Fecal tryptophan metabolite profiling in newborns in relation to microbiota and antibiotic treatment

In the first days of life, the newborns' intestinal microbiota develops simultaneously with the intestinal gut barrier and follows intestinal immunity. The mode of delivery shows significant impact on microbial development and, thus, the initiation of the tryptophan catabolism pathway. Further antibiotics (ATB) treatment of mothers before or during delivery affects the microbial and tryptophan metabolite composition of stool of the caesarean- and vaginal-delivered newborns. The determination of microbiome and levels of tryptophan microbial metabolites in meconium and stool can characterize intestinal colonization of a newborn. From 134 samples from the Central European Longitudinal Studies of Parents and Children: The Next Generation (CELSPAC: TNG) cohort study, 16S rRNA gene sequencing was performed, and microbial tryptophan metabolites were quantified using ultra-high-performance liquid chromatography with triple-quadrupole mass spectrometry. Microbial diversity and concentrations of tryptophan metabolites were significantly higher in stool compared to meconium. Treatment of mothers with ATB before or during delivery affects metabolite composition and microbial diversity in stool of vaginal- and caesarean-delivered newborns. Correlation of microbial and metabolite composition shows significant positive correlations of indol-3-lactic acid, N-acetyl-tryptophan and indol-3-acetic acid with Bifidobacterium, Bacteroides and Peptoclostridium. The positive effect of vaginal delivery on newborns' microbiome development is degraded when mother is treated with ATB before or during delivery. KEY POINTS: • Antibiotic treatment diminishes the positive effects of vaginal delivery. • Antibiotic treatment affects metabolite and microbial composition in newborns. • Bifidobacterium and Peptoclostridium could be the producer of indole-lactic acid.

Antibiotic subclasses differentially perturb the gut microbiota in kidney transplant recipients

Introduction

The impact of antibiotics on the gut microbiota in kidney transplant recipients is not well characterized. In this study, we determine the impact of different subclasses of antibiotics on the gut microbiota in a cohort of 168 kidney transplant recipients.

Methods

Gut microbiome profiling was performed on 510 fecal specimens using 16S rRNA gene sequencing of the V4-V5 hypervariable region. We classified fecal specimens by antibiotic exposure into 5 categories: Beta-lactam, Fluoroquinolone (FQ), Beta-lactam & FQ Group, Other Antibiotics, and No Antibiotic (No Abx). Mixed-effects regression models were utilized to identify changes in microbial diversity and in the centered log-ratio (CLR) transformed abundance of genera while adjusting for important covariates.

Results

Antibiotic administration was associated with a significant decrease in the Shannon alpha diversity index, a decreased abundance of 11 taxa including Eubacterium and Ruminococcus, and an increased abundance of 16 taxa including Enterococcus and Staphylococcus. Exposure to Beta-lactam antibiotics was associated with an increased abundance of 10 taxa including Enterococcus and a decreased abundance of 5 taxa including Eubacterium while exposure to FQ antibiotics was associated with an increased abundance of 3 taxa and a decreased abundance of 4 taxa including Ruminococcus.

Conclusions

Beta-lactam antibiotics and FQ antibiotics have a profound impact on the gut microbiota in kidney transplant recipients. Given the link of the gut microbiota to infectious complications, antibiotic associated changes in the microbiota may lead to an increased risk for further infections.

Ecological associations distribution modelling of marine plankton at a global scale

Marine plankton communities form intricate networks of interacting organisms at the base of the food chain, and play a central role in regulating ocean biogeochemical cycles and climate. However, predicting plankton community shifts in response to climate change remains challenging. While species distribution models are valuable tools for predicting changes in species biogeography under climate change scenarios, they generally overlook the key role of biotic interactions, which can significantly shape ecological processes and ecosystem responses. Here, we introduce a novel statistical framework, association distribution modelling (ADM), designed to model and predict ecological associations distribution in space and time. Applied on a Tara Oceans genome-resolved metagenomics dataset, the present-day biogeography of ADM-inferred marine plankton associations revealed four major biogeographic biomes organized along a latitudinal gradient. We predicted the evolution of these biome-specific communities in response to a climate change scenario, highlighting differential responses to environmental change. Finally, we explored the functional potential of impacted plankton communities, focusing on carbon fixation, outlining the predicted evolution of its geographical distribution and implications for ecosystem function.This article is part of the theme issue 'Connected interactions: enriching food web research by spatial and social interactions'.

Distinct anal microbiome is correlated with anal cancer precursors in MSM with HIV

OBJECTIVES

Anal cancer risk is elevated in MSM with HIV (MSMWH). Anal high-risk human papillomavirus (hr-HPV) infection is necessary but insufficient to develop high-grade squamous intraepithelial lesion (HSIL), the anal cancer precursor, suggesting additional factors. We sought to determine whether the microbiome of the anal canal is distinct by comparing it with the microbiome of stool. We also sought to determine whether changes in the anal microbiome are associated with HSIL among MSMWH.

DESIGN

Cross-sectional comparison of the microbiome of the anal canal with the microbiome of stool in MSMWH and cross-sectional comparison of the anal microbiome of MSMWH with anal HSIL with the anal microbiome of MSMWH without anal HSIL.

METHODS

Sterile swabs were used to sample the anus of MSMWH for microbiome and HPV testing, followed by high-resolution anoscopy. Stool samples were mailed from home. 16S sequencing was used for bacterial identification. Measures of alpha diversity, beta diversity, and differential abundance analysis were used to compare samples.

RESULTS

One hundred sixty-six anal samples and 103 matching stool samples were sequenced. Beta diversity showed clustering of stool and anal samples. Of hr-HPV-positive MSMWH, 31 had HSIL and 13 had no SIL. Comparison of the microbiome between these revealed 28 different species. The highest-fold enrichment among MSMWH/hr-HPV/HSIL included pro-inflammatory and carcinogenic Prevotella, Parasuterella, Hungatella, Sneathia, and Fusobacterium species. The anti-inflammatory Anaerostipes caccae showed the greatest reduction among MSMWH/hr-HPV/HSIL.

CONCLUSION

The anal microbiome is distinct from stool. A pro-inflammatory and carcinogenic environment may be associated with anal HSIL.

Correlated Responses to Selection for Intramuscular Fat on the Gut Microbiome in Rabbits

Intramuscular fat (IMF) content is important for meat production and human health, where the host genetics and its microbiome greatly contribute to its variation. The aim of this study is to describe the consequences of the genetic modification of IMF by selecting the taxonomic composition of the microbiome, using rabbits from the 10th generation of a divergent selection experiment for IMF (high (H) and low (L) lines differ by 3.8 standard deviations). The selection altered the composition of the gut microbiota. Correlated responses were better distinguished at the genus level (51 genera) than at the phylum level (10 phyla). The H-line was enriched in Hungateiclostridium, Limosilactobacillus, Legionella, Lysinibacillus, Phorphyromonas, Methanosphaera, Desulfovibrio, and Akkermansia, while the L-line was enriched in Escherichia, Methanobrevibacter, Fonticella, Candidatus Amulumruptor, Methanobrevibacter, Exiguobacterium, Flintibacter, and Coprococcus, among other genera with smaller line differences. A microbial biomarker generated from the abundance of four of these genera classified the lines with 78% accuracy in a logit regression. Our results demonstrate different gut microbiome compositions in hosts with divergent IMF genotypes. Furthermore, we provide a microbial biomarker to be used as an indicator of hosts genetically predisposed to accumulate muscle lipids, which opens up the opportunity for research to develop probiotics or microbiome-based breeding strategies targeting IMF.

Do fish gut microbiotas vary across spatial scales? A case study of Diplodus vulgaris in the Mediterranean Sea

BACKGROUND

Biogeography has been linked to differences in gut microbiota in several animals. However, the existence of such a relationship in fish is not clear yet. So far, it seems to depend on the fish species studied. However, most studies of fish gut microbiotas are based on single populations. In this study, we investigated the gut microbiota of fish from three wild populations of the two-banded sea bream Diplodus vulgaris (Geoffroy Saint-Hilaire, 1817) to determine whether its diversity, structure and potential functionality reflect the geographic origin of the fish, at large and small geographical scale. Additionally, we explored the host- and environmental-related factors explaining this relationship.

RESULTS

We showed that the taxonomy and potential functionality of the mucosa-associated gut microbiota of Diplodus vulgaris differ to varying degrees depending on the spatial scale considered. At large scale, we observed that both the taxonomical structure and the potential functionality of the fish microbiota differed significantly between populations. In contrast, the taxonomical diversity of the microbial community displayed a significant relationship with factors other than the geographic origin of the fish (i.e. sampling date). On the other hand, at small scale, the different composition and diversity of the microbiota differ according to the characteristics of the habitat occupied by the fish. Specifically, we identified the presence of Posidonia oceanica in the benthic habitat as predictor of both the microbiota composition and diversity. Lastly, we reported the enrichment of functions related to the metabolism of xenobiotics (i.e. drugs and 4-aminobenzoate) in a population and we indicated it as a potential target of future monitoring.

CONCLUSIONS

With this study, we confirmed the importance of investigating the gut microbiota of wild fish species using multiple populations, taking into account the different habitats occupied by the individuals. Furthermore, we underscored the use of the biodegradation potential of the gut microbiota as an alternative means of monitoring emerging contaminants in Mediterranean fish.

A GLM-based zero-inflated generalized Poisson factor model for analyzing microbiome data

Motivation

High-throughput sequencing technology facilitates the quantitative analysis of microbial communities, improving the capacity to investigate the associations between the human microbiome and diseases. Our primary motivating application is to explore the association between gut microbes and obesity. The complex characteristics of microbiome data, including high dimensionality, zero inflation, and over-dispersion, pose new statistical challenges for downstream analysis.

Results

We propose a GLM-based zero-inflated generalized Poisson factor analysis (GZIGPFA) model to analyze microbiome data with complex characteristics. The GZIGPFA model is based on a zero-inflated generalized Poisson (ZIGP) distribution for modeling microbiome count data. A link function between the generalized Poisson rate and the probability of excess zeros is established within the generalized linear model (GLM) framework. The latent parameters of the GZIGPFA model constitute a low-rank matrix comprising a low-dimensional score matrix and a loading matrix. An alternating maximum likelihood algorithm is employed to estimate the unknown parameters, and cross-validation is utilized to determine the rank of the model in this study. The proposed GZIGPFA model demonstrates superior performance and advantages through comprehensive simulation studies and real data applications.

An esophagus cell atlas reveals dynamic rewiring during active eosinophilic esophagitis and remission

Coordinated cell interactions within the esophagus maintain homeostasis, and disruption can lead to eosinophilic esophagitis (EoE), a chronic inflammatory disease with poorly understood pathogenesis. We profile 421,312 individual cells from the esophageal mucosa of 7 healthy and 15 EoE participants, revealing 60 cell subsets and functional alterations in cell states, compositions, and interactions that highlight previously unclear features of EoE. Active disease displays enrichment of ALOX15+ macrophages, PRDM16+ dendritic cells expressing the EoE risk gene ATP10A, and cycling mast cells, with concomitant reduction of TH17 cells. Ligand-receptor expression uncovers eosinophil recruitment programs, increased fibroblast interactions in disease, and IL-9+IL-4+IL-13+ TH2 and endothelial cells as potential mast cell interactors. Resolution of inflammation-associated signatures includes mast and CD4+ TRM cell contraction and cell type-specific downregulation of eosinophil chemoattractant, growth, and survival factors. These cellular alterations in EoE and remission advance our understanding of eosinophilic inflammation and opportunities for therapeutic intervention.

Including microbiome information in a multi-trait genomic evaluation: a case study on longitudinal growth performance in beef cattle

BACKGROUND

Growth rate is an important component of feed conversion efficiency in cattle and varies across the different stages of the finishing period. The metabolic effect of the rumen microbiome is essential for cattle growth, and investigating the genomic and microbial factors that underlie this temporal variation can help maximize feed conversion efficiency at each growth stage.

RESULTS

By analysing longitudinal body weights during the finishing period and genomic and metagenomic data from 359 beef cattle, our study demonstrates that the influence of the host genome on the functional rumen microbiome contributes to the temporal variation in average daily gain (ADG) in different months (ADG1, ADG2, ADG3, ADG4). Five hundred and thirty-three additive log-ratio transformed microbial genes (alr-MG) had non-zero genomic correlations (rg) with at least one ADG-trait (ranging from |0.21| to |0.42|). Only a few alr-MG correlated with more than one ADG-trait, which suggests that a differential host-microbiome determinism underlies ADG at different stages. These alr-MG were involved in ribosomal biosynthesis, energy processes, sulphur and aminoacid metabolism and transport, or lipopolysaccharide signalling, among others. We selected two alternative subsets of 32 alr-MG that had a non-uniform or a uniform rg sign with all the ADG-traits, regardless of the rg magnitude, and used them to develop a microbiome-driven breeding strategy based on alr-MG only, or combined with ADG-traits, which was aimed at shaping the rumen microbiome towards increased ADG at all finishing stages. Combining alr-MG information with ADG records increased prediction accuracy of genomic estimated breeding values (GEBV) by 11 to 22% relative to the direct breeding strategy (using ADG-traits only), whereas using microbiome information, only, achieved lower accuracies (from 7 to 41%). Predicted selection responses varied consistently with accuracies. Restricting alr-MG based on their rg sign (uniform subset) did not yield a gain in the predicted response compared to the non-uniform subset, which is explained by the absence of alr-MG showing non-zero rg at least with more than one of the ADG-traits.

CONCLUSIONS

Our work sheds light on the role of the microbial metabolism in the growth trajectory of beef cattle at the genomic level and provides insights into the potential benefits of using microbiome information in future genomic breeding programs to accurately estimate GEBV and increase ADG at each finishing stage in beef cattle.

Preliminary exploratory research on the application value of oral and intestinal meta-genomics in predicting subjects' occupations-A case study of the distinction between students and migrant workers

Background

In the field of forensic science, accurately determining occupation of an individual can greatly assist in resolving cases such as criminal investigations or disaster victim identifications. However, estimating occupation can be challenging due to the intricate relationship between occupation and various factors, including gender, age, living environment, health status, medication use, and lifestyle habits such as alcohol consumption and smoking. All of these factors can impact the composition of oral or gut microbial community of an individual.

Methods and results

In this study, we collected saliva and feces samples from individuals representing different occupational sectors, specifically students and manual laborers. We then performed metagenomic sequencing on the DNA extracted from these samples to obtain data that could be analyzed for taxonomic and functional annotations in five different databases. The correlation between occupation with microbial information was assisted from the perspective of α and β diversity, showing that individuals belonging to the two occupations hold significantly different oral and gut microbial communities, and that this correlation is basically not affected by gender, drinking, and smoking in our datasets. Finally, random forest (RF) models were built with recursive feature elimination (RFE) processes. Models with 100% accuracy in both training and testing sets were constructed based on three species in saliva samples or on a single pathway annotated by the KEGG database in fecal samples, namely, "ko04145" or Phagosome.

Conclusion

Although this study may have limited representativeness due to its small sample size, it provides preliminary evidence of the potential of using microbiome information for occupational inference.

Processing of corn-based dog foods through pelleting, baking and extrusion and their effect on apparent total tract digestibility and colonic health of adult dogs

Different food processing parameters may alter starch granule structure and its cooking degree. With lower thermomechanical energy, more resistant starch (RS) is retained in the food, which may benefit gastrointestinal (GI) health. The objective of this study was to determine the effect of food processing on dietary utilization and dog gut health. Experimental diets containing 56% corn as the sole starch source were produced through pelleting, baking, and extrusion and compared to a baked control diet in which the corn was replaced with dextrose. The extruded diet resulted in the highest level (P < 0.05) of in vitro starch cook and lowest RS, while baked was intermediate and pelleted had the lowest starch cook and highest RS. To evaluate the in vivo effects of these treatments, 12 dogs were adapted to foods for 9 d, and feces were collected for 5 d in a replicated 4 × 4 Latin square design. Feces were scored for consistency using an ordinal scale, and parametric data included apparent digestibility (ATTD), parameters indicative of gut health, and the microbial composition, which was centered log-ratio transformed before operational taxonomic unit (OTU) analyses. Fecal scores were analyzed by ordinal logistic regression, and parametric data were analyzed as mixed models. Overall ATTD was greater (P < 0.05) in extruded, followed by baked and pelleted. Dogs fed the control had osmotic diarrhea, whereas dogs fed the other treatments had mostly acceptable fecal scores, with extrusion leading to the best fecal quality. The control also led to high fecal pH and low SCFAs, indicating dysbiosis. All corn foods had similar (P > 0.05) fecal SCFAs and extruded tended (P = 0.055) to promote higher fecal butyrate than baked and pelleted. The microbiome of dogs fed the corn foods had similar α diversity indices, and OTUs at the species and phyla levels were mostly alike and different from the control. In conclusion, the higher levels of in vitro RS did not translate into a better in vivo fermentation profile, and extruded kibble performed best regarding fecal quality, ATTD, and fecal SCFAs.

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.

The effect of hydrostatic pressure on the activity and community composition of hydrocarbon-degrading bacteria in Arctic seawater

IMPORTANCE

Increased ship traffic in the Arctic region raises the risk of oil spills. With an average sea depth of 1,000 m, there is a growing concern over the potential release of oil sinking in the form of marine oil snow into deep Arctic waters. At increasing depth, the oil-degrading community is exposed to increasing hydrostatic pressure, which can reduce microbial activity. However, microbes thriving in polar regions may adapt to low temperature by modulation of membrane fluidity, which is also a well-known adaptation to high hydrostatic pressure. At mild hydrostatic pressures up to 8-12 MPa, we did not observe an altered microbial activity or community composition, whereas comparable studies using deep-sea or sub-Arctic microbial communities with in situ temperatures of 4-5°C showed pressure-induced effects at 10-15 MPa. Our results suggest that the psychrophilic nature of the underwater microbial communities in the Arctic may be featured by specific traits that enhance their fitness at increasing hydrostatic pressure.

A flexible quasi-likelihood model for microbiome abundance count data

In this article, we present a flexible model for microbiome count data. We consider a quasi-likelihood framework, in which we do not make any assumptions on the distribution of the microbiome count except that its variance is an unknown but smooth function of the mean. By comparing our model to the negative binomial generalized linear model (GLM) and Poisson GLM in simulation studies, we show that our flexible quasi-likelihood method yields valid inferential results. Using a real microbiome study, we demonstrate the utility of our method by examining the relationship between adenomas and microbiota. We also provide an R package "fql" for the application of our method.

Microbiability of milk composition and genetic control of microbiota effects in sheep

Recently, high-dimensional omics data are becoming available in larger quantities, and models have been developed that integrate them with genomics to understand in finer detail the relationship between genotype and phenotype, and thus improve the performance of genetic evaluations. Our objectives are to quantify the effect of the inclusion of microbiome data in the genetic evaluation for dairy traits in sheep, through the estimation of the heritability, microbiability, and how the microbiome effect on dairy traits decomposes into genetic and nongenetic parts. In this study we analyzed milk and rumen samples of 795 Lacaune dairy ewes. We included, as phenotype, dairy traits and milk fatty acids and proteins composition; as omics measurements, 16S rRNA rumen bacterial abundances; and as genotyping, 54K SNP chip for all ewes. Two nested genomic models were used: a first model to predict the individual contributions of the genetic and microbial abundances to phenotypes, and a second model to predict the additive genetic effect of the microbial community. In addition, microbiome-wide association studies for all dairy traits were applied using the 2,059 rumen bacterial abundances, and the genetic correlations between microbiome principal components and dairy traits were estimated. Results showed that in general the inclusion of both genetic and microbiome effect did not improve the fit of the model compared with the model with the genetic effect only. In addition, for all dairy traits the total heritability was equal to the direct heritability after fitting microbiota effects, due to a microbiability being almost zero for most dairy traits and heritability of the microbial community was very close to zero. Microbiome-wide association studies did not show operational taxonomic units with major effect for any of the dairy traits evaluated, and the genetic correlations between the first 5 principal components and dairy traits were low to moderate. So far, we can conclude that, using a substantial data set of 795 Lacaune dairy ewes, rumen bacterial abundances do not provide improved genetic evaluation for dairy traits in sheep.

Modeling idiopathic autism in forebrain organoids reveals an imbalance of excitatory cortical neuron subtypes during early neurogenesis

Idiopathic autism spectrum disorder (ASD) is highly heterogeneous, and it remains unclear how convergent biological processes in affected individuals may give rise to symptoms. Here, using cortical organoids and single-cell transcriptomics, we modeled alterations in the forebrain development between boys with idiopathic ASD and their unaffected fathers in 13 families. Transcriptomic changes suggest that ASD pathogenesis in macrocephalic and normocephalic probands involves an opposite disruption of the balance between excitatory neurons of the dorsal cortical plate and other lineages such as early-generated neurons from the putative preplate. The imbalance stemmed from divergent expression of transcription factors driving cell fate during early cortical development. While we did not find genomic variants in probands that explained the observed transcriptomic alterations, a significant overlap between altered transcripts and reported ASD risk genes affected by rare variants suggests a degree of gene convergence between rare forms of ASD and the developmental transcriptome in idiopathic ASD.

Enrichment of human nasopharyngeal bacteriome with bacteria from dust after short-term exposure to indoor environment: a pilot study

BACKGROUND

Indoor dust particles are an everyday source of human exposure to microorganisms and their inhalation may directly affect the microbiota of the respiratory tract. We aimed to characterize the changes in human nasopharyngeal bacteriome after short-term exposure to indoor (workplace) environments.

METHODS

In this pilot study, nasopharyngeal swabs were taken from 22 participants in the morning and after 8 h of their presence at the workplace. At the same time points, indoor dust samples were collected from the participants' households (16 from flats and 6 from houses) and workplaces (8 from a maternity hospital - NEO, 6 from a pediatric hospital - ENT, and 8 from a research center - RCX). 16S rRNA sequencing analysis was performed on these human and environmental matrices.

RESULTS

Staphylococcus and Corynebacterium were the most abundant genera in both indoor dust and nasopharyngeal samples. The analysis indicated lower bacterial diversity in indoor dust samples from flats compared to houses, NEO, ENT, and RCX (p < 0.05). Participants working in the NEO had the highest nasopharyngeal bacterial diversity of all groups (p < 0.05). After 8 h of exposure to the workplace environment, enrichment of the nasopharynx with several new bacterial genera present in the indoor dust was observed in 76% of study participants; however, no significant changes were observed at the level of the nasopharyngeal bacterial diversity (p > 0.05, Shannon index). These "enriching" bacterial genera overlapped between the hospital workplaces - NEO and ENT but differed from those in the research center - RCX.

CONCLUSIONS

The results suggest that although the composition of nasopharyngeal bacteriome is relatively stable during the day. Short-term exposure to the indoor environment can result in the enrichment of the nasopharynx with bacterial DNA from indoor dust; the bacterial composition, however, varies by the indoor workplace environment.

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.

A Bayesian joint model for compositional mediation effect selection in microbiome data

Analyzing multivariate count data generated by high-throughput sequencing technology in microbiome research studies is challenging due to the high-dimensional and compositional structure of the data and overdispersion. In practice, researchers are often interested in investigating how the microbiome may mediate the relation between an assigned treatment and an observed phenotypic response. Existing approaches designed for compositional mediation analysis are unable to simultaneously determine the presence of direct effects, relative indirect effects, and overall indirect effects, while quantifying their uncertainty. We propose a formulation of a Bayesian joint model for compositional data that allows for the identification, estimation, and uncertainty quantification of various causal estimands in high-dimensional mediation analysis. We conduct simulation studies and compare our method's mediation effects selection performance with existing methods. Finally, we apply our method to a benchmark data set investigating the sub-therapeutic antibiotic treatment effect on body weight in early-life mice.

Candida expansion in the gut of lung cancer patients associates with an ecological signature that supports growth under dysbiotic conditions

Candida species overgrowth in the human gut is considered a prerequisite for invasive candidiasis, but our understanding of gut bacteria promoting or restricting this overgrowth is still limited. By integrating cross-sectional mycobiome and shotgun metagenomics data from the stool of 75 male and female cancer patients at risk but without systemic candidiasis, bacterial communities in high Candida samples display higher metabolic flexibility yet lower contributional diversity than those in low Candida samples. We develop machine learning models that use only bacterial taxa or functional relative abundances to predict the levels of Candida genus and species in an external validation cohort with an AUC of 78.6-81.1%. We propose a mechanism for intestinal Candida overgrowth based on an increase in lactate-producing bacteria, which coincides with a decrease in bacteria that regulate short chain fatty acid and oxygen levels. Under these conditions, the ability of Candida to harness lactate as a nutrient source may enable Candida to outcompete other fungi in the gut.

Predicting feed efficiency traits in growing lambs from their ruminal microbiota

Selecting feed-efficient sheep could improve the sustainability of this livestock production. However, most sheep breeding companies cannot afford to record feed intake to select feed-efficient animals. Past studies underlined the potential of omics data, including microbiota metabarcoding data, as proxies for feed efficiency. The study involved 277 Romane lambs from two lines divergently selected for residual feed intake (RFI). There were two objectives: check the consequences of selecting for feed efficiency over the rumen microbiota, and assess the predictive ability of the rumen microbiota for host traits. The study assessed two contrasting diets (concentrate diet and mixed diet) and two microbial groups (prokaryotes and eukaryotes). Discriminant analyses did not highlight any significant effect of sheep selection for residual feed intake on the rumen microbiota composition. Indeed, prokaryotic and eukaryotic microbiota compositions poorly discriminated the RFI lines, with averaged balanced error rates ranging from 45% to 55%. Correlations between host traits (feed efficiency and production traits) and their predictions from microbiota data varied between -0.07 and 0.56, depending on the trait, diet and sequencing. Feed intake was the most accurately predicted trait. However, predictions from fixed effects and BW were more accurate than or as accurate as predictions from the microbiota. Environmental effects can greatly affect the variability of microbiota compositions. Considering batch and environmental effects should be paramount when the predictive ability of the microbiota is assessed. This study argues why metabarcoding the rumen microbiota is not the best way to predict meat sheep production traits: fixed effects and BW were more cost-effective proxies and they led to similar or better predictive accuracies than microbiota metabarcoding (16S and 18S sequencing).

Combing fecal microbial community data to identify consistent obesity-specific microbial signatures and shared metabolic pathways

Obesity is associated with altered gut microbiome composition but data across different populations remain inconsistent. We meta-analyzed publicly available 16S-rRNA sequence datasets from 18 different studies and identified differentially abundant taxa and functional pathways of the obese gut microbiome. Most differentially abundant genera (Odoribacter, Oscillospira, Akkermansia, Alistipes, and Bacteroides) were depleted in obesity, indicating a deficiency of commensal microbes in the obese gut microbiome. From microbiome functional pathways, elevated lipid biosynthesis and depleted carbohydrate and protein degradation suggested metabolic adaptation to high-fat, low-carbohydrate, and low-protein diets in obese individuals. Machine learning models trained on the 18 studies were modest in predicting obesity with a median AUC of 0.608 using 10-fold cross-validation. The median AUC increased to 0.771 when models were trained in eight studies designed for investigating obesity-microbiome association. By meta-analyzing obesity-associated microbiota signatures, we identified obesity-associated depleted taxa that may be exploited to mitigate obesity and related metabolic diseases.

Winners and Losers of Atlantification: The Degree of Ocean Warming Affects the Structure of Arctic Microbial Communities

Arctic microbial communities (i.e., protists and bacteria) are increasingly subjected to an intrusion of new species via Atlantification and an uncertain degree of ocean warming. As species differ in adaptive traits, these oceanic conditions may lead to compositional changes with functional implications for the ecosystem. In June 2021, we incubated water from the western Fram Strait at three temperatures (2 °C, 6 °C, and 9 °C), mimicking the current and potential future properties of the Arctic Ocean. Our results show that increasing the temperature to 6 °C only minorly affects the community, while an increase to 9 °C significantly lowers the diversity and shifts the composition. A higher relative abundance of large hetero- and mixotrophic protists was observed at 2 °C and 6 °C compared to a higher abundance of intermediate-sized temperate diatoms at 9 °C. The compositional differences at 9 °C led to a higher chlorophyll a:POC ratio, but the C:N ratio remained similar. Our results contradict the common assumption that smaller organisms and heterotrophs are favored under warming and strongly indicate a thermal limit between 6 °C and 9 °C for many Arctic species. Consequently, the magnitude of temperature increase is a crucial factor for microbial community reorganization and the ensuing ecological consequences in the future Arctic Ocean.

Compositionality, sparsity, spurious heterogeneity, and other data-driven challenges for machine learning algorithms within plant microbiome studies

The plant-associated microbiome is a key component of plant systems, contributing to their health, growth, and productivity. The application of machine learning (ML) in this field promises to help untangle the relationships involved. However, measurements of microbial communities by high-throughput sequencing pose challenges for ML. Noise from low sample sizes, soil heterogeneity, and technical factors can impact the performance of ML. Additionally, the compositional and sparse nature of these datasets can impact the predictive accuracy of ML. We review recent literature from plant studies to illustrate that these properties often go unmentioned. We expand our analysis to other fields to quantify the degree to which mitigation approaches improve the performance of ML and describe the mathematical basis for this. With the advent of accessible analytical packages for microbiome data including learning models, researchers must be familiar with the nature of their datasets.

Pairwise ratio-based differential abundance analysis of infant microbiome 16S sequencing data

Differential abundance analysis of infant 16S microbial sequencing data is complicated by challenging data properties, including high sparsity, extreme dispersion and the relative nature of the information contained within the data. In this study, we propose a pairwise ratio analysis that uses the compositional data analysis principle of subcompositional coherence and merges it with a beta-binomial regression model. The resulting method provides a flexible and easily interpretable approach to infant 16S sequencing data differential abundance analysis that does not require zero imputation. We evaluate the proposed method using infant 16S data from clinical trials and demonstrate that the proposed method has the power to detect differences, and demonstrate how its results can be used to gain insights. We further evaluate the method using data-inspired simulations and compare its power against related methods. Our results indicate that power is high for pairwise differential abundance analysis of taxon pairs that have a large abundance. In contrast, results for sparse taxon pairs show a decrease in power and substantial variability in method performance. While our method shows promising performance on well-measured subcompositions, we advise strong filtering steps in order to avoid excessive numbers of underpowered comparisons in practical applications.

Impact of Data and Study Characteristics on Microbiome Volatility Estimates

The human microbiome is a dynamic community of bacteria, viruses, fungi, and other microorganisms. Both the composition of the microbiome (the microbes that are present and their relative abundances) and the temporal variability of the microbiome (the magnitude of changes in their composition across time, called volatility) has been associated with human health. However, the effect of unbalanced sampling intervals and differential read depth on the estimates of microbiome volatility has not been thoroughly assessed. Using four publicly available gut and vaginal microbiome time series, we subsampled the datasets to several sampling intervals and read depths and then compared additive, multiplicative, centered log ratio (CLR)-based, qualitative, and distance-based measures of microbiome volatility between the conditions. We find that longer sampling intervals are associated with larger quantitative measures of change (particularly for common taxa), but not with qualitative measures of change or distance-based volatility quantification. A lower sequencing read depth is associated with smaller multiplicative, CLR-based, and qualitative measures of change (particularly for less common taxa). Strategic subsampling may serve as a useful sensitivity analysis in unbalanced longitudinal studies investigating clinical associations with microbiome volatility.

Integrating data from spontaneous and induced trans-10 shift of ruminal biohydrogenation reveals discriminant bacterial community changes at the OTU level

Introduction

Microbial digestion is of key importance for ruminants, and disturbances can affect efficiency and quality of products for human consumers. Ruminal biohydrogenation of dietary unsaturated fatty acids leads to a wide variety of specific fatty acids. Some dietary conditions can affect the pathways of this transformation, leading to trans-10 fatty acids rather than the more usual trans-11 fatty acids, this change resulting in milk fat depression in dairy cows.

Materials and methods

We combined data from an induced and spontaneous trans-10 shift of ruminal biohydrogenation, providing new insight on bacterial changes at different taxonomic levels. A trans-10 shift was induced using dietary addition of concentrate and/or unsaturated fat, and the spontaneous milk fat depression was observed in a commercial dairy herd.

Results and discussion

Most changes of microbial community related to bacteria that are not known to be involved in the biohydrogenation process, suggesting that the trans-10 shift may represent the biochemical marker of a wide change of bacterial community. At OTU level, sparse discriminant analysis revealed strong associations between this change of biohydrogenation pathway and some taxa, especially three taxa belonging to [Eubacterium] coprostanoligenes group, Muribaculaceae and Lachnospiraceae NK3A20 group, that could both be microbial markers of this disturbance and candidates for studies relative to their ability to produce trans-10 fatty acids.

Bacterial and metabolic phenotypes associated with inadequate response to ursodeoxycholic acid treatment in primary biliary cholangitis

Primary biliary cholangitis (PBC) is a chronic cholestatic liver disease with ursodeoxycholic acid (UDCA) as first-line treatment. Poor response to UDCA is associated with a higher risk of progressing to cirrhosis, but the underlying mechanisms are unclear. UDCA modulates the composition of primary and bacterial-derived bile acids (BAs). We characterized the phenotypic response to UDCA based on BA and bacterial profiles of PBC patients treated with UDCA. Patients from the UK-PBC cohort (n = 419) treated with UDCA for a minimum of 12-months were assessed using the Barcelona dynamic response criteria. BAs from serum, urine, and feces were analyzed using Ultra-High-Performance Liquid Chromatography-Mass Spectrometry and fecal bacterial composition measured using 16S rRNA gene sequencing. We identified 191 non-responders, 212 responders, and a subgroup of responders with persistently elevated liver biomarkers (n = 16). Responders had higher fecal secondary and tertiary BAs than non-responders and lower urinary bile acid abundances, with the exception of 12-dehydrocholic acid, which was higher in responders. The sub-group of responders with poor liver function showed lower alpha-diversity evenness, lower abundance of fecal secondary and tertiary BAs than the other groups and lower levels of phyla with BA-deconjugation capacity (Actinobacteriota/Actinomycetota, Desulfobacterota, Verrucomicrobiota) compared to responders. UDCA dynamic response was associated with an increased capacity to generate oxo-/epimerized secondary BAs. 12-dehydrocholic acid is a potential biomarker of treatment response. Lower alpha-diversity and lower abundance of bacteria with BA deconjugation capacity might be associated with an incomplete response to treatment in some patients.

Lost in the woods: Forest vegetation, and not topography, most affects the connectivity of mesh radio networks for public safety

Real-time data- and location-sharing using mesh networking radios paired with smartphones may improve situational awareness and safety in remote environments lacking communications infrastructure. Despite being increasingly used for wildland fire and public safety applications, there has been little formal evaluation of the network connectivity of these devices. The objectives of this study were to 1) characterize the connectivity of mesh networks in variable forest and topographic conditions; 2) evaluate the abilities of lidar and satellite remote sensing data to predict connectivity; and 3) assess the relative importance of the predictive metrics. A large field experiment was conducted to test the connectivity of a network of one mobile and five stationary goTenna Pro mesh radios on 24 Public Land Survey System sections approximately 260 ha in area in northern Idaho. Dirichlet regression was used to predict connectivity using 1) both lidar- and satellite-derived metrics (LIDSAT); 2) lidar-derived metrics only (LID); and 3) satellite-derived metrics only (SAT). On average the full network was connected only 32.6% of the time (range: 0% to 90.5%) and the mobile goTenna was disconnected from all other devices 18.2% of the time (range: 0% to 44.5%). RMSE for the six connectivity levels ranged from 0.101 to 0.314 for the LIDSAT model, from 0.103 to 0.310 for the LID model, and from 0.121 to 0.313 for the SAT model. Vegetation-related metrics affected connectivity more than topography. Developed models may be used to predict the connectivity of real-time mesh networks over large spatial extents using remote sensing data in order to forecast how well similar networks are expected to perform for wildland firefighting, forestry, and public safety applications. However, safety professionals should be aware of the impacts of vegetation on connectivity.

Development of a predictive algorithm to identify pre-school children at risk for behavior changes associated with sleep-related breathing disorders

STUDY OBJECTIVES

Children with late-onset (2-5 years) or persistent (3 months-5 years) sleep-related breathing disorder (SRBD) have an increased risk of behavior problems compared to children with no or early-onset SRBD. We sought to determine whether a combination of urine metabolites and sleep questionnaires could identify children at risk for SRBD-associated behavior problems.

METHODS

Urine and data were analyzed from the Edmonton site of the CHILD birth cohort study. We measured urine metabolites (random, mid-stream) at age three-years among a sub-cohort of participants (n = 165). Random Forest with a Boruta wrapper was used to identify important metabolites (creatinine-corrected, z-scores) for late/persistent SRBD versus no/early SRBD (reference). An algorithm was subsequently generated to predict late/persistent SRBD in children with a history of snoring using a metabolite composite score (z-scores < or ≥ 0) plus the SDBeasy score defined as [age (yrs.) of most recent positive SRBD]² - [age (yrs.) first reported ever snoring]².

RESULTS

Of the 165 children with SRBD data, 40 participants had late/persistent SRBD. Seven urinary metabolites in addition to the SDBeasy score were confirmed as important for late/persistent SRBD (AUC = 0.87). Among children with an ever-snoring history and a metabolite composite score ≥0, those with SDBeasy score ≥3 were over 13-fold more likely to have late/persistent SRBD (OR 13.7; 95%CI: 3.0, 62.1; p = 0.001). This algorithm has a Sensitivity of 69.6%, Specificity of 85.7% and a positive likelihood ratio (+LR) of 4.9.

CONCLUSIONS

We developed a predictive algorithm using a combination of questionnaires and urine metabolites at age three-years to identify children with late/persistent SRBD by five-years of age.

Host genetic control on rumen microbiota and its impact on dairy traits in sheep

BACKGROUND

Milk yield and fine composition in sheep depend on the volatile and long-chain fatty acids, microbial proteins, vitamins produced through feedstuff digestion by the rumen microbiota. In cattle, the host genome has been shown to have a low to moderate genetic control on rumen microbiota abundance but a high control on dairy traits with heritabilities higher than 0.30. There is little information on the genetic correlations and quantitative trait loci (QTL) that simultaneously affect rumen microbiota abundance and dairy traits in ruminants, especially in sheep. Thus, our aim was to quantify the effect of the host genetics on rumen bacterial abundance and the genetic correlations between rumen bacterial abundance and several dairy traits, and to identify QTL that are associated with both rumen bacterial abundance and milk traits.

RESULTS

Our results in Lacaune sheep show that the heritability of rumen bacterial abundance ranges from 0 to 0.29 and that the heritability of 306 operational taxonomic units (OTU) is significantly different from 0. Of these 306 OTU, 96 that belong mainly to the Prevotellaceae, Lachnospiraceae and Ruminococcaceae bacterial families show strong genetic correlations with milk fatty acids and proteins (absolute values ranging from 0.33 to 0.99). Genome-wide association studies revealed a QTL for alpha-lactalbumin concentration in milk on Ovis aries chromosome (OAR) 11, and six QTL for rumen bacterial abundances i.e., for two OTU belonging to the genera Prevotella (OAR3 and 5), Rikeneleaceae_RC9_gut_group (OAR5), Ruminococcus (OAR5), an unknown genus of order Clostridia UCG-014 (OAR10), and CAG-352 (OAR11). None of these detected regions are simultaneously associated with rumen bacterial abundance and dairy traits, but the bacterial families Prevotellaceae, Lachnospiraceae and F082 show colocalized signals on OAR3, 5, 15 and 26.

CONCLUSIONS

In Lacaune dairy sheep, rumen microbiota abundance is partially controlled by the host genetics and is poorly genetically linked with milk protein and fatty acid compositions, and three main bacterial families, Prevotellaceae, Lachnospiraceae and F082, show specific associations with OAR3, 5, 15 and 26.

A pilot study characterizing longitudinal changes in fecal microbiota of patients with Hirschsprung-associated enterocolitis

PURPOSE

Hirschsprung disease is a neurointestinal disease that occurs due to failure of enteric neural crest-derived cells to complete their rostrocaudal migration along the gut mesenchyme, resulting in aganglionosis along variable lengths of the distal bowel. Despite the effective surgery that removes the aganglionic segment, children with Hirschsprung disease remain at high risk for developing a potentially life-threatening enterocolitis (Hirschsprung-associated enterocolitis). Although the etiology of this enterocolitis remains poorly understood, several recent studies in both mouse models and in human subjects suggest potential involvement of gastrointestinal microbiota in the underlying pathogenesis of Hirschsprung-associated enterocolitis.

METHODS

We present the first study to exploit the Illumina MiSeq next-generation sequencing platform within a longitudinal framework focused on microbiomes of Hirschsprung-associated enterocolitis in five patients. We analyzed bacterial communities from fecal samples collected at different timepoints starting from active enterocolitis and progressing into remission.

RESULTS

We observed compositional differences between patients largely attributable to variability in age at the time of sample collection. Remission samples across patients exhibited compositional similarity, including enrichment of Blautia, while active enterocolitis samples showed substantial variability in composition.

CONCLUSIONS

Overall, our findings provide continued support for the role of GI microbiota in the pathogenesis of Hirschsprung-associated enterocolitis.

Microbiome-driven breeding strategy potentially improves beef fatty acid profile benefiting human health and reduces methane emissions

BACKGROUND

Healthier ruminant products can be achieved by adequate manipulation of the rumen microbiota to increase the flux of beneficial fatty acids reaching host tissues. Genomic selection to modify the microbiome function provides a permanent and accumulative solution, which may have also favourable consequences in other traits of interest (e.g. methane emissions). Possibly due to a lack of data, this strategy has never been explored.

RESULTS

This study provides a comprehensive identification of ruminal microbial mechanisms under host genomic influence that directly or indirectly affect the content of unsaturated fatty acids in beef associated with human dietary health benefits C18:3n-3, C20:5n-3, C22:5n-3, C22:6n-3 or cis-9, trans-11 C18:2 and trans-11 C18:1 in relation to hypercholesterolemic saturated fatty acids C12:0, C14:0 and C16:0, referred to as N3 and CLA indices. We first identified that ~27.6% (1002/3633) of the functional core additive log-ratio transformed microbial gene abundances (alr-MG) in the rumen were at least moderately host-genomically influenced (HGFC). Of these, 372 alr-MG were host-genomically correlated with the N3 index (n=290), CLA index (n=66) or with both (n=16), indicating that the HGFC influence on beef fatty acid composition is much more complex than the direct regulation of microbial lipolysis and biohydrogenation of dietary lipids and that N3 index variation is more strongly subjected to variations in the HGFC than CLA. Of these 372 alr-MG, 110 were correlated with the N3 and/or CLA index in the same direction, suggesting the opportunity for enhancement of both indices simultaneously through a microbiome-driven breeding strategy. These microbial genes were involved in microbial protein synthesis (aroF and serA), carbohydrate metabolism and transport (galT, msmX), lipopolysaccharide biosynthesis (kdsA, lpxD, lpxB), or flagellar synthesis (flgB, fliN) in certain genera within the Proteobacteria phyla (e.g. Serratia, Aeromonas). A microbiome-driven breeding strategy based on these microbial mechanisms as sole information criteria resulted in a positive selection response for both indices (1.36±0.24 and 0.79±0.21 sd of N3 and CLA indices, at 2.06 selection intensity). When evaluating the impact of our microbiome-driven breeding strategy to increase N3 and CLA indices on the environmental trait methane emissions (g/kg of dry matter intake), we obtained a correlated mitigation response of -0.41±0.12 sd.

CONCLUSION

This research provides insight on the possibility of using the ruminal functional microbiome as information for host genomic selection, which could simultaneously improve several microbiome-driven traits of interest, in this study exemplified with meat quality traits and methane emissions. Video Abstract.

Be aware of the allele-specific bias and compositional effects in multi-template PCR

High-throughput sequencing of amplicon libraries is the most widespread and one of the most effective ways to study the taxonomic structure of microbial communities, even despite growing accessibility of whole metagenome sequencing. Due to the targeted amplification, the method provides unparalleled resolution of communities, but at the same time perturbs initial community structure thereby reducing data robustness and compromising downstream analyses. Experimental research of the perturbations is largely limited to comparative studies on different PCR protocols without considering other sources of experimental variation related to characteristics of the initial microbial composition itself. Here we analyse these sources and demonstrate how dramatically they effect the relative abundances of taxa during the PCR cycles. We developed the mathematical model of the PCR amplification assuming the heterogeneity of amplification efficiencies and considering the compositional nature of data. We designed the experiment-five consecutive amplicon cycles (22-26) with 12 replicates for one real human stool microbial sample-and estimated the dynamics of the microbial community in line with the model. We found the high heterogeneity in amplicon efficiencies of taxa that leads to the non-linear and substantial (up to fivefold) changes in relative abundances during PCR. The analysis of possible sources of heterogeneity revealed the significant association between amplicon efficiencies and the energy of secondary structures of the DNA templates. The result of our work highlights non-trivial changes in the dynamics of real-life microbial communities due to their compositional nature. Obtained effects are specific not only for amplicon libraries, but also for any studies of metagenome dynamics.

Three approaches to supervised learning for compositional data with pairwise logratios

Logratios between pairs of compositional parts (pairwise logratios) are the easiest to interpret in compositional data analysis, and include the well-known additive logratios as particular cases. When the number of parts is large (sometimes even larger than the number of cases), some form of logratio selection is needed. In this article, we present three alternative stepwise supervised learning methods to select the pairwise logratios that best explain a dependent variable in a generalized linear model, each geared for a specific problem. The first method features unrestricted search, where any pairwise logratio can be selected. This method has a complex interpretation if some pairs of parts in the logratios overlap, but it leads to the most accurate predictions. The second method restricts parts to occur only once, which makes the corresponding logratios intuitively interpretable. The third method uses additive logratios, so that K-1 selected logratios involve a K-part subcomposition. Our approach allows logratios or non-compositional covariates to be forced into the models based on theoretical knowledge, and various stopping criteria are available based on information measures or statistical significance with the Bonferroni correction. We present an application on a dataset from a study predicting Crohn's disease.

Pathogenic variants damage cell composition and single cell transcription in cardiomyopathies

Pathogenic variants in genes that cause dilated cardiomyopathy (DCM) and arrhythmogenic cardiomyopathy (ACM) convey high risks for the development of heart failure through unknown mechanisms. Using single-nucleus RNA sequencing, we characterized the transcriptome of 880,000 nuclei from 18 control and 61 failing, nonischemic human hearts with pathogenic variants in DCM and ACM genes or idiopathic disease. We performed genotype-stratified analyses of the ventricular cell lineages and transcriptional states. The resultant DCM and ACM ventricular cell atlas demonstrated distinct right and left ventricular responses, highlighting genotype-associated pathways, intercellular interactions, and differential gene expression at single-cell resolution. Together, these data illuminate both shared and distinct cellular and molecular architectures of human heart failure and suggest candidate therapeutic targets.

MiCloud: A unified web platform for comprehensive microbiome data analysis

The recent advance in massively parallel sequencing has enabled accurate microbiome profiling at a dramatically lowered cost. Then, the human microbiome has been the subject of intensive investigation in public health and medicine. In the meanwhile, researchers have developed lots of microbiome data analysis methods, protocols, and/or tools. Among those, especially, the web platforms can be highlighted because of the user-friendly interfaces and streamlined protocols for a long sequence of analytic procedures. However, existing web platforms can handle only a categorical trait of interest, cross-sectional study design, and the analysis with no covariate adjustment. We therefore introduce here a unified web platform, named MiCloud, for a binary or continuous trait of interest, cross-sectional or longitudinal/family-based study design, and with or without covariate adjustment. MiCloud handles all such types of analyses for both ecological measures (i.e., alpha and beta diversity indices) and microbial taxa in relative abundance on different taxonomic levels (i.e., phylum, class, order, family, genus and species). Importantly, MiCloud also provides a unified analytic protocol that streamlines data inputs, quality controls, data transformations, statistical methods and visualizations with vastly extended utility and flexibility that are suited to microbiome data analysis. We illustrate the use of MiCloud through the United Kingdom twin study on the association between gut microbiome and body mass index adjusting for age. MiCloud can be implemented on either the web server (http://micloud.kr) or the user's computer (https://github.com/wg99526/micloudgit).

Next generation restoration metrics: Using soil eDNA bacterial community data to measure trajectories towards rehabilitation targets

In post-mining rehabilitation, successful mine closure planning requires specific, measurable, achievable, relevant and time-bound (SMART) completion criteria, such as returning ecological communities to match a target level of similarity to reference sites. Soil microbiota are fundamentally linked to the restoration of degraded ecosystems, helping to underpin ecological functions and plant communities. High-throughput sequencing of soil eDNA to characterise these communities offers promise to help monitor and predict ecological progress towards reference states. Here we demonstrate a novel methodology for monitoring and evaluating ecological restoration using three long-term (>25 year) case study post-mining rehabilitation soil eDNA-based bacterial community datasets. Specifically, we developed rehabilitation trajectory assessments based on similarity to reference data from restoration chronosequence datasets. Recognising that numerous alternative options for microbiota data processing have potential to influence these assessments, we comprehensively examined the influence of standard versus compositional data analyses, different ecological distance measures, sequence grouping approaches, eliminating rare taxa, and the potential for excessive spatial autocorrelation to impact on results. Our approach reduces the complexity of information that often overwhelms ecologically-relevant patterns in microbiota studies, and enables prediction of recovery time, with explicit inclusion of uncertainty in assessments. We offer a step change in the development of quantitative microbiota-based SMART metrics for measuring rehabilitation success. Our approach may also have wider applications where restorative processes facilitate the shift of microbiota towards reference states.

Bovine host genome acts on rumen microbiome function linked to methane emissions

Our study provides substantial evidence that the host genome affects the comprehensive function of the microbiome in the rumen of bovines. Of 1,107/225/1,141 rumen microbial genera/metagenome assembled uncultured genomes (RUGs)/genes identified from whole metagenomics sequencing, 194/14/337 had significant host genomic effects (heritabilities ranging from 0.13 to 0.61), revealing that substantial variation of the microbiome is under host genomic control. We found 29/22/115 microbial genera/RUGs/genes host-genomically correlated (|0.59| to |0.93|) with emissions of the potent greenhouse gas methane (CH4), highlighting the strength of a common host genomic control of specific microbial processes and CH4. Only one of these microbial genes was directly involved in methanogenesis (cofG), whereas others were involved in providing substrates for archaea (e.g. bcd and pccB), important microbial interspecies communication mechanisms (ABC.PE.P), host-microbiome interaction (TSTA3) and genetic information processes (RP-L35). In our population, selection based on abundances of the 30 most informative microbial genes provided a mitigation potential of 17% of mean CH4 emissions per generation, which is higher than for selection based on measured CH4 using respiration chambers (13%), indicating the high potential of microbiome-driven breeding to cumulatively reduce CH4 emissions and mitigate climate change.

Bacteria, Protists, and Fungi May Hold Clues of Seamount Impact on Diversity and Connectivity of Deep-Sea Pelagic Communities

The topography and hydrography around seamounts have a strong influence on plankton biogeography. The intrinsic properties of various biological taxa inherently also shape their distribution. Therefore, it is hypothesized that different pelagic groups respond differently to effects of seamounts regarding their distribution and connectivity patterns. Herein, bacterial, protist, and fungal diversity was investigated across the water column around the Kocebu Guyot in the western Pacific Ocean. A higher connectivity was detected for bacteria than for protists and an extremely low connectivity for fungi, which might be attributed to parasitic and commensal interactions of many fungal taxa. The seamount enhanced the vertical connectivity of bacterial and protist communities, but significantly reduced protist connectivity along horizontal dimension. Such effects provide ecological opportunities for eukaryotic adaption and diversification. All the bacterial, protist, and fungal communities were more strongly affected by deterministic than stochastic processes. Drift appeared to have a more significant role in influencing the fungal community than other groups. Our study indicates the impact of seamounts on the pelagic community distribution and connectivity and highlights the mechanism of horizontally restricted dispersal combined with vertical mixing, which promotes the diversification of eukaryotic life.

A Simultaneous Feature Selection and Compositional Association Test for Detecting Sparse Associations in High-Dimensional Metagenomic Data

Numerous metagenomic studies aim to discover associations between the microbial composition of an environment (e.g., gut, skin, oral) and a phenotype of interest. Multivariate analysis is often performed in these studies without critical a priori knowledge of which taxa are associated with the phenotype being studied. This approach typically reduces statistical power in settings where the true associations among only a few taxa are obscured by high dimensionality (i.e., sparse association signals). At the same time, low sample size and compositional sample space constraints may reduce beyond-study generalizability if not properly accounted for. To address these difficulties, we developed the Selection-Energy-Permutation (SelEnergyPerm) method, a nonparametric group association test with embedded feature selection that directly accounts for compositional constraints using parsimonious logratio signatures between taxonomic features, for characterizing and understanding alterations in microbial community structure. Simulation results show SelEnergyPerm selects small independent sets of logratios that capture strong associations in a range of scenarios. Additionally, our simulation results demonstrate SelEnergyPerm consistently detects/rejects associations in synthetic data with sparse, dense, or no association signals. We demonstrate the novel benefits of our method in four case studies utilizing publicly available 16S amplicon and whole-genome sequencing datasets. Our R implementation of Selection-Energy-Permutation, including an example demonstration and the code to generate all of the scenarios used here, is available at https://www.github.com/andrew84830813/selEnergyPermR.

A global microbiome survey of vineyard soils highlights the microbial dimension of viticultural terroirs

The microbial biodiversity found in different vitivinicultural regions is an important determinant of wine terroir. It should be studied and preserved, although it may, in the future, be subjected to manipulation by precision agriculture and oenology. Here, we conducted a global survey of vineyards' soil microbial communities. We analysed soil samples from 200 vineyards on four continents to establish the basis for the development of a vineyard soil microbiome's map, representing microbial biogeographical patterns on a global scale. This study describes vineyard microbial communities worldwide and establishes links between vineyard locations and microbial biodiversity on different scales: between continents, countries, and between different regions within the same country. Climate data correlates with fungal alpha diversity but not with prokaryotes alpha diversity, while spatial distance, on a global and national scale, is the main variable explaining beta-diversity in fungal and prokaryotes communities. Proteobacteria, Actinobacteria and Acidobacteria phyla, and Archaea genus Nitrososphaera dominate prokaryotic communities in soil samples while the overall fungal community is dominated by the genera Solicoccozyma, Mortierella and Alternaria. Finally, we used microbiome data to develop a predictive model based on random forest analyses to discriminate between microbial patterns and to predict the geographical source of the samples with reasonable precision.

Beware to ignore the rare: how imputing zero-values can improve the quality of 16S rRNA gene studies results

BACKGROUND

16S rRNA-gene sequencing is a valuable approach to characterize the taxonomic content of the whole bacterial population inhabiting a metabolic and spatial niche, providing an important opportunity to study bacteria and their role in many health and environmental mechanisms. The analysis of data produced by amplicon sequencing, however, brings very specific methodological issues that need to be properly addressed to obtain reliable biological conclusions. Among these, 16S count data tend to be very sparse, with many null values reflecting species that are present but got unobserved due to the multiplexing constraints. However, current data workflows do not consider a step in which the information about unobserved species is recovered.

RESULTS

In this work, we evaluate for the first time the effects of introducing in the 16S data workflow a new preprocessing step, zero-imputation, to recover this lost information. Due to the lack of published zero-imputation methods specifically designed for 16S count data, we considered a set of zero-imputation strategies available for other frameworks, and benchmarked them using in silico 16S count data reflecting different experimental designs. Additionally, we assessed the effect of combining zero-imputation and normalization, i.e. the only preprocessing step in current 16S workflow. Overall, we benchmarked 35 16S preprocessing pipelines assessing their ability to handle data sparsity, identify species presence/absence, recovery sample proportional abundance distributions, and improve typical downstream analyses such as computation of alpha and beta diversity indices and differential abundance analysis.

CONCLUSIONS

The results clearly show that 16S data analysis greatly benefits from a properly-performed zero-imputation step, despite the choice of the right zero-imputation method having a pivotal role. In addition, we identify a set of best-performing pipelines that could be a valuable indication for data analysts.

Changes in sedentary behavior patterns during the transition from childhood to adolescence and their association with adiposity: a prospective study based on compositional data analysis

Background

To date, no longitudinal study using a compositional approach has examined sedentary behavior (SB) patterns in relation to adiposity in the pediatric population. Therefore, our aims were to (1) investigate the changes in SB patterns and adiposity from childhood to adolescence, (2) analyze the prospective compositional associations between changes in SB patterns and adiposity, and (3) estimate the changes in adiposity associated with substituting SB with physical activity (PA) of different intensities.

Methods

The study presents a longitudinal design with a 5-year follow-up. A total of 88 participants (61% girls) were included in the analysis. PA and SB were monitored for seven consecutive days using a hip-worn accelerometer. Adiposity markers (fat mass percentage [FM%], fat mass index [FMI], and visceral adiposity tissue [VAT]) were assessed using the multi-frequency bioimpedance analysis. The prospective associations were examined using compositional data analysis.

Results

Over the follow-up period, the proportion of time spent in total SB increased by 154.8 min/day (p < 0.001). The increase in total SB was caused mainly by an increase in middle and long sedentary bouts, as these SB periods increased by 79.8 min/day and 62.0 min/day (p < 0.001 for both), respectively. FM%, FMI, and VAT increased by 2.4% points, 1.0 kg/m², and 31.5 cm² (p < 0.001 for all), respectively. Relative to the remaining movement behaviors, the increase in time spent in middle sedentary bouts was significantly associated with higher FM% (β_ilr1 = 0.27, 95% confidence interval [CI]: 0.02 to 0.53) at follow-up. Lower VAT by 3.3% (95% CI: 0.8 to 5.7), 3.8% (95% CI: 0.03 to 7.4), 3.9% (95% CI: 0.8 to 6.9), and 3.8% (95% CI: 0.7 to 6.9) was associated with substituting 15 min/week spent in total SB and in short, middle, and long sedentary bouts, respectively, with an equivalent amount of time spent in vigorous PA.

Conclusions

This study showed unfavorable changes in SB patterns and adiposity status in the transition from childhood to adolescence. Incorporating high-intensity PA at the expense of SB appears to be an appropriate approach to reduce the risk of excess adiposity in the pediatric population.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13690-021-00755-5.

The Mouse Gastrointestinal Bacteria Catalogue enables translation between the mouse and human gut microbiotas via functional mapping

Human health and disease have increasingly been shown to be impacted by the gut microbiota, and mouse models are essential for investigating these effects. However, the compositions of human and mouse gut microbiotas are distinct, limiting translation of microbiota research between these hosts. To address this, we constructed the Mouse Gastrointestinal Bacteria Catalogue (MGBC), a repository of 26,640 high-quality mouse microbiota-derived bacterial genomes. This catalog enables species-level analyses for mapping functions of interest and identifying functionally equivalent taxa between the microbiotas of humans and mice. We have complemented this with a publicly deposited collection of 223 bacterial isolates, including 62 previously uncultured species, to facilitate experimental investigation of individual commensal bacteria functions in vitro and in vivo. Together, these resources provide the ability to identify and test functionally equivalent members of the host-specific gut microbiotas of humans and mice and support the informed use of mouse models in human microbiota research.

Nutrient Intake and Gut Microbial Genera Changes after a 4-Week Placebo Controlled Galacto-Oligosaccharides Intervention in Young Females

Recent interest in the gut-brain-axis has highlighted the potential of prebiotics to impact wellbeing, and to affect behavioral change in humans. In this clinical trial, we examined the impact of four-weeks daily supplementation of galacto-oligosaccharides (GOS) on self-reported nutrient intake and relationships on gut microbiota in a four-week two-armed parallel double-blind placebo controlled GOS supplement trial in young adult females. Food diaries and stool samples were collected prior to and following 28 days of supplement consumption. It was found that four weeks of GOS supplementation influenced macronutrient intake, as evident by reduced carbohydrate and sugars and increased fats intake. Further analysis showed that the reduction in carbohydrates was predicted by increasing abundances of Bifidobacterium in the GOS group in comparison to the placebo group. This suggests that Bifidobacterium increase via GOS supplementation may help improve the gut microbiota composition by altering the desire for specific types of carbohydrates and boosting Bifidobacterium availability when fiber intake is below recommended levels, without compromising appetite for fiber from food.

Diversity of archaea and niche preferences among putative ammonia-oxidizing Nitrososphaeria dominating across European arable soils

Archaeal communities in arable soils are dominated by Nitrososphaeria, a class within Thaumarchaeota comprising all known ammonia-oxidizing archaea (AOA). AOA are key players in the nitrogen cycle and defining their niche specialization can help predicting effects of environmental change on these communities. However, hierarchical effects of environmental filters on AOA and the delineation of niche preferences of nitrososphaerial lineages remain poorly understood. We used phylogenetic information at fine scale and machine learning approaches to identify climatic, edaphic and geomorphological drivers of Nitrososphaeria and other archaea along a 3000 km European gradient. Only limited insights into the ecology of the low-abundant archaeal classes could be inferred, but our analyses underlined the multifactorial nature of niche differentiation within Nitrososphaeria. Mean annual temperature, C:N ratio and pH were the best predictors of their diversity, evenness and distribution. Thresholds in the predictions could be defined for C:N ratio and cation exchange capacity. Furthermore, multiple, independent and recent specializations to soil pH were detected in the Nitrososphaeria phylogeny. The coexistence of widespread ecophysiological differences between closely related soil Nitrososphaeria highlights that their ecology is best studied at fine phylogenetic scale.