Automatic policing of biochemical annotations using genomic correlations

Transcriptomic Analysis Reveals the Role of tmRNA on Biofilm Formation in Bacillus subtilis

Bacillus strains are widely distributed in terrestrial and marine environments, and some of them are used as biocontrol organisms for their biofilm-formation ability. In Bacillus subtilis, biofilm formation is fine-tuned by a complex network, a clear understanding of which still requires study. In bacteria, tmRNA, encoded by the ssrA gene, catalyzes trans-translation that can rescue ribosomes stalled on mRNA transcripts lacking a functional stop codon. tmRNA also affects physiological bioprocesses in some bacteria. In this study, we constructed a ssrA mutant in B. subtilis and found that the biofilm formation in the ssrA mutant was largely impaired. Moreover, we isolated a biofilm-formation suppressor of ssrA, in which the biofilm formation was restored to a level even stronger than that in the wild type. We further performed RNAseq assays with the wild type, ssrA mutant, and suppressor of ssrA for comparisons of their transcriptomes. By analyzing the transcriptomic data, we predicted the possible functions of some differentially expressed genes (DEGs) in the tmRNA regulation of biofilm formation in B. subtilis. Finally, we found that the overexpression of two DEGs, acoA and yhjR, could restore the biofilm formation in the ssrA mutant, indicating that AcoA and YhjR were immediate regulators involved in the tmRNA regulatory web controlling biofilm formation in B. subtilis. Our data can improve the knowledge about the molecular network involved in Bacillus biofilm formation and provide new targets for manipulation of Bacillus biofilms for future investigation.

Parallel evolution of non-homologous isofunctional enzymes in methionine biosynthesis

Experimental validation of enzyme function is crucial for genome interpretation, but it remains challenging because it cannot be scaled up to accommodate the constant accumulation of genome sequences. We tackled this issue for the MetA and MetX enzyme families, phylogenetically unrelated families of acyl-L-homoserine transferases involved in L-methionine biosynthesis. Members of these families are prone to incorrect annotation because MetX and MetA enzymes are assumed to always use acetyl-CoA and succinyl-CoA, respectively. We determined the enzymatic activities of 100 enzymes from diverse species, and interpreted the results by structural classification of active sites based on protein structure modeling. We predict that >60% of the 10,000 sequences from these families currently present in databases are incorrectly annotated, and suggest that acetyl-CoA was originally the sole substrate of these isofunctional enzymes, which evolved to use exclusively succinyl-CoA in the most recent bacteria. We also uncovered a divergent subgroup of MetX enzymes in fungi that participate only in L-cysteine biosynthesis as O-succinyl-L-serine transferases.

Assignment of function to a domain of unknown function: DUF1537 is a new kinase family in catabolic pathways for acid sugars

Using a large-scale "genomic enzymology" approach, we (i) assigned novel ATP-dependent four-carbon acid sugar kinase functions to members of the DUF1537 protein family (domain of unknown function; Pfam families PF07005 and PF17042) and (ii) discovered novel catabolic pathways for d-threonate, l-threonate, and d-erythronate. The experimentally determined ligand specificities of several solute binding proteins (SBPs) for TRAP (tripartite ATP-independent permease) transporters for four-carbon acids, including d-erythronate and l-erythronate, were used to constrain the substrates for the catabolic pathways that degrade the SBP ligands to intermediates in central carbon metabolism. Sequence similarity networks and genome neighborhood networks were used to identify the enzyme components of the pathways. Conserved genome neighborhoods encoded SBPs as well as permease components of the TRAP transporters, members of the DUF1537 family, and a member of the 4-hydroxy-l-threonine 4-phosphate dehydrogenase (PdxA) oxidative decarboxylase, class II aldolase, or ribulose 1,5-bisphosphate carboxylase/oxygenase, large subunit (RuBisCO) superfamily. Because the characterized substrates of members of the PdxA, class II aldolase, and RuBisCO superfamilies are phosphorylated, we postulated that the members of the DUF1537 family are novel ATP-dependent kinases that participate in catabolic pathways for four-carbon acid sugars. We determined that (i) the DUF1537/PdxA pair participates in a pathway for the conversion of d-threonate to dihydroxyacetone phosphate and CO2 and (ii) the DUF1537/class II aldolase pair participates in pathways for the conversion of d-erythronate and l-threonate (epimers at carbon-3) to dihydroxyacetone phosphate and CO2 The physiological importance of these pathways was demonstrated in vivo by phenotypic and genetic analyses.

Successful conversion of the Bacillus subtilis BirA Group II biotin protein ligase into a Group I ligase

Group II biotin protein ligases (BPLs) are characterized by the presence of an N-terminal DNA binding domain that allows transcriptional regulation of biotin biosynthetic and transport genes whereas Group I BPLs lack this N-terminal domain. The Bacillus subtilis BPL, BirA, is classified as a Group II BPL based on sequence predictions of an N-terminal helix-turn-helix motif and mutational alteration of its regulatory properties. We report evidence that B. subtilis BirA is a Group II BPL that regulates transcription at three genomic sites: bioWAFDBI, yuiG and yhfUTS. Moreover, unlike the paradigm Group II BPL, E. coli BirA, the N-terminal DNA binding domain can be deleted from Bacillus subtilis BirA without adverse effects on its ligase function. This is the first example of successful conversion of a Group II BPL to a Group I BPL with retention of full ligase activity.

Simple topological properties predict functional misannotations in a metabolic network

Motivation: Misannotation in sequence databases is an important obstacle for automated tools for gene function annotation, which rely extensively on comparison with sequences with known function. To improve current annotations and prevent future propagation of errors, sequence-independent tools are, therefore, needed to assist in the identification of misannotated gene products. In the case of enzymatic functions, each functional assignment implies the existence of a reaction within the organism’s metabolic network; a first approximation to a genome-scale metabolic model can be obtained directly from an automated genome annotation. Any obvious problems in the network, such as dead end or disconnected reactions, can, therefore, be strong indications of misannotation.

Results: We demonstrate that a machine-learning approach using only network topological features can successfully predict the validity of enzyme annotations. The predictions are tested at three different levels. A random forest using topological features of the metabolic network and trained on curated sets of correct and incorrect enzyme assignments was found to have an accuracy of up to 86% in 5-fold cross-validation experiments. Further cross-validation against unseen enzyme superfamilies indicates that this classifier can successfully extrapolate beyond the classes of enzyme present in the training data. The random forest model was applied to several automated genome annotations, achieving an accuracy of in most cases when validated against recent genome-scale metabolic models. We also observe that when applied to draft metabolic networks for multiple species, a clear negative correlation is observed between predicted annotation quality and phylogenetic distance to the major model organism for biochemistry (Escherichia coli for prokaryotes and Homo sapiens for eukaryotes).

Contact:j.pinney@imperial.ac.uk

Supplementary information:Supplementary data are available at Bioinformatics online.

Explaining microbial phenotypes on a genomic scale: GWAS for microbes

There is an increasing availability of complete or draft genome sequences for microbial organisms. These data form a potentially valuable resource for genotype-phenotype association and gene function prediction, provided that phenotypes are consistently annotated for all the sequenced strains. In this review, we address the requirements for successful gene-trait matching. We outline a basic protocol for microbial functional genomics, including genome assembly, annotation of genotypes (including single nucleotide polymorphisms, orthologous groups and prophages), data pre-processing, genotype-phenotype association, visualization and interpretation of results. The methodologies for association described herein can be applied to other data types, opening up possibilities to analyze transcriptome-phenotype associations, and correlate microbial population structure or activity, as measured by metagenomics, to environmental parameters.

Global probabilistic annotation of metabolic networks enables enzyme discovery

Annotation of organism-specific metabolic networks is one of the main challenges of systems biology. Importantly, due to inherent uncertainty of computational annotations, predictions of biochemical function need to be treated probabilistically. We present a global probabilistic approach to annotate genome-scale metabolic networks that integrates sequence homology and context-based correlations under a single principled framework. The developed method for Global Biochemical reconstruction Using Sampling (GLOBUS) not only provides annotation probabilities for each functional assignment, but also suggests likely alternative functions. GLOBUS is based on statistical Gibbs sampling of probable metabolic annotations and is able to make accurate functional assignments even in cases of remote sequence identity to known enzymes. We apply GLOBUS to genomes of Bacillus subtilis and Staphylococcus aureus, and validate the method predictions by experimentally demonstrating the 6-phosphogluconolactonase activity of ykgB and the role of the sps pathway for rhamnose biosynthesis in B. subtilis.

MIRAGE: a functional genomics-based approach for metabolic network model reconstruction and its application to cyanobacteria networks

Genome-scale metabolic network reconstructions are considered a key step in quantifying the genotype-phenotype relationship. We present a novel gap-filling approach, MetabolIc Reconstruction via functionAl GEnomics (MIRAGE), which identifies missing network reactions by integrating metabolic flux analysis and functional genomics data. MIRAGE's performance is demonstrated on the reconstruction of metabolic network models of E. coli and Synechocystis sp. and validated via existing networks for these species. Then, it is applied to reconstruct genome-scale metabolic network models for 36 sequenced cyanobacteria amenable for constraint-based modeling analysis and specifically for metabolic engineering. The reconstructed network models are supplied via standard SBML files.

Recent advances in mapping environmental microbial metabolisms through 13C isotopic fingerprints

After feeding microbes with a defined (13)C substrate, unique isotopic patterns (isotopic fingerprints) can be formed in their metabolic products. Such labelling information not only can provide novel insights into functional pathways but also can determine absolute carbon fluxes through the metabolic network via metabolic modelling approaches. This technique has been used for finding pathways that may have been mis-annotated in the past, elucidating new enzyme functions, and investigating cell metabolisms in microbial communities. In this review paper, we summarize the applications of (13)C approaches to analyse novel cell metabolisms for the past 3 years. The isotopic fingerprints (defined as unique isotopomers useful for pathway identifications) have revealed the operations of the Entner-Doudoroff pathway, the reverse tricarboxylic acid cycle, new enzymes for biosynthesis of central metabolites, diverse respiration routes in phototrophic metabolism, co-metabolism of carbon nutrients and novel CO(2) fixation pathways. This review also discusses new isotopic methods to map carbon fluxes in global metabolisms, as well as potential factors influencing the metabolic flux quantification (e.g. metabolite channelling, the isotopic purity of (13)C substrates and the isotopic effect). Although (13)C labelling is not applicable to all biological systems (e.g. microbial communities), recent studies have shown that this method has a significant value in functional characterization of poorly understood micro-organisms, including species relevant for biotechnology and human health.

Analysis of the microbial gene landscape and transcriptome for aromatic pollutants and alkane degradation using a novel internally calibrated microarray system

Despite various efforts to develop tools to detect and compare the catabolic potential and activity for pollutant degradation in environmental samples, there is still a need for an open-source, curated and reliable array method. We developed a custom array system including a novel normalization strategy that can be applied to any microarray design, allowing the calculation of the reliability of signals and make cross-experimental comparisons. Array probes, which are fully available to the scientific community, were designed from knowledge-based curated databases for key aromatic catabolic gene families and key alkane degradation genes. This design assigns signals to the respective protein subfamilies, thus directly inferring function and substrate specificity. Experimental procedures were optimized using DNA of four genome sequenced biodegradation strains and reliability of signals assessed through a novel normalization procedure, where a plasmid containing four artificial targets in increased copy numbers and co-amplified with the environmental DNA served as an internal calibration curve. The array system was applied to assess the catabolic gene landscape and transcriptome of aromatic contaminated environmental samples, confirming the abundance of catabolic gene subfamilies previously detected by functional metagenomics but also revealing the presence of previously undetected catabolic groups and specifically their expression under pollutant stress.

A road map for the development of community systems (CoSy) biology

Microbial interactions are essential for all global geochemical cycles and have an important role in human health and disease. Although we possess general knowledge about the major processes within a microbial community, we are presently unable to decipher what role individual microorganisms have and how their individual actions influence others in the community. We also have limited knowledge with which to predict the effects of microbial interactions and community composition on the environment and vice versa. In this Opinion article, we describe how community systems (CoSy) biology will enable us to decode these complex relationships and will therefore improve our understanding of individual members of the community and the modes of interactions in which they engage.

Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods

Reconstructed microbial metabolic networks facilitate a mechanistic description of the genotype-phenotype relationship through the deployment of constraint-based reconstruction and analysis (COBRA) methods. As reconstructed networks leverage genomic data for insight and phenotype prediction, the development of COBRA methods has accelerated following the advent of whole-genome sequencing. Here, we describe a phylogeny of COBRA methods that has rapidly evolved from the few early methods, such as flux balance analysis and elementary flux mode analysis, into a repertoire of more than 100 methods. These methods have enabled genome-scale analysis of microbial metabolism for numerous basic and applied uses, including antibiotic discovery, metabolic engineering and modelling of microbial community behaviour.

A metabolic network approach for the identification and prioritization of antimicrobial drug targets

For many infectious diseases, novel treatment options are needed in order to address problems with cost, toxicity and resistance to current drugs. Systems biology tools can be used to gain valuable insight into pathogenic processes and aid in expediting drug discovery. In the past decade, constraint-based modeling of genome-scale metabolic networks has become widely used. Focusing on pathogen metabolic networks, we review in silico strategies used to identify effective drug targets and highlight recent successes as well as limitations associated with such computational analyses. We further discuss how accounting for the host environment and even targeting the host may offer new therapeutic options. These systems-level approaches are beginning to provide novel avenues for drug targeting against infectious agents.

Inference of functional properties from large-scale analysis of enzyme superfamilies

As increasingly large amounts of data from genome and other sequencing projects become available, new approaches are needed to determine the functions of the proteins these genes encode. We show how large-scale computational analysis can help to address this challenge by linking functional information to sequence and structural similarities using protein similarity networks. Network analyses using three functionally diverse enzyme superfamilies illustrate the use of these approaches for facile updating and comparison of available structures for a large superfamily, for creation of functional hypotheses for metagenomic sequences, and to summarize the limits of our functional knowledge about even well studied superfamilies.

The human metabolic reconstruction Recon 1 directs hypotheses of novel human metabolic functions

Background

Metabolic network reconstructions formalize our knowledge of metabolism. Gaps in these networks pinpoint regions of metabolism where biological components and functions are "missing." At the same time, a major challenge in the post genomic era involves characterisation of missing biological components to complete genome annotation.

Results

We used the human metabolic network reconstruction RECON 1 and established constraint-based modelling tools to uncover novel functions associated with human metabolism. Flux variability analysis identified 175 gaps in RECON 1 in the form of blocked reactions. These gaps were unevenly distributed within metabolic pathways but primarily found in the cytosol and often caused by compounds whose metabolic fate, rather than production, is unknown. Using a published algorithm, we computed gap-filling solutions comprised of non-organism specific metabolic reactions capable of bridging the identified gaps. These candidate solutions were found to be dependent upon the reaction environment of the blocked reaction. Importantly, we showed that automatically generated solutions could produce biologically realistic hypotheses of novel human metabolic reactions such as of the fate of iduronic acid following glycan degradation and of N-acetylglutamate in amino acid metabolism.

Conclusions

The results demonstrate how metabolic models can be utilised to direct hypotheses of novel metabolic functions in human metabolism; a process that we find is heavily reliant upon manual curation and biochemical insight. The effectiveness of a systems approach for novel biochemical pathway discovery in mammals is demonstrated and steps required to tailor future gap filling algorithms to mammalian metabolic networks are proposed.

COMBREX: a project to accelerate the functional annotation of prokaryotic genomes

COMBREX (http://combrex.bu.edu) is a project to increase the speed of the functional annotation of new bacterial and archaeal genomes. It consists of a database of functional predictions produced by computational biologists and a mechanism for experimental biochemists to bid for the validation of those predictions. Small grants are available to support successful bids.

Accurate protein structure annotation through competitive diffusion of enzymatic functions over a network of local evolutionary similarities

High-throughput Structural Genomics yields many new protein structures without known molecular function. This study aims to uncover these missing annotations by globally comparing select functional residues across the structural proteome. First, Evolutionary Trace Annotation, or ETA, identifies which proteins have local evolutionary and structural features in common; next, these proteins are linked together into a proteomic network of ETA similarities; then, starting from proteins with known functions, competing functional labels diffuse link-by-link over the entire network. Every node is thus assigned a likelihood z-score for every function, and the most significant one at each node wins and defines its annotation. In high-throughput controls, this competitive diffusion process recovered enzyme activity annotations with 99% and 97% accuracy at half-coverage for the third and fourth Enzyme Commission (EC) levels, respectively. This corresponds to false positive rates 4-fold lower than nearest-neighbor and 5-fold lower than sequence-based annotations. In practice, experimental validation of the predicted carboxylesterase activity in a protein from Staphylococcus aureus illustrated the effectiveness of this approach in the context of an increasingly drug-resistant microbe. This study further links molecular function to a small number of evolutionarily important residues recognizable by Evolutionary Tracing and it points to the specificity and sensitivity of functional annotation by competitive global network diffusion. A web server is at http://mammoth.bcm.tmc.edu/networks.

Bacillus subtilis YqjG is required for genetic competence development

Members of the evolutionary conserved Oxa1/Alb3/YidC family have been shown to play an important role in membrane protein insertion, folding and/or assembly. Bacillus subtilis contains two YidC-like proteins, denoted as SpoIIIJ and YqjG. SpoIIIJ and YqjG are largely exchangeable, but SpoIIIJ is essential for spore formation and YqjG cannot complement this activity. To elucidate the role of YqjG, we determined the membrane proteome and functional aspects of B. subtilis cells devoid of SpoIIIJ, YqjG or both. The data show that SpoIIIJ and YqjG have complementary functions in membrane protein insertion and assembly. The reduced levels of F(1)F(O) ATP synthase in cells devoid of both SpoIIIJ and YqjG are due to a defective assembly of the F(1)-domain onto the F(0)-domain. Importantly, for the first time, a specific function is demonstrated for YqjG in genetic competence development.

Multi-species integrative biclustering

We describe an algorithm, multi-species cMonkey, for the simultaneous biclustering of heterogeneous multiple-species data collections and apply the algorithm to a group of bacteria containing Bacillus subtilis, Bacillus anthracis, and Listeria monocytogenes. The algorithm reveals evolutionary insights into the surprisingly high degree of conservation of regulatory modules across these three species and allows data and insights from well-studied organisms to complement the analysis of related but less well studied organisms.

Hierarchical evolution of the bacterial sporulation network

Genome sequencing of multiple species makes it possible to understand the main principles behind the evolution of developmental regulatory networks. It is especially interesting to analyze the evolution of well-defined model systems in which conservation patterns can be directly correlated with the functional roles of various network components. Endospore formation (sporulation), extensively studied in Bacillus subtilis, is driven by such a model bacterial network of cellular development and differentiation. In this review, we analyze the evolution of the sporulation network in multiple endospore-forming bacteria. Importantly, the network evolution is not random but primarily follows the hierarchical organization and functional logic of the sporulation process. Specifically, the sporulation sigma factors and the master regulator of sporulation, Spo0A, are conserved in all considered spore-formers. The sequential activation of these global regulators is also strongly conserved. The feed-forward loops, which are likely used to fine-tune waves of gene expression within regulatory modules, show an intermediate level of conservation. These loops are less conserved than the sigma factors but significantly more than the structural sporulation genes, which form the lowest level in the functional and evolutionary hierarchy of the sporulation network. Interestingly, in spore-forming bacteria, gene regulation is more conserved than gene presence for sporulation genes, while the opposite is true for non-sporulation genes. The observed patterns suggest that, by understanding the functional organization of a developmental network in a model organism, it is possible to understand the logic behind the evolution of this network in multiple related species.

Philosophy of science. Machine science

Soon, computers could generate many useful hypotheses with little help from humans.

Systems biology of microbial metabolism

One current challenge in metabolic systems biology is to map out the regulation networks that control metabolism. From progress in this area, we conclude that non-transcriptional mechanisms (e.g. metabolite-protein interactions and protein phosphorylation) are highly relevant in actually controlling metabolic function. Furthermore, recent results highlight more functions of enzymes and metabolites than currently appreciated in genome-scale metabolic reconstructions, thereby adding another level of complexity. Combining experimental analyses and modeling efforts we are also beginning to understand how metabolic behavior emerges. Particularly, we recognize that metabolism is not simply a dull workhorse process but rather takes very active control of itself and other cellular processes, rendering true system-level understanding of metabolism possibly more difficult than for other cellular systems.