MetaRibo-Seq measures translation in microbiomes

Advances in ribosome profiling technologies

Ribosome profiling (or Ribo-seq) has emerged as a powerful approach for revealing the regulatory mechanisms of protein synthesis, on the basis of deep sequencing of ribosome footprints. Recent innovations in Ribo-seq technologies have significantly enhanced their sensitivity, specificity, and resolution. In this review, we outline emerging Ribo-seq derivatives that overcome barriers in low inputs, rRNA contamination, data calibration, and single-cell applications. These advances enable detailed insights into translational control across diverse biological contexts.

Targeting the Gut Microbiota's Role in Host Energy Absorption With Precision Nutrition Interventions for the Prevention and Treatment of Obesity

The field of precision nutrition aims to develop dietary approaches based on individual biological factors such as genomics or the gut microbiota. The gut microbiota, which is the highly individualized and complex community of microbes residing in the colon, is a key contributor to human physiology. Although gut microbes play multiple roles in the metabolism of nutrients, their role in modulating the absorption of dietary energy from foods that escape digestion in the small intestine has the potential to variably affect energy balance and, thus, body weight. The fate of this energy, and its subsequent impact on body weight, is well described in rodents and is emerging in humans. This narrative review is focused on recent clinical evidence of the role of the gut microbiota in human energy balance, specifically its impact on energy available to the human host. Despite recent progress, remaining gaps in knowledge present opportunities for developing and implementing strategies to understand causal microbial mechanisms related to energy balance. We propose that implementing rigorous microbiota-focused measurements in the context of innovative clinical trial designs will elucidate integrated diet-host-gut microbiota mechanisms. These mechanisms are primed to be targets for precision nutrition interventions to optimize energy balance to achieve desired weight outcomes. Given the magnitude and impact of the obesity epidemic, implementing these interventions within comprehensive weight management paradigms has the potential to be of public health significance.

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.

Modern microbiology: Embracing complexity through integration across scales

Microbes were the only form of life on Earth for most of its history, and they still account for the vast majority of life's diversity. They convert rocks to soil, produce much of the oxygen we breathe, remediate our sewage, and sustain agriculture. Microbes are vital to planetary health as they maintain biogeochemical cycles that produce and consume major greenhouse gases and support large food webs. Modern microbiologists analyze nucleic acids, proteins, and metabolites; leverage sophisticated genetic tools, software, and bioinformatic algorithms; and process and integrate complex and heterogeneous datasets so that microbial systems may be harnessed to address contemporary challenges in health, the environment, and basic science. Here, we consider an inevitably incomplete list of emergent themes in our discipline and highlight those that we recognize as the archetypes of its modern era that aim to address the most pressing problems of the 21st century.

Mining human microbiomes reveals an untapped source of peptide antibiotics

Drug-resistant bacteria are outpacing traditional antibiotic discovery efforts. Here, we computationally screened 444,054 previously reported putative small protein families from 1,773 human metagenomes for antimicrobial properties, identifying 323 candidates encoded in small open reading frames (smORFs). To test our computational predictions, 78 peptides were synthesized and screened for antimicrobial activity in vitro, with 70.5% displaying antimicrobial activity. As these compounds were different compared with previously reported antimicrobial peptides, we termed them smORF-encoded peptides (SEPs). SEPs killed bacteria by targeting their membrane, synergizing with each other, and modulating gut commensals, indicating a potential 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. Our report supports the existence of hundreds of antimicrobials in the human microbiome amenable to clinical translation.

Metaproteomics-informed stoichiometric modeling reveals the responses of wetland microbial communities to oxygen and sulfate exposure

Climate changes significantly impact greenhouse gas emissions from wetland soil. Specifically, wetland soil may be exposed to oxygen (O2) during droughts, or to sulfate (SO42-) as a result of sea level rise. How these stressors - separately and together - impact microbial food webs driving carbon cycling in the wetlands is still not understood. To investigate this, we integrated geochemical analysis, proteogenomics, and stoichiometric modeling to characterize the impact of elevated SO42- and O2 levels on microbial methane (CH4) and carbon dioxide (CO2) emissions. The results uncovered the adaptive responses of this community to changes in SO42- and O2 availability and identified altered microbial guilds and metabolic processes driving CH4 and CO2 emissions. Elevated SO42- reduced CH4 emissions, with hydrogenotrophic methanogenesis more suppressed than acetoclastic. Elevated O2 shifted the greenhouse gas emissions from CH4 to CO2. The metabolic effects of combined SO42- and O2 exposures on CH4 and CO2 emissions were similar to those of O2 exposure alone. The reduction in CH4 emission by increased SO42- and O2 was much greater than the concomitant increase in CO2 emission. Thus, greater SO42- and O2 exposure in wetlands is expected to reduce the aggregate warming effect of CH4 and CO2. Metaproteomics and stoichiometric modeling revealed a unique subnetwork involving carbon metabolism that converts lactate and SO42- to produce acetate, H2S, and CO2 when SO42- is elevated under oxic conditions. This study provides greater quantitative resolution of key metabolic processes necessary for the prediction of CH4 and CO2 emissions from wetlands under future climate scenarios.

Ribosome profiling reveals the fine-tuned response of Escherichia coli to mild and severe acid stress

IMPORTANCE

Bacteria react very differently to survive in acidic environments, such as the human gastrointestinal tract. Escherichia coli is one of the extremely acid-resistant bacteria and has a variety of acid-defense mechanisms. Here, we provide the first genome-wide overview of the adaptations of E. coli K-12 to mild and severe acid stress at both the transcriptional and translational levels. Using ribosome profiling and RNA sequencing, we uncover novel adaptations to different degrees of acidity, including previously hidden stress-induced small proteins and novel key transcription factors for acid defense, and report mRNAs with pH-dependent differential translation efficiency. In addition, we distinguish between acid-specific adaptations and general stress response mechanisms using denoising autoencoders. This workflow represents a powerful approach that takes advantage of next-generation sequencing techniques and machine learning to systematically analyze bacterial stress responses.

Comparative whole-genome analysis of China and global epidemic Pseudomonas aeruginosa high-risk clones

OBJECTIVES

The various sequence types (STs) of Pseudomonas aeruginosa (P. aeruginosa) high-risk clones (HiRiCs) have been sporadically reported in China, but the systematic analysis of genomes for these STs remains limited. This study aimed to address the evolutionary pathways underlying the emergence of HiRiCs and their routes of dissemination from Chinese and global perspectives.

METHODS

The phylogenetic analysis was performed based on 416 newly sequenced clinical P. aeruginosa strains from Guangdong (GD), published genome sequences of 282 Chinese isolates, and 868 HiRiCs isolates from other countries. The genomic comparison study of global HiRiC ST244 was conducted to detect the model of global dissemination and local separation driven by association regional-specific antibiotic resistance genes. Furthermore, the evolutionary route of the emerging, China-specific HiRiC ST1971 was explored using Most Recent Common Ancestor (MRCA) analysis.

RESULTS

Based on comparative genomics analysis, we found a clear geographical separation of ST244 isolates, yet with an association between ST244 isolates from GD and America. We identified a set of 38 AMR genes that contribute to the geographical separation in ST244, and we identified genetic determinants either positively (MexB) and negatively (opmD) associated with GD ST244. For the China-unique HiRiC ST1971, its evolutionary history across different continents before emerging as ST1971 in China was also deduced.

CONCLUSION

This study provides insight into the specific genetics underlying regional differences among globally disseminated P. aeruginosa HiRiCs (ST244) as well as new understanding of the dissemination and evolution of a regional HiRiC (ST1971). Understanding the genetics of these and other HiRiCs may assist in controlling their emergence and further spread.

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.

Identification of over ten thousand candidate structured RNAs in viruses and phages

Structured RNAs play crucial roles in viruses, exerting influence over both viral and host gene expression. However, the extensive diversity of structured RNAs and their ability to act in cis or trans positions pose challenges for predicting and assigning their functions. While comparative genomics approaches have successfully predicted candidate structured RNAs in microbes on a large scale, similar efforts for viruses have been lacking. In this study, we screened over 5 million DNA and RNA viral sequences, resulting in the prediction of 10,006 novel candidate structured RNAs. These predictions are widely distributed across taxonomy and ecosystem. We found transcriptional evidence for 206 of these candidate structured RNAs in the human fecal microbiome. These candidate RNAs exhibited evidence of nucleotide covariation, indicative of selective pressure maintaining the predicted secondary structures. Our analysis revealed a diverse repertoire of candidate structured RNAs, encompassing a substantial number of putative tRNAs or tRNA-like structures, Rho-independent transcription terminators, and potentially cis-regulatory structures consistently positioned upstream of genes. In summary, our findings shed light on the extensive diversity of structured RNAs in viruses, offering a valuable resource for further investigations into their functional roles and implications in viral gene expression and pave the way for a deeper understanding of the intricate interplay between viruses and their hosts at the molecular level.

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.

Atlas of mRNA translation and decay for bacteria

Regulation of messenger RNA stability is pivotal for programmed gene expression in bacteria and is achieved by a myriad of molecular mechanisms. By bulk sequencing of 5' monophosphorylated mRNA decay intermediates (5'P), we show that cotranslational mRNA degradation is conserved among both Gram-positive and -negative bacteria. We demonstrate that, in species with 5'-3' exonucleases, the exoribonuclease RNase J tracks the trailing ribosome to produce an in vivo single-nucleotide toeprint of the 5' position of the ribosome. In other species lacking 5'-3' exonucleases, ribosome positioning alters endonucleolytic cleavage sites. Using our metadegradome (5'P degradome) sequencing approach, we characterize 5'P mRNA decay intermediates in 96 species including Bacillus subtilis, Escherichia coli, Synechocystis spp. and Prevotella copri and identify codon- and gene-level ribosome stalling responses to stress and drug treatment. We also apply 5'P sequencing to complex clinical and environmental microbiomes and demonstrate that metadegradome sequencing provides fast, species-specific posttranscriptional characterization of responses to drug or environmental perturbations. Finally we produce a degradome atlas for 96 species to enable analysis of mechanisms of RNA degradation in bacteria. Our work paves the way for the application of metadegradome sequencing to investigation of posttranscriptional regulation in unculturable species and complex microbial communities.

Guild and Niche Determination Enable Targeted Alteration of the Microbiome

Microbiome science has greatly contributed to our understanding of microbial life and its essential roles for the environment and human health1-5. However, the nature of microbial interactions and how microbial communities respond to perturbations remains poorly understood, resulting in an often descriptive and correlation-based approach to microbiome research6-8. Achieving causal and predictive microbiome science would require direct functional measurements in complex communities to better understand the metabolic role of each member and its interactions with others. In this study we present a new approach that integrates transcription and translation measurements to predict competition and substrate preferences within microbial communities, consequently enabling the selective manipulation of the microbiome. By performing metatranscriptomic (metaRNA-Seq) and metatranslatomic (metaRibo-Seq) analysis in complex samples, we classified microbes into functional groups (i.e. guilds) and demonstrated that members of the same guild are competitors. Furthermore, we predicted preferred substrates based on importer proteins, which specifically benefited selected microbes in the community (i.e. their niche) and simultaneously impaired their competitors. We demonstrated the scalability of microbial guild and niche determination to natural samples and its ability to successfully manipulate microorganisms in complex microbiomes. Thus, the approach enhances the design of pre- and probiotic interventions to selectively alter members within microbial communities, advances our understanding of microbial interactions, and paves the way for establishing causality in microbiome science.

Managing the Transition to Widespread Metagenomic Monitoring: Policy Considerations for Future Biosurveillance

The technological possibilities and future public health importance of metagenomic sequencing have received extensive attention, but there has been little discussion about the policy and regulatory issues that need to be addressed if metagenomic sequencing is adopted as a key technology for biosurveillance. In this article, we introduce metagenomic monitoring as a possible path to eventually replacing current infectious disease monitoring models. Many key enablers are technological, whereas others are not. We therefore highlight key policy challenges and implementation questions that need to be addressed for "widespread metagenomic monitoring" to be possible. Policymakers must address pitfalls like fragmentation of the technological base, private capture of benefits, privacy concerns, the usefulness of the system during nonpandemic times, and how the future systems will enable better response. If these challenges are addressed, the technological and public health promise of metagenomic sequencing can be realized.

INRI-seq enables global cell-free analysis of translation initiation and off-target effects of antisense inhibitors

Ribosome profiling (Ribo-seq) is a powerful method for the transcriptome-wide assessment of protein synthesis rates and the study of translational control mechanisms. Yet, Ribo-seq also has limitations. These include difficulties with the analysis of translation-modulating molecules such as antibiotics, which are often toxic or challenging to deliver into living cells. Here, we have developed in vitro Ribo-seq (INRI-seq), a cell-free method to analyze the translational landscape of a fully customizable synthetic transcriptome. Using Escherichia coli as an example, we show how INRI-seq can be used to analyze the translation initiation sites of a transcriptome of interest. We also study the global impact of direct translation inhibition by antisense peptide nucleic acid (PNA) to analyze PNA off-target effects. Overall, INRI-seq presents a scalable, sensitive method to study translation initiation in a transcriptome-wide manner without the potentially confounding effects of extracting ribosomes from living cells.

Thousands of small, novel genes predicted in global phage genomes

Small genes (<150 nucleotides) have been systematically overlooked in phage genomes. We employ a large-scale comparative genomics approach to predict >40,000 small-gene families in ∼2.3 million phage genome contigs. We find that small genes in phage genomes are approximately 3-fold more prevalent than in host prokaryotic genomes. Our approach enriches for small genes that are translated in microbiomes, suggesting the small genes identified are coding. More than 9,000 families encode potentially secreted or transmembrane proteins, more than 5,000 families encode predicted anti-CRISPR proteins, and more than 500 families encode predicted antimicrobial proteins. By combining homology and genomic-neighborhood analyses, we reveal substantial novelty and diversity within phage biology, including small phage genes found in multiple host phyla, small genes encoding proteins that play essential roles in host infection, and small genes that share genomic neighborhoods and whose encoded proteins may share related functions.

tRNA modification dynamics from individual organisms to metaepitranscriptomics of microbiomes

tRNA is the most extensively modified RNA in cells. On average, a bacterial tRNA contains 8 modifications per molecule and a eukaryotic tRNA contains 13 modifications per molecule. Recent studies reveal that tRNA modifications are highly dynamic and respond extensively to environmental conditions. Functions of tRNA modification dynamics include enhanced, on-demand decoding of specific codons in response genes and regulation of tRNA fragment biogenesis. This review summarizes recent advances in the studies of tRNA modification dynamics in biological processes, tRNA modification erasers, and human-associated bacteria. Furthermore, we use the term "metaepitranscriptomics" to describe the potential and approach of tRNA modification studies in natural biological communities such as microbiomes. tRNA is highly modified in cells, and tRNA modifications respond extensively to environmental conditions to enhance translation of specific genes and produce tRNA fragments on demand. We review recent advances in tRNA sequencing methods, tRNA modification dynamics in biological processes, and tRNA modification studies in natural communities such as the microbiomes.

RiboChat: a chat-style web interface for analysis and annotation of ribosome profiling data

The increasing volume of ribosome profiling (Ribo-seq) data, computational complexity of its data processing and operational handicap of related analytical procedures present a daunting set of informatics challenges. These impose a substantial barrier to researchers particularly with no or limited bioinformatics expertise in analyzing and decoding translation information from Ribo-seq data, thus driving the need for a new research paradigm for data computation and information extraction. In this knowledge base, we herein present a novel interactive web platform, RiboChat (https://db.cngb.org/ribobench/chat.html), for direct analyzing and annotating Ribo-seq data in the form of a chat conversation. It consists of a user-friendly web interface and a backend cloud-computing service. When typing a data analysis question into the chat window, the object-text detection module will be run to recognize relevant keywords from the input text. Based on the features identified in the input, individual analytics modules are then scored to find the perfect-matching candidate. The corresponding analytics module will be further executed after checking the completion status of the uploading of datasets and configured parameters. Overall, RiboChat represents an important step forward in the emerging direction of next-generation data analytics and will enable the broad research community to conveniently decipher translation information embedded within Ribo-seq data.

Identifying Small Open Reading Frames in Prokaryotes with Ribosome Profiling

Small proteins encoded by open reading frames (ORFs) shorter than 50 codons (small ORFs [sORFs]) are often overlooked by annotation engines and are difficult to characterize using traditional biochemical techniques. Ribosome profiling has tremendous potential to empirically improve the annotations of prokaryotic genomes. Recent improvements in ribosome profiling methods for bacterial model organisms have revealed many new sORFs in well-characterized genomes. Antibiotics that trap ribosomes just after initiation have played a key role in these developments by allowing the unambiguous identification of the start codons (and, hence, the reading frame) for novel ORFs. Here, we describe these new methods and highlight critical controls and considerations for adapting ribosome profiling to different prokaryotic species.

Small Proteins; Big Questions

In recent years, there has been increased appreciation that a whole category of proteins, small proteins of around 50 amino acids or fewer in length, has been missed by annotation as well as by genetic and biochemical assays. With the increased recognition that small proteins are stable within cells and have regulatory functions, there has been intensified study of these proteins. As a result, important questions about small proteins in bacteria and archaea are coming to the fore. Here, we give an overview of these questions, the initial answers, and the approaches needed to address these questions more fully. More detailed discussions of how small proteins can be identified by ribosome profiling and mass spectrometry approaches are provided by two accompanying reviews (N. Vazquez-Laslop, C. M. Sharma, A. S. Mankin, and A. R. Buskirk, J Bacteriol 204:e00294-21, 2022, https://doi.org/10.1128/JB.00294-21; C. H. Ahrens, J. T. Wade, M. M. Champion, and J. D. Langer, J Bacteriol 204:e00353-21, 2022, https://doi.org/10.1128/JB.00353-21). We are excited by the prospects of new insights and possible therapeutic approaches coming from this emerging field.

Genome-Scale Metabolic Modeling Enables In-Depth Understanding of Big Data

Genome-scale metabolic models (GEMs) enable the mathematical simulation of the metabolism of archaea, bacteria, and eukaryotic organisms. GEMs quantitatively define a relationship between genotype and phenotype by contextualizing different types of Big Data (e.g., genomics, metabolomics, and transcriptomics). In this review, we analyze the available Big Data useful for metabolic modeling and compile the available GEM reconstruction tools that integrate Big Data. We also discuss recent applications in industry and research that include predicting phenotypes, elucidating metabolic pathways, producing industry-relevant chemicals, identifying drug targets, and generating knowledge to better understand host-associated diseases. In addition to the up-to-date review of GEMs currently available, we assessed a plethora of tools for developing new GEMs that include macromolecular expression and dynamic resolution. Finally, we provide a perspective in emerging areas, such as annotation, data managing, and machine learning, in which GEMs will play a key role in the further utilization of Big Data.

Translation complex stabilization on messenger RNA and footprint profiling to study the RNA responses and dynamics of protein biosynthesis in the cells

During protein biosynthesis, ribosomes bind to messenger (m)RNA, locate its protein-coding information, and translate the nucleotide triplets sequentially as codons into the corresponding sequence of amino acids, forming proteins. Non-coding mRNA features, such as 5' and 3' untranslated regions (UTRs), start sites or stop codons of different efficiency, stretches of slower or faster code and nascent polypeptide interactions can alter the translation rates transcript-wise. Most of the homeostatic and signal response pathways of the cells converge on individual mRNA control, as well as alter the global translation output. Among the multitude of approaches to study translational control, one of the most powerful is to infer the locations of translational complexes on mRNA based on the mRNA fragments protected by these complexes from endonucleolytic hydrolysis, or footprints. Translation complex profiling by high-throughput sequencing of the footprints allows to quantify the transcript-wise, as well as global, alterations of translation, and uncover the underlying control mechanisms by attributing footprint locations and sizes to different configurations of the translational complexes. The accuracy of all footprint profiling approaches critically depends on the fidelity of footprint generation and many methods have emerged to preserve certain or multiple configurations of the translational complexes, often in challenging biological material. In this review, a systematic summary of approaches to stabilize translational complexes on mRNA for footprinting is presented and major findings are discussed. Future directions of translation footprint profiling are outlined, focusing on the fidelity and accuracy of inference of the native in vivo translation complex distribution on mRNA.

Simultaneous ribosome profiling of hundreds of microbes from the human microbiome

Ribosome profiling enables sequencing of ribosome-bound fragments of RNA, revealing which transcripts are being translated as well as the position of ribosomes along mRNAs. Although ribosome profiling has been applied to cultured bacterial isolates, its application to uncultured, mixed communities has been challenging. We present MetaRibo-Seq, a protocol that enables the application of ribosome profiling directly to the human fecal microbiome. MetaRibo-Seq is a benchmarked method that includes several modifications to existing ribosome profiling protocols, specifically addressing challenges involving fecal sample storage, purity and input requirements. We also provide a computational workflow to quality control and trim reads, de novo assemble a reference metagenome with metagenomic reads, align MetaRibo-Seq reads to the reference, and assess MetaRibo-Seq library quality ( https://github.com/bhattlab/bhattlab_workflows/tree/master/metariboseq ). This MetaRibo-Seq protocol enables researchers in standard molecular biology laboratories to study translation in the fecal microbiome in ~5 d.

HRIBO: high-throughput analysis of bacterial ribosome profiling data

MOTIVATION

Ribosome profiling (Ribo-seq) is a powerful approach based on deep sequencing of cDNA libraries generated from ribosome-protected RNA fragments to explore the translatome of a cell, and is especially useful for the detection of small proteins (50-100 amino acids) that are recalcitrant to many standard biochemical and in silico approaches. While pipelines are available to analyze Ribo-seq data, none are designed explicitly for the automatic processing and analysis of data from bacteria, nor are they focused on the discovery of unannotated open reading frames (ORFs).

RESULTS

We present HRIBO (High-throughput annotation by Ribo-seq), a workflow to enable reproducible and high-throughput analysis of bacterial Ribo-seq data. The workflow performs all required pre-processing and quality control steps. Importantly, HRIBO outputs annotation-independent ORF predictions based on two complementary bacteria-focused tools, and integrates them with additional feature information and expression values. This facilitates the rapid and high-confidence discovery of novel ORFs and their prioritization for functional characterization.

AVAILABILITY AND IMPLEMENTATION

HRIBO is a free and open source project available under the GPL-3 license at: https://github.com/RickGelhausen/HRIBO.

Quantitative Control for Stoichiometric Protein Synthesis

Bacterial protein synthesis rates have evolved to maintain preferred stoichiometries at striking precision, from the components of protein complexes to constituents of entire pathways. Setting relative protein production rates to be well within a factor of two requires concerted tuning of transcription, RNA turnover, and translation, allowing many potential regulatory strategies to achieve the preferred output. The last decade has seen a greatly expanded capacity for precise interrogation of each step of the central dogma genome-wide. Here, we summarize how these technologies have shaped the current understanding of diverse bacterial regulatory architectures underpinning stoichiometric protein synthesis. We focus on the emerging expanded view of bacterial operons, which encode diverse primary and secondary mRNA structures for tuning protein stoichiometry. Emphasis is placed on how quantitative tuning is achieved. We discuss the challenges and open questions in the application of quantitative, genome-wide methodologies to the problem of precise protein production. Expected final online publication date for the Annual Review of Microbiology, Volume 75 is October 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Comparative genomics identifies thousands of candidate structured RNAs in human microbiomes

BACKGROUND

Structured RNAs play varied bioregulatory roles within microbes. To date, hundreds of candidate structured RNAs have been predicted using informatic approaches that search for motif structures in genomic sequence data. The human microbiome contains thousands of species and strains of microbes. Yet, much of the metagenomic data from the human microbiome remains unmined for structured RNA motifs primarily due to computational limitations.

RESULTS

We sought to apply a large-scale, comparative genomics approach to these organisms to identify candidate structured RNAs. With a carefully constructed, though computationally intensive automated analysis, we identify 3161 conserved candidate structured RNAs in intergenic regions, as well as 2022 additional candidate structured RNAs that may overlap coding regions. We validate the RNA expression of 177 of these candidate structures by analyzing small fragment RNA-seq data from four human fecal samples.

CONCLUSIONS

This approach identifies a wide variety of candidate structured RNAs, including tmRNAs, antitoxins, and likely ribosome protein leaders, from a wide variety of taxa. Overall, our pipeline enables conservative predictions of thousands of novel candidate structured RNAs from human microbiomes.

Meeting the complexity of plant nutrient metabolism with multi-omics approaches

This article comments on:

Henriet C, Balliau T, Aime D, Le Signor C, Kreplak J, Zivy M, Gallardo K, Vernoud V. 2021. Proteomics of developing pea seeds reveals a complex antioxidant network underlying the response to sulfur deficiency and water stress. Journal of Experimental Botany 72, 2611–2626.

Cross-species RNA-seq for deciphering host-microbe interactions

The human body is constantly exposed to microorganisms, which entails manifold interactions between human cells and diverse commensal or pathogenic bacteria. The cellular states of the interacting cells are decisive for the outcome of these encounters such as whether bacterial virulence programmes and host defence or tolerance mechanisms are induced. This Review summarizes how next-generation RNA sequencing (RNA-seq) has become a primary technology to study host-microbe interactions with high resolution, improving our understanding of the physiological consequences and the mechanisms at play. We illustrate how the discriminatory power and sensitivity of RNA-seq helps to dissect increasingly complex cellular interactions in time and space down to the single-cell level. We also outline how future transcriptomics may answer currently open questions in host-microbe interactions and inform treatment schemes for microbial disorders.

Automated Prediction and Annotation of Small Open Reading Frames in Microbial Genomes

Small open reading frames (smORFs) and their encoded microproteins play central roles in microbes. However, there is a vast unexplored space of smORFs within human-associated microbes. A recent bioinformatic analysis used evolutionary conservation signals to enhance prediction of small protein families. To facilitate the annotation of specific smORFs, we introduce SmORFinder. This tool combines profile hidden Markov models of each smORF family and deep learning models that better generalize to smORF families not seen in the training set, resulting in predictions enriched for Ribo-seq translation signals. Feature importance analysis reveals that the deep learning models learn to identify Shine-Dalgarno sequences, deprioritize the wobble position in each codon, and group codon synonyms found in the codon table. A core-genome analysis of 26 bacterial species identifies several core smORFs of unknown function. We pre-compute smORF annotations for thousands of RefSeq isolate genomes and Human Microbiome Project metagenomes and provide these data through a public web portal.

Structured RNA Contaminants in Bacterial Ribo-Seq

Ribosome profiling (Ribo-Seq) is a powerful method to study translation in bacteria. However, Ribo-Seq signal can be observed across RNAs that one would not expect to be bound by ribosomes. For example, Escherichia coli Ribo-Seq libraries also capture reads from most noncoding RNAs (ncRNAs). While some of these ncRNAs may overlap coding regions, this alone does not explain the majority of observed signal across ncRNAs. These fragments of ncRNAs in Ribo-Seq data pass all size selection steps of the Ribo-Seq protocol and survive hours of micrococcal nuclease (MNase) treatment. In this work, we specifically focus on Ribo-Seq signal across ncRNAs and provide evidence to suggest that RNA structure, as opposed to ribosome binding, protects them from degradation and allows them to persist in the Ribo-Seq sequencing library preparation. By inspecting these "contaminant reads" in bacterial Ribo-Seq, we show that data previously disregarded in bacterial Ribo-Seq experiments may, in fact, be used to gain partial information regarding the in vivo secondary structure of ncRNAs.IMPORTANCE Structured ncRNAs are pivotal mediators of bioregulation in bacteria, and their functions are often reliant on their specific structures. Here, we first inspect Ribo-Seq reads across noncoding regions, identifying contaminant reads in these libraries. We observe that contaminant reads in bacterial Ribo-Seq experiments that are often disregarded, in fact, strongly overlap with structured regions of ncRNAs. We then perform several bioinformatic analyses to determine why these contaminant reads may persist in Ribo-Seq libraries. Finally, we highlight some structured RNA contaminants in Ribo-Seq and support the hypothesis that structures in the RNA protect them from MNase digestion. We conclude that researchers should be cautious when interpreting Ribo-Seq signal as coding without considering signal distribution. These findings also may enable us to partially resolve RNA structures, identify novel structured RNAs, and elucidate RNA structure-function relationships in bacteria at a large scale and in vivo through the reanalysis of existing Ribo-Seq data sets.