Large-Scale Analyses of Human Microbiomes Reveal Thousands of Small, Novel Genes

Dual quorum-sensing control of purine biosynthesis drives pathogenic fitness of Enterococcus faecalis

Enterococcus faecalis is a resident of the human gut, though upon translocation to the blood or body tissues, it can be pathogenic. Here we discover and characterize two peptide-based quorum-sensing systems that transcriptionally modulate de novo purine biosynthesis in E. faecalis . Using a comparative genomic analysis, we find that most enterococcal species do not encode this system; E. moraviensis , E. haemoperoxidus and E. caccae , three species that are closely related to E. faecalis , encode one of the two systems, and only E. faecalis encodes both systems. We show that these systems are important for the intracellular survival of E. faecalis within macrophages and for the fitness of E. faecalis in a murine wound infection model. Taken together, we combine comparative genomics, microbiological, bacterial genetics, transcriptomics, targeted proteomics and animal model experiments to describe a paired quorum sensing mechanism that directly influences central metabolism and impacts the pathogenicity of E. faecalis .

Large-scale investigation of species-specific orphan genes in the human gut microbiome elucidates their evolutionary origins

Species-specific genes, also known as orphans, are ubiquitous across life's domains. In prokaryotes, species-specific orphan genes (SSOGs) are mostly thought to originate in external elements such as viruses followed by horizontal gene transfer, whereas the scenario of native origination, through rapid divergence or de novo, is mostly dismissed. However, quantitative evidence supporting either scenario is lacking. Here, we systematically analyzed genomes from 4644 human gut microbiome species and identified more than 600,000 unique SSOGs, representing an average of 2.6% of a given species' pangenome. These sequences are mostly rare within each species yet show signs of purifying selection. Overall, SSOGs use optimal codons less frequently, and their proteins are more disordered than those of conserved genes (i.e., non-SSOGs). Importantly, across species, the GC content of SSOGs closely matches that of conserved ones. In contrast, the ∼5% of SSOGs that share similarity to known viral sequences have distinct characteristics, including lower GC content. Thus, SSOGs with similarity to viruses differ from the remaining SSOGs, contrasting an external origination scenario for most of them. By examining the orthologous genomic region in closely related species, we show that a small subset of SSOGs likely evolved natively de novo and find that these genes also differ in their properties from the remaining SSOGs. Our results challenge the notion that external elements are the dominant source of prokaryotic genetic novelty and will enable future studies into the biological role and relevance of species-specific genes in the human gut.

Discovery of antimicrobial peptides in the global microbiome with machine learning

Novel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machine-learning-based approach to predict antimicrobial peptides (AMPs) within the global microbiome and leverage a vast dataset of 63,410 metagenomes and 87,920 prokaryotic genomes from environmental and host-associated habitats to create the AMPSphere, a comprehensive catalog comprising 863,498 non-redundant peptides, few of which match existing databases. AMPSphere provides insights into the evolutionary origins of peptides, including by duplication or gene truncation of longer sequences, and we observed that AMP production varies by habitat. To validate our predictions, we synthesized and tested 100 AMPs against clinically relevant drug-resistant pathogens and human gut commensals both in vitro and in vivo. A total of 79 peptides were active, with 63 targeting pathogens. These active AMPs exhibited antibacterial activity by disrupting bacterial membranes. In conclusion, our approach identified nearly one million prokaryotic AMP sequences, an open-access resource for antibiotic discovery.

PSPI: A deep learning approach for prokaryotic small protein identification

Small Proteins (SPs) are pivotal in various cellular functions such as immunity, defense, and communication. Despite their significance, identifying them is still in its infancy. Existing computational tools are tailored to specific eukaryotic species, leaving only a few options for SP identification in prokaryotes. In addition, these existing tools still have suboptimal performance in SP identification. To fill this gap, we introduce PSPI, a deep learning-based approach designed specifically for predicting prokaryotic SPs. We showed that PSPI had a high accuracy in predicting generalized sets of prokaryotic SPs and sets specific to the human metagenome. Compared with three existing tools, PSPI was faster and showed greater precision, sensitivity, and specificity not only for prokaryotic SPs but also for eukaryotic ones. We also observed that the incorporation of (n, k)-mers greatly enhances the performance of PSPI, suggesting that many SPs may contain short linear motifs. The PSPI tool, which is freely available at https://www.cs.ucf.edu/∼xiaoman/tools/PSPI/, will be useful for studying SPs as a tool for identifying prokaryotic SPs and it can be trained to identify other types of SPs as well.

Sequencing-based analysis of microbiomes

Microbiomes occupy a range of niches and, in addition to having diverse compositions, they have varied functional roles that have an impact on agriculture, environmental sciences, and human health and disease. The study of microbiomes has been facilitated by recent technological and analytical advances, such as cheaper and higher-throughput DNA and RNA sequencing, improved long-read sequencing and innovative computational analysis methods. These advances are providing a deeper understanding of microbiomes at the genomic, transcriptional and translational level, generating insights into their function and composition at resolutions beyond the species level.

Structural features and substrate engagement in peptide-modifying radical SAM enzymes

In recent years, the biological significance of ribosomally synthesized, post-translationally modified peptides (RiPPs) and the intriguing chemistry catalyzed by their tailoring enzymes has garnered significant attention. A subgroup of bacterial radical S-adenosylmethionine (rSAM) enzymes can activate C-H bonds in peptides, which leads to the production of a diverse range of RiPPs. The remarkable ability of these enzymes to facilitate various chemical processes, to generate and harbor high-energy radical species, and to accommodate large substrates with a high degree of flexibility is truly intriguing. The wide substrate scope and diversity of the chemistry performed by rSAM enzymes raise one question: how does the protein environment facilitate these distinct chemical conversions while sharing a similar structural fold? In this review, we discuss recent advances in the field of RiPP-rSAM enzymes, with a particular emphasis on domain architectures and substrate engagements identified by biophysical and structural characterizations. We provide readers with a comparative analysis of six examples of RiPP-rSAM enzymes with experimentally characterized structures. Linking the structural elements and the nature of rSAM-catalyzed RiPP production will provide insight into the functional engineering of enzyme activity to harness their catalytic power in broader applications.

Challenges in computational discovery of bioactive peptides in 'omics data

Peptides have a plethora of activities in biological systems that can potentially be exploited biotechnologically. Several peptides are used clinically, as well as in industry and agriculture. The increase in available 'omics data has recently provided a large opportunity for mining novel enzymes, biosynthetic gene clusters, and molecules. While these data primarily consist of DNA sequences, other types of data provide important complementary information. Due to their size, the approaches proven successful at discovering novel proteins of canonical size cannot be naïvely applied to the discovery of peptides. Peptides can be encoded directly in the genome as short open reading frames (smORFs), or they can be derived from larger proteins by proteolysis. Both of these peptide classes pose challenges as simple methods for their prediction result in large numbers of false positives. Similarly, functional annotation of larger proteins, traditionally based on sequence similarity to infer orthology and then transferring functions between characterized proteins and uncharacterized ones, cannot be applied for short sequences. The use of these techniques is much more limited and alternative approaches based on machine learning are used instead. Here, we review the limitations of traditional methods as well as the alternative methods that have recently been developed for discovering novel bioactive peptides with a focus on prokaryotic genomes and metagenomes.

Identification of 3-ketocapnine reductase activity within the human microbiota

The microbial synthesis of sulfonolipids within the human body is likely involved in maintaining human health or causing diseases. However, the enzymes responsible for their biosynthesis remain largely unknown. In this study, we identified and verified the role of 3-ketocapnine reductase, the third-step enzyme, in the four-step conversion of l-phosphoserine into sulfobacin B both in vivo and in vitro. This finding builds upon our previous research into sulfonolipid biosynthesis, which focused on the vaginal bacterium Chryseobacterium gleum DSM 16776 and the gut bacterium Alistipes finegoldii DSM 17242. Through comprehensive gene mapping, we demonstrate the widespread presence of potential sulfonolipid biosynthetic genes across diverse bacterial species inhabiting various regions of the human body. These findings shed light on the prevalence of sulfonolipid-like metabolites within the human microbiota, suggesting a potential role for these lipid molecules in influencing the intricate biointeractions within the complex microbial ecosystem of the human body.

Exploring the roles of ribosomal peptides in prokaryote-phage interactions through deep learning-enabled metagenome mining

BACKGROUND

Microbial secondary metabolites play a crucial role in the intricate interactions within the natural environment. Among these metabolites, ribosomally synthesized and post-translationally modified peptides (RiPPs) are becoming a promising source of therapeutic agents due to their structural diversity and functional versatility. However, their biosynthetic capacity and ecological functions remain largely underexplored.

RESULTS

Here, we aim to explore the biosynthetic profile of RiPPs and their potential roles in the interactions between microbes and viruses in the ocean, which encompasses a vast diversity of unique biomes that are rich in interactions and remains chemically underexplored. We first developed TrRiPP to identify RiPPs from ocean metagenomes, a deep learning method that detects RiPP precursors in a hallmark gene-independent manner to overcome the limitations of classic methods in processing highly fragmented metagenomic data. Applying this method to metagenomes from the global ocean microbiome, we uncover a diverse array of previously uncharacterized putative RiPP families with great novelty and diversity. Through correlation analysis based on metatranscriptomic data, we observed a high prevalence of antiphage defense-related and phage-related protein families that were co-expressed with RiPP families. Based on this putative association between RiPPs and phage infection, we constructed an Ocean Virus Database (OVD) and established a RiPP-involving host-phage interaction network through host prediction and co-expression analysis, revealing complex connectivities linking RiPP-encoding prokaryotes, RiPP families, viral protein families, and phages. These findings highlight the potential of RiPP families involved in prokaryote-phage interactions and coevolution, providing insights into their ecological functions in the ocean microbiome.

CONCLUSIONS

This study provides a systematic investigation of the biosynthetic potential of RiPPs from the ocean microbiome at a global scale, shedding light on the essential insights into the ecological functions of RiPPs in prokaryote-phage interactions through the integration of deep learning approaches, metatranscriptomic data, and host-phage connectivity. This study serves as a valuable example of exploring the ecological functions of bacterial secondary metabolites, particularly their associations with unexplored microbial interactions. Video Abstract.

A survey of experimental and computational identification of small proteins

Small proteins (SPs) are typically characterized as eukaryotic proteins shorter than 100 amino acids and prokaryotic proteins shorter than 50 amino acids. Historically, they were disregarded because of the arbitrary size thresholds to define proteins. However, recent research has revealed the existence of many SPs and their crucial roles. Despite this, the identification of SPs and the elucidation of their functions are still in their infancy. To pave the way for future SP studies, we briefly introduce the limitations and advancements in experimental techniques for SP identification. We then provide an overview of available computational tools for SP identification, their constraints, and their evaluation. Additionally, we highlight existing resources for SP research. This survey aims to initiate further exploration into SPs and encourage the development of more sophisticated computational tools for SP identification in prokaryotes and microbiomes.

Natural products from the human microbiome: an emergent frontier in organic synthesis and drug discovery

Often referred to as the "second genome", the human microbiome is at the epicenter of complex inter-habitat biochemical networks like the "gut-brain axis", which has emerged as a significant determinant of cognition, overall health and well-being, as well as resistance to antibiotics and susceptibility to diseases. As part of a broader understanding of the nexus between the human microbiome, diseases and microbial interactions, whether encoded secondary metabolites (natural products) play crucial signalling roles has been the subject of intense scrutiny in the recent past. A major focus of these activities involves harvesting the genomic potential of the human microbiome via bioinformatics guided genome mining and culturomics. Through these efforts, an impressive number of structurally intriguing antibiotics, with enhanced chemical diversity vis-à-vis conventional antibiotics have been isolated from human commensal bacteria, thereby generating considerable interest in their total synthesis and expanding their therapeutic space for drug discovery. These developments augur well for the discovery of new drugs and antibiotics, particularly in the context of challenges posed by mycobacterial resistance and emerging new diseases. The current landscape of various synthetic campaigns and drug discovery initiatives on antibacterial natural products from the human microbiome is captured in this review with an intent to stimulate further activities in this interdisciplinary arena among the new generation.

Blap-6, a Novel Antifungal Peptide from the Chinese Medicinal Beetle Blaps rhynchopetera against Cryptococcus neoformans

Cryptococcus neoformans (C. neoformans) is a pathogenic fungus that can cause life-threatening meningitis, particularly in individuals with compromised immune systems. The current standard treatment involves the combination of amphotericin B and azole drugs, but this regimen often leads to inevitable toxicity in patients. Therefore, there is an urgent need to develop new antifungal drugs with improved safety profiles. We screened antimicrobial peptides from the hemolymph transcriptome of Blaps rhynchopetera (B. rhynchopetera), a folk Chinese medicine. We found an antimicrobial peptide named blap-6 that exhibited potent activity against bacteria and fungi. Blap-6 is composed of 17 amino acids (KRCRFRIYRWGFPRRRF), and it has excellent antifungal activity against C. neoformans, with a minimum inhibitory concentration (MIC) of 0.81 μM. Blap-6 exhibits strong antifungal kinetic characteristics. Mechanistic studies revealed that blap-6 exerts its antifungal activity by penetrating and disrupting the integrity of the fungal cell membrane. In addition to its direct antifungal effect, blap-6 showed strong biofilm inhibition and scavenging activity. Notably, the peptide exhibited low hemolytic and cytotoxicity to human cells and may be a potential candidate antimicrobial drug for fungal infection caused by C. neoformans.

sOCP: a framework predicting smORF coding potential based on TIS and in-frame features and effectively applied in the human genome

Small open reading frames (smORFs) have been acknowledged to play various roles on essential biological pathways and affect human beings from diabetes to tumorigenesis. Predicting smORFs in silico is quite a prerequisite for processing the omics data. Here, we proposed the smORF-coding-potential-predicting framework, sOCP, which provides functions to construct a model for predicting novel smORFs in some species. The sOCP model constructed in human was based on in-frame features and the nucleotide bias around the start codon, and the small feature subset was proved to be competent enough and avoid overfitting problems for complicated models. It showed more advanced prediction metrics than previous methods and could correlate closely with experimental evidence in a heterogeneous dataset. The model was applied to Rattus norvegicus and exhibited satisfactory performance. We then scanned smORFs with ATG and non-ATG start codons from the human genome and generated a database containing about a million novel smORFs with coding potential. Around 72 000 smORFs are located on the lncRNA regions of the genome. The smORF-encoded peptides may be involved in biological pathways rare for canonical proteins, including glucocorticoid catabolic process and the prokaryotic defense system. Our work provides a model and database for human smORF investigation and a convenient tool for further smORF prediction in other species.

ProTInSeq: transposon insertion tracking by ultra-deep DNA sequencing to identify translated large and small ORFs

Identifying open reading frames (ORFs) being translated is not a trivial task. ProTInSeq is a technique designed to characterize proteomes by sequencing transposon insertions engineered to express a selection marker when they occur in-frame within a protein-coding gene. In the bacterium Mycoplasma pneumoniae, ProTInSeq identifies 83% of its annotated proteins, along with 5 proteins and 153 small ORF-encoded proteins (SEPs; ≤100 aa) that were not previously annotated. Moreover, ProTInSeq can be utilized for detecting translational noise, as well as for relative quantification and transmembrane topology estimation of fitness and non-essential proteins. By integrating various identification approaches, the number of initially annotated SEPs in this bacterium increases from 27 to 329, with a quarter of them predicted to possess antimicrobial potential. Herein, we describe a methodology complementary to Ribo-Seq and mass spectroscopy that can identify SEPs while providing other insights in a proteome with a flexible and cost-effective DNA ultra-deep sequencing approach.

The Cryptic Bacterial Microproteome

Microproteins encoded by small open reading frames (smORFs) comprise the "dark matter" of proteomes. Although functional microproteins were identified in diverse organisms from all three domains of life, bacterial smORFs remain poorly characterized. In this comprehensive study of intergenic smORFs (ismORFs, 15-70 codons) in 5,668 bacterial genomes of the family Enterobacteriaceae, we identified 67,297 clusters of ismORFs subject to purifying selection. The ismORFs mainly code for hydrophobic, potentially transmembrane, unstructured, or minimally structured microproteins. Using AlphaFold Multimer, we predicted interactions of some of the predicted microproteins encoded by transcribed ismORFs with proteins encoded by neighboring genes, revealing the potential of microproteins to regulate the activity of various proteins, particularly, under stress. We compiled a catalog of predicted microprotein families with different levels of evidence from synteny analysis, structure prediction, and transcription and translation data. This study offers a resource for investigation of biological functions of microproteins.

Microbiota-gut-brain axis and its therapeutic applications in neurodegenerative diseases

The human gastrointestinal tract is populated with a diverse microbial community. The vast genetic and metabolic potential of the gut microbiome underpins its ubiquity in nearly every aspect of human biology, including health maintenance, development, aging, and disease. The advent of new sequencing technologies and culture-independent methods has allowed researchers to move beyond correlative studies toward mechanistic explorations to shed light on microbiome-host interactions. Evidence has unveiled the bidirectional communication between the gut microbiome and the central nervous system, referred to as the "microbiota-gut-brain axis". The microbiota-gut-brain axis represents an important regulator of glial functions, making it an actionable target to ameliorate the development and progression of neurodegenerative diseases. In this review, we discuss the mechanisms of the microbiota-gut-brain axis in neurodegenerative diseases. As the gut microbiome provides essential cues to microglia, astrocytes, and oligodendrocytes, we examine the communications between gut microbiota and these glial cells during healthy states and neurodegenerative diseases. Subsequently, we discuss the mechanisms of the microbiota-gut-brain axis in neurodegenerative diseases using a metabolite-centric approach, while also examining the role of gut microbiota-related neurotransmitters and gut hormones. Next, we examine the potential of targeting the intestinal barrier, blood-brain barrier, meninges, and peripheral immune system to counteract glial dysfunction in neurodegeneration. Finally, we conclude by assessing the pre-clinical and clinical evidence of probiotics, prebiotics, and fecal microbiota transplantation in neurodegenerative diseases. A thorough comprehension of the microbiota-gut-brain axis will foster the development of effective therapeutic interventions for the management of neurodegenerative diseases.

CRISPR-based screening of small RNA modulators of bile susceptibility in Bacteroides thetaiotaomicron

Microbiota-centric interventions are limited by our incomplete understanding of the gene functions of many of its constituent species. This applies in particular to small RNAs (sRNAs), which are emerging as important regulators in microbiota species yet tend to be missed by traditional functional genomics approaches. Here, we establish CRISPR interference (CRISPRi) in the abundant microbiota member Bacteroides thetaiotaomicron for genome-wide sRNA screens. By assessing the abundance of different protospacer-adjacent motifs, we identify the Prevotella bryantii B14 Cas12a as a suitable nuclease for CRISPR screens in these bacteria and generate an inducible Cas12a expression system. Using a luciferase reporter strain, we infer guide design rules and use this knowledge to assemble a computational pipeline for automated gRNA design. By subjecting the resulting guide library to a phenotypic screen, we uncover the sRNA BatR to increase susceptibility to bile salts through the regulation of genes involved in Bacteroides cell surface structure. Our study lays the groundwork for unlocking the genetic potential of these major human gut mutualists and, more generally, for identifying hidden functions of bacterial sRNAs.

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.

Randomly barcoded transposon mutant libraries for gut commensals I: Strategies for efficient library construction

Randomly barcoded transposon mutant libraries are powerful tools for studying gene function and organization, assessing gene essentiality and pathways, discovering potential therapeutic targets, and understanding the physiology of gut bacteria and their interactions with the host. However, construction of high-quality libraries with uniform representation can be challenging. In this review, we survey various strategies for barcoded library construction, including transposition systems, methods of transposon delivery, optimal library size, and transconjugant selection schemes. We discuss the advantages and limitations of each approach, as well as factors to consider when selecting a strategy. In addition, we highlight experimental and computational advances in arraying condensed libraries from mutant pools. We focus on examples of successful library construction in gut bacteria and their application to gene function studies and drug discovery. Given the need for understanding gene function and organization in gut bacteria, we provide a comprehensive guide for researchers to construct randomly barcoded transposon mutant libraries.

Identification of proteoforms of short open reading frame-encoded peptides in Blautia producta under different cultivation conditions

IMPORTANCE

The identification of short open reading frame-encoded peptides (SEP) and different proteoforms in single cultures of gut microbes offers new insights into a largely neglected part of the microbial proteome landscape. This is of particular importance as SEP provide various predicted functions, such as acting as antimicrobial peptides, maintaining cell homeostasis under stress conditions, or even contributing to the virulence pattern. They are, thus, taking a poorly understood role in structure and function of microbial networks in the human body. A better understanding of SEP in the context of human health requires a precise understanding of the abundance of SEP both in commensal microbes as well as pathogens. For the gut beneficial B. producta, we demonstrate the importance of specific environmental conditions for biosynthesis of SEP expanding previous findings about their role in microbial interactions.

Examining the functional space of gut microbiome-derived peptides

The human gut microbiome contains thousands of small, novel peptides that could play a role in microbe-microbe and host-microbe interactions, contributing to human health and disease. Although these peptides have not yet been systematically characterized, computational tools can be used to elucidate the bioactivities they may have. This article proposes probing the functional space of gut microbiome-derived peptides (MDPs) using in silico approaches for three bioactivities: antimicrobial, anticancer, and nucleomodulins. Machine learning programs that support peptide and protein queries are provided for each bioactivity. Considering the biases of an activity-centric approach, activity-agnostic tools using structural and chemical similarity and target prediction are also described. Gut MDPs represent a vast functional space that can not only contribute to our understanding of microbiome interactions but potentially even serve as a source of life-changing therapeutics.

Subcellular localization and relevant mechanisms of human cancer-related micropeptides

Rapid advances in high-quality sequencing and bioinformatics have invalidated the argument that noncoding RNAs (ncRNAs) are junk transcripts that do not encode proteins. Increasing evidence suggests that small open reading frames (sORFs) in ncRNAs can encode micropeptides and polypeptides within 100 amino acids in length. Several micropeptides have been characterized and proven to have various functions in human physiology and pathology, particularly in cancer. The present review mainly highlights the latest studies on ncRNA-encoded micropeptides in different cancers and categorizes them based on their subcellular localization, thereby providing a theoretical basis for micropeptide applications in the early diagnosis and prognosis of cancer and as therapeutic targets. However, considering the inherent characteristics of micropeptides and the limitations of the assay technology methods, more detailed information is warranted.

Small proteins in bacteria - Big challenges in prediction and identification

Proteins with up to 100 amino acids have been largely overlooked due to the challenges associated with predicting and identifying them using traditional methods. Recent advances in bioinformatics and machine learning, DNA sequencing, RNA and Ribo-seq technologies, and mass spectrometry (MS) have greatly facilitated the detection and characterisation of these elusive proteins in recent years. This has revealed their crucial role in various cellular processes including regulation, signalling and transport, as toxins and as folding helpers for protein complexes. Consequently, the systematic identification and characterisation of these proteins in bacteria have emerged as a prominent field of interest within the microbial research community. This review provides an overview of different strategies for predicting and identifying these proteins on a large scale, leveraging the power of these advanced technologies. Furthermore, the review offers insights into the future developments that may be expected in this field.

Selection of a de novo gene that can promote survival of Escherichia coli by modulating protein homeostasis pathways

Cellular novelty can emerge when non-functional loci become functional genes in a process termed de novo gene birth. But how proteins with random amino acid sequences beneficially integrate into existing cellular pathways remains poorly understood. We screened ~108 genes, generated from random nucleotide sequences and devoid of homology to natural genes, for their ability to rescue growth arrest of Escherichia coli cells producing the ribonuclease toxin MazF. We identified ~2,000 genes that could promote growth, probably by reducing transcription from the promoter driving toxin expression. Additionally, one random protein, named Random antitoxin of MazF (RamF), modulated protein homeostasis by interacting with chaperones, leading to MazF proteolysis and a consequent loss of its toxicity. Finally, we demonstrate that random proteins can improve during evolution by identifying beneficial mutations that turned RamF into a more efficient inhibitor. Our work provides a mechanistic basis for how de novo gene birth can produce functional proteins that effectively benefit cells evolving under stress.

Microproteins-Discovery, structure, and function

Advances in proteogenomic technologies have revealed hundreds to thousands of translated small open reading frames (sORFs) that encode microproteins in genomes across evolutionary space. While many microproteins have now been shown to play critical roles in biology and human disease, a majority of recently identified microproteins have little or no experimental evidence regarding their functionality. Computational tools have some limitations for analysis of short, poorly conserved microprotein sequences, so additional approaches are needed to determine the role of each member of this recently discovered polypeptide class. A currently underexplored avenue in the study of microproteins is structure prediction and determination, which delivers a depth of functional information. In this review, we provide a brief overview of microprotein discovery methods, then examine examples of microprotein structures (and, conversely, intrinsic disorder) that have been experimentally determined using crystallography, cryo-electron microscopy, and NMR, which provide insight into their molecular functions and mechanisms. Additionally, we discuss examples of predicted microprotein structures that have provided insight or context regarding their function. Analysis of microprotein structure at the angstrom level, and confirmation of predicted structures, therefore, has potential to identify translated microproteins that are of biological importance and to provide molecular mechanism for their in vivo roles.

StORF-Reporter: finding genes between genes

Large regions of prokaryotic genomes are currently without any annotation, in part due to well-established limitations of annotation tools. For example, it is routine for genes using alternative start codons to be misreported or completely omitted. Therefore, we present StORF-Reporter, a tool that takes an annotated genome and returns regions that may contain missing CDS genes from unannotated regions. StORF-Reporter consists of two parts. The first begins with the extraction of unannotated regions from an annotated genome. Next, Stop-ORFs (StORFs) are identified in these unannotated regions. StORFs are open reading frames that are delimited by stop codons and thus can capture those genes most often missing in genome annotations. We show this methodology recovers genes missing from canonical genome annotations. We inspect the results of the genomes of model organisms, the pangenome of Escherichia coli, and a set of 5109 prokaryotic genomes of 247 genera from the Ensembl Bacteria database. StORF-Reporter extended the core, soft-core and accessory gene collections, identified novel gene families and extended families into additional genera. The high levels of sequence conservation observed between genera suggest that many of these StORFs are likely to be functional genes that should now be considered for inclusion in canonical annotations.

The inhibitory mechanism of a small protein reveals its role in antimicrobial peptide sensing

A large number of small membrane proteins have been uncovered in bacteria, but their mechanism of action has remained mostly elusive. Here, we investigate the mechanism of a physiologically important small protein, MgrB, which represses the activity of the sensor kinase PhoQ and is widely distributed among enterobacteria. The PhoQ/PhoP two-component system is a master regulator of the bacterial virulence program and interacts with MgrB to modulate bacterial virulence, fitness, and drug resistance. A combination of cross-linking approaches with functional assays and protein dynamic simulations revealed structural rearrangements due to interactions between MgrB and PhoQ near the membrane/periplasm interface and along the transmembrane helices. These interactions induce the movement of the PhoQ catalytic domain and the repression of its activity. Without MgrB, PhoQ appears to be much less sensitive to antimicrobial peptides, including the commonly used C18G. In the presence of MgrB, C18G promotes MgrB to dissociate from PhoQ, thus activating PhoQ via derepression. Our findings reveal the inhibitory mechanism of the small protein MgrB and uncover its importance in antimicrobial peptide sensing.

HiFun: homology independent protein function prediction by a novel protein-language self-attention model

Protein function prediction based on amino acid sequence alone is an extremely challenging but important task, especially in metagenomics/metatranscriptomics field, in which novel proteins have been uncovered exponentially from new microorganisms. Many of them are extremely low homology to known proteins and cannot be annotated with homology-based or information integrative methods. To overcome this problem, we proposed a Homology Independent protein Function annotation method (HiFun) based on a unified deep-learning model by reassembling the sequence as protein language. The robustness of HiFun was evaluated using the benchmark datasets and metrics in the CAFA3 challenge. To navigate the utility of HiFun, we annotated 2 212 663 unknown proteins and discovered novel motifs in the UHGP-50 catalog. We proved that HiFun can extract latent function related structure features which empowers it ability to achieve function annotation for non-homology proteins. HiFun can substantially improve newly proteins annotation and expand our understanding of microorganisms' adaptation in various ecological niches. Moreover, we provided a free and accessible webservice at http://www.unimd.org/HiFun, requiring only protein sequences as input, offering researchers an efficient and practical platform for predicting protein functions.

Computational exploration of the global microbiome for antibiotic discovery

Novel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machine learning-based approach to predict prokaryotic antimicrobial peptides (AMPs) by leveraging a vast dataset of 63,410 metagenomes and 87,920 microbial genomes. This led to the creation of AMPSphere, a comprehensive catalog comprising 863,498 non-redundant peptides, the majority of which were previously unknown. We observed that AMP production varies by habitat, with animal-associated samples displaying the highest proportion of AMPs compared to other habitats. Furthermore, within different human-associated microbiota, strain-level differences were evident. To validate our predictions, we synthesized and experimentally tested 50 AMPs, demonstrating their efficacy against clinically relevant drug-resistant pathogens both in vitro and in vivo. These AMPs exhibited antibacterial activity by targeting the bacterial membrane. Additionally, AMPSphere provides valuable insights into the evolutionary origins of peptides. In conclusion, our approach identified AMP sequences within prokaryotic microbiomes, opening up new avenues for the discovery of antibiotics.

Human gut metagenomic mining reveals an untapped source of peptide antibiotics

Drug-resistant bacteria are outpacing traditional antibiotic discovery efforts. Here, we computationally mined 444,054 families of putative small proteins from 1,773 human gut metagenomes, identifying 323 peptide antibiotics encoded in small open reading frames (smORFs). To test our computational predictions, 78 peptides were synthesized and screened for antimicrobial activity in vitro, with 59% displaying activity against either pathogens or commensals. Since these peptides were unique compared to previously reported antimicrobial peptides, we termed them smORF-encoded peptides (SEPs). SEPs killed bacteria by targeting their membrane, synergized with each other, and modulated gut commensals, indicating that they may play a role in reconfiguring microbiome communities in addition to counteracting pathogens. The lead candidates were anti-infective in both murine skin abscess and deep thigh infection models. Notably, prevotellin-2 from Prevotella copri presented activity comparable to the commonly used antibiotic polymyxin B. We report the discovery of hundreds of peptide sequences in the human gut.

S2P intramembrane protease RseP degrades small membrane proteins and suppresses the cytotoxicity of intrinsic toxin HokB

The site2-protease (S2P) family of intramembrane proteases (IMPs) is conserved in all kingdoms of life and cleaves transmembrane proteins within the membrane to regulate and maintain various cellular activities. RseP, an Escherichia coli S2P peptidase, is involved in the regulation of gene expression through the regulated cleavage of the two target membrane proteins (RseA and FecR) and in membrane quality control through the proteolytic elimination of remnant signal peptides. RseP is expected to have additional substrates and to be involved in other cellular processes. Recent studies have shown that cells express small membrane proteins (SMPs; single-spanning membrane proteins of approximately 50-100 amino acid residues) with crucial cellular functions. However, little is known about their metabolism, which affects their functions. This study investigated the possible RseP-catalyzed cleavage of E. coli SMPs based on the apparent similarity of the sizes and structures of SMPs to those of remnant signal peptides. We screened SMPs cleaved by RseP in vivo and in vitro and identified 14 SMPs, including HokB, an endogenous toxin that induces persister formation, as potential substrates. We demonstrated that RseP suppresses the cytotoxicity and biological functions of HokB. The identification of several SMPs as novel potential substrates of RseP provides a clue to a comprehensive understanding of the cellular roles of RseP and other S2P peptidases and highlights a novel aspect of the regulation of SMPs. IMPORTANCE Membrane proteins play an important role in cell activity and survival. Thus, understanding their dynamics, including proteolytic degradation, is crucial. E. coli RseP, an S2P family intramembrane protease, cleaves membrane proteins to regulate gene expression in response to environmental changes and to maintain membrane quality. To identify novel substrates of RseP, we screened small membrane proteins (SMPs), a group of proteins that have recently been shown to have diverse cellular functions, and identified 14 potential substrates. We also showed that RseP suppresses the cytotoxicity of the intrinsic toxin, HokB, an SMP that has been reported to induce persister cell formation, by degrading it. These findings provide new insights into the cellular roles of S2P peptidases and the functional regulation of SMPs.

CX3CL1 promotes M1 macrophage polarization and osteoclast differentiation through NF-κB signaling pathway in ankylosing spondylitis in vitro

BACKGROUND

Ankylosing spondylitis (AS) is an autoimmune disease with a genetic correlation and is characterized by inflammation in the axial skeleton and sacroiliac joints. Many AS patients also have inflammatory bowel diseases (IBD), but the underlying causes of intestinal inflammation and osteoporosis in AS are not well understood. CX3CL1, a protein involved in inflammation, has been found to be up-regulated in AS patients and AS-model mice.

METHODS

The authors investigated the effects of CX3CL1 on AS by studying its impact on macrophage polarization, inflammation factors, and osteoclast differentiation. Furthermore, the effects of inhibiting the NF-κB pathway and blocking CX3CL1 were assessed using BAY-117082 and anti-CX3CL1 mAb, respectively. AS model mice were used to evaluate the effects of anti-CX3CL1 mAb on limb thickness, spine rupture, and intestinal tissue damage.

RESULTS

The authors found that CX3CL1 increased the expression of M1-type macrophage markers and inflammation factors, and promoted osteoclast differentiation. This effect was mediated through the NF-κB signaling pathway. Inhibition of the NF-κB pathway prevented M1-type macrophage polarization, reduced inflammation levels, and inhibited osteoclast differentiation. Injection of anti-CX3CL1 mAb alleviated limb thickness, spine rupture, and intestinal tissue damage in AS model mice by inhibiting M1-type macrophage polarization and reducing intestinal tissue inflammation.

CONCLUSIONS

The study demonstrated that up-regulated CX3CL1 promotes M1-type macrophage polarization and osteoclast differentiation through the NF-κB signaling pathway. Inhibition of this pathway and blocking CX3CL1 can alleviate inflammation and bone destruction in AS. These findings contribute to a better understanding of the pathogenesis of AS and provide a basis for clinical diagnosis and treatment.

Regulation of acetate tolerance by small ORF-encoded polypeptides modulating efflux pump specificity in Methylomonas sp. DH-1

BACKGROUND

Methanotrophs have emerged as promising hosts for the biological conversion of methane into value-added chemicals, including various organic acids. Understanding the mechanisms of acid tolerance is essential for improving organic acid production. WatR, a LysR-type transcriptional regulator, was initially identified as involved in lactate tolerance in a methanotrophic bacterium Methylomonas sp. DH-1. In this study, we investigated the role of WatR as a regulator of cellular defense against weak organic acids and identified novel target genes of WatR.

RESULTS

By conducting an investigation into the genome-wide binding targets of WatR and its role in transcriptional regulation, we identified genes encoding an RND-type efflux pump (WatABO pump) and previously unannotated small open reading frames (smORFs), watS1 to watS5, as WatR target genes activated in response to acetate. The watS1 to watS5 genes encode polypeptides of approximately 50 amino acids, and WatS1 to WatS4 are highly homologous with one predicted transmembrane domain. Deletion of the WatABO pump genes resulted in decreased tolerance against formate, acetate, lactate, and propionate, suggesting its role as an efflux pump for a wide range of weak organic acids. WatR repressed the basal expression of watS genes but activated watS and WatABO pump genes in response to acetate stress. Overexpression of watS1 increased tolerance to acetate but not to other acids, only in the presence of the WatABO pump. Therefore, WatS1 may increase WatABO pump specificity toward acetate, switching the general weak acid efflux pump to an acetate-specific efflux pump for efficient cellular defense against acetate stress.

CONCLUSIONS

Our study has elucidated the role of WatR as a key transcription factor in the cellular defense against weak organic acids, particularly acetate, in Methylomonas sp. DH-1. We identified the genes encoding WatABO efflux pump and small polypeptides (WatS1 to WatS5), as the target genes regulated by WatR for this specific function. These findings offer valuable insights into the mechanisms underlying weak acid tolerance in methanotrophic bacteria, thereby contributing to the development of bioprocesses aimed at converting methane into value-added chemicals.

Urinary Tract Infection and Microbiome

Urinary tract infection is one of the most common bacterial infections and can cause major burdens, not only to individuals but also to an entire society. Current knowledge of the microbial communities in the urinary tract has increased exponentially due to next-generation sequencing and expanded quantitative urine culture. We now acknowledge a dynamic urinary tract microbiome that we once thought was sterile. Taxonomic studies have identified the normal core microbiota of the urinary tract, and studies on the changes in microbiome due to sexuality and age have set the foundation for microbiome studies in pathologic states. Urinary tract infection is not only caused by invading uropathogenic bacteria but also by changes to the uromicrobiome milieu, and interactions with other microbial communities can also contribute. Recent studies have provided insights into the pathogenesis of recurrent urinary tract infections and antimicrobial resistance. New therapeutic options for urinary tract infections also show promise; however, further research is needed to fully understand the implications of the urinary microbiome in urinary tract infections.

Antimicrobial Peptides in the Global Microbiome: Biosynthetic Genes and Resistance Determinants

Antimicrobial peptides are a promising new class of antimicrobials that could address the antibiotic resistance crisis, which poses a major threat to human health. These peptides are present in all kingdoms of life, but especially in microorganisms, having multiple origins in diverse taxa. To date, there has been no global study on the diversity of antimicrobial peptides, the hosts in which these occur, and the potential for resistance to these agents. Here, we investigated the diversity and number of antimicrobial peptides in four main habitats (aquatic, terrestrial, human, and engineered) by analyzing 52,515 metagenome-assembled genomes. The number of antimicrobial peptides was higher in the human gut microbiome than in other habitats, and most hosts of antimicrobial peptides were habitat-specific. The relative abundance of genes that confer resistance to antimicrobial peptides varied between habitats and was generally low, except for the built environment and on human skin. The horizontal transfer of potential resistance genes among these habitats was probably constrained by ecological barriers. We systematically quantified the risk of each resistance determinant to human health and found that nearly half of them pose a threat, especially those that confer resistance to multiple AMPs and polymyxin B. Our results help identify the biosynthetic potential of antimicrobial peptides in the global microbiome, further identifying peptides with a low risk of developing resistance.

Quantifying Shared and Unique Gene Content across 17 Microbial Ecosystems

Measuring microbial diversity is traditionally based on microbe taxonomy. Here, in contrast, we aimed to quantify heterogeneity in microbial gene content across 14,183 metagenomic samples spanning 17 ecologies, including 6 human associated, 7 nonhuman host associated, and 4 in other nonhuman host environments. In total, we identified 117,629,181 nonredundant genes. The vast majority of genes (66%) occurred in only one sample (i.e., "singletons"). In contrast, we found 1,864 sequences present in every metagenome, but not necessarily every bacterial genome. Additionally, we report data sets of other ecology-associated genes (e.g., abundant in only gut ecosystems) and simultaneously demonstrated that prior microbiome gene catalogs are both incomplete and inaccurately cluster microbial genetic life (e.g., at gene sequence identities that are too restrictive). We provide our results and the sets of environmentally differentiating genes described above at http://www.microbial-genes.bio. IMPORTANCE The amount of shared genetic elements has not been quantified between the human microbiome and other host- and non-host-associated microbiomes. Here, we made a gene catalog of 17 different microbial ecosystems and compared them. We show that most species shared between environment and human gut microbiomes are pathogens and that prior gene catalogs described as "nearly complete" are far from it. Additionally, over two-thirds of all genes only appear in a single sample, and only 1,864 genes (0.001%) are found in all types of metagenomes. These results highlight the large diversity between metagenomes and reveal a new, rare class of genes, those found in every type of metagenome, but not every microbial genome.

Microbiome-based enrichment pattern mining has enabled a deeper understanding of the biome-species-function relationship

Microbes live in diverse habitats (i.e. biomes), yet their species and genes were biome-specific, forming enrichment patterns. These enrichment patterns have mirrored the biome-species-function relationship, which is shaped by ecological and evolutionary principles. However, a grand picture of these enrichment patterns, as well as the roles of external and internal factors in driving these enrichment patterns, remain largely unexamined. In this work, we have examined the enrichment patterns based on 1705 microbiome samples from four representative biomes (Engineered, Gut, Freshwater, and Soil). Moreover, an "enrichment sphere" model was constructed to elucidate the regulatory principles behind these patterns. The driving factors for this model were revealed based on two case studies: (1) The copper-resistance genes were enriched in Soil biomes, owing to the copper contamination and horizontal gene transfer. (2) The flagellum-related genes were enriched in the Freshwater biome, due to high fluidity and vertical gene accumulation. Furthermore, this enrichment sphere model has valuable applications, such as in biome identification for metagenome samples, and in guiding 3D structure modeling of proteins. In summary, the enrichment sphere model aims towards creating a bluebook of the biome-species-function relationships and be applied in many fields.

Prediction of protein-coding small ORFs in multi-species using integrated sequence-derived features and the random forest model

Proteins encoded by small open reading frames (sORFs) can serve as functional elements playing important roles in vivo. Such sORFs also constitute the potential pool for facilitating the de novo gene birth, driving evolutionary innovation and species diversity. Therefore, their theoretical and experimental identification has become a critical issue. Herein, we proposed a protein-coding sORFs prediction method merely based on integrative sequence-derived features. Our prediction performance is better or comparable compared with other nine prevalent methods, which shows that our method can provide a relatively reliable research tool for the prediction of protein-coding sORFs. Our method allows users to estimate the potential expression of a queried sORF, which has been demonstrated by the correlation analysis between our possibility estimation and codon adaption index (CAI). Based on the features that we used, we demonstrated that the sequence features of the protein-coding sORFs in the two domains have significant differences implying that it might be a relatively hard task in terms of cross-domain prediction, hence domain-specific models were developed, which allowed users to predict protein-coding sORFs both in eukaryotes and prokaryotes. Finally, a web-server was developed and provided to boost and facilitate the study of the related field, which is freely available at http://guolab.whu.edu.cn/codingCapacity/index.html.

Mycobacterium smegmatis: The Vanguard of Mycobacterial Research

The genus Mycobacterium contains several slow-growing human pathogens, including Mycobacterium tuberculosis, Mycobacterium leprae, and Mycobacterium avium. Mycobacterium smegmatis is a nonpathogenic and fast growing species within this genus. In 1990, a mutant of M. smegmatis, designated mc²155, that could be transformed with episomal plasmids was isolated, elevating M. smegmatis to model status as the ideal surrogate for mycobacterial research. Classical bacterial models, such as Escherichia coli, were inadequate for mycobacteria research because they have low genetic conservation, different physiology, and lack the novel envelope structure that distinguishes the Mycobacterium genus. By contrast, M. smegmatis encodes thousands of conserved mycobacterial gene orthologs and has the same cell architecture and physiology. Dissection and characterization of conserved genes, structures, and processes in genetically tractable M. smegmatis mc²155 have since provided previously unattainable insights on these same features in its slow-growing relatives. Notably, tuberculosis (TB) drugs, including the first-line drugs isoniazid and ethambutol, are active against M. smegmatis, but not against E. coli, allowing the identification of their physiological targets. Furthermore, Bedaquiline, the first new TB drug in 40 years, was discovered through an M. smegmatis screen. M. smegmatis has become a model bacterium, not only for M. tuberculosis, but for all other Mycobacterium species and related genera. With a repertoire of bioinformatic and physical resources, including the recently established Mycobacterial Systems Resource, M. smegmatis will continue to accelerate mycobacterial research and advance the field of microbiology.

Microbiome dysbiosis and epigenetic modulations in lung cancer: From pathogenesis to therapy

The lung microbiome plays an essential role in maintaining healthy lung function, including host immune homeostasis. Lung microbial dysbiosis or disruption of the gut-lung axis can contribute to lung carcinogenesis by causing DNA damage, inducing genomic instability, or altering the host's susceptibility to carcinogenic insults. Thus far, most studies have reported the association of microbial composition in lung cancer. Mechanistic studies describing host-microbe interactions in promoting lung carcinogenesis are limited. Considering cancer as a multifaceted disease where epigenetic dysregulation plays a critical role, epigenetic modifying potentials of microbial metabolites and toxins and their roles in lung tumorigenesis are not well studied. The current review explains microbial dysbiosis and epigenetic aberrations in lung cancer and potential therapeutic opportunities.

Deciphering the Microbiome: Integrating Theory, New Technologies, and Inclusive Science

The diversity and functional significance of microbiomes have become increasingly clear through the extensive sampling of Earth's many habitats and the rapid adoption of new sequencing technologies. However, much remains unknown about what makes a "healthy" microbiome, how to restore a disrupted microbiome, and how microbiomes assemble. In December 2019, we convened a workshop that focused on how to identify potential "rules of life" that govern microbiome structure and function. This collection of mSystems Perspective pieces reflects many of the main challenges and opportunities in the field identified by both in-person and virtual workshop participants. By borrowing conceptual and theoretical approaches from other fields, including economics and philosophy, these pieces suggest new ways to dissect microbiome patterns and processes. The application of conceptual advances, including trait-based theory and community coalescence, is providing new insights on how to predict and manage microbiome diversity and function. Technological and analytical advances, including deep transfer learning, metabolic models, and advances in analytical chemistry, are helping us sift through complex systems to pinpoint mechanisms of microbiome assembly and dynamics. Integration of all of these advancements (theory, concepts, technology) across biological and spatial scales is providing dramatically improved temporal and spatial resolution of microbiome dynamics. This integrative microbiome research is happening in a new moment in science where academic institutions, scientific societies, and funding agencies must act collaboratively to support and train a diverse and inclusive community of microbiome scientists.

Strain level and comprehensive microbiome analysis in inflammatory bowel disease via multi-technology meta-analysis identifies key bacterial influencers of disease

Objective

Inflammatory bowel disease (IBD) is a heterogenous disease in which the microbiome has been shown to play an important role. However, the precise homeostatic or pathological functions played by bacteria remain unclear. Most published studies report taxa-disease associations based on single-technology analysis of a single cohort, potentially biasing results to one clinical protocol, cohort, and molecular analysis technology. To begin to address this key question, precise identification of the bacteria implicated in IBD across cohorts is necessary.

Methods

We sought to take advantage of the numerous and diverse studies characterizing the microbiome in IBD to develop a multi-technology meta-analysis (MTMA) as a platform for aggregation of independently generated datasets, irrespective of DNA-profiling technique, in order to uncover the consistent microbial modulators of disease. We report the largest strain-level survey of IBD, integrating microbiome profiles from 3,407 samples from 21 datasets spanning 15 cohorts, three of which are presented for the first time in the current study, characterized using three DNA-profiling technologies, mapping all nucleotide data against known, culturable strain reference data.

Results

We identify several novel IBD associations with culturable strains that have so far remained elusive, including two genome-sequenced but uncharacterized Lachnospiraceae strains consistently decreased in both the gut luminal and mucosal contents of patients with IBD, and demonstrate that these strains are correlated with inflammation-related pathways that are known mechanisms targeted for treatment. Furthermore, comparative MTMA at the species versus strain level reveals that not all significant strain associations resulted in a corresponding species-level significance and conversely significant species associations are not always re-captured at the strain level.

Conclusion

We propose MTMA for uncovering experimentally testable strain-disease associations that, as demonstrated here, are beneficial in discovering mechanisms underpinning microbiome impact on disease or novel targets for therapeutic interventions.

Deciphering microbial gene function using natural language processing

Revealing the function of uncharacterized genes is a fundamental challenge in an era of ever-increasing volumes of sequencing data. Here, we present a concept for tackling this challenge using deep learning methodologies adopted from natural language processing (NLP). We repurpose NLP algorithms to model "gene semantics" based on a biological corpus of more than 360 million microbial genes within their genomic context. We use the language models to predict functional categories for 56,617 genes and find that out of 1369 genes associated with recently discovered defense systems, 98% are inferred correctly. We then systematically evaluate the "discovery potential" of different functional categories, pinpointing those with the most genes yet to be characterized. Finally, we demonstrate our method's ability to discover systems associated with microbial interaction and defense. Our results highlight that combining microbial genomics and language models is a promising avenue for revealing gene functions in microbes.

Leveraging Substrate Promiscuity of a Radical S-Adenosyl-L-methionine RiPP Maturase toward Intramolecular Peptide Cross-Linking Applications

Radical S-adenosyl-l-methionine (RS) enzymes operate on a variety of substrates and catalyze a wide range of complex radical-mediated transformations. Radical non-α-carbon thioether peptides (ranthipeptides) are a class of ribosomally synthesized and post-translationally modified peptides (RiPPs). The RS enzyme PapB catalyzes the formation of thioether cross-links between Cys/Asp (or Cys/Glu) residues located in six Cys-X3-Asp/Glu motifs. In this report, using a minimal substrate that contains a single cross-link motif, we explore the substrate scope of the PapB and show that the enzyme is highly promiscuous and will accept a variety of Cys-X n -Asp sequences where n = 0-6. Moreover, we show that the enzyme will introduce in-line and nested thioether cross-links independently in peptide sequences that contain two motifs derived from the wild-type sequence. Additionally, the enzyme accepts peptides that contain d-amino acids at either the Cys or the Asp position. These observations are leveraged to produce a thioether cyclized analogue of the FDA-approved therapeutic agent octreotide, with a Cys-Glu cross-link replacing the disulfide that is found in the drug. These findings highlight the remarkable substrate tolerance of PapB and show the utility of RS RiPP maturases in biotechnological applications.

Molecular tools for probing the microbiome

The microbiome plays essential roles in health and disease. Our understanding of the imbalances that can arise in the microbiome and their consequences is held back by a lack of technologies that selectively knock out members of these microbial communities. Antibiotics and fecal transplants, the existing methods for manipulating the microbiota of the gastrointestinal tract, are not sufficiently pinpointed to reveal how particular microbial genes, strains, or species affect human health. A toolset for the precise manipulation of the microbiome could significantly advance disease diagnosis and treatment. Here, we provide an overview of current and future strategies for the development of molecular tools that can be used to probe the microbiome without producing off-target effects.

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.

Role of microbial secreted proteins in gut microbiota-host interactions

The mammalian gut microbiota comprises a variety of commensals including potential probiotics and pathobionts, influencing the host itself. Members of the microbiota can intervene with host physiology by several mechanisms, including the secretion of a relatively well-reported set of metabolic products. Another microbiota influence mechanism is the use of secreted proteins (i.e., the secretome), impacting both the host and other community members. While widely reported and studied in pathogens, this mechanism remains understood to a lesser extent in commensals, and this knowledge is increasing in recent years. In the following minireview, we assess the current literature covering different studies, concerning the functions of secretable proteins from members of the gut microbiota (including commensals, pathobionts, and probiotics). Their effect on host physiology and health, and how these effects can be harnessed by postbiotic products, are also discussed.