A phylogenetic transform enhances analysis of compositional microbiota data

Pre-challenge gut microbial signature predicts RhCMV/SIV vaccine efficacy in rhesus macaques

Rhesus cytomegalovirus expressing simian immunodeficiency virus (RhCMV/SIV) vaccines protect ~59% of vaccinated rhesus macaques against repeated limiting-dose intra-rectal exposure with highly pathogenic SIVmac239M, but the exact mechanism responsible for the vaccine efficacy is unknown. It is becoming evident that complex interactions exist between gut microbiota and the host immune system. Here, we aimed to investigate if the rhesus gut microbiome impacts RhCMV/SIV vaccine-induced protection. Three groups of 15 rhesus macaques naturally pre-exposed to RhCMV were vaccinated with RhCMV/SIV vaccines. Rectal swabs were collected longitudinally both before SIV challenge (after vaccination) and post-challenge and were profiled using 16S rRNA based microbiome analysis. We identified ~2,400 16S rRNA amplicon sequence variants (ASVs), representing potential bacterial species/strains. Global gut microbial profiles were strongly associated with each of the three vaccination groups, and all animals tended to maintain consistent profiles throughout the pre-challenge phase. Despite vaccination group differences, by using newly developed compositional data analysis techniques, we identified a common gut microbial signature predictive of vaccine protection outcome across the three vaccination groups. Part of this microbial signature persisted even after SIV challenge. We also observed a strong correlation between this microbial signature and an early signature derived from whole blood transcriptomes in the same animals. Our findings indicate that changes in gut microbiomes are associated with RhCMV/SIV vaccine-induced protection and early host response to vaccination in rhesus macaques.IMPORTANCEThe human immunodeficiency virus (HIV) has infected millions of people worldwide. Unfortunately, still there is no vaccine that can prevent or treat HIV infection. A promising pre-clinical HIV vaccine based on rhesus cytomegalovirus (RhCMV) expressing simian immunodeficiency virus (SIV) antigens (RhCMV/SIV) provides sustained, durable protection against SIV challenge in ~59% of vaccinated rhesus macaques. There is an urgent need to understand the cause of this protection vs non-protection outcome. In this study, we profiled the gut microbiomes of 45 RhCMV/SIV vaccinated rhesus macaques and identified gut microbial signatures that were predictive of RhCMV/SIV vaccination groups and vaccine protection outcomes. These vaccine protection-associated microbial features were significantly correlated with early vaccine-induced host immune signatures in whole blood from the same animals. These findings show that the gut microbiome may be involved in RhCMV/SIV vaccine-induced protection, warranting further research into the impact of the gut microbiome in human vaccine trials.

A Graph-Informed Modeling Framework Empowering Gene Pathway Discovery

This study introduces a novel graph-informed modeling framework for improving the statistical analysis of gene expression data, particularly in the context of identifying differentially expressed gene pathways and gene expression-assisted disease classification in a high-dimensional data setting. By integrating gene regulatory network information into hypothesis testing for the difference between mean vectors and linear discriminant analysis, we aim to effectively capture and utilize previously validated external gene interaction information. Our method leverages a block-coordinate descent approach which enables us to incorporate mixed graph information into linear structural equation modeling, accommodating directed/undirected edges and potential cycles in gene regulatory networks. Extensive simulations under various data scenarios have demonstrated the effectiveness of our approach with improved power for gene pathway tests and disease classification over existing methods. An application to a lung cancer dataset from the Cancer Genome Atlas Program (TCGA) further exemplifies the potential of our graph-informed approach in empowering the detection of differentially expressed gene pathways and gene expression-based classification of different lung cancer stages. Our findings underscore the potential utility of incorporating gene regulatory network information in gene pathway analysis, setting the stage for future advancements in gene pathway discovery, disease diagnosis, and treatment strategies.

Microbiota and the volatile profile of avian nests are associated with each other and with the intensity of parasitism

Bacteria have been suggested as being partially responsible for avian nest odours and, thus, volatiles from their metabolism could influence the intensity of selection pressures due to parasites detecting olfactory cues of their hosts. Here, we tested this hypothesis by exploring intraspecific and interspecific variability in microbial environments, volatile profiles and intensity of ectoparasitism by Carnus hemapterus in the nests of 10 avian species. As expected, we found that (i) alpha and beta diversity of microbial and volatile profiles were associated with each other. Moreover, (ii) alpha diversity of bacteria and volatiles of the nest environment, as well as some particular bacteria and volatiles, was associated with the intensity of parasitism at early and late stages of the nestling period. Finally, (iii) alpha diversity of the nest microbiota, as well as some particular bacteria and volatiles, was correlated with fledging success. When considering them together, the results support the expected links between the microbial environment and nest odours in different bird species, and between the microbial environment and both ectoparasitism intensity and fledging success. Relative abundances of particular volatiles and bacteria predicted ectoparasitism and/or fledging success. Future research should prioritise experimental approaches directed to determine the role of bacteria and volatiles in the outcomes of host-ectoparasite interactions.

Relative dispersion ratios following fecal microbiota transplant elucidate principles governing microbial migration dynamics

Microorganisms frequently migrate from one ecosystem to another. Yet, despite the potential importance of this process in modulating the environment and the microbial ecosystem, our understanding of the fundamental forces that govern microbial dispersion is still lacking. Moreover, while theoretical models and in-vitro experiments have highlighted the contribution of species interactions to community assembly, identifying such interactions in vivo, specifically in communities as complex as the human gut, remains challenging. To address this gap, here we introduce a robust and rigorous computational framework, termed Relative Dispersion Ratio (RDR) analysis, and leverage data from well-characterized fecal microbiota transplant trials, to rigorously pinpoint dependencies between taxa during the colonization of human gastrointestinal tract. Our analysis identifies numerous pairwise dependencies between co-colonizing microbes during migration between gastrointestinal environments. We further demonstrate that identified dependencies agree with previously reported findings from in-vitro experiments and population-wide distribution patterns. Finally, we explore metabolic dependencies between these taxa and characterize the functional properties that facilitate effective dispersion. Collectively, our findings provide insights into the principles and determinants of community dynamics following ecological translocation, informing potential opportunities for precise community design.

Volatiles of symbiotic bacterial origin explain ectoparasitism and fledging success of hoopoes

BACKGROUND

Some parasites use olfactory cues to detect their hosts and, since bacterial symbionts are partially responsible for animal odours, they could influence host parasitism. By autoclaving nest materials of hoopoe (Upupa epops) nests before reproduction started, we explored the hypothetical links between host-associated bacteria, volatiles and parasitism. During the nestling stage, we (i) estimated the level of ectoparasitism by chewing lice (Suborder Mallophaga) in adult hoopoe females and by Carnus haemapterus flies in nestlings, and (ii) characterized microbial communities and volatile profiles of nest environments (nest material and nest cavity, respectively) and uropygial secretions.

RESULTS

Experimental nests had less diverse bacterial communities and more diverse volatile profiles than control nests, while occupants experienced lower intensity of parasitism in experimental than in control nests. The experiment also affected beta diversity of the microbial communities of nest material and of the volatiles of the nestling uropygial secretions. Moreover, microbial communities of uropygial secretions and of nest materials covaried with their volatile profiles, while the volatile profile of the bird secretions explained nest volatile profile. Finally, a subset of the volatiles and bacteria detected in the nest material and uropygial secretions were associated with the ectoparasitism intensity of both adult females and nestlings, and with fledging success.

CONCLUSIONS

These results show that a component of animal odours is linked with the microbial communities of the host and its reproductive environment, and emphasize that the associations between bacteria, ectoparasitism and reproductive success are partially mediated by volatiles of bacterial origin. Future work should focus on mechanisms underlying the detected patterns.

Interpretable metric learning in comparative metagenomics: The adaptive Haar-like distance

Random forests have emerged as a promising tool in comparative metagenomics because they can predict environmental characteristics based on microbial composition in datasets where β-diversity metrics fall short of revealing meaningful relationships between samples. Nevertheless, despite this efficacy, they lack biological insight in tandem with their predictions, potentially hindering scientific advancement. To overcome this limitation, we leverage a geometric characterization of random forests to introduce a data-driven phylogenetic β-diversity metric, the adaptive Haar-like distance. This new metric assigns a weight to each internal node (i.e., split or bifurcation) of a reference phylogeny, indicating the relative importance of that node in discerning environmental samples based on their microbial composition. Alongside this, a weighted nearest-neighbors classifier, constructed using the adaptive metric, can be used as a proxy for the random forest while maintaining accuracy on par with that of the original forest and another state-of-the-art classifier, CoDaCoRe. As shown in datasets from diverse microbial environments, however, the new metric and classifier significantly enhance the biological interpretability and visualization of high-dimensional metagenomic samples.

The impact of antibiotics on the gut microbiota of children recovering from watery diarrhoea

Infectious diarrhoeal diseases remain a substantial health burden in young children in low- and middle-income countries. The disease and its variable treatment options significantly alter the gut microbiome, which may affect clinical outcomes and overall gut health. Antibiotics are often prescribed, but their impact on the gut microbiome during recovery is unclear. Here, we used 16S rRNA sequencing to investigate changes in the gut microbiota in Vietnamese children with acute watery diarrhoea, and highlight the impact of antibiotic treatment on these changes. Our analyses identified that, regardless of treatment, recovery was characterised by reductions in Streptococcus and Rothia species and expansion of Bacteroides/Phocaeicola, Lachnospiraceae and Ruminococcacae taxa. Antibiotic treatment significantly delayed the temporal increases in alpha- and beta-diversity within patients, resulting in distinctive patterns of taxonomic change. These changes included a pronounced, transient overabundance of Enterococcus species and depletion of Bifidobacterium pseudocatenulatum. Our findings demonstrate that antibiotic treatment slows gut microbiota recovery in children following watery diarrhoea.

Beyond Normalization: Incorporating Scale Uncertainty in Microbiome and Gene Expression Analysis

Though statistical normalizations are often used in differential abundance or differential expression analysis to address sample-to-sample variation in sequencing depth, we offer a better alternative. These normalizations often make strong, implicit assumptions about the scale of biological systems (e.g., microbial load). Thus, analyses are susceptible to even slight errors in these assumptions, leading to elevated rates of false positives and false negatives. We introduce scale models as a generalization of normalizations so researchers can model potential errors in assumptions about scale. By incorporating scale models into the popular ALDEx2 software, we enhance the reproducibility of analyses while often drastically decreasing false positive and false negative rates. We design scale models that are guaranteed to reduce false positives compared to equivalent normalizations. At least in the context of ALDEx2, we recommend using scale models over normalizations in all practical situations.

Heterogeneous lineage-specific arginine deiminase expression within dental microbiome species

Arginine catabolism by the bacterial arginine deiminase system (ADS) has anticariogenic properties through the production of ammonia, which modulates the pH of the oral environment. Given the potential protective capacity of the ADS pathway, the exploitation of ADS-competent oral microbes through pre- or probiotic applications is a promising therapeutic target to prevent tooth decay. To date, most investigations of the ADS in the oral cavity and its relation to caries have focused on indirect measures of activity or on specific bacterial groups, yet the pervasiveness and rate of expression of the ADS operon in diverse mixed microbial communities in oral health and disease remain an open question. Here, we use a multivariate approach, combining ultra-deep metatranscriptomic sequencing with paired metataxonomic and in vitro citrulline quantification to characterize the microbial community and ADS operon expression in healthy and late-stage cavitated teeth. While ADS activity is higher in healthy teeth, we identify multiple bacterial lineages with upregulated ADS activity on cavitated teeth that are distinct from those found on healthy teeth using both reference-based mapping and de novo assembly methods. Our dual metataxonomic and metatranscriptomic approach demonstrates the importance of species abundance for gene expression data interpretation and that patterns of differential expression can be skewed by low-abundance groups. Finally, we identify several potential candidate probiotic bacterial lineages within species that may be useful therapeutic targets for the prevention of tooth decay and propose that the development of a strain-specific, mixed-microbial probiotic may be a beneficial approach given the heterogeneity of taxa identified here across health groups.

IMPORTANCE

Tooth decay is the most common preventable chronic disease, affecting more than two billion people globally. The development of caries on teeth is primarily a consequence of acid production by cariogenic bacteria that inhabit the plaque microbiome. Other bacterial strains in the oral cavity may suppress or prevent tooth decay by producing ammonia as a byproduct of the arginine deiminase metabolic pathway, increasing the pH of the plaque biofilm. While the benefits of arginine metabolism on oral health have been extensively documented in specific bacterial groups, the prevalence and consistency of arginine deiminase system (ADS) activity among oral bacteria in a community context remain an open question. In the current study, we use a multi-omics approach to document the pervasiveness of the expression of the ADS operon in both health and disease to better understand the conditions in which ADS activity may prevent tooth decay.

Host-microbiome associations in saliva predict COVID-19 severity

Established evidence indicates that oral microbiota plays a crucial role in modulating host immune responses to viral infection. Following severe acute respiratory syndrome coronavirus 2, there are coordinated microbiome and inflammatory responses within the mucosal and systemic compartments that are unknown. The specific roles the oral microbiota and inflammatory cytokines play in the pathogenesis of coronavirus disease 2019 (COVID-19) are yet to be explored. Here, we evaluated the relationships between the salivary microbiome and host parameters in different groups of COVID-19 severity based on their oxygen requirement. Saliva and blood samples (n = 80) were collected from COVID-19 and from noninfected individuals. We characterized the oral microbiomes using 16S ribosomal RNA gene sequencing and evaluated saliva and serum cytokines and chemokines using multiplex analysis. Alpha diversity of the salivary microbial community was negatively associated with COVID-19 severity, while diversity increased with health. Integrated cytokine evaluations of saliva and serum showed that the oral host response was distinct from the systemic response. The hierarchical classification of COVID-19 status and respiratory severity using multiple modalities separately (i.e. microbiome, salivary cytokines, and systemic cytokines) and simultaneously (i.e. multimodal perturbation analyses) revealed that the microbiome perturbation analysis was the most informative for predicting COVID-19 status and severity, followed by the multimodal. Our findings suggest that oral microbiome and salivary cytokines may be predictive of COVID-19 status and severity, whereas atypical local mucosal immune suppression and systemic hyperinflammation provide new cues to understand the pathogenesis in immunologically compromised populations.

Proportion-based normalizations outperform compositional data transformations in machine learning applications

BACKGROUND

Normalization, as a pre-processing step, can significantly affect the resolution of machine learning analysis for microbiome studies. There are countless options for normalization scheme selection. In this study, we examined compositionally aware algorithms including the additive log ratio (alr), the centered log ratio (clr), and a recent evolution of the isometric log ratio (ilr) in the form of balance trees made with the PhILR R package. We also looked at compositionally naïve transformations such as raw counts tables and several transformations that are based on relative abundance, such as proportions, the Hellinger transformation, and a transformation based on the logarithm of proportions (which we call "lognorm").

RESULTS

In our evaluation, we used 65 metadata variables culled from four publicly available datasets at the amplicon sequence variant (ASV) level with a random forest machine learning algorithm. We found that different common pre-processing steps in the creation of the balance trees made very little difference in overall performance. Overall, we found that the compositionally aware data transformations such as alr, clr, and ilr (PhILR) performed generally slightly worse or only as well as compositionally naïve transformations. However, relative abundance-based transformations outperformed most other transformations by a small but reliably statistically significant margin.

CONCLUSIONS

Our results suggest that minimizing the complexity of transformations while correcting for read depth may be a generally preferable strategy in preparing data for machine learning compared to more sophisticated, but more complex, transformations that attempt to better correct for compositionality. Video Abstract.

Pre-challenge gut microbial signature predicts RhCMV/SIV vaccine efficacy in rhesus macaques

Background

RhCMV/SIV vaccines protect ∼59% of vaccinated rhesus macaques against repeated limiting-dose intra-rectal exposure with highly pathogenic SIVmac239M, but the exact mechanism responsible for the vaccine efficacy is not known. It is becoming evident that complex interactions exist between gut microbiota and the host immune system. Here we aimed to investigate if the rhesus gut microbiome impacts RhCMV/SIV vaccine-induced protection.

Methods

Three groups of 15 rhesus macaques naturally pre-exposed to RhCMV were vaccinated with RhCMV/SIV vaccines. Rectal swabs were collected longitudinally both before SIV challenge (after vaccination) and post challenge and were profiled using 16S rRNA based microbiome analysis.

Results

We identified ∼2,400 16S rRNA amplicon sequence variants (ASVs), representing potential bacterial species/strains. Global gut microbial profiles were strongly associated with each of the three vaccination groups, and all animals tended to maintain consistent profiles throughout the pre-challenge phase. Despite vaccination group differences, using newly developed compositional data analysis techniques we identified a common gut microbial signature predictive of vaccine protection outcome across the three vaccination groups. Part of this microbial signature persisted even after SIV challenge. We also observed a strong correlation between this microbial signature and an early signature derived from whole blood transcriptomes in the same animals.

Conclusions

Our findings indicate that changes in gut microbiomes are associated with RhCMV/SIV vaccine-induced protection and early host response to vaccination in rhesus macaques.

Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research

Every year, 11% of infants are born preterm with significant health consequences, with the vaginal microbiome a risk factor for preterm birth. We crowdsource models to predict (1) preterm birth (PTB; <37 weeks) or (2) early preterm birth (ePTB; <32 weeks) from 9 vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from public raw data via phylogenetic harmonization. The predictive models are validated on two independent unpublished datasets representing 331 samples from 148 pregnant individuals. The top-performing models (among 148 and 121 submissions from 318 teams) achieve area under the receiver operator characteristic (AUROC) curve scores of 0.69 and 0.87 predicting PTB and ePTB, respectively. Alpha diversity, VALENCIA community state types, and composition are important features in the top-performing models, most of which are tree-based methods. This work is a model for translation of microbiome data into clinically relevant predictive models and to better understand preterm birth.

Disturbance frequency directs microbial community succession in marine biofilms exposed to shear

IMPORTANCE

Disturbances are major drivers of community succession in many microbial systems; however, relatively little is known about marine biofilm community succession, especially under antifouling disturbance. Antifouling technologies exert strong local disturbances on marine biofilms, and resulting biomass losses can be accompanied by shifts in biofilm community composition and succession. We address this gap in knowledge by bridging microbial ecology with antifouling technology development. We show that disturbance by shear can strongly alter marine biofilm community succession, acting as a selective filter influenced by frequency of exposure. Examining marine biofilm succession patterns with and without shear revealed stable associations between key prokaryotic and eukaryotic taxa, highlighting the importance of cross-domain assessment in future marine biofilm research. Describing how compounded top-down and bottom-up disturbances shape the succession of marine biofilms is valuable for understanding the assembly and stability of these complex microbial communities and predicting species invasiveness.

Another tick bites the dust: exploring the association of microbial composition with a broad transmission competence of tick vector species

IMPORTANCE

Some tick species are competent to transmit more than one pathogen while other species are, until now, known to be competent to transmit only one single or any pathogen. Such a difference in vector competence for one or more pathogens might be related to the microbiome, and understanding what differentiates these two groups of ticks could help us control several diseases aiming at the bacteria groups that contribute to such a broad vector competence. Using 16S rRNA from tick species that could be classified into these groups, genera such as Rickettsia and Staphylococcus seemed to be associated with such a broad vector competence. Our results highlight differences in tick species when they are divided based on the number of pathogens they are competent to transmit. These findings are the first step into understanding the relationship between one single tick species and the pathogens it transmits.

Age-dependent changes in the gut microbiota and serum metabolome correlate with renal function and human aging

Human aging is invariably accompanied by a decline in renal function, a process potentially exacerbated by uremic toxins originating from gut microbes. Based on a registered household Chinese Guangxi longevity cohort (n = 151), we conducted comprehensive profiling of the gut microbiota and serum metabolome of individuals from 22 to 111 years of age and validated the findings in two independent East Asian aging cohorts (Japan aging cohort n = 330, Yunnan aging cohort n = 80), identifying unique age-dependent differences in the microbiota and serum metabolome. We discovered that the influence of the gut microbiota on serum metabolites intensifies with advancing age. Furthermore, mediation analyses unveiled putative causal relationships between the gut microbiota (Escherichia coli, Odoribacter splanchnicus, and Desulfovibrio piger) and serum metabolite markers related to impaired renal function (p-cresol, N-phenylacetylglutamine, 2-oxindole, and 4-aminohippuric acid) and aging. The fecal microbiota transplantation experiment demonstrated that the feces of elderly individuals could influence markers related to impaired renal function in the serum. Our findings reveal novel links between age-dependent alterations in the gut microbiota and serum metabolite markers of impaired renal function, providing novel insights into the effects of microbiota-metabolite interplay on renal function and healthy aging.

Isolated Grauer's gorilla populations differ in diet and gut microbiome

The animal gut microbiome has been implicated in a number of key biological processes, ranging from digestion to behaviour, and has also been suggested to facilitate local adaptation. Yet studies in wild animals rarely compare multiple populations that differ ecologically, which is the level at which local adaptation may occur. Further, few studies simultaneously characterize diet and gut microbiome from the same sample, despite their probable interdependence. Here, we investigate the interplay between diet and gut microbiome in three geographically isolated populations of the critically endangered Grauer's gorilla (Gorilla beringei graueri), which we show to be genetically differentiated. We find population- and social group-specific dietary and gut microbial profiles and covariation between diet and gut microbiome, despite the presence of core microbial taxa. There was no detectable effect of age, and only marginal effects of sex and genetic relatedness on the microbiome. Diet differed considerably across populations, with the high-altitude population consuming a lower diversity of plants compared to low-altitude populations, consistent with plant availability constraining dietary choices. The observed pattern of covariation between diet and gut microbiome is probably a result of long-term social and environmental factors. Our study suggests that the gut microbiome is sufficiently plastic to support flexible food selection and hence contribute to local adaptation.

Statistical normalization methods in microbiome data with application to microbiome cancer research

Mounting evidence has shown that gut microbiome is associated with various cancers, including gastrointestinal (GI) tract and non-GI tract cancers. But microbiome data have unique characteristics and pose major challenges when using standard statistical methods causing results to be invalid or misleading. Thus, to analyze microbiome data, it not only needs appropriate statistical methods, but also requires microbiome data to be normalized prior to statistical analysis. Here, we first describe the unique characteristics of microbiome data and the challenges in analyzing them (Section 2). Then, we provide an overall review on the available normalization methods of 16S rRNA and shotgun metagenomic data along with examples of their applications in microbiome cancer research (Section 3). In Section 4, we comprehensively investigate how the normalization methods of 16S rRNA and shotgun metagenomic data are evaluated. Finally, we summarize and conclude with remarks on statistical normalization methods (Section 5). Altogether, this review aims to provide a broad and comprehensive view and remarks on the promises and challenges of the statistical normalization methods in microbiome data with microbiome cancer research examples.

Community composition and the environment modulate the population dynamics of type VI secretion in human gut bacteria

Understanding the relationship between the composition of the human gut microbiota and the ecological forces shaping it is of great importance; however, knowledge of the biogeographical and ecological relationships between physically interacting taxa is limited. Interbacterial antagonism may play an important role in gut community dynamics, yet the conditions under which antagonistic behaviour is favoured or disfavoured by selection in the gut are not well understood. Here, using genomics, we show that a species-specific type VI secretion system (T6SS) repeatedly acquires inactivating mutations in Bacteroides fragilis in the human gut. This result implies a fitness cost to the T6SS, but we could not identify laboratory conditions under which such a cost manifests. Strikingly, experiments in mice illustrate that the T6SS can be favoured or disfavoured in the gut depending on the strains and species in the surrounding community and their susceptibility to T6SS antagonism. We use ecological modelling to explore the conditions that could underlie these results and find that community spatial structure modulates interaction patterns among bacteria, thereby modulating the costs and benefits of T6SS activity. Our findings point towards new integrative models for interrogating the evolutionary dynamics of type VI secretion and other modes of antagonistic interaction in microbiomes.

Impact of Chronic Exposure to Arsenate through Drinking Water on the Intestinal Barrier

Chronic exposure to inorganic arsenic (As) [As(III) + As(V)], which affects millions of people, increases the incidence of some kinds of cancer and other noncarcinogenic pathologies. Although the oral pathway is the main source of exposure, in vivo studies conducted to verify the intestinal toxicity of this metalloid are scarce and are mainly focused on evaluating the toxicity of As(III). The aim of this study was to evaluate the effect of chronic exposure (6 months) of BALB/c mice to As(V) (15-60 mg/L) via drinking water on the different components of the intestinal barrier and to determine the possible mechanisms involved. The results show that chronic exposure to As(V) generates a situation of oxidative stress (increased lipid peroxidation and reactive species) and inflammation (increased contents of several proinflammatory cytokines and neutrophil infiltrations) in the intestinal tissues. There is also evidence of an altered expression of constituent proteins of the intercellular junctions (Cldn1, Cldn3, and Ocln) and the mucus layer (Muc2) and changes in the composition of the gut microbiota and the metabolism of short-chain fatty acids. All of these toxic effects eventually may lead to the disruption of the intestinal barrier, which shows an increased paracellular permeability. Moreover, signs of endotoxemia are observed in the serum of As(V)-treated animals (increases in lipopolysaccharide-binding protein LBP and the proinflammatory cytokine IL-1β). The data obtained suggest that chronic exposure to As(V) via drinking water affects the intestinal environment.

Microbial community dynamics and cycling of plutonium and iron in a seasonally stratified and radiologically contaminated pond

Plutonium (Pu) cycling and mobility in the environment can be impacted by the iron cycle and microbial community dynamics. We investigated the spatial and temporal changes of the microbiome in an iron (Fe)-rich, plutonium-contaminated, monomictic reservoir (Pond B, Savannah River Site, South Carolina, USA). The microbial community composition varied with depth during seasonal thermal stratification and was strongly correlated with redox. During stratification, Fe(II) oxidizers (e.g., Ferrovum, Rhodoferax, Chlorobium) were most abundant in the hypoxic/anoxic zones, while Fe(III) reducers (e.g., Geothrix, Geobacter) dominated the deep, anoxic zone. Sulfate reducers and methanogens were present in the anoxic layer, likely contributing to iron and plutonium cycling. Multinomial regression of predicted functions/pathways identified metabolisms highly associated with stratification (within the top 5%), including iron reduction, methanogenesis, C1 compound utilization, fermentation, and aromatic compound degradation. Two sediment cores collected at the Inlet and Outlet of the pond were dominated by putative fermenters and organic matter (OM) degraders. Overall, microbiome analyses revealed the potential for three microbial impacts on the plutonium and iron biogeochemical cycles: (1) plutonium bioaccumulation throughout the water column, (2) Pu-Fe-OM-aggregate formation by Fe(II) oxidizers under microaerophilic/aerobic conditions, and (3) Pu-Fe-OM-aggregate or sediment reductive dissolution and organic matter degradation in the deep, anoxic waters.

Phylogenetic variation in raw cow milk microbiota and the impact of forage combinations and use of silage inoculants

Introduction

The microbiota of bulk tank raw milk is known to be closely related to that of microbial niches of the on-farm environment. Preserved forage types are partof this ecosystem and previous studies have shown variations in their microbial ecology. However, little is known of the microbiota of forage ration combinations and the transfer rates of associated species to milk.

Methods

We identified raw milk bacteria that may originate from forage rations encompassing either hay (H) or grass/legume silage uninoculated (GL) as the only forage type, or a combination of GL and corn silage uninoculated (GLC), or grass/legume and corn silage both inoculated (GLICI). Forage and milk samples collected in the fall and spring from 24 dairy farms were analyzed using 16S rRNA gene high-throughput sequencing following a treatment with propidium monoazide to account for viable cells.

Results and discussion

Three community types separating H, GL, and GLICI forage were identified. While the H community was co-dominated by Enterobacteriaceae, Microbacteriaceae, Beijerinckiaceae, and Sphingomonadaceae, the GL and GLICI communities showed high proportions of Leuconostocaceae and Acetobacteraceae, respectively. Most of the GLC and GLICI rations were similar, suggesting that in the mixed forage rations involving grass/legume and corn silage, the addition of inoculant in one or both types of feed does not considerably change the microbiota. Raw milk samples were not grouped in the same way, as the GLC milk was phylogenetically different from that of GLICI across sampling periods. Raw milk communities, including the GLICI group for which cows were fed inoculated forage, were differentiated by Enterobacteriaceae and other Proteobacteria, instead of by lactic acid bacteria. Of the 113 amplicon sequence variants (ASVs) shared between forage rations and corresponding raw milk, bacterial transfer rates were estimated at 18 to 31%. Silage-based forage rations, particularly those including corn, share more ASVs with raw milk produced on corresponding farms compared to that observed in the milk from cows fed hay. These results show the relevance of cow forage rations as sources of bacteria that contaminate milk and serve to advance our knowledge of on-farm raw milk contamination.

A Synthetic Formula Amino Acid Diet Leads to Microbiome Dysbiosis, Reduced Colon Length, Inflammation, and Altered Locomotor Activity in C57BL/6J Mice

The effects of synthetic, free-amino acid diets, similar to those prescribed as supplements for (phenylketonuria) PKU patients, on gut microbiota and overall health are not well understood. In the current, multidisciplinary study, we examined the effects of a synthetically-derived, low-fiber, amino acid diet on behavior, cognition, gut microbiome composition, and inflammatory markers. A cohort of 20 male C57BL/6J mice were randomly assigned to either a standard or synthetic diet (n = 10) at post-natal day 21 and maintained for 13 weeks. Sequencing of the 16S rRNA gene from fecal samples revealed decreased bacterial diversity, increased abundance of bacteria associated with disease, such as Prevotella, and a downward shift in gut microbiota associated with fermentation pathways in the synthetic diet group. Furthermore, there were decreased levels of short chain fatty acids and shortening of the colon in mice consuming the synthetic diet. Finally, we measured TNF-α, IL-6, and IL-10 in serum, the hippocampus, and colon, and found that the synthetic diet significantly increased IL-6 production in the hippocampus. These results demonstrate the importance of a multidisciplinary approach to future diet and microbiome studies, as diet not only impacts the gut microbiome composition but potentially systemic health as well.

Gut Microbiome and Lipidome Signatures in Irritable Bowel Syndrome Patients from a Low-Income, Food-Desert Area: A Pilot Study

Irritable bowel syndrome (IBS) is a common gastroenterological disorder with triggers such as fructose. We showed that our IBS patients suffering from socioeconomic challenges have a significantly high consumption of high-fructose corn syrup (HFCS). Here, we characterize gut microbial dysbiosis and fatty acid changes, with respect to IBS, HFCS consumption, and socioeconomic factors. Fecal samples from IBS patients and healthy controls were subjected to microbiome and lipidome analyses. We assessed phylogenetic diversity and community composition of the microbiomes, and used linear discriminant analysis effect size (LEfSe), analysis of compositions of microbiomes (ANCOM) on highly co-occurring subcommunities (modules), least absolute shrinkage and selection operator (LASSO) on phylogenetic isometric log-ratio transformed (PhILR) taxon abundances to identify differentially abundant taxa. Based on a Procrustes randomization test, the microbiome and lipidome datasets correlated significantly (p = 0.002). Alpha diversity correlated with economic factors (p < 0.001). Multiple subsets of the phylogenetic tree were associated with HFCS consumption (p < 0.001). In IBS patients, relative abundances of potentially beneficial bacteria such as Monoglobaceae, Lachnospiraceae, and Ruminococcaceae were lower (p = 0.007), and Eisenbergiella, associated with inflammatory disorders, was higher. In IBS patients, certain saturated fatty acids were higher and unsaturated fatty acids were lower (p < 0.05). Our study aims first to underscore the influence of HFCS consumption and socioeconomic factors on IBS pathophysiology, and provides new insights that inform patient care.

Developing a New Phylogeny-Driven Random Forest Model for Functional Metagenomics

Metagenomics is an unobtrusive science linking microbial genes to biological functions or environmental states. Classifying microbial genes into their functional repertoire is an important task in the downstream analysis of Metagenomic studies. The task involves Machine Learning (ML) based supervised methods to achieve good classification performance. Random Forest (RF) has been applied rigorously to microbial gene abundance profiles, mapping them to functional phenotypes. The current research targets tuning RF by the evolutionary ancestry of microbial phylogeny, developing a Phylogeny-RF model for functional classification of metagenomes. This method facilitates capturing the effects of phylogenetic relatedness in an ML classifier itself rather than just applying a supervised classifier over the raw abundances of microbial genes. The idea is rooted in the fact that closely related microbes by phylogeny are highly correlated and tend to have similar genetic and phenotypic traits. Such microbes behave similarly; and hence tend to be selected together, or one of these could be dropped from the analysis, to improve the ML process. The proposed Phylogeny-RF algorithm has been compared with state-of-the-art classification methods including RF and the phylogeny-aware methods of MetaPhyl and PhILR, using three real-world 16S rRNA metagenomic datasets. It has been observed that the proposed method not only achieved significantly better performance than the traditional RF model but also performed better than the other phylogeny-driven benchmarks (p < 0.05). For example, Phylogeny-RF attained a highest AUC of 0.949 and Kappa of 0.891 over soil microbiomes in comparison to other benchmarks.

A metabarcoding analysis of the wrackbed microbiome indicates a phylogeographic break along the North Sea-Baltic Sea transition zone

Sandy beaches are biogeochemical hotspots that bridge marine and terrestrial ecosystems via the transfer of organic matter, such as seaweed (termed wrack). A keystone of this unique ecosystem is the microbial community, which helps to degrade wrack and re-mineralize nutrients. However, little is known about this community. Here, we characterize the wrackbed microbiome as well as the microbiome of a primary consumer, the seaweed fly Coelopa frigida, and examine how they change along one of the most studied ecological gradients in the world, the transition from the marine North Sea to the brackish Baltic Sea. We found that polysaccharide degraders dominated both microbiomes, but there were still consistent differences between wrackbed and fly samples. Furthermore, we observed a shift in both microbial communities and functionality between the North and Baltic Sea driven by changes in the frequency of different groups of known polysaccharide degraders. We hypothesize that microbes were selected for their abilities to degrade different polysaccharides corresponding to a shift in polysaccharide content in the different seaweed communities. Our results reveal the complexities of both the wrackbed microbial community, with different groups specialized to different roles, and the cascading trophic consequences of shifts in the near shore algal community.

Mild SARS-CoV-2 infection results in long-lasting microbiota instability

Viruses targeting mammalian cells can indirectly alter the gut microbiota, potentially compounding their phenotypic effects. Multiple studies have observed a disrupted gut microbiota in severe cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection that require hospitalization. Yet, despite demographic shifts in disease severity resulting in a large and continuing burden of non-hospitalized infections, we still know very little about the impact of mild SARS-CoV-2 infection on the gut microbiota in the outpatient setting. To address this knowledge gap, we longitudinally sampled 14 SARS-CoV-2-positive subjects who remained outpatient and 4 household controls. SARS-CoV-2 cases exhibited a significantly less stable gut microbiota relative to controls. These results were confirmed and extended in the K18-humanized angiotensin-converting enzyme 2 mouse model, which is susceptible to SARS-CoV-2 infection. All of the tested SARS-CoV-2 variants significantly disrupted the mouse gut microbiota, including USA-WA1/2020 (the original variant detected in the USA), Delta, and Omicron. Surprisingly, despite the fact that the Omicron variant caused the least severe symptoms in mice, it destabilized the gut microbiota and led to a significant depletion in Akkermansia muciniphila. Furthermore, exposure of wild-type C57BL/6J mice to SARS-CoV-2 disrupted the gut microbiota in the absence of severe lung pathology. IMPORTANCE Taken together, our results demonstrate that even mild cases of SARS-CoV-2 can disrupt gut microbial ecology. Our findings in non-hospitalized individuals are consistent with studies of hospitalized patients, in that reproducible shifts in gut microbial taxonomic abundance in response to SARS-CoV-2 have been difficult to identify. Instead, we report a long-lasting instability in the gut microbiota. Surprisingly, our mouse experiments revealed an impact of the Omicron variant, despite producing the least severe symptoms in genetically susceptible mice, suggesting that despite the continued evolution of SARS-CoV-2, it has retained its ability to perturb the intestinal mucosa. These results will hopefully renew efforts to study the mechanisms through which Omicron and future SARS-CoV-2 variants alter gastrointestinal physiology, while also considering the potentially broad consequences of SARS-CoV-2-induced microbiota instability for host health and disease.

Droplet-based high-throughput single microbe RNA sequencing by smRandom-seq

Bacteria colonize almost all parts of the human body and can differ significantly. However, the population level transcriptomics measurements can only describe the average bacteria population behaviors, ignoring the heterogeneity among bacteria. Here, we report a droplet-based high-throughput single-microbe RNA-seq assay (smRandom-seq), using random primers for in situ cDNA generation, droplets for single-microbe barcoding, and CRISPR-based rRNA depletion for mRNA enrichment. smRandom-seq showed a high species specificity (99%), a minor doublet rate (1.6%), a reduced rRNA percentage (32%), and a sensitive gene detection (a median of ~1000 genes per single E. coli). Furthermore, smRandom-seq successfully captured transcriptome changes of thousands of individual E. coli and discovered a few antibiotic resistant subpopulations displaying distinct gene expression patterns of SOS response and metabolic pathways in E. coli population upon antibiotic stress. smRandom-seq provides a high-throughput single-microbe transcriptome profiling tool that will facilitate future discoveries in microbial resistance, persistence, microbe-host interaction, and microbiome research.

Impact of HIV on the Oral Microbiome of Children Living in Sub-Saharan Africa, Determined by Using an rpoC Gene Fragment Metataxonomic Approach

Children living with HIV have a higher prevalence of oral diseases, including caries, but the mechanisms underlying this higher prevalence are not well understood. Here, we test the hypothesis that HIV infection is associated with a more cariogenic oral microbiome, characterized by an increase in bacteria involved in the pathogenesis of caries. We present data generated from supragingival plaques collected from 484 children representing three exposure groups: (i) children living with HIV (HI), (ii) children who were perinatally exposed but uninfected (HEU), and (iii) unexposed and therefore uninfected children (HUU). We found that the microbiome of HI children is distinct from those of HEU and HUU children and that this distinction is more pronounced in diseased teeth than healthy teeth, suggesting that the impact of HIV is more severe as caries progresses. Moreover, we report both an increase in bacterial diversity and a decrease in community similarity in our older HI cohort compared to our younger HI cohort, which may in part be a prolonged effect of HIV and/or its treatment. Finally, while Streptococcus mutans is often a dominant species in late-stage caries, it tended to be found at lower frequency in our HI cohort than in other groups. Our results highlight the taxonomic diversity of the supragingival plaque microbiome and suggest that broad and increasingly individualistic ecological shifts are responsible for the pathogenesis of caries in children living with HIV, coupled with a diverse and possibly severe impact on known cariogenic taxa that potentially exacerbates caries. IMPORTANCE Since its recognition as a global epidemic in the early 1980s, approximately 84.2 million people have been diagnosed with HIV and 40.1 million people have died from AIDS-related illnesses. The development and increased global availability of antiretroviral treatment (ART) regimens have dramatically reduced the mortality rate of HIV and AIDS, yet approximately 1.5 million new infections were reported in 2021, 51% of which are in sub-Saharan Africa. People living with HIV have a higher prevalence of caries and other chronic oral diseases, the mechanisms of which are not well understood. Here, we used a novel genetic approach to characterize the supragingival plaque microbiome of children living with HIV and compared it to the microbiomes of uninfected and perinatally exposed children to better understand the role of oral bacteria in the etiology of tooth decay in the context of HIV exposure and infection.

Non-destructive insect metabarcoding for surveillance and biosecurity in citrus orchards: recording the good, the bad and the psyllids

Background

The Australian citrus industry remains one of the few in the world to be unaffected by the African and the Asian citrus psyllids, Trioza erytreae Del Guercio and Diaphorina citri Kuwayama, respectively, and the diseases their vectored bacteria can cause. Surveillance, early detection, and strict quarantine measures are therefore fundamental to safeguard Australian citrus. However, long-term targeted surveillance for exotic citrus pests can be a time-consuming and expensive activity, often relying on manually screening large numbers of trap samples and morphological identification of specimens, which requires a high level of taxonomic knowledge.

Methods

Here we evaluated the use of non-destructive insect metabarcoding for exotic pest surveillance in citrus orchards. We conducted an 11-week field trial, between the months of December and February, at a horticultural research farm (SuniTAFE Smart Farm) in the Northwest of Victoria, Australia, and processed more than 250 samples collected from three types of invertebrate traps across four sites.

Results

The whole-community metabarcoding data enabled comparisons between different trapping methods, demonstrated the spatial variation of insect diversity across the same orchard, and highlighted how comprehensive assessment of insect biodiversity requires use of multiple complimentary trapping methods. In addition to revealing the diversity of native psyllid species in citrus orchards, the non-targeted metabarcoding approach identified a diversity of other pest and beneficial insects and arachnids within the trap bycatch, and recorded the presence of the triozid Casuarinicola cf warrigalensis for the first time in Victoria. Ultimately, this work highlights how a non-targeted surveillance approach for insect monitoring coupled with non-destructive DNA metabarcoding can provide accurate and high-throughput species identification for biosecurity and biodiversity monitoring.

Algae drive convergent bacterial community assembly at low dilution frequency

Microbial community assembly is a complex dynamical process that determines community structure and function. The interdependence of inter-species interactions and nutrient availability presents a challenge for understanding community assembly. We sought to understand how external nutrient supply rate modulated interactions to affect the assembly process. A statistical decomposition of taxonomic structures of bacterial communities assembled with and without algae and at varying dilution frequencies allowed the separation of the effects of biotic (presence of algae) and abiotic (dilution frequency) factors on community assembly. For infrequent dilutions, the algae strongly impact community assembly, driving initially diverse bacterial consortia to converge to a common structure. Analyzing sequencing data revealed that this convergence is largely mediated by a decline in the relative abundance of specific taxa in the presence of algae. This study shows that complex phototroph-heterotroph communities can be powerful model systems for understanding assembly processes relevant to the global ecosystem functioning.

It's all relative: Regression analysis with compositional predictors

Compositional data reside in a simplex and measure fractions or proportions of parts to a whole. Most existing regression methods for such data rely on log-ratio transformations that are inadequate or inappropriate in modeling high-dimensional data with excessive zeros and hierarchical structures. Moreover, such models usually lack a straightforward interpretation due to the interrelation between parts of a composition. We develop a novel relative-shift regression framework that directly uses proportions as predictors. The new framework provides a paradigm shift for regression analysis with compositional predictors and offers a superior interpretation of how shifting concentration between parts affects the response. New equi-sparsity and tree-guided regularization methods and an efficient smoothing proximal gradient algorithm are developed to facilitate feature aggregation and dimension reduction in regression. A unified finite-sample prediction error bound is derived for the proposed regularized estimators. We demonstrate the efficacy of the proposed methods in extensive simulation studies and a real gut microbiome study. Guided by the taxonomy of the microbiome data, the framework identifies important taxa at different taxonomic levels associated with the neurodevelopment of preterm infants.

Differences in the gut microbiome across typical ageing and in Parkinson's disease

The microbiota-gut-brain axis' role in Parkinson's disease (PD) pathophysiology, and how this differs from typical ageing, is poorly understood. Presently, gut-bacterial diversity, taxonomic abundance and metabolic bacterial pathways were compared across healthy young (n = 22, 18-35 years), healthy older (n = 33, 50-80 years), and PD groups (n = 18, 50-80 years) using shotgun sequencing and compositional data analysis. Associations between the gut-microbiome and PD symptoms, and between lifestyle factors (fibre intake, physical activity, and sleep) and the gut-microbiome were conducted. Alpha-diversity did not differ between PD participants and older adults, whilst beta-diversity differed between these groups. Lower abundance of Butyricimonas synergistica, a butyrate-producer, was associated with worse PD non-motor symptoms in the PD group. Regarding typical ageing, Bifidobacterium bifidum, was greater in the younger compared to older group, with no difference between the older and PD group. Abundance of metabolic pathways related to butyrate production did not differ among the groups, while 100 other metabolic pathways differed among the three groups. Sleep efficiency was positively associated with Roseburia inulinivorans in the older group. These results highlight the relevance of gut-microbiota to PD and that reduced butyrate-production may be involved with PD pathophysiology. Future studies should account for lifestyle factors when investigating gut-microbiomes across ageing and in PD.

Reduced microbial diversity of the nasopharyngeal microbiome in household contacts with latent tuberculosis infection

The upper respiratory tract is an obliged pathway for respiratory pathogens and a healthy microbiota may support the host's mucosal immunity preventing infection. We analyzed the nasopharyngeal microbiome in tuberculosis household contacts (HHCs) and its association with latent tuberculosis infection (TBI). A prospective cohort of HHCs was established and latent TBI status was assessed by serial interferon-γ release assay (IGRA). Nasopharyngeal swabs collected at baseline were processed for 16S rRNA gene sequencing. The 82 participants included in the analysis were classified as: (a) non-TBI [IGRA negative at baseline and follow-up, no active TB (n = 31)], (b) pre-TBI [IGRA negative at baseline but converted to IGRA positive or developed active TB at follow-up (n = 16)], and (c) TBI [IGRA positive at enrollment (n = 35)]. Predominant phyla were Actinobacteriota, Proteobacteria, Firmicutes and Bacteroidota. TBI group had a lower alpha diversity compared to non-TBI (p_adj = 0.04) and pre-TBI (p_adj = 0.04). Only TBI and non-TBI had beta diversity differences (p_adj = 0.035). Core microbiomes' had unique genera, and genus showed differential abundance among groups. HHCs with established latent TBI showed reduced nasopharyngeal microbial diversity with distinctive taxonomical composition. Whether a pre-existing microbiome feature favors, are a consequence, or protects against Mycobacterium tuberculosis needs further investigation.

Host-Microbiome Associations in Saliva Predict COVID-19 Severity

Established evidence indicates that oral microbiota plays a crucial role in modulating host immune responses to viral infection. Following Severe Acute Respiratory Syndrome Coronavirus 2 - SARS-CoV-2 - there are coordinated microbiome and inflammatory responses within the mucosal and systemic compartments that are unknown. The specific roles that the oral microbiota and inflammatory cytokines play in the pathogenesis of COVID-19 are yet to be explored. We evaluated the relationships between the salivary microbiome and host parameters in different groups of COVID-19 severity based on their Oxygen requirement. Saliva and blood samples (n = 80) were collected from COVID-19 and from non-infected individuals. We characterized the oral microbiomes using 16S ribosomal RNA gene sequencing and evaluated saliva and serum cytokines using Luminex multiplex analysis. Alpha diversity of the salivary microbial community was negatively associated with COVID-19 severity. Integrated cytokine evaluations of saliva and serum showed that the oral host response was distinct from the systemic response. The hierarchical classification of COVID-19 status and respiratory severity using multiple modalities separately (i.e., microbiome, salivary cytokines, and systemic cytokines) and simultaneously (i.e., multi-modal perturbation analyses) revealed that the microbiome perturbation analysis was the most informative for predicting COVID-19 status and severity, followed by the multi-modal. Our findings suggest that oral microbiome and salivary cytokines may be predictive of COVID-19 status and severity, whereas atypical local mucosal immune suppression and systemic hyperinflammation provide new cues to understand the pathogenesis in immunologically naïve populations.

The Effect of Residual Pesticide Application on Microbiomes of the Storage Mite Tyrophagus putrescentiae

Arthropods can host well-developed microbial communities, and such microbes can degrade pesticides and confer tolerance to most types of pests. Two cultures of the stored-product mite Tyrophagus putrescentiae, one with a symbiotic microbiome containing Wolbachia and the other without Wolbachia, were compared on pesticide residue (organophosphate: pirimiphos-methyl and pyrethroid: deltamethrin, deltamethrin + piperonyl butoxide)-containing diets. The microbiomes from mite bodies, mite feces and debris from the spent mite diet were analyzed using barcode sequencing. Pesticide tolerance was different among mite cultures and organophosphate and pyrethroid pesticides. The pesticide residues influenced the microbiome composition in both cultures but without any remarkable trend for mite cultures with and without Wolbachia. The most influenced bacterial taxa were Bartonella-like and Bacillus for both cultures and Wolbachia for the culture containing this symbiont. However, there was no direct evidence of any effect of Wolbachia on pesticide tolerance. The high pesticide concentration residues in diets reduced Wolbachia, Bartonella-like and Bacillus in mites of the symbiotic culture. This effect was low for Bartonella-like and Bacillus in the asymbiotic microbiome culture. The results showed that the microbiomes of mites are affected by pesticide residues in the diets, but the effect is not systemic. No actual detoxification effect by the microbiome was observed for the tested pesticides.

Kernel-based genetic association analysis for microbiome phenotypes identifies host genetic drivers of beta-diversity

BACKGROUND

Understanding human genetic influences on the gut microbiota helps elucidate the mechanisms by which genetics may influence health outcomes. Typical microbiome genome-wide association studies (GWAS) marginally assess the association between individual genetic variants and individual microbial taxa. We propose a novel approach, the covariate-adjusted kernel RV (KRV) framework, to map genetic variants associated with microbiome beta-diversity, which focuses on overall shifts in the microbiota. The KRV framework evaluates the association between genetics and microbes by comparing similarity in genetic profiles, based on groups of variants at the gene level, to similarity in microbiome profiles, based on the overall microbiome composition, across all pairs of individuals. By reducing the multiple-testing burden and capturing intrinsic structure within the genetic and microbiome data, the KRV framework has the potential of improving statistical power in microbiome GWAS.

RESULTS

We apply the covariate-adjusted KRV to the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) in a two-stage (first gene-level, then variant-level) genome-wide association analysis for gut microbiome beta-diversity. We have identified an immunity-related gene, IL23R, reported in a previous microbiome genetic association study and discovered 3 other novel genes, 2 of which are involved in immune functions or autoimmune disorders. In addition, simulation studies show that the covariate-adjusted KRV has a greater power than other microbiome GWAS methods that rely on univariate microbiome phenotypes across a range of scenarios.

CONCLUSIONS

Our findings highlight the value of the covariate-adjusted KRV as a powerful microbiome GWAS approach and support an important role of immunity-related genes in shaping the gut microbiome composition. Video Abstract.

Exploring the microbiome of oral epithelial dysplasia as a predictor of malignant progression

A growing body of research associates the oral microbiome and oral cancer. Well-characterized clinical samples with outcome data are required to establish relevant associations between the microbiota and disease. The objective of this study was to characterize the community variations and the functional implications of the microbiome in low-grade oral epithelial dysplasia (OED) using 16S rRNA gene sequencing from annotated archival swabs in progressing (P) and non-progressing (NP) OED. We characterised the microbial community in 90 OED samples - 30 swabs from low-grade OED that progressed to cancer (cases) and 60 swabs from low-grade OED that did not progress after a minimum of 5 years of follow up (matched control subjects). There were small but significant differences between P and NP samples in terms of alpha diversity as well as beta diversity in conjunction with other clinical factors such as age and smoking status for both taxa and functional predictions. Across all samples, the most abundant genus was Streptococcus, followed by Haemophilus, Rothia, and Neisseria. Taxa and predicted functions were identified that were significantly differentially abundant with progression status (all Ps and NPs), when samples were grouped broadly by the number of years between sampling and progression or in specific time to progression for Ps only. However, these differentially abundant features were typically present only at low abundances. For example, Campylobacter was present in slightly higher abundance in Ps (1.72%) than NPs (1.41%) and this difference was significant when Ps were grouped by time to progression. Furthermore, several of the significantly differentially abundant functions were linked to the Campylobacteraceae family in Ps and may justify further investigation. Larger cohort studies to further explore the microbiome as a potential biomarker of risk in OED are warranted.

From the Mountain to the Valley: Drivers of Groundwater Prokaryotic Communities along an Alpine River Corridor

Rivers are the “tip of the iceberg”, with the underlying groundwater being the unseen freshwater majority. Microbial community composition and the dynamics of shallow groundwater ecosystems are thus crucial, due to their potential impact on ecosystem processes and functioning. In early summer and late autumn, samples of river water from 14 stations and groundwater from 45 wells were analyzed along a 300 km transect of the Mur River valley, from the Austrian alps to the flats at the Slovenian border. The active and total prokaryotic communities were characterized using high-throughput gene amplicon sequencing. Key physico-chemical parameters and stress indicators were recorded. The dataset was used to challenge ecological concepts and assembly processes in shallow aquifers. The groundwater microbiome is analyzed regarding its composition, change with land use, and difference to the river. Community composition and species turnover differed significantly. At high altitudes, dispersal limitation was the main driver of groundwater community assembly, whereas in the lowland, homogeneous selection explained the larger share. Land use was a key determinant of the groundwater microbiome composition. The alpine region was more diverse and richer in prokaryotic taxa, with some early diverging archaeal lineages being highly abundant. This dataset shows a longitudinal change in prokaryotic communities that is dependent on regional differences affected by geomorphology and land use.

Decision Tree Ensembles Utilizing Multivariate Splits Are Effective at Investigating Beta Diversity in Medically Relevant 16S Amplicon Sequencing Data

Developing an understanding of how microbial communities vary across conditions is an important analytical step. We used 16S rRNA data isolated from human stool samples to investigate whether learned dissimilarities, such as those produced using unsupervised decision tree ensembles, can be used to improve the analysis of the composition of bacterial communities in patients suffering from Crohn's disease and adenomas/colorectal cancers. We also introduce a workflow capable of learning dissimilarities, projecting them into a lower dimensional space, and identifying features that impact the location of samples in the projections. For example, when used with the centered log ratio transformation, our new workflow (TreeOrdination) could identify differences in the microbial communities of Crohn's disease patients and healthy controls. Further investigation of our models elucidated the global impact amplicon sequence variants (ASVs) had on the locations of samples in the projected space and how each ASV impacted individual samples in this space. Furthermore, this approach can be used to integrate patient data easily into the model and results in models that generalize well to unseen data. Models employing multivariate splits can improve the analysis of complex high-throughput sequencing data sets because they are better able to learn about the underlying structure of the data set. IMPORTANCE There is an ever-increasing level of interest in accurately modeling and understanding the roles that commensal organisms play in human health and disease. We show that learned representations can be used to create informative ordinations. We also demonstrate that the application of modern model introspection algorithms can be used to investigate and quantify the impacts of taxa in these ordinations, and that the taxa identified by these approaches have been associated with immune-mediated inflammatory diseases and colorectal cancer.

Local environment drives rapid shifts in composition and phylogenetic clustering of seagrass microbiomes

Plant microbiomes depend on environmental conditions, stochasticity, host species, and genotype identity. Eelgrass (Zostera marina) is a unique system for plant-microbe interactions as a marine angiosperm growing in a physiologically-challenging environment with anoxic sediment, periodic exposure to air at low tide, and fluctuations in water clarity and flow. We tested the influence of host origin versus environment on eelgrass microbiome composition by transplanting 768 plants among four sites within Bodega Harbor, CA. Over three months following transplantation, we sampled microbial communities monthly on leaves and roots and sequenced the V4-V5 region of the 16S rRNA gene to assess community composition. The main driver of leaf and root microbiome composition was destination site; more modest effects of host origin site did not last longer than one month. Community phylogenetic analyses suggested that environmental filtering structures these communities, but the strength and nature of this filtering varies among sites and over time and roots and leaves show opposing gradients in clustering along a temperature gradient. We demonstrate that local environmental differences create rapid shifts in associated microbial community composition with potential functional implications for rapid host acclimation under shifting environmental conditions.

Intestinal homeostasis disruption in mice chronically exposed to arsenite-contaminated drinking water

Chronic exposure to inorganic arsenic [As(III) and As(V)] affects about 200 million people, and is linked to a greater incidence of certain types of cancer. Drinking water is the main route of exposure, so, in endemic areas, the intestinal mucosa is constantly exposed to the metalloid. However, studies on the intestinal toxicity of inorganic As are scarce. The objective of this study was to evaluate the toxicity of a chronic exposure to As(III) on the intestinal mucosa and its associated microbiota. For this purpose, BALB/c mice were exposed during 6 months through drinking water to As(III) (15 and 30 mg/L). Treatment with As(III) increased reactive oxygen species (43-64%) and lipid peroxidation (8-51%). A pro-inflammatory response was also observed, evidenced by an increase in fecal lactoferrin (23-29%) and mucosal neutrophil infiltration. As(III) also induced an increase in the colonic levels of pro-inflammatory cytokines (24-201%) and the activation of some pro-inflammatory signaling pathways. Reductions in the number of goblet cells and mucus production were also observed. Moreover, As(III) exposure resulted in changes in gut microbial alpha diversity but no differences in beta diversity. This suggested that the abundance of some taxa was significantly affected by As(III), although the composition of the population did not show significant alterations. Analysis of differential taxa agreed with this, 21 ASVs were affected in abundance or variability, especially ASVs from the family Muribaculaceae. Intestinal microbiota metabolism was also affected, as reductions in fecal concentration of short-chain fatty acids were observed. The effects observed on different components of the intestinal barrier may be responsible of the increased permeability in As(III) treated mice, evidenced by an increase in fecal albumin (48-66%). Moreover, serum levels of Lipopolysaccharide binding proteins and TNF-α were increased in animals treated with 30 mg/L of As(III), suggesting a low-level systemic inflammation.

Community composition and the environment modulate the population dynamics of type VI secretion in human gut bacteria

Understanding the relationship between the composition of the human gut microbiota and the ecological forces shaping it is of high importance as progress towards therapeutic modulation of the microbiota advances. However, given the inaccessibility of the gastrointestinal tract, our knowledge of the biogeographical and ecological relationships between physically interacting taxa has been limited to date. It has been suggested that interbacterial antagonism plays an important role in gut community dynamics, but in practice the conditions under which antagonistic behavior is favored or disfavored by selection in the gut environment are not well known. Here, using phylogenomics of bacterial isolate genomes and analysis of infant and adult fecal metagenomes, we show that the contact-dependent type VI secretion system (T6SS) is repeatedly lost from the genomes of Bacteroides fragilis in adults compare to infants. Although this result implies a significant fitness cost to the T6SS, but we could not identify in vitro conditions under which such a cost manifests. Strikingly, however, experiments in mice illustrated that the B. fragilis T6SS can be favored or disfavored in the gut environment, depending on the strains and species in the surrounding community and their susceptibility to T6SS antagonism. We use a variety of ecological modeling techniques to explore the possible local community structuring conditions that could underlie the results of our larger scale phylogenomic and mouse gut experimental approaches. The models illustrate robustly that the pattern of local community structuring in space can modulate the extent of interactions between T6SS-producing, sensitive, and resistant bacteria, which in turn control the balance of fitness costs and benefits of performing contact-dependent antagonistic behavior. Taken together, our genomic analyses, in vivo studies, and ecological theory point toward new integrative models for interrogating the evolutionary dynamics of type VI secretion and other predominant modes of antagonistic interaction in diverse microbiomes.

Non-destructive insect metabarcoding as a surveillance tool for the Australian grains industry: a first trial for the iMapPESTS smart trap

Surveillance and long-term monitoring of insect pest populations are of paramount importance to limit dispersal and inform pest management. Molecular methods have been employed in diagnostics, surveillance and monitoring for the past few decades, often paired with more traditional techniques relying on morphological examinations. Within this context, the ‘iMapPESTS: Sentinel Surveillance for Agriculture’ project was conceptualised to enhance on-farm pest management decision-making via development and deployment of smart traps, able to collect insects, as well as recording associated environmental data. Here, we compared an iMapPESTS ‘Sentinel’ smart trap to an alternative suction trap over a 10-week period. We used a non-destructive insect metabarcoding approach complemented by insect morphological diagnostics to assess and compare aphid species presence and diversity across trap samples and time. Furthermore, we paired this with environmental data recorded throughout the sampling period. This methodology recorded a total of 497 different taxa from 70 traps over a 10-week period in the grain-growing region in western Victoria. This included not only the 14 aphid target species, but an additional 12 aphid species, including a new record for Victoria. Ultimately, with more than 450 bycatch species detected, this highlighted the value of insect metabarcoding, not only for pest surveillance, but also at a broader ecosystem level, with potential applications in integrated pest management and biocontrol.

Microbiome Alterations in Alcohol Use Disorder and Alcoholic Liver Disease

Microbiome alterations are emerging as one of the most important factors that influence the course of alcohol use disorder (AUD). Recent advances in bioinformatics enable more robust and accurate characterization of changes in the composition of the microbiome. In this study, our objective was to provide the most comprehensive and up-to-date evaluation of microbiome alterations associated with AUD and alcoholic liver disease (ALD). To achieve it, we have applied consistent, state of art bioinformatic workflow to raw reads from multiple 16S rRNA sequencing datasets. The study population consisted of 122 patients with AUD, 75 with ALD, 54 with non-alcoholic liver diseases, and 260 healthy controls. We have found several microbiome alterations that were consistent across multiple datasets. The most consistent changes included a significantly lower abundance of multiple butyrate-producing families, including Ruminococcaceae, Lachnospiraceae, and Oscillospiraceae in AUD compared to HC and further reduction of these families in ALD compared with AUD. Other important results include an increase in endotoxin-producing Proteobacteria in AUD, with the ALD group having the largest increase. All of these alterations can potentially contribute to increased intestinal permeability and inflammation associated with AUD and ALD.

Rumen and cecum bacteria of beef cattle that differ in feed efficiency fed a forage diet

Most of the research addressing feed efficiency and the microbiota has been conducted in cattle fed grain diets, although cattle evolved to consume forage diets. Our hypothesis was that the bacteria in the rumen and cecum differed in cattle that have a common feed intake but had different ^average daily body weight gains (ADG) on a forage diet. Heifers (n = 134) were 606 ± 1 d of age and weighed 476 ± 3 kg at the start of the 84-d feeding study. Heifers were offered ad libitum access to a totally mixed ration that consisted of 86% ground brome hay, 10% wet distillers grains with solubles, and 4% mineral supplement as dry matter. Feed intake and body weight gain were measured, and gain was calculated. Heifers with the least (n = 8) and greatest (n = 8) ADG within 0.32 SD of the mean daily dry matter intake were selected for sampling. Digesta samples from the rumen and cecum were collected, and subsequent 16S analysis was conducted to identify Amplicon Sequence Variants. There were no differences in Alpha and Beta diversity between ADG classification within sample sites (P > 0.05). Both sample sites contained calculated balances of sister clades using phylogenetic isometric log ratio transferred data that differed across ADG classification. These findings suggest that bacteria did not differ at the community level, but there was structural difference at the clade level.

Best practice for wildlife gut microbiome research: A comprehensive review of methodology for 16S rRNA gene investigations

Extensive research in well-studied animal models underscores the importance of commensal gastrointestinal (gut) microbes to animal physiology. Gut microbes have been shown to impact dietary digestion, mediate infection, and even modify behavior and cognition. Given the large physiological and pathophysiological contribution microbes provide their host, it is reasonable to assume that the vertebrate gut microbiome may also impact the fitness, health and ecology of wildlife. In accordance with this expectation, an increasing number of investigations have considered the role of the gut microbiome in wildlife ecology, health, and conservation. To help promote the development of this nascent field, we need to dissolve the technical barriers prohibitive to performing wildlife microbiome research. The present review discusses the 16S rRNA gene microbiome research landscape, clarifying best practices in microbiome data generation and analysis, with particular emphasis on unique situations that arise during wildlife investigations. Special consideration is given to topics relevant for microbiome wildlife research from sample collection to molecular techniques for data generation, to data analysis strategies. Our hope is that this article not only calls for greater integration of microbiome analyses into wildlife ecology and health studies but provides researchers with the technical framework needed to successfully conduct such investigations.

Mild SARS-CoV-2 infection results in long-lasting microbiota instability

Viruses targeting mammalian cells can indirectly alter the gut microbiota, potentially compounding their phenotypic effects. Multiple studies have observed a disrupted gut microbiota in severe cases of SARS-CoV-2 infection that require hospitalization. Yet, despite demographic shifts in disease severity resulting in a large and continuing burden of non-hospitalized infections, we still know very little about the impact of mild SARS-CoV-2 infection on the gut microbiota in the outpatient setting. To address this knowledge gap, we longitudinally sampled 14 SARS-CoV-2 positive subjects who remained outpatient and 4 household controls. SARS-CoV-2 cases exhibited a significantly less stable gut microbiota relative to controls, as long as 154 days after their positive test. These results were confirmed and extended in the K18-hACE2 mouse model, which is susceptible to SARS-CoV-2 infection. All of the tested SARS-CoV-2 variants significantly disrupted the mouse gut microbiota, including USA-WA1/2020 (the original variant detected in the United States), Delta, and Omicron. Surprisingly, despite the fact that the Omicron variant caused the least severe symptoms in mice, it destabilized the gut microbiota and led to a significant depletion in Akkermansia muciniphila . Furthermore, exposure of wild-type C57BL/6J mice to SARS-CoV-2 disrupted the gut microbiota in the absence of severe lung pathology.

IMPORTANCE

Taken together, our results demonstrate that even mild cases of SARS-CoV-2 can disrupt gut microbial ecology. Our findings in non-hospitalized individuals are consistent with studies of hospitalized patients, in that reproducible shifts in gut microbial taxonomic abundance in response to SARS-CoV-2 have been difficult to identify. Instead, we report a long-lasting instability in the gut microbiota. Surprisingly, our mouse experiments revealed an impact of the Omicron variant, despite producing the least severe symptoms in genetically susceptible mice, suggesting that despite the continued evolution of SARS-CoV-2 it has retained its ability to perturb the intestinal mucosa. These results will hopefully renew efforts to study the mechanisms through which Omicron and future SARS-CoV-2 variants alter gastrointestinal physiology, while also considering the potentially broad consequences of SARS-CoV-2-induced microbiota instability for host health and disease.

The devil is in the details: Variable impacts of season, BMI, sampling site temperature, and presence of insects on the post-mortem microbiome

Background

Post-mortem microbial communities are increasingly investigated as proxy evidence for a variety of factors of interest in forensic science. The reported predictive power of the microbial community to determine aspects of the individual's post-mortem history (e.g., the post-mortem interval) varies substantially among published research. This observed variation is partially driven by the local environment or the individual themselves. In the current study, we investigated the impact of BMI, sex, insect activity, season, repeat sampling, decomposition time, and temperature on the microbial community sampled from donated human remains in San Marcos, TX using a high-throughput gene-fragment metabarcoding approach.

Materials and methods

In the current study, we investigated the impact of BMI, sex, insect activity, season, repeat sampling, decomposition time, and temperature on the microbial community sampled from donated human remains in San Marcos, TX using a high-throughput gene-fragment metabarcoding approach.

Results

We found that season, temperature at the sampling site, BMI, and sex had a significant effect on the post-mortem microbiome, the presence of insects has a homogenizing influence on the total bacterial community, and that community consistency from repeat sampling decreases as the decomposition process progresses. Moreover, we demonstrate the importance of temperature at the site of sampling on the abundance of important diagnostic taxa.

Conclusion

The results of this study suggest that while the bacterial community or specific bacterial species may prove to be useful for forensic applications, a clearer understanding of the mechanisms underpinning microbial decomposition will greatly increase the utility of microbial evidence in forensic casework.

Meta-analysis of caries microbiome studies can improve upon disease prediction outcomes

As one of the most prevalent infective diseases worldwide, it is crucial that we not only know the constituents of the oral microbiome in dental caries but also understand its functionality. Herein, we present a reproducible meta-analysis to effectively report the key components and the associated functional signature of the oral microbiome in dental caries. Publicly available sequencing data were downloaded from online repositories and subjected to a standardized analysis pipeline before analysis. Meta-analyses identified significant differences in alpha and beta diversities of carious microbiomes when compared to healthy ones. Additionally, machine learning and receiver operator characteristic analysis showed an ability to discriminate between healthy and disease microbiomes. We identified from importance values, as derived from random forest analyses, a group of genera, notably containing Selenomonas, Aggregatibacter, Actinomyces and Treponema, which can be predictive of dental caries. Finally, we propose the most appropriate study design for investigating the microbiome of dental caries by synthesizing the studies, which had the most accurate differentiation based on random forest modelling. In conclusion, we have developed a non-biased, reproducible pipeline, which can be applied to microbiome meta-analyses of multiple diseases, but importantly we have derived from our meta-analysis a key group of organisms that can be used to identify individuals at risk of developing dental caries based on oral microbiome inhabitants.