Annotation error in public databases: misannotation of molecular function in enzyme superfamilies

PhyloGenes: An online phylogenetics and functional genomics resource for plant gene function inference

We aim to enable the accurate and efficient transfer of knowledge about gene function gained from Arabidopsis thaliana and other model organisms to other plant species. This knowledge transfer is frequently challenging in plants due to duplications of individual genes and whole genomes in plant lineages. Such duplications result in complex evolutionary relationships between related genes, which may have similar sequences but highly divergent functions. In such cases, functional inference requires more than a simple sequence similarity calculation. We have developed an online resource, PhyloGenes (phylogenes.org), that displays precomputed phylogenetic trees for plant gene families along with experimentally validated function information for individual genes within the families. A total of 40 plant genomes and 10 non-plant model organisms are represented in over 8,000 gene families. Evolutionary events such as speciation and duplication are clearly labeled on gene trees to distinguish orthologs from paralogs. Nearly 6,000 families have at least one member with an experimentally supported annotation to a Gene Ontology (GO) molecular function or biological process term. By displaying experimentally validated gene functions associated to individual genes within a tree, PhyloGenes enables functional inference for genes of uncharacterized function, based on their evolutionary relationships to experimentally studied genes, in a visually traceable manner. For the many families containing genes that have evolved to perform different functions, PhyloGenes facilitates the use of evolutionary history to determine the most likely function of genes that have not been experimentally characterized. Future work will enrich the resource by incorporating additional gene function datasets such as plant gene expression atlas data.

Inferring the molecular and phenotypic impact of amino acid variants with MutPred2

Identifying pathogenic variants and underlying functional alterations is challenging. To this end, we introduce MutPred2, a tool that improves the prioritization of pathogenic amino acid substitutions over existing methods, generates molecular mechanisms potentially causative of disease, and returns interpretable pathogenicity score distributions on individual genomes. Whilst its prioritization performance is state-of-the-art, a distinguishing feature of MutPred2 is the probabilistic modeling of variant impact on specific aspects of protein structure and function that can serve to guide experimental studies of phenotype-altering variants. We demonstrate the utility of MutPred2 in the identification of the structural and functional mutational signatures relevant to Mendelian disorders and the prioritization of de novo mutations associated with complex neurodevelopmental disorders. We then experimentally validate the functional impact of several variants identified in patients with such disorders. We argue that mechanism-driven studies of human inherited disease have the potential to significantly accelerate the discovery of clinically actionable variants.

Discovery of novel pathways for carbohydrate metabolism

Closing the gap between the increasing availability of complete genome sequences and the discovery of novel enzymes in novel metabolic pathways is a significant challenge. Here, we review recent examples of assignment of in vitro enzymatic activities and in vivo metabolic functions to uncharacterized proteins, with a focus on enzymes and metabolic pathways involved in the catabolism and biosynthesis of monosaccharides and polysaccharides. The most effective approaches are based on analyses of sequence-function space in protein families that provide clues for the predictions of the functions of the uncharacterized enzymes. As summarized in this Opinion, this approach allows the discovery of the catabolism of new molecules, new pathways for common molecules, and new enzymatic chemistries.

Understanding the causes of errors in eukaryotic protein-coding gene prediction: a case study of primate proteomes

Background

Recent advances in sequencing technologies have led to an explosion in the number of genomes available, but accurate genome annotation remains a major challenge. The prediction of protein-coding genes in eukaryotic genomes is especially problematic, due to their complex exon–intron structures. Even the best eukaryotic gene prediction algorithms can make serious errors that will significantly affect subsequent analyses.

Results

We first investigated the prevalence of gene prediction errors in a large set of 176,478 proteins from ten primate proteomes available in public databases. Using the well-studied human proteins as a reference, a total of 82,305 potential errors were detected, including 44,001 deletions, 27,289 insertions and 11,015 mismatched segments where part of the correct protein sequence is replaced with an alternative erroneous sequence. We then focused on the mismatched sequence errors that cause particular problems for downstream applications. A detailed characterization allowed us to identify the potential causes for the gene misprediction in approximately half (5446) of these cases. As a proof-of-concept, we also developed a simple method which allowed us to propose improved sequences for 603 primate proteins.

Conclusions

Gene prediction errors in primate proteomes affect up to 50% of the sequences. Major causes of errors include undetermined genome regions, genome sequencing or assembly issues, and limitations in the models used to represent gene exon–intron structures. Nevertheless, existing genome sequences can still be exploited to improve protein sequence quality. Perspectives of the work include the characterization of other types of gene prediction errors, as well as the development of a more comprehensive algorithm for protein sequence error correction.

Isofunctional Clustering and Conformational Analysis of the Arsenate Reductase Superfamily Reveals Nine Distinct Clusters

Arsenate reductase (ArsC) is a superfamily of enzymes that reduce arsenate. Due to active site similarities, some ArsC can function as low-molecular weight protein tyrosine phosphatases (LMW-PTPs). Broad superfamily classifications align with redox partners (Trx- or Grx-linked). To understand this superfamily's mechanistic diversity, the ArsC superfamily is classified on the basis of active site features utilizing the tools TuLIP (two-level iterative clustering process) and autoMISST (automated multilevel iterative sequence searching technique). This approach identified nine functionally relevant (perhaps isofunctional) protein groups. Five groups exhibit distinct ArsC mechanisms. Three are Grx-linked: group 4AA (classical ArsC), group 3AAA (YffB-like), and group 5BAA. Two are Trx-linked: groups 6AAAAA and 7AAAAAAAA. One is an Spx-like transcriptional regulatory group, group 5AAA. Three are potential LMW-PTP groups: groups 7BAAAA, and 7AAAABAA, which have not been previously identified, and the well-studied LMW-PTP family group 8AAA. Molecular dynamics simulations were utilized to explore functional site details. In several families, we confirm and add detail to literature-based mechanistic information. Mechanistic roles are hypothesized for conserved active site residues in several families. In three families, simulations of the unliganded structure sample specific conformational ensembles, which are proposed to represent either a more ligand-binding-competent conformation or a pathway toward a more binding-competent state; these active sites may be designed to traverse high-energy barriers to the lower-energy conformations necessary to more readily bind ligands. This more detailed biochemical understanding of ArsC and ArsC-like PTP mechanisms opens possibilities for further understanding of arsenate bioremediation and the LMW-PTP mechanism.

UniRule: a unified rule resource for automatic annotation in the UniProt Knowledgebase

MOTIVATION

The number of protein records in the UniProt Knowledgebase (UniProtKB: https://www.uniprot.org) continues to grow rapidly as a result of genome sequencing and the prediction of protein-coding genes. Providing functional annotation for these proteins presents a significant and continuing challenge.

RESULTS

In response to this challenge, UniProt has developed a method of annotation, known as UniRule, based on expertly curated rules, which integrates related systems (RuleBase, HAMAP, PIRSR, PIRNR) developed by the members of the UniProt consortium. UniRule uses protein family signatures from InterPro, combined with taxonomic and other constraints, to select sets of reviewed proteins which have common functional properties supported by experimental evidence. This annotation is propagated to unreviewed records in UniProtKB that meet the same selection criteria, most of which do not have (and are never likely to have) experimentally verified functional annotation. Release 2020_01 of UniProtKB contains 6496 UniRule rules which provide annotation for 53 million proteins, accounting for 30% of the 178 million records in UniProtKB. UniRule provides scalable enrichment of annotation in UniProtKB.

AVAILABILITY AND IMPLEMENTATION

UniRule rules are integrated into UniProtKB and can be viewed at https://www.uniprot.org/unirule/. UniRule rules and the code required to run the rules, are publicly available for researchers who wish to annotate their own sequences. The implementation used to run the rules is known as UniFIRE and is available at https://gitlab.ebi.ac.uk/uniprot-public/unifire.

An Improved Approach to Identify Bacterial Pathogens to Human in Environmental Metagenome

The identification of bacterial pathogens to humans is critical for environmental microbial risk assessment. However, current methods for identifying pathogens in environmental samples are limited in their ability to detect highly diverse bacterial communities and accurately differentiate pathogens from commensal bacteria. In the present study, we suggest an improved approach using a combination of identification results obtained from multiple databases, including the multilocus sequence typing (MLST) database, virulence factor database (VFDB), and pathosystems resource integration center (PATRIC) databases to resolve current challenges. By integrating the identification results from multiple databases, potential bacterial pathogens in metagenomes were identified and classified into eight different groups. Based on the distribution of genes in each group, we proposed an equation to calculate the metagenomic pathogen identification index (MPII) of each metagenome based on the weighted abundance of identified sequences in each database. We found that the accuracy of pathogen identification was improved by using combinations of multiple databases compared to that of individual databases. When the approach was applied to environmental metagenomes, metagenomes associated with activated sludge were estimated with higher MPII than other environments (i.e., drinking water, ocean water, ocean sediment, and freshwater sediment). The calculated MPII values were statistically distinguishable among different environments (p<0.05). These results demonstrate that the suggested approach allows more for more accurate identification of the pathogens associated with metagenomes.

Review on the Computational Genome Annotation of Sequences Obtained by Next-Generation Sequencing

Next-Generation Sequencing (NGS) has made it easier to obtain genome-wide sequence data and it has shifted the research focus into genome annotation. The challenging tasks involved in annotation rely on the currently available tools and techniques to decode the information contained in nucleotide sequences. This information will improve our understanding of general aspects of life and evolution and improve our ability to diagnose genetic disorders. Here, we present a summary of both structural and functional annotations, as well as the associated comparative annotation tools and pipelines. We highlight visualization tools that immensely aid the annotation process and the contributions of the scientific community to the annotation. Further, we discuss quality-control practices and the need for re-annotation, and highlight the future of annotation.

Activity-based protein profiling of rice (Oryza sativa L.) bran serine hydrolases

Rice bran is an underutilized agricultural by-product with economic importance. The unique phytochemicals and fatty acid compositions of bran have been targeted for nutraceutical development. The endogenous lipases and hydrolases are responsible for the rapid deterioration of rice bran. Hence, we attempted to provide the first comprehensive profiling of active serine hydrolases (SHs) present in rice bran proteome by activity-based protein profiling (ABPP) strategy. The active site-directed fluorophosphonate probe (rhodamine and biotin-conjugated) was used for the detection and identification of active SHs. ABPP revealed 55 uncharacterized active-SHs and are representing five different known enzyme families. Based on motif and domain analyses, one of the uncharacterized and miss annotated SHs (Os12Ssp, storage protein) was selected for biochemical characterization by overexpressing in yeast. The purified recombinant protein authenticated the serine protease activity in time and protein-dependent studies. Os12Ssp exhibited the maximum activity at a pH between 7.0 and 8.0. The protease activity was inhibited by the covalent serine protease inhibitor, which suggests that the ABPP approach is indeed reliable than the sequence-based annotations. Collectively, the comprehensive knowledge generated from this study would be useful in expanding the current understanding of rice bran SHs and paves the way for better utilization/stabilization of rice bran.

Detecting and correcting misclassified sequences in the large-scale public databases

Motivation

As the cost of sequencing decreases, the amount of data being deposited into public repositories is increasing rapidly. Public databases rely on the user to provide metadata for each submission that is prone to user error. Unfortunately, most public databases, such as non-redundant (NR), rely on user input and do not have methods for identifying errors in the provided metadata, leading to the potential for error propagation. Previous research on a small subset of the NR database analyzed misclassification based on sequence similarity. To the best of our knowledge, the amount of misclassification in the entire database has not been quantified. We propose a heuristic method to detect potentially misclassified taxonomic assignments in the NR database. We applied a curation technique and quality control to find the most probable taxonomic assignment. Our method incorporates provenance and frequency of each annotation from manually and computationally created databases and clustering information at 95% similarity.

Results

We found more than two million potentially taxonomically misclassified proteins in the NR database. Using simulated data, we show a high precision of 97% and a recall of 87% for detecting taxonomically misclassified proteins. The proposed approach and findings could also be applied to other databases.

Availability and implementation

Source code, dataset, documentation, Jupyter notebooks and Docker container are available at https://github.com/boalang/nr.

Supplementary information

Supplementary data are available at Bioinformatics online.

Sequence-based bioprospecting of myo-inositol oxygenase (Miox) reveals new homologues that increase glucaric acid production in Saccharomyces cerevisiae

myo-Inositol oxygenase (Miox) is a rate-limiting enzyme for glucaric acid production via microbial fermentation. The enzyme converts myo-inositol to glucuronate, which is further converted to glucaric acid, a natural compound with industrial uses that range from detergents to pharmaceutical synthesis to polymeric materials. More than 2,000 Miox sequences are available in the Uniprot database but only thirteen are classified as reviewed in Swiss-Prot (August 2019). In this study, sequence similarity networks were used to identify new homologues to be expressed in Saccharomyces cerevisiae for glucaric acid production. The expression of four homologues did not lead to product formation. Some of these enzymes may have a defective "dynamic lid" - a structural feature important to close the reaction site - which might explain the lack of activity. Thirty-one selected Miox sequences did allow for product formation, of which twenty-five were characterized for the first time. Expression of Talaromyces marneffei Miox led to the accumulation of 1.76 ± 0.33 g glucaric acid/L from 20 g glucose/L and 10 g/L myo-inositol. Specific glucaric acid titer with TmMiox increased 44 % compared to the often-used Arabidopsis thaliana variant AtMiox4 (0.258 vs. 0.179 g glucaric acid/g biomass). AtMiox4 activity decreased from 12.47 to 0.40 nmol/min/mg protein when cells exited exponential phase during growth on glucose, highlighting the importance of future research on Miox stability in order to further improve microbial production of glucaric acid.

Antibiotic susceptibility signatures identify potential antimicrobial targets in the Acinetobacter baumannii cell envelope

A unique, protective cell envelope contributes to the broad drug resistance of the nosocomial pathogen Acinetobacter baumannii. Here we use transposon insertion sequencing to identify A. baumannii mutants displaying altered susceptibility to a panel of diverse antibiotics. By examining mutants with antibiotic susceptibility profiles that parallel mutations in characterized genes, we infer the function of multiple uncharacterized envelope proteins, some of which have roles in cell division or cell elongation. Remarkably, mutations affecting a predicted cell wall hydrolase lead to alterations in lipooligosaccharide synthesis. In addition, the analysis of altered susceptibility signatures and antibiotic-induced morphology patterns allows us to predict drug synergies; for example, certain beta-lactams appear to work cooperatively due to their preferential targeting of specific cell wall assembly machineries. Our results indicate that the pathogen may be effectively inhibited by the combined targeting of multiple pathways critical for envelope growth.

MtcB, a member of the MttB superfamily from the human gut acetogen Eubacterium limosum, is a cobalamin-dependent carnitine demethylase

The trimethylamine methyltransferase MttB is the first described member of a superfamily comprising thousands of microbial proteins. Most members of the MttB superfamily are encoded by genes that lack the codon for pyrrolysine characteristic of trimethylamine methyltransferases, raising questions about the activities of these proteins. The superfamily member MtcB is found in the human intestinal isolate Eubacterium limosum ATCC 8486, an acetogen that can grow by demethylation of l-carnitine. Here, we demonstrate that MtcB catalyzes l-carnitine demethylation. When growing on l-carnitine, E. limosum excreted the unusual biological product norcarnitine as well as acetate, butyrate, and caproate. Cellular extracts of E. limosum grown on l-carnitine, but not lactate, methylated cob-(I)alamin or tetrahydrofolate using l-carnitine as methyl donor. MtcB, along with the corrinoid protein MtqC and the methylcorrinoid:tetrahydrofolate methyltransferase MtqA, were much more abundant in E. limosum cells grown on l-carnitine than on lactate. Recombinant MtcB methylates either cob(I)alamin or Co(I)-MtqC in the presence of l-carnitine and, to a much lesser extent, γ-butyrobetaine. Other quaternary amines were not substrates. Recombinant MtcB, MtqC, and MtqA methylated tetrahydrofolate via l-carnitine, forming a key intermediate in the acetogenic Wood-Ljungdahl pathway. To our knowledge, MtcB methylation of cobalamin or Co(I)-MtqC represents the first described mechanism of biological l-carnitine demethylation. The conversion of l-carnitine and its derivative γ-butyrobetaine to trimethylamine by the gut microbiome has been linked to cardiovascular disease. The activities of MtcB and related proteins in E. limosum might demethylate proatherogenic quaternary amines and contribute to the perceived health benefits of this human gut symbiont.

RidA Proteins Protect against Metabolic Damage by Reactive Intermediates

The Rid (YjgF/YER057c/UK114) protein superfamily was first defined by sequence homology with available protein sequences from bacteria, archaea, and eukaryotes (L. Parsons, N. Bonander, E. Eisenstein, M. Gilson, et al., Biochemistry 42:80-89, 2003, https://doi.org/10.1021/bi020541w). The archetypal subfamily, RidA (reactive intermediate deaminase A), is found in all domains of life, with the vast majority of free-living organisms carrying at least one RidA homolog. In over 2 decades, close to 100 reports have implicated Rid family members in cellular processes in prokaryotes, yeast, plants, and mammals. Functional roles have been proposed for Rid enzymes in amino acid biosynthesis, plant root development and nutrient acquisition, cellular respiration, and carcinogenesis. Despite the wealth of literature and over a dozen high-resolution structures of different RidA enzymes, their biochemical function remained elusive for decades. The function of the RidA protein was elucidated in a bacterial model system despite (i) a minimal phenotype of ridA mutants, (ii) the enzyme catalyzing a reaction believed to occur spontaneously, and (iii) confusing literature on the pleiotropic effects of RidA homologs in prokaryotes and eukaryotes. Subsequent work provided the physiological framework to support the RidA paradigm in Salmonella enterica by linking the phenotypes of mutants lacking ridA to the accumulation of the reactive metabolite 2-aminoacrylate (2AA), which damaged metabolic enzymes. Conservation of enamine/imine deaminase activity of RidA enzymes from all domains raises the likelihood that, despite the diverse phenotypes, the consequences when RidA is absent are due to accumulated 2AA (or a similar reactive enamine) and the diversity of metabolic phenotypes can be attributed to differences in metabolic network architecture. The discovery of the RidA paradigm in S. enterica laid a foundation for assessing the role of Rid enzymes in diverse organisms and contributed fundamental lessons on metabolic network evolution and diversity in microbes. This review describes the studies that defined the conserved function of RidA, the paradigm of enamine stress in S. enterica, and emerging studies that explore how this paradigm differs in other organisms. We focus primarily on the RidA subfamily, while remarking on our current understanding of the other Rid subfamilies. Finally, we describe the current status of the field and pose questions that will drive future studies on this widely conserved protein family to provide fundamental new metabolic information.

Computational Approaches for Unraveling the Effects of Variation in the Human Genome and Microbiome

The past two decades of analytical efforts have highlighted how much more remains to be learned about the human genome and, particularly, its complex involvement in promoting disease development and progression. While numerous computational tools exist for the assessment of the functional and pathogenic effects of genome variants, their precision is far from satisfactory, particularly for clinical use. Accumulating evidence also suggests that the human microbiome's interaction with the human genome plays a critical role in determining health and disease states. While numerous microbial taxonomic groups and molecular functions of the human microbiome have been associated with disease, the reproducibility of these findings is lacking. The human microbiome–genome interaction in healthy individuals is even less well understood. This review summarizes the available computational methods built to analyze the effect of variation in the human genome and microbiome. We address the applicability and precision of these methods across their possible uses. We also briefly discuss the exciting, necessary, and now possible integration of the two types of data to improve the understanding of pathogenicity mechanisms.

Alternative carbohydrate pathways - enzymes, functions and engineering

Metabolic engineering is crucial in the development of production strains for platform chemicals, pharmaceuticals and biomaterials from renewable resources. The central carbon metabolism (CCM) of heterotrophs plays an essential role in the conversion of biomass to the cellular building blocks required for growth. Yet, engineering the CCM ultimately aims toward a maximization of flux toward products of interest. The most abundant dissimilative carbohydrate pathways amongst prokaryotes (and eukaryotes) are the Embden-Meyerhof-Parnas (EMP) and the Entner-Doudoroff (ED) pathways, which build the basics for heterotrophic metabolic chassis strains. Although the EMP is regarded as the textbook example of a carbohydrate pathway owing to its central role in production strains like Escherichia coli, Saccharomyces cerevisiae and Bacillus subtilis, it is either modified, complemented or even replaced by alternative carbohydrate pathways in different organisms. The ED pathway also plays key roles in biotechnological relevant bacteria, like Zymomonas mobilis and Pseudomonas putida, and its importance was recently discovered in photoautotrophs and marine microorganisms. In contrast to the EMP, the ED pathway and its variations are not evolutionary optimized for high ATP production and it differs in key principles such as protein cost, energetics and thermodynamics, which can be exploited in the construction of unique metabolic designs. Single ED pathway enzymes and complete ED pathway modules have been used to rewire carbon metabolisms in production strains and for the construction of cell-free enzymatic pathways. This review focuses on the differences of the ED and EMP pathways including their variations and discusses the use of alternative pathway strategies for in vivo and cell-free metabolic engineering.

The ortholog conjecture revisited: the value of orthologs and paralogs in function prediction

MOTIVATION

The computational prediction of gene function is a key step in making full use of newly sequenced genomes. Function is generally predicted by transferring annotations from homologous genes or proteins for which experimental evidence exists. The 'ortholog conjecture' proposes that orthologous genes should be preferred when making such predictions, as they evolve functions more slowly than paralogous genes. Previous research has provided little support for the ortholog conjecture, though the incomplete nature of the data cast doubt on the conclusions.

RESULTS

We use experimental annotations from over 40 000 proteins, drawn from over 80 000 publications, to revisit the ortholog conjecture in two pairs of species: (i) Homo sapiens and Mus musculus and (ii) Saccharomyces cerevisiae and Schizosaccharomyces pombe. By making a distinction between questions about the evolution of function versus questions about the prediction of function, we find strong evidence against the ortholog conjecture in the context of function prediction, though questions about the evolution of function remain difficult to address. In both pairs of species, we quantify the amount of information that would be ignored if paralogs are discarded, as well as the resulting loss in prediction accuracy. Taken as a whole, our results support the view that the types of homologs used for function transfer are largely irrelevant to the task of function prediction. Maximizing the amount of data used for this task, regardless of whether it comes from orthologs or paralogs, is most likely to lead to higher prediction accuracy.

AVAILABILITY AND IMPLEMENTATION

https://github.com/predragradivojac/oc.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Non-homology-based prediction of gene functions in maize (Zea mays ssp. mays)

Advances in genome sequencing and annotation have eased the difficulty of identifying new gene sequences. Predicting the functions of these newly identified genes remains challenging. Genes descended from a common ancestral sequence are likely to have common functions. As a result, homology is widely used for gene function prediction. This means functional annotation errors also propagate from one species to another. Several approaches based on machine learning classification algorithms were evaluated for their ability to accurately predict gene function from non-homology gene features. Among the eight supervised classification algorithms evaluated, random-forest-based prediction consistently provided the most accurate gene function prediction. Non-homology-based functional annotation provides complementary strengths to homology-based annotation, with higher average performance in Biological Process GO terms, the domain where homology-based functional annotation performs the worst, and weaker performance in Molecular Function GO terms, the domain where the accuracy of homology-based functional annotation is highest. GO prediction models trained with homology-based annotations were able to successfully predict annotations from a manually curated "gold standard" GO annotation set. Non-homology-based functional annotation based on machine learning may ultimately prove useful both as a method to assign predicted functions to orphan genes which lack functionally characterized homologs, and to identify and correct functional annotation errors which were propagated through homology-based functional annotations.

GapMind: Automated Annotation of Amino Acid Biosynthesis

GapMind is a Web-based tool for annotating amino acid biosynthesis in bacteria and archaea (http://papers.genomics.lbl.gov/gaps). GapMind incorporates many variant pathways and 130 different reactions, and it analyzes a genome in just 15 s. To avoid error-prone transitive annotations, GapMind relies primarily on a database of experimentally characterized proteins. GapMind correctly handles fusion proteins and split proteins, which often cause errors for best-hit approaches. To improve GapMind's coverage, we examined genetic data from 35 bacteria that grow in defined media without amino acids, and we filled many gaps in amino acid biosynthesis pathways. For example, we identified additional genes for arginine synthesis with succinylated intermediates in Bacteroides thetaiotaomicron, and we propose that Dyella japonica synthesizes tyrosine from phenylalanine. Nevertheless, for many bacteria and archaea that grow in minimal media, genes for some steps still cannot be identified. To help interpret potential gaps, GapMind checks if they match known gaps in related microbes that can grow in minimal media. GapMind should aid the identification of microbial growth requirements.IMPORTANCE Many microbes can make all of the amino acids (the building blocks of proteins). In principle, we should be able to predict which amino acids a microbe can make, and which it requires as nutrients, by checking its genome sequence for all of the necessary genes. However, in practice, it is difficult to check for all of the alternative pathways. Furthermore, new pathways and enzymes are still being discovered. We built an automated tool, GapMind, to annotate amino acid biosynthesis in bacterial and archaeal genomes. We used GapMind to list gaps: cases where a microbe makes an amino acid but a complete pathway cannot be identified in its genome. We used these gaps, together with data from mutants, to identify new pathways and enzymes. However, for most bacteria and archaea, we still do not know how they can make all of the amino acids.

ARGDIT: a validation and integration toolkit for Antimicrobial Resistance Gene Databases

MOTIVATION

Antimicrobial resistance is currently one of the main challenges in public health due to the excessive use of antimicrobials in medical treatments and agriculture. The advancements in high-throughput next-generation sequencing and development of bioinformatics tools allow simultaneous detection and identification of antimicrobial resistance genes (ARGs) from clinical, food and environment samples, to monitor the prevalence and track the dissemination of these ARGs. Such analyses are however reliant on a comprehensive database of ARGs with accurate sequence content and annotation. Most of the current ARG databases are therefore manually curated, but this is a time-consuming process and the resulting curation errors could be hard to detect. Several secondary ARG databases consolidate contents from different source ARG databases, and hence modifications in the primary databases might not be propagated and updated promptly in the secondary ARG databases.

RESULTS

To address these problems, a validation and integration toolkit called ARGDIT was developed to validate ARG database fidelity, and merge multiple primary ARG databases into a single consolidated secondary ARG database with optional automated sequence re-annotation. Experimental results demonstrated the effectiveness of this toolkit in identifying errors such as sequence annotation typos in current ARG databases and generating an integrated non-redundant ARG database with structured annotation. A toolkit-oriented workflow is also proposed to minimize the efforts in validating, curating and merging multiple ARG protein or coding sequence databases. Database developers therefore benefit from faster update cycles and lower costs for database maintenance, while ARG pipeline users can easily evaluate the reference ARG database quality.

AVAILABILITY AND IMPLEMENTATION

ARGDIT is available at https://github.com/phglab/ARGDIT.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Metabolic coessentiality mapping identifies C12orf49 as a regulator of SREBP processing and cholesterol metabolism

Coessentiality mapping has been useful to systematically cluster genes into biological pathways and identify gene functions1-3. Here, using the debiased sparse partial correlation (DSPC) method3, we construct a functional coessentiality map for cellular metabolic processes across human cancer cell lines. This analysis reveals 35 modules associated with known metabolic pathways and further assigns metabolic functions to unknown genes. In particular, we identify C12orf49 as an essential regulator of cholesterol and fatty acid metabolism in mammalian cells. Mechanistically, C12orf49 localizes to the Golgi, binds membrane-bound transcription factor peptidase, site 1 (MBTPS1, site 1 protease) and is necessary for the cleavage of its substrates, including sterol regulatory element binding protein (SREBP) transcription factors. This function depends on the evolutionarily conserved uncharacterized domain (DUF2054) and promotes cell proliferation under cholesterol depletion. Notably, c12orf49 depletion in zebrafish blocks dietary lipid clearance in vivo, mimicking the phenotype of mbtps1 mutants. Finally, in an electronic health record (EHR)-linked DNA biobank, C12orf49 is associated with hyperlipidaemia through phenome analysis. Altogether, our findings reveal a conserved role for C12orf49 in cholesterol and lipid homeostasis and provide a platform to identify unknown components of other metabolic pathways.

Identification and Expression Profiling of Toll-Like Receptors of Brown Trout (Salmo trutta) during Proliferative Kidney Disease

Proliferative kidney disease is an emerging disease among salmonids in Europe and North America caused by the myxozoan parasite Tetracapsuloides bryosalmonae. The decline of endemic brown trout (Salmo trutta) in the Alpine streams of Europe is fostered by T. bryosalmonae infection. Toll-like receptors (TLRs) are a family of pattern recognition receptors that acts as sentinels of the immune system against the invading pathogens. However, little is known about the TLRs' response in salmonids against the myxozoan infection. In the present study, we identified and evaluated TLR1, TLR19, and TLR13-like genes of brown trout using data-mining and phylogenetic analysis. The expression pattern of TLRs was examined in the posterior kidney of brown trout infected with T. bryosalmonae at various time points. Typical Toll/interleukin-1 receptor protein domain was found in all tested TLRs. However, TLR13-like chr2 had a short amino acid sequence with no LRR domain. Phylogenetic analysis illustrated that TLR orthologs are conserved across vertebrates. Similarly, a conserved synteny gene block arrangement was observed in the case of TLR1 and TLR19 across fish species. Interestingly, all tested TLRs showed their maximal relative expression from 6 to 10 weeks post-exposure to the parasite. Our results suggest that these TLRs may play an important role in the innate defense mechanism of brown trout against the invading T. bryosalmonae.

The novel E. coli cell division protein, YtfB, plays a role in eukaryotic cell adhesion

Characterisation of protein function based solely on homology searches may overlook functions under specific environmental conditions, or the possibility of a protein having multiple roles. In this study we investigated the role of YtfB, a protein originally identified in a genome-wide screen to cause inhibition of cell division, and has demonstrated to localise to the Escherichia coli division site with some degree of glycan specificity. Interestingly, YtfB also shows homology to the virulence factor OapA from Haemophilus influenzae, which is important for adherence to epithelial cells, indicating the potential of additional function(s) for YtfB. Here we show that E. coli YtfB binds to N’acetylglucosamine and mannobiose glycans with high affinity. The loss of ytfB results in a reduction in the ability of the uropathogenic E. coli strain UTI89 to adhere to human kidney cells, but not to bladder cells, suggesting a specific role in the initial adherence stage of ascending urinary tract infections. Taken together, our results suggest a role for YtfB in adhesion to specific eukaryotic cells, which may be additional, or complementary, to its role in cell division. This study highlights the importance of understanding the possible multiple functions of proteins based on homology, which may be specific to different environmental conditions.

Microbial genome analysis: the COG approach

For the past 20 years, the Clusters of Orthologous Genes (COG) database had been a popular tool for microbial genome annotation and comparative genomics. Initially created for the purpose of evolutionary classification of protein families, the COG have been used, apart from straightforward functional annotation of sequenced genomes, for such tasks as (i) unification of genome annotation in groups of related organisms; (ii) identification of missing and/or undetected genes in complete microbial genomes; (iii) analysis of genomic neighborhoods, in many cases allowing prediction of novel functional systems; (iv) analysis of metabolic pathways and prediction of alternative forms of enzymes; (v) comparison of organisms by COG functional categories; and (vi) prioritization of targets for structural and functional characterization. Here we review the principles of the COG approach and discuss its key advantages and drawbacks in microbial genome analysis.

PATRIC as a unique resource for studying antimicrobial resistance

The Pathosystems Resource Integration Center (PATRIC, www.patricbrc.org) is designed to provide researchers with the tools and services that they need to perform genomic and other ‘omic’ data analyses. In response to mounting concern over antimicrobial resistance (AMR), the PATRIC team has been developing new tools that help researchers understand AMR and its genetic determinants. To support comparative analyses, we have added AMR phenotype data to over 15 000 genomes in the PATRIC database, often assembling genomes from reads in public archives and collecting their associated AMR panel data from the literature to augment the collection. We have also been using this collection of AMR metadata to build machine learning-based classifiers that can predict the AMR phenotypes and the genomic regions associated with resistance for genomes being submitted to the annotation service. Likewise, we have undertaken a large AMR protein annotation effort by manually curating data from the literature and public repositories. This collection of 7370 AMR reference proteins, which contains many protein annotations (functional roles) that are unique to PATRIC and RAST, has been manually curated so that it projects stably across genomes. The collection currently projects to 1 610 744 proteins in the PATRIC database. Finally, the PATRIC Web site has been expanded to enable AMR-based custom page views so that researchers can easily explore AMR data and design experiments based on whole genomes or individual genes.

A benchmark study of ab initio gene prediction methods in diverse eukaryotic organisms

Background

The draft genome assemblies produced by new sequencing technologies present important challenges for automatic gene prediction pipelines, leading to less accurate gene models. New benchmark methods are needed to evaluate the accuracy of gene prediction methods in the face of incomplete genome assemblies, low genome coverage and quality, complex gene structures, or a lack of suitable sequences for evidence-based annotations.

Results

We describe the construction of a new benchmark, called G3PO (benchmark for Gene and Protein Prediction PrOgrams), designed to represent many of the typical challenges faced by current genome annotation projects. The benchmark is based on a carefully validated and curated set of real eukaryotic genes from 147 phylogenetically disperse organisms, and a number of test sets are defined to evaluate the effects of different features, including genome sequence quality, gene structure complexity, protein length, etc. We used the benchmark to perform an independent comparative analysis of the most widely used ab initio gene prediction programs and identified the main strengths and weaknesses of the programs. More importantly, we highlight a number of features that could be exploited in order to improve the accuracy of current prediction tools.

Conclusions

The experiments showed that ab initio gene structure prediction is a very challenging task, which should be further investigated. We believe that the baseline results associated with the complex gene test sets in G3PO provide useful guidelines for future studies.

Using multiple reference genomes to identify and resolve annotation inconsistencies

BACKGROUND

Advances in sequencing technologies have led to the release of reference genomes and annotations for multiple individuals within more well-studied systems. While each of these new genome assemblies shares significant portions of synteny between each other, the annotated structure of gene models within these regions can differ. Of particular concern are split-gene misannotations, in which a single gene is incorrectly annotated as two distinct genes or two genes are incorrectly annotated as a single gene. These misannotations can have major impacts on functional prediction, estimates of expression, and many downstream analyses.

RESULTS

We developed a high-throughput method based on pairwise comparisons of annotations that detect potential split-gene misannotations and quantifies support for whether the genes should be merged into a single gene model. We demonstrated the utility of our method using gene annotations of three reference genomes from maize (B73, PH207, and W22), a difficult system from an annotation perspective due to the size and complexity of the genome. On average, we found several hundred of these potential split-gene misannotations in each pairwise comparison, corresponding to 3-5% of gene models across annotations. To determine which state (i.e. one gene or multiple genes) is biologically supported, we utilized RNAseq data from 10 tissues throughout development along with a novel metric and simulation framework. The methods we have developed require minimal human interaction and can be applied to future assemblies to aid in annotation efforts.

CONCLUSIONS

Split-gene misannotations occur at appreciable frequency in maize annotations. We have developed a method to easily identify and correct these misannotations. Importantly, this method is generic in that it can utilize any type of short-read expression data. Failure to account for split-gene misannotations has serious consequences for biological inference, particularly for expression-based analyses.

Using multiple reference genomes to identify and resolve annotation inconsistencies

BACKGROUND

Advances in sequencing technologies have led to the release of reference genomes and annotations for multiple individuals within more well-studied systems. While each of these new genome assemblies shares significant portions of synteny between each other, the annotated structure of gene models within these regions can differ. Of particular concern are split-gene misannotations, in which a single gene is incorrectly annotated as two distinct genes or two genes are incorrectly annotated as a single gene. These misannotations can have major impacts on functional prediction, estimates of expression, and many downstream analyses.

RESULTS

We developed a high-throughput method based on pairwise comparisons of annotations that detect potential split-gene misannotations and quantifies support for whether the genes should be merged into a single gene model. We demonstrated the utility of our method using gene annotations of three reference genomes from maize (B73, PH207, and W22), a difficult system from an annotation perspective due to the size and complexity of the genome. On average, we found several hundred of these potential split-gene misannotations in each pairwise comparison, corresponding to 3-5% of gene models across annotations. To determine which state (i.e. one gene or multiple genes) is biologically supported, we utilized RNAseq data from 10 tissues throughout development along with a novel metric and simulation framework. The methods we have developed require minimal human interaction and can be applied to future assemblies to aid in annotation efforts.

CONCLUSIONS

Split-gene misannotations occur at appreciable frequency in maize annotations. We have developed a method to easily identify and correct these misannotations. Importantly, this method is generic in that it can utilize any type of short-read expression data. Failure to account for split-gene misannotations has serious consequences for biological inference, particularly for expression-based analyses.

Misannotations of the genes encoding sugar N-formyltransferases

Tens of thousands of bacterial genome sequences are now known due to the development of rapid and inexpensive sequencing technologies. An important key in utilizing these vast amounts of data in a biologically meaningful way is to infer the function of the proteins encoded in the genomes via bioinformatics techniques. Whereas these approaches are absolutely critical to the annotation of gene function, there are still issues of misidentifications, which must be experimentally corrected. For example, many of the bacterial DNA sequences encoding sugar N-formyltransferases have been annotated as l-methionyl-tRNA transferases in the databases. These mistakes may be due in part to the fact that until recently the structures and functions of these enzymes were not well known. Herein we describe the misannotation of two genes, WP_088211966.1 and WP_096244125.1, from Shewanella spp. and Pseudomonas congelans, respectively. Although the proteins encoded by these genes were originally suggested to function as l-methionyl-tRNA transferases, we demonstrate that they actually catalyze the conversion of dTDP-4-amino-4,6-dideoxy-d-glucose to dTDP-4-formamido-4,6-dideoxy-d-glucose utilizing N10 -formyltetrahydrofolate as the carbon source. For this analysis, the genes encoding these enzymes were cloned and the corresponding proteins purified. X-ray structures of the two proteins were determined to high resolution and kinetic analyses were conducted. Both enzymes display classical Michaelis-Menten kinetics and adopt the characteristic three-dimensional structural fold previously observed for other sugar N-formyltransferases. The results presented herein will aid in the future annotation of these fascinating enzymes.

Unique Shine-Dalgarno Sequences in Cyanobacteria and Chloroplasts Reveal Evolutionary Differences in Their Translation Initiation

Microorganisms require efficient translation to grow and replicate rapidly, and translation is often rate-limited by initiation. A prominent feature that facilitates translation initiation in bacteria is the Shine-Dalgarno (SD) sequence. However, there is much debate over its conservation in Cyanobacteria and in chloroplasts which presumably originated from endosymbiosis of ancient Cyanobacteria. Elucidating the utilization of SD sequences in Cyanobacteria and in chloroplasts is therefore important to understand whether 1) SD role in Cyanobacterial translation has been reduced prior to chloroplast endosymbiosis or 2) translation in Cyanobacteria and in plastid has been subjected to different evolutionary pressures. To test these alternatives, we employed genomic, proteomic, and transcriptomic data to trace differences in SD usage among Synechocystis species, Microcystis aeruginosa, cyanophages, Nicotiana tabacum chloroplast, and Arabidopsis thaliana chloroplast. We corrected their mis-annotated 16S rRNA 3' terminus using an RNA-Seq-based approach to determine their SD/anti-SD locational constraints using an improved measurement DtoStart. We found that cyanophages well-mimic Cyanobacteria in SD usage because both have been under the same selection pressure for SD-mediated initiation. Whereas chloroplasts lost this similarity because the need for SD-facilitated initiation has been reduced in plastids having much reduced genome size and different ribosomal proteins as a result of host-symbiont coevolution. Consequently, SD sequence significantly increases protein expression in Cyanobacteria but not in chloroplasts, and only Cyanobacterial genes compensate for a lack of SD sequence by having weaker secondary structures at the 5' UTR. Our results suggest different evolutionary pressures operate on translation initiation in Cyanobacteria and in chloroplast.

Identification and characterization of parvalbumin-like protein in Trichophyton violaceum

Parvalbumins play crucial physiological roles in neuromuscular systems of vertebrates, such as cell-cycle, development of neurons, contraction of muscles, and regulation of intracellular calcium. To perform these neuromuscular functions, parvalbumin may be in associated with other proteins including calbindin, carbonic anhydrase, and cytochrome oxidase. Humans may show an IgE-specific hypersensitivity to parvalbumins after consumption of some distinct fish species. While this protein is abundant in fish muscles, literature review of publications related to fish parvalbumins, do not point to the presence of parvalbumins in eukaryotic microbes. In this study, we propose that distantly related parvalbumins may be found in some non-fish species. Bioinformatics studies such as multiple sequence alignment (MSA), phylogenetic analysis as well as molecular-based experiments indicate that, at least two parvalbumins sequences (UniProt IDs: A0A178F775 and A0A178F7E4) with EF-hand domains and Ca²⁺-binding sites could be identified in Trichophyton violaceum, a pathogenic fungal species. It was determined that both genes consisted of a single exon and encoded for parvalbumin proteins possessing conserved amino acid motifs. Antigenicity prediction revealed antigenic sites located in both sides of the Ca²⁺-binding site of the first EF-hand domain. Our phylogenetic analysis revealed that one of parvalbumins (UniProt ID: 0A178F775) can be evolved to other parvalbumins in T. violaceum (UniProt ID: A0A178F7E4) and fish species through evolutionary phenomenon. To confirm our in-silico findings, we designed three primer pairs to detect one of the T. violaceum parvalbumins (UniProt ID: A0A178F7E4) by polymerase chain reaction (PCR); one primer pair showed a strong and specific band in agarose gel electrophoresis. To evaluate the specificity of the method, the primers were tested on extracted DNA from Trichophyton rubrum and T. mentagrophytes. The results demonstrated that the evaluated parvalbumin gene (UniProt ID: A0A178F7E4) was T. violaceum-specific and this pathogenic fungus can be differentiated from T. rubrum and T. mentagrophytes through identification of parvalbumin genes. Further studies are necessary to unravel the biochemical and physiological functions of parvalbumins in T. violaceum.

PhyloToL: A Taxon/Gene-Rich Phylogenomic Pipeline to Explore Genome Evolution of Diverse Eukaryotes

Estimating multiple sequence alignments (MSAs) and inferring phylogenies are essential for many aspects of comparative biology. Yet, many bioinformatics tools for such analyses have focused on specific clades, with greatest attention paid to plants, animals, and fungi. The rapid increase in high-throughput sequencing (HTS) data from diverse lineages now provides opportunities to estimate evolutionary relationships and gene family evolution across the eukaryotic tree of life. At the same time, these types of data are known to be error-prone (e.g., substitutions, contamination). To address these opportunities and challenges, we have refined a phylogenomic pipeline, now named PhyloToL, to allow easy incorporation of data from HTS studies, to automate production of both MSAs and gene trees, and to identify and remove contaminants. PhyloToL is designed for phylogenomic analyses of diverse lineages across the tree of life (i.e., at scales of >100 My). We demonstrate the power of PhyloToL by assessing stop codon usage in Ciliophora, identifying contamination in a taxon- and gene-rich database and exploring the evolutionary history of chromosomes in the kinetoplastid parasite Trypanosoma brucei, the causative agent of African sleeping sickness. Benchmarking PhyloToL’s homology assessment against that of OrthoMCL and a published paper on superfamilies of bacterial and eukaryotic organellar outer membrane pore-forming proteins demonstrates the power of our approach for determining gene family membership and inferring gene trees. PhyloToL is highly flexible and allows users to easily explore HTS data, test hypotheses about phylogeny and gene family evolution and combine outputs with third-party tools (e.g., PhyloChromoMap, iGTP).

Whole-genome comparison between the type strain of Halobacterium salinarum (DSM 3754T ) and the laboratory strains R1 and NRC-1

Halobacterium salinarum is an extremely halophilic archaeon that is widely distributed in hypersaline environments and was originally isolated as a spoilage organism of salted fish and hides. The type strain 91-R6 (DSM 3754T ) has seldom been studied and its genome sequence has only recently been determined by our group. The exact relationship between the type strain and two widely used model strains, NRC-1 and R1, has not been described before. The genome of Hbt. salinarum strain 91-R6 consists of a chromosome (2.17 Mb) and two large plasmids (148 and 102 kb, with 39,230 bp being duplicated). Cytosine residues are methylated (m4 C) within CTAG motifs. The genomes of type and laboratory strains are closely related, their chromosomes sharing average nucleotide identity (ANIb) values of 98% and in silico DNA-DNA hybridization (DDH) values of 95%. The chromosomes are completely colinear, do not show genome rearrangement, and matching segments show <1% sequence difference. Among the strain-specific sequences are three large chromosomal replacement regions (>10 kb). The well-studied AT-rich island (61 kb) of the laboratory strains is replaced by a distinct AT-rich sequence (47 kb) in 91-R6. Another large replacement (91-R6: 78 kb, R1: 44 kb) codes for distinct homologs of proteins involved in motility and N-glycosylation. Most (107 kb) of plasmid pHSAL1 (91-R6) is very closely related to part of plasmid pHS3 (R1) and codes for essential genes (e.g. arginine-tRNA ligase and the pyrimidine biosynthesis enzyme aspartate carbamoyltransferase). Part of pHS3 (42.5 kb total) is closely related to the largest strain-specific sequence (164 kb) in the type strain chromosome. Genome sequencing unraveled the close relationship between the Hbt. salinarum type strain and two well-studied laboratory strains at the DNA and protein levels. Although an independent isolate, the type strain shows a remarkably low evolutionary difference to the laboratory strains.

Mis-annotations of a promising antibiotic target in high-priority gram-negative pathogens

The rise of antibiotic resistance combined with the lack of new products entering the market has led to bacterial infections becoming one of the biggest threats to global health. Therefore, there is an urgent need to identify novel antibiotic targets, such as dihydrodipicolinate synthase (DHDPS), an enzyme involved in the production of essential metabolites in cell wall and protein synthesis. Here, we utilised a 7-residue sequence motif to identify mis-annotation of multiple DHDPS genes in the high-priority Gram-negative bacteria Acinetobacter baumannii and Klebsiella pneumoniae. We subsequently confirmed these mis-annotations using a combination of enzyme kinetics and X-ray crystallography. Thus, this study highlights the need to ensure genes encoding promising drug targets, like DHDPS, are annotated correctly, especially for clinically important pathogens. PDB ID: 6UE0.

The ammonia-lyases: enzymes that use a wide range of approaches to catalyze the same type of reaction

The paradigm that protein structure determines protein function has been clearly established. What is less clear is whether a specific protein structure is always required to carry out a specific function. Numerous cases are now known where there is no apparent connection between the biological function of a protein and the other members of its structural class, and where functionally related proteins can have quite diverse structures. A set of enzymes with these diverse properties, the ammonia-lyases, will be examined in this review. These are a class of enzymes that catalyze a relatively straightforward deamination reaction. However, the individual enzymes of this class possess a wide variety of different structures, utilize a diverse set of cofactors, and appear to catalyze this related reaction through a range of different mechanisms. This review aims to address a basic question: if there is not a specific protein structure and active site architecture that is both required and sufficient to define a catalyst for a given chemical reaction, then what factor(s) determine the structure and the mechanism that is selected to catalyze a particular reaction?

Mechanistic insights into bacterial metabolic reprogramming from omics-integrated genome-scale models

Understanding the adaptive responses of individual bacterial strains is crucial for microbiome engineering approaches that introduce new functionalities into complex microbiomes, such as xenobiotic compound metabolism for soil bioremediation. Adaptation requires metabolic reprogramming of the cell, which can be captured by multi-omics, but this data remains formidably challenging to interpret and predict. Here we present a new approach that combines genome-scale metabolic modeling with transcriptomics and exometabolomics, both of which are common tools for studying dynamic population behavior. As a realistic demonstration, we developed a genome-scale model of Pseudomonas veronii 1YdBTEX2, a candidate bioaugmentation agent for accelerated metabolism of mono-aromatic compounds in soil microbiomes, while simultaneously collecting experimental data of P. veronii metabolism during growth phase transitions. Predictions of the P. veronii growth rates and specific metabolic processes from the integrated model closely matched experimental observations. We conclude that integrative and network-based analysis can help build predictive models that accurately capture bacterial adaptation responses. Further development and testing of such models may considerably improve the successful establishment of bacterial inoculants in more complex systems.

A strategy for large-scale comparison of evolutionary- and reaction-based classifications of enzyme function

Determining the molecular function of enzymes discovered by genome sequencing represents a primary foundation for understanding many aspects of biology. Historically, classification of enzyme reactions has used the enzyme nomenclature system developed to describe the overall reactions performed by biochemically characterized enzymes, irrespective of their associated sequences. In contrast, functional classification and assignment for the millions of protein sequences of unknown function now available is largely done in two computational steps, first by similarity-based assignment of newly obtained sequences to homologous groups, followed by transferring to them the known functions of similar biochemically characterized homologs. Due to the fundamental differences in their etiologies and practice, `how' these chemistry- and evolution-centric functional classification systems relate to each other has been difficult to explore on a large scale. To investigate this issue in a new way, we integrated two published ontologies that had previously described each of these classification systems independently. The resulting infrastructure was then used to compare the functional assignments obtained from each classification system for the well-studied and functionally diverse enolase superfamily. Mapping these function assignments to protein structure and reaction similarity networks shows a profound and complex disconnect between the homology- and chemistry-based classification systems. This conclusion mirrors previous observations suggesting that except for closely related sequences, facile annotation transfer from small numbers of characterized enzymes to the huge number uncharacterized homologs to which they are related is problematic. Our extension of these comparisons to large enzyme superfamilies in a computationally intelligent manner provides a foundation for new directions in protein function prediction for the huge proportion of sequences of unknown function represented in major databases. Interactive sequence, reaction, substrate and product similarity networks computed for this work for the enolase and two other superfamilies are freely available for download from the Structure Function Linkage Database Archive (http://sfld.rbvi.ucsf.edu).

Full-Length Transcriptome Survey and Expression Analysis of Parasitoid Wasp Chouioia cunea upon Exposure to 1-Dodecene

Chouioia cunea (Yang) is an endoparasitic wasp which parasitizes pupae and thus plays an important role in the biological control of the fall webworm (Hyphantria cunea Drury), an important quarantine pest in the entire world and a major invasive pest in China. For the purposes of investigating which proteins are involved in the response of C. cunea to 1-Docecene, one of the chemical compounds of pupae of H. cunea with a significant attracting action to mated female C. cunea, 11.5 Gb transcriptome data was sequenced on the PacBio RS II platform from 1-day old C. cunea adults to generate a reference assembly. Afterwards, 46.88 Gb of clean RNA-Seq data were obtained to assess the transcriptional response of these insects before and after the stimulation with 1-Docecene. After removing redundancy using CD-HIT, a sequence structure analysis predicted 29,105 complete coding sequence (CDS) regions, 51,458 single-sequence repeats (SSRs), and 2,375 long non-coding RNAs. Based on the early transcriptome sequencing in our laboratory, we revealed some new sequences corresponding to chemosensory genes such as odorant binding proteins (OBPs), odorant receptor (OR), gustatory receptors(GRs). Results of quantitative real-time PCR experiments revealed that CcOBP7, CcOBP18, CcCSP4, CcOR2, and CcGR18 were up-regulated after 1-Dodecene stimulation. In addition, the expression of 31 genes, including 1 gene related to phospholipid biosynthesis and 2 genes related to transmembrane transport were up-regulated after 1-Dodecene stimulation; meanwhile, the expression of 22 genes, including 5 genes related to protein phosphorylation and protein serine/threonine kinase activity were significantly down-regulated after 1-Dodecene stimulation. These results suggest that the attraction of adult C. cunea to 1-dodecane is associated with the transmembrane signal transduction and dephosphorylation of some proteins. Our findings will provide useful targets for further studies on the molecular mechanism of host recognition in C. cunea.

A global analysis of function and conservation of catalytic residues in enzymes

The catalytic residues of an enzyme comprise the amino acids located in the active center responsible for accelerating the enzyme-catalyzed reaction. These residues lower the activation energy of reactions by performing several catalytic functions. Decades of enzymology research has established general themes regarding the roles of specific residues in these catalytic reactions, but it has been more difficult to explore these roles in a more systematic way. Here, we review the data on the catalytic residues of 648 enzymes, as annotated in the Mechanism and Catalytic Site Atlas (M-CSA), and compare our results with those in previous studies. We structured this analysis around three key properties of the catalytic residues: amino acid type, catalytic function, and sequence conservation in homologous proteins. As expected, we observed that catalysis is mostly accomplished by a small set of residues performing a limited number of catalytic functions. Catalytic residues are typically highly conserved, but to a smaller degree in homologues that perform different reactions or are nonenzymes (pseudoenzymes). Cross-analysis yielded further insights revealing which residues perform particular functions and how often. We obtained more detailed specificity rules for certain functions by identifying the chemical group upon which the residue acts. Finally, we show the mutation tolerance of the catalytic residues based on their roles. The characterization of the catalytic residues, their functions, and conservation, as presented here, is key to understanding the impact of mutations in evolution, disease, and enzyme design. The tools developed for this analysis are available at the M-CSA website and allow for user specific analysis of the same data.

On the structure and function of Escherichia coli YjhC: An oxidoreductase involved in bacterial sialic acid metabolism

Human pathogenic and commensal bacteria have evolved the ability to scavenge host-derived sialic acids and subsequently degrade them as a source of nutrition. Expression of the Escherichia coli yjhBC operon is controlled by the repressor protein nanR, which regulates the core machinery responsible for the import and catabolic processing of sialic acid. The role of the yjhBC encoded proteins is not known-here, we demonstrate that the enzyme YjhC is an oxidoreductase/dehydrogenase involved in bacterial sialic acid degradation. First, we demonstrate in vivo using knockout experiments that YjhC is broadly involved in carbohydrate metabolism, including that of N-acetyl-d-glucosamine, N-acetyl-d-galactosamine and N-acetylneuraminic acid. Differential scanning fluorimetry demonstrates that YjhC binds N-acetylneuraminic acid and its lactone variant, along with NAD(H), which is consistent with its role as an oxidoreductase. Next, we solved the crystal structure of YjhC in complex with the NAD(H) cofactor to 1.35 Å resolution. The protein fold belongs to the Gfo/Idh/MocA protein family. The dimeric assembly observed in the crystal form is confirmed through solution studies. Ensemble refinement reveals a flexible loop region that may play a key role during catalysis, providing essential contacts to stabilize the substrate-a unique feature to YjhC among closely related structures. Guided by the structure, in silico docking experiments support the binding of sialic acid and several common derivatives in the binding pocket, which has an overall positive charge distribution. Taken together, our results verify the role of YjhC as a bona fide oxidoreductase/dehydrogenase and provide the first evidence to support its involvement in sialic acid metabolism.

Metagenomics: Is it a powerful tool to obtain lipases for application in biocatalysis?

In recent years, metagenomic strategies have been widely used to isolate and identify new enzymes from uncultivable components of microbial communities. Among these enzymes, various lipases have been obtained from metagenomic libraries from different environments and characterized. Although many of these lipases have characteristics that could make them interesting for application in biocatalysis, relatively little work has been done to evaluate their potential to catalyze industrially important reactions. In the present article, we highlight the latest research on lipases obtained through metagenomic tools, focusing on studies of activity and stability and investigations of application in biocatalysis. We also discuss the challenges of metagenomic approaches for the bioprospecting of new lipases.

Predicting bacterial virulence factors - evaluation of machine learning and negative data strategies

Bacterial proteins dubbed virulence factors (VFs) are a highly diverse group of sequences, whose only obvious commonality is the very property of being, more or less directly, involved in virulence. It is therefore tempting to speculate whether their prediction, based on direct sequence similarity (seqsim) to known VFs, could be enhanced or even replaced by using machine-learning methods. Specifically, when trained on a large and diverse set of VFs, such may be able to detect putative, non-trivial characteristics shared by otherwise unrelated VF families and therefore better predict novel VFs with insignificant similarity to each individual family. We therefore first reassess the performance of dimer-based Support Vector Machines, as used in the widely used MP3 method, in light of seqsim-only and seqsim/dimer-hybrid classifiers. We then repeat the analysis with a novel, considerably more diverse data set, also addressing the important problem of negative data selection. Finally, we move on to the real-world use case of proteome-wide VF prediction, outlining different approaches to estimating specificity in this scenario. We find that direct seqsim is of unparalleled importance and therefore should always be exploited. Further, we observe strikingly low correlations between different feature and classifier types when ranking proteins by VF likeness. We therefore propose a 'best of each world' approach to prioritize proteins for experimental testing, focussing on the top predictions of each classifier. Further, classifiers for individual VF families should be developed.

Enumerating consistent sub-graphs of directed acyclic graphs: an insight into biomedical ontologies

Motivation

Modern problems of concept annotation associate an object of interest (gene, individual, text document) with a set of interrelated textual descriptors (functions, diseases, topics), often organized in concept hierarchies or ontologies. Most ontology can be seen as directed acyclic graphs (DAGs), where nodes represent concepts and edges represent relational ties between these concepts. Given an ontology graph, each object can only be annotated by a consistent sub-graph; that is, a sub-graph such that if an object is annotated by a particular concept, it must also be annotated by all other concepts that generalize it. Ontologies therefore provide a compact representation of a large space of possible consistent sub-graphs; however, until now we have not been aware of a practical algorithm that can enumerate such annotation spaces for a given ontology.

Results

We propose an algorithm for enumerating consistent sub-graphs of DAGs. The algorithm recursively partitions the graph into strictly smaller graphs until the resulting graph becomes a rooted tree (forest), for which a linear-time solution is computed. It then combines the tallies from graphs created in the recursion to obtain the final count. We prove the correctness of this algorithm, propose several practical accelerations, evaluate it on random graphs and then apply it to characterize four major biomedical ontologies. We believe this work provides valuable insights into the complexity of concept annotation spaces and its potential influence on the predictability of ontological annotation.

Availability and implementation

https://github.com/shawn-peng/counting-consistent-sub-DAG

Supplementary information

Supplementary data are available at Bioinformatics online.

HFSP: high speed homology-driven function annotation of proteins

Motivation

The rapid drop in sequencing costs has produced many more (predicted) protein sequences than can feasibly be functionally annotated with wet-lab experiments. Thus, many computational methods have been developed for this purpose. Most of these methods employ homology-based inference, approximated via sequence alignments, to transfer functional annotations between proteins. The increase in the number of available sequences, however, has drastically increased the search space, thus significantly slowing down alignment methods.

Results

Here we describe homology-derived functional similarity of proteins (HFSP), a novel computational method that uses results of a high-speed alignment algorithm, MMseqs2, to infer functional similarity of proteins on the basis of their alignment length and sequence identity. We show that our method is accurate (85% precision) and fast (more than 40-fold speed increase over state-of-the-art). HFSP can help correct at least a 16% error in legacy curations, even for a resource of as high quality as Swiss-Prot. These findings suggest HFSP as an ideal resource for large-scale functional annotation efforts.

Supplementary information

Supplementary data are available at Bioinformatics online.

PTBP1 mRNA isoforms and regulation of their translation

Polypyrimidine tract-binding proteins (PTBPs) are RNA binding proteins that regulate a number of posttranscriptional events. Human PTBP1 transits between the nucleus and cytoplasm and is thought to regulate RNA processes in both. However, information about PTBP1 mRNA isoforms and regulation of PTPB1 expression remains incomplete. Here we mapped the major PTBP1 mRNA isoforms in HEK293T cells and identified alternative 5' and 3' untranslated regions (5'-UTRs, 3'-UTRs), as well as alternative splicing patterns in the protein coding region. We also assessed how the observed PTBP1 mRNA isoforms contribute to PTBP1 expression in different phases of the cell cycle. Previously, PTBP1 mRNAs were shown to crosslink to eukaryotic translation initiation factor 3 (eIF3). We find that eIF3 binds differently to each PTBP1 mRNA isoform in a cell cycle dependent manner. We also observe a strong correlation between eIF3 binding to PTBP1 mRNAs and repression of PTBP1 levels during the S phase of the cell cycle. Our results provide evidence of translational regulation of PTBP1 protein levels during the cell cycle, which may affect downstream regulation of alternative splicing and translation mediated by PTBP1 protein isoforms.

Microbial functional diversity: From concepts to applications

Functional diversity is increasingly recognized by microbial ecologists as the essential link between biodiversity patterns and ecosystem functioning, determining the trophic relationships and interactions between microorganisms, their participation in biogeochemical cycles, and their responses to environmental changes. Consequently, its definition and quantification have practical and theoretical implications. In this opinion paper, we present a synthesis on the concept of microbial functional diversity from its definition to its application. Initially, we revisit to the original definition of functional diversity, highlighting two fundamental aspects, the ecological unit under study and the functional traits used to characterize it. Then, we discuss how the particularities of the microbial world disallow the direct application of the concepts and tools developed for macroorganisms. Next, we provide a synthesis of the literature on the types of ecological units and functional traits available in microbial functional ecology. We also provide a list of more than 400 traits covering a wide array of environmentally relevant functions. Lastly, we provide examples of the use of functional diversity in microbial systems based on the different units and traits discussed herein. It is our hope that this paper will stimulate discussions and help the growing field of microbial functional ecology to realize a potential that thus far has only been attained in macrobial ecology.

The continuing expansion of CAZymes and their families

Carbohydrate-active enzymes (CAZymes) catalyze the assembly and breakdown of glycans and glycoconjugates. Some have been discovered, studied and exploited for numerous applications long ago. For instance, amylase and invertase were isolated in the second half of the 19th century and lysozyme was the first enzyme whose 3-D structure was determined. In spite of this early start, the number of families of carbohydrate-active enzymes continues to grow steadily in the early 21st century. This review examines the CAZyme families reported during the last two years and posits that the current expansion will continue in the future, progressively uncovering the massive diversity of glycans whose breakdown requires a large diversity of bespoke enzymes.

Functional Annotation of ABHD14B, an Orphan Serine Hydrolase Enzyme

The metabolic serine hydrolase family is, arguably, one of the largest functional enzyme classes in mammals, including humans, comprising 1-2% of the total proteome. This enzyme family uses a conserved nucleophilic serine residue in the active site to perform diverse hydrolytic reactions and consists of proteases, lipases, esterases, amidases, and transacylases, which are prototypical members of this family. In humans, this enzyme family consists of >250, of which approximately 40% members remain unannotated, in terms of both their endogenous substrates and the biological pathways that they regulate. The enzyme ABHD14B, an outlying member of this family, is also known as CCG1/TAF_II250-interacting factor B, as it was found to be associated with transcription initiation factor TFIID. The crystal structure of human ABHD14B was determined more than a decade ago; however, its endogenous substrates remain elusive. In this paper, we annotate ABHD14B as a lysine deacetylase (KDAC), showing this enzyme's ability to transfer an acetyl group from a post-translationally acetylated lysine to coenzyme A (CoA), to yield acetyl-CoA, while regenerating the free amine of protein lysine residues. We validate these findings by in vitro biochemical assays using recombinantly purified human ABHD14B in conjunction with cellular studies in a mammalian cell line by knocking down ABHD14B and by identification of a putative substrate binding site. Finally, we report the development and characterization of a much-needed, exquisitely selective ABHD14B antibody, and using it, we map the cellular and tissue distribution of ABHD14B and prospective metabolic pathways that this enzyme might biologically regulate.

SamPler - a novel method for selecting parameters for gene functional annotation routines

BACKGROUND

As genome sequencing projects grow rapidly, the diversity of organisms with recently assembled genome sequences peaks at an unprecedented scale, thereby highlighting the need to make gene functional annotations fast and efficient. However, the (high) quality of such annotations must be guaranteed, as this is the first indicator of the genomic potential of every organism. Automatic procedures help accelerating the annotation process, though decreasing the confidence and reliability of the outcomes. Manually curating a genome-wide annotation of genes, enzymes and transporter proteins function is a highly time-consuming, tedious and impractical task, even for the most proficient curator. Hence, a semi-automated procedure, which balances the two approaches, will increase the reliability of the annotation, while speeding up the process. In fact, a prior analysis of the annotation algorithm may leverage its performance, by manipulating its parameters, hastening the downstream processing and the manual curation of assigning functions to genes encoding proteins.

RESULTS

Here SamPler, a novel strategy to select parameters for gene functional annotation routines is presented. This semi-automated method is based on the manual curation of a randomly selected set of genes/proteins. Then, in a multi-dimensional array, this sample is used to assess the automatic annotations for all possible combinations of the algorithm's parameters. These assessments allow creating an array of confusion matrices, for which several metrics are calculated (accuracy, precision and negative predictive value) and used to reach optimal values for the parameters.

CONCLUSIONS

The potential of this methodology is demonstrated with four genome functional annotations performed in merlin, an in-house user-friendly computational framework for genome-scale metabolic annotation and model reconstruction. For that, SamPler was implemented as a new plugin for the merlin tool.