Phyletic profiling with cliques of orthologs is enhanced by signatures of paralogy relationships

Identification of new SdiA regulon members of Escherichia coli, Enterobacter cloacae, and Salmonella enterica serovars Typhimurium and Typhi

Bacteria can coordinate behavior in response to population density through the production, release, and detection of small molecules, a phenomenon known as quorum sensing. Salmonella enterica is among a group of Enterobacteriaceae that can detect signaling molecules of the N-acyl homoserine lactone (AHL) type but lack the ability to produce them. The AHLs are detected by the LuxR-type transcription factor, SdiA. This enables a behavior known as eavesdropping, where organisms can sense the signaling molecules of other species of bacteria. The role of SdiA remains largely unknown. Here, we use RNA-seq to more completely identify the sdiA regulons of two clinically significant serovars of Salmonella enterica: Typhimurium and Typhi. We find that their sdiA regulons are largely conserved despite the significant differences in pathogenic strategy and host range of these two serovars. Previous studies identified sdiA-regulated genes in Escherichia coli and Enterobacter cloacae, but there is surprisingly no overlap in regulon membership between the different species. This led us to individually test orthologs of each regulon member in the other species and determine that there is indeed some overlap. Unfortunately, the functions of most sdiA-regulated genes are unknown, with the overall function of eavesdropping in these organisms remaining unclear.

IMPORTANCE

Many bacterial species detect their own population density through the production, release, and detection of small molecules (quorum sensing). Salmonella and other Enterobacteriaceae have a modified system that detects the N-acyl-homoserine lactones of other bacteria through the solo quorum sensing receptor SdiA, a behavior known as eavesdropping. The roles of sdiA-dependent eavesdropping in the lifecycles of these bacteria are unknown. In this study, we identify sdiA-dependent transcriptional responses in two clinically relevant serovars of Salmonella, Typhimurium and Typhi, and note that their responses are partially conserved. We also demonstrate for the first time that sdiA-dependent regulation of genes is partially conserved in Enterobacter cloacae and Escherichia coli as well, indicating a degree of commonality in eavesdropping among the Enterobacteriaceae.

Assembling bacterial puzzles: piecing together functions into microbial pathways

Functional metagenomics enables the study of unexplored bacterial diversity, gene families, and pathways essential to microbial communities. However, discovering biological insights with these data is impeded by the scarcity of quality annotations. Here, we use a co-occurrence-based analysis of predicted microbial protein functions to uncover pathways in genomic and metagenomic biological systems. Our approach, based on phylogenetic profiles, improves the identification of functional relationships, or participation in the same biochemical pathway, between enzymes over a comparable homology-based approach. We optimized the design of our profiles to identify potential pathways using minimal data, clustered functionally related enzyme pairs into multi-enzymatic pathways, and evaluated our predictions against reference pathways in the KEGG database. We then demonstrated a novel extension of this approach to predict inter-bacterial protein interactions amongst members of a marine microbiome. Most significantly, we show our method predicts emergent biochemical pathways between known and unknown functions. Thus, our work establishes a basis for identifying the potential functional capacities of the entire metagenome, capturing previously unknown and abstract functions into discrete putative pathways.

Interrogation of RNA-protein interaction dynamics in bacterial growth

Characterising RNA-protein interaction dynamics is fundamental to understand how bacteria respond to their environment. In this study, we have analysed the dynamics of 91% of the Escherichia coli expressed proteome and the RNA-interaction properties of 271 RNA-binding proteins (RBPs) at different growth phases. We find that 68% of RBPs differentially bind RNA across growth phases and characterise 17 previously unannotated proteins as bacterial RBPs including YfiF, a ncRNA-binding protein. While these new RBPs are mostly present in Proteobacteria, two of them are orthologs of human mitochondrial proteins associated with rare metabolic disorders. Moreover, we reveal novel RBP functions for proteins such as the chaperone HtpG, a new stationary phase tRNA-binding protein. For the first time, the dynamics of the bacterial RBPome have been interrogated, showcasing how this approach can reveal the function of uncharacterised proteins and identify critical RNA-protein interactions for cell growth which could inform new antimicrobial therapies.

FinO/ProQ-family proteins: an evolutionary perspective

RNA-binding proteins are key actors of post-transcriptional networks. Almost exclusively studied in the light of their interactions with RNA ligands and the associated functional events, they are still poorly understood as evolutionary units. In this review, we discuss the FinO/ProQ family of bacterial RNA chaperones, how they evolve and spread across bacterial populations and what properties and opportunities they provide to their host cells. We reflect on major conserved and divergent themes within the family, trying to understand how the same ancestral RNA-binding fold, augmented with additional structural elements, could yield either highly specialised proteins or, on the contrary, globally acting regulatory hubs with a pervasive impact on gene expression. We also consider dominant convergent evolutionary trends that shaped their RNA chaperone activity and recurrently implicated the FinO/ProQ-like proteins in bacterial DNA metabolism, translation and virulence. Finally, we offer a new perspective in which FinO/ProQ-family regulators emerge as active evolutionary players with both negative and positive roles, significantly impacting the evolutionary modes and trajectories of their bacterial hosts.

The minimal meningococcal ProQ protein has an intrinsic capacity for structure-based global RNA recognition

FinO-domain proteins are a widespread family of bacterial RNA-binding proteins with regulatory functions. Their target spectrum ranges from a single RNA pair, in the case of plasmid-encoded FinO, to global RNA regulons, as with enterobacterial ProQ. To assess whether the FinO domain itself is intrinsically selective or promiscuous, we determine in vivo targets of Neisseria meningitidis, which consists of solely a FinO domain. UV-CLIP-seq identifies associations with 16 small non-coding sRNAs and 166 mRNAs. Meningococcal ProQ predominantly binds to highly structured regions and generally acts to stabilize its RNA targets. Loss of ProQ alters transcript levels of >250 genes, demonstrating that this minimal ProQ protein impacts gene expression globally. Phenotypic analyses indicate that ProQ promotes oxidative stress resistance and DNA damage repair. We conclude that FinO domain proteins recognize some abundant type of RNA shape and evolve RNA binding selectivity through acquisition of additional regions that constrain target recognition.

FinO-domain proteins are bacterial RNA-binding proteins with a wide range of target specificities. Here, the authors employ UV CLIP-seq and show that minimal ProQ protein of Neisseria meningitidis binds to various small non-coding RNAs and mRNAs involved in virulence.

Identifying orthologs with OMA: A primer

The Orthologous Matrix (OMA) is a method and database that allows users to identify orthologs among many genomes. OMA provides three different types of orthologs: pairwise orthologs, OMA Groups and Hierarchical Orthologous Groups (HOGs). This Primer is organized in two parts. In the first part, we provide all the necessary background information to understand the concepts of orthology, how we infer them and the different subtypes of orthology in OMA, as well as what types of analyses they should be used for. In the second part, we describe protocols for using the OMA browser to find a specific gene and its various types of orthologs. By the end of the Primer, readers should be able to (i) understand homology and the different types of orthologs reported in OMA, (ii) understand the best type of orthologs to use for a particular analysis; (iii) find particular genes of interest in the OMA browser; and (iv) identify orthologs for a given gene.  The data can be freely accessed from the OMA browser at https://omabrowser.org.

In vitro and in vivo efficacy of Caenorhabditis elegans recombinant antimicrobial protein against Gram-negative bacteria

Antimicrobial peptides (AMPs) are short, positively charged host defense peptides, found in various life forms from microorganisms to humans. AMPs are gaining more attention as substitutes for antibiotics in order to combat the risk posed by multi-drug- resistant pathogens. The nematode Caenorhabditis elegans relies solely on its innate immune defense to cope with its challenging life-style. Bacterial infection in C. elegans leads to induction of antimicrobial proteins, defensins, nemapores, cecropins, and neuropeptide-like proteins, which act to limit bacterial proliferation. This study reports how the C. elegans recombinant antibacterial factor (ABF-1) rapidly inhibited bacterial growth (Salmonella Typhi, Klebsiella pneumonia, Shigella sonnei and Vibrio alginolyticus). The ABF-1 exposure on S. Typhi, showed differential regulation in cell-cycle, DNA repair mechanism, membrane stability, and stress related proteins. The exogenous supply of ABF-1 protein has extended C. elegans survival by reducing the bacterial colony forming units on the nematode intestine. Together, these findings indicate the valuable and potential therapeutic applications of ABF-1 protein as antimicrobial agents against intracellular pathogens.

Combining learning and constraints for genome-wide protein annotation

BACKGROUND

The advent of high-throughput experimental techniques paved the way to genome-wide computational analysis and predictive annotation studies. When considering the joint annotation of a large set of related entities, like all proteins of a certain genome, many candidate annotations could be inconsistent, or very unlikely, given the existing knowledge. A sound predictive framework capable of accounting for this type of constraints in making predictions could substantially contribute to the quality of machine-generated annotations at a genomic scale.

RESULTS

We present OCELOT, a predictive pipeline which simultaneously addresses functional and interaction annotation of all proteins of a given genome. The system combines sequence-based predictors for functional and protein-protein interaction (PPI) prediction with a consistency layer enforcing (soft) constraints as fuzzy logic rules. The enforced rules represent the available prior knowledge about the classification task, including taxonomic constraints over each GO hierarchy (e.g. a protein labeled with a GO term should also be labeled with all ancestor terms) as well as rules combining interaction and function prediction. An extensive experimental evaluation on the Yeast genome shows that the integration of prior knowledge via rules substantially improves the quality of the predictions. The system largely outperforms GoFDR, the only high-ranking system at the last CAFA challenge with a readily available implementation, when GoFDR is given access to intra-genome information only (as OCELOT), and has comparable or better results (depending on the hierarchy and performance measure) when GoFDR is allowed to use information from other genomes. Our system also compares favorably to recent methods based on deep learning.

Quantifying changes in the bacterial thiol redox proteome during host-pathogen interaction

Phagocyte-derived production of a complex mixture of different oxidants is a major mechanism of the host defense against microbial intruders. On the protein level, a major target of these oxidants is the thiol group of the amino acid cysteine in proteins. Oxidation of thiol groups is a widespread regulatory post-translational protein modification. It is used by bacteria to respond to and to overcome oxidative stress. Numerous redox proteomic studies have shown that protein thiols in bacteria, such as Escherichia coli react towards a number of oxidants in specific ways. However, our knowledge about protein thiols in bacteria exposed to the complex mixture of oxidants encountered in the phagolysosome is still limited. In this study, we used a quantitative redox proteomic method (OxICAT) to assess the in vivo thiol oxidation status of phagocytized E. coli. The majority (65.5%) of identified proteins harbored thiols that were significantly oxidized (> 30%) upon phagocytosis. A substantial number of these proteins are from major metabolic pathways or are involved in cell detoxification and stress response, suggesting a systemic breakdown of the bacterial cysteine proteome in phagocytized bacteria. 16 of the oxidized proteins provide E. coli with a significant growth advantage in the presence of H₂O₂, when compared to deletion mutants lacking these proteins, and 11 were shown to be essential under these conditions.

The evolutionary signal in metagenome phyletic profiles predicts many gene functions

BACKGROUND

The function of many genes is still not known even in model organisms. An increasing availability of microbiome DNA sequencing data provides an opportunity to infer gene function in a systematic manner.

RESULTS

We evaluated if the evolutionary signal contained in metagenome phyletic profiles (MPP) is predictive of a broad array of gene functions. The MPPs are an encoding of environmental DNA sequencing data that consists of relative abundances of gene families across metagenomes. We find that such MPPs can accurately predict 826 Gene Ontology functional categories, while drawing on human gut microbiomes, ocean metagenomes, and DNA sequences from various other engineered and natural environments. Overall, in this task, the MPPs are highly accurate, and moreover they provide coverage for a set of Gene Ontology terms largely complementary to standard phylogenetic profiles, derived from fully sequenced genomes. We also find that metagenomes approximated from taxon relative abundance obtained via 16S rRNA gene sequencing may provide surprisingly useful predictive models. Crucially, the MPPs derived from different types of environments can infer distinct, non-overlapping sets of gene functions and therefore complement each other. Consistently, simulations on > 5000 metagenomes indicate that the amount of data is not in itself critical for maximizing predictive accuracy, while the diversity of sampled environments appears to be the critical factor for obtaining robust models.

CONCLUSIONS

In past work, metagenomics has provided invaluable insight into ecology of various habitats, into diversity of microbial life and also into human health and disease mechanisms. We propose that environmental DNA sequencing additionally constitutes a useful tool to predict biological roles of genes, yielding inferences out of reach for existing comparative genomics approaches.

Improvement of isopropanol tolerance of Escherichia coli using adaptive laboratory evolution and omics technologies

Isopropanol (IPA) is the secondary alcohol that can be dehydrated to yield propylene. To produce IPA using microorganisms, a significant issue is that the toxicity of IPA causes retardation or inhibition of cell growth, decreasing the yield. One possible strategy to overcome this problem is to improve IPA tolerance of production organisms. For the understanding of tolerance to IPA, we performed parallel adaptive laboratory evolution (ALE) of Escherichia coli under IPA stress. To identify the genotypic change during ALE, we performed genome re-sequencing analyses of obtained tolerant strains. To verify which mutations were contributed to IPA tolerance, we constructed the mutant strains and quantify the IPA tolerance of the constructed mutants. From these analyses, we found that five mutations (relA, marC, proQ, yfgO, and rraA) provided the increase of IPA tolerance. To understand the phenotypic change during ALE, we performed transcriptome analysis of tolerant strains. From transcriptome analysis, we found that expression levels of genes related to biosynthetic pathways of amino acids, iron ion homeostasis, and energy metabolisms were changed in the tolerant strains. Results from these experiments provide fundamental bases for designing IPA tolerant strains for industrial purposes.

Molecular mechanism of mRNA repression in trans by a ProQ-dependent small RNA

Research into post-transcriptional control of mRNAs by small noncoding RNAs (sRNAs) in the model bacteria Escherichia coli and Salmonella enterica has mainly focused on sRNAs that associate with the RNA chaperone Hfq. However, the recent discovery of the protein ProQ as a common binding partner that stabilizes a distinct large class of structured sRNAs suggests that additional RNA regulons exist in these organisms. The cellular functions and molecular mechanisms of these new ProQ-dependent sRNAs are largely unknown. Here, we report in Salmonella Typhimurium the mode-of-action of RaiZ, a ProQ-dependent sRNA that is made from the 3' end of the mRNA encoding ribosome-inactivating protein RaiA. We show that RaiZ is a base-pairing sRNA that represses in trans the mRNA of histone-like protein HU-α. RaiZ forms an RNA duplex with the ribosome-binding site of hupA mRNA, facilitated by ProQ, to prevent 30S ribosome loading and protein synthesis of HU-α. Similarities and differences between ProQ- and Hfq-mediated regulation will be discussed.

Modes of antibacterial action of curcumin under dark and light conditions: A toxicoproteomics approach

Curcumin is a potent natural food-grade antimicrobial compound. Exposure to light further enhances its antimicrobial capacity. Proteomic methods were used in this study for investigating the mechanistic aspects of the antibacterial curcumin effects in the dark and upon illumination. Escherichia coli cells exposed to water-dispersible curcumin-methyl-β-cyclodextrin inclusion complex under dark and light conditions were compared with the non-treated cells kept under the same illumination regimes. Curcumin treatment in the dark evoked adaptive responses aimed at mitigation of oxidative stress, DNA protection, proteostasis, modulation of redox state via changing NADH level, and gasotransmitter (H₂S and NH₃) biosynthesis. Although part of these phenomena were also present in E. coli treated under light, the light-induced curcumin toxicity was prevailed by maladaptive responses. The ROS burst induced upon curcumin treatment under light overrode the cellular adaptive mechanisms disrupting the iron metabolism, deregulating the iron-sulfur cluster biosynthesis and eventually leading to cell death. The toxicoproteomic findings were validated by transcriptomic analysis and by assessment of intracellular ROS, NADH, NADPH and iron levels.

SIGNIFICANCE

The results of this study elucidate putative mechanistic basis of antibacterial effects of curcumin, suggesting ways towards more efficient contamination control. In particular, the antimicrobial efficacy of curcumin can be potentiated by targeting bacterial systems that remediate its dark toxicity by free radical detoxification and modulation of cell redox status. To the best of the authors' knowledge, this is the first proteomic study differentiating between the dark and light-induced antimicrobial activity of curcumin.

Metabolic Pathway Assignment of Plant Genes based on Phylogenetic Profiling-A Feasibility Study

Despite many developed experimental and computational approaches, functional gene annotation remains challenging. With the rapidly growing number of sequenced genomes, the concept of phylogenetic profiling, which predicts functional links between genes that share a common co-occurrence pattern across different genomes, has gained renewed attention as it promises to annotate gene functions based on presence/absence calls alone. We applied phylogenetic profiling to the problem of metabolic pathway assignments of plant genes with a particular focus on secondary metabolism pathways. We determined phylogenetic profiles for 40,960 metabolic pathway enzyme genes with assigned EC numbers from 24 plant species based on sequence and pathway annotation data from KEGG and Ensembl Plants. For gene sequence family assignments, needed to determine the presence or absence of particular gene functions in the given plant species, we included data of all 39 species available at the Ensembl Plants database and established gene families based on pairwise sequence identities and annotation information. Aside from performing profiling comparisons, we used machine learning approaches to predict pathway associations from phylogenetic profiles alone. Selected metabolic pathways were indeed found to be composed of gene families of greater than expected phylogenetic profile similarity. This was particularly evident for primary metabolism pathways, whereas for secondary pathways, both the available annotation in different species as well as the abstraction of functional association via distinct pathways proved limiting. While phylogenetic profile similarity was generally not found to correlate with gene co-expression, direct physical interactions of proteins were reflected by a significantly increased profile similarity suggesting an application of phylogenetic profiling methods as a filtering step in the identification of protein-protein interactions. This feasibility study highlights the potential and challenges associated with phylogenetic profiling methods for the detection of functional relationships between genes as well as the need to enlarge the set of plant genes with proven secondary metabolism involvement as well as the limitations of distinct pathways as abstractions of relationships between genes.

Computational Methods for Annotation Transfers from Sequence

Surveys of public sequence resources show that experimentally supported functional information is still completely missing for a considerable fraction of known proteins and is clearly incomplete for an even larger portion. Bioinformatics methods have long made use of very diverse data sources alone or in combination to predict protein function, with the understanding that different data types help elucidate complementary biological roles. This chapter focuses on methods accepting amino acid sequences as input and producing GO term assignments directly as outputs; the relevant biological and computational concepts are presented along with the advantages and limitations of individual approaches.

Identifying and Tackling Emergent Vulnerability in Drug-Resistant Mycobacteria

The global mechanisms and associated molecular alterations that occur in drug-resistant mycobacteria are poorly understood. To address this, we obtain genomics data and then construct a genome-scale response network in isoniazid-resistant Mycobacterium smegmatis and apply a network-mining algorithm. Through this, we decipher global alterations in an unbiased manner and identify emergent vulnerabilities in resistant bacilli, of which redox response was prominent. Using phenotypic profiling, we find that resistant bacilli exhibit collateral sensitivity to several compounds that block antioxidant responses. We find that nanogram/milliliter concentrations of ebselen, vancomycin, and phenylarsine oxide, in combination with isoniazid, are highly effective against Mycobacterium tuberculosis H37Rv and three clinical drug-resistant strains. Dynamic measurements of cytoplasmic redox potential revealed a surprisingly diminished capacity of clinical drug-resistant strains to counteract oxidative stress, providing a mechanistic basis for efficient and synergistic mycobactericidal activity of the drug combinations. Ebselen and vancomycin appear to be promising repurposable drugs.

Combining Shigella Tn-seq data with gold-standard E. coli gene deletion data suggests rare transitions between essential and non-essential gene functionality

BACKGROUND

Gene essentiality - whether or not a gene is necessary for cell growth - is a fundamental component of gene function. It is not well established how quickly gene essentiality can change, as few studies have compared empirical measures of essentiality between closely related organisms.

RESULTS

Here we present the results of a Tn-seq experiment designed to detect essential protein coding genes in the bacterial pathogen Shigella flexneri 2a 2457T on a genome-wide scale. Superficial analysis of this data suggested that 481 protein-coding genes in this Shigella strain are critical for robust cellular growth on rich media. Comparison of this set of genes with a gold-standard data set of essential genes in the closely related Escherichia coli K12 BW25113 revealed that an excessive number of genes appeared essential in Shigella but non-essential in E. coli. Importantly, and in converse to this comparison, we found no genes that were essential in E. coli and non-essential in Shigella, implying that many genes were artefactually inferred as essential in Shigella. Controlling for such artefacts resulted in a much smaller set of discrepant genes. Among these, we identified three sets of functionally related genes, two of which have previously been implicated as critical for Shigella growth, but which are dispensable for E. coli growth.

CONCLUSIONS

The data presented here highlight the small number of protein coding genes for which we have strong evidence that their essentiality status differs between the closely related bacterial taxa E. coli and Shigella. A set of genes involved in acetate utilization provides a canonical example. These results leave open the possibility of developing strain-specific antibiotic treatments targeting such differentially essential genes, but suggest that such opportunities may be rare in closely related bacteria.

Extensive complementarity between gene function prediction methods

MOTIVATION

The number of sequenced genomes rises steadily but we still lack the knowledge about the biological roles of many genes. Automated function prediction (AFP) is thus a necessity. We hypothesized that AFP approaches that draw on distinct genome features may be useful for predicting different types of gene functions, motivating a systematic analysis of the benefits gained by obtaining and integrating such predictions.

RESULTS

Our pipeline amalgamates 5 133 543 genes from 2071 genomes in a single massive analysis that evaluates five established genomic AFP methodologies. While 1227 Gene Ontology (GO) terms yielded reliable predictions, the majority of these functions were accessible to only one or two of the methods. Moreover, different methods tend to assign a GO term to non-overlapping sets of genes. Thus, inferences made by diverse genomic AFP methods display a striking complementary, both gene-wise and function-wise. Because of this, a viable integration strategy is to rely on a single most-confident prediction per gene/function, rather than enforcing agreement across multiple AFP methods. Using an information-theoretic approach, we estimate that current databases contain 29.2 bits/gene of known Escherichia coli gene functions. This can be increased by up to 5.5 bits/gene using individual AFP methods or by 11 additional bits/gene upon integration, thereby providing a highly-ranking predictor on the Critical Assessment of Function Annotation 2 community benchmark. Availability of more sequenced genomes boosts the predictive accuracy of AFP approaches and also the benefit from integrating them.

AVAILABILITY AND IMPLEMENTATION

The individual and integrated GO predictions for the complete set of genes are available from http://gorbi.irb.hr/ CONTACT: fran.supek@irb.hrSupplementary information: Supplementary materials are available at Bioinformatics online.

The Code of Silence: Widespread Associations Between Synonymous Codon Biases and Gene Function

Some mutations in gene coding regions exchange one synonymous codon for another, and thus do not alter the amino acid sequence of the encoded protein. Even though they are often called 'silent,' these mutations may exhibit a plethora of effects on the living cell. Therefore, they are often selected during evolution, causing synonymous codon usage biases in genomes. Comparative analyses of bacterial, archaeal, fungal, and human cancer genomes have found many links between a gene's biological role and the accrual of synonymous mutations during evolution. In particular, highly expressed genes in certain functional categories are enriched with optimal codons, which are decoded by the abundant tRNAs, thus enhancing the speed and accuracy of the translating ribosome. The set of genes exhibiting codon adaptation differs between genomes, and these differences show robust associations to organismal phenotypes. In addition to selection for translation efficiency, other distinct codon bias patterns have been found in: amino acid starvation genes, cyclically expressed genes, tissue-specific genes in animals and plants, oxidative stress response genes, cellular differentiation genes, and oncogenes. In addition, genomes of organisms harboring tRNA modifications exhibit particular codon preferences. The evolutionary trace of codon bias patterns across orthologous genes may be examined to learn about a gene's relevance to various phenotypes, or, more generally, its function in the cell.

Gene expression profiles of wild-type and isoniazid-resistant strains of Mycobacterium smegmatis

The global variations in the gene expression pattern of drug treated (½X) and laboratory evolved drug-resistant strains (2XR and 4XR) of Mycobacterium smegmatis were obtained and compared with the M. smegmatis mc² 155 (WT) strain. The genes exhibiting two-fold change and p-value <0.05 under the treated conditions have been considered as differentially expressed genes (DEGs). Overall, the numbers of DEGs observed are 1529 in ½X (596—up, 933—down), 1381 (899—up, 482—down) in 2XR and 716 in 4XR (267—up, 449—down) conditions. The data is publicly available through the GEO database with accession number GSE64132.

Global Shifts in Genome and Proteome Composition Are Very Tightly Coupled

The amino acid composition (AAC) of proteomes differs greatly between microorganisms and is associated with the environmental niche they inhabit, suggesting that these changes may be adaptive. Similarly, the oligonucleotide composition of genomes varies and may confer advantages at the DNA/RNA level. These influences overlap in protein-coding sequences, making it difficult to gauge their relative contributions. We disentangle these effects by systematically evaluating the correspondence between intergenic nucleotide composition, where protein-level selection is absent, the AAC, and ecological parameters of 909 prokaryotes. We find that G + C content, the most frequently used measure of genomic composition, cannot capture diversity in AAC and across ecological contexts. However, di-/trinucleotide composition in intergenic DNA predicts amino acid frequencies of proteomes to the point where very little cross-species variability remains unexplained (91% of variance accounted for). Qualitatively similar results were obtained for 49 fungal genomes, where 80% of the variability in AAC could be explained by the composition of introns and intergenic regions. Upon factoring out oligonucleotide composition and phylogenetic inertia, the residual AAC is poorly predictive of the microbes' ecological preferences, in stark contrast with the original AAC. Moreover, highly expressed genes do not exhibit more prominent environment-related AAC signatures than lowly expressed genes, despite contributing more to the effective proteome. Thus, evolutionary shifts in overall AAC appear to occur almost exclusively through factors shaping the global oligonucleotide content of the genome. We discuss these results in light of contravening evidence from biophysical data and further reading frame-specific analyses that suggest that adaptation takes place at the protein level.

Enrichment of Triticum aestivum gene annotations using ortholog cliques and gene ontologies in other plants

Background

While the gargantuan multi-nation effort of sequencing T. aestivum gets close to completion, the annotation process for the vast number of wheat genes and proteins is in its infancy. Previous experimental studies carried out on model plant organisms such as A. thaliana and O. sativa provide a plethora of gene annotations that can be used as potential starting points for wheat gene annotations, proven that solid cross-species gene-to-gene and protein-to-protein correspondences are provided.

Results

DNA and protein sequences and corresponding annotations for T. aestivum and 9 other plant species were collected from Ensembl Plants release 22 and curated. Cliques of predicted 1-to-1 orthologs were identified and an annotation enrichment model was defined based on existing gene-GO term associations and phylogenetic relationships among wheat and 9 other plant species. A total of 13 cliques of size 10 were identified, which represent putative functionally equivalent genes and proteins in the 10 plant species. Eighty-five new and more specific GO terms were associated with wheat genes in the 13 cliques of size 10, which represent a 65% increase compared with the previously 130 known GO terms. Similar expression patterns for 4 genes from Arabidopsis, barley, maize and rice in cliques of size 10 provide experimental evidence to support our model. Overall, based on clique size equal or larger than 3, our model enriched the existing gene-GO term associations for 7,838 (8%) wheat genes, of which 2,139 had no previous annotation.

Conclusions

Our novel comparative genomics approach enriches existing T. aestivum gene annotations based on cliques of predicted 1-to-1 orthologs, phylogenetic relationships and existing gene ontologies from 9 other plant species.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1496-2) contains supplementary material, which is available to authorized users.

Phylogenetic profiling: how much input data is enough?

Phylogenetic profiling is a well-established approach for predicting gene function based on patterns of gene presence and absence across species. Much of the recent developments have focused on methodological improvements, but relatively little is known about the effect of input data size on the quality of predictions. In this work, we ask: how many genomes and functional annotations need to be considered for phylogenetic profiling to be effective? Phylogenetic profiling generally benefits from an increased amount of input data. However, by decomposing this improvement in predictive accuracy in terms of the contribution of additional genomes and of additional annotations, we observed diminishing returns in adding more than ∼100 genomes, whereas increasing the number of annotations remained strongly beneficial throughout. We also observed that maximising phylogenetic diversity within a clade of interest improves predictive accuracy, but the effect is small compared to changes in the number of genomes under comparison. Finally, we show that these findings are supported in light of the Open World Assumption, which posits that functional annotation databases are inherently incomplete. All the tools and data used in this work are available for reuse from http://lab.dessimoz.org/14_phylprof. Scripts used to analyse the data are available on request from the authors.

The OMA orthology database in 2015: function predictions, better plant support, synteny view and other improvements

The Orthologous Matrix (OMA) project is a method and associated database inferring evolutionary relationships amongst currently 1706 complete proteomes (i.e. the protein sequence associated for every protein-coding gene in all genomes). In this update article, we present six major new developments in OMA: (i) a new web interface; (ii) Gene Ontology function predictions as part of the OMA pipeline; (iii) better support for plant genomes and in particular homeologs in the wheat genome; (iv) a new synteny viewer providing the genomic context of orthologs; (v) statically computed hierarchical orthologous groups subsets downloadable in OrthoXML format; and (vi) possibility to export parts of the all-against-all computations and to combine them with custom data for 'client-side' orthology prediction. OMA can be accessed through the OMA Browser and various programmatic interfaces at http://omabrowser.org.

Complexity of gene expression evolution after duplication: protein dosage rebalancing

Ongoing debates about functional importance of gene duplications have been recently intensified by a heated discussion of the “ortholog conjecture” (OC). Under the OC, which is central to functional annotation of genomes, orthologous genes are functionally more similar than paralogous genes at the same level of sequence divergence. However, a recent study challenged the OC by reporting a greater functional similarity, in terms of gene ontology (GO) annotations and expression profiles, among within-species paralogs compared to orthologs. These findings were taken to indicate that functional similarity of homologous genes is primarily determined by the cellular context of the genes, rather than evolutionary history. Subsequent studies suggested that the OC appears to be generally valid when applied to mammalian evolution but the complete picture of evolution of gene expression also has to incorporate lineage-specific aspects of paralogy. The observed complexity of gene expression evolution after duplication can be explained through selection for gene dosage effect combined with the duplication-degeneration-complementation model. This paper discusses expression divergence of recent duplications occurring before functional divergence of proteins encoded by duplicate genes.

Inferring gene function from evolutionary change in signatures of translation efficiency

Background

The genetic code is redundant, meaning that most amino acids can be encoded by more than one codon. Highly expressed genes tend to use optimal codons to increase the accuracy and speed of translation. Thus, codon usage biases provide a signature of the relative expression levels of genes, which can, uniquely, be quantified across the domains of life.

Results

Here we describe a general statistical framework to exploit this phenomenon and to systematically associate genes with environments and phenotypic traits through changes in codon adaptation. By inferring evolutionary signatures of translation efficiency in 911 bacterial and archaeal genomes while controlling for confounding effects of phylogeny and inter-correlated phenotypes, we linked 187 gene families to 24 diverse phenotypic traits. A series of experiments in Escherichia coli revealed that 13 of 15, 19 of 23, and 3 of 6 gene families with changes in codon adaptation in aerotolerant, thermophilic, or halophilic microbes. Respectively, confer specific resistance to, respectively, hydrogen peroxide, heat, and high salinity. Further, we demonstrate experimentally that changes in codon optimality alone are sufficient to enhance stress resistance. Finally, we present evidence that multiple genes with altered codon optimality in aerobes confer oxidative stress resistance by controlling the levels of iron and NAD(P)H.

Conclusions

Taken together, these results provide experimental evidence for a widespread connection between changes in translation efficiency and phenotypic adaptation. As the number of sequenced genomes increases, this novel genomic context method for linking genes to phenotypes based on sequence alone will become increasingly useful.