Microbial Dark Matter: from Discovery to Applications

Throwing a spotlight on genomic dark matter: the power and potential of transposon-insertion sequencing

Linking genotype to phenotype is a central goal in biology. In the microbiological field, transposon mutagenesis is a technique that has been widely used since the 1970's to facilitate this connection. The development of modern 'omics approaches and next-generation sequencing, have allowed high-throughput association between genes and their putative function. In 2009, four different variations of modern transposon-insertion sequencing (TIS) approaches were published, being referred to as transposon-directed insertion-site sequencing (TraDIS), transposon sequencing (Tn-seq), insertion sequencing (INSeq) and high-throughput insertion tracking by deep sequencing (HITS). These approaches exploit a similar concept to allow estimation of the essentiality or contribution to fitness of each gene in any bacterial genome. The main rationale is to perform a comparative analysis of the abundance of specific transposon mutants under one or more selective conditions. The approaches themselves only vary in the transposon used for mutagenesis, and in the methodology used for sequencing library preparation. In this review, we discuss how TIS approaches have been used to facilitate a major shift in our fundamental understanding of bacterial biology in a range of areas. We focus on several aspects including pathogenesis, biofilm development, polymicrobial interactions in various ecosystems, and antimicrobial resistance. These studies have provided new insight into bacterial physiology and revealed predicted functions for hundreds of genes previously representing genomic 'dark matter'. We also discuss how TIS approaches have been used to understand complex bacterial systems and interactions and how future developments of TIS could continue to accelerate and enrich our understanding of bacterial biology.

Multimodal metagenomic analysis reveals microbial InDels as superior biomarkers for pediatric Crohn's disease

BACKGROUND AND AIMS

The gut microbiome is closely associated with pediatric Crohn's disease (CD), while the multidimensional microbial signature and their capabilities for distinguishing pediatric CD are underexplored. This study aims to characterize the microbial alterations in pediatric CD and develop a robust classification model.

METHODS

A total of 1175 fecal metagenomic sequencing samples, predominantly from 3 cohorts of pediatric CD patients, were re-analyzed from raw sequencing data using uniform process pipelines to obtain multidimensional microbial alterations in pediatric CD, including taxonomic profiles, functional profiles, and multi-type genetic variants. Random forest algorithms were used to construct classification models after comparing multiple machine learning algorithms.

RESULTS

We found pediatric CD samples exhibited reduced microbial diversity and unique microbial characteristics. Pronounced abundance differences in 45 species and 1357 KEGG orthology genes. Particularly, Enterocloster bolteae emerged as a pivotal pediatric CD-associated species. Additionally, we identified a vast amount of microbial genetic variants linked to pediatric CD, including 192 structural variants, 1256 insertions/deletions (InDels), and 3567 single nucleotide variants, with a considerable portion of these variants located in non-genic regions. The InDel-based model outperformed other predictive models against multidimensional microbial signatures, achieving an area under the ROC curve (AUC) of 0.982. The robustness and disease specificity were further confirmed in an independent CD cohort (AUC = 0.996) and 5 other microbiome-associated pediatric cohorts.

CONCLUSIONS

Our study provided a comprehensive landscape of microbial alterations in pediatric CD and introduced a highly effective diagnostic model rooted in microbial InDels, which contributes to the development of noninvasive diagnostic tools and targeted therapies.

A review of neural networks for metagenomic binning

One of the main goals of metagenomic studies is to describe the taxonomic diversity of microbial communities. A crucial step in metagenomic analysis is metagenomic binning, which involves the (supervised) classification or (unsupervised) clustering of metagenomic sequences. Various machine learning models have been applied to address this task. In this review, the contributions of artificial neural networks (ANN) in the context of metagenomic binning are detailed, addressing both supervised, unsupervised, and semi-supervised approaches. 34 ANN-based binning tools are systematically compared, detailing their architectures, input features, datasets, advantages, disadvantages, and other relevant aspects. The findings reveal that deep learning approaches, such as convolutional neural networks and autoencoders, achieve higher accuracy and scalability than traditional methods. Gaps in benchmarking practices are highlighted, and future directions are proposed, including standardized datasets and optimization of architectures, for third-generation sequencing. This review provides support to researchers in identifying trends and selecting suitable tools for the metagenomic binning problem.

Absolute quantification of the living skin microbiome overcomes relic-DNA bias and reveals specific patterns across volunteers

BACKGROUND

As the first line of defense against external pathogens, the skin and its resident microbiota are responsible for protection and eubiosis. Innovations in DNA sequencing have significantly increased our knowledge of the skin microbiome. However, current characterizations do not discriminate between DNA from live cells and remnant DNA from dead organisms (relic DNA), resulting in a combined readout of all microorganisms that were and are currently present on the skin rather than the actual living population of the microbiome. Additionally, most methods lack the capability for absolute quantification of the microbial load on the skin, complicating the extrapolation of clinically relevant information.

RESULTS

Here, we integrated relic-DNA depletion with shotgun metagenomics and bacterial load determination to quantify live bacterial cell abundances across different skin sites. Though we discovered up to 90% of microbial DNA from the skin to be relic DNA, we saw no significant effect of this on the relative abundances of taxa determined by shotgun sequencing. Relic-DNA depletion prior to sequencing strengthened underlying patterns between microbiomes across volunteers and reduced intraindividual similarity. We determined the absolute abundance and the fraction of population alive for several common skin taxa across body sites and found taxa-specific differential abundance of live bacteria across regions to be different from estimates generated by total DNA (live + dead) sequencing.

CONCLUSIONS

Our results reveal the significant bias relic DNA has on the quantification of low biomass samples like the skin. The reduced intraindividual similarity across samples following relic-DNA depletion highlights the bias introduced by traditional (total DNA) sequencing in diversity comparisons across samples. The divergent levels of cell viability measured across different skin sites, along with the inconsistencies in taxa differential abundance determined by total vs live cell DNA sequencing, suggest an important hypothesis for certain sites being susceptible to pathogen infection. Overall, our study demonstrates a characterization of the skin microbiome that overcomes relic-DNA bias to provide a baseline for live microbiota that will further improve mechanistic studies of infection, disease progression, and the design of therapies for the skin. Video Abstract.

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.

Differentiation of microbial communities in coastal seawater before and during an Akashiwo sanguinea (Dinophyceae) bloom in the urban area of Antofagasta city (northern Chile)

Harmful algal blooms (HABs) are highly relevant for public health, the economy, the ecosystem, and biodiversity stability in southern Chile (40° to 53°S), where they occur regularly and are frequently monitored. However, HAB events and their associated microbes in northern Chile (17° to 30°S) remain unknown and difficult to track due to a lack of monitoring, particularly in urban areas. We investigated changes in microbial communities in coastal seawater before and during an Akashiwo sanguinea bloom (B) at two sampling points in Antofagasta city (23°38'39S, 70°24'39W). Seawater samples (filtered at 1 and 0.2 μm) were collected during distinct bloom phases (control, prebloom, and in-bloom), and 16S and 18S rDNA gene libraries were constructed and analyzed via the DNA metabarcoding technique. Our findings indicate that species diversity within the 16S rDNA-based community was greater during the prebloom phase than during the control and in-bloom phases. Conversely, species diversity within the 18S rDNA-based community was lower during the in-bloom phase than during the control and prebloom phases. Independent of the sampling points and fractions, principal coordinate analysis (PCoA) revealed distinct differences in both the 16S and 18S rDNA-based communities between the analyzed bloom phases. Our analysis further revealed that the 16S rDNA-based community was dominated by the Flavobacteriaceae and Rhodobacteraceae families, whose abundance decreased and increased, respectively, during the bloom. As anticipated, the structure of the 18S rDNA-based community was predominantly governed by the Gymnodiniaceae family, specifically the Akashiwo genus, during the bloom. The differentiation in microbial communities was more pronounced in taxa with low abundances than in the dominant taxa. Additionally, co-occurrence network analysis revealed predominantly positive relationships within the microbial communities, particularly during the bloom event. Our analysis also identified several bacterial genera as keystone taxa within the microbial communities, notably members of Pseudomonadota and Bacteroidota.

The potential of including the microbiome as biomarker in population-based health studies: methods and benefits

The key role of our microbiome in influencing our health status, and its relationship with our environment and lifestyle or health behaviors, have been shown in the last decades. Therefore, the human microbiome has the potential to act as a biomarker or indicator of health or exposure to health risks in the general population, if information on the microbiome can be collected in population-based health surveys or cohorts. It could then be associated with epidemiological participant data such as demographic, clinical or exposure profiles. However, to our knowledge, microbiome sampling has not yet been included as biological evidence of health or exposure to health risks in large population-based studies representative of the general population. In this mini-review, we first highlight some practical considerations for microbiome sampling and analysis that need to be considered in the context of a population study. We then present some examples of topics where the microbiome could be included as biological evidence in population-based health studies for the benefit of public health, and how this could be developed in the future. In doing so, we aim to highlight the benefits of having microbiome data available at the level of the general population, combined with epidemiological data from health surveys, and hence how microbiological data could be used in the future to assess human health. We also stress the challenges that remain to be overcome to allow the use of this microbiome data in order to improve proactive public health policies.

Modulation of gut microbiome in response to the combination of Escherichia coli Nissle 1917 and sugars: a pilot study using host-free system reflecting impact on interpersonal microbiome

INTRODUCTION

The differential effects of probiotic, prebiotic, and synbiotic formulations on human health are dictated by the inter-individual gut microbial profile. The effects of probiotics such as Escherichia coli Nissle 1917 (ECN) on gut microbiota may vary according to the microbiome profiles of individuals and may be influenced by the presence of certain carbohydrates, which can impact microbial community structure and treatment results.

METHOD

Processed fecal samples from donors having contrasting lifestyles, dietary patterns, and disease histories were mixed with 5 × 106 CFU/mL ECN with or without 1% (w/v) sugars (glucose, galactose, or rice starch) in a host-free system. Post-incubation, 16 s rRNA sequencing was performed. Microbial diversity and taxonomic abundance were computed in relation to the probiotic, prebiotic, and synbiotic treatment effects and interpersonal microbiome variance.

RESULT

Baseline gut microbial profiles showed significant inter-individual variations. ECN treatment alone had a limited impact on the inter-personal gut microbial diversity and abundance. Prebiotics caused a substantial enrichment in Actinobacteria, but there were differences in the responses at the order and genus levels, with enrichment shown in Bifidobacterium, Collinsella, and Megasphaera. Subject B exhibited enrichment in Proteobacteria and Cyanobacteria, but subject A showed more diversified taxonomic alterations as a consequence of the synbiotic treatments. Despite negligible difference in the α-diversity, probiotic, prebiotic, and synbiotic treatments independently resulted in distinct segregation in microbial communities at the β-diversity level. The core microbiota was altered only under prebiotic and synbiotic treatment. Significant correlations primarily for minor phyla were identified under prebiotic and synbiotic treatment.

CONCLUSION

The interindividual microbiome composition strongly influences the effectiveness of personalized diet and treatment plans. The responsiveness to dietary strategies varies according to individual microbiome profiles influenced by health, diet, and lifestyle. Therefore, tailored approaches that consider individual microbiome compositions are crucial for maximizing gut health and treatment results.

Bacillus species are core microbiota of resistant maize cultivars that induce host metabolic defense against corn stalk rot

BACKGROUND

Microbes colonizing each compartment of terrestrial plants are indispensable for maintaining crop health. Although corn stalk rot (CSR) is a severe disease affecting maize (Zea mays) worldwide, the mechanisms underlying host-microbe interactions across vertical compartments in maize plants, which exhibit heterogeneous CSR-resistance, remain largely uncharacterized.

RESULTS

Here, we investigated the microbial communities associated with CSR-resistant and CSR-susceptible maize cultivars using multi-omics analysis coupled with experimental verification. Maize cultivars resistant to CSR reshaped the microbiota and recruited Bacillus species with three phenotypes against Fusarium graminearum including niche pre-emption, potential secretion of antimicrobial compounds, and no inhibition to alleviate pathogen stress. By inducing the expression of Tyrosine decarboxylase 1 (TYDC1), encoding an enzyme that catalyzes the production of tyramine and dopamine, Bacillus isolates that do not directly suppress pathogen infection induced the synthesis of berberine, an isoquinoline alkaloid that inhibits pathogen growth. These beneficial bacteria were recruited from the rhizosphere and transferred to the stems but not grains of the CSR-resistant plants.

CONCLUSIONS

The current study offers insight into how maize plants respond to and interact with their microbiome and lays the foundation for preventing and treating soil-borne pathogens. Video Abstract.

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.

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.

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.

Successful Isolation of Diverse Verrucomicrobiota Strains through the Duckweed-Microbes Co-cultivation Method

The "duckweed-microbes co-cultivation method" is a microbial isolation technique that effectively recovers diverse microbes, including rarely cultivated bacterial phyla, from environmental samples. In this method, aseptic duckweed and microbes collected from an environmental sample are co-cultivated for several days, and duckweed-associated microbes are then isolated from its roots using a conventional agar plate-based cultivation method. We herein propose several improvements to the method in order to specifically obtain members of the rarely cultivated bacterial phylum, Verrucomicrobiota. In systems using river water as the inoculum, the marked enrichment of Verrucomicrobiota was observed after 10 days of co-cultivation, particularly in the roots and co-cultivated media. We also successfully isolated 44 strains belonging to subdivisions 1, 3, and 4 of the phylum Verrucomicrobiota from these systems. This was achieved by changing the concentration of nitrogen in the co-cultivation medium, which is known to affect duckweed growth and/or metabolism, and by subjecting the fronds and co-cultivated media as well as the roots after co-cultivation to microbial isolation.

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.

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.

Marine Bioprospecting, Biocatalysis and Process Development

Oceans possess tremendous diversity in microbial life. The enzymatic machinery that marine bacteria present is the result of extensive evolution to assist cell survival under the harsh and continuously changing conditions found in the marine environment. Several bacterial cells and enzymes are already used at an industrial scale, but novel biocatalysts are still needed for sustainable industrial applications, with benefits for both public health and the environment. Metagenomic techniques have enabled the discovery of novel biocatalysts, biosynthetic pathways, and microbial identification without their cultivation. However, a key stage for application of novel biocatalysts is the need for rapid evaluation of the feasibility of the bioprocess. Cultivation of not-yet-cultured bacteria is challenging and requires new methodologies to enable growth of the bacteria present in collected environmental samples, but, once a bacterium is isolated, its enzyme activities are easily measured. High-throughput screening techniques have also been used successfully, and innovative in vitro screening platforms to rapidly identify relevant enzymatic activities continue to improve. Small-scale approaches and process integration could improve the study and development of new bioprocesses to produce commercially interesting products. In this work, the latest studies related to (i) the growth of marine bacteria under laboratorial conditions, (ii) screening techniques for bioprospecting, and (iii) bioprocess development using microreactors and miniaturized systems are reviewed and discussed.

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.