MOSCA: An Automated Pipeline for Integrated Metagenomics and Metatranscriptomics Data Analysis

Chapter 4 - Computational and Statistical Considerations in the Analysis of Metagenomic Data

BackgroundMicrobial communities of the mouse gut have been extensively studied; however, their functional roles and regulation are yet to be elucidated. Metagenomic and metatranscriptomic analyses may allow us a comprehensive profiling of bacterial composition and functions of the complex gut microbiota. The present study aimed to investigate the active functions of the microbial communities in the murine cecum by analyzing both metagenomic and metatranscriptomic data on specific bacterial species within the microbial communities, in addition to the whole microbiome.ResultsBacterial composition of the healthy mouse gut microbiome was profiled using the following three different approaches: 16S rRNA-based profiling based on amplicon and shotgun sequencing data, and genome-based profiling based on shotgun sequencing data. Consistently, Bacteroidetes, Firmicutes, and Deferribacteres emerged as the major phyla. Based on NCBI taxonomy, Muribaculaceae, Lachnospiraceae, and Deferribacteraceae were the predominant families identified in each phylum. The genes for carbohydrate metabolism were upregulated in Muribaculaceae, while genes for cofactors and vitamin metabolism and amino acid metabolism were upregulated in Deferribacteraceae. The genes for translation were commonly enhanced in all three families. Notably, combined analysis of metagenomic and metatranscriptomic sequencing data revealed that the functions of translation and metabolism were largely upregulated in all three families in the mouse gut environment. The ratio of the genes in the metagenome and their expression in the metatranscriptome indicated higher expression of carbohydrate metabolism in Muribaculum, Duncaniella, and Mucispirillum.ConclusionsWe demonstrated a fundamental methodology for linking genomic and transcriptomic datasets to examine functional activities of specific bacterial species in a complicated microbial environment. We investigated the normal flora of the mouse gut using three different approaches and identified Muribaculaceae, Lachnospiraceae, and Deferribacteraceae as the predominant families. The functional distribution of these families was reflected in the entire microbiome. By comparing the metagenomic and metatranscriptomic data, we found that the expression rates differed for different functional categories in the mouse gut environment. Application of these methods to track microbial transcription in individuals over time, or before and after administration of a specific stimulus will significantly facilitate future development of diagnostics and treatments.

EBI's metagenomics pipeline: an automated pipeline for the analysis of metagenomic data

BackgroundNext-Generation-Sequencing (NGS) enables detection of microorganisms present in biological and other matrices of various origin and nature, allowing not only the identification of known phyla and strains but also the discovery of novel ones. The large amount of metagenomic shotgun data produced by NGS require comprehensive and user-friendly pipelines for data analysis, that speed up the bioinformatics steps, relieving the users from the need to manually perform complex and time-consuming tasks.ResultsWe describe here HOME-BIO (sHOtgun MEtagenomic analysis of BIOlogical entities), an exhaustive pipeline for metagenomics data analysis, comprising three independent analytical modules designed for an inclusive analysis of large NGS datasets.ConclusionsHOME-BIO is a powerful and easy-to-use tool that can be run also by users with limited computational expertise. It allows in-depth analyses by removing low-complexity/ problematic reads, integrating the analytical steps that lead to a comprehensive taxonomy profile of each sample by querying different source databases, and it is customizable according to specific users’ needs.

Heavy metals have been severely polluting the environment. However, the response mechanism of microbial communities to short-term heavy metals stress remains unclear. In this study, metagenomics (MG) and metatranscriptomics (MT) was performed to observe the microbial response to short-term Cr(VI) stress. MG data showed that 99.1% of species were similar in the control and Cr(VI) treated groups. However, MT data demonstrated that 83% of the microbes were active in which 58.7% increased, while the relative abundance of 41.3% decreased after short-term Cr(VI) incubation. The MT results also revealed 9% of microbes were dormant in samples. Genes associated with oxidative stress, Cr(VI) transport, resistance, and reduction, as well as genes with unknown functions were 2-10 times upregulated after Cr(VI) treatment. To further confirm the function of unknown genes, two genes (314 and 494) were selected to detect the Cr(VI) resistance and reduction ability. The results showed that these genes significantly increased the Cr(VI) remediation ability of Escherichia coli. MT results also revealed an increase in the expression of some rare genera (at least two times) after Cr(VI) treatment, indicating these rare species played a crucial role in microbial response to short-term Cr(VI) stress. In summary, MT is an efficient way to understand the role of active and dormant microbes in specific environmental conditions.

Rationale: Two molecular phenotypes have been identified in acute respiratory distress syndrome (ARDS). In the ROSE (Reevaluation of Systemic Early Neuromuscular Blockade) trial of cisatracurium in moderate to severe ARDS, we addressed three unanswered questions: 1) Do the same phenotypes emerge in a more severe ARDS cohort with earlier recruitment; 2) Do phenotypes respond differently to neuromuscular blockade? and 3) What biological pathways most differentiate inflammatory phenotypes?Methods: We performed latent class analysis in ROSE using preenrollment clinical and protein biomarkers. In a subset of patients (n = 134), we sequenced whole-blood RNA using enrollment and Day 2 samples and performed differential gene expression and pathway analyses. Informed by the differential gene expression analysis, we measured additional plasma proteins and evaluated their abundance relative to gene expression amounts.Measurements and Main Results: In ROSE, we identified the hypoinflammatory (60.4%) and hyperinflammatory (39.6%) phenotypes with similar biological and clinical characteristics as prior studies, including higher mortality at Day 90 for the hyperinflammatory phenotype (30.3% vs. 61.6%; P < 0.0001). We observed no treatment interaction between the phenotypes and randomized groups for mortality. The hyperinflammatory phenotype was enriched for genes associated with innate immune response, tissue remodeling, and zinc metabolism at Day 0 and collagen synthesis and neutrophil degranulation at Day 2. Longitudinal changes in gene expression patterns differed dependent on survivorship. For most highly expressed genes, we observed correlations with their corresponding plasma proteins' abundance. However, for the class-defining plasma proteins in the latent class analysis, no correlation was observed with their corresponding genes' expression.Conclusions: The hyperinflammatory and hypoinflammatory phenotypes have different clinical, protein, and dynamic transcriptional characteristics. These findings support the clinical and biological potential of molecular phenotypes to advance precision care in ARDS.

Microbiome is an ecological community of commensal, symbiotic, and pathogenic microorganisms that share the same environment. The study of microbiome, i.e., genetic material sampled directly from environmental samples is called metagenomics. In recent years, genome sequencing methods have dramatically improved and the number and variety of sequenced genomes has rapidly increased. New technology has significantly increased the variety and complexity of the microbiome research and ever-larger datasets present new challenges in analysis of metagenomic data. Two main tasks in metagenomic analysis are classification of sequenced metagenomic data into taxonomic group of any rank, such as a species, family, or class, and assembly of the data into longer contiguous sequences. The final aim of both tasks is to correctly identify species presented in the metagenomic sample. This has various applications in medicine (e.g. infectious disease diagnosis), development of biofuels, biotechnology, agriculture, and many other areas. In this paper, we present a description of common procedures and methods for metagenomic data analysis and challenges facing these procedures. We give an overview of existing software tools and a review of public genome databases used for metagenomic analysis. Finally, we explore possible improvements to the existing methods for metagenomic classification and assembly.

Differential gene expression (DGE) analysis has gained significant attention with the advent of next-generation sequencing technologies, leading to the development of a wide range of methods and tools for DGE analysis. We performed bibliometric analysis using Biblioshiny and VOSviewer software to investigate the trends over the investigated period. Relevant papers with differential gene expression related terms as the subjects from 2005 to 2023 were retrieved from the Web of Science database. Network maps were generated using Biblioshiny and VOSviewer software to illustrate the published trends over the investigated period. A total of 729 studies were examined to reveal trends in the DGE analysis methodologies, tools, and packages. In the analysis, co-authorship, bibliographic coupling, and co-occurrence analyses were conducted for country, institution, source, author, and keyword productivity. It was found that the output and citation numbers increased after 2005. During the study period, the USA and China emerged as the leading contributors to the field. The temporal study revealed a significant increase in publications at certain times, followed by period of slight decrease. The greatest fall was observed between 2008 and 2010. Despite these decreases, DGE analysis remains a critical topic in genomics due to its essential role in understanding the mechanisms of any disease, gene function, and therapeutic targets. This trend suggests that current methods and tools are considered sufficiently powerful for identifying key informative genes associated with diverse diseases.

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.

1024 Background: HER2-low metastatic breast cancer (mBC) has emerged as a clinically significant subgroup since approval of trastuzumab deruxtecan. However, its relevance as a distinct subtype remains debated. We aimed to identify HER2-related gene signatures, examine the molecular characteristics of HER2-low mBC, and investigate the potential for classifying HER2-low mBC into subgroups with profiles resembling either HER2-positive (HER2+) or HER2-negative (HER2-) mBC. Methods: We performed differential gene expression (DGE) analysis using the TCGA-BRCA dataset, comparing HER2+ and HER2- samples to identify a 17-gene HER2 expression signature. The findings were validated on archival samples from a UAE clinical center, where DGE analysis was repeated to compare HER2+ and HER2-zero mBC. HER2-low samples were then classified into two subsets; positive-like and zero-like based on their HER2 signature gene expression level. Lastly, Gene Set Enrichment Analysis (GSEA) was conducted to evaluate the molecular characteristics of these subsets. Results: In the TCGA-BRCA dataset (n = 409; median age 59 years, range 26–90), 20.5% of samples were HER2+, and 79.5% were HER2-. DGE analysis identified a 17-gene HER2 signature that effectively separated HER2+ and HER2- mBC. These 17 genes including GSDMB, ERBB2, and MED1, are associated with HER2 expression. In the UAE cohort (n = 69; median age 52 years, range 24–84), comprising 7.25% HER2+, 60.87% HER2-low, and 31.88% HER2-zero mBC, 7 genes (GSDMB, GRB7, ERBB2, STARD3, PGAP3, MIEN1, TCAP) from the 17-gene HER2 signature were similarly upregulated in the HER2+ samples. We observed an increasing trend in HER2 signature expression across the groups, from HER2-zero to HER2-low and then HER2+ mBC showing the highest expression. This trend was supported by separation across HER2 status with gene-level expression, Gene Set Variation Analysis scores and Principal Component Analysis (PCA). Next, HER2-low mBC were classified into two distinct subgroups based on distance-to-centroid approach with PCA. Based on distance to HER2+ and HER2-zero centroids, HER2-low mBC were classified into positive-like and zero-like subgroups respectively. Separation of the positive-like and zero-like samples were observed with DGE analysis and expression of the HER2 signature genes. GSEA of HER2-low positive-like samples indicate increased activation of ERBB2 oncogenic pathway and suppression of gene sets involved in immune-mediated pathways compared to HER2-low zero-like mBC. Conclusions: This study reveals the potential utility of transcriptomic HER2 signature to characterize HER2-related molecular features in mBC, revealing a gradient of HER2 signature expression across HER2+, HER2-low, and HER2-zero. Identification of suppressed immune-mediated pathways in HER2-low positive-like mBC suggests combination with immune mediators as potential treatment strategy.

MGnify is EMBL-EBI’s microbiome data resource which enables the analysis and exploration of metagenomic, metatranscriptomic, amplicon and assembly data. This webinar will showcase the recently released MGnify Proteins website which provides information regarding the proteins identified through MGnify’s analysis pipeline. In MGnify Proteins the information about proteins are accompanied by metadata such as underlying biological domains and encountered environments. The protein sequence data is further utilised in other MGnify resources such as MGnifams, to generate protein families, models and structures.

EBI Metagenomics ( https://www.ebi.ac.uk/metagenomics/ ) is a freely available hub for the analysis and exploration of metagenomic, metatranscriptomic, amplicon and assembly data. The resource provides rich functional and taxonomic analyses of user-submitted sequences, as well as analysis of publicly available metagenomic datasets held within the European Nucleotide Archive. This presentation provides an overview of the resource and analysis pipeline components. It also outlines ways in which the resource can be used for data discovery and describes a number of recently added and forthcoming functionalities.

Microorganisms are everywhere. Recent studies showed that the mixture of microbes or the microbiome on the human body plays important roles in human physiology and diseases. Metagenomic sequencing is a key technology for studying microbiomes. It produces massive amounts of data in the form of short sequencing reads. A single metagenomic sample can contain 10 7 to 10 8 reads of about 100-nucleotide (nt) length each in a typical shotgun metagenomic sequencing study. They contain rich information about microbiomes and their functions, but reading out those information from the huge highly fragmented data has multiple challenges for mathematical models, bioinformatics methods, and computer algorithms. In this paper, we review the basic bioinformatics tasks and existing methods in processing and analyzing metagenomic data, and discuss remaining open challenges and practical observations. The aim of the paper is to provide readers a whole picture of metagenomic data processing and analysis, and a reference and perspective to start with for computational scientists who are interested in this exciting field.

A key goal of conservation is to protect the biodiversity of wild species to support their continued evolution and survival. Conservation practice has long been guided by genetic, ecological, and demographic indicators of risk. Cope's rule suggests that species tend to evolve larger body sizes over time. Here, we provide strong evidence to support the inclusion of body size when formulating wildlife conservation strategies. The gut microbiome can mirror the physiological and environmental adaptation status of the host. This study established a connection between body size and the gut microbiome in the Felidae family using 70 fecal samples collected from 18 individuals through metagenomic data analysis and mining metagenome-assembled genomes (MAGs). Two enterotypes were identified in the Felidae gut: Bacteroides and Clostridium. Medium-sized felids predominantly harbored Clostridium, associated with pathogenicity, whereas large and small felids harbored both beneficial Bacteroides and pathogenic Clostridium. Species that evolved larger body sizes over time exhibited distinct changes in gut microbial communities, such as enhanced nutrient extraction and metabolic capabilities. Larger felids exhibited a more diverse, stable gut microbiome engaged in metabolic processes and extensive host interactions, indicating an evolved functional role in various biological processes. Conversely, that of smaller felids is less diverse, with more viruses and pathogenic elements primarily involved in chemical synthesis. These findings provide essential insights for developing conservation strategies that consider the nutritional needs of different-sized feline species, control the transmission of pathogens, and allocate resources based on their unique gut microbiome characteristics.IMPORTANCEBody size is a fundamental trait that varies greatly among taxa and has important implications for life history and ecology. Cope's rule suggests that species tend to evolve larger body sizes over time. However, its correlation to body size evolution remains unclear. This study aimed to establish a connection between body size and the gut microbiome in the Felidae family through metagenomic data analysis. Our results support Cope's rule, illustrating that increased body size correlates with shifts in the gut microbiome, enhancing survival and adaptability.

Anti-PD-1 immunotherapy has a large impact on cancer treatment but the rate of positive treatment outcomes is 40-45% and depends on many factors. One of the factors affecting the outcome of immunotherapy is the gut microbiota composition. This effect has been demonstrated both in model objects and in clinical patients groups. However, in order to identify clear causal relationships between microbiota and anti-PD1 immunotherapy response, it is necessary to expand the number of patients and experimental samples. This work presents an analysis of metagenomic data obtained using whole-genome sequencing of stool samples from melanoma patients (n=45) with different responses to anti-PD1 therapy. The analysis of the differential representation of microbial species has shown a difference in the composition of the microbiota between the experimental groups. Results of this study indicate existence of a strong link between the composition of the gut microbiota and the outcome of anti-PD1 therapy. Expansion of similar research may help develop additional predictive tools for the outcome of anti-PD1 cancer immunotherapy, as well as increase its effectiveness.