Understanding sequencing data as compositions: an outlook and review

Metabolic complexity drives divergence in microbial communities

Microbial communities are shaped by environmental metabolites, but the principles that govern whether different communities will converge or diverge in any given condition remain unknown, posing fundamental questions about the feasibility of microbiome engineering. Here we studied the longitudinal assembly dynamics of a set of natural microbial communities grown in laboratory conditions of increasing metabolic complexity. We found that different microbial communities tend to become similar to each other when grown in metabolically simple conditions, but they diverge in composition as the metabolic complexity of the environment increases, a phenomenon we refer to as the divergence-complexity effect. A comparative analysis of these communities revealed that this divergence is driven by community diversity and by the assortment of specialist taxa capable of degrading complex metabolites. An ecological model of community dynamics indicates that the hierarchical structure of metabolism itself, where complex molecules are enzymatically degraded into progressively simpler ones that then participate in cross-feeding between community members, is necessary and sufficient to recapitulate our experimental observations. In addition to helping understand the role of the environment in community assembly, the divergence-complexity effect can provide insight into which environments support multiple community states, enabling the search for desired ecosystem functions towards microbiome engineering applications.

Integration and holistic analysis of multiple multidimensional soil data sets

Complex matrices such as soil have a range of measurable characteristics, and thus data to describe them can be considered multidimensional. These characteristics can be strongly influenced by factors that introduce confounding effects that hinder analyses. Traditional statistical approaches lack the flexibility and granularity required to adequately evaluate such matrices, particularly those with large dataset of varying data types (i.e. quantitative non-compositional, quantitative compositional). We present a statistical workflow designed to effectively analyse complex, multidimensional systems, even in the presence of confounding variables. The developed methodology involves exploratory analysis to identify the presence of confounding variables, followed by data decomposition (including strategies for both compositional and non-compositional quantitative data) to minimise the influence of these confounding factors such as sampling site/location. These data processing methods then allow for common patterns to be highlighted in the data, including the identification of biomarkers and determination of non-trivial associations between variables. We demonstrate the utility of this statistical workflow by jointly analysing the chemical composition and fungal biodiversity of New Zealand vineyard soils that have been managed with either organic low-input or conventional input approaches. By applying this pipeline, we were able to identify biomarkers that distinguish viticultural soil from both approaches and also unearth links and associations between the chemical and metagenomic profiles. While soil is an example of a system that can require this type of statistical methodology, there are a range of biological and ecological systems that are challenging to analyse due to the complex interplay of global and local effects. Utilising our developed pipeline will greatly enhance the way that these systems can be studied and the quality and impact of insight gained from their analysis.

Primary nasal influenza infection rewires tissue-scale memory response dynamics

The nasal mucosa is often the initial site of respiratory viral infection, replication, and transmission. Understanding how infection shapes tissue-scale primary and memory responses is critical for designing mucosal therapeutics and vaccines. We generated a single-cell RNA-sequencing atlas of the murine nasal mucosa, sampling three regions during primary influenza infection and rechallenge. Compositional analysis revealed restricted infection to the respiratory mucosa with stepwise changes in immune and epithelial cell subsets and states. We identified and characterized a rare subset of Krt13+ nasal immune-interacting floor epithelial (KNIIFE) cells, which concurrently increased with tissue-resident memory T (TRM)-like cells. Proportionality analysis, cell-cell communication inference, and microscopy underscored the CXCL16-CXCR6 axis between KNIIFE and TRM cells. Secondary influenza challenge induced accelerated and coordinated myeloid and lymphoid responses without epithelial proliferation. Together, this atlas serves as a reference for viral infection in the upper respiratory tract and highlights the efficacy of local coordinated memory responses.

Systematic evaluation with practical guidelines for single-cell and spatially resolved transcriptomics data simulation under multiple scenarios

BACKGROUND

Single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) have led to groundbreaking advancements in life sciences. To develop bioinformatics tools for scRNA-seq and SRT data and perform unbiased benchmarks, data simulation has been widely adopted by providing explicit ground truth and generating customized datasets. However, the performance of simulation methods under multiple scenarios has not been comprehensively assessed, making it challenging to choose suitable methods without practical guidelines.

RESULTS

We systematically evaluated 49 simulation methods developed for scRNA-seq and/or SRT data in terms of accuracy, functionality, scalability, and usability using 152 reference datasets derived from 24 platforms. SRTsim, scDesign3, ZINB-WaVE, and scDesign2 have the best accuracy performance across various platforms. Unexpectedly, some methods tailored to scRNA-seq data have potential compatibility for simulating SRT data. Lun, SPARSim, and scDesign3-tree outperform other methods under corresponding simulation scenarios. Phenopath, Lun, Simple, and MFA yield high scalability scores but they cannot generate realistic simulated data. Users should consider the trade-offs between method accuracy and scalability (or functionality) when making decisions. Additionally, execution errors are mainly caused by failed parameter estimations and appearance of missing or infinite values in calculations. We provide practical guidelines for method selection, a standard pipeline Simpipe ( https://github.com/duohongrui/simpipe ; https://doi.org/10.5281/zenodo.11178409 ), and an online tool Simsite ( https://www.ciblab.net/software/simshiny/ ) for data simulation.

CONCLUSIONS

No method performs best on all criteria, thus a good-yet-not-the-best method is recommended if it solves problems effectively and reasonably. Our comprehensive work provides crucial insights for developers on modeling gene expression data and fosters the simulation process for users.

Microbiome compositional data analysis for survival studies

The growing interest in studying the relationship between the human microbiome and our health has also extended to time-to-event studies where researchers explore the connection between the microbiome and the occurrence of a specific event of interest. The analysis of microbiome obtained through high throughput sequencing techniques requires the use of specialized Compositional Data Analysis (CoDA) methods designed to accommodate its compositional nature. There is a limited availability of statistical tools for microbiome analysis that incorporate CoDA, and this is even more pronounced in the context of survival analysis. To fill this methodological gap, we present coda4microbiome for survival studies, a new methodology for the identification of microbial signatures in time-to-event studies. The algorithm implements an elastic-net penalized Cox regression model adapted to compositional covariates. We illustrate coda4microbiome algorithm for survival studies with a case study about the time to develop type 1 diabetes for non-obese diabetic mice. Our algorithm identified a bacterial signature composed of 21 genera associated with diabetes development. coda4microbiome for survival studies is integrated in the R package coda4microbiome as an extension of the existing functions for cross-sectional and longitudinal studies.

Site properties, environmental factors, and crop identify influence soil bacterial communities more than municipal biosolid application

Reducing the environmental impact of Canadian field crop agriculture, including the reliance on conventional synthesised fertilisers, are key societal targets for establishing long-term sustainable practices. Municipal biosolids (MSB) are an abundant, residual organic material, rich in phosphate, nitrogen and other oligo-nutrients, that could be used in conjunction with conventional fertilisers to decrease their use. Though MBS have previously been shown to be an effective fertiliser substitute for different crops, including corn and soybean, there remain key knowledge gaps concerning the impact of MBS on the resident soil bacterial communities in agro-ecosystems. We hypothesised that the MBS fertiliser amendment would not significantly impact the structure or function of the soil bacterial communities, nor contribute to the spread of human pathogenic bacteria, in corn or soybean agricultural systems. In field experiments, fertiliser regimes for both crops were amended with MBS, and compared to corn and soybean plots with standard fertiliser treatments. We repeated this across four different agricultural sites in Quebec, over 2021 and 2022. We sampled MBS-treated, and untreated soils, and identified the composition of the soil bacterial communities via 16S rRNA metabarcoding. We found no indication that the MBS fertiliser amendment altered the structure of the soil bacterial communities, but rather that the soil type and crop identities were the most significant factors in structuring the bacterial communities. Moreover, there was no evidence that the MBS-treated soils were enriched in potential human bacterial pathogens over the two years of our study. Our analysis indicates that not only can MBS function as substitutes for conventional, synthesised fertilisers, but that they also do not disrupt the structure of the resident soil bacterial communities in the short term. Finally, we suggest that the use of MBS in agro-ecosystems poses no greater concern to the public than existing soil bacterial communities. This highlights the significant role MBS could potentially have in reducing the use of conventional industrial fertilisers and improving agricultural production, without risking environmental contamination.

RIP-seq reveals RNAs that interact with RNA polymerase and primary sigma factors in bacteria

Bacteria have evolved structured RNAs that can associate with RNA polymerase (RNAP). Two of them have been known so far-6S RNA and Ms1 RNA but it is unclear if any other types of RNAs binding to RNAP exist in bacteria. To identify all RNAs interacting with RNAP and the primary σ factors, we have established and performed native RIP-seq in Bacillus subtilis, Corynebacterium glutamicum, Streptomyces coelicolor, Mycobacterium smegmatis and the pathogenic Mycobacterium tuberculosis. Besides known 6S RNAs in B. subtilis and Ms1 in M. smegmatis, we detected MTS2823, a homologue of Ms1, on RNAP in M. tuberculosis. In C. glutamicum, we discovered novel types of structured RNAs that associate with RNAP. Furthermore, we identified other species-specific RNAs including full-length mRNAs, revealing a previously unknown landscape of RNAs interacting with the bacterial transcription machinery.

Kelp and sea urchin settlement mediated by biotic interactions with benthic coralline algal species

Species interactions can influence key ecological processes that support community assembly and composition. For example, coralline algae encompass extensive diversity and may play a major role in regime shifts from kelp forests to urchin-dominated barrens through their role in inducing invertebrate larval metamorphosis and influencing kelp spore settlement. In a series of laboratory experiments, we tested the hypothesis that different coralline communities facilitate the maintenance of either ecosystem state by either promoting or inhibiting early recruitment of kelps or urchins. Coralline algae significantly increased red urchin metamorphosis compared with a control, while they had varying effects on kelp settlement. Urchin metamorphosis and density of juvenile canopy kelps did not differ significantly across coralline species abundant in both kelp forests and urchin barrens, suggesting that recruitment of urchin and canopy kelps does not depend on specific corallines. Non-calcified fleshy red algal crusts promoted the highest mean urchin metamorphosis percentage and showed some of the lowest canopy kelp settlement. In contrast, settlement of one subcanopy kelp species was reduced on crustose corallines, but elevated on articulated corallines, suggesting that articulated corallines, typically absent in urchin barrens, may need to recover before this subcanopy kelp could return. Coralline species differed in surface bacterial microbiome composition; however, urchin metamorphosis was not significantly different when microbiomes were removed with antibiotics. Our results clarify the role played by coralline algal species in kelp forest community assembly and could have important implications for kelp forest recovery.

mbDecoda: a debiased approach to compositional data analysis for microbiome surveys

Potentially pathogenic or probiotic microbes can be identified by comparing their abundance levels between healthy and diseased populations, or more broadly, by linking microbiome composition with clinical phenotypes or environmental factors. However, in microbiome studies, feature tables provide relative rather than absolute abundance of each feature in each sample, as the microbial loads of the samples and the ratios of sequencing depth to microbial load are both unknown and subject to considerable variation. Moreover, microbiome abundance data are count-valued, often over-dispersed and contain a substantial proportion of zeros. To carry out differential abundance analysis while addressing these challenges, we introduce mbDecoda, a model-based approach for debiased analysis of sparse compositions of microbiomes. mbDecoda employs a zero-inflated negative binomial model, linking mean abundance to the variable of interest through a log link function, and it accommodates the adjustment for confounding factors. To efficiently obtain maximum likelihood estimates of model parameters, an Expectation Maximization algorithm is developed. A minimum coverage interval approach is then proposed to rectify compositional bias, enabling accurate and reliable absolute abundance analysis. Through extensive simulation studies and analysis of real-world microbiome datasets, we demonstrate that mbDecoda compares favorably with state-of-the-art methods in terms of effectiveness, robustness and reproducibility.

Accelerated nitrogen cycling on Mediterranean seagrass leaves at volcanic CO2 vents

Seagrass meadows form highly productive and diverse ecosystems in coastal areas worldwide, where they are increasingly exposed to ocean acidification (OA). Efficient nitrogen (N) cycling and uptake are essential to maintain plant productivity, but the effects of OA on N transformations in these systems are poorly understood. Here we show that complete N cycling occurs on leaves of the Mediterranean seagrass Posidonia oceanica at a volcanic CO2 vent near Ischia Island (Italy), with OA affecting both N gain and loss while the epiphytic microbial community structure remains largely unaffected. Daily leaf-associated N2 fixation contributes to 35% of the plant's N demand under ambient pH, while it contributes to 45% under OA. Nitrification potential is only detected under OA, and N-loss via N2 production increases, although the balance remains decisively in favor of enhanced N gain. Our work highlights the role of the N-cycling microbiome in seagrass adaptation to OA, with key N transformations accelerating towards increased N gain.

Primary nasal viral infection rewires the tissue-scale memory response

The nasal mucosa is frequently the initial site of respiratory viral infection, replication, and transmission. Recent work has started to clarify the independent responses of epithelial, myeloid, and lymphoid cells to viral infection in the nasal mucosa, but their spatiotemporal coordination and relative contributions remain unclear. Furthermore, understanding whether and how primary infection shapes tissue-scale memory responses to secondary challenge is critical for the rational design of nasal-targeting therapeutics and vaccines. Here, we generated a single-cell RNA-sequencing (scRNA-seq) atlas of the murine nasal mucosa sampling three distinct regions before and during primary and secondary influenza infection. Primary infection was largely restricted to respiratory mucosa and induced stepwise changes in cell type, subset, and state composition over time. Type I Interferon (IFN)-responsive neutrophils appeared 2 days post infection (dpi) and preceded transient IFN-responsive/cycling epithelial cell responses 5 dpi, which coincided with broader antiviral monocyte and NK cell accumulation. By 8 dpi, monocyte-derived macrophages (MDMs) expressing Cxcl9 and Cxcl16 arose alongside effector cytotoxic CD8 and Ifng-expressing CD4 T cells. Following viral clearance (14 dpi), rare, previously undescribed Krt13+ nasal immune-interacting floor epithelial (KNIIFE) cells expressing multiple genes with immune communication potential increased concurrently with tissue-resident memory T (TRM)-like cells and early IgG+/IgA+ plasmablasts. Proportionality analysis coupled with cell-cell communication inference, alongside validation by in situ microscopy, underscored the CXCL16-CXCR6 signaling axis between MDMs and effector CD8 T cells 8dpi and KNIIFE cells and TRM cells 14 dpi. Secondary influenza challenge with a homologous or heterologous strain administered 60 dpi induced an accelerated and coordinated myeloid and lymphoid response without epithelial proliferation, illustrating how tissue-scale memory to natural infection engages both myeloid and lymphoid cells to reduce epithelial regenerative burden. Together, this atlas serves as a reference for viral infection in the upper respiratory tract and highlights the efficacy of local coordinated memory responses upon rechallenge.

Hypometabolism to survive the long polar night and subsequent successful return to light in the diatom Fragilariopsis cylindrus

Diatoms, the main eukaryotic phytoplankton of the polar marine regions, are essential for the maintenance of food chains specific to Arctic and Antarctic ecosystems, and are experiencing major disturbances under current climate change. As such, it is fundamental to understand the physiological mechanisms and associated molecular basis of their endurance during the long polar night. Here, using the polar diatom Fragilariopsis cylindrus, we report an integrative analysis combining transcriptomic, microscopic and biochemical approaches to shed light on the strategies used to survive the polar night. We reveal that in prolonged darkness, diatom cells enter a state of quiescence with reduced metabolic and transcriptional activity, during which no cell division occurs. We propose that minimal energy is provided by respiration and degradation of protein, carbohydrate and lipid stores and that homeostasis is maintained by autophagy in prolonged darkness. We also report internal structural changes that manifest the morphological acclimation of cells to darkness, including the appearance of a large vacuole. Our results further show that immediately following a return to light, diatom cells are able to use photoprotective mechanisms and rapidly resume photosynthesis, demonstrating the remarkable robustness of polar diatoms to prolonged darkness at low temperature.

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.

Rarefaction is currently the best approach to control for uneven sequencing effort in amplicon sequence analyses

Considering it is common to find as much as 100-fold variation in the number of 16S rRNA gene sequences across samples in a study, researchers need to control for the effect of uneven sequencing effort. How to do this has become a contentious question. Some have argued that rarefying or rarefaction is "inadmissible" because it omits valid data. A number of alternative approaches have been developed to normalize and rescale the data that purport to be invariant to the number of observations. I generated community distributions based on 12 published data sets where I was able to assess the ability of multiple methods to control for uneven sequencing effort. Rarefaction was the only method that could control for variation in uneven sequencing effort when measuring commonly used alpha and beta diversity metrics. Next, I compared the false detection rate and power to detect true differences between simulated communities with a known effect size using various alpha and beta diversity metrics. Although all methods of controlling for uneven sequencing effort had an acceptable false detection rate when samples were randomly assigned to two treatment groups, rarefaction was consistently able to control for differences in sequencing effort when sequencing depth was confounded with treatment group. Finally, the statistical power to detect differences in alpha and beta diversity metrics was consistently the highest when using rarefaction. These simulations underscore the importance of using rarefaction to normalize the number of sequences across samples in amplicon sequencing analyses.

IMPORTANCE

Sequencing 16S rRNA gene fragments has become a fundamental tool for understanding the diversity of microbial communities and the factors that affect their diversity. Due to technical challenges, it is common to observe wide variation in the number of sequences that are collected from different samples within the same study. However, the diversity metrics used by microbial ecologists are sensitive to differences in sequencing effort. Therefore, tools are needed to control for the uneven levels of sequencing. This simulation-based analysis shows that despite a longstanding controversy, rarefaction is the most robust approach to control for uneven sequencing effort. The controversy started because of confusion over the definition of rarefaction and violation of assumptions that are made by methods that have been borrowed from other fields. Microbial ecologists should use rarefaction.

Zero is not absence: censoring-based differential abundance analysis for microbiome data

MOTIVATION

Microbiome data analysis faces the challenge of sparsity, with many entries recorded as zeros. In differential abundance analysis, the presence of excessive zeros in data violates distributional assumptions and creates ties, leading to an increased risk of type I errors and reduced statistical power.

RESULTS

We developed a novel normalization method, called censoring-based analysis of microbiome proportions (CAMP), for microbiome data by treating zeros as censored observations, transforming raw read counts into tie-free time-to-event-like data. This enables the use of survival analysis techniques, like the Cox proportional hazards model, for differential abundance analysis. Extensive simulations demonstrate that CAMP achieves proper type I error control and high power. Applying CAMP to a human gut microbiome dataset, we identify 60 new differentially abundant taxa across geographic locations, showcasing its usefulness. CAMP overcomes sparsity challenges, enabling improved statistical analysis and providing valuable insights into microbiome data in various contexts.

AVAILABILITY AND IMPLEMENTATION

The R package is available at https://github.com/lapsumchan/CAMP.

Consistent spatial patterns in microbial taxa of red squirrel gut microbiomes

Gut microbiomes are diverse ecosystems whose drivers of variation remain largely unknown, especially in time and space. We analysed a dataset with over 900 red squirrel (Tamiasciurus hudsonicus) gut microbiome samples to identify the drivers of gut microbiome composition in this territorial rodent. The large-scale spatiotemporal replication in the data analysed was an essential component of understanding the assembly of these microbial communities. We identified that the spatial location of the sampled squirrels in their local environment is a key contributor to gut microbial community composition. The non-core gut microbiome (present in less than 75% of gut microbiome samples) had highly localised spatial patterns throughout different seasons and different study areas in the host squirrel population. The core gut microbiome, on the other hand, showed some spatial patterns, though fewer than in the non-core gut microbiome. Environmental transmission of microbiota is the likely contributor to the spatiotemporal distribution observed in the North American red squirrel gut microbiome.

Seafloor incubation experiments at deep-sea hydrothermal vents reveal distinct biogeographic signatures of autotrophic communities

The discharge of hydrothermal vents on the seafloor provides energy sources for dynamic and productive ecosystems, which are supported by chemosynthetic microbial populations. These populations use the energy gained by oxidizing the reduced chemicals contained within the vent fluids to fix carbon and support multiple trophic levels. Hydrothermal discharge is ephemeral and chemical composition of such fluids varies over space and time, which can result in geographically distinct microbial communities. To investigate the foundational members of the community, microbial growth chambers were placed within the hydrothermal discharge at Axial Seamount (Juan de Fuca Ridge), Magic Mountain Seamount (Explorer Ridge), and Kama'ehuakanaloa Seamount (Hawai'i hotspot). Campylobacteria were identified within the nascent communities, but different amplicon sequence variants were present at Axial and Kama'ehuakanaloa Seamounts, indicating that geography in addition to the composition of the vent effluent influences microbial community development. Across these vent locations, dissolved iron concentration was the strongest driver of community structure. These results provide insights into nascent microbial community structure and shed light on the development of diverse lithotrophic communities at hydrothermal vents.

Deep learning in spatially resolved transcriptfomics: a comprehensive technical view

Spatially resolved transcriptomics (SRT) is a pioneering method for simultaneously studying morphological contexts and gene expression at single-cell precision. Data emerging from SRT are multifaceted, presenting researchers with intricate gene expression matrices, precise spatial details and comprehensive histology visuals. Such rich and intricate datasets, unfortunately, render many conventional methods like traditional machine learning and statistical models ineffective. The unique challenges posed by the specialized nature of SRT data have led the scientific community to explore more sophisticated analytical avenues. Recent trends indicate an increasing reliance on deep learning algorithms, especially in areas such as spatial clustering, identification of spatially variable genes and data alignment tasks. In this manuscript, we provide a rigorous critique of these advanced deep learning methodologies, probing into their merits, limitations and avenues for further refinement. Our in-depth analysis underscores that while the recent innovations in deep learning tailored for SRT have been promising, there remains a substantial potential for enhancement. A crucial area that demands attention is the development of models that can incorporate intricate biological nuances, such as phylogeny-aware processing or in-depth analysis of minuscule histology image segments. Furthermore, addressing challenges like the elimination of batch effects, perfecting data normalization techniques and countering the overdispersion and zero inflation patterns seen in gene expression is pivotal. To support the broader scientific community in their SRT endeavors, we have meticulously assembled a comprehensive directory of readily accessible SRT databases, hoping to serve as a foundation for future research initiatives.

A robust microbiome signature for autism spectrum disorder across different studies using machine learning

Autism spectrum disorder (ASD) is a highly complex neurodevelopmental disorder characterized by deficits in sociability and repetitive behaviour, however there is a great heterogeneity within other comorbidities that accompany ASD. Recently, gut microbiome has been pointed out as a plausible contributing factor for ASD development as individuals diagnosed with ASD often suffer from intestinal problems and show a differentiated intestinal microbial composition. Nevertheless, gut microbiome studies in ASD rarely agree on the specific bacterial taxa involved in this disorder. Regarding the potential role of gut microbiome in ASD pathophysiology, our aim is to investigate whether there is a set of bacterial taxa relevant for ASD classification by using a sibling-controlled dataset. Additionally, we aim to validate these results across two independent cohorts as several confounding factors, such as lifestyle, influence both ASD and gut microbiome studies. A machine learning approach, recursive ensemble feature selection (REFS), was applied to 16S rRNA gene sequencing data from 117 subjects (60 ASD cases and 57 siblings) identifying 26 bacterial taxa that discriminate ASD cases from controls. The average area under the curve (AUC) of this specific set of bacteria in the sibling-controlled dataset was 81.6%. Moreover, we applied the selected bacterial taxa in a tenfold cross-validation scheme using two independent cohorts (a total of 223 samples-125 ASD cases and 98 controls). We obtained average AUCs of 74.8% and 74%, respectively. Analysis of the gut microbiome using REFS identified a set of bacterial taxa that can be used to predict the ASD status of children in three distinct cohorts with AUC over 80% for the best-performing classifiers. Our results indicate that the gut microbiome has a strong association with ASD and should not be disregarded as a potential target for therapeutic interventions. Furthermore, our work can contribute to use the proposed approach for identifying microbiome signatures across other 16S rRNA gene sequencing datasets.

Aberrant epithelial cell interaction promotes esophageal squamous-cell carcinoma development and progression

Epithelial-mesenchymal transition (EMT) and proliferation play important roles in epithelial cancer formation and progression, but what molecules and how they trigger EMT is largely unknown. Here we performed spatial transcriptomic and functional analyses on samples of multistage esophageal squamous-cell carcinoma (ESCC) from mice and humans to decipher these critical issues. By investigating spatiotemporal gene expression patterns and cell-cell interactions, we demonstrated that the aberrant epithelial cell interaction via EFNB1-EPHB4 triggers EMT and cell cycle mediated by downstream SRC/ERK/AKT signaling. The aberrant epithelial cell interaction occurs within the basal layer at early precancerous lesions, which expands to the whole epithelial layer and strengthens along the cancer development and progression. Functional analysis revealed that the aberrant EFNB1-EPHB4 interaction is caused by overexpressed ΔNP63 due to TP53 mutation, the culprit in human ESCC tumorigenesis. Our results shed new light on the role of TP53-TP63/ΔNP63-EFNB1-EPHB4 axis in EMT and cell proliferation in epithelial cancer formation.

Unveiling microbiome changes in Mediterranean octocorals during the 2022 marine heatwaves: quantifying key bacterial symbionts and potential pathogens

BACKGROUND

Climate change has accelerated the occurrence and severity of heatwaves in the Mediterranean Sea and poses a significant threat to the octocoral species that form the foundation of marine animal forests (MAFs). As coral health intricately relies on the symbiotic relationships established between corals and microbial communities, our goal was to gain a deeper understanding of the role of bacteria in the observed tissue loss of key octocoral species following the unprecedented heatwaves in 2022.

RESULTS

Using amplicon sequencing and taxon-specific qPCR analyses, we unexpectedly found that the absolute abundance of the major bacterial symbionts, Spirochaetaceae (C. rubrum) and Endozoicomonas (P. clavata), remained, in most cases, unchanged between colonies with 0% and 90% tissue loss. These results suggest that the impairment of coral health was not due to the loss of the main bacterial symbionts. However, we observed a significant increase in the total abundance of bacterial opportunists, including putative pathogens such as Vibrio, which was not evident when only their relative abundance was considered. In addition, there was no clear relation between bacterial symbiont loss and the intensity of thermal stress, suggesting that factors other than temperature may have influenced the differential response of octocoral microbiomes at different sampling sites.

CONCLUSIONS

Our results indicate that tissue loss in octocorals is not directly caused by the decline of the main bacterial symbionts but by the proliferation of opportunistic and pathogenic bacteria. Our findings thus underscore the significance of considering both relative and absolute quantification approaches when evaluating the impact of stressors on coral microbiome as the relative quantification does not accurately depict the actual changes in the microbiome. Consequently, this research enhances our comprehension of the intricate interplay between host organisms, their microbiomes, and environmental stressors, while offering valuable insights into the ecological implications of heatwaves on marine animal forests. Video Abstract.

Engraftment of essential functions through multiple fecal microbiota transplants in chronic antibiotic-resistant pouchitis-a case study using metatranscriptomics

BACKGROUND

Ileal pouch-anal anastomosis (IPAA) is the standard of care after total proctocolectomy for ulcerative colitis (UC). Around 50% of patients will experience pouchitis, an idiopathic inflammatory condition. Antibiotics are the backbone of treatment of pouchitis; however, antibiotic-resistant pouchitis develops in 5-10% of those patients. It has been shown that fecal microbiota transplantation (FMT) is an effective treatment for UC, but results for FMT antibiotic-resistant pouchitis are inconsistent.

METHODS

To uncover which metabolic activities were transferred to the recipients during FMT and helped the remission, we performed a longitudinal case study of the gut metatranscriptomes from three patients and their donors. The patients were treated by two to three FMTs, and stool samples were analyzed for up to 140 days.

RESULTS

Reduced expression in pouchitis patients compared to healthy donors was observed for genes involved in biosynthesis of amino acids, cofactors, and B vitamins. An independent metatranscriptome dataset of UC patients showed a similar result. Other functions including biosynthesis of butyrate, metabolism of bile acids, and tryptophan were also much lower expressed in pouchitis. After FMT, these activities transiently increased, and the overall metatranscriptome profiles closely mirrored those of the respective donors with notable fluctuations during the subsequent weeks. The levels of the clinical marker fecal calprotectin were concordant with the metatranscriptome data. Faecalibacterium prausnitzii represented the most active species contributing to butyrate synthesis via the acetyl-CoA pathway. Remission occurred after the last FMT in all patients and was characterized by a microbiota activity profile distinct from donors in two of the patients.

CONCLUSIONS

Our study demonstrates the clear but short-lived activity engraftment of donor microbiota, particularly the butyrate biosynthesis after each FMT. The data suggest that FMT triggers shifts in the activity of patient microbiota towards health which need to be repeated to reach critical thresholds. As a case study, these insights warrant cautious interpretation, and validation in larger cohorts is necessary for generalized applications. In the long run, probiotics with high taxonomic diversity consisting of well characterized strains could replace FMT to avoid the costly screening of donors and the risk of transferring unwanted genetic material. Video Abstract.

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.

Carbon amendments in soil microcosms induce uneven response on H2 oxidation activity and microbial community composition

High-affinity H2-oxidizing bacteria (HA-HOB) thriving in soil are responsible for the most important sink of atmospheric H2. Their activity increases with soil organic carbon content, but the incidence of different carbohydrate fractions on the process has received little attention. Here we tested the hypothesis that carbon amendments impact HA-HOB activity and diversity differentially depending on their recalcitrance and their concentration. Carbon sources (sucrose, starch, cellulose) and application doses (0, 0.1, 1, 3, 5% Ceq soildw-1) were manipulated in soil microcosms. Only 0.1% Ceq soildw-1 cellulose treatment stimulated the HA-HOB activity. Sucrose amendments induced the most significant changes, with an abatement of 50% activity at 1% Ceq soildw-1. This was accompanied with a loss of bacterial and fungal alpha diversity and a reduction of high-affinity group 1 h/5 [NiFe]-hydrogenase gene (hhyL) abundance. A quantitative classification framework was elaborated to assign carbon preference traits to 16S rRNA gene, ITS and hhyL genotypes. The response was uneven at the taxonomic level, making carbon preference a difficult trait to predict. Overall, the results suggest that HA-HOB activity is more susceptible to be stimulated by low doses of recalcitrant carbon, while labile carbon-rich environment is an unfavorable niche for HA-HOB, inducing catabolic repression of hydrogenase.

Genetic hypogonadal mouse model reveals niche-specific influence of reproductive axis and sex on intestinal microbial communities

BACKGROUND

The gut microbiome has been linked to many diseases with sex bias including autoimmune, metabolic, neurological, and reproductive disorders. While numerous studies report sex differences in fecal microbial communities, the role of the reproductive axis in this differentiation is unclear and it is unknown how sex differentiation affects microbial diversity in specific regions of the small and large intestine.

METHODS

We used a genetic hypogonadal mouse model that does not produce sex steroids or go through puberty to investigate how sex and the reproductive axis impact bacterial diversity within the intestine. Using 16S rRNA gene sequencing, we analyzed alpha and beta diversity and taxonomic composition of fecal and intestinal communities from the lumen and mucosa of the duodenum, ileum, and cecum from adult female (n = 20) and male (n = 20) wild-type mice and female (n = 17) and male (n = 20) hypogonadal mice.

RESULTS

Both sex and reproductive axis inactivation altered bacterial composition in an intestinal section and niche-specific manner. Hypogonadism was significantly associated with bacteria from the Bacteroidaceae, Eggerthellaceae, Muribaculaceae, and Rikenellaceae families, which have genes for bile acid metabolism and mucin degradation. Microbial balances between males and females and between hypogonadal and wild-type mice were also intestinal section-specific. In addition, we identified 3 bacterial genera (Escherichia Shigella, Lachnoclostridium, and Eggerthellaceae genus) with higher abundance in wild-type female mice throughout the intestinal tract compared to both wild-type male and hypogonadal female mice, indicating that activation of the reproductive axis leads to female-specific differentiation of the gut microbiome. Our results also implicated factors independent of the reproductive axis (i.e., sex chromosomes) in shaping sex differences in intestinal communities. Additionally, our detailed profile of intestinal communities showed that fecal samples do not reflect bacterial diversity in the small intestine.

CONCLUSIONS

Our results indicate that sex differences in the gut microbiome are intestinal niche-specific and that sampling feces or the large intestine may miss significant sex effects in the small intestine. These results strongly support the need to consider both sex and reproductive status when studying the gut microbiome and while developing microbial-based therapies.

Addressing erroneous scale assumptions in microbe and gene set enrichment analysis

By applying Differential Set Analysis (DSA) to sequence count data, researchers can determine whether groups of microbes or genes are differentially enriched. Yet sequence count data suffer from a scale limitation: these data lack information about the scale (i.e., size) of the biological system under study, leading some authors to call these data compositional (i.e., proportional). In this article, we show that commonly used DSA methods that rely on normalization make strong, implicit assumptions about the unmeasured system scale. We show that even small errors in these scale assumptions can lead to positive predictive values as low as 9%. To address this problem, we take three novel approaches. First, we introduce a sensitivity analysis framework to identify when modeling results are robust to such errors and when they are suspect. Unlike standard benchmarking studies, this framework does not require ground-truth knowledge and can therefore be applied to both simulated and real data. Second, we introduce a statistical test that provably controls Type-I error at a nominal rate despite errors in scale assumptions. Finally, we discuss how the impact of scale limitations depends on a researcher's scientific goals and provide tools that researchers can use to evaluate whether their goals are at risk from erroneous scale assumptions. Overall, the goal of this article is to catalyze future research into the impact of scale limitations in analyses of sequence count data; to illustrate that scale limitations can lead to inferential errors in practice; yet to also show that rigorous and reproducible scale reliant inference is possible if done carefully.

scDiffCom: a tool for differential analysis of cell-cell interactions provides a mouse atlas of aging changes in intercellular communication

Dysregulation of intercellular communication is a hallmark of aging. To better quantify and explore changes in intercellular communication, we present scDiffCom and scAgeCom. scDiffCom is an R package, relying on approximately 5,000 curated ligand-receptor interactions, that performs differential intercellular communication analysis between two conditions from single-cell transcriptomics data. Built upon scDiffCom, scAgeCom is an atlas of age-related cell-cell communication changes covering 23 mouse tissues from 58 single-cell RNA sequencing datasets from Tabula Muris Senis and the Calico murine aging cell atlas. It offers a comprehensive resource of tissue-specific and sex-specific aging dysregulations and highlights age-related intercellular communication changes widespread across the whole body, such as the upregulation of immune system processes and inflammation, the downregulation of developmental processes, angiogenesis and extracellular matrix organization and the deregulation of lipid metabolism. Our analysis emphasizes the relevance of the specific ligands, receptors and cell types regulating these processes. The atlas is available online ( https://scagecom.org ).

Importance of mobile genetic elements for dissemination of antimicrobial resistance in metagenomic sewage samples across the world

We are facing an ever-growing threat from increasing antimicrobial resistance (AMR) in bacteria. To mitigate this, we need a better understanding of the global spread of antimicrobial resistance genes (ARGs). ARGs are often spread among bacteria by horizontal gene transfer facilitated by mobile genetic elements (MGE). Here we use a dataset consisting of 677 metagenomic sequenced sewage samples from 97 countries or regions to study how MGEs are geographically distributed and how they disseminate ARGs worldwide. The ARGs, MGEs, and bacterial abundance were calculated by reference-based read mapping. We found systematic differences in the abundance of MGEs and ARGs, where some elements were prevalent on all continents while others had higher abundance in separate geographic areas. Different MGEs tended to be localized to temperate or tropical climate zones, while different ARGs tended to separate according to continents. This suggests that the climate is an important factor influencing the local flora of MGEs. MGEs were also found to be more geographically confined than ARGs. We identified several integrated MGEs whose abundance correlated with the abundance of ARGs and bacterial genera, indicating the ability to mobilize and disseminate these genes. Some MGEs seemed to be more able to mobilize ARGs and spread to more bacterial species. The host ranges of MGEs seemed to differ between elements, where most were associated with bacteria of the same family. We believe that our method could be used to investigate the population dynamics of MGEs in complex bacterial populations.

Overview of data preprocessing for machine learning applications in human microbiome research

Although metagenomic sequencing is now the preferred technique to study microbiome-host interactions, analyzing and interpreting microbiome sequencing data presents challenges primarily attributed to the statistical specificities of the data (e.g., sparse, over-dispersed, compositional, inter-variable dependency). This mini review explores preprocessing and transformation methods applied in recent human microbiome studies to address microbiome data analysis challenges. Our results indicate a limited adoption of transformation methods targeting the statistical characteristics of microbiome sequencing data. Instead, there is a prevalent usage of relative and normalization-based transformations that do not specifically account for the specific attributes of microbiome data. The information on preprocessing and transformations applied to the data before analysis was incomplete or missing in many publications, leading to reproducibility concerns, comparability issues, and questionable results. We hope this mini review will provide researchers and newcomers to the field of human microbiome research with an up-to-date point of reference for various data transformation tools and assist them in choosing the most suitable transformation method based on their research questions, objectives, and data characteristics.

clrDV: a differential variability test for RNA-Seq data based on the skew-normal distribution

Background

Pathological conditions may result in certain genes having expression variance that differs markedly from that of the control. Finding such genes from gene expression data can provide invaluable candidates for therapeutic intervention. Under the dominant paradigm for modeling RNA-Seq gene counts using the negative binomial model, tests of differential variability are challenging to develop, owing to dependence of the variance on the mean.

Methods

Here, we describe clrDV, a statistical method for detecting genes that show differential variability between two populations. We present the skew-normal distribution for modeling gene-wise null distribution of centered log-ratio transformation of compositional RNA-seq data.

Results

Simulation results show that clrDV has false discovery rate and probability of Type II error that are on par with or superior to existing methodologies. In addition, its run time is faster than its closest competitors, and remains relatively constant for increasing sample size per group. Analysis of a large neurodegenerative disease RNA-Seq dataset using clrDV successfully recovers multiple gene candidates that have been reported to be associated with Alzheimer's disease.

Holobiont responses of mesophotic precious red coral Corallium rubrum to thermal anomalies

Marine heat waves (MHWs) have increased in frequency and intensity worldwide, causing mass mortality of benthic organisms and loss of biodiversity in shallow waters. The Mediterranean Sea is no exception, with shallow populations of habitat-forming octocorals facing the threat of local extinction. The mesophotic zone, which is less affected by MHWs, may be of ecological importance in conservation strategies for these species. However, our understanding of the response of mesophotic octocoral holobionts to changes in seawater temperature remains limited. To address this knowledge gap, we conducted a study on an iconic Mediterranean octocoral, the red coral Corallium rubrum sampled at 60 m depth and 15 °C. We exposed the colonies to temperatures they occasionally experience (18 °C) and temperatures that could occur at the end of the century if global warming continues (21 °C). We also tested their response to extremely cold and warm temperatures (12 °C and 24 °C). Our results show a high tolerance of C. rubrum to a two-month long exposure to temperatures ranging from 12 to 21 °C as no colony showed signs of tissue loss, reduced feeding ability, stress-induced gene expression, or disruption of host-bacterial symbioses. At 24 °C, however, we measured a sharp decrease in the relative abundance of Spirochaetaceae, which are the predominant bacterial symbionts under healthy conditions, along with a relative increase in Vibrionaceae. Tissue loss and overexpression of the tumor necrosis factor receptor 1 gene were also observed after two weeks of exposure. In light of ongoing global warming, our study helps predict the consequences of MHWs on mesophotic coralligenous reefs and the biodiversity that depends on them.

Metabolic complexity drives divergence in microbial communities

Microbial communities are shaped by the metabolites available in their environment, but the principles that govern whether different communities will converge or diverge in any given condition remain unknown, posing fundamental questions about the feasibility of microbiome engineering. To this end, we studied the longitudinal assembly dynamics of a set of natural microbial communities grown in laboratory conditions of increasing metabolic complexity. We found that different microbial communities tend to become similar to each other when grown in metabolically simple conditions, but diverge in composition as the metabolic complexity of the environment increases, a phenomenon we refer to as the divergence-complexity effect. A comparative analysis of these communities revealed that this divergence is driven by community diversity and by the diverse assortment of specialist taxa capable of degrading complex metabolites. An ecological model of community dynamics indicates that the hierarchical structure of metabolism itself, where complex molecules are enzymatically degraded into progressively smaller ones, is necessary and sufficient to recapitulate all of our experimental observations. In addition to pointing to a fundamental principle of community assembly, the divergence-complexity effect has important implications for microbiome engineering applications, as it can provide insight into which environments support multiple community states, enabling the search for desired ecosystem functions.

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.

Single-cell quantification of ribosome occupancy in early mouse development

Translation regulation is critical for early mammalian embryonic development¹. However, previous studies had been restricted to bulk measurements², precluding precise determination of translation regulation including allele-specific analyses. Here, to address this challenge, we developed a novel microfluidic isotachophoresis (ITP) approach, named RIBOsome profiling via ITP (Ribo-ITP), and characterized translation in single oocytes and embryos during early mouse development. We identified differential translation efficiency as a key mechanism regulating genes involved in centrosome organization and N⁶-methyladenosine modification of RNAs. Our high-coverage measurements enabled, to our knowledge, the first analysis of allele-specific ribosome engagement in early development. These led to the discovery of stage-specific differential engagement of zygotic RNAs with ribosomes and reduced translation efficiency of transcripts exhibiting allele-biased expression. By integrating our measurements with proteomics data, we discovered that ribosome occupancy in germinal vesicle-stage oocytes is the predominant determinant of protein abundance in the zygote. The Ribo-ITP approach will enable numerous applications by providing high-coverage and high-resolution ribosome occupancy measurements from ultra-low input samples including single cells.

Moving beyond DNA: towards functional analysis of the vaginal microbiome by non-sequencing-based methods

Over the last two decades, sequencing-based methods have revolutionised our understanding of niche-specific microbial complexity. In the lower female reproductive tract, these approaches have enabled identification of bacterial compositional structures associated with health and disease. Application of metagenomics and metatranscriptomics strategies have provided insight into the putative function of these communities but it is increasingly clear that direct measures of microbial and host cell function are required to understand the contribution of microbe-host interactions to pathophysiology. Here we explore and discuss current methods and approaches, many of which rely upon mass-spectrometry, being used to capture functional insight into the vaginal mucosal interface. In addition to improving mechanistic understanding, these methods offer innovative solutions for the development of diagnostic and therapeutic strategies designed to improve women's health.

Sexual transmission may drive pair similarity of the cloacal microbiome in a polyandrous species

All animals host a microbial community within and on their reproductive organs, known as the reproductive microbiome. In free-living birds, studies on the sexual transmission of bacteria have typically focused on a few pathogens instead of the bacterial community as a whole, despite a potential link to reproductive function. Theory predicts higher sexual transmission of the reproductive microbiome in females via the males' ejaculates and higher rates of transmission in promiscuous systems. We studied the cloacal microbiome of breeding individuals of a socially polyandrous, sex-role-reversed shorebird, the red phalarope (Phalaropus fulicarius). We expected (i) higher microbial diversity in females compared to males; (ii) low compositional differentiation between sexes; (iii) lower variation in composition between individuals (i.e. microbiome dispersion) in females than in males; (iv) convergence in composition as the breeding season progresses as a consequence of sexual transmission and/or shared habitat use; and (v) higher similarity in microbial composition between social pair members than between two random opposite-sex individuals. We found no or small between-sex differences in cloacal microbiome diversity/richness and composition. Dispersion of predicted functional pathways was lower in females than in males. As predicted, microbiome dispersion decreased with sampling date relative to clutch initiation of the social pair. Microbiome composition was significantly more similar among social pair members than among two random opposite-sex individuals. Pair membership explained 21.5% of the variation in taxonomic composition and 10.1% of functional profiles, whereas temporal and sex effects explained only 0.6%-1.6%. Consistent with evidence of functional convergence of reproductive microbiomes within pairs, some select taxa and predicted functional pathways were less variable between social pair members than between random opposite-sex individuals. As predicted if sexual transmission of the reproductive microbiome is high, sex differences in microbiome composition were weak in a socially polyandrous system with frequent copulations. Moreover, high within-pair similarity in microbiome composition, particularly for a few taxa spanning the spectrum of the beneficial-pathogenic axis, demonstrates the link between mating behaviour and the reproductive microbiome. Our study is consistent with the hypothesis that sexual transmission plays an important role in driving reproductive microbiome ecology and evolution.

Interneuron diversity in the human dorsal striatum

Deciphering the striatal interneuron diversity is key to understanding the basal ganglia circuit and to untangle the complex neurological and psychiatric diseases affecting this brain structure. We performed snRNA-seq of postmortem human caudate nucleus and putamen samples to elucidate the diversity and abundance of interneuron populations and their transcriptional structure in the human dorsal striatum. We propose a new taxonomy of striatal interneurons with eight main classes and fourteen subclasses and provide their specific markers and some quantitative FISH validation, particularly for a novel PTHLH-expressing population. For the most abundant populations, PTHLH and TAC3, we found matching known mouse interneuron populations based on key functional genes such as ion channels and synaptic receptors. Remarkably, human TAC3 and mouse Th populations share important similarities including the expression of the neuropeptide tachykinin 3. Finally, we were able to integrate other published datasets supporting the generalizability of this new harmonized taxonomy.

Characterizing sediment functional traits and ecological consequences respond to increasing antibiotic pollution

Current studies have shown that the taxonomic structures of ecologically important microbial communities are altered by antibiotic exposure, but the resulting effects on functional potentials and subsequent biogeochemical processes are poorly understood. However, this knowledge is indispensable for developing an accurate projection of nutrient dynamics in the future. Using metagenomic analyses, here we explored the responses of taxonomical and functional structures of a sediment microbial community, and their links with key biogeochemical processes to increasing antibiotic pollution from the pristine inlet to the outfall sites along an aquaculture discharge channel. We identified sharply contrasting sedimentary microbial communities and functional traits along increasing antibiotic pollution. Functional structures exhibited steeper distance-decay relationships than taxonomical structures along both the antibiotic distance and physicochemical distance, revealing higher functional sensitivity. Sediment enzyme activities were significantly and positively coupled with the relative abundances of their coding genes, thus the abundances of genes were indicative of functional potentials. The nitrogen cycling pathways were commonly inhibited by antibiotics, but not for the first step of nitrification, which could synergistically mitigate nitrous oxide emission. However, antibiotic pollution stimulated methanogens and inhibited methanotrophs, thereby promoting methane efflux. Furthermore, microbes could adapt to antibiotic pollution through enriched potential of sulfate uptake. Antibiotics indirectly affected taxonomic structures through alterations in network topological features, which in turn affected sediment functional structures and biogeochemical processes. Notably, only 13 antibiotics concentration-discriminatory genes contributed an overall 95.9% accuracy in diagnosing in situ antibiotic concentrations, in which just two indicators were antibiotic resistance genes. Our study comprehensively integrates sediment compositional and functional traits, biotic interactions, and enzymatic activities, thus generating a better understanding about ecological consequences of increasing antibiotics pollution. KEY POINTS: • Contrasting functional traits respond to increasing antibiotic pollution. • Antibiotics pollution stimulates CH₄ efflux, while mitigating N₂O emission and may drive an adaptive response of enriched sulfate uptake. • Indicator genes contribute 95.9% accuracy in diagnosing antibiotic concentrations.

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.

Oral intake of Lactiplantibacillus pentosus LPG1 Produces a Beneficial Regulation of Gut Microbiota in Healthy Persons: A Randomised, Placebo-Controlled, Single-Blind Trial

The search for vegetable-origin probiotic microorganisms is a recent area of interest. This study conducted a phase I clinical trial to assess the effects of oral administration of Lactiplantibacillus pentosus LPG1, a natural strain with probiotic potential isolated from table olive fermentations, on the gut microbiota. The trial was a randomised, placebo-controlled, single-blind study involving 39 healthy volunteers. Group A (n = 20) ingested one capsule/day of L. pentosus LPG1 containing 1 × 10¹⁰ UFC/capsule, while Group B (n = 19) received one capsule/day containing only dextrose (placebo). The capsules were taken during breakfast for 30 consecutive days. Human stool samples were collected from all volunteers at the beginning (baseline) and at the end of the study (post-intervention) and were subjected to 16S rRNA metataxonomic analysis using Illumina MiSeq. Sequencing data at the genus level were statistically analysed using traditional methods and compositional data analysis (CoDA). After treatment, the alpha diversity in Group B (placebo) decreased according to an increase in the Berger and Parker dominance index (p-value < 0.05); moreover, dominance D increased and Simpson 1-D index decreased (p-value < 0.10). The Lactobacillus genus in the faeces was included in the CoDA signature balances (selbal and coda4microbiome) and played a notable role in distinguishing samples from baseline and post-intervention in Group A (LPG1). Additionally, ingesting L. pentosus LPG1 modified the gut microbiota post-intervention, increasing the presence of Parabacteroides and Agathobacter, but reducing Prevotella. These findings suggest that L. pentosus LPG1 is a potentially beneficial gut microbiota modulator in healthy persons.

Assessing arthropod diversity metrics derived from stream environmental DNA: spatiotemporal variation and paired comparisons with manual sampling

Background

Benthic invertebrate (BI) surveys have been widely used to characterize freshwater environmental quality but can be challenging to implement at desired spatial scales and frequency. Environmental DNA (eDNA) allows an alternative BI survey approach, one that can potentially be implemented more rapidly and cheaply than traditional methods.

Methods

We evaluated eDNA analogs of BI metrics in the Potomac River watershed of the eastern United States. We first compared arthropod diversity detected with primers targeting mitochondrial 16S (mt16S) and cytochrome c oxidase 1 (cox1 or COI) loci to that detected by manual surveys conducted in parallel. We then evaluated spatial and temporal variation in arthropod diversity metrics with repeated sampling in three focal parks. We also investigated technical factors such as filter type used to capture eDNA and PCR inhibition treatment.

Results

Our results indicate that genus-level assessment of eDNA compositions is achievable at both loci with modest technical noise, although database gaps remain substantial at mt16S for regional taxa. While the specific taxa identified by eDNA did not strongly overlap with paired manual surveys, some metrics derived from eDNA compositions were rank-correlated with previously derived biological indices of environmental quality. Repeated sampling revealed statistical differences between high- and low-quality sites based on taxonomic diversity, functional diversity, and tolerance scores weighted by taxon proportions in transformed counts. We conclude that eDNA compositions are efficient and informative of stream condition. Further development and validation of scoring schemes analogous to commonly used biological indices should allow increased application of the approach to management needs.

scGGAN: single-cell RNA-seq imputation by graph-based generative adversarial network

Single-cell RNA sequencing (scRNA-seq) data are typically with a large number of missing values, which often results in the loss of critical gene signaling information and seriously limit the downstream analysis. Deep learning-based imputation methods often can better handle scRNA-seq data than shallow ones, but most of them do not consider the inherent relations between genes, and the expression of a gene is often regulated by other genes. Therefore, it is essential to impute scRNA-seq data by considering the regional gene-to-gene relations. We propose a novel model (named scGGAN) to impute scRNA-seq data that learns the gene-to-gene relations by Graph Convolutional Networks (GCN) and global scRNA-seq data distribution by Generative Adversarial Networks (GAN). scGGAN first leverages single-cell and bulk genomics data to explore inherent relations between genes and builds a more compact gene relation network to jointly capture the homogeneous and heterogeneous information. Then, it constructs a GCN-based GAN model to integrate the scRNA-seq, gene sequencing data and gene relation network for generating scRNA-seq data, and trains the model through adversarial learning. Finally, it utilizes data generated by the trained GCN-based GAN model to impute scRNA-seq data. Experiments on simulated and real scRNA-seq datasets show that scGGAN can effectively identify dropout events, recover the biologically meaningful expressions, determine subcellular states and types, improve the differential expression analysis and temporal dynamics analysis. Ablation experiments confirm that both the gene relation network and gene sequence data help the imputation of scRNA-seq data.

PLSDA-batch: a multivariate framework to correct for batch effects in microbiome data

Microbial communities are highly dynamic and sensitive to changes in the environment. Thus, microbiome data are highly susceptible to batch effects, defined as sources of unwanted variation that are not related to and obscure any factors of interest. Existing batch effect correction methods have been primarily developed for gene expression data. As such, they do not consider the inherent characteristics of microbiome data, including zero inflation, overdispersion and correlation between variables. We introduce new multivariate and non-parametric batch effect correction methods based on Partial Least Squares Discriminant Analysis (PLSDA). PLSDA-batch first estimates treatment and batch variation with latent components, then subtracts batch-associated components from the data. The resulting batch-effect-corrected data can then be input in any downstream statistical analysis. Two variants are proposed to handle unbalanced batch x treatment designs and to avoid overfitting when estimating the components via variable selection. We compare our approaches with popular methods managing batch effects, namely, removeBatchEffect, ComBat and Surrogate Variable Analysis, in simulated and three case studies using various visual and numerical assessments. We show that our three methods lead to competitive performance in removing batch variation while preserving treatment variation, especially for unbalanced batch $\times $ treatment designs. Our downstream analyses show selections of biologically relevant taxa. This work demonstrates that batch effect correction methods can improve microbiome research outputs. Reproducible code and vignettes are available on GitHub.

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.

Bioinformatic and Statistical Analysis of Microbiome Data

Since advances in next-generation sequencing (NGS) technique enabled to investigate uncultured microbiota and their genomes in unbiased manner, many microbiome researches have been reporting strong evidences for close links of microbiome to human health and disease. Bioinformatic and statistical analysis of NGS-based microbiome data are essential components in those microbiome researches to explore the complex composition of microbial community and understand the functions of community members in relation to host and environment. This chapter introduces bioinformatic analysis methods that generate taxonomy and functional feature count table along with phylogenetic tree from raw NGS microbiome data and then introduce statistical methods and machine learning approaches for analyzing the outputs of the bioinformatic analysis to infer the biodiversity of a microbial community and unravel host-microbiome association. Understanding the advantages and limitations of the analysis methods will help readers use the methods correctly in microbiome data analysis and may give a new opportunity to develop new analytic techniques for microbiome research.

Microbial rewilding in the gut microbiomes of captive ring-tailed lemurs (Lemur catta) in Madagascar

Microbial rewilding, whereby exposure to naturalistic environments can modulate or augment gut microbiomes and improve host-microbe symbiosis, is being harnessed as an innovative approach to human health, one that may also have significant value to animal care and conservation. To test for microbial rewilding in animal microbiomes, we used a unique population of wild-born ring-tailed lemurs (Lemur catta) that were initially held as illegal pets in unnatural settings and, subsequently, relocated to a rescue center in Madagascar where they live in naturalistic environments. Using amplicon and shotgun metagenomic sequencing of lemur and environmental microbiomes, we found multiple lines of evidence for microbial rewilding in lemurs that were transitioned from unnatural to naturalistic environments: A lemur's duration of exposure to naturalistic settings significantly correlated with (a) increased compositional similarly to the gut communities of wild lemurs, (b) decreased proportions of antibiotic resistance genes that were likely acquired via human contact during pethood, and (c) greater covariation with soil microbiomes from natural habitats. Beyond the inherent psychosocial value of naturalistic environments, we find that actions, such as providing appropriate diets, minimizing contact with humans, and increasing exposure to natural environmental consortia, may assist in maximizing host-microbe symbiosis in animals under human care.

Assessing and removing the effect of unwanted technical variations in microbiome data

Varying technologies and experimental approaches used in microbiome studies often lead to irreproducible results due to unwanted technical variations. Such variations, often unaccounted for and of unknown source, may interfere with true biological signals, resulting in misleading biological conclusions. In this work, we aim to characterize the major sources of technical variations in microbiome data and demonstrate how in-silico approaches can minimize their impact. We analyzed 184 pig faecal metagenomes encompassing 21 specific combinations of deliberately introduced factors of technical and biological variations. Using the novel Removing Unwanted Variations-III-Negative Binomial (RUV-III-NB), we identified several known experimental factors, specifically storage conditions and freeze-thaw cycles, as likely major sources of unwanted variation in metagenomes. We also observed that these unwanted technical variations do not affect taxa uniformly, with freezing samples affecting taxa of class Bacteroidia the most, for example. Additionally, we benchmarked the performances of different correction methods, including ComBat, ComBat-seq, RUVg, RUVs, and RUV-III-NB. While RUV-III-NB performed consistently robust across our sensitivity and specificity metrics, most other methods did not remove unwanted variations optimally. Our analyses suggest that a careful consideration of possible technical confounders is critical during experimental design of microbiome studies, and that the inclusion of technical replicates is necessary to efficiently remove unwanted variations computationally.

The community ecology perspective of omics data

The measurement of uncharacterized pools of biological molecules through techniques such as metabarcoding, metagenomics, metatranscriptomics, metabolomics, and metaproteomics produces large, multivariate datasets. Analyses of these datasets have successfully been borrowed from community ecology to characterize the molecular diversity of samples (ɑ-diversity) and to assess how these profiles change in response to experimental treatments or across gradients (β-diversity). However, sample preparation and data collection methods generate biases and noise which confound molecular diversity estimates and require special attention. Here, we examine how technical biases and noise that are introduced into multivariate molecular data affect the estimation of the components of diversity (i.e., total number of different molecular species, or entities; total number of molecules; and the abundance distribution of molecular entities). We then explore under which conditions these biases affect the measurement of ɑ- and β-diversity and highlight how novel methods commonly used in community ecology can be adopted to improve the interpretation and integration of multivariate molecular data. Video Abstract.

Elevated dietary carbohydrate and glycemic intake associate with an altered oral microbial ecosystem in two large U.S. cohorts

The human oral microbiome is associated with chronic diseases including cancer. However, our understanding of its relationship with diet is limited. We assessed the associations between carbohydrate and glycemic index (GI) with oral microbiome composition in 834 non-diabetic subjects from the NCI-PLCO and ACS-CPSII cohorts. The oral microbiome was characterized using 16Sv3-4 rRNA-sequencing from oral mouthwash samples. Daily carbohydrate and GI were assessed from food frequency questionnaires. We used linear regression, permutational MANOVA, and negative binomial Generalized Linear Models (GLM) to test associations of diet with α- and β-diversity and taxon abundance (adjusting for age, sex, cohort, BMI, smoking, caloric intake, and alcohol). A q-value (FDR-adjusted P-value) of <0.05 was considered significant. Oral bacterial α-diversity trended higher in participants in the highest quintiles of carbohydrate intake, with marginally increased richness and Shannon diversity (p-trend=0.06 and 0.07). Greater carbohydrate intake was associated with greater abundance of class Fusobacteriia (q=0.02) and genus Leptotrichia (q=0.01) and with lesser abundance of an Actinomyces OTU (q=4.7E-04). Higher GI was significantly related to greater abundance of genus Gemella (q=0.001). This large, nationwide study provides evidence that diets high in carbohydrates and GI may influence the oral microbiome.

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.