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To ensure sustainable aquaculture, it is essential to understand the path 'from feed to fish', whereby the gut microbiome plays an important role in digestion and metabolism, ultimately influencing host health and growth. Previous work has reported the taxonomic composition of the Atlantic salmon (Salmo salar) gut microbiome; however, functional insights are lacking. Here we present the Salmon Microbial Genome Atlas consisting of 211 high-quality bacterial genomes, recovered by cultivation (n = 131) and gut metagenomics (n = 80) from wild and farmed fish both in freshwater and seawater. Bacterial genomes were taxonomically assigned to 14 different orders, including 35 distinctive genera and 29 previously undescribed species. Using metatranscriptomics, we functionally characterized key bacterial populations, across five phyla, in the salmon gut. This included the ability to degrade diet-derived fibres and release vitamins and other exometabolites with known beneficial effects, which was supported by genome-scale metabolic modelling and in vitro cultivation of selected bacterial species coupled with untargeted metabolomic studies. Together, the Salmon Microbial Genome Atlas provides a genomic and functional resource to enable future studies on salmon nutrition and health., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

Microorganisms play a primary role in regulating biogeochemical cycles and are a valuable source of enzymes that have biotechnological applications, such as carbohydrate-active enzymes (CAZymes). However, the inability to culture the majority of microorganisms that exist in natural ecosystems restricts access to potentially novel bacteria and beneficial CAZymes. While commonplace molecular-based culture-independent methods such as metagenomics enable researchers to study microbial communities directly from environmental samples, recent progress in long-read sequencing technologies are advancing the field. We outline key methodological stages that are required as well as describe specific protocols that are currently used for long-read metagenomic projects dedicated to CAZyme discovery., (© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

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., Competing Interests: No competing interests were disclosed., (Copyright: © 2021 Mehta S et al.)

The rumen microbiome constitutes a dense and complex mixture of anaerobic bacteria, archaea, protozoa, virus and fungi. Collectively, rumen microbial populations interact closely in order to degrade and ferment complex plant material into nutrients for host metabolism, a process which also produces other by-products, such as methane gas. Our understanding of the rumen microbiome and its functions are of both scientific and industrial interest, as the metabolic functions are connected to animal health and nutrition, but at the same time contribute significantly to global greenhouse gas emissions. While many of the major microbial members of the rumen microbiome are acknowledged, advances in modern culture-independent meta-omic techniques, such as metaproteomics, enable deep exploration into active microbial populations involved in essential rumen metabolic functions. Meaningful and accurate metaproteomic analyses are highly dependent on representative samples, precise protein extraction and fractionation, as well as a comprehensive and high-quality protein sequence database that enables precise protein identification and quantification. This review focuses on the application of rumen metaproteomics, and its potential towards understanding the complex rumen microbiome and its metabolic functions. We present and discuss current methods in sample handling, protein extraction and data analysis for rumen metaproteomics, and finally emphasize the potential of (meta)genome-integrated metaproteomics for accurate reconstruction of active microbial populations in the rumen., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Meta-omic techniques have progressed rapidly in the past decade and are frequently used in microbial ecology to study microorganisms in their natural ecosystems independent from culture restrictions. Metaproteomics, in combination with metagenomics, enables quantitative assessment of expressed proteins and pathways from individual members of the consortium. Together, metaproteomics and metagenomics can provide a detailed understanding of which organisms occupy specific metabolic niches, how they interact, and how they utilize nutrients, and these insights can be obtained directly from environmental samples. Here, we outline key aspects of sample preparation, database generation, and other methodological considerations that are required for successful quantitative metaproteomic analyses and we describe case studies on the integration with metagenomics for enhanced functional output.

Viruses represent the most abundant life forms on the planet. Recent experimental and computational improvements have led to a dramatic increase in the number of viral genome sequences identified primarily from metagenomic samples. As a result of the expanding catalog of metagenomic viral sequences, there exists a need for a comprehensive computational platform integrating all these sequences with associated metadata and analytical tools. Here we present IMG/VR (https://img.jgi.doe.gov/vr/), the largest publicly available database of 3908 isolate reference DNA viruses with 264 413 computationally identified viral contigs from >6000 ecologically diverse metagenomic samples. Approximately half of the viral contigs are grouped into genetically distinct quasi-species clusters. Microbial hosts are predicted for 20 000 viral sequences, revealing nine microbial phyla previously unreported to be infected by viruses. Viral sequences can be queried using a variety of associated metadata, including habitat type and geographic location of the samples, or taxonomic classification according to hallmark viral genes. IMG/VR has a user-friendly interface that allows users to interrogate all integrated data and interact by comparing with external sequences, thus serving as an essential resource in the viral genomics community., (© The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Microorganisms play a primary role in regulating biogeochemical cycles and are a valuable source of enzymes that have biotechnological applications, such as carbohydrate-active enzymes (CAZymes). However, the inability to culture the majority of microorganisms that exist in natural ecosystems using common culture-dependent techniques restricts access to potentially novel cellulolytic bacteria and beneficial enzymes. The development of molecular-based culture-independent methods such as metagenomics enables researchers to study microbial communities directly from environmental samples, and presents a platform from which enzymes of interest can be sourced. We outline key methodological stages that are required as well as describe specific protocols that are currently used for metagenomic projects dedicated to CAZyme discovery.

DNA assembly is a core methodological step in metagenomic pipelines used to study the structure and function within microbial communities. Here we investigate the utility of Pacific Biosciences long and high accuracy circular consensus sequencing (CCS) reads for metagenomic projects. We compared the application and performance of both PacBio CCS and Illumina HiSeq data with assembly and taxonomic binning algorithms using metagenomic samples representing a complex microbial community. Eight SMRT cells produced approximately 94 Mb of CCS reads from a biogas reactor microbiome sample that averaged 1319 nt in length and 99.7% accuracy. CCS data assembly generated a comparative number of large contigs greater than 1 kb, to those assembled from a ~190x larger HiSeq dataset (~18 Gb) produced from the same sample (i.e approximately 62% of total contigs). Hybrid assemblies using PacBio CCS and HiSeq contigs produced improvements in assembly statistics, including an increase in the average contig length and number of large contigs. The incorporation of CCS data produced significant enhancements in taxonomic binning and genome reconstruction of two dominant phylotypes, which assembled and binned poorly using HiSeq data alone. Collectively these results illustrate the value of PacBio CCS reads in certain metagenomics applications.