Carlos P. Cantalapiedra

Chemoreception plays a central role in microbial adaptability, influencing both community structure and interactions with the environment. However, many chemosensitive microorganisms occur at low abundances in natural ecosystems, which has limited their detection and study using conventional metagenomic sequencing. Here, we employed a custom-designed target capture sequencing approach—encompassing all known chemoreceptor genes from both cultured and uncultured microorganisms—to uncover and characterize the vast chemosensory potential and biodiversity within the rarest fractions of the microbiome. Compared to standard environmental sequencing methods, our approach enhanced the detection of chemoreceptor (CR) genes and their associated sensing domains by orders of magnitude across diverse environments, including the rhizosphere, phyllosphere, soil, aquatic ecosystems, the human gut, and bovine rumen. This enabled the identification of thousands of low-abundance chemosensitive microorganisms that remained undetectable using conventional sequencing approaches, including known plant pathogens and symbionts. Phylogenetic analysis of the most divergent CR genes revealed evidence for novel chemosensitive species, potentially representing new bacterial phyla and classes. Our study provides a new perspective on the chemoreception capabilities of environmental microbes and opens new avenues for discovering and characterizing novel microbial sensing mechanisms.

Many of the Earth’s microbes remain uncultured and understudied, limiting our understanding of the functional and evolutionary aspects of their genetic material, which remain largely overlooked in most metagenomic studies¹. Here we analysed 149,842 environmental genomes from multiple habitats^2–6 and compiled a curated catalogue of 404,085 functionally and evolutionarily significant novel (FESNov) gene families exclusive to uncultivated prokaryotic taxa. All FESNov families span multiple species, exhibit strong signals of purifying selection and qualify as new orthologous groups, thus nearly tripling the number of bacterial and archaeal gene families described to date. The FESNov catalogue is enriched in clade-specific traits, including 1,034 novel families that can distinguish entire uncultivated phyla, classes and orders, probably representing synapomorphies that facilitated their evolutionary divergence. Using genomic context analysis and structural alignments we predicted functional associations for 32.4% of FESNov families, including 4,349 high-confidence associations with important biological processes. These predictions provide a valuable hypothesis-driven framework that we used for experimental validatation of a new gene family involved in cell motility and a novel set of antimicrobial peptides. We also demonstrate that the relative abundance profiles of novel families can discriminate between environments and clinical conditions, leading to the discovery of potentially new biomarkers associated with colorectal cancer. We expect this work to enhance future metagenomics studies and expand our knowledge of the genetic repertory of uncultivated organisms.

The eggNOG (evolutionary gene genealogy Non-supervised Orthologous Groups) database is a bioinformatics resource providing orthology data and comprehensive functional information for organisms from all domains of life. Here, we present a major update of the database and website (version 6.0), which increases the number of covered organisms to 12 535 reference species, expands functional annotations, and implements new functionality. In total, eggNOG 6.0 provides a hierarchy of over 17M orthologous groups (OGs) computed at 1601 taxonomic levels, spanning 10 756 bacterial, 457 archaeal and 1322 eukaryotic organisms. OGs have been thoroughly annotated using recent knowledge from functional databases, including KEGG, Gene Ontology, UniProtKB, BiGG, CAZy, CARD, PFAM and SMART. eggNOG also offers phylogenetic trees for all OGs, maximising utility and versatility for end users while allowing researchers to investigate the evolutionary history of speciation and duplication events as well as the phylogenetic distribution of functional terms within each OG. Furthermore, the eggNOG 6.0 website contains new functionality to mine orthology and functional data with ease, including the possibility of generating phylogenetic profiles for multiple OGs across species or identifying single-copy OGs at custom taxonomic levels. eggNOG 6.0 is available at http://eggnog6.embl.de.

Phylogenomics data have grown exponentially over the last decades. It is currently common for genome-wide projects to generate hundreds or even thousands of phylogenetic trees and multiple sequence alignments, which may also be very large in size. However, the analysis and interpretation of such data still depends on custom bioinformatic and visualisation workflows that are largely unattainable for non-expert users. Here, we present PhyloCloud, an online platform aimed at hosting, indexing and exploring large phylogenetic tree collections, providing also seamless access to common analyses and operations, such as node annotation, searching, topology editing, automatic tree rooting, orthology detection and more. In addition, PhyloCloud provides quick access to tools that allow users to build their own phylogenies using fast predefined workflows, graphically compare tree topologies, or query taxonomic databases such as NBCI or GTDB. Finally, PhyloCloud offers a novel tree visualisation system based on ETE Toolkit v4.0, which can be used to explore very large trees and enhance them with custom annotations and multiple sequence alignments. The platform allows for sharing tree collections and specific tree views via private links, or make them fully public, serving also as a repository of phylogenomic data. PhyloCloud is available at https://phylocloud.cgmlab.org

Microbial genes encode the majority of the functional repertoire of life on earth. However, despite increasing efforts in metagenomic sequencing of various habitats^1,2,3, little is known about the distribution of genes across the global biosphere, with implications for human and planetary health. Here we constructed a non-redundant gene catalogue of 303 million species-level genes (clustered at 95% nucleotide identity) from 13,174 publicly available metagenomes across 14 major habitats and use it to show that most genes are specific to a single habitat. The small fraction of genes found in multiple habitats is enriched in antibiotic-resistance genes and markers for mobile genetic elements. By further clustering these species-level genes into 32 million protein families, we observed that a small fraction of these families contain the majority of the genes (0.6% of families account for 50% of the genes). The majority of species-level genes and protein families are rare. Furthermore, species-level genes, and in particular the rare ones, show low rates of positive (adaptive) selection, supporting a model in which most genetic variability observed within each protein family is neutral or nearly neutral.

Sponges and evolutionary origins: Sponges represent our distant animal relatives. They do not have a nervous system but do have a simple body for filter feeding. Surveying the cell types in the freshwater sponge Spongilla lacustris , Musser et al . found that many genes important in synaptic communication are expressed in cells of the small digestive chambers. They found secretory machinery characteristic of the presynapse in small multipolar cells contacting all other cells and also the receptive apparatus of the postsynapse in the choanocytes that generate water flow and digest microbial food. These results suggest that the first directed communication in animals may have evolved to regulate feeding, serving as a starting point on the long path toward nervous system evolution. —BAP

Even though automated functional annotation of genes represents a fundamental step in most genomic and metagenomic workflows, it remains challenging at large scales. Here, we describe a major upgrade to eggNOG-mapper, a tool for functional annotation based on precomputed orthology assignments, now optimized for vast (meta)genomic data sets. Improvements in version 2 include a full update of both the genomes and functional databases to those from eggNOG v5, as well as several efficiency enhancements and new features. Most notably, eggNOG-mapper v2 now allows for: 1) de novo gene prediction from raw contigs, 2) built-in pairwise orthology prediction, 3) fast protein domain discovery, and 4) automated GFF decoration. eggNOG-mapper v2 is available as a standalone tool or as an online service at http://eggnog-mapper.embl.de.

Even though automated functional annotation of genes represents a fundamental step in most genomic and metagenomic workflows, it remains challenging at large scales. Here, we describe a major upgrade to eggNOG-mapper, a tool for functional annotation based on precomputed orthology assignments, now optimized for vast (meta)genomic data sets. Improvements in version 2 include a full update of both the genomes and functional databases to those from eggNOG v5, as well as several efficiency enhancements and new features. Most notably, eggNOG-mapper v2 now allows: (i) de novo gene prediction from raw contigs, (ii) built-in pairwise orthology prediction, (iii) fast protein domain discovery, and (iv) automated GFF decoration. eggNOG-mapper v2 is available as a standalone tool or as an online service at http://eggnog-mapper.embl.de .

The Spanish Barley Core Collection (SBCC) is a source of genetic variability of potential interest for breeding, particularly for adaptation to Mediterranean environments. Two backcross populations (BC₂F₅) were developed using the elite cultivar Cierzo as the recurrent parent. The donor parents, namely SBCC042 and SBCC073, were selected from the SBCC lines due to their outstanding yield in drought environments. Flowering time, yield and drought-related traits were evaluated in two field trials in Zaragoza (Spain) during the 2014–15 and 2015–16 seasons and validated in the 2017–18 season. Two hundred sixty-four lines of each population were genotyped with the Barley Illumina iSelect 50k SNP chip. Genetic maps for each population were generated. The map for SBCC042 × Cierzo contains 12,893 SNPs distributed in 9 linkage groups. The map for SBCC073 × Cierzo includes 12,026 SNPs in 7 linkage groups. Both populations shared two QTL hotspots. There are QTLs for flowering time, thousand-kernel weight (TKW), and hectoliter weight on a segment of 23 Mb at ~515 Mb on chromosome 1H, which encompasses the HvFT3 gene. In both populations, flowering was accelerated by the landrace allele, which also increased the TKW. In the same region, better soil coverage was contributed by SBCC042 but coincident with a lower hectoliter weight. The second large hotspot was on chromosome 6H and contained QTLs with wide intervals for grain yield, plant height and TKW. Landrace alleles contributed to increased plant height and TKW and reduced grain yield. Only SBCC042 contributed favorable alleles for “green area,” with three significant QTLs that increased ground coverage after winter, which might be exploited as an adaptive trait of this landrace. Some genes of interest found in or very close to the peaks of the QTLs are highlighted. Strategies to deploy the QTLs found for breeding and pre-breeding are proposed.