Functional characterization of prokaryotic dark matter: the road so far and what lies ahead

Functional and evolutionary significance of unknown genes from uncultivated taxa

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 habitats2-6 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.

Effect of castration timing and weaning strategy on the taxonomic and functional profile of ruminal bacteria and archaea of beef calves

BACKGROUND

Beef cattle experience several management challenges across their lifecycle. Castration and weaning, two major interventions in the early life of beef cattle, can have a substantial impact on animal performance. Despite the key role of the rumen microbiome on productive traits of beef cattle, the effect of castration timing and weaning strategy on this microbial community has not been formally described. We assessed the effect of four castration time windows (at birth, turnout, pre-weaning and weaning) and two weaning strategies (fence-line and truck transportation) on the rumen microbiome in a randomized controlled study with 32 male calves across 3 collection days (i.e., time points). Ruminal fluid samples were submitted to shotgun metagenomic sequencing and changes in the taxonomic (microbiota) and functional profile (metagenome) of the rumen microbiome were described.

RESULTS

Using a comprehensive yet stringent taxonomic classification approach, we identified 10,238 unique taxa classified under 40 bacterial and 7 archaeal phyla across all samples. Castration timing had a limited long-term impact on the rumen microbiota and was not associated with changes in alpha and beta diversity. The interaction of collection day and weaning strategy was associated with changes in the rumen microbiota, which experienced a significant decrease in alpha diversity and shifts in beta diversity within 48 h post-weaning, especially in calves abruptly weaned by truck transportation. Calves weaned using a fence-line weaning strategy had lower relative abundance of Bacteroides, Lachnospira, Fibrobacter and Ruminococcus genera compared to calves weaned by truck transportation. Some genes involved in the hydrogenotrophic methanogenesis pathway (fwdB and fwdF) had higher relative abundance in fence-line-weaned calves post-weaning. The antimicrobial resistance gene tetW consistently represented more than 50% of the resistome across time, weaning and castration groups, without significant changes in relative abundance.

CONCLUSIONS

Within the context of this study, castration timing had limited long-term effects on the rumen microbiota, while weaning strategy had short-term effects on the rumen microbiota and methane-associated metagenome, but not on the rumen resistome.

Single-cell transcriptomics and data analyses for prokaryotes-Past, present and future concepts

Transcriptomics, or more specifically mRNA sequencing, is a powerful tool to study gene expression at the single-cell level (scRNA-seq) which enables new insights into a plethora of biological processes. While methods for single-cell RNA-seq in eukaryotes are well established, application to prokaryotes is still challenging. Reasons for that are rigid and diverse cell wall structures hampering lysis, the lack of polyadenylated transcripts impeding mRNA enrichment, and minute amounts of RNA requiring amplification steps before sequencing. Despite those obstacles, several promising scRNA-seq approaches for bacteria have been published recently, albeit difficulties in the experimental workflow and data processing and analysis remain. In particular, bias is often introduced by amplification which makes it difficult to distinguish between technical noise and biological variation. Future optimization of experimental procedures and data analysis algorithms are needed for the improvement of scRNA-seq but also to aid in the emergence of prokaryotic single-cell multi-omics. to help address 21st century challenges in the biotechnology and health sector.

Elucidating the functional roles of prokaryotic proteins using big data and artificial intelligence

Annotating protein sequences according to their biological functions is one of the key steps in understanding microbial diversity, metabolic potentials, and evolutionary histories. However, even in the best-studied prokaryotic genomes, not all proteins can be characterized by classical in vivo, in vitro, and/or in silico methods-a challenge rapidly growing alongside the advent of next-generation sequencing technologies and their enormous extension of 'omics' data in public databases. These so-called hypothetical proteins (HPs) represent a huge knowledge gap and hidden potential for biotechnological applications. Opportunities for leveraging the available 'Big Data' have recently proliferated with the use of artificial intelligence (AI). Here, we review the aims and methods of protein annotation and explain the different principles behind machine and deep learning algorithms including recent research examples, in order to assist both biologists wishing to apply AI tools in developing comprehensive genome annotations and computer scientists who want to contribute to this leading edge of biological research.