MetaTrans: an open-source pipeline for metatranscriptomics

metaTP: a meta-transcriptome data analysis pipeline with integrated automated workflows

BACKGROUND

The accessibility of sequencing technologies has enabled meta-transcriptomic studies to provide a deeper understanding of microbial ecology at the transcriptional level. Analyzing omics data involves multiple steps that require the use of various bioinformatics tools. With the increasing availability of public microbiome datasets, conducting meta-analyses can reveal new insights into microbiome activity. However, the reproducibility of data is often compromised due to variations in processing methods for sample omics data. Therefore, it is essential to develop efficient analytical workflows that ensure repeatability, reproducibility, and the traceability of results in microbiome research.

RESULTS

We developed metaTP, a pipeline that integrates bioinformatics tools for analyzing meta-transcriptomic data comprehensively. The pipeline includes quality control, non-coding RNA removal, transcript expression quantification, differential gene expression analysis, functional annotation, and co-expression network analysis. To quantify mRNA expression, we rely on reference indexes built using protein-coding sequences, which help overcome the limitations of database analysis. Additionally, metaTP provides a function for calculating the topological properties of gene co-expression networks, offering an intuitive explanation for correlated gene sets in high-dimensional datasets. The use of metaTP is anticipated to support researchers in addressing microbiota-related biological inquiries and improving the accessibility and interpretation of microbiota RNA-Seq data.

CONCLUSIONS

We have created a conda package to integrate the tools into our pipeline, making it a flexible and versatile tool for handling meta-transcriptomic sequencing data. The metaTP pipeline is freely available at: https://github.com/nanbei45/metaTP .

Advancing Gut Microbiome Research: The Shift from Metagenomics to Multi-Omics and Future Perspectives

The gut microbiome, a dynamic and integral component of human health, has co-evolved with its host, playing essential roles in metabolism, immunity, and disease prevention. Traditional microbiome studies, primarily focused on microbial composition, have provided limited insights into the functional and mechanistic interactions between microbiota and their host. The advent of multi-omics technologies has transformed microbiome research by integrating genomics, transcriptomics, proteomics, and metabolomics, offering a comprehensive, systems-level understanding of microbial ecology and host-microbiome interactions. These advances have propelled innovations in personalized medicine, enabling more precise diagnostics and targeted therapeutic strategies. This review highlights recent breakthroughs in microbiome research, demonstrating how these approaches have elucidated microbial functions and their implications for health and disease. Additionally, it underscores the necessity of standardizing multi-omics methodologies, conducting large-scale cohort studies, and developing novel platforms for mechanistic studies, which are critical steps toward translating microbiome research into clinical applications and advancing precision medicine.

Metatranscriptomics for Understanding the Microbiome in Food and Nutrition Science

Microbiome science has greatly expanded our understanding of the diverse composition and function of gut microorganisms over the past decades. With its rich microbial composition, the microbiome hosts numerous functionalities essential for metabolizing food ingredients and nutrients, resulting in the production of active metabolites that affect food fermentation or gut health. Most of these processes are mediated by microbial enzymes such as carbohydrate-active enzymes and amino acid metabolism enzymes. Metatranscriptomics enables the capture of active transcripts within the microbiome, providing invaluable functional insights into metabolic activities. Given the inter-kingdom complexity of the microbiome, metatranscriptomics could further elucidate the activities of fungi, archaea, and bacteriophages in the microbial ecosystem. Despite its potential, the application of metatranscriptomics in food and nutrition sciences remains limited but is growing. This review highlights the latest advances in food science (e.g., flavour formation and food enzymology) and nutrition science (e.g., dietary fibres, proteins, minerals, and probiotics), emphasizing the integration of metatranscriptomics with other technologies to address key research questions. Ultimately, metatranscriptomics represents a powerful tool for uncovering the microbiome activity, particularly in relation to active metabolic processes.

Unveiling the role of emerging metagenomics for the examination of hypersaline environments

Hypersaline ecosystems are distributed all over the globe. They are subjected to poly-extreme stresses and are inhabited by halophilic microorganisms possessing multiple adaptations. The halophiles have many biotechnological applications such as nutrient supplements, antioxidant synthesis, salt tolerant enzyme production, osmolyte synthesis, biofuel production, electricity generation etc. However, halophiles are still underexplored in terms of complex ecological interactions and functions as compared to other niches. The advent of metagenomics and the recent advancement of next-generation sequencing tools have made it feasible to investigate the microflora of an ecosystem, its interactions and functions. Both target gene and shotgun metagenomic approaches are commonly employed for the taxonomic, phylogenetic, and functional analyses of the hypersaline microbial communities. This review discusses different types of hypersaline niches, their residential microflora, and an overview of the metagenomic approaches used to investigate them. Various applications, hurdles and the recent advancements in metagenomic approaches have also been focused on here for their better understanding and utilization in the study of hypersaline microbiome.

MOSCA 2.0: A bioinformatics framework for metagenomics, metatranscriptomics and metaproteomics data analysis and visualization

The analysis of meta-omics data requires the utilization of several bioinformatics tools and proficiency in informatics. The integration of multiple meta-omics data is even more challenging, and the outputs of existing bioinformatics solutions are not always easy to interpret. Here, we present a meta-omics bioinformatics pipeline, Meta-Omics Software for Community Analysis (MOSCA), which aims to overcome these limitations. MOSCA was initially developed for analysing metagenomics (MG) and metatranscriptomics (MT) data. Now, it also performs MG and metaproteomics (MP) integrated analysis, and MG/MT analysis was upgraded with an additional iterative binning step, metabolic pathways mapping, and several improvements regarding functional annotation and data visualization. MOSCA handles raw sequencing data and mass spectra and performs pre-processing, assembly, annotation, binning and differential gene/protein expression analysis. MOSCA shows taxonomic and functional analysis in large tables, performs metabolic pathways mapping, generates Krona plots and shows gene/protein expression results in heatmaps, improving omics data visualization. MOSCA is easily run from a single command while also providing a web interface (MOSGUITO). Relevant features include an extensive set of customization options, allowing tailored analyses to suit specific research objectives, and the ability to restart the pipeline from intermediary checkpoints using alternative configurations. Two case studies showcased MOSCA results, giving a complete view of the anaerobic microbial communities from anaerobic digesters and insights on the role of specific microorganisms. MOSCA represents a pivotal advancement in meta-omics research, offering an intuitive, comprehensive, and versatile solution for researchers seeking to unravel the intricate tapestry of microbial communities.

Metatranscriptomics: A Tool for Clinical Metagenomics

In the field of bioinformatics, amplicon sequencing of 16S rRNA genes has long been used to investigate community membership and taxonomic abundance in microbiome studies. As we can observe, shotgun metagenomics has become the dominant method in this field. This is largely owing to advancements in sequencing technology, which now allow for random sequencing of the entire genetic content of a microbiome. Furthermore, this method allows profiling both genes and the microbiome's membership. Although these methods have provided extensive insights into various microbiomes, they solely assess the existence of organisms or genes, without determining their active role within the microbiome. Microbiome scholarship now includes metatranscriptomics to decipher how a community of microorganisms responds to changing environmental conditions over a period of time. Metagenomic studies identify the microbes that make up a community but metatranscriptomics explores the diversity of active genes within that community, understanding their expression profile and observing how these genes respond to changes in environmental conditions. This expert review article offers a critical examination of the computational metatranscriptomics tools for studying the transcriptomes of microbial communities. First, we unpack the reasons behind the need for community transcriptomics. Second, we explore the prospects and challenges of metatranscriptomic workflows, starting with isolation and sequencing of the RNA community, then moving on to bioinformatics approaches for quantifying RNA features, and statistical techniques for detecting differential expression in a community. Finally, we discuss strengths and shortcomings in relation to other microbiome analysis approaches, pipelines, use cases and limitations, and contextualize metatranscriptomics as a tool for clinical metagenomics.

Nematodes alter the taxonomic and functional profiles of benthic bacterial communities: A metatranscriptomic approach

Marine sediments cover 70% of the Earth's surface, and harbour diverse bacterial communities critical for marine biogeochemical processes, which affect climate change, biodiversity and ecosystem functioning. Nematodes, the most abundant and species-rich metazoan organisms in marine sediments, in turn, affect benthic bacterial communities and bacterial-mediated ecological processes, but the underlying mechanisms by which they affect biogeochemical cycles remain poorly understood. Here, we demonstrate using a metatranscriptomic approach that nematodes alter the taxonomic and functional profiles of benthic bacterial communities. We found particularly strong stimulation of nitrogen-fixing and methane-oxidizing bacteria in the presence of nematodes, as well as increased functional activity associated with methane metabolism and degradation of various carbon compounds. This study provides empirical evidence that the presence of nematodes results in taxonomic and functional shifts in active bacterial communities, indicating that nematodes may play an important role in benthic ecosystem processes.

Metagenomics and metatranscriptomics as potential driving forces for the exploration of diversity and functions of micro-eukaryotes in soil

Micro-eukaryotes are ubiquitous and play vital roles in diverse ecological systems, yet their diversity and functions are scarcely known. This may be due to the limitations of formerly used conventional culture-based methods. Metagenomics and metatranscriptomics are enabling to unravel the genomic, metabolic, and phylogenetic diversity of micro-eukaryotes inhabiting in different ecosystems in a more comprehensive manner. The in-depth study of structural and functional characteristics of micro-eukaryote community residing in soil is crucial for the complete understanding of this major ecosystem. This review provides a deep insight into the methodologies employed under these approaches to study soil micro-eukaryotic organisms. Furthermore, the review describes available computational tools, pipelines, and database sources and their manipulation for the analysis of sequence data of micro-eukaryotic origin. The challenges and limitations of these approaches are also discussed in detail. In addition, this review summarizes the key findings of metagenomic and metatranscriptomic studies on soil micro-eukaryotes. It also highlights the exploitation of these methods to study the structural as well as functional profiles of soil micro-eukaryotic community and to screen functional eukaryotic protein coding genes for biotechnological applications along with the future perspectives in the field.

Rational probe design for efficient rRNA depletion and improved metatranscriptomic analysis of human microbiomes

The microbiota that colonize the human gut and other tissues are dynamic, varying both in composition and functional state between individuals and over time. Gene expression measurements can provide insights into microbiome composition and function. However, efficient and unbiased removal of microbial ribosomal RNA (rRNA) presents a barrier to acquiring metatranscriptomic data. Here we describe a probe set that achieves efficient enzymatic rRNA removal of complex human-associated microbial communities. We demonstrate that the custom probe set can be further refined through an iterative design process to efficiently deplete rRNA from a range of human microbiome samples. Using synthetic nucleic acid spike-ins, we show that the rRNA depletion process does not introduce substantial quantitative error in gene expression profiles. Successful rRNA depletion allows for efficient characterization of taxonomic and functional profiles, including during the development of the human gut microbiome. The pan-human microbiome enzymatic rRNA depletion probes described here provide a powerful tool for studying the transcriptional dynamics and function of the human microbiome.

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

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

MetaPro: a scalable and reproducible data processing and analysis pipeline for metatranscriptomic investigation of microbial communities

BACKGROUND

Whole microbiome RNASeq (metatranscriptomics) has emerged as a powerful technology to functionally interrogate microbial communities. A key challenge is how best to process, analyze, and interpret these complex datasets. In a typical application, a single metatranscriptomic dataset may comprise from tens to hundreds of millions of sequence reads. These reads must first be processed and filtered for low quality and potential contaminants, before being annotated with taxonomic and functional labels and subsequently collated to generate global bacterial gene expression profiles.

RESULTS

Here, we present MetaPro, a flexible, massively scalable metatranscriptomic data analysis pipeline that is cross-platform compatible through its implementation within a Docker framework. MetaPro starts with raw sequence read input (single-end or paired-end reads) and processes them through a tiered series of filtering, assembly, and annotation steps. In addition to yielding a final list of bacterial genes and their relative expression, MetaPro delivers a taxonomic breakdown based on the consensus of complementary prediction algorithms, together with a focused breakdown of enzymes, readily visualized through the Cytoscape network visualization tool. We benchmark the performance of MetaPro against two current state-of-the-art pipelines and demonstrate improved performance and functionality.

CONCLUSIONS

MetaPro represents an effective integrated solution for the processing and analysis of metatranscriptomic datasets. Its modular architecture allows new algorithms to be deployed as they are developed, ensuring its longevity. To aid user uptake of the pipeline, MetaPro, together with an established tutorial that has been developed for educational purposes, is made freely available at https://github.com/ParkinsonLab/MetaPro . The software is freely available under the GNU general public license v3. Video Abstract.

Metatranscriptomic analysis of the gut microbiome of black soldier fly larvae reared on lignocellulose-rich fiber diets unveils key lignocellulolytic enzymes

Recently, interest in the black soldier fly larvae (BSFL) gut microbiome has received increased attention primarily due to their role in waste bioconversion. However, there is a lack of information on the positive effect on the activities of the gut microbiomes and enzymes (CAZyme families) acting on lignocellulose. In this study, BSFL were subjected to lignocellulose-rich diets: chicken feed (CF), chicken manure (CM), brewers' spent grain (BSG), and water hyacinth (WH). The mRNA libraries were prepared, and RNA-Sequencing was conducted using the PCR-cDNA approach through the MinION sequencing platform. Our results demonstrated that BSFL reared on BSG and WH had the highest abundance of Bacteroides and Dysgonomonas. The presence of GH51 and GH43_16 enzyme families in the gut of BSFL with both α-L-arabinofuranosidases and exo-alpha-L-arabinofuranosidase 2 were common in the BSFL reared on the highly lignocellulosic WH and BSG diets. Gene clusters that encode hemicellulolytic arabinofuranosidases in the CAZy family GH51 were also identified. These findings provide novel insight into the shift of gut microbiomes and the potential role of BSFL in the bioconversion of various highly lignocellulosic diets to fermentable sugars for subsequent value-added products (bioethanol). Further research on the role of these enzymes to improve existing technologies and their biotechnological applications is crucial.

The hitchhikers' guide to RNA sequencing and functional analysis

DNA and RNA sequencing technologies have revolutionized biology and biomedical sciences, sequencing full genomes and transcriptomes at very high speeds and reasonably low costs. RNA sequencing (RNA-Seq) enables transcript identification and quantification, but once sequencing has concluded researchers can be easily overwhelmed with questions such as how to go from raw data to differential expression (DE), pathway analysis and interpretation. Several pipelines and procedures have been developed to this effect. Even though there is no unique way to perform RNA-Seq analysis, it usually follows these steps: 1) raw reads quality check, 2) alignment of reads to a reference genome, 3) aligned reads' summarization according to an annotation file, 4) DE analysis and 5) gene set analysis and/or functional enrichment analysis. Each step requires researchers to make decisions, and the wide variety of options and resulting large volumes of data often lead to interpretation challenges. There also seems to be insufficient guidance on how best to obtain relevant information and derive actionable knowledge from transcription experiments. In this paper, we explain RNA-Seq steps in detail and outline differences and similarities of different popular options, as well as advantages and disadvantages. We also discuss non-coding RNA analysis, multi-omics, meta-transcriptomics and the use of artificial intelligence methods complementing the arsenal of tools available to researchers. Lastly, we perform a complete analysis from raw reads to DE and functional enrichment analysis, visually illustrating how results are not absolute truths and how algorithmic decisions can greatly impact results and interpretation.

TbasCO: trait-based comparative 'omics identifies ecosystem-level and niche-differentiating adaptations of an engineered microbiome

A grand challenge in microbial ecology is disentangling the traits of individual populations within complex communities. Various cultivation-independent approaches have been used to infer traits based on the presence of marker genes. However, marker genes are not linked to traits with complete fidelity, nor do they capture important attributes, such as the timing of gene expression or coordination among traits. To address this, we present an approach for assessing the trait landscape of microbial communities by statistically defining a trait attribute as a shared transcriptional pattern across multiple organisms. Leveraging the KEGG pathway database as a trait library and the Enhanced Biological Phosphorus Removal (EBPR) model microbial ecosystem, we demonstrate that a majority (65%) of traits present in 10 or more genomes have niche-differentiating expression attributes. For example, while many genomes containing high-affinity phosphorus transporter pstABCS display a canonical attribute (e.g. up-regulation under phosphorus starvation), we identified another attribute shared by many genomes where transcription was highest under high phosphorus conditions. Taken together, we provide a novel framework for unravelling the functional dynamics of uncultivated microorganisms by assigning trait-attributes through genome-resolved time-series metatranscriptomics.

Prospects of advanced metagenomics and meta-omics in the investigation of phytomicrobiome to forecast beneficial and pathogenic response

Microorganisms dwell in diverse plant niches as non-axenic biotic components that are beneficial as well pathogenic for the host. They improve nutrients-uptake, stress tolerance, phytohormone synthesis, and strengthening the defense system through phyllosphere, rhizosphere, and endosphere. The negative consequences of the microbial communities are largely in the form of diseases characterized by certain symptoms such as gall, cankers, rots etc. Uncultivable and unspecified nature of different phytomicrobiomes communities is a challenge in the management of plant disease, a leading cause for the loss of the plant products. Metagenomics has opened a new gateway for the exploration of microorganisms that are hitherto unknown, enables investigation of the functional aspect of microbial gene products through metatranscriptomics and metabolomics. Metagenomics offers advantages of characterizing previously unknown microorganisms from extreme environments like hot springs, glaciers, deep seas, animal gut etc. besides bioprospecting gene products such as Taq polymerase, bor encoded indolotryptoline, hydrolases, and polyketides. This review provides a detailed account of the phytomicrobiome networks and highlights the importance and limitations of metagenomics and other meta-omics approaches for the understanding of plant microbial diversity with special focus on the disease control and its management.

Multi-Omics Strategies for Investigating the Microbiome in Toxicology Research

Microbial communities on and within the host contact environmental pollutants, toxic compounds, and other xenobiotic compounds. These communities of bacteria, fungi, viruses, and archaea possess diverse metabolic potential to catabolize compounds and produce new metabolites. Microbes alter chemical disposition thus making the microbiome a natural subject of interest for toxicology. Sequencing and metabolomics technologies permit the study of microbiomes altered by acute or long-term exposure to xenobiotics. These investigations have already contributed to and are helping to re-interpret traditional understandings of toxicology. The purpose of this review is to provide a survey of the current methods used to characterize microbes within the context of toxicology. This will include discussion of commonly used techniques for conducting omic-based experiments, their respective strengths and deficiencies, and how forward-looking techniques may address present shortcomings. Finally, a perspective will be provided regarding common assumptions that currently impede microbiome studies from producing causal explanations of toxicologic mechanisms.

MTD: a unique pipeline for host and meta-transcriptome joint and integrative analyses of RNA-seq data

Ribonucleic acid (RNA)-seq data contain not only host transcriptomes but also nonhost information that comprises transcripts from active microbiota in the host cells. Therefore, joint and integrative analyses of both host and meta-transcriptome can reveal gene expression of the microbial community in a given sample as well as the correlative and interactive dynamics of the host response to the microbiome. However, there are no convenient tools that can systemically analyze host-microbiota interactions through simultaneously quantifying the host and meta-transcriptome in the same sample at the tissue and the single-cell level. This poses a challenge for interested researchers with limited expertise in bioinformatics. Here, we developed a software pipeline that can comprehensively and synergistically analyze and correlate the host and meta-transcriptome in a single sample using bulk and single-cell RNA-seq data. This pipeline, named meta-transcriptome detector (MTD), can extensively identify and quantify microbiome, including viruses, bacteria, protozoa, fungi, plasmids and vectors, in the host cells and correlate the microbiome with the host transcriptome. MTD is easy to install and run, involving only a few lines of simple commands. It offers researchers with unique genomics insights into host responses to microorganisms.

Response and resilience of anammox consortia to nutrient starvation

BACKGROUND

It is of critical importance to understand how anammox consortia respond to disturbance events and fluctuations in the wastewater treatment reactors. Although the responses of anammox consortia to operational parameters (e.g., temperature, dissolved oxygen, nutrient concentrations) have frequently been reported in previous studies, less is known about their responses and resilience when they suffer from nutrient interruption.

RESULTS

Here, we investigated the anammox community states and transcriptional patterns before and after a short-term nutrient starvation (3 days) to determine how anammox consortia respond to and recover from such stress. The results demonstrated that the remarkable changes in transcriptional patterns, rather than the community compositions were associated with the nutritional stress. The divergent expression of genes involved in anammox reactions, especially the hydrazine synthase complex (HZS), and nutrient transportation might function as part of a starvation response mechanism in anammox bacteria. In addition, effective energy conservation and substrate supply strategies (ATP accumulation, upregulated amino acid biosynthesis, and enhanced protein degradation) and synergistic interactions between anammox bacteria and heterotrophs might benefit their survival during starvation and the ensuing recovery of the anammox process. Compared with abundant heterotrophs in the anammox system, the overall transcription pattern of the core autotrophic producers (i.e., anammox bacteria) was highly resilient and quickly returned to its pre-starvation state, further contributing to the prompt recovery when the feeding was resumed.

CONCLUSIONS

These findings provide important insights into nutritional stress-induced changes in transcriptional activities in the anammox consortia and would be beneficial for the understanding of the capacity of anammox consortia in response to stress and process stability in the engineered ecosystems. Video Abstract.

Dysbiotic microbes and how to find them: a review of microbiome profiling in prostate cancer

The role of the microbiota in human health and disease is well established, including its effects on several cancer types. However, the role of microbial dysbiosis in prostate cancer development, progression, and response to treatment is less well understood. This knowledge gap could perhaps be implicated in the lack of better risk stratification and prognostic tools that incorporate risk factors such as bacterial infections and inflammatory signatures. With over a decade’s research investigating associations between microbiome and prostate carcinogenesis, we are ever closer to finding the crucial biological link between the two. Yet, definitive answers remain elusive, calling for continued research into this field. In this review, we outline the three frequently used NGS based analysis methodologies that are used for microbiome profiling, thereby serving as a quick guide for future microbiome research. We next provide a detailed overview of the current knowledge of the role of the human microbiome in prostate cancer development, progression, and treatment response. Finally, we describe proposed mechanisms of host-microbe interactions that could lead to prostate cancer development, progression or treatment response.

BiG-MAP: an Automated Pipeline To Profile Metabolic Gene Cluster Abundance and Expression in Microbiomes

Microbial gene clusters encoding the biosynthesis of primary and secondary metabolites play key roles in shaping microbial ecosystems and driving microbiome-associated phenotypes. Although effective approaches exist to evaluate the metabolic potential of such bacteria through identification of these metabolic gene clusters in their genomes, no automated pipelines exist to profile the abundance and expression levels of such gene clusters in microbiome samples to generate hypotheses about their functional roles, and to find associations with phenotypes of interest. Here, we describe BiG-MAP, a bioinformatic tool to profile abundance and expression levels of gene clusters across metagenomic and metatranscriptomic data and evaluate their differential abundance and expression under different conditions. To illustrate its usefulness, we analyzed 96 metagenomic samples from healthy and caries-associated human oral microbiome samples and identified 252 gene clusters, including unreported ones, that were significantly more abundant in either phenotype. Among them, we found the muc operon, a gene cluster known to be associated with tooth decay. Additionally, we found a putative reuterin biosynthetic gene cluster from a Streptococcus strain to be enriched but not exclusively found in healthy samples; metabolomic data from the same samples showed masses with fragmentation patterns consistent with (poly)acrolein, which is known to spontaneously form from the products of the reuterin pathway and has been previously shown to inhibit pathogenic Streptococcus mutans strains. Thus, we show how BiG-MAP can be used to generate new hypotheses on potential drivers of microbiome-associated phenotypes and prioritize the experimental characterization of relevant gene clusters that may mediate them. IMPORTANCE Microbes play an increasingly recognized role in determining host-associated phenotypes by producing small molecules that interact with other microorganisms or host cells. The production of these molecules is often encoded in syntenic genomic regions, also known as gene clusters. With the increasing numbers of (multi)omics data sets that can help in understanding complex ecosystems at a much deeper level, there is a need to create tools that can automate the process of analyzing these gene clusters across omics data sets. This report presents a new software tool called BiG-MAP, which allows assessing gene cluster abundance and expression in microbiome samples using metagenomic and metatranscriptomic data. Here, we describe the tool and its functionalities, as well as its validation using a mock community. Finally, using an oral microbiome data set, we show how it can be used to generate hypotheses regarding the functional roles of gene clusters in mediating host phenotypes.

Demystifying emerging bulk RNA-Seq applications: the application and utility of bioinformatic methodology

Significant innovations in next-generation sequencing techniques and bioinformatics tools have impacted our appreciation and understanding of RNA. Practical RNA sequencing (RNA-Seq) applications have evolved in conjunction with sequence technology and bioinformatic tools advances. In most projects, bulk RNA-Seq data is used to measure gene expression patterns, isoform expression, alternative splicing and single-nucleotide polymorphisms. However, RNA-Seq holds far more hidden biological information including details of copy number alteration, microbial contamination, transposable elements, cell type (deconvolution) and the presence of neoantigens. Recent novel and advanced bioinformatic algorithms developed the capacity to retrieve this information from bulk RNA-Seq data, thus broadening its scope. The focus of this review is to comprehend the emerging bulk RNA-Seq-based analyses, emphasizing less familiar and underused applications. In doing so, we highlight the power of bulk RNA-Seq in providing biological insights.

Metatranscriptomics for the Human Microbiome and Microbial Community Functional Profiling

Shotgun metatranscriptomics (MTX) is an increasingly practical way to survey microbial community gene function and regulation at scale. This review begins by summarizing the motivations for community transcriptomics and the history of the field. We then explore the principles, best practices, and challenges of contemporary MTX workflows: beginning with laboratory methods for isolation and sequencing of community RNA, followed by informatics methods for quantifying RNA features, and finally statistical methods for detecting differential expression in a community context. In thesecond half of the review, we survey important biological findings from the MTX literature, drawing examples from the human microbiome, other (nonhuman) host-associated microbiomes, and the environment. Across these examples, MTX methods prove invaluable for probing microbe-microbe and host-microbe interactions, the dynamics of energy harvest and chemical cycling, and responses to environmental stresses. We conclude with a review of open challenges in the MTX field, including making assays and analyses more robust, accessible, and adaptable to new technologies; deciphering roles for millions of uncharacterized microbial transcripts; and solving applied problems such as biomarker discovery and development of microbial therapeutics.

Statistical approaches for differential expression analysis in metatranscriptomics

Motivation

Metatranscriptomics (MTX) has become an increasingly practical way to profile the functional activity of microbial communities in situ. However, MTX remains underutilized due to experimental and computational limitations. The latter are complicated by non-independent changes in both RNA transcript levels and their underlying genomic DNA copies (as microbes simultaneously change their overall abundance in the population and regulate individual transcripts), genetic plasticity (as whole loci are frequently gained and lost in microbial lineages) and measurement compositionality and zero-inflation. Here, we present a systematic evaluation of and recommendations for differential expression (DE) analysis in MTX.

Results

We designed and assessed six statistical models for DE discovery in MTX that incorporate different combinations of DNA and RNA normalization and assumptions about the underlying changes of gene copies or species abundance within communities. We evaluated these models on multiple simulated and real multi-omic datasets. Models adjusting transcripts relative to their encoding gene copies as a covariate were significantly more accurate in identifying DE from MTX in both simulated and real datasets. Moreover, we show that when paired DNA measurements (metagenomic data) are not available, models normalizing MTX measurements within-species while also adjusting for total-species RNA balance sensitivity, specificity and interpretability of DE detection, as does filtering likely technical zeros. The efficiency and accuracy of these models pave the way for more effective MTX-based DE discovery in microbial communities.

Availability and implementation

The analysis code and synthetic datasets used in this evaluation are available online at http://huttenhower.sph.harvard.edu/mtx2021.

Supplementary information

Supplementary data are available at Bioinformatics online.

The gutSMASH web server: automated identification of primary metabolic gene clusters from the gut microbiota

Anaerobic bacteria from the human microbiome produce a wide array of molecules at high concentrations that can directly or indirectly affect the host. The production of these molecules, mostly derived from their primary metabolism, is frequently encoded in metabolic gene clusters (MGCs). However, despite the importance of microbiome-derived primary metabolites, no tool existed to predict the gene clusters responsible for their production. For this reason, we recently introduced gutSMASH. gutSMASH can predict 41 different known pathways, including MGCs involved in bioenergetics, but also putative ones that are candidates for novel pathway discovery. To make the tool more user-friendly and accessible, we here present the gutSMASH web server, hosted at https://gutsmash.bioinformatics.nl/. The user can either input the GenBank assembly accession or upload a genome file in FASTA or GenBank format. Optionally, the user can enable additional analyses to obtain further insights into the predicted MGCs. An interactive HTML output (viewable online or downloadable for offline use) provides a user-friendly way to browse functional gene annotations and sequence comparisons with reference gene clusters as well as gene clusters predicted in other genomes. Thus, this web server provides the community with a streamlined and user-friendly interface to analyze the metabolic potential of gut microbiomes.

The impact of the Fungus-Host-Microbiota interplay upon Candida albicans infections: current knowledge and new perspectives

Candida albicans is a major fungal pathogen of humans. It exists as a commensal in the oral cavity, gut or genital tract of most individuals, constrained by the local microbiota, epithelial barriers and immune defences. Their perturbation can lead to fungal outgrowth and the development of mucosal infections such as oropharyngeal or vulvovaginal candidiasis, and patients with compromised immunity are susceptible to life-threatening systemic infections. The importance of the interplay between fungus, host and microbiota in driving the transition from C. albicans commensalism to pathogenicity is widely appreciated. However, the complexity of these interactions, and the significant impact of fungal, host and microbiota variability upon disease severity and outcome, are less well understood. Therefore, we summarise the features of the fungus that promote infection, and how genetic variation between clinical isolates influences pathogenicity. We discuss antifungal immunity, how this differs between mucosae, and how individual variation influences a person's susceptibility to infection. Also, we describe factors that influence the composition of gut, oral and vaginal microbiotas, and how these affect fungal colonisation and antifungal immunity. We argue that a detailed understanding of these variables, which underlie fungal-host-microbiota interactions, will present opportunities for directed antifungal therapies that benefit vulnerable patients.

Best practices on the differential expression analysis of multi-species RNA-seq

Advances in transcriptome sequencing allow for simultaneous interrogation of differentially expressed genes from multiple species originating from a single RNA sample, termed dual or multi-species transcriptomics. Compared to single-species differential expression analysis, the design of multi-species differential expression experiments must account for the relative abundances of each organism of interest within the sample, often requiring enrichment methods and yielding differences in total read counts across samples. The analysis of multi-species transcriptomics datasets requires modifications to the alignment, quantification, and downstream analysis steps compared to the single-species analysis pipelines. We describe best practices for multi-species transcriptomics and differential gene expression.

Network analyses in microbiome based on high-throughput multi-omics data

Together with various hosts and environments, ubiquitous microbes interact closely with each other forming an intertwined system or community. Of interest, shifts of the relationships between microbes and their hosts or environments are associated with critical diseases and ecological changes. While advances in high-throughput Omics technologies offer a great opportunity for understanding the structures and functions of microbiome, it is still challenging to analyse and interpret the omics data. Specifically, the heterogeneity and diversity of microbial communities, compounded with the large size of the datasets, impose a tremendous challenge to mechanistically elucidate the complex communities. Fortunately, network analyses provide an efficient way to tackle this problem, and several network approaches have been proposed to improve this understanding recently. Here, we systemically illustrate these network theories that have been used in biological and biomedical research. Then, we review existing network modelling methods of microbial studies at multiple layers from metagenomics to metabolomics and further to multi-omics. Lastly, we discuss the limitations of present studies and provide a perspective for further directions in support of the understanding of microbial communities.

Comparative Fungal Community Analyses Using Metatranscriptomics and Internal Transcribed Spacer Amplicon Sequencing from Norway Spruce

The health, growth, and fitness of boreal forest trees are impacted and improved by their associated microbiomes. Microbial gene expression and functional activity can be assayed with RNA sequencing (RNA-Seq) data from host samples. In contrast, phylogenetic marker gene amplicon sequencing data are used to assess taxonomic composition and community structure of the microbiome. Few studies have considered how much of this structural and taxonomic information is included in transcriptomic data from matched samples. Here, we described fungal communities using both host-derived RNA-Seq and fungal ITS1 DNA amplicon sequencing to compare the outcomes between the methods. We used a panel of root and needle samples from the coniferous tree species Picea abies (Norway spruce) growing in untreated (nutrient-deficient) and nutrient-enriched plots at the Flakaliden forest research site in boreal northern Sweden. We show that the relationship between samples and alpha and beta diversity indicated by the fungal transcriptome is in agreement with that generated by the ITS data, while also identifying a lack of taxonomic overlap due to limitations imposed by current database coverage. Furthermore, we demonstrate how metatranscriptomics data additionally provide biologically informative functional insights. At the community level, there were changes in starch and sucrose metabolism, biosynthesis of amino acids, and pentose and glucuronate interconversions, while processing of organic macromolecules, including aromatic and heterocyclic compounds, was enriched in transcripts assigned to the genus Cortinarius.

IMPORTANCE A deeper understanding of microbial communities associated with plants is revealing their importance for plant health and productivity. RNA extracted from plant field samples represents the host and other organisms present. Typically, gene expression studies focus on the plant component or, in a limited number of studies, expression in one or more associated organisms. However, metatranscriptomic data are rarely used for taxonomic profiling, which is currently performed using amplicon approaches. We created an assembly-based, reproducible, and hardware-agnostic workflow to taxonomically and functionally annotate fungal RNA-Seq data obtained from Norway spruce roots, which we compared to matching ITS amplicon sequencing data. While we identified some limitations and caveats, we show that functional, taxonomic, and compositional insights can all be obtained from RNA-Seq data. These findings highlight the potential of metatranscriptomics to advance our understanding of interaction, response, and effect between host plants and their associated microbial communities.

ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework

The Earth Microbiome Project (EMP) aided in understanding the role of microbial communities and the influence of collective genetic material (the 'microbiome') and microbial diversity patterns across the habitats of our planet. With the evolution of new sequencing technologies, researchers can now investigate the microbiome and map its influence on the environment and human health. Advances in bioinformatics methods for next-generation sequencing (NGS) data analysis have helped researchers to gain an in-depth knowledge about the taxonomic and genetic composition of microbial communities. Metagenomic-based methods have been the most commonly used approaches for microbiome analysis; however, it primarily extracts information about taxonomic composition and genetic potential of the microbiome under study, lacking quantification of the gene products (RNA and proteins). On the other hand, metatranscriptomics, the study of a microbial community's RNA expression, can reveal the dynamic gene expression of individual microbial populations and the community as a whole, ultimately providing information about the active pathways in the microbiome.  In order to address the analysis of NGS data, the ASaiM analysis framework was previously developed and made available via the Galaxy platform. Although developed for both metagenomics and metatranscriptomics, the original publication demonstrated the use of ASaiM only for metagenomics, while thorough testing for metatranscriptomics data was lacking.  In the current study, we have focused on validating and optimizing the tools within ASaiM for metatranscriptomics data. As a result, we deliver a robust workflow that will enable researchers to understand dynamic functional response of the microbiome in a wide variety of metatranscriptomics studies. This improved and optimized ASaiM-metatranscriptomics (ASaiM-MT) workflow is publicly available via the ASaiM framework, documented and supported with training material so that users can interrogate and characterize metatranscriptomic data, as part of larger meta-omic studies of microbiomes.

ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework

The Human Microbiome Project (HMP) aided in understanding the role of microbial communities and the influence of collective genetic material (the 'microbiome') in human health and disease. With the evolution of new sequencing technologies, researchers can now investigate the microbiome and map its influence on human health. Advances in bioinformatics methods for next-generation sequencing (NGS) data analysis have helped researchers to gain an in-depth knowledge about the taxonomic and genetic composition of microbial communities. Metagenomic-based methods have been the most commonly used approaches for microbiome analysis; however, it primarily extracts information about taxonomic composition and genetic potential of the microbiome under study, lacking quantification of the gene products (RNA and proteins). Conversely, metatranscriptomics, the study of a microbial community's RNA expression, can reveal the dynamic gene expression of individual microbial populations and the community as a whole, ultimately providing information about the active pathways in the microbiome.  In order to address the analysis of NGS data, the ASaiM analysis framework was previously developed and made available via the Galaxy platform. Although developed for both metagenomics and metatranscriptomics, the original publication demonstrated the use of ASaiM only for metagenomics, while thorough testing for metatranscriptomics data was lacking.  In the current study, we have focused on validating and optimizing the tools within ASaiM for metatranscriptomics data. As a result, we deliver a robust workflow that will enable researchers to understand dynamic functional response of the microbiome in a wide variety of metatranscriptomics studies. This improved and optimized ASaiM-metatranscriptomics (ASaiM-MT) workflow is publicly available via the ASaiM framework, documented and supported with training material so that users can interrogate and characterize metatranscriptomic data, as part of larger meta-omic studies of microbiomes.

gNOMO: a multi-omics pipeline for integrated host and microbiome analysis of non-model organisms

The study of bacterial symbioses has grown exponentially in the recent past. However, existing bioinformatic workflows of microbiome data analysis do commonly not integrate multiple meta-omics levels and are mainly geared toward human microbiomes. Microbiota are better understood when analyzed in their biological context; that is together with their host or environment. Nevertheless, this is a limitation when studying non-model organisms mainly due to the lack of well-annotated sequence references. Here, we present gNOMO, a bioinformatic pipeline that is specifically designed to process and analyze non-model organism samples of up to three meta-omics levels: metagenomics, metatranscriptomics and metaproteomics in an integrative manner. The pipeline has been developed using the workflow management framework Snakemake in order to obtain an automated and reproducible pipeline. Using experimental datasets of the German cockroach Blattella germanica, a non-model organism with very complex gut microbiome, we show the capabilities of gNOMO with regard to meta-omics data integration, expression ratio comparison, taxonomic and functional analysis as well as intuitive output visualization. In conclusion, gNOMO is a bioinformatic pipeline that can easily be configured, for integrating and analyzing multiple meta-omics data types and for producing output visualizations, specifically designed for integrating paired-end sequencing data with mass spectrometry from non-model organisms.

Reconstructing ribosomal genes from large scale total RNA meta-transcriptomic data

MOTIVATION

Technological advances in meta-transcriptomics have enabled a deeper understanding of the structure and function of microbial communities. 'Total RNA' meta-transcriptomics, sequencing of total reverse transcribed RNA, provides a unique opportunity to investigate both the structure and function of active microbial communities from all three domains of life simultaneously. A major step of this approach is the reconstruction of full-length taxonomic marker genes such as the small subunit ribosomal RNA. However, current tools for this purpose are mainly targeted towards analysis of amplicon and metagenomic data and thus lack the ability to handle the massive and complex datasets typically resulting from total RNA experiments.

RESULTS

In this work, we introduce MetaRib, a new tool for reconstructing ribosomal gene sequences from total RNA meta-transcriptomic data. MetaRib is based on the popular rRNA assembly program EMIRGE, together with several improvements. We address the challenge posed by large complex datasets by integrating sub-assembly, dereplication and mapping in an iterative approach, with additional post-processing steps. We applied the method to both simulated and real-world datasets. Our results show that MetaRib can deal with larger datasets and recover more rRNA genes, which achieve around 60 times speedup and higher F1 score compared to EMIRGE in simulated datasets. In the real-world dataset, it shows similar trends but recovers more contigs compared with a previous analysis based on random sub-sampling, while enabling the comparison of individual contig abundances across samples for the first time.

AVAILABILITY AND IMPLEMENTATION

The source code of MetaRib is freely available at https://github.com/yxxue/MetaRib.

CONTACT

yaxin.xue@uib.no or Inge.Jonassen@uib.no.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Sputum microbiome profiling in COPD: beyond singular pathogen detection

Culture-independent microbial sequencing techniques have revealed that the respiratory tract harbours a complex microbiome not detectable by conventional culturing methods. The contribution of the microbiome to chronic obstructive pulmonary disease (COPD) pathobiology and the potential for microbiome-based clinical biomarkers in COPD are still in the early phases of investigation. Sputum is an easily obtainable sample and has provided a wealth of information on COPD pathobiology, and thus has been a preferred sample type for microbiome studies. Although the sputum microbiome likely reflects the respiratory microbiome only in part, there is increasing evidence that microbial community structure and diversity are associated with disease severity and clinical outcomes, both in stable COPD and during the exacerbations. Current evidence has been limited to mainly cross-sectional studies using 16S rRNA gene sequencing, attempting to answer the question 'who is there?' Longitudinal studies using standardised protocols are needed to answer outstanding questions including differences between sputum sampling techniques. Further, with advancing technologies, microbiome studies are shifting beyond the examination of the 16S rRNA gene, to include whole metagenome and metatranscriptome sequencing, as well as metabolome characterisation. Despite being technically more challenging, whole-genome profiling and metabolomics can address the questions 'what can they do?' and 'what are they doing?' This review provides an overview of the basic principles of high-throughput microbiome sequencing techniques, current literature on sputum microbiome profiling in COPD, and a discussion of the associated limitations and future perspectives.

Metatranscriptomics: an approach for retrieving novel eukaryotic genes from polluted and related environments

Metatranscriptomics, a subset of metagenomics, provides valuable information about the whole gene expression profiling of complex microbial communities of an ecosystem. Metagenomic studies mainly focus on the genomic content and identification of microbes present within a community, while metatranscriptomics provides the diversity of the active genes within such community, their expression profile and how these levels change due to change in environmental conditions. Metatranscriptomics has been applied to different types of environments, from the study of human microbiomes, to those found in plants, animals, within soils and in aquatic systems. Metatranscriptomics, based on the utilization of mRNA isolated from environmental samples, is a suitable approach to mine the eukaryotic gene pool for genes of biotechnological relevance. Also, it is imperative to develop different bioinformatic pipelines to analyse the data obtained from metatranscriptomic analysis. In the present review, we summarise the metatranscriptomics applied to soil environments to study the functional diversity, and discuss approaches for isolating the genes involved in organic matter degradation and providing tolerance to toxic metals, role of metatranscriptomics in microbiome research, various bioinformatics pipelines used in data analysis and technical challenges for gaining biologically meaningful insight of this approach.

Fecal metatranscriptomics and glycomics suggest that bovine milk oligosaccharides are fully utilized by healthy adults

Human milk oligosaccharides play a vital role in the development of the gut microbiome in the human infant. Although oligosaccharides derived from bovine milk (BMO) differ in content and profile with those derived from human milk (HMO), several oligosaccharide structures are shared between the species. BMO are commercial alternatives to HMO, but their fate in the digestive tract of healthy adult consumers is unknown. Healthy human subjects consumed two BMO doses over 11-day periods each and provided fecal samples. Metatranscriptomics of fecal samples were conducted to determine microbial and host gene expression in response to the supplement. Fecal samples were also analyzed by mass spectrometry to determine levels of undigested BMO. No changes were observed in microbial gene expression across all participants. Repeated sampling enabled subject-specific analyses: four of six participants had minor, yet statistically significant, changes in microbial gene expression. No significant change was observed in the gene expression of host cells exfoliated in stool. Levels of BMO excreted in feces after supplementation were not significantly different from baseline and were not correlated with dosage or expressed microbial enzyme levels. Collectively, these data suggest that BMO are fully fermented in the human gastrointestinal tract upstream of the distal colon. Additionally, the unaltered host transcriptome provides further evidence for the safety of BMO as a dietary supplement or food ingredient. Further research is needed to investigate potential health benefits of this completely fermentable prebiotic that naturally occurs in cow's milk.

Exploring bacterial pathogen community dynamics in freshwater beach sediments: A tale of two lakes

Pathogenic bacteria associated with freshwater ecosystems can pose significant health risks particularly where recreational water use is popular. Common water quality assessments involve quantifying indicator Escherichia coli within the water column but neglect to consider physical and geochemical factors and contributions from the sediment. In this study, we used high-throughput sequencing to investigate sediment microbial communities at four freshwater public beaches in southern Ontario, Canada and analysed community structure, function, and gene expression with relation to geographical characteristics. Our results indicate that beach sediments at the sediment-water interface could serve as potential sources of bacterial contamination under low-energy environments with tightly packed small sediment particles compared with high-energy environments. Further, the absence of pathogens but expression of pathogenic transcripts suggests occurrence of alternate gene acquisition. Pathogenicity at these locations included expression of Salmonella virulence factors, genes involved in pertussis, and antimicrobial resistance. Finally, we introduce a proposed universal bacterial pathogen model to consider the combined and synergistic processes used by these microbes. To our knowledge, this is the first study of its kind to investigate chemolithotrophic activity related to pathogens within bed sediment at freshwater beaches. This work helps advance current understanding of health risks in these environments.

Microbial metatranscriptomic investigations across contaminant gradients of the Detroit River

Microbial community function in freshwater sediments is influenced by the presence and persistence of anthropogenic pollutants, yet simultaneously imposes significant control on their transformation. Thus, microbes provide valuable ecosystem services in terms of biodegradation and bioindicators of compromised habitats. From a remediation perspective it is valuable to leverage the suite of microbial genes at the transcriptional level that are active in either natural versus stressed environments to provide insight into the cycling and fate of contaminants. Metatranscriptomic analysis of total bacterial and archaeal messenger RNA (mRNA) is a useful tool in this facet and was applied to sediments sampled from the Detroit River; a binational Area of Concern (AOC) in the Great Lakes. Previously established sediment surveys and modelling delineated the river into contaminant gradients based on concentrations of polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), and metals. Differential expression analysis through DESeq2 revealed that microbial transcripts associated with nitrate reduction, methanogenesis, and beta-oxidation were significant in legacy polluted sediments and linked with energetic pathways key in the generation of cellular currencies (acetyl-CoA, succinyl-CoA). Gluconeogenesis and polyester synthesis also showed high abundance in contaminated regions, along with increased expression of stress response genes and transposons, despite decreases in community α-diversity. Aromatic cleavage genes were detected, but in low abundance across the contaminant gradient. These results suggest that microbial communities within the Detroit River exploit unique anabolic and catabolic pathways to derive and store energy from legacy organic contaminants while simultaneously recruiting stress-response and gene transfer mechanisms to cope with xenobiotic pressures. By coupling well-resolved chemical datasets with metatranscriptomics, this study adds to the spatial understanding of in-situ microbial activities in pristine and perturbed freshwater sediments.

Advances and Challenges in Metatranscriptomic Analysis

Sequencing-based analyses of microbiomes have traditionally focused on addressing the question of community membership and profiling taxonomic abundance through amplicon sequencing of 16 rRNA genes. More recently, shotgun metagenomics, which involves the random sequencing of all genomic content of a microbiome, has dominated this arena due to advancements in sequencing technology throughput and capability to profile genes as well as microbiome membership. While these methods have revealed a great number of insights into a wide variety of microbiomes, both of these approaches only describe the presence of organisms or genes, and not whether they are active members of the microbiome. To obtain deeper insights into how a microbial community responds over time to their changing environmental conditions, microbiome scientists are beginning to employ large-scale metatranscriptomics approaches. Here, we present a comprehensive review on computational metatranscriptomics approaches to study microbial community transcriptomes. We review the major advancements in this burgeoning field, compare strengths and weaknesses to other microbiome analysis methods, list available tools and workflows, and describe use cases and limitations of this method. We envision that this field will continue to grow exponentially, as will the scope of projects (e.g. longitudinal studies of community transcriptional responses to perturbations over time) and the resulting data. This review will provide a list of options for computational analysis of these data and will highlight areas in need of development.

The Role of the Microbiome in Drug Response

The microbiome is known to regulate many aspects of host health and disease and is increasingly being recognized as a key mediator of drug action. However, investigating the complex multidirectional relationships between drugs, the microbiota, and the host is a challenging endeavor, and the biological mechanisms that underpin these interactions are often not well understood. In this review, we outline the current evidence that supports a role for the microbiota as a contributor to both the therapeutic benefits and side effects of drugs, with a particular focus on those used to treat mental disorders, type 2 diabetes, and cancer. We also provide a snapshot of the experimental and computational tools that are currently available for the dissection of drug-microbiota-host interactions. The advancement of knowledge in this area may ultimately pave the way for the development of novel microbiota-based strategies that can be used to improve treatment outcomes.

To assemble or not to resemble-A validated Comparative Metatranscriptomics Workflow (CoMW)

BACKGROUND

Metatranscriptomics has been used widely for investigation and quantification of microbial communities' activity in response to external stimuli. By assessing the genes expressed, metatranscriptomics provides an understanding of the interactions between different major functional guilds and the environment. Here, we present a de novo assembly-based Comparative Metatranscriptomics Workflow (CoMW) implemented in a modular, reproducible structure. Metatranscriptomics typically uses short sequence reads, which can either be directly aligned to external reference databases ("assembly-free approach") or first assembled into contigs before alignment ("assembly-based approach"). We also compare CoMW (assembly-based implementation) with an assembly-free alternative workflow, using simulated and real-world metatranscriptomes from Arctic and temperate terrestrial environments. We evaluate their accuracy in precision and recall using generic and specialized hierarchical protein databases.

RESULTS

CoMW provided significantly fewer false-positive results, resulting in more precise identification and quantification of functional genes in metatranscriptomes. Using the comprehensive database M5nr, the assembly-based approach identified genes with only 0.6% false-positive results at thresholds ranging from inclusive to stringent compared with the assembly-free approach, which yielded up to 15% false-positive results. Using specialized databases (carbohydrate-active enzyme and nitrogen cycle), the assembly-based approach identified and quantified genes with 3-5 times fewer false-positive results. We also evaluated the impact of both approaches on real-world datasets.

CONCLUSIONS

We present an open source de novo assembly-based CoMW. Our benchmarking findings support assembling short reads into contigs before alignment to a reference database because this provides higher precision and minimizes false-positive results.

Bioinformatics tools for quantitative and functional metagenome and metatranscriptome data analysis in microbes

Metagenomic and metatranscriptomic sequencing approaches are more frequently being used to link microbiota to important diseases and ecological changes. Many analyses have been used to compare the taxonomic and functional profiles of microbiota across habitats or individuals. While a large portion of metagenomic analyses focus on species-level profiling, some studies use strain-level metagenomic analyses to investigate the relationship between specific strains and certain circumstances. Metatranscriptomic analysis provides another important insight into activities of genes by examining gene expression levels of microbiota. Hence, combining metagenomic and metatranscriptomic analyses will help understand the activity or enrichment of a given gene set, such as drug-resistant genes among microbiome samples. Here, we summarize existing bioinformatics tools of metagenomic and metatranscriptomic data analysis, the purpose of which is to assist researchers in deciding the appropriate tools for their microbiome studies. Additionally, we propose an Integrated Meta-Function mapping pipeline to incorporate various reference databases and accelerate functional gene mapping procedures for both metagenomic and metatranscriptomic analyses.

Gut microbiota in ALS: possible role in pathogenesis?

Introduction: The gut microbiota has important roles in maintaining human health. The microbiota and its metabolic byproducts could play a role in the pathogenesis of neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS). Areas covered: The authors evaluate the methods of assessing the gut microbiota, and also review how the gut microbiota affects the various physiological functions of the gut. The authors then consider how gut dysbiosis could theoretically affect the pathogenesis of ALS. They present the current evidence regarding the composition of the gut microbiota in ALS and in rodent models of ALS. Finally, the authors review therapies that could improve gut dysbiosis in the context of ALS. Expert opinion: Currently reported studies suggest some instances of gut dysbiosis in ALS patients and mouse models; however, these studies are limited, and more information with well-controlled larger datasets is required to make a definitive judgment about the role of the gut microbiota in ALS pathogenesis. Overall this is an emerging field that is worthy of further investigation. The authors advocate for larger studies using modern metagenomic techniques to address the current knowledge gaps.

Microbial metabolic strategies for overcoming low-oxygen in naturalized freshwater reservoirs surrounding the Athabasca Oil Sands: A proxy for End-Pit Lakes?

The success and sustainability of aquatic ecosystems are driven by the complex, cooperative metabolism of microbes. Ecological engineering strategies often strive to harness this syntrophic synergy of microbial metabolism for the reclamation of contaminated environments worldwide. Currently, there is a significant knowledge gap in our understanding of how the natural microbial ecology overcomes thermodynamic limitations in recovering contaminated environments. Here, we used in-situ metatranscriptomics and associated metataxonomic analyses on sediments collected from naturalized freshwater man-made reservoirs within the Athabasca Oil Sands region of Alberta, Canada. These reservoirs are unique since they are untouched by industrial mining processes and serve as representative endpoints for model landscape reconstruction. Results indicate that a microbial syntrophic cooperation has been established represented by the oxygenic and anoxygenic phototrophs, sustained through the efficient use of novel cellular mechanistic adaptations tailored to these unique thermodynamic conditions. Specifically, chemotaxis transcripts (cheY & MCPs-methyl-accepting chemotaxis proteins) were highly expressed, suggesting a highly active microbial response to gradients in environmental stimuli, resulting indirectly from hydrocarbon compound alteration. A high expression of photosynthetic activity, likely sustaining nutrient delivery to the similarly highly expressed methanogenic community acting in syntrophy during the breakdown of organics. Overall the more diversified functionality within sub-oxic sample locations indicates an ability to maintain efficient metabolism under thermodynamic constraints. This is the first study to holistically identify and characterize these types of in-situ, metabolic processes and address their thermodynamic feasibility within a global context for large landscape reconstruction. These characterizations of regional, natural landscapes surrounding the oil sands mining operation are severely lacking, but truly provide invaluable insight into end-point goals and targets for reclamation procedures.

Microbial abundance, activity and population genomic profiling with mOTUs2

Metagenomic sequencing has greatly improved our ability to profile the composition of environmental and host-associated microbial communities. However, the dependency of most methods on reference genomes, which are currently unavailable for a substantial fraction of microbial species, introduces estimation biases. We present an updated and functionally extended tool based on universal (i.e., reference-independent), phylogenetic marker gene (MG)-based operational taxonomic units (mOTUs) enabling the profiling of >7700 microbial species. As more than 30% of them could not previously be quantified at this taxonomic resolution, relative abundance estimates based on mOTUs are more accurate compared to other methods. As a new feature, we show that mOTUs, which are based on essential housekeeping genes, are demonstrably well-suited for quantification of basal transcriptional activity of community members. Furthermore, single nucleotide variation profiles estimated using mOTUs reflect those from whole genomes, which allows for comparing microbial strain populations (e.g., across different human body sites).

Seasonal and ecohydrological regulation of active microbial populations involved in DOC, CO2, and CH4 fluxes in temperate rainforest soil

The Pacific coastal temperate rainforest (PCTR) is a global hot-spot for carbon cycling and export. Yet the influence of microorganisms on carbon cycling processes in PCTR soil is poorly characterized. We developed and tested a conceptual model of seasonal microbial carbon cycling in PCTR soil through integration of geochemistry, micro-meteorology, and eukaryotic and prokaryotic ribosomal amplicon (rRNA) sequencing from 216 soil DNA and RNA libraries. Soil moisture and pH increased during the wet season, with significant correlation to net CO₂ flux in peat bog and net CH₄ flux in bog forest soil. Fungal succession in these sites was characterized by the apparent turnover of Archaeorhizomycetes phylotypes accounting for 41% of ITS libraries. Anaerobic prokaryotes, including Syntrophobacteraceae and Methanomicrobia increased in rRNA libraries during the wet season. Putatively active populations of these phylotypes and their biogeochemical marker genes for sulfate and CH₄ cycling, respectively, were positively correlated following rRNA and metatranscriptomic network analysis. The latter phylotype was positively correlated to CH₄ fluxes (r = 0.46, p < 0.0001). Phylotype functional assignments were supported by metatranscriptomic analysis. We propose that active microbial populations respond primarily to changes in hydrology, pH, and nutrient availability. The increased microbial carbon export observed over winter may have ramifications for climate-soil feedbacks in the PCTR.

The gut microbiome and aquatic toxicology: An emerging concept for environmental health

The microbiome plays an essential role in the health and onset of diseases in all animals, including humans. The microbiome has emerged as a central theme in environmental toxicology because microbes interact with the host immune system in addition to its role in chemical detoxification. Pathophysiological changes in the gastrointestinal tissue caused by ingested chemicals and metabolites generated from microbial biodegradation can lead to systemic adverse effects. The present critical review dissects what we know about the impacts of environmental contaminants on the microbiome of aquatic species, with special emphasis on the gut microbiome. We highlight some of the known major gut epithelium proteins in vertebrate hosts that are targets for chemical perturbation, proteins that also directly cross-talk with the microbiome. These proteins may act as molecular initiators for altered gut function, and we propose a general framework for an adverse outcome pathway that considers gut dysbiosis as a major contributing factor to adverse apical endpoints. We present 2 case studies, nanomaterials and hydrocarbons, with special emphasis on the Deepwater Horizon oil spill, to illustrate how investigations into the microbiome can improve understanding of adverse outcomes. Lastly, we present strategies to functionally relate chemical-induced gut dysbiosis with adverse outcomes because this is required to demonstrate cause-effect relationships. Further investigations into the toxicant-microbiome relationship may prove to be a major breakthrough for improving animal and human health. Environ Toxicol Chem 2018;37:2758-2775. © 2018 SETAC.

A de novo approach to disentangle partner identity and function in holobiont systems

BACKGROUND

Study of meta-transcriptomic datasets involving non-model organisms represents bioinformatic challenges. The production of chimeric sequences and our inability to distinguish the taxonomic origins of the sequences produced are inherent and recurrent difficulties in de novo assembly analyses. As the study of holobiont meta-transcriptomes is affected by challenges invoked above, we propose an innovative bioinformatic approach to tackle such difficulties and tested it on marine models as a proof of concept.

RESULTS

We considered three holobiont models, of which two transcriptomes were previously published and a yet unpublished transcriptome, to analyze and sort their raw reads using Short Read Connector, a k-mer based similarity method. Before assembly, we thus defined four distinct categories for each holobiont meta-transcriptome: host reads, symbiont reads, shared reads, and unassigned reads. Afterwards, we observed that independent de novo assemblies for each category led to a diminution of the number of chimeras compared to classical assembly methods. Moreover, the separation of each partner's transcriptome offered the independent and comparative exploration of their functional diversity in the holobiont. Finally, our strategy allowed to propose new functional annotations for two well-studied holobionts (a Cnidaria-Dinophyta, a Porifera-Bacteria) and a first meta-transcriptome from a planktonic Radiolaria-Dinophyta system forming widespread symbiotic association for which our knowledge is considerably limited.

CONCLUSIONS

In contrast to classical assembly approaches, our bioinformatic strategy generates less de novo assembled chimera and allows biologists to study separately host and symbiont data from a holobiont mixture. The pre-assembly separation of reads using an efficient tool as Short Read Connector is an effective way to tackle meta-transcriptomic challenges and offers bright perpectives to study holobiont systems composed of either well-studied or poorly characterized symbiotic lineages and ultimately expand our knowledge about these associations.

SAMSA2: a standalone metatranscriptome analysis pipeline

Background

Complex microbial communities are an area of growing interest in biology. Metatranscriptomics allows researchers to quantify microbial gene expression in an environmental sample via high-throughput sequencing. Metatranscriptomic experiments are computationally intensive because the experiments generate a large volume of sequence data and each sequence must be compared with reference sequences from thousands of organisms.

Results

SAMSA2 is an upgrade to the original Simple Annotation of Metatranscriptomes by Sequence Analysis (SAMSA) pipeline that has been redesigned for standalone use on a supercomputing cluster. SAMSA2 is faster due to the use of the DIAMOND aligner, and more flexible and reproducible because it uses local databases. SAMSA2 is available with detailed documentation, and example input and output files along with examples of master scripts for full pipeline execution.

Conclusions

SAMSA2 is a rapid and efficient metatranscriptome pipeline for analyzing large RNA-seq datasets in a supercomputing cluster environment. SAMSA2 provides simplified output that can be examined directly or used for further analyses, and its reference databases may be upgraded, altered or customized to fit the needs of any experiment.

How to normalize metatranscriptomic count data for differential expression analysis

Background

Differential expression analysis on the basis of RNA-Seq count data has become a standard tool in transcriptomics. Several studies have shown that prior normalization of the data is crucial for a reliable detection of transcriptional differences. Until now it has not been clear whether and how the transcriptomic approach can be used for differential expression analysis in metatranscriptomics.

Methods

We propose a model for differential expression in metatranscriptomics that explicitly accounts for variations in the taxonomic composition of transcripts across different samples. As a main consequence the correct normalization of metatranscriptomic count data under this model requires the taxonomic separation of the data into organism-specific bins. Then the taxon-specific scaling of organism profiles yields a valid normalization and allows us to recombine the scaled profiles into a metatranscriptomic count matrix. This matrix can then be analyzed with statistical tools for transcriptomic count data. For taxon-specific scaling and recombination of scaled counts we provide a simple R script.

Results

When applying transcriptomic tools for differential expression analysis directly to metatranscriptomic data with an organism-independent (global) scaling of counts the resulting differences may be difficult to interpret. The differences may correspond to changing functional profiles of the contributing organisms but may also result from a variation of taxonomic abundances. Taxon-specific scaling eliminates this variation and therefore the resulting differences actually reflect a different behavior of organisms under changing conditions. In simulation studies we show that the divergence between results from global and taxon-specific scaling can be drastic. In particular, the variation of organism abundances can imply a considerable increase of significant differences with global scaling. Also, on real metatranscriptomic data, the predictions from taxon-specific and global scaling can differ widely. Our studies indicate that in real data applications performed with global scaling it might be impossible to distinguish between differential expression in terms of transcriptomic changes and differential composition in terms of changing taxonomic proportions.

Conclusions

As in transcriptomics, a proper normalization of count data is also essential for differential expression analysis in metatranscriptomics. Our model implies a taxon-specific scaling of counts for normalization of the data. The application of taxon-specific scaling consequently removes taxonomic composition variations from functional profiles and therefore provides a clear interpretation of the observed functional differences.

TaxMapper: an analysis tool, reference database and workflow for metatranscriptome analysis of eukaryotic microorganisms

Background

High-throughput sequencing (HTS) technologies are increasingly applied to analyse complex microbial ecosystems by mRNA sequencing of whole communities, also known as metatranscriptome sequencing. This approach is at the moment largely limited to prokaryotic communities and communities of few eukaryotic species with sequenced genomes. For eukaryotes the analysis is hindered mainly by a low and fragmented coverage of the reference databases to infer the community composition, but also by lack of automated workflows for the task.

Results

From the databases of the National Center for Biotechnology Information and Marine Microbial Eukaryote Transcriptome Sequencing Project, 142 references were selected in such a way that the taxa represent the main lineages within each of the seven supergroups of eukaryotes and possess predominantly complete transcriptomes or genomes. From these references, we created an annotated microeukaryotic reference database. We developed a tool called TaxMapper for a reliably mapping of sequencing reads against this database and filtering of unreliable assignments. For filtering, a classifier was trained and tested on each of the following: sequences of taxa in the database, sequences of taxa related to those in the database, and random sequences. Additionally, TaxMapper is part of a metatranscriptomic Snakemake workflow developed to perform quality assessment, functional and taxonomic annotation and (multivariate) statistical analysis including environmental data. The workflow is provided and described in detail to empower researchers to apply it for metatranscriptome analysis of any environmental sample.

Conclusions

TaxMapper shows superior performance compared to standard approaches, resulting in a higher number of true positive taxonomic assignments. Both the TaxMapper tool and the workflow are available as open-source code at Bitbucket under the MIT license: https://bitbucket.org/dbeisser/taxmapperand as a Bioconda package: https://bioconda.github.io/recipes/taxmapper/README.html.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-4168-6) contains supplementary material, which is available to authorized users.