Inherent differential microbial assemblages and functions associated with corals exhibiting different thermal phenotypes.

Certain coral individuals exhibit enhanced resistance to thermal bleaching, yet the specific microbial assemblages and their roles in these phenotypes remain unclear. We compared the microbial communities of thermal bleaching-resistant (TBR) and thermal bleaching-sensitive (TBS) corals using metabarcoding and metagenomics. Our multidomain approach revealed stable distinct microbial compositions between thermal phenotypes.

Genome-centric analyses of 165 metagenomes show that mobile genetic elements are crucial for the transmission of antimicrobial resistance genes to pathogens in activated sludge and wastewater.

Unlabelled: Wastewater is considered a reservoir of antimicrobial resistance genes (ARGs), where the abundant antimicrobial-resistant bacteria and mobile genetic elements facilitate horizontal gene transfer. However, the prevalence and extent of these phenomena in different taxonomic groups that inhabit wastewater are still not fully understood. Here, we determined the presence of ARGs in metagenome-assembled genomes (MAGs) and evaluated the risks of MAG-carrying ARGs in potential human pathogens.

MuDoGeR: Multi-Domain Genome recovery from metagenomes made easy.

Several computational frameworks and workflows that recover genomes from prokaryotes, eukaryotes and viruses from metagenomes exist. Yet, it is difficult for scientists with little bioinformatics experience to evaluate quality, annotate genes, dereplicate, assign taxonomy and calculate relative abundance and coverage of genomes belonging to different domains. MuDoGeR is a user-friendly tool tailored for those familiar with Unix command-line environment that makes it easy to recover genomes of prokaryotes, eukaryotes and viruses from metagenomes, either alone or in combination.

The AnimalAssociatedMetagenomeDB reveals a bias towards livestock and developed countries and blind spots in functional-potential studies of animal-associated microbiomes.

Background: Metagenomic data can shed light on animal-microbiome relationships and the functional potential of these communities. Over the past years, the generation of metagenomics data has increased exponentially, and so has the availability and reusability of data present in public repositories. However, identifying which datasets and associated metadata are available is not straightforward.

PredicTF: prediction of bacterial transcription factors in complex microbial communities using deep learning.

Background: Transcription factors (TFs) are proteins controlling the flow of genetic information by regulating cellular gene expression. A better understanding of TFs in a bacterial community context may open novel revenues for exploring gene regulation in ecosystems where bacteria play a key role. Here we describe PredicTF, a platform supporting the prediction and classification of novel bacterial TF in single species and complex microbial communities.

OrtSuite: from genomes to prediction of microbial interactions within targeted ecosystem processes.

The high complexity found in microbial communities makes the identification of microbial interactions challenging. To address this challenge, we present OrtSuite, a flexible workflow to predict putative microbial interactions based on genomic content of microbial communities and targeted to specific ecosystem processes. The pipeline is composed of three user-friendly bash commands.

Metagenomes, metatranscriptomes and microbiomes of naturally decomposing deadwood.

Deadwood represents significant carbon (C) stock in a temperate forests. Its decomposition and C mobilization is accomplished by decomposer microorganisms - fungi and bacteria - who also supply the foodweb of commensalist microbes. Due to the ecosystem-level importance of deadwood habitat as a C and nutrient stock with significant nitrogen fixation, the deadwood microbiome composition and function are critical to understanding the microbial processes related to its decomposition.

Mining Synergistic Microbial Interactions: A Roadmap on How to Integrate Multi-Omics Data.

Mining interspecies interactions remain a challenge due to the complex nature of microbial communities and the need for computational power to handle big data. Our meta-analysis indicates that genetic potential alone does not resolve all issues involving mining of microbial interactions. Nevertheless, it can be used as the starting point to infer synergistic interspecies interactions and to limit the search space (i.

Complementary Roles of Wood-Inhabiting Fungi and Bacteria Facilitate Deadwood Decomposition.

Forests accumulate and store large amounts of carbon (C), and a substantial fraction of this stock is contained in deadwood. This transient pool is subject to decomposition by deadwood-associated organisms, and in this process it contributes to CO emissions. Although fungi and bacteria are known to colonize deadwood, little is known about the microbial processes that mediate carbon and nitrogen (N) cycling in deadwood.

TerrestrialMetagenomeDB: a public repository of curated and standardized metadata for terrestrial metagenomes.

Microbiome studies focused on the genetic potential of microbial communities (metagenomics) became standard within microbial ecology. MG-RAST and the Sequence Read Archive (SRA), the two main metagenome repositories, contain over 202 858 public available metagenomes and this number has increased exponentially. However, mining databases can be challenging due to misannotated, misleading and decentralized data.

Marine probiotics: increasing coral resistance to bleaching through microbiome manipulation.

Although the early coral reef-bleaching warning system (NOAA/USA) is established, there is no feasible treatment that can minimize temperature bleaching and/or disease impacts on corals in the field. Here, we present the first attempts to extrapolate the widespread and well-established use of bacterial consortia to protect or improve health in other organisms (e.g.

Fungal biomarker discovery by integration of classifiers.

Background: The human immune system is responsible for protecting the host from infection. However, in immunocompromised individuals the risk of infection increases substantially with possible drastic consequences. In extreme, systemic infection can lead to sepsis which is responsible for innumerous deaths worldwide.