Unannotated Proteins Research Articles

Circuitry Linking the Global Csr- and σE-Dependent Cell Envelope Stress Response Systems.

CsrA of Escherichia coli is an RNA-binding protein that globally regulates a wide variety of cellular processes and behaviors, including carbon metabolism, motility, biofilm formation, and the stringent response. CsrB and CsrC are small RNAs (sRNAs) that sequester CsrA, thereby preventing CsrA-mRNA interaction. RpoE (σE) is the extracytoplasmic stress response sigma factor of E. coli Previous RNA sequencing (RNA-seq) studies identified rpoE mRNA as a CsrA target. Here, we explored the regulation of rpoE by CsrA and found that CsrA represses rpoE translation. Gel mobility shift, footprint, and toeprint studies identified three CsrA binding sites in the rpoE leader transcript, one of which overlaps the rpoE Shine-Dalgarno (SD) sequence, while another overlaps the rpoE translation initiation codon. Coupled in vitro transcription-translation experiments showed that CsrA represses rpoE translation by binding to these sites. We further demonstrate that σE indirectly activates the transcription of csrB and csrC, leading to increased sequestration of CsrA, such that repression of rpoE by CsrA is reduced. We propose that the Csr system fine-tunes the σE-dependent cell envelope stress response. We also identified a 51-amino-acid coding sequence whose stop codon overlaps the rpoE start codon and demonstrate that rpoE is translationally coupled with this upstream open reading frame (ORF51). The loss of coupling reduces rpoE translation by more than 50%. Identification of a translationally coupled ORF upstream of rpoE suggests that this previously unannotated protein may participate in the cell envelope stress response. In keeping with existing nomenclature, we named ORF51 rseD, resulting in an operon arrangement of rseD-rpoE-rseA-rseB-rseC IMPORTANCE CsrA posttranscriptionally represses genes required for bacterial stress responses, including the stringent response, catabolite repression, and the RpoS (σS)-mediated general stress response. We show that CsrA represses the translation of rpoE, encoding the extracytoplasmic stress response sigma factor, and that σE indirectly activates the transcription of csrB and csrC, resulting in reciprocal regulation of these two global regulatory systems. These findings suggest that extracytoplasmic stress leads to derepression of rpoE translation by CsrA, and CsrA-mediated repression helps reset RpoE abundance to prestress levels once envelope damage is repaired. The discovery of an ORF, rseD, translationally coupled with rpoE adds further complexity to translational control of rpoE.

Comparative Omics and Trait Analyses of Marine Pseudoalteromonas Phages Advance the Phage OTU Concept.

Viruses influence the ecology and evolutionary trajectory of microbial communities. Yet our understanding of their roles in ecosystems is limited by the paucity of model systems available for hypothesis generation and testing. Further, virology is limited by the lack of a broadly accepted conceptual framework to classify viral diversity into evolutionary and ecologically cohesive units. Here, we introduce genomes, structural proteomes, and quantitative host range data for eight Pseudoalteromonas phages isolated from Helgoland (North Sea, Germany) and use these data to advance a genome-based viral operational taxonomic unit (OTU) definition. These viruses represent five new genera and inform 498 unaffiliated or unannotated protein clusters (PCs) from global virus metagenomes. In a comparison of previously sequenced Pseudoalteromonas phage isolates (n = 7) and predicted prophages (n = 31), the eight phages are unique. They share a genus with only one other isolate, Pseudoalteromonas podophage RIO-1 (East Sea, South Korea) and two Pseudoalteromonas prophages. Mass-spectrometry of purified viral particles identified 12–20 structural proteins per phage. When combined with 3-D structural predictions, these data led to the functional characterization of five previously unidentified major capsid proteins. Protein functional predictions revealed mechanisms for hijacking host metabolism and resources. Further, they uncovered a hybrid sipho-myovirus that encodes genes for Mu-like infection rarely described in ocean systems. Finally, we used these data to evaluate a recently introduced definition for virus populations that requires members of the same population to have >95% average nucleotide identity across at least 80% of their genes. Using physiological traits and genomics, we proposed a conceptual model for a viral OTU definition that captures evolutionarily cohesive and ecologically distinct units. In this trait-based framework, sensitive hosts are considered viral niches, while host ranges and infection efficiencies are tracked as viral traits. Quantitative host range assays revealed conserved traits within virus OTUs that break down between OTUs, suggesting the defined units capture niche and fitness differentiation. Together these analyses provide a foundation for model system-based hypothesis testing that will improve our understanding of marine copiotrophs, as well as phage–host interactions on the ocean particles and aggregates where Pseudoalteromonas thrive.

A new distributed alignment-free approach to compare whole proteomes

Phylogeny inference has moved in recent years from the analysis of a single or few proteins to that of whole proteomes. However, the reconstruction of evolutionary trees for big number of species poses a significant computational challenge when using complete proteomes, even when relatively fast pairwise sequence comparison algorithms are used. We present a distributed approach that relies on the computation of distance measures based on maximal shared substrings within a bounded Hamming distance. The distributed system we built to implement this approach is flexible in that it supports a variety of design choices. It is based on the Spark framework and covers all the steps required by our approach, starting from the initial indexing of a set of FASTA sequences up to producing a report detailing the distances among these sequences, ranked according to a user-defined measure. Here we apply it to compare all proteins of selected organisms, divide them into groups and perform the comparisons within each group separately. The groups include: the functionally characterized proteins, the ribosomal proteins, and the unannotated proteins. We compute the average distances within the groups and evaluate their relationship and ability to capture the evolutionary closeness of organisms. We run experiments on selected species using a Hadoop computing cluster running Spark. The results show that the system implementing our approach is scalable and accurate.

Proteogenomic analysis reveals alternative splicing and translation as part of the abscisic acid response in Arabidopsis seedlings.

In eukaryotes, mechanisms such as alternative splicing (AS) and alternative translation initiation (ATI) contribute to organismal protein diversity. Specifically, splicing factors play crucial roles in responses to environment and development cues; however, the underlying mechanisms are not well investigated in plants. Here, we report the parallel employment of short-read RNA sequencing, single molecule long-read sequencing and proteomic identification to unravel AS isoforms and previously unannotated proteins in response to abscisic acid (ABA) treatment. Combining the data from the two sequencing methods, approximately 83.4% of intron-containing genes were alternatively spliced. Two AS types, which are referred to as alternative first exon (AFE) and alternative last exon (ALE), were more abundant than intron retention (IR); however, by contrast to AS events detected under normal conditions, differentially expressed AS isoforms were more likely to be translated. ABA extensively affects the AS pattern, indicated by the increasing number of non-conventional splicing sites. This work also identified thousands of unannotated peptides and proteins by ATI based on mass spectrometry and a virtual peptide library deduced from both strands of coding regions within the Arabidopsis genome. The results enhance our understanding of AS and alternative translation mechanisms under normal conditions, and in response to ABA treatment.

Unbiased Protein Association Study on the Public Human Proteome Reveals Biological Connections between Co-Occurring Protein Pairs.

Mass-spectrometry-based, high-throughput proteomics experiments produce large amounts of data. While typically acquired to answer specific biological questions, these data can also be reused in orthogonal ways to reveal new biological knowledge. We here present a novel method for such orthogonal data reuse of public proteomics data. Our method elucidates biological relationships between proteins based on the co-occurrence of these proteins across human experiments in the PRIDE database. The majority of the significantly co-occurring protein pairs that were detected by our method have been successfully mapped to existing biological knowledge. The validity of our novel method is substantiated by the extremely few pairs that can be mapped to existing knowledge based on random associations between the same set of proteins. Moreover, using literature searches and the STRING database, we were able to derive meaningful biological associations for unannotated protein pairs that were detected using our method, further illustrating that as-yet unknown associations present highly interesting targets for follow-up analysis.

Insights into Ciliary Genes and Evolution from Multi-Level Phylogenetic Profiling.

Cilia (flagella) are important eukaryotic organelles, present in the Last Eukaryotic Common Ancestor, and are involved in cell motility and integration of extracellular signals. Ciliary dysfunction causes a class of genetic diseases, known as ciliopathies, however current knowledge of the underlying mechanisms is still limited and a better characterization of genes is needed. As cilia have been lost independently several times during evolution and they are subject to important functional variation between species, ciliary genes can be investigated through comparative genomics. We performed phylogenetic profiling by predicting orthologs of human protein-coding genes in 100 eukaryotic species. The analysis integrated three independent methods to predict a consensus set of 274 ciliary genes, including 87 new promising candidates. A fine-grained analysis of the phylogenetic profiles allowed a partitioning of ciliary genes into modules with distinct evolutionary histories and ciliary functions (assembly, movement, centriole, etc.) and thus propagation of potential annotations to previously undocumented genes. The cilia/basal body localization was experimentally confirmed for five of these previously unannotated proteins (LRRC23, LRRC34, TEX9, WDR27, and BIVM), validating the relevance of our approach. Furthermore, our multi-level analysis sheds light on the core gene sets retained in gamete-only flagellates or Ecdysozoa for instance. By combining gene-centric and species-oriented analyses, this work reveals new ciliary and ciliopathy gene candidates and provides clues about the evolution of ciliary processes in the eukaryotic domain. Additionally, the positive and negative reference gene sets and the phylogenetic profile of human genes constructed during this study can be exploited in future work.

Mutation in an Unannotated Protein Confers Carbapenem Resistance in Mycobacterium tuberculosis.

β-Lactams are the most widely used antibacterials. Among β-lactams, carbapenems are considered the last line of defense against recalcitrant infections. As recent developments have prompted consideration of carbapenems for treatment of drug-resistant tuberculosis, it is only a matter of time before Mycobacterium tuberculosis strains resistant to these drugs will emerge. In the present study, we investigated the genetic basis that confers such resistance. To our surprise, instead of mutations in the known β-lactam targets, a single nucleotide polymorphism in the Rv2421c-Rv2422 intergenic region was common among M. tuberculosis mutants selected with meropenem or biapenem. We present data supporting the hypothesis that this locus harbors a previously unidentified gene that encodes a protein. This protein binds to β-lactams, slowly hydrolyzes the chromogenic β-lactam nitrocefin, and is inhibited by select penicillins and carbapenems and the β-lactamase inhibitor clavulanate. The mutation results in a W62R substitution that reduces the protein's nitrocefin-hydrolyzing activity and binding affinities for carbapenems.

Annotation transfer by homology among closely related genomes helps to identify protein function in Plasmodium species

Malaria is a major killer disease causing heavy loss in terms of life and medical expenditure. The genome of Plasmodium falciparum sequenced in 2002 but more than 60% of its meaning still remains unknown. We compared protein sequences from P. falciparum in the genomic sequences with P. vivax and P. knowlesi through standalone blast tool from NCBI. We found many proteins annotated in one species sharing high similarity with unannotated proteins in the other/s species. Based on the 'annotation transfer by homology' method we annotated 715 hypothetical proteins. By the latest information available as of 1st September our method annotated 343 protein sequences. Gene Ontology annotation with BLAST2GO revealed 128 proteins with Enzyme Codes among the 561 sequences mapped with GO terms. The kognitor results showed 542 proteins similar to proteins belonging to various KOG categories. More attention should be paid to these 'hypothetical' proteins as they can reveal unknown insights on the biology of organisms and have an impact in the field of drug discovery and medicine.

YeATSAM analysis of the walnut and chickpea transcriptome reveals key genes undetected by current annotation tools

Background: The transcriptome, a treasure trove of gene space information, remains severely under-used by current genome annotation methods. Methods: Here, we present an annotation method in the YeATS suite (YeATSAM), based on information encoded by the transcriptome, that demonstrates artifacts of the assembler, which must be addressed to achieve proper annotation. Results and Discussion: YeATSAM was applied to the transcriptome obtained from twenty walnut tissues and compared to MAKER-P annotation of the recently published walnut genome sequence (WGS). MAKER-P and YeATSAM both failed to annotate several hundred proteins found by the other. Although many of these unannotated proteins have repetitive sequences (possibly transposable elements), other crucial proteins were excluded by each method. An egg cell-secreted protein and a homer protein were undetected by YeATSAM, although these did not produce any transcripts. Importantly, MAKER-P failed to classify key photosynthesis-related proteins, which we show emanated from Trinity assembly artifacts potentially not handled by MAKER-P. Also, no proteins from the large berberine bridge enzyme (BBE) family were annotated by MAKER-P. BBE is implicated in biosynthesis of several alkaloids metabolites, like anti-microbial berberine. As further validation, YeATSAM identified ~1000 genes that are not annotated in the NCBI database by Gnomon. YeATSAM used a RNA-seq derived chickpea ( Cicer arietinum L.) transcriptome assembled using Newbler v2.3. Conclusions: Since the current version of YeATSAM does not have an ab initio module, we suggest a combined annotation scheme using both MAKER-P and YeATSAM to comprehensively and accurately annotate the WGS.

YeATSAM analysis of the walnut and chickpea transcriptome reveals key genes undetected by current annotation tools.

Background: The transcriptome, a treasure trove of gene space information, remains severely under-used by current genome annotation methods. Methods: Here, we present an annotation method in the YeATS suite (YeATSAM), based on information encoded by the transcriptome, that demonstrates artifacts of the assembler, which must be addressed to achieve proper annotation. Results and Discussion:YeATSAM was applied to the transcriptome obtained from twenty walnut tissues and compared to MAKER-P annotation of the recently published walnut genome sequence (WGS). MAKER-P and YeATSAM both failed to annotate several hundred proteins found by the other. Although many of these unannotated proteins have repetitive sequences (possibly transposable elements), other crucial proteins were excluded by each method. An egg cell-secreted protein and a homer protein were undetected by YeATSAM, although these did not produce any transcripts. Importantly, MAKER-P failed to classify key photosynthesis-related proteins, which we show emanated from Trinity assembly artifacts potentially not handled by MAKER-P. Also, no proteins from the large berberine bridge enzyme (BBE) family were annotated by MAKER-P. BBE is implicated in biosynthesis of several alkaloids metabolites, like anti-microbial berberine. As further validation, YeATSAM identified ~1000 genes that are not annotated in the NCBI database by Gnomon. YeATSAM used a RNA-seq derived chickpea ( Cicer arietinum L.) transcriptome assembled using Newbler v2.3. Conclusions:Since the current version of YeATSAM does not have an ab initio module, we suggest a combined annotation scheme using both MAKER-P and YeATSAM to comprehensively and accurately annotate the WGS.

Community-Wide Evaluation of Computational Function Prediction.

A biological experiment is the most reliable way of assigning function to a protein. However, in the era of high-throughput sequencing, scientists are unable to carry out experiments to determine the function of every single gene product. Therefore, to gain insights into the activity of these molecules and guide experiments, we must rely on computational means to functionally annotate the majority of sequence data. To understand how well these algorithms perform, we have established a challenge involving a broad scientific community in which we evaluate different annotation methods according to their ability to predict the associations between previously unannotated protein sequences and Gene Ontology terms. Here we discuss the rationale, benefits, and issues associated with evaluating computational methods in an ongoing community-wide challenge.

Inferring the Functions of Proteins from the Interrelationships between Functional Categories.

This study proposes a new method to determine the functions of an unannotated protein. The proteins and amino acid residues mentioned in biomedical texts associated with an unannotated protein can be considered as characteristics terms for , which are highly predictive of the potential functions of . Similarly, proteins and amino acid residues mentioned in biomedical texts associated with proteins annotated with a functional category can be considered as characteristics terms of . We introduce in this paper an information extraction system called IFP_IFC that predicts the functions of an unannotated protein by representing and each functional category by a vector of weights. Each weight reflects the degree of association between a characteristic term and (or a characteristic term and ). First, IFP_IFC constructs a network, whose nodes represent the different functional categories, and its edges the interrelationships between the nodes. Then, it determines the functions of by employing random walks with restarts on the mentioned network. The walker is the vector of . Finally, is assigned to the functional categories of the nodes in the network that are visited most by the walker. We evaluated the quality of IFP_IFC by comparing it experimentally with two other systems. Results showed marked improvement.

An efficient method for protein function annotation based on multilayer protein networks

BackgroundAccurate annotation of protein functions is still a big challenge for understanding life in the post-genomic era. Many computational methods based on protein-protein interaction (PPI) networks have been proposed to predict the function of proteins. However, the precision of these predictions still needs to be improved, due to the incompletion and noise in PPI networks. Integrating network topology and biological information could improve the accuracy of protein function prediction and may also lead to the discovery of multiple interaction types between proteins. Current algorithms generate a single network, which is archived using a weighted sum of all types of protein interactions.MethodThe influences of different types of interactions on the prediction of protein functions are not the same. To address this, we construct multilayer protein networks (MPN) by integrating PPI networks, the domain of proteins, and information on protein complexes. In the MPN, there is more than one type of connections between pairwise proteins. Different types of connections reflect different roles and importance in protein function prediction. Based on the MPN, we propose a new protein function prediction method, named function prediction based on multilayer protein networks (FP-MPN). Given an un-annotated protein, the FP-MPN method visits each layer of the MPN in turn and generates a set of candidate neighbors with known functions. A set of predicted functions for the testing protein is then formed and all of these functions are scored and sorted. Each layer plays different importance on the prediction of protein functions. A number of top-ranking functions are selected to annotate the unknown protein.ConclusionsThe method proposed in this paper was a better predictor when used on Saccharomyces cerevisiae protein data than other function prediction methods previously used. The proposed FP-MPN method takes different roles of connections in protein function prediction into account to reduce the artificial noise by introducing biological information.Electronic supplementary materialThe online version of this article (doi:10.1186/s40246-016-0087-x) contains supplementary material, which is available to authorized users.

ProtNN: fast and accurate protein 3D-structure classification in structural and topological space.

BackgroundStudying the functions and structures of proteins is important for understanding the molecular mechanisms of life. The number of publicly available protein structures has increasingly become extremely large. Still, the classification of a protein structure remains a difficult, costly, and time consuming task. The difficulties are often due to the essential role of spatial and topological structures in the classification of protein structures.ResultsWe propose ProtNN, a novel classification approach for protein 3D-structures. Given an unannotated query protein structure and a set of annotated proteins, ProtNN assigns to the query protein the class with the highest number of votes across the k nearest neighbor reference proteins, where k is a user-defined parameter. The search of the nearest neighbor annotated structures is based on a protein-graph representation model and pairwise similarities between vector embedding of the query and the reference protein structures in structural and topological spaces.ConclusionsWe demonstrate through an extensive experimental evaluation that ProtNN is able to accurately classify several datasets in an extremely fast runtime compared to state-of-the-art approaches. We further show that ProtNN is able to scale up to a whole PDB dataset in a single-process mode with no parallelization, with a gain of thousands order of magnitude in runtime compared to state-of-the-art approaches.

Proteomic Analysis of the Excretory and Secretory Proteins of Haemonchus contortus (HcESP) Binding to Goat PBMCs In Vivo Revealed Stage-Specific Binding Profiles.

Haemonchus contortus is a parasitic gastrointestinal nematode, and its excretory and secretory products (HcESPs) interact extensively with the host cells. In this study, we report the interaction of proteins from HcESPs at different developmental stages to goat peripheral blood mononuclear cells (PBMCs) in vivo using liquid chromatography-tandem mass spectrometry. A total of 407 HcESPs that interacted with goat PBMCs at different time points were identified from a H. contortus protein database using SEQUEST searches. The L4 and L5 stages of H. contortus represented a higher proportion of the identified proteins compared with the early and late adult stages. Both stage-specific interacting proteins and proteins that were common to multiple stages were identified. Forty-seven interacting proteins were shared among all stages. The gene ontology (GO) distributions of the identified goat PBMC-interacting proteins were nearly identical among all developmental stages, with high representation of binding and catalytic activity. Cellular, metabolic and single-organism processes were also annotated as major biological processes, but interestingly, more proteins were annotated as localization processes at the L5 stage than at the L4 and adult stages. Based on the clustering of homologous proteins, we improved the functional annotations of un-annotated proteins identified at different developmental stages. Some unnamed H. contortus ATP-binding cassette proteins, including ADP-ribosylation factor and P-glycoprotein-9, were identified by STRING protein clustering analysis.

JUMPg: An Integrative Proteogenomics Pipeline Identifying Unannotated Proteins in Human Brain and Cancer Cells.

Proteogenomics is an emerging approach to improve gene annotation and interpretation of proteomics data. Here we present JUMPg, an integrative proteogenomics pipeline including customized database construction, tag-based database search, peptide-spectrum match filtering, and data visualization. JUMPg creates multiple databases of DNA polymorphisms, mutations, splice junctions, partially trypticity, as well as protein fragments translated from the whole transcriptome in all six frames upon RNA-seq de novo assembly. We use a multistage strategy to search these databases sequentially, in which the performance is optimized by re-searching only unmatched high-quality spectra and reusing amino acid tags generated by the JUMP search engine. The identified peptides/proteins are displayed with gene loci using the UCSC genome browser. Then, the JUMPg program is applied to process a label-free mass spectrometry data set of Alzheimer's disease postmortem brain, uncovering 496 new peptides of amino acid substitutions, alternative splicing, frame shift, and "non-coding gene" translation. The novel protein PNMA6BL specifically expressed in the brain is highlighted. We also tested JUMPg to analyze a stable-isotope labeled data set of multiple myeloma cells, revealing 991 sample-specific peptides that include protein sequences in the immunoglobulin light chain variable region. Thus, the JUMPg program is an effective proteogenomics tool for multiomics data integration.

Protein function prediction based on data fusion and functional interrelationship

One of the challenging tasks of bioinformatics is to predict more accurate and confident protein functions from genomics and proteomics datasets. Computational approaches use a variety of high throughput experimental data, such as protein-protein interaction (PPI), protein sequences and phylogenetic profiles, to predict protein functions. This paper presents a method that uses transductive multi-label learning algorithm by integrating multiple data sources for classification. Multiple proteomics datasets are integrated to make inferences about functions of unknown proteins and use a directed bi-relational graph to assign labels to unannotated proteins. Our method, bi-relational graph based transductive multi-label function annotation (Bi-TMF) uses functional correlation and topological PPI network properties on both the training and testing datasets to predict protein functions through data fusion of the individual kernel result. The main purpose of our proposed method is to enhance the performance of classifier integration for protein function prediction algorithms. Experimental results demonstrate the effectiveness and efficiency of Bi-TMF on multi-sources datasets in yeast, human and mouse benchmarks. Bi-TMF outperforms other recently proposed methods.

The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins.

Genomics, epigenomics, transcriptomics, proteomics and metabolomics efforts rapidly generate a plethora of data on the activity and levels of biomolecules within mammalian cells. At the same time, curation projects that organize knowledge from the biomedical literature into online databases are expanding. Hence, there is a wealth of information about genes, proteins and their associations, with an urgent need for data integration to achieve better knowledge extraction and data reuse. For this purpose, we developed the Harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins from over 70 major online resources. We extracted, abstracted and organized data into ∼72 million functional associations between genes/proteins and their attributes. Such attributes could be physical relationships with other biomolecules, expression in cell lines and tissues, genetic associations with knockout mouse or human phenotypes, or changes in expression after drug treatment. We stored these associations in a relational database along with rich metadata for the genes/proteins, their attributes and the original resources. The freely available Harmonizome web portal provides a graphical user interface, a web service and a mobile app for querying, browsing and downloading all of the collected data. To demonstrate the utility of the Harmonizome, we computed and visualized gene–gene and attribute–attribute similarity networks, and through unsupervised clustering, identified many unexpected relationships by combining pairs of datasets such as the association between kinase perturbations and disease signatures. We also applied supervised machine learning methods to predict novel substrates for kinases, endogenous ligands for G-protein coupled receptors, mouse phenotypes for knockout genes, and classified unannotated transmembrane proteins for likelihood of being ion channels. The Harmonizome is a comprehensive resource of knowledge about genes and proteins, and as such, it enables researchers to discover novel relationships between biological entities, as well as form novel data-driven hypotheses for experimental validation.Database URL: http://amp.pharm.mssm.edu/Harmonizome.

Leaderless Transcripts and Small Proteins Are Common Features of the Mycobacterial Translational Landscape.

RNA-seq technologies have provided significant insight into the transcription networks of mycobacteria. However, such studies provide no definitive information on the translational landscape. Here, we use a combination of high-throughput transcriptome and proteome-profiling approaches to more rigorously understand protein expression in two mycobacterial species. RNA-seq and ribosome profiling in Mycobacterium smegmatis, and transcription start site (TSS) mapping and N-terminal peptide mass spectrometry in Mycobacterium tuberculosis, provide complementary, empirical datasets to examine the congruence of transcription and translation in the Mycobacterium genus. We find that nearly one-quarter of mycobacterial transcripts are leaderless, lacking a 5’ untranslated region (UTR) and Shine-Dalgarno ribosome-binding site. Our data indicate that leaderless translation is a major feature of mycobacterial genomes and is comparably robust to leadered initiation. Using translational reporters to systematically probe the cis-sequence requirements of leaderless translation initiation in mycobacteria, we find that an ATG or GTG at the mRNA 5’ end is both necessary and sufficient. This criterion, together with our ribosome occupancy data, suggests that mycobacteria encode hundreds of small, unannotated proteins at the 5’ ends of transcripts. The conservation of small proteins in both mycobacterial species tested suggests that some play important roles in mycobacterial physiology. Our translational-reporter system further indicates that mycobacterial leadered translation initiation requires a Shine Dalgarno site in the 5’ UTR and that ATG, GTG, TTG, and ATT codons can robustly initiate translation. Our combined approaches provide the first comprehensive view of mycobacterial gene structures and their non-canonical mechanisms of protein expression.

Regulation of Met1-linked polyubiquitin signalling by the deubiquitinase OTULIN.

Modification of proteins with Met1‐linked ‘linear’ ubiquitin chains has emerged as a key regulatory signal to control inflammatory signalling via the master regulator, the transcription factor nuclear factor κB (NF‐κB). While the assembly machinery, the linear ubiquitin chain assembly complex (LUBAC), and receptors for this ubiquitin chain type have been known for years, it was less clear which deubiquitinating enzymes (DUBs) hydrolyse Met1 linkages specifically. In 2013, two labs reported the previously unannotated protein FAM105B/OTULIN to be this missing Met1 linkage‐specific DUB. Structural studies have revealed how OTULIN achieves its remarkable specificity, employing a mechanism of ubiquitin‐assisted catalysis in which a glutamate residue on the substrate complements the active site of the enzyme. The specificity of OTULIN enables it to regulate global levels of Met1‐linked polyubiquitin in cells. This ability led to investigations of NF‐κB activation from new angles, and also revealed involvement of Met1‐polyubiquitin in Wnt signalling. Interestingly, OTULIN directly interacts with LUBAC, and this interaction is dynamic and can be regulated by OTULIN phosphorylation. This provides a new paradigm for how individual linkage types can be regulated by dedicated enzyme complexes mediating assembly and removal. Here we review what has been learned about OTULIN's mechanism, regulation and function, discuss the open questions in the field, and discuss how DUBs regulate the NF‐κB response.