SubtiWiki in 2018: from genes and proteins to functional network annotation of the model organism Bacillus subtilis

Living cells are made up of individual parts, i.e. the genome, the proteins, the RNA and lipid molecules as well as the metabolites and ions. However, life depends on the functional interaction among these components which is often organized in networks. Here, we present the recent development of SubtiWiki, the integrated database for the model bacterium Bacillus subtilis (http://subtiwiki.uni-goettingen.de/). SubtiWiki is based on a relational database and provides access to published information about the genes and proteins of B. subtilis and about metabolic and regulatory pathways. We have included a network visualization tool that can be used to visualize regulatory as well as protein-protein interaction networks. The resulting interactive graphical presentations allow the user to detect novel associations and thus to develop novel hypotheses that can then be tested experimentally. To facilitate the mobile use of SubtiWiki, we provide enhanced versions of the SubtiWiki App that are available for iOS and Android devices. Importantly, the App allows to link private notes and pictures to the gene/protein pages that can be synchronized on multiple devices. SubtiWiki has become one of the most complete resources of knowledge on a living organism.

Bacillus subtilis, the model Gram-positive bacterium: 20 years of annotation refinement

Genome annotation is, nowadays, performed via automatic pipelines that cannot discriminate between right and wrong annotations. Given their importance in increasing the accuracy of the genome annotations of other organisms, it is critical that the annotations of model organisms reflect the current annotation gold standard. The genome of Bacillus subtilis strain 168 was sequenced twenty years ago. Using a combination of inductive, deductive and abductive reasoning, we present a unique, manually curated annotation, essentially based on experimental data. This reveals how this bacterium lives in a plant niche, while carrying a paleome operating system common to Firmicutes and Tenericutes. Dozens of new genomic objects and an extensive literature survey have been included for the sequence available at the INSDC (AccNum AL009126.3). We also propose an extension to Demerec's nomenclature rules that will help investigators connect to this type of curated annotation via the use of common gene names.

AureoWiki ̵ The repository of the Staphylococcus aureus research and annotation community

In light of continuously accumulating data and knowledge on major human pathogens, comprehensive and up-to-date sources of easily accessible information are urgently required. The AureoWiki database (http://aureowiki.med.uni-greifswald.de) provides detailed information on the genes and proteins of clinically and experimentally relevant S. aureus strains, currently covering NCTC 8325, COL, Newman, USA300_FPR3757, and N315. By implementing a pan-genome approach, AureoWiki facilitates the transfer of knowledge gained in studies with different S. aureus strains, thus supporting functional annotation and better understanding of this organism. All data related to a given gene or gene product is compiled on a strain-specific gene page. The gene pages contain sequence-based information complemented by data on, for example, protein function and localization, transcriptional regulation, and gene expression. The information provided is connected via links to other databases and published literature. Importantly, orthologous genes of the individual strains, which are linked by a pan-genome gene identifier and a unified gene name, are presented side by side using strain-specific tabs. The respective pan-genome gene page contains an orthologue table for 32 S. aureus strains, a multiple-strain genome viewer, a protein sequence alignment as well as other comparative information. The data collected in AureoWiki is also accessible through various download options in order to support bioinformatics applications. In addition, based on two large-scale gene expression data sets, AureoWiki provides graphical representations of condition-dependent mRNA levels and protein profiles under various laboratory and infection-related conditions.

Reconstruction of gene networks using prior knowledge

Background

Reconstructing gene regulatory networks (GRNs) from expression data is a challenging task that has become essential to the understanding of complex regulatory mechanisms in cells. The major issues are the usually very high ratio of number of genes to sample size, and the noise in the available data. Integrating biological prior knowledge to the learning process is a natural and promising way to partially compensate for the lack of reliable expression data and to increase the accuracy of network reconstruction algorithms.

Results

In this manuscript, we present PriorPC, a new algorithm based on the PC algorithm. PC algorithm is one of the most popular methods for Bayesian network reconstruction. The result of PC is known to depend on the order in which conditional independence tests are processed, especially for large networks. PriorPC uses prior knowledge to exclude unlikely edges from network estimation and introduces a particular ordering for the conditional independence tests. We show on synthetic data that the structural accuracy of networks obtained with PriorPC is greatly improved compared to PC.

Conclusion

PriorPC improves structural accuracy of inferred gene networks by using soft priors which assign to edges a probability of existence. It is robust to false prior which is not avoidable in the context of biological data. PriorPC is also fast and scales well for large networks which is important for its applicability to real data.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-015-0233-4) contains supplementary material, which is available to authorized users.

SubtiWiki 2.0--an integrated database for the model organism Bacillus subtilis

To understand living cells, we need knowledge of each of their parts as well as about the interactions of these parts. To gain rapid and comprehensive access to this information, annotation databases are required. Here, we present SubtiWiki 2.0, the integrated database for the model bacterium Bacillus subtilis (http://subtiwiki.uni-goettingen.de/). SubtiWiki provides text-based access to published information about the genes and proteins of B. subtilis as well as presentations of metabolic and regulatory pathways. Moreover, manually curated protein-protein interactions diagrams are linked to the protein pages. Finally, expression data are shown with respect to gene expression under 104 different conditions as well as absolute protein quantification for cytoplasmic proteins. To facilitate the mobile use of SubtiWiki, we have now expanded it by Apps that are available for iOS and Android devices. Importantly, the App allows to link private notes and pictures to the gene/protein pages. Today, SubtiWiki has become one of the most complete collections of knowledge on a living organism in one single resource.

SubtiWiki-a database for the model organism Bacillus subtilis that links pathway, interaction and expression information

Genome annotation and access to information from large-scale experimental approaches at the genome level are essential to improve our understanding of living cells and organisms. This is even more the case for model organisms that are the basis to study pathogens and technologically important species. We have generated SubtiWiki, a database for the Gram-positive model bacterium Bacillus subtilis (http://subtiwiki.uni-goettingen.de/). In addition to the established companion modules of SubtiWiki, SubtiPathways and SubtInteract, we have now created SubtiExpress, a third module, to visualize genome scale transcription data that are of unprecedented quality and density. Today, SubtiWiki is one of the most complete collections of knowledge on a living organism in one single resource.

Robust data-driven incorporation of prior knowledge into the inference of dynamic regulatory networks

MOTIVATION

Inferring global regulatory networks (GRNs) from genome-wide data is a computational challenge central to the field of systems biology. Although the primary data currently used to infer GRNs consist of gene expression and proteomics measurements, there is a growing abundance of alternate data types that can reveal regulatory interactions, e.g. ChIP-Chip, literature-derived interactions, protein-protein interactions. GRN inference requires the development of integrative methods capable of using these alternate data as priors on the GRN structure. Each source of structure priors has its unique biases and inherent potential errors; thus, GRN methods using these data must be robust to noisy inputs.

RESULTS

We developed two methods for incorporating structure priors into GRN inference. Both methods [Modified Elastic Net (MEN) and Bayesian Best Subset Regression (BBSR)] extend the previously described Inferelator framework, enabling the use of prior information. We test our methods on one synthetic and two bacterial datasets, and show that both MEN and BBSR infer accurate GRNs even when the structure prior used has significant amounts of error (>90% erroneous interactions). We find that BBSR outperforms MEN at inferring GRNs from expression data and noisy structure priors.

AVAILABILITY AND IMPLEMENTATION

Code, datasets and networks presented in this article are available at http://bonneaulab.bio.nyu.edu/software.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Metabolic adaptation and protein complexes in prokaryotes

Protein complexes are classified and have been charted in several large-scale screening studies in prokaryotes. These complexes are organized in a factory-like fashion to optimize protein production and metabolism. Central components are conserved between different prokaryotes; major complexes involve carbohydrate, amino acid, fatty acid and nucleotide metabolism. Metabolic adaptation changes protein complexes according to environmental conditions. Protein modification depends on specific modifying enzymes. Proteins such as trigger enzymes display condition-dependent adaptation to different functions by participating in several complexes. Several bacterial pathogens adapt rapidly to intracellular survival with concomitant changes in protein complexes in central metabolism and optimize utilization of their favorite available nutrient source. Regulation optimizes protein costs. Master regulators lead to up- and downregulation in specific subnetworks and all involved complexes. Long protein half-life and low level expression detaches protein levels from gene expression levels. However, under optimal growth conditions, metabolite fluxes through central carbohydrate pathways correlate well with gene expression. In a system-wide view, major metabolic changes lead to rapid adaptation of complexes and feedback or feedforward regulation. Finally, prokaryotic enzyme complexes are involved in crowding and substrate channeling. This depends on detailed structural interactions and is verified for specific effects by experiments and simulations.

Building the repertoire of dispensable chromosome regions in Bacillus subtilis entails major refinement of cognate large-scale metabolic model

The nonessential regions in bacterial chromosomes are ill-defined due to incomplete functional information. Here, we establish a comprehensive repertoire of the genome regions that are dispensable for growth of Bacillus subtilis in a variety of media conditions. In complex medium, we attempted deletion of 157 individual regions ranging in size from 2 to 159 kb. A total of 146 deletions were successful in complex medium, whereas the remaining regions were subdivided to identify new essential genes (4) and coessential gene sets (7). Overall, our repertoire covers ∼76% of the genome. We screened for viability of mutant strains in rich defined medium and glucose minimal media. Experimental observations were compared with predictions by the iBsu1103 model, revealing discrepancies that led to numerous model changes, including the large-scale application of model reconciliation techniques. We ultimately produced the iBsu1103V2 model and generated predictions of metabolites that could restore the growth of unviable strains. These predictions were experimentally tested and demonstrated to be correct for 27 strains, validating the refinements made to the model. The iBsu1103V2 model has improved considerably at predicting loss of viability, and many insights gained from the model revisions have been integrated into the Model SEED to improve reconstruction of other microbial models.

BioNLP Shared Task--The Bacteria Track

BACKGROUND

We present the BioNLP 2011 Shared Task Bacteria Track, the first Information Extraction challenge entirely dedicated to bacteria. It includes three tasks that cover different levels of biological knowledge. The Bacteria Gene Renaming supporting task is aimed at extracting gene renaming and gene name synonymy in PubMed abstracts. The Bacteria Gene Interaction is a gene/protein interaction extraction task from individual sentences. The interactions have been categorized into ten different sub-types, thus giving a detailed account of genetic regulations at the molecular level. Finally, the Bacteria Biotopes task focuses on the localization and environment of bacteria mentioned in textbook articles. We describe the process of creation for the three corpora, including document acquisition and manual annotation, as well as the metrics used to evaluate the participants' submissions.

RESULTS

Three teams submitted to the Bacteria Gene Renaming task; the best team achieved an F-score of 87%. For the Bacteria Gene Interaction task, the only participant's score had reached a global F-score of 77%, although the system efficiency varies significantly from one sub-type to another. Three teams submitted to the Bacteria Biotopes task with very different approaches; the best team achieved an F-score of 45%. However, the detailed study of the participating systems efficiency reveals the strengths and weaknesses of each participating system.

CONCLUSIONS

The three tasks of the Bacteria Track offer participants a chance to address a wide range of issues in Information Extraction, including entity recognition, semantic typing and coreference resolution. We found common trends in the most efficient systems: the systematic use of syntactic dependencies and machine learning. Nevertheless, the originality of the Bacteria Biotopes task encouraged the use of interesting novel methods and techniques, such as term compositionality, scopes wider than the sentence.

Global impact of protein arginine phosphorylation on the physiology of Bacillus subtilis

Reversible protein phosphorylation is an important and ubiquitous protein modification in all living cells. Here we report that protein phosphorylation on arginine residues plays a physiologically significant role. We detected 121 arginine phosphorylation sites in 87 proteins in the gram-positive model organism Bacillus subtilis in vivo. Moreover, we provide evidence that protein arginine phosphorylation has a functional role and is involved in the regulation of many critical cellular processes, such as protein degradation, motility, competence, and stringent and stress responses. Our results suggest that in B. subtilis the combined activity of a protein arginine kinase and phosphatase allows a rapid and reversible regulation of protein activity and that protein arginine phosphorylation can play a physiologically important and regulatory role in bacteria.

Comparative analysis of gene content evolution in phytoplasmas and mycoplasmas

Phytoplasmas and mycoplasmas are two groups of important pathogens in the bacterial class Mollicutes. Because of their economical and clinical importance, these obligate pathogens have attracted much research attention. However, difficulties involved in the empirical study of these bacteria, particularly the fact that phytoplasmas have not yet been successfully cultivated outside of their hosts despite decades of attempts, have greatly hampered research progress. With the rapid advancements in genome sequencing, comparative genome analysis provides a new approach to facilitate our understanding of these bacteria. In this study, our main focus is to investigate the evolution of gene content in phytoplasmas, mycoplasmas, and their common ancestor. By using a phylogenetic framework for comparative analysis of 12 complete genome sequences, we characterized the putative gains and losses of genes in these obligate parasites. Our results demonstrated that the degradation of metabolic capacities in these bacteria has occurred predominantly in the common ancestor of Mollicutes, prior to the evolutionary split of phytoplasmas and mycoplasmas. Furthermore, we identified a list of genes that are acquired by the common ancestor of phytoplasmas and are conserved across all strains with complete genome sequences available. These genes include several putative effectors for the interactions with hosts and may be good candidates for future functional characterization.

Condition-dependent transcriptome reveals high-level regulatory architecture in Bacillus subtilis

Bacteria adapt to environmental stimuli by adjusting their transcriptomes in a complex manner, the full potential of which has yet to be established for any individual bacterial species. Here, we report the transcriptomes of Bacillus subtilis exposed to a wide range of environmental and nutritional conditions that the organism might encounter in nature. We comprehensively mapped transcription units (TUs) and grouped 2935 promoters into regulons controlled by various RNA polymerase sigma factors, accounting for ~66% of the observed variance in transcriptional activity. This global classification of promoters and detailed description of TUs revealed that a large proportion of the detected antisense RNAs arose from potentially spurious transcription initiation by alternative sigma factors and from imperfect control of transcription termination.

Global network reorganization during dynamic adaptations of Bacillus subtilis metabolism

Adaptation of cells to environmental changes requires dynamic interactions between metabolic and regulatory networks, but studies typically address only one or a few layers of regulation. For nutritional shifts between two preferred carbon sources of Bacillus subtilis, we combined statistical and model-based data analyses of dynamic transcript, protein, and metabolite abundances and promoter activities. Adaptation to malate was rapid and primarily controlled posttranscriptionally compared with the slow, mainly transcriptionally controlled adaptation to glucose that entailed nearly half of the known transcription regulation network. Interactions across multiple levels of regulation were involved in adaptive changes that could also be achieved by controlling single genes. Our analysis suggests that global trade-offs and evolutionary constraints provide incentives to favor complex control programs.

SubtiWiki--a comprehensive community resource for the model organism Bacillus subtilis

In the post-genomic era, most components of a cell are known and they can be quantified by large-scale functional genomics approaches. However, genome annotation is the bottleneck that hampers our understanding of living cells and organisms. Up-to-date functional annotation is of special importance for model organisms that provide a frame of reference for studies with other relevant organisms. We have generated a Wiki-type database for the Gram-positive model bacterium Bacillus subtilis, SubtiWiki (http://subtiwiki.uni-goettingen.de/). This Wiki is centered around the individual genes and gene products of B. subtilis and provides information on each aspect of gene function and expression as well as protein activity and its control. SubtiWiki is accompanied by two companion databases SubtiPathways and SubtInteract that provide graphical representations of B. subtilis metabolism and its regulation and of protein-protein interactions, respectively. The diagrams of both databases are easily navigatable using the popular Google maps API, and they are extensively linked with the SubtiWiki gene pages. Moreover, each gene/gene product was assigned to one or more functional categories and transcription factor regulons. Pages for the specific categories and regulons provide a rapid overview of functionally related genes/proteins. Today, SubtiWiki can be regarded as one of the most complete inventories of knowledge on a living organism in one single resource.

Systems-wide temporal proteomic profiling in glucose-starved Bacillus subtilis

Functional genomics of the Gram-positive model organism Bacillus subtilis reveals valuable insights into basic concepts of cell physiology. In this study, we monitor temporal changes in the proteome, transcriptome and extracellular metabolome of B. subtilis caused by glucose starvation. For proteomic profiling, a combination of in vivo metabolic labelling and shotgun mass spectrometric analysis was carried out for five different proteomic subfractions (cytosolic, integral membrane, membrane, surface and extracellular proteome fraction), leading to the identification of ∼52% of the predicted proteome of B. subtilis. Quantitative proteomic and corresponding transcriptomic data were analysed with Voronoi treemaps linking functional classification and relative expression changes of gene products according to their fate in the stationary phase. The obtained data comprise the first comprehensive profiling of changes in the membrane subfraction and allow in-depth analysis of major physiological processes, including monitoring of protein degradation.

Identifying the transcripts and proteins that fluctuate in response to stimuli provides important information for understanding cell physiology. In this study, 52% of the Bacillus subtilis predicted proteome is identified following glucose starvation, revealing further insight into protein dynamics at a global scale.

Regulon of the N-acetylglucosamine utilization regulator NagR in Bacillus subtilis

N-Acetylglucosamine (GlcNAc) is the most abundant carbon-nitrogen biocompound on earth and has been shown to be an important source of nutrients for both catabolic and anabolic purposes in Bacillus species. In this work we show that the GntR family regulator YvoA of Bacillus subtilis serves as a negative transcriptional regulator of GlcNAc catabolism gene expression. YvoA represses transcription by binding a 16-bp sequence upstream of nagP encoding the GlcNAc-specific EIIBC component of the sugar phosphotransferase system involved in GlcNAc transport and phosphorylation, as well as another very similar 16-bp sequence upstream of the nagAB-yvoA locus, wherein nagA codes for N-acetylglucosamine-6-phosphate deacetylase and nagB codes for the glucosamine-6-phosphate (GlcN-6-P) deaminase. In vitro experiments demonstrated that GlcN-6-P acts as an inhibitor of YvoA DNA-binding activity, as occurs for its Streptomyces ortholog, DasR. Interestingly, we observed that the expression of nag genes was still activated upon addition of GlcNAc in a ΔyvoA mutant background, suggesting the existence of an auxiliary transcriptional control instance. Initial computational prediction of the YvoA regulon showed a distribution of YvoA binding sites limited to nag genes and therefore suggests renaming YvoA to NagR, for N-acetylglucosamine utilization regulator. Whole-transcriptome studies showed significant repercussions of nagR deletion for several major B. subtilis regulators, probably indirectly due to an excess of the crucial molecules acetate, ammonia, and fructose-6-phosphate, resulting from complete hydrolysis of GlcNAc. We discuss a model deduced from NagR-mediated gene expression, which highlights clear connections with pathways for GlcNAc-containing polymer biosynthesis and adaptation to growth under oxygen limitation.

SPABBATS: A pathway-discovery method based on Boolean satisfiability that facilitates the characterization of suppressor mutants

Background

Several computational methods exist to suggest rational genetic interventions that improve the productivity of industrial strains. Nonetheless, these methods are less effective to predict possible genetic responses of the strain after the intervention. This problem requires a better understanding of potential alternative metabolic and regulatory pathways able to counteract the targeted intervention.

Results

Here we present SPABBATS, an algorithm based on Boolean satisfiability (SAT) that computes alternative metabolic pathways between input and output species in a reconstructed network. The pathways can be constructed iteratively in order of increasing complexity. SPABBATS allows the accumulation of intermediates in the pathways, which permits discovering pathways missed by most traditional pathway analysis methods. In addition, we provide a proof of concept experiment for the validity of the algorithm. We deleted the genes for the glutamate dehydrogenases of the Gram-positive bacterium Bacillus subtilis and isolated suppressor mutant strains able to grow on glutamate as single carbon source. Our SAT approach proposed candidate alternative pathways which were decisive to pinpoint the exact mutation of the suppressor strain.

Conclusions

SPABBATS is the first application of SAT techniques to metabolic problems. It is particularly useful for the characterization of metabolic suppressor mutants and can be used in a synthetic biology setting to design new pathways with specific input-output requirements.

CellPublisher: a web platform for the intuitive visualization and sharing of metabolic, signalling and regulatory pathways

SUMMARY

Systems biology relies increasingly on collaborations between several groups with different expertise. Therefore, the systems biology community is adopting standards that allow effective communication of concepts, as well as transmission and processing of pathway information. The Systems Biology Graphical Notation (SBGN) is a graphical language for biological pathways that has both a biological as well as a computational meaning. The program CellDesigner allows the codification of biological phenomena in an SBGN compliant form. CellPublisher is a web server that allows the conversion of CellDesigner files to web-based navigatable diagrams based on the user interface of Google maps. Thus, CellPublisher complements CellDesigner by facilitating the understanding of complex diagrams and by providing the possibility to share any CellDesigner diagram online with collaborators and get their feedback. Due to the intuitive interface of the online diagrams, CellPublisher serves as a basis for discovery of novel properties of the modelled networks.

AVAILABILITY

The freely available web server and the documentation can be accessed at: http://cellpublisher.gobics.de/. The source code and the offline version for Microsoft Windows are freely available at http://sourceforge.net/projects/cellpublisher/.

CONTACT

jstuelk@gwdg.de

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Physical interactions between tricarboxylic acid cycle enzymes in Bacillus subtilis: evidence for a metabolon

The majority of all proteins of a living cell is active in complexes rather than in an isolated way. These protein-protein interactions are of high relevance for many biological functions. In addition to many well established protein complexes an increasing number of protein-protein interactions, which form rather transient complexes has recently been discovered. The formation of such complexes seems to be a common feature especially for metabolic pathways. In the Gram-positive model organism Bacillus subtilis, we identified a protein complex of three citric acid cycle enzymes. This complex consists of the citrate synthase, the isocitrate dehydrogenase, and the malate dehydrogenase. Moreover, fumarase and aconitase interact with malate dehydrogenase and with each other. These five enzymes catalyze sequential reaction of the TCA cycle. Thus, this interaction might be important for a direct transfer of intermediates of the TCA cycle and thus for elevated metabolic fluxes via substrate channeling. In addition, we discovered a link between the TCA cycle and gluconeogenesis through a flexible interaction of two proteins: the association between the malate dehydrogenase and phosphoenolpyruvate carboxykinase is directly controlled by the metabolic flux. The phosphoenolpyruvate carboxykinase links the TCA cycle with gluconeogenesis and is essential for B. subtilis growing on gluconeogenic carbon sources. Only under gluconeogenic growth conditions an interaction of these two proteins is detectable and disappears under glycolytic growth conditions.