MetaQUBIC: a computational pipeline for gene-level functional profiling of metagenome and metatranscriptome

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.

Review on computer-assisted biosynthetic capacities elucidation to assess metabolic interactions and communication within microbial communities

Microbial communities thrive through interactions and communication, which are challenging to study as most microorganisms are not cultivable. To address this challenge, researchers focus on the extracellular space where communication events occur. Exometabolomics and interactome analysis provide insights into the molecules involved in communication and the dynamics of their interactions. Advances in sequencing technologies and computational methods enable the reconstruction of taxonomic and functional profiles of microbial communities using high-throughput multi-omics data. Network-based approaches, including community flux balance analysis, aim to model molecular interactions within and between communities. Despite these advances, challenges remain in computer-assisted biosynthetic capacities elucidation, requiring continued innovation and collaboration among diverse scientists. This review provides insights into the current state and future directions of computer-assisted biosynthetic capacities elucidation in studying microbial communities.

Inference of disease-associated microbial gene modules based on metagenomic and metatranscriptomic data

The identification of microbial characteristics associated with diseases is crucial for disease diagnosis and therapy. However, the presence of heterogeneity, high dimensionality, and large amounts of microbial data presents tremendous challenges in discovering key microbial features. In this paper, we present IDAM, a novel computational method for inferring disease-associated gene modules from metagenomic and metatranscriptomic data. This method integrates gene context conservation (uber-operons) and regulatory mechanisms (gene co-expression patterns) within a mathematical graph model to explore gene modules associated with specific diseases. It alleviates reliance on prior meta-data. We applied IDAM to publicly available datasets from inflammatory bowel disease, melanoma, type 1 diabetes mellitus, and irritable bowel syndrome. The results demonstrated the superior performance of IDAM in inferring disease-associated characteristics compared to existing popular tools. Furthermore, we showcased the high reproducibility of the gene modules inferred by IDAM using independent cohorts with inflammatory bowel disease. We believe that IDAM can be a highly advantageous method for exploring disease-associated microbial characteristics. The source code of IDAM is freely available at https://github.com/OSU-BMBL/IDAM, and the web server can be accessed at https://bmblx.bmi.osumc.edu/idam/.

Omics profiles of fecal and oral microbiota change in irritable bowel syndrome patients with diarrhea and symptom exacerbation

BACKGROUND

Irritable bowel syndrome (IBS) is a disorder of gut-brain interaction, including dysregulation of the hypothalamic-pituitary-adrenal axis with salivary cortisol changes. However, the role of gastrointestinal microbiota during IBS symptom exacerbation remains unclear. We tested the hypothesis that the microbial species, gene transcripts, and chemical composition of fecal and oral samples are altered during the exacerbation of IBS symptoms.

METHODS

Fecal, salivary, and dental plaque samples were collected at baseline from 43 men with IBS with diarrhea (IBS-D) and 40 healthy control (HC) men. Samples in the IBS-D patients were also collected during symptom exacerbation. The composition of the fecal microbiota was determined by analyzing the 16S rRNA gene, RNA-based metatranscriptome, and metabolites in samples from HC and IBS patients with and without symptom exacerbation. Oral samples were also analyzed using omics approaches.

RESULTS

The fecal microbiota during IBS symptom exacerbation exhibited significant differences in the phylogenic pattern and short-chain fatty acid compared with fecal samples during defecation when symptoms were not exacerbated. Although there were no significant differences in the phylogenic pattern of fecal microbiota abundance between HCs and IBS-D patients, significant differences were detected in the expression patterns of bacterial transcriptomes related to butyrate production and neuroendocrine hormones, including tryptophan-serotonin-melatonin synthesis and glutamine/GABA. The composition of plaque microbiota was different between HC and IBS-D patients during normal defecation.

CONCLUSIONS

Our findings suggest that colonic host-microbial interactions are altered in IBS-D patients during exacerbation of symptoms. There were no overlaps between feces and oral microbiomes.

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.

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.