Microbial Dark Matter: from Discovery to Applications

Description of the first cultured representative of "Candidatus Synoicihabitans" genus, isolated from deep-sea sediment of South China Sea

In this study we describe the first cultured representative of Candidatus Synoicihabitans genus, a novel strain designated as LMO-M01T, isolated from deep-sea sediment of South China Sea. This bacterium is a facultative aerobe, Gram-negative, non-motile, and has a globular-shaped morphology, with light greenish, small, and circular colonies. Analysis of the 16S rRNA gene sequences of strain LMO-M01T showed less than 93% similarity to its closest cultured members. Furthermore, employing advanced phylogenomic methods such as comparative genome analysis, average nucleotide identity (ANI), average amino acids identity (AAI), and digital DNA-DNA hybridization (dDDH), placed this novel species within the candidatus genus Synoicihabitans of the family Opitutaceae, Phylum Verrucomicrobiota. The genomic analysis of strain LMO-M01T revealed 175 genes, encoding putative carbohydrate-active enzymes. This suggests its metabolic potential to degrade and utilize complex polysaccharides, indicating a significant role in carbon cycling and nutrient turnover in deep-sea sediment. In addition, the strain's physiological capacity to utilize diverse biopolymers such as lignin, xylan, starch, and agar as sole carbon source opens up possibilities for sustainable energy production and environmental remediation. Moreover, the genome sequence of this newly isolated strain has been identified across diverse ecosystems, including marine sediment, fresh water, coral, soil, plants, and activated sludge highlighting its ecological significance and adaptability to various environments. The recovery of strain LMO-M01T holds promise for taxonomical, ecological and biotechnological applications. Based on the polyphasic data, we propose that this ecologically important strain LMO-M01T represents a novel genus (previously Candidatus) within the family Opitutaceae of phylum Verrucomicrobiota, for which the name Synoicihabitans lomoniglobus gen. nov., sp. nov. was proposed. The type of strain is LMO-M01T (= CGMCC 1.61593T = KCTC 92913T).

Waste to resource: Mining antimicrobial peptides in sludge from metagenomes using machine learning

The emergence of antibiotic-resistant bacteria poses a huge threat to the treatment of infections. Antimicrobial peptides are a class of short peptides that widely exist in organisms and are considered as potential substitutes for traditional antibiotics. Here, we use metagenomics combined with machine learning to find antimicrobial peptides from environmental metagenomes and successfully obtained 16,044,909 predicted AMPs. We compared the abundance of potential antimicrobial peptides in natural environments and engineered environments, and found that engineered environments also have great potential. Further, we chose sludge as a typical engineered environmental sample, and tried to mine antimicrobial peptides from it. Through metaproteome analysis and correlation analysis, we mined 27 candidate AMPs from sludge. We successfully synthesized 25 peptides by chemical synthesis, and experimentally verified that 21 peptides had antibacterial activity against the 4 strains tested. Our work highlights the potential for mining new antimicrobial peptides from engineered environments and demonstrates the effectiveness of mining antimicrobial peptides from sludge.

Peeling off the layers from microbial dark matter (MDM): recent advances, future challenges, and opportunities

Microbes represent the most common organisms on Earth; however, less than 2% of microbial species in the environment can undergo cultivation for study under laboratory conditions, and the rest of the enigmatic, microbial world remains mysterious, constituting a kind of "microbial dark matter" (MDM). In the last two decades, remarkable progress has been made in culture-dependent and culture-independent techniques. More recently, studies of MDM have relied on culture-independent techniques to recover genetic material through either unicellular genomics or shotgun metagenomics to construct single-amplified genomes (SAGs) and metagenome-assembled genomes (MAGs), respectively, which provide information about evolution and metabolism. Despite the remarkable progress made in the past decades, the functional diversity of MDM still remains uncharacterized. This review comprehensively summarizes the recently developed culture-dependent and culture-independent techniques for characterizing MDM, discussing major challenges, opportunities, and potential applications. These activities contribute to expanding our knowledge of the microbial world and have implications for various fields including Biotechnology, Bioprospecting, Functional genomics, Medicine, Evolutionary and Planetary biology. Overall, this review aims to peel off the layers from MDM, shed light on recent advancements, identify future challenges, and illuminate the exciting opportunities that lie ahead in unraveling the secrets of this intriguing microbial realm.

Severe acute respiratory syndrome Coronavirus-2 GenoAnalyzer and mutagenic anomaly detector using FCMFI and NSCE

In order to deepen our understanding of the virus and help guide the creation of efficient therapies, this study uses artificial intelligence tools to thoroughly explore the genetic sequences of the SARS-CoV-2 virus. The process starts by using the Fuzzy Closure Miner for Frequent Itemsets (FCMFI) on a large corpus of SARS-CoV-2 genomic sequences to reveal hidden patterns, including nucleotides base sequences, repeating motifs, and corresponding interchanges. Then, using the Nucleotide Sequence Comprehension Engine (NSCE) technique, we were able to precisely define the genomic areas for mutation analysis. Structured and unstructured proteins are both strongly impacted by virus mutations, with spike proteins that are linked to the severity of COVID-19 pneumonia being particularly affected. Notably, the Mutagenic Anomaly Detector shows a 65 % efficiency boost in computing genome mutation rates compared to conventional point mutation analysis, while GenoAnalyzer offers a remarkable 93.33 % improvement over existing approaches in recognizing common genomic sequence patterns. These results highlight the potential of FCMFI to reveal complex genomic patterns and significant insights in COVID-19 genetic sequences when combined with mutation analysis. The Mutagenic Anomaly Detector and GenoAnalyzer show promise for revealing hidden genomic patterns and precisely estimating the SARS-CoV-2 mutation rate.

Modelling of microbial interactions in anaerobic digestion: from black to glass box

Anaerobic and microaerophilic environments are pervasive in nature, providing essential contributions to the maintenance of human health, biogeochemical cycles and the Earth's climate. These ecological niches are characterised by low free oxygen and oxidants, or lack thereof. Under these conditions, interactions between species are essential for supporting the growth of syntrophic species and maintaining thermodynamic feasibility of anaerobic fermentation. Kinetic models provide a simplified view of complex metabolic networks, while genome-scale metabolic models and flux-balance analysis (FBA) aim to unravel these systems as a whole. The target of this review is to outline the main similarities, differences and challenges associated with kinetic and metabolic modelling, and describe state-of-the-art modelling practices for studying syntrophies in the anaerobic digestion (AD) case study.

The Y-ome Conundrum: Insights into Uncharacterized Genes and Approaches for Functional Annotation

The ever-increasing availability of genome sequencing data has revealed a substantial number of uncharacterized genes without known functions across various organisms. The first comprehensive genome sequencing of E. coli K12 revealed that more than 50% of its open reading frames corresponded to transcripts with no known functions. The group of protein-coding genes without a functional description and/or a recognized pathway, beginning with the letter "Y", is classified as the "y-ome". Several efforts have been made to elucidate the functions of these genes and to recognize their role in biological processes. This review provides a brief update on various strategies employed when studying the y-ome, such as high-throughput experimental approaches, comparative omics, metabolic engineering, gene expression analysis, and data integration techniques. Additionally, we highlight recent advancements in functional annotation methods, including the use of machine learning, network analysis, and functional genomics approaches. Novel approaches are required to produce more precise functional annotations across the genome to reduce the number of genes with unknown functions.

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.

A thermophilic chemolithoautotrophic bacterial consortium suggests a mutual relationship between bacteria in extreme oligotrophic environments

A thermophilic, chemolithoautotrophic, and aerobic microbial consortium (termed carbonitroflex) growing in a nutrient-poor medium and an atmosphere containing N₂, O₂, CO₂, and CO is investigated as a model to expand our understanding of extreme biological systems. Here we show that the consortium is dominated by Carbonactinospora thermoautotrophica (strain StC), followed by Sphaerobacter thermophilus, Chelatococcus spp., and Geobacillus spp. Metagenomic analysis of the consortium reveals a mutual relationship among bacteria, with C. thermoautotrophica StC exhibiting carboxydotrophy and carbon-dioxide storage capacity. C. thermoautotrophica StC, Chelatococcus spp., and S. thermophilus harbor genes encoding CO dehydrogenase and formate oxidase. No pure cultures were obtained under the original growth conditions, indicating that a tightly regulated interactive metabolism might be required for group survival and growth in this extreme oligotrophic system. The breadwinner hypothesis is proposed to explain the metabolic flux model and highlight the vital role of C. thermoautotrophica StC (the sole keystone species and primary carbon producer) in the survival of all consortium members. Our data may contribute to the investigation of complex interactions in extreme environments, exemplifying the interconnections and dependency within microbial communities.

The microbiome-product colibactin hits unique cellular targets mediating host–microbe interaction

The human microbiota produces molecules that are evolved to interact with the diverse cellular machinery of both the host and microbes, mediating health and diseases. One of the most puzzling microbiome molecules is colibactin, a genotoxin encoded in some commensal and extraintestinal microbes and is implicated in initiating colorectal cancer. The colibactin cluster was discovered more than 15 years ago, and most of the research studies have been focused on revealing the biosynthesis and precise structure of the cryptic encoded molecule(s) and the mechanism of carcinogenesis. In 2022, the Balskus group revealed that colibactin not only hits targets in the eukaryotic cell machinery but also in the prokaryotic cell. To that end, colibactin crosslinks the DNA resulting in activation of the SOS signaling pathway, leading to prophage induction from bacterial lysogens and modulation of virulence genes in pathogenic species. These unique activities of colibactin highlight its ecological role in shaping gut microbial communities and further consequences that impact human health. This review dives in-depth into the molecular mechanisms underpinning colibactin cellular targets in eukaryotic and prokaryotic cells, aiming to understand the fine details of the role of secreted microbiome chemistry in mediating host–microbe and microbe–microbe interactions. This understanding translates into a better realization of microbiome potential and how this could be advanced to future microbiome-based therapeutics or diagnostic biomarkers.

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

Eight-hundred thousand to one trillion prokaryotic species may inhabit our planet. Yet, fewer than two-hundred thousand prokaryotic species have been described. This uncharted fraction of microbial diversity, and its undisclosed coding potential, is known as the "microbial dark matter" (MDM). Next-generation sequencing has allowed to collect a massive amount of genome sequence data, leading to unprecedented advances in the field of genomics. Still, harnessing new functional information from the genomes of uncultured prokaryotes is often limited by standard classification methods. These methods often rely on sequence similarity searches against reference genomes from cultured species. This hinders the discovery of unique genetic elements that are missing from the cultivated realm. It also contributes to the accumulation of prokaryotic gene products of unknown function among public sequence data repositories, highlighting the need for new approaches for sequencing data analysis and classification. Increasing evidence indicates that these proteins of unknown function might be a treasure trove of biotechnological potential. Here, we outline the challenges, opportunities, and the potential hidden within the functional dark matter (FDM) of prokaryotes. We also discuss the pitfalls surrounding molecular and computational approaches currently used to probe these uncharted waters, and discuss future opportunities for research and applications.