Current research on the prokaryotic low abundance taxa, the prokaryotic rare biosphere, is growing, leading to a greater understanding of the mechanisms underlying organismal rarity and its relevance in ecology. From this emerging knowledge it is possible to envision innovative approaches in biotechnology applicable to several sectors. Bioremediation and bioprospecting are two of the most promising areas where such approaches could find feasible implementation, involving possible new solutions to the decontamination of polluted sites and to the discovery of novel gene variants and pathways based on the attributes of rare microbial communities. Bioremediation can be improved through the realization that diverse rare species can grow abundant and degrade different pollutants or possibly transfer useful genes. Further, most of the prokaryotic diversity found in virtually all environments belongs in the rare biosphere and remains uncultivatable, suggesting great bioprospecting potential within this vast and understudied genetic pool. This Mini Review argues that knowledge of the ecophysiology of rare prokaryotes can aid the development of future, efficient biotechnology-based processes, products and services. However, this promise may only be fulfilled through improvements in (and optimal blending of) advanced microbial culturing and physiology, metagenomics, genome annotation and editing, and synthetic biology, to name a few areas of relevance. In the future, it will be important to understand how activity profiles relate with abundance, as some rare taxa can remain rare and increase activity, whereas other taxa can grow abundant. The metabolic mechanisms behind those patterns can be useful in designing biotechnological processes.

Research on marine microbial communities is growing, but studies are hard to compare because of variation in seawater sampling protocols. To help researchers in the inter-comparison of studies that use different seawater sampling methodologies, as well as to help them design future sampling campaigns, we developed the EuroMarine Open Science Exploration initiative (EMOSE). Within the EMOSE framework, we sampled thousands of liters of seawater from a single station in the NW Mediterranean Sea (Service d’Observation du Laboratoire Arago [SOLA], Banyuls-sur-Mer), during one single day. The resulting dataset includes multiple seawater processing approaches, encompassing different material-type kinds of filters (cartridge membrane and flat membrane), three different size fractionations (>0.22 µm, 0.22–3 µm, 3–20 µm and >20 µm), and a number of different seawater volumes ranging from 1 L up to 1000 L. We show that the volume of seawater that is filtered does not have a significant effect on prokaryotic and protist diversity, independently of the sequencing strategy. However, there was a clear difference in alpha and beta diversity between size fractions and between these and “whole water” (with no pre-fractionation). Overall, we recommend care when merging data from datasets that use filters of different pore size, but we consider that the type of filter and volume should not act as confounding variables for the tested sequencing strategies. To the best of our knowledge, this is the first time a publicly available dataset effectively allows for the clarification of the impact of marine microbiome methodological options across a wide range of protocols, including large-scale variations in sampled volume.

The microbial rare biosphere, composed of low-abundance microorganisms in a community, lacks a standardized delineation method for its definition. Currently, most studies rely on arbitrary thresholds to define the microbial rare biosphere (e.g., 0.1% relative abundance per sample), hampering comparisons across studies. To address this challenge, we present ulrb (Unsupervised Learning based Definition of the Rare Biosphere), available as an R package. ulrb uses unsupervised machine learning to optimally classify taxa into abundance categories (e.g., rare, intermediate, or abundant) within microbial communities. We show that ulrb is more consistent than threshold-based approaches and can be applied to data derived from common microbial ecology protocols and non-microbial studies. ulrb can be used to identify different types of rarity and is statistically valid for the analysis of various dataset sizes. In conclusion, ulrb discerns rare from abundant organisms in a user-independent manner, finding applicability in selected ecological datasets.

Bacterial natural products (NPs) are still a major source of new drug leads. Polyketides (PKs) and non-ribosomal peptides (NRP) are two pharmaceutically important families of NPs and recent studies have revealed Antarctica to harbor endemic polyketide synthase (PKS) and non-ribosomal peptide synthetase (NRPS) genes, likely to be involved in the production of novel metabolites. Despite this, the diversity of secondary metabolites genes in Antarctica is still poorly explored. In this study, a computational bioprospection approach was employed to study the diversity and identity of PKS and NRPS genes to one of the most biodiverse areas in maritime Antarctica—Maxwell Bay. Amplicon sequencing of soil samples targeting ketosynthase (KS) and adenylation (AD) domains of PKS and NRPS genes, respectively, revealed abundant and unexplored chemical diversity in this peninsula. About 20% of AD domain sequences were only distantly related to characterized biosynthetic genes. Several PKS and NRPS genes were found to be closely associated to recently described metabolites including those from uncultured and candidate phyla. The combination of new approaches in computational biology and new culture-dependent and -independent strategies is thus critical for the recovery of the potential novel chemistry encoded in Antarctica microorganisms.

Background: High-throughput sequencing of the full-length 16S rRNA gene has improved the taxonomic classification of prokaryotes found in natural environments. However, sequencing of shorter regions from the same gene, like the V4-V5 region, can provide more cost-effective high throughput. It is unclear which approach best describes prokaryotic communities from underexplored environments. In this study, we hypothesize that high-throughput full-length 16S rRNA gene sequencing combined with adequate taxonomic databases improves the taxonomic description of prokaryotic communities from underexplored environments in comparison with high-throughput sequencing of a short region of the 16S rRNA gene. Results: To test our hypothesis, we compared taxonomic profiles of seawater samples from the Arctic Ocean using: full-length and V4-V5 16S rRNA gene sequencing in combination with either the Genome Taxonomy Database (GTDB) or the Silva taxonomy database. Our results show that all combinations of sequencing strategies and taxonomic databases present similar results at higher taxonomic levels. However, at lower taxonomic levels, namely family, genus, and most notably species level, the full-length approach led to higher proportions of Amplicon Sequence Variants (ASVs) assigned to formally valid taxa. Hence, the best taxonomic description was obtained by the full-length and GTDB combination, which in some cases allowed for the identification of intraspecific diversity of ASVs. Conclusions: We conclude that coupling high-throughput full-length 16S rRNA gene sequencing with GTDB improves the description of microbiome profiling at lower taxonomic ranks. The improvements reported here provide more context for scientists to discuss microbial community dynamics within a solid taxonomic framework in environments like the Arctic Ocean with still underrepresented microbiome sequences in public databases.