REPARATION: ribosome profiling assisted (re-)annotation of bacterial genomes

Uncovering the small proteome of Methanosarcina mazei using Ribo-seq and peptidomics under different nitrogen conditions

The mesophilic methanogenic archaeal model organism Methanosarcina mazei strain Gö1 is crucial for climate and environmental research due to its ability to produce methane. Here, we establish a Ribo-seq protocol for M. mazei strain Gö1 under two growth conditions (nitrogen sufficiency and limitation). The translation of 93 previously annotated and 314 unannotated small ORFs, coding for proteins ≤ 70 amino acids, is predicted with high confidence based on Ribo-seq data. LC-MS analysis validates the translation for 62 annotated small ORFs and 26 unannotated small ORFs. Epitope tagging followed by immunoblotting analysis confirms the translation of 13 out of 16 selected unannotated small ORFs. A comprehensive differential transcription and translation analysis reveals that 29 of 314 unannotated small ORFs are differentially regulated in response to nitrogen availability at the transcriptional and 49 at the translational level. A high number of reported small RNAs are emerging as dual-function RNAs, including sRNA154, the central regulatory small RNA of nitrogen metabolism. Several unannotated small ORFs are conserved in Methanosarcina species and overproducing several (small ORF encoded) small proteins suggests key physiological functions. Overall, the comprehensive analysis opens an avenue to elucidate the function(s) of multitudinous small proteins and dual-function RNAs in M. mazei.

Proteins à la carte: riboproteogenomic exploration of bacterial N-terminal proteoform expression

In recent years, it has become evident that the true complexity of bacterial proteomes remains underestimated. Gene annotation tools are known to propagate biases and overlook certain classes of truly expressed proteins, particularly proteoforms-protein isoforms arising from a single gene. Recent (re-)annotation efforts heavily rely on ribosome profiling by providing a direct readout of translation to fully describe bacterial proteomes. In this study, we employ a robust riboproteogenomic pipeline to conduct a systematic census of expressed N-terminal proteoform pairs, representing two isoforms encoded by a single gene raised by annotated and alternative translation initiation, in Salmonella. Intriguingly, conditional-dependent changes in relative utilization of annotated and alternative translation initiation sites (TIS) were observed in several cases. This suggests that TIS selection is subject to regulatory control, adding yet another layer of complexity to our understanding of bacterial proteomes.

IMPORTANCE

With the emerging theme of genes within genes comprising the existence of alternative open reading frames (ORFs) generated by translation initiation at in-frame start codons, mechanisms that control the relative utilization of annotated and alternative TIS need to be unraveled and our molecular understanding of resulting proteoforms broadened. Utilizing complementary ribosome profiling strategies to map ORF boundaries, we uncovered dual-encoding ORFs generated by in-frame TIS usage in Salmonella. Besides demonstrating that alternative TIS usage may generate proteoforms with different characteristics, such as differential localization and specialized function, quantitative aspects of conditional retapamulin-assisted ribosome profiling (Ribo-RET) translation initiation maps offer unprecedented insights into the relative utilization of annotated and alternative TIS, enabling the exploration of gene regulatory mechanisms that control TIS usage and, consequently, the translation of N-terminal proteoform pairs.

Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges

Machine learning (ML), particularly deep learning (DL), has made rapid and substantial progress in synthetic biology in recent years. Biotechnological applications of biosystems, including pathways, enzymes, and whole cells, are being probed frequently with time. The intricacy and interconnectedness of biosystems make it challenging to design them with the desired properties. ML and DL have a synergy with synthetic biology. Synthetic biology can be employed to produce large data sets for training models (for instance, by utilizing DNA synthesis), and ML/DL models can be employed to inform design (for example, by generating new parts or advising unrivaled experiments to perform). This potential has recently been brought to light by research at the intersection of engineering biology and ML/DL through achievements like the design of novel biological components, best experimental design, automated analysis of microscopy data, protein structure prediction, and biomolecular implementations of ANNs (Artificial Neural Networks). I have divided this review into three sections. In the first section, I describe predictive potential and basics of ML along with myriad applications in synthetic biology, especially in engineering cells, activity of proteins, and metabolic pathways. In the second section, I describe fundamental DL architectures and their applications in synthetic biology. Finally, I describe different challenges causing hurdles in the progress of ML/DL and synthetic biology along with their solutions.

ORFeus: a computational method to detect programmed ribosomal frameshifts and other non-canonical translation events

BACKGROUND

In canonical protein translation, ribosomes initiate translation at a specific start codon, maintain a single reading frame throughout elongation, and terminate at the first in-frame stop codon. However, ribosomal behavior can deviate at each of these steps, sometimes in a programmed manner. Certain mRNAs contain sequence and structural elements that cause ribosomes to begin translation at alternative start codons, shift reading frame, read through stop codons, or reinitiate on the same mRNA. These processes represent important translational control mechanisms that can allow an mRNA to encode multiple functional protein products or regulate protein expression. The prevalence of these events remains uncertain, due to the difficulty of systematic detection.

RESULTS

We have developed a computational model to infer non-canonical translation events from ribosome profiling data.

CONCLUSION

ORFeus identifies known examples of alternative open reading frames and recoding events across different organisms and enables transcriptome-wide searches for novel events.

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.

Exposing the small protein load of bacterial life

The ever-growing repertoire of genomic techniques continues to expand our understanding of the true diversity and richness of prokaryotic genomes. Riboproteogenomics laid the foundation for dynamic studies of previously overlooked genomic elements. Most strikingly, bacterial genomes were revealed to harbor robust repertoires of small open reading frames (sORFs) encoding a diverse and broadly expressed range of small proteins, or sORF-encoded polypeptides (SEPs). In recent years, continuous efforts led to great improvements in the annotation and characterization of such proteins, yet many challenges remain to fully comprehend the pervasive nature of small proteins and their impact on bacterial biology. In this work, we review the recent developments in the dynamic field of bacterial genome reannotation, catalog the important biological roles carried out by small proteins and identify challenges obstructing the way to full understanding of these elusive proteins.

Unraveling the small proteome of the plant symbiont Sinorhizobium meliloti by ribosome profiling and proteogenomics

The soil-dwelling plant symbiont Sinorhizobium meliloti is a major model organism of Alphaproteobacteria. Despite numerous detailed OMICS studies, information about small open reading frame (sORF)-encoded proteins (SEPs) is largely missing, because sORFs are poorly annotated and SEPs are hard to detect experimentally. However, given that SEPs can fulfill important functions, identification of translated sORFs is critical for analyzing their roles in bacterial physiology. Ribosome profiling (Ribo-seq) can detect translated sORFs with high sensitivity, but is not yet routinely applied to bacteria because it must be adapted for each species. Here, we established a Ribo-seq procedure for S. meliloti 2011 based on RNase I digestion and detected translation for 60% of the annotated coding sequences during growth in minimal medium. Using ORF prediction tools based on Ribo-seq data, subsequent filtering, and manual curation, the translation of 37 non-annotated sORFs with ≤ 70 amino acids was predicted with confidence. The Ribo-seq data were supplemented by mass spectrometry (MS) analyses from three sample preparation approaches and two integrated proteogenomic search database (iPtgxDB) types. Searches against standard and 20-fold smaller Ribo-seq data-informed custom iPtgxDBs confirmed 47 annotated SEPs and identified 11 additional novel SEPs. Epitope tagging and Western blot analysis confirmed the translation of 15 out of 20 SEPs selected from the translatome map. Overall, by combining MS and Ribo-seq approaches, the small proteome of S. meliloti was substantially expanded by 48 novel SEPs. Several of them are part of predicted operons and/or are conserved from Rhizobiaceae to Bacteria, suggesting important physiological functions.

From prediction to function: Current practices and challenges towards the functional characterization of type III effectors

The type III secretion system (T3SS) is a well-studied pathogenicity determinant of many bacteria through which effectors (T3Es) are translocated into the host cell, where they exercise a wide range of functions to deceive the host cell's immunity and to establish a niche. Here we look at the different approaches that are used to functionally characterize a T3E. Such approaches include host localization studies, virulence screenings, biochemical activity assays, and large-scale omics, such as transcriptomics, interactomics, and metabolomics, among others. By means of the phytopathogenic Ralstonia solanacearum species complex (RSSC) as a case study, the current advances of these methods will be explored, alongside the progress made in understanding effector biology. Data obtained by such complementary methods provide crucial information to comprehend the entire function of the effectome and will eventually lead to a better understanding of the phytopathogen, opening opportunities to tackle it.

Revealing the small proteome of Haloferax volcanii by combining ribosome profiling and small-protein optimized mass spectrometry

In contrast to extensively studied prokaryotic 'small' transcriptomes (encompassing all small noncoding RNAs), small proteomes (here defined as including proteins ≤70 aa) are only now entering the limelight. The absence of a complete small protein catalogue in most prokaryotes precludes our understanding of how these molecules affect physiology. So far, archaeal genomes have not yet been analyzed broadly with a dedicated focus on small proteins. Here, we present a combinatorial approach, integrating experimental data from small protein-optimized mass spectrometry (MS) and ribosome profiling (Ribo-seq), to generate a high confidence inventory of small proteins in the model archaeon Haloferax volcanii. We demonstrate by MS and Ribo-seq that 67% of the 317 annotated small open reading frames (sORFs) are translated under standard growth conditions. Furthermore, annotation-independent analysis of Ribo-seq data showed ribosomal engagement for 47 novel sORFs in intergenic regions. A total of seven of these were also detected by proteomics, in addition to an eighth novel small protein solely identified by MS. We also provide independent experimental evidence in vivo for the translation of 12 sORFs (annotated and novel) using epitope tagging and western blotting, underlining the validity of our identification scheme. Several novel sORFs are conserved in Haloferax species and might have important functions. Based on our findings, we conclude that the small proteome of H. volcanii is larger than previously appreciated, and that combining MS with Ribo-seq is a powerful approach for the discovery of novel small protein coding genes in archaea.

Synthetic Biology Meets Machine Learning

This chapter outlines the myriad applications of machine learning (ML) in synthetic biology, specifically in engineering cell and protein activity, and metabolic pathways. Though by no means comprehensive, the chapter highlights several prominent computational tools applied in the field and their potential use cases. The examples detailed reinforce how ML algorithms can enhance synthetic biology research by providing data-driven insights into the behavior of living systems, even without detailed knowledge of their underlying mechanisms. By doing so, ML promises to increase the efficiency of research projects by modeling hypotheses in silico that can then be tested through experiments. While challenges related to training dataset generation and computational costs remain, ongoing improvements in ML tools are paving the way for smarter and more streamlined synthetic biology workflows that can be readily employed to address grand challenges across manufacturing, medicine, engineering, agriculture, and beyond.

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.

Hidden in plain sight: challenges in proteomics detection of small ORF-encoded polypeptides

Genomic studies of bacteria have long pointed toward widespread prevalence of small open reading frames (sORFs) encoding for short proteins, <100 amino acids in length. Despite the mounting genomic evidence of their robust expression, relatively little progress has been made in their mass spectrometry-based detection and various blanket statements have been used to explain this observed discrepancy. In this study, we provide a large-scale riboproteogenomics investigation of the challenging nature of proteomic detection of such small proteins as informed by conditional translation data. A panel of physiochemical properties alongside recently developed mass spectrometry detectability metrics was interrogated to provide a comprehensive evidence-based assessment of sORF-encoded polypeptide (SEP) detectability. Moreover, a large-scale proteomics and translatomics compendium of proteins produced by Salmonella Typhimurium (S. Typhimurium), a model human pathogen, across a panel of growth conditions is presented and used in support of our in silico SEP detectability analysis. This integrative approach is used to provide a data-driven census of small proteins expressed by S. Typhimurium across growth phases and infection-relevant conditions. Taken together, our study pinpoints current limitations in proteomics-based detection of novel small proteins currently missing from bacterial genome annotations.

RiboReport - benchmarking tools for ribosome profiling-based identification of open reading frames in bacteria

Small proteins encoded by short open reading frames (ORFs) with 50 codons or fewer are emerging as an important class of cellular macromolecules in diverse organisms. However, they often evade detection by proteomics or in silico methods. Ribosome profiling (Ribo-seq) has revealed widespread translation in genomic regions previously thought to be non-coding, driving the development of ORF detection tools using Ribo-seq data. However, only a handful of tools have been designed for bacteria, and these have not yet been systematically compared. Here, we aimed to identify tools that use Ribo-seq data to correctly determine the translational status of annotated bacterial ORFs and also discover novel translated regions with high sensitivity. To this end, we generated a large set of annotated ORFs from four diverse bacterial organisms, manually labeled for their translation status based on Ribo-seq data, which are available for future benchmarking studies. This set was used to investigate the predictive performance of seven Ribo-seq-based ORF detection tools (REPARATION_blast, DeepRibo, Ribo-TISH, PRICE, smORFer, ribotricer and SPECtre), as well as IRSOM, which uses coding potential and RNA-seq coverage only. DeepRibo and REPARATION_blast robustly predicted translated ORFs, including sORFs, with no significant difference for ORFs in close proximity to other genes versus stand-alone genes. However, no tool predicted a set of novel, experimentally verified sORFs with high sensitivity. Start codon predictions with smORFer show the value of initiation site profiling data to further improve the sensitivity of ORF prediction tools in bacteria. Overall, we find that bacterial tools perform well for sORF detection, although there is potential for improving their performance, applicability, usability and reproducibility.

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 Protein Enrichment Improves Proteomics Detection of sORF Encoded Polypeptides

With the rapid growth in the number of sequenced genomes, genome annotation efforts became almost exclusively reliant on automated pipelines. Despite their unquestionable utility, these methods have been shown to underestimate the true complexity of the studied genomes, with small open reading frames (sORFs; ORFs typically considered shorter than 300 nucleotides) and, in consequence, their protein products (sORF encoded polypeptides or SEPs) being the primary example of a poorly annotated and highly underexplored class of genomic elements. With the advent of advanced translatomics such as ribosome profiling, reannotation efforts have progressed a great deal in providing translation evidence for numerous, previously unannotated sORFs. However, proteomics validation of these riboproteogenomics discoveries remains challenging due to their short length and often highly variable physiochemical properties. In this work we evaluate and compare tailored, yet easily adaptable, protein extraction methodologies for their efficacy in the extraction and concomitantly proteomics detection of SEPs expressed in the prokaryotic model pathogen Salmonella typhimurium (S. typhimurium). Further, an optimized protocol for the enrichment and efficient detection of SEPs making use of the of amphipathic polymer amphipol A8-35 and relying on differential peptide vs. protein solubility was developed and compared with global extraction methods making use of chaotropic agents. Given the versatile biological functions SEPs have been shown to exert, this work provides an accessible protocol for proteomics exploration of this fascinating class of small proteins.

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.

Trips-Viz: an environment for the analysis of public and user-generated ribosome profiling data

Trips-Viz (https://trips.ucc.ie/) is an interactive platform for the analysis and visualization of ribosome profiling (Ribo-Seq) and shotgun RNA sequencing (RNA-seq) data. This includes publicly available and user generated data, hence Trips-Viz can be classified as a database and as a server. As a database it provides access to many processed Ribo-Seq and RNA-seq data aligned to reference transcriptomes which has been expanded considerably since its inception. Here, we focus on the server functionality of Trips-viz which also has been greatly improved. Trips-viz now enables visualisation of proteomics data from a large number of processed mass spectrometry datasets. It can be used to support translation inferred from Ribo-Seq data. Users are now able to upload a custom reference transcriptome as well as data types other than Ribo-Seq/RNA-Seq. Incorporating custom data has been streamlined with RiboGalaxy (https://ribogalaxy.ucc.ie/) integration. The other new functionality is the rapid detection of translated open reading frames (ORFs) through a simple easy to use interface. The analysis of differential expression has been also improved via integration of DESeq2 and Anota2seq in addition to a number of other improvements of existing Trips-viz features.

smORFer: a modular algorithm to detect small ORFs in prokaryotes

Emerging evidence places small proteins (≤50 amino acids) more centrally in physiological processes. Yet, their functional identification and the systematic genome annotation of their cognate small open-reading frames (smORFs) remains challenging both experimentally and computationally. Ribosome profiling or Ribo-Seq (that is a deep sequencing of ribosome-protected fragments) enables detecting of actively translated open-reading frames (ORFs) and empirical annotation of coding sequences (CDSs) using the in-register translation pattern that is characteristic for genuinely translating ribosomes. Multiple identifiers of ORFs that use the 3-nt periodicity in Ribo-Seq data sets have been successful in eukaryotic smORF annotation. They have difficulties evaluating prokaryotic genomes due to the unique architecture (e.g. polycistronic messages, overlapping ORFs, leaderless translation, non-canonical initiation etc.). Here, we present a new algorithm, smORFer, which performs with high accuracy in prokaryotic organisms in detecting putative smORFs. The unique feature of smORFer is that it uses an integrated approach and considers structural features of the genetic sequence along with in-frame translation and uses Fourier transform to convert these parameters into a measurable score to faithfully select smORFs. The algorithm is executed in a modular way, and dependent on the data available for a particular organism, different modules can be selected for smORF search.

Bacterial riboproteogenomics: the era of N-terminal proteoform existence revealed

With the rapid increase in the number of sequenced prokaryotic genomes, relying on automated gene annotation became a necessity. Multiple lines of evidence, however, suggest that current bacterial genome annotations may contain inconsistencies and are incomplete, even for so-called well-annotated genomes. We here discuss underexplored sources of protein diversity and new methodologies for high-throughput genome reannotation. The expression of multiple molecular forms of proteins (proteoforms) from a single gene, particularly driven by alternative translation initiation, is gaining interest as a prominent contributor to bacterial protein diversity. In consequence, riboproteogenomic pipelines were proposed to comprehensively capture proteoform expression in prokaryotes by the complementary use of (positional) proteomics and the direct readout of translated genomic regions using ribosome profiling. To complement these discoveries, tailored strategies are required for the functional characterization of newly discovered bacterial proteoforms.

Monitoring Bacterial Translation Rates Genome-Wide

Modern DNA sequencing technologies have allowed for the sequencing of tens of thousands of bacterial genomes. While this explosion of information has brought about new insights into the diversity of the prokaryotic world, much less is known of the identity of proteins encoded within these genomes, as well as their rates of production. The advent of ribosome profiling, or the deep sequencing of ribosome-protected footprints, has recently enabled the systematic evaluation of every protein-coding region in a given experimental condition, the rates of protein production for each gene, and the variability in translation rates across each message. Here, I provide an update to the bacterial ribosome profiling approach, with a particular emphasis on a simplified strategy to reduce cloning time.

Identification of novel translated small ORFs in Escherichia coli using complementary ribosome profiling approaches

Small proteins of <51 amino acids are abundant across all domains of life but are often overlooked because their small size makes them difficult to predict computationally, and they are refractory to standard proteomic approaches. Ribosome profiling has been used to infer the existence of small proteins by detecting the translation of the corresponding open reading frames (ORFs). Detection of translated short ORFs by ribosome profiling can be improved by treating cells with drugs that stall ribosomes at specific codons. Here, we combine the analysis of ribosome profiling data for Escherichia coli cells treated with antibiotics that stall ribosomes at either start or stop codons. Thus, we identify ribosome-occupied start and stop codons with high sensitivity for ∼400 novel putative ORFs. The newly discovered ORFs are mostly short, with 365 encoding proteins of <51 amino acids. We validate translation of several selected short ORFs, and show that many likely encode unstable proteins. Moreover, we present evidence that most of the newly identified short ORFs are not under purifying selection, suggesting they do not impact cell fitness, although a small subset have the hallmarks of functional ORFs. IMPORTANCE Small proteins of <51 amino acids are abundant across all domains of life but are often overlooked because their small size makes them difficult to predict computationally, and they are refractory to standard proteomic approaches. Recent studies have discovered small proteins by mapping the location of translating ribosomes on RNA using a technique known as ribosome profiling. Discovery of translated sORFs using ribosome profiling can be improved by treating cells with drugs that trap initiating ribosomes. Here, we show that combining these data with equivalent data for cells treated with a drug that stalls terminating ribosomes facilitates the discovery of small proteins. We use this approach to discover 365 putative genes that encode small proteins in Escherichia coli.

Identification of Translation Start Sites in Bacterial Genomes

The knowledge of translation start sites is crucial for annotation of genes in bacterial genomes. However, systematic mapping of start codons in bacterial genes has mainly relied on predictions based on protein conservation and mRNA sequence features which, although useful, are not always accurate. We recently found that the pleuromutilin antibiotic retapamulin (RET) is a specific inhibitor of translation initiation that traps ribosomes specifically at start codons, and we used it in combination with ribosome profiling to map start codons in the Escherichia coli genome. This genome-wide strategy, that was named Ribo-RET, not only verifies the position of start codons in already annotated genes but also enables identification of previously unannotated open reading frames and reveals the presence of internal start sites within genes. Here, we provide a detailed Ribo-RET protocol for E. coli. Ribo-RET can be adapted for mapping the start codons of the protein-coding sequences in a variety of bacterial species.

Machine learning for metabolic engineering: A review

Machine learning provides researchers a unique opportunity to make metabolic engineering more predictable. In this review, we offer an introduction to this discipline in terms that are relatable to metabolic engineers, as well as providing in-depth illustrative examples leveraging omics data and improving production. We also include practical advice for the practitioner in terms of data management, algorithm libraries, computational resources, and important non-technical issues. A variety of applications ranging from pathway construction and optimization, to genetic editing optimization, cell factory testing, and production scale-up are discussed. Moreover, the promising relationship between machine learning and mechanistic models is thoroughly reviewed. Finally, the future perspectives and most promising directions for this combination of disciplines are examined.

The Small Toxic Salmonella Protein TimP Targets the Cytoplasmic Membrane and Is Repressed by the Small RNA TimR

Next-generation sequencing (NGS) has enabled the revelation of a vast number of genomes from organisms spanning all domains of life. To reduce complexity when new genome sequences are annotated, open reading frames (ORFs) shorter than 50 codons in length are generally omitted. However, it has recently become evident that this procedure sorts away ORFs encoding small proteins of high biological significance. For instance, tailored small protein identification approaches have shown that bacteria encode numerous small proteins with important physiological functions. As the number of predicted small ORFs increase, it becomes important to characterize the corresponding proteins. In this study, we discovered a conserved but previously overlooked small enterobacterial protein. We show that this protein, which we dubbed TimP, is a potent toxin that inhibits bacterial growth by targeting the cell membrane. Toxicity is relieved by a small regulatory RNA, which binds the toxin mRNA to inhibit toxin synthesis.

Small proteins are gaining increased attention due to their important functions in major biological processes throughout the domains of life. However, their small size and low sequence conservation make them difficult to identify. It is therefore not surprising that enterobacterial ryfA has escaped identification as a small protein coding gene for nearly 2 decades. Since its identification in 2001, ryfA has been thought to encode a noncoding RNA and has been implicated in biofilm formation in Escherichia coli and pathogenesis in Shigella dysenteriae. Although a recent ribosome profiling study suggested ryfA to be translated, the corresponding protein product was not detected. In this study, we provide evidence that ryfA encodes a small toxic inner membrane protein, TimP, overexpression of which causes cytoplasmic membrane leakage. TimP carries an N-terminal signal sequence, indicating that its membrane localization is Sec-dependent. Expression of TimP is repressed by the small RNA (sRNA) TimR, which base pairs with the timP mRNA to inhibit its translation. In contrast to overexpression, endogenous expression of TimP upon timR deletion permits cell growth, possibly indicating a toxicity-independent function in the bacterial membrane.

Lost and Found: Re-searching and Re-scoring Proteomics Data Aids Genome Annotation and Improves Proteome Coverage

Delineation of open reading frames (ORFs) causes persistent inconsistencies in prokaryote genome annotation. We demonstrate that by advanced (re)analysis of omics data, a higher proteome coverage and sensitive detection of unannotated ORFs can be achieved, which can be exploited for conditional bacterial genome (re)annotation, which is especially relevant in view of annotating the wealth of sequenced prokaryotic genomes obtained in recent years.

Prokaryotic genome annotation is heavily dependent on automated gene annotation pipelines that are prone to propagate errors and underestimate genome complexity. We describe an optimized proteogenomic workflow that uses ribosome profiling (ribo-seq) and proteomic data for Salmonella enterica serovar Typhimurium to identify unannotated proteins or alternative protein forms. This data analysis encompasses the searching of cofragmenting peptides and postprocessing with extended peptide-to-spectrum quality features, including comparison to predicted fragment ion intensities. When this strategy is applied, an enhanced proteome depth is achieved, as well as greater confidence for unannotated peptide hits. We demonstrate the general applicability of our pipeline by reanalyzing public Deinococcus radiodurans data sets. Taken together, our results show that systematic reanalysis using available prokaryotic (proteome) data sets holds great promise to assist in experimentally based genome annotation.

IMPORTANCE Delineation of open reading frames (ORFs) causes persistent inconsistencies in prokaryote genome annotation. We demonstrate that by advanced (re)analysis of omics data, a higher proteome coverage and sensitive detection of unannotated ORFs can be achieved, which can be exploited for conditional bacterial genome (re)annotation, which is especially relevant in view of annotating the wealth of sequenced prokaryotic genomes obtained in recent years.

Recommendations for bacterial ribosome profiling experiments based on bioinformatic evaluation of published data

Ribosome profiling (RIBO-Seq) has improved our understanding of bacterial translation, including finding many unannotated genes. However, protocols for RIBO-Seq and corresponding data analysis are not yet standardized. Here, we analyzed 48 RIBO-Seq samples from nine studies of Escherichia coli K12 grown in lysogeny broth medium and particularly focused on the size-selection step. We show that for conventional expression analysis, a size range between 22 and 30 nucleotides is sufficient to obtain protein-coding fragments, which has the advantage of removing many unwanted rRNA and tRNA reads. More specific analyses may require longer reads and a corresponding improvement in rRNA/tRNA depletion. There is no consensus about the appropriate sequencing depth for RIBO-Seq experiments in prokaryotes, and studies vary significantly in total read number. Our analysis suggests that 20 million reads that are not mapping to rRNA/tRNA are required for global detection of translated annotated genes. We also highlight the influence of drug-induced ribosome stalling, which causes bias at translation start sites. The resulting accumulation of reads at the start site may be especially useful for detecting weakly expressed genes. As different methods suit different questions, it may not be possible to produce a "one-size-fits-all" ribosome profiling data set. Therefore, experiments should be carefully designed in light of the scientific questions of interest. We propose some basic characteristics that should be reported with any new RIBO-Seq data sets. Careful attention to the factors discussed should improve prokaryotic gene detection and the comparability of ribosome profiling data sets.

MetaRibo-Seq measures translation in microbiomes

No method exists to measure large-scale translation of genes in uncultured organisms in microbiomes. To overcome this limitation, we develop MetaRibo-Seq, a method for simultaneous ribosome profiling of tens to hundreds of organisms in microbiome samples. MetaRibo-Seq was benchmarked against gold-standard Ribo-Seq in a mock microbial community and applied to five different human fecal samples. Unlike RNA-Seq, Ribo-Seq signal of a predicted gene suggests it encodes a translated protein. We demonstrate two applications of this technique: First, MetaRibo-Seq identifies small genes, whose identification until now has been challenging. For example, MetaRibo-Seq identifies 2,091 translated, previously unannotated small protein families from five fecal samples, more than doubling the number of small proteins predicted to exist in this niche. Second, the combined application of RNA-Seq and MetaRibo-Seq identifies differences in the translation of transcripts. In summary, MetaRibo-Seq enables comprehensive translational profiling in microbiomes and identifies previously unannotated small proteins.

Defining the functions of individual organisms or communities within microbiomes is a challenging task. Here, the authors develop MetaRibo-Seq, a method for simultaneous high-throughput ribosome profiling of organisms in uncultured microbiome samples.

Computational methods for ribosome profiling data analysis

Since the introduction of the ribosome profiling technique in 2009 its popularity has greatly increased. It is widely used for the comprehensive assessment of gene expression and for studying the mechanisms of regulation at the translational level. As the number of ribosome profiling datasets being produced continues to grow, so too does the need for reliable software that can provide answers to the biological questions it can address. This review describes the computational methods and tools that have been developed to analyze ribosome profiling data at the different stages of the process. It starts with initial routine processing of raw data and follows with more specific tasks such as the identification of translated open reading frames, differential gene expression analysis, or evaluation of local or global codon decoding rates. The review pinpoints challenges associated with each step and explains the ways in which they are currently addressed. In addition it provides a comprehensive, albeit incomplete, list of publicly available software applicable to each step, which may be a beneficial starting point to those unexposed to ribosome profiling analysis. The outline of current challenges in ribosome profiling data analysis may inspire computational biologists to search for novel, potentially superior, solutions that will improve and expand the bioinformatician's toolbox for ribosome profiling data analysis. This article is characterized under: Translation > Ribosome Structure/Function RNA Evolution and Genomics > Computational Analyses of RNA Translation > Translation Mechanisms Translation > Translation Regulation.

DeepRibo: a neural network for precise gene annotation of prokaryotes by combining ribosome profiling signal and binding site patterns

Annotation of gene expression in prokaryotes often finds itself corrected due to small variations of the annotated gene regions observed between different (sub)-species. It has become apparent that traditional sequence alignment algorithms, used for the curation of genomes, are not able to map the full complexity of the genomic landscape. We present DeepRibo, a novel neural network utilizing features extracted from ribosome profiling information and binding site sequence patterns that shows to be a precise tool for the delineation and annotation of expressed genes in prokaryotes. The neural network combines recurrent memory cells and convolutional layers, adapting the information gained from both the high-throughput ribosome profiling data and ribosome binding translation initiation sequence region into one model. DeepRibo is designed as a single model trained on a variety of ribosome profiling experiments, used for the identification of open reading frames in prokaryotes without a priori knowledge of the translational landscape. Through extensive validation of the model trained on various sets of data, multiple species sequence similarity, mass spectrometry and Edman degradation verified proteins, the effectiveness of DeepRibo is highlighted.

De Novo Emergence of Peptides That Confer Antibiotic Resistance

The origin of novel genes and beneficial functions is of fundamental interest in evolutionary biology. New genes can originate from different mechanisms, including horizontal gene transfer, duplication-divergence, and de novo from noncoding DNA sequences. Comparative genomics has generated strong evidence for de novo emergence of genes in various organisms, but experimental demonstration of this process has been limited to localized randomization in preexisting structural scaffolds. This bypasses the basic requirement of de novo gene emergence, i.e., lack of an ancestral gene. We constructed highly diverse plasmid libraries encoding randomly generated open reading frames and expressed them in Escherichia coli to identify short peptides that could confer a beneficial and selectable phenotype in vivo (in a living cell). Selections on antibiotic-containing agar plates resulted in the identification of three peptides that increased aminoglycoside resistance up to 48-fold. Combining genetic and functional analyses, we show that the peptides are highly hydrophobic, and by inserting into the membrane, they reduce membrane potential, decrease aminoglycoside uptake, and thereby confer high-level resistance. This study demonstrates that randomized DNA sequences can encode peptides that confer selective benefits and illustrates how expression of random sequences could spark the origination of new genes. In addition, our results also show that this question can be addressed experimentally by expression of highly diverse sequence libraries and subsequent selection for specific functions, such as resistance to toxic compounds, the ability to rescue auxotrophic/temperature-sensitive mutants, and growth on normally nonused carbon sources, allowing the exploration of many different phenotypes.IMPORTANCE De novo gene origination from nonfunctional DNA sequences was long assumed to be implausible. However, recent studies have shown that large fractions of genomic noncoding DNA are transcribed and translated, potentially generating new genes. Experimental validation of this process so far has been limited to comparative genomics, in vitro selections, or partial randomizations. Here, we describe selection of novel peptides in vivo using fully random synthetic expression libraries. The peptides confer aminoglycoside resistance by inserting into the bacterial membrane and thereby partly reducing membrane potential and decreasing drug uptake. Our results show that beneficial peptides can be selected from random sequence pools in vivo and support the idea that expression of noncoding sequences could spark the origination of new genes.

Small open reading frames and cellular stress responses

Small open reading frames (smORFs) encoding polypeptides of less than 100 amino acids in eukaryotes (50 amino acids in prokaryotes) were historically excluded from genome annotation. However, recent advances in genomics, ribosome footprinting, and proteomics have revealed thousands of translated smORFs in genomes spanning evolutionary space. These smORFs can encode functional polypeptides, or act as cis-translational regulators. Herein we review evidence that some smORF-encoded polypeptides (SEPs) participate in stress responses in both prokaryotes and eukaryotes, and that some upstream ORFs (uORFs) regulate stress-responsive translation of downstream cistrons in eukaryotic cells. These studies provide insight into a regulated subclass of smORFs and suggest that at least some SEPs may participate in maintenance of cellular homeostasis under stress.

Ribosomal stalling landscapes revealed by high-throughput inverse toeprinting of mRNA libraries

High-throughput inverse toeprinting identifies peptide-encoding transcripts that induce ribosome stalling and allows the systematic analysis of sequence-dependent translational events.

Although it is known that the amino acid sequence of a nascent polypeptide can impact its rate of translation, dedicated tools to systematically investigate this process are lacking. Here, we present high-throughput inverse toeprinting, a method to identify peptide-encoding transcripts that induce ribosomal stalling in vitro. Unlike ribosome profiling, inverse toeprinting protects the entire coding region upstream of a stalled ribosome, making it possible to work with random or focused transcript libraries that efficiently sample the sequence space. We used inverse toeprinting to characterize the stalling landscapes of free and drug-bound Escherichia coli ribosomes, obtaining a comprehensive list of arrest motifs that were validated in vivo, along with a quantitative measure of their pause strength. Thanks to the modest sequencing depth and small amounts of material required, inverse toeprinting provides a highly scalable and versatile tool to study sequence-dependent translational processes.

PausePred and Rfeet: webtools for inferring ribosome pauses and visualizing footprint density from ribosome profiling data

The process of translation is characterized by irregularities in the local decoding rates of specific mRNA codons. This includes the occurrences of long pauses that can take place when ribosomes decode certain peptide sequences, encounter strong RNA secondary structures, or decode "hungry" codons. Examples are known where such pausing or stalling is used for regulating protein synthesis. This can be achieved at the level of translation via direct alteration of ribosome progression through mRNA or by altering mRNA stability via NoGo decay. Ribosome pausing has also been implicated in the cotranslational folding of proteins. Ribosome profiling data often are used for inferring the locations of ribosome pauses. However, no dedicated online software is available for this purpose. Here we present PausePred (https://pausepred.ucc.ie/), which can be used to infer ribosome pauses from ribosome profiling (Ribo-seq) data. Peaks of ribosome footprint density are scored based on their magnitude relative to the background density within the surrounding area. The scoring allows the comparison of peaks across the transcriptome or genome. In addition to the score, PausePred reports the coordinates of the pause, the footprint density at the pause site, and the surrounding nucleotide sequence. The pauses can be visualized in the context of Ribo-seq and RNA-seq density plots generated for specific transcripts or genomic regions with the Rfeet tool. PausePred does not require input on the location of protein coding ORFs (although gene annotations can be optionally supplied). As a result, it can be used universally and its output does not depend on ever evolving annotations.

Identifying New Small Proteins in Escherichia coli

The number of small proteins (SPs) encoded in the Escherichia coli genome is unknown, as current bioinformatics and biochemical techniques make short gene and small protein identification challenging. One method of small protein identification involves adding an epitope tag to the 3′ end of a short open reading frame (sORF) on the chromosome, with synthesis confirmed by immunoblot assays. In this study, this strategy was used to identify new E. coli small proteins, tagging 80 sORFs in the E. coli genome, and assayed for protein synthesis. The selected sORFs represent diverse sequence characteristics, including degrees of sORF conservation, predicted transmembrane domains, sORF direction with respect to flanking genes, ribosome binding site (RBS) prediction, and ribosome profiling results. Of 80 sORFs, 36 resulted in encoded synthesized proteins—a 45% success rate. Modeling of detected versus non‐detected small proteins analysis showed predictions based on RBS prediction, transcription data, and ribosome profiling had statistically‐significant correlation with protein synthesis; however, there was no correlation between current sORF annotation and protein synthesis. These results suggest substantial numbers of small proteins remain undiscovered in E. coli, and existing bioinformatics techniques must continue to improve to facilitate identification.

Proteogenomics in Aid of Host-Pathogen Interaction Studies: A Bacterial Perspective

By providing useful tools to study host-pathogen interactions, next-generation omics has recently enabled the study of gene expression changes in both pathogen and infected host simultaneously. However, since great discriminative power is required to study pathogen and host simultaneously throughout the infection process, the depth of quantitative gene expression profiling has proven to be unsatisfactory when focusing on bacterial pathogens, thus preferentially requiring specific strategies or the development of novel methodologies based on complementary omics approaches. In this review, we focus on the difficulties encountered when making use of proteogenomics approaches to study bacterial pathogenesis. In addition, we review different omics strategies (i.e., transcriptomics, proteomics and secretomics) and their applications for studying interactions of pathogens with their host.