and multiple regression will explore how differences between synthetic and experimental data characteristics may affect the results.

Conclusions

Synthetic data enables the validation of findings through controlled experiments. We assess how well synthetic data replicates experimental data, try to validate previous findings with the most recent versions of the DA methods and delineate the strengths and limitations of synthetic data in benchmark studies. Moreover, to our knowledge this is the first computational benchmark study to systematically incorporate synthetic data for validating differential abundance methods while strictly adhering to a pre-specified study protocol following SPIRIT guidelines, contributing to transparency, reproducibility, and unbiased research.

Metagenomic analyses of gut microbiome composition and function with age in a wild bird; little change, except increased transposase gene abundance

Studies on wild animals, mostly undertaken using 16S metabarcoding, have yielded ambiguous evidence regarding changes in the gut microbiome (GM) with age and senescence. Furthermore, variation in GM function has rarely been studied in such wild populations, despite GM metabolic characteristics potentially being associated with host senescent declines. Here, we used 7 years of repeated sampling of individuals and shotgun metagenomic sequencing to investigate taxonomic and functional changes in the GM of Seychelles warblers (Acrocephalus sechellensis) with age. Our results suggest that taxonomic GM species richness declines with age and in the terminal year, with this terminal decline occurring consistently across all ages. Taxonomic and functional GM composition also shifted with host age. However, the changes we identified occurred linearly with age (or even mainly during early years prior to the onset of senescence in this species) with little evidence of accelerated change in later life or during their terminal year. Therefore, the results suggest that changes in the GM with age are not linked to senescence. Interestingly, we found a significant increase in the abundance of a group of transposase genes with age, which may accumulate passively or due to increased transposition induced as a result of stressors that arise with age. These findings reveal taxonomic and functional GM changes with age, but not senescence, in a wild vertebrate and provide a blueprint for future wild functional GM studies linked to age and senescence.

Microbial fuel cells to monitor natural attenuation around groundwater plumes

This research presents a straightforward and economically efficient design for a microbial fuel cell (MFC) that can be conveniently integrated into a borehole to monitor natural attenuation in groundwater. The design employs conventional, transparent, and reusable PVC bailers with graphite tape and granular activated carbon to create high surface area electrodes. These electrodes are connected across redox environments in nested boreholes through a wire and variable resistor setup. The amended electrodes were installed in pre-existing boreholes surrounding a groundwater plume near a former gasworks facility. Among all the MFC locations tested, the MFC at the plume fringe exhibited the highest electrical response and displayed significant variations in the differential abundance of key bacterial and archaeal taxa between the anode and cathode electrodes. The other MFC configurations in the plume center and uncontaminated groundwater showed little to no electrical response, suggesting minimal microbial activity. This straightforward approach enables informed decision-making regarding effectively monitoring, enhancing, or designing degradation strategies for groundwater plumes. It offers a valuable tool for understanding and managing contaminant degradation in such environments.

Statistical methods for comparing two independent exponential-gamma means with application to single cell protein data

In genomic study, log transformation is a common prepossessing step to adjust for skewness in data. This standard approach often assumes that log-transformed data is normally distributed, and two sample t-test (or its modifications) is used for detecting differences between two experimental conditions. However, recently it was shown that two sample t-test can lead to exaggerated false positives, and the Wilcoxon-Mann-Whitney (WMW) test was proposed as an alternative for studies with larger sample sizes. In addition, studies have demonstrated that the specific distribution used in modeling genomic data has profound impact on the interpretation and validity of results. The aim of this paper is three-fold: 1) to present the Exp-gamma distribution (exponential-gamma distribution stands for log-transformed gamma distribution) as a proper biological and statistical model for the analysis of log-transformed protein abundance data from single-cell experiments; 2) to demonstrate the inappropriateness of two sample t-test and the WMW test in analyzing log-transformed protein abundance data; 3) to propose and evaluate statistical inference methods for hypothesis testing and confidence interval estimation when comparing two independent samples under the Exp-gamma distributions. The proposed methods are applied to analyze protein abundance data from a single-cell dataset.

Cefazolin shifts the kidney microbiota to promote a lithogenic environment

Clinical studies of the urinary tract microbiome, termed urobiome, suggest a direct, antibiotic-dependent, impact of the urobiome on kidney physiology. However, evidence for kidney bacteria comes from indirect sources or infected tissue. Further, it is unclear how antibiotics impact kidney bacteria. Here we show direct evidence for the presence of bacteria in the kidneys, with microniches in nephrons. In murine kidneys, administration of cefazolin, a commonly used perioperative antibiotic, led to a loss of uroprotective Lactobacillus spp. and proliferation of Enterobacteriaceae (which includes many known uropathogens). This effect was dependent on treatment duration, with recovery post treatment. Uroprotective L. crispatus and a strain of stone-associated E. coli differentially influenced calcium oxalate (CaOx) crystallization through the incorporation of CaOx inhibitors or promoters, respectively. In humans, microbial signatures were identified in the kidney, with unique niches between the glomeruli and tubules, established through RNA sequencing analysis and direct imaging of two independent populations. Collectively, findings support the hypothesis that the kidneys harbor a stable and antibiotic-responsive microbiota that can influence CaOx lithogenesis. The presence of unique, age-dependent microbial signatures in the glomeruli and tubuli carry implications for non-infectious kidney diseases.

Revisiting microgenderome: detecting and cataloguing sexually unique and enriched species in human microbiomes

BACKGROUND

Microgenderome or arguably more accurately microsexome refers to studies on sexual dimorphism of human microbiomes aimed at investigating bidirectional interactions between human microbiomes, sex hormones, and immune systems. It is important because of its implications to disease susceptibility and therapy, in which men and women demonstrate divergence in many diseases especially autoimmune diseases. In a previous report [1], we presented analyses of several key ecological aspects of microgenderome by leveraging the large datasets of the HMP (human microbiome project) but failed to offer species-level composition differences such as sexually unique species (US) and enriched species (ES). Existing approaches, for such tasks, including differential species relative abundance analysis and differential network analysis, possess certain limitations given that virtually all rely on species abundance alone or are univariate, while ignoring species distribution information across samples. Obviously, it is both species abundance and distribution that shape/drive the structure and dynamics of human microbiomes, and both should be equally responsible for the universal heterogeneity of microbiomes including the sexual dimorphism.

RESULTS

Here, we fill the gap by taking advantages of a recently developed computational algorithm, species specificity, and specificity diversity (SSD) framework (refer to the companion article) to reanalyze the HMP and complementary seminovaginal microbiome datasets. The SSD framework can randomly search and catalogue the sexually specific unique/enriched species with statistical rigor, guided by species specificity (a synthetic metric of abundance and distribution) and specificity diversity (SD). The SSD framework reveals that men seem to have more unique species than women in their gut and reproductive system microbiomes, but women seem to have more unique species than men in the airway, oral, and skin microbiomes, which is likely due to sexual dimorphism in the hormone and immune systems. We further investigate co-dependency and heterogeneity of those sexually unique/enriched species across 15 body sites, with core/periphery network analyses.

CONCLUSIONS

This study not only produced sexually unique/enriched species in the human microbiomes and analyzed their codependency and heterogeneity but also further validated the robustness of the SSD framework presented in the companion article, by performing all negative control tests based on the HMP gut microbiome samples.

Species specificity and specificity diversity (SSD) framework: a novel method for detecting the unique and enriched species associated with disease by leveraging the microbiome heterogeneity

BACKGROUND

Differentiating the microbiome changes associated with diseases is challenging but critically important. Majority of existing efforts have been focused on a community level, but the discerning power of community or holistic metrics such as diversity analysis seems limited. This prompts many researchers to believe that the promise should be downward to species or even strain level-effectively and efficiently identifying unique or enriched species in diseased microbiomes with statistical rigor. Nevertheless, virtually, all species-level approaches such as differential abundance and differential network analysis methods exclusively rely on species abundances without considering species distribution information, while it can be said that distribution is equally, if not more, important than abundance in shaping the spatiotemporal heterogeneity of community compositions.

RESULTS

Here, we fill the gap by developing a novel framework-species specificity and specificity diversity (SSD)-that synthesizes both abundance and distribution information to differentiate microbiomes, at both species and community scales, under different environmental gradients such as the healthy and diseased treatments. The proposed SSD framework consists of three essential elements. The first is species specificity (SS), a concept that reincarnates the traditional specialist-generalist continuum and is defined by Mariadassou et al. (Ecol Lett 18:974-82, 2015). The SS synthesizes a species' local prevalence (distribution) and global abundance information and attaches specificity measure to each species in a specific habitat (e.g., healthy or diseased treatment). The second element is a new concept to introduce here, the (species) specificity diversity (SD), which is inspired by traditional species (abundance) diversity in community ecology and measures the diversity of specificity (a proxy for metacommunity heterogeneity, essentially) with Renyi's entropy. The third element is a pair of statistical tests based on the principle of permutation tests.

CONCLUSIONS

The SSD framework can (i) identify and catalogue lists of unique species (US), significantly enriched species (ES) in each treatment based on SS and specificity permutation (SP) test and (ii) measure the holistic differences between assemblages (or treatments) based on SD and specificity diversity permutation (SDP) test. Both capacities can be enabling technologies for general comparative microbiome research including risk assessment, diagnosis, and treatment of microbiome-associated diseases.

GEMimp: An Accurate and Robust Imputation Method for Microbiome Data Using Graph Embedding Neural Network

Microbiome research has increasingly underscored the profound link between microbial compositions and human health, with numerous studies establishing a strong correlation between microbiome characteristics and various diseases. However, the analysis of microbiome data is frequently compromised by inherent sparsity issues, characterized by a substantial presence of observed zeros. These zeros not only skew the abundance distribution of microbial species but also undermine the reliability of scientific conclusions drawn from such data. Addressing this challenge, we introduce GEMimp, an innovative imputation method designed to infuse robustness into microbiome data analysis. GEMimp leverages the node2vec algorithm, which incorporates both Breadth-First Search (BFS) and Depth-First Search (DFS) strategies in its random walks sampling process. This approach enables GEMimp to learn nuanced, low-dimensional representations of each taxonomic unit, facilitating the reconstruction of their similarity networks with unprecedented accuracy. Our comparative analysis pits GEMimp against state-of-the-art imputation methods including SAVER, MAGIC and mbImpute. The results unequivocally demonstrate that GEMimp outperforms its counterparts by achieving the highest Pearson correlation coefficient when compared to the original raw dataset. Furthermore, GEMimp shows notable proficiency in identifying significant taxa, enhancing the detection of disease-related taxa and effectively mitigating the impact of sparsity on both simulated and real-world datasets, such as those pertaining to Type 2 Diabetes (T2D) and Colorectal Cancer (CRC). These findings collectively highlight the strong effectiveness of GEMimp, allowing for better analysis on microbial data. With alleviation of sparsity issues, it could be greatly facilitated in downstream analyses and even in the field of microbiology.

A novel computational approach for the mining of signature pathways using species co-occurrence networks in gut microbiomes

BACKGROUND

Advances in metagenome sequencing data continue to enable new methods for analyzing biological systems. When handling microbial profile data, metagenome sequencing has proven to be far more comprehensive than traditional methods such as 16s rRNA data, which rely on partial sequences. Microbial community profiling can be used to obtain key biological insights that pave the way for more accurate understanding of complex systems that are critical for advancing biomedical research and healthcare. However, such attempts have mostly used partial or incomplete data to accurately capture those associations.

METHODS

This study introduces a novel computational approach for the identification of co-occurring microbial communities using the abundance and functional roles of species-level microbiome data. The proposed approach is then used to identify signature pathways associated with inflammatory bowel disease (IBD). Furthermore, we developed a computational pipeline to identify microbial species co-occurrences from metagenome data at various granularity levels.

RESULTS

When comparing the IBD group to a control group, we show that certain co-occurring communities of species are enriched for potential pathways. We also show that the identified co-occurring microbial species operate as a community to facilitate pathway enrichment.

CONCLUSIONS

The obtained findings suggest that the proposed network model, along with the computational pipeline, provide a valuable analytical tool to analyze complex biological systems and extract pathway signatures that can be used to diagnose certain health conditions.

Pregnancy during COVID 19 pandemic associated with differential gut microbiome composition as compared to pre-pandemic

The first two years of the COVID-19 pandemic and subsequent health mandates resulted in significant disruptions to daily life, creating a period of heightened psychosocial stress in myriad aspects. Understanding the impact of this period on pregnant individuals' bacteriomes is crucial as pregnancy is a period of heightened vulnerability to stress and its sequelae, anxiety and mood disorders, which have been demonstrated to alter gut microbiome composition. In a prospective cohort study (N = 12-26) conducted from February 2019 to August 2021, we examined psychometric responses and rectal microbiome swabs from pregnant individuals. Full-length 16 S rRNA sequencing followed by calculation of diversity metrics and relative abundance values were used to interrogate fecal microbiome community composition across pandemic groups. Distinct shifts in bacterial diversity and composition were observed during early to late pregnancy in the pandemic group, including lower relative abundance of pathogenic and lesser-known taxa. However, distribution of stress and depressive symptoms did not significantly differ from the pre-pandemic period while the correlation between stress and depressive symptoms dissipated during the pandemic. Our findings suggest that living through the COVID-19 pandemic altered the gut microbiome of pregnant individuals, independent of perceived stress.

Exploring microbial diversity in the rhizosphere: a comprehensive review of metagenomic approaches and their applications

The rhizosphere, the soil region influenced by plant roots, represents a dynamic microenvironment where intricate interactions between plants and microorganisms shape soil health, nutrient cycling, and plant growth. Soil microorganisms are integral players in the transformation of materials, the dynamics of energy flows, and the intricate cycles of biogeochemistry. Considerable research has been dedicated to investigating the abundance, diversity, and intricacies of interactions among different microbes, as well as the relationships between plants and microbes present in the rhizosphere. Metagenomics, a powerful suite of techniques, has emerged as a transformative tool for dissecting the genetic repertoire of complex microbial communities inhabiting the rhizosphere. The review systematically navigates through various metagenomic approaches, ranging from shotgun metagenomics, enabling unbiased analysis of entire microbial genomes, to targeted sequencing of the 16S rRNA gene for taxonomic profiling. Each approach's strengths and limitations are critically evaluated, providing researchers with a nuanced understanding of their applicability in different research contexts. A central focus of the review lies in the practical applications of rhizosphere metagenomics in various fields including agriculture. By decoding the genomic content of rhizospheric microbes, researchers gain insights into their functional roles in nutrient acquisition, disease suppression, and overall plant health. The review also addresses the broader implications of metagenomic studies in advancing our understanding of microbial diversity and community dynamics in the rhizosphere. It serves as a comprehensive guide for researchers, agronomists, and policymakers, offering a roadmap for harnessing metagenomic approaches to unlock the full potential of the rhizosphere microbiome in promoting sustainable agriculture.

Metagenome comparison (MC): A new framework for detecting unique/enriched OMUs (operational metagenomic units) derived from whole-genome sequencing reads

BACKGROUND

Current methods for comparing metagenomes, derived from whole-genome sequencing reads, include top-down metrics or parametric models such as metagenome-diversity, and bottom-up, non-parametric, model-free machine learning approaches like Naïve Bayes for k-mer-profiling. However, both types are limited in their ability to effectively and comprehensively identify and catalogue unique or enriched metagenomic genes, a critical task in comparative metagenomics. This challenge is significant and complex due to its NP-hard nature, which means computational time grows exponentially, or even faster, with the problem size, rendering it impractical for even the fastest supercomputers without heuristic approximation algorithms.

METHOD

In this study, we introduce a new framework, MC (Metagenome-Comparison), designed to exhaustively detect and catalogue unique or enriched metagenomic genes (MGs) and their derivatives, including metagenome functional gene clusters (MFGC), or more generally, the operational metagenomic unit (OMU) that can be considered the counterpart of the OTU (operational taxonomic unit) from amplicon sequencing reads. The MC is essentially a heuristic search algorithm guided by pairs of new metrics (termed MG-specificity or OMU-specificity, MG-specificity diversity or OMU-specificity diversity). It is further constrained by statistical significance (P-value) implemented as a pair of statistical tests.

RESULTS

We evaluated the MC using large metagenomic datasets related to obesity, diabetes, and IBD, and found that the proportions of unique and enriched metagenomic genes ranged from 0.001% to 0.08 % and 0.08%-0.82 % respectively, and less than 10 % for the MFGC.

CONCLUSION

The MC provides a robust method for comparing metagenomes at various scales, from baseline MGs to various function/pathway clusters of metagenomes, collectively termed OMUs.

The Role of the Microbiome in Pediatric Respiratory Diseases

Numerous studies have examined the role of the microbiome and microbiome-based therapeutics in many childhood airway and lung diseases. In this narrative review, the authors first give a brief overview of the current methods used in microbiome research. The authors then review the literature linking the microbiome with (1) early-life acute respiratory infections due to respiratory syncytial virus, (2) childhood asthma onset, (3) cystic fibrosis, and (4) bronchopulmonary dysplasia, focusing on recent studies that have used culture-independent methods to characterize the respiratory or gut microbiome in the pediatric population.

Assessment of Distinct Gut Microbiome Signatures in a Diverse Cohort of Patients Undergoing Definitive Treatment for Rectal Cancer

Introduction

Disparities in incidence and outcome of rectal cancer are multifactorial in etiology but may be due, in part, to differences in gut microbiome composition. We used serial robust statistical approaches to assess baseline gut microbiome composition in a diverse cohort of patients with rectal cancer receiving definitive treatment.

Methods

Microbiome composition was compared by age at diagnosis (< 50 vs ≥ 50 years), race and ethnicity (White Hispanic vs non-Hispanic), and response to therapy. Alpha diversity was assessed using the Shannon, Chao1, and Simpson diversity measures. Beta diversity was explored using both Bray-Curtis dissimilarity and Aitchison distance with principal coordinate analysis. To minimize false-positive findings, we used two distinct methods for differential abundance testing: LinDA and MaAsLin2 (all statistics two-sided, Benjamini-Hochberg corrected false discovery rate < 0.05).

Results

Among 64 patients (47% White Hispanic) with median age 51 years, beta diversity metrics showed significant clustering by race and ethnicity (p < 0.001 by both metrics) and by onset (Aitchison p = 0.022, Bray-Curtis p = 0.035). White Hispanic patients had enrichment of bacterial family Prevotellaceae (LinDA fold change 5.32, MaAsLin2 fold change 5.11, combined adjusted p = 0.0007). No significant differences in microbiome composition were associated with neoadjuvant therapy response.

Conclusion

We identified distinct gut microbiome signatures associated with race and ethnicity and age of onset in a diverse cohort of patients undergoing definitive treatment for rectal cancer.

A prospective pilot study of gut microbiome in cerebral vasospasm and delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage

A recent systematic review indicated that gut-microbiota-brain axis contributes to growth and rupture of intracranial aneurysms. However, gaps were detected in the role of intestinal microbiome in cerebral vasospasm (CVS) after aneurysmal subarachnoid hemorrhage (aSAH). This is the first pilot study aiming to test study feasibility and identify differences in gut microbiota between subjects with and without CVS following aSAH. A prospective nested case-control pilot study with 1:1 matching was conducted recruiting subjects with aSAH: cases with CVS; and controls without CVS based on the clinical picture and structured bedside transcranial Doppler (TCD). Fecal samples for microbiota analyses by means of 16S rRNA gene amplicon sequencing were collected within the first 96 h after ictus. Operational taxonomic unit tables were constructed, diversity metrics calculated, phylogenetic trees built, and differential abundance analysis (DAA) performed. At baseline, the groups did not differ significantly in basic demographic and aneurysm-related characteristics (p > 0.05). Alpha-diversity (richness and Shannon Index) was significantly reduced in cases of middle cerebral artery (MCA) vasospasm (p < 0.05). In DAA, relative abundance of genus Acidaminococcus was associated with MCA vasospasm (p = 0.00013). Two butyrate-producing genera, Intestinimonas and Butyricimonas, as well as [Clostridium] innocuum group had the strongest negative correlation with the mean blood flow velocity in anterior cerebral arteries (p < 0.01; rho = - 0.63; - 0.57, and - 0.57, respectively). In total, 16 gut microbial genera were identified to correlate with TCD parameters, and two intestinal genera correlated with outcome upon discharge. In this pilot study, we prove study feasibility and present the first preliminary evidence of gut microbiome signature associating with CVS as a significant cause of stroke in subjects with aSAH.

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.