DEG 5.0, a database of essential genes in both prokaryotes and eukaryotes

PREDICTION OF ESSENTIAL PROTEINS BASED ON EDGE CLUSTERING COEFFICIENT AND GENE ONTOLOGY INFORMATION

Essential proteins are important for the survival and development of organisms. Lots of centrality algorithms based on network topology have been proposed to detect essential proteins and achieve good results. However, most of them only focus on the network topology, but ignore the false positive (FP) interactions in protein–protein interaction (PPI) network. In this paper, gene ontology (GO) information is proposed to measure the reliability of the edges in PPI network and we propose a novel algorithm for identifying essential proteins, named EGC algorithm. EGC algorithm integrates topology character of PPI network and GO information. To validate the performance of EGC algorithm, we use EGC and other nine methods (DC, BC, CC, SC, EC, LAC, NC, PEC and CoEWC) to identify the essential proteins in the two different yeast PPI networks: DIP and MIPS. The results show that EGC is better than the other nine methods, which means adding GO information can help in predicting essential proteins.

A new method for predicting essential proteins based on dynamic network topology and complex information

Predicting essential proteins is highly significant because organisms can not survive or develop even if only one of these proteins is missing. Improvements in high-throughput technologies have resulted in a large number of available protein-protein interactions. By taking advantage of these interaction data, researchers have proposed many computational methods to identify essential proteins at the network level. Most of these approaches focus on the topology of a static protein interaction network. However, the protein interaction network changes with time and condition. This important inherent dynamics of the protein interaction network is overlooked by previous methods. In this paper, we introduce a new method named CDLC to predict essential proteins by integrating dynamic local average connectivity and in-degree of proteins in complexes. CDLC is applied to the protein interaction network of Saccharomyces cerevisiae. The results show that CDLC outperforms five other methods (Degree Centrality (DC), Local Average Connectivity-based method (LAC), Sum of ECC (SoECC), PeC and Co-Expression Weighted by Clustering coefficient (CoEWC)). In particular, CDLC could improve the prediction precision by more than 45% compared with DC methods. CDLC is also compared with the latest algorithm CEPPK, and a higher precision is achieved by CDLC. CDLC is available as Supplementary materials. The default settings of active threshold and alpha-parameter are 0.8 and 0.1, respectively.

Unraveling novel broad-spectrum antibacterial targets in food and waterborne pathogens using comparative genomics and protein interaction network analysis

Food and waterborne diseases are a growing concern in terms of human morbidity and mortality worldwide, even in the 21st century, emphasizing the need for new therapeutic interventions for these diseases. The current study aims at prioritizing broad-spectrum antibacterial targets, present in multiple food and waterborne bacterial pathogens, through a comparative genomics strategy coupled with a protein interaction network analysis. The pathways unique and common to all the pathogens under study (viz., methane metabolism, d-alanine metabolism, peptidoglycan biosynthesis, bacterial secretion system, two-component system, C5-branched dibasic acid metabolism), identified by comparative metabolic pathway analysis, were considered for the analysis. The proteins/enzymes involved in these pathways were prioritized following host non-homology analysis, essentiality analysis, gut flora non-homology analysis and protein interaction network analysis. The analyses revealed a set of promising broad-spectrum antibacterial targets, present in multiple food and waterborne pathogens, which are essential for bacterial survival, non-homologous to host and gut flora, and functionally important in the metabolic network. The identified broad-spectrum candidates, namely, integral membrane protein/virulence factor (MviN), preprotein translocase subunits SecB and SecG, carbon storage regulator (CsrA), and nitrogen regulatory protein P-II 1 (GlnB), contributed by the peptidoglycan pathway, bacterial secretion systems and two-component systems, were also found to be present in a wide range of other disease-causing bacteria. Cytoplasmic proteins SecG, CsrA and GlnB were considered as drug targets, while membrane proteins MviN and SecB were classified as vaccine targets. The identified broad-spectrum targets can aid in the design and development of antibacterial agents not only against food and waterborne pathogens but also against other pathogens.

Prediction of essential proteins based on overlapping essential modules

Many computational methods have been proposed to identify essential proteins by using the topological features of interactome networks. However, the precision of essential protein discovery still needs to be improved. Researches show that majority of hubs (essential proteins) in the yeast interactome network are essential due to their involvement in essential complex biological modules and hubs can be classified into two categories: date hubs and party hubs. In this study, combining with gene expression profiles, we propose a new method to predict essential proteins based on overlapping essential modules, named POEM. In POEM, the original protein interactome network is partitioned into many overlapping essential modules. The frequencies and weighted degrees of proteins in these modules are employed to decide which categories does a protein belong to? The comparative results show that POEM outperforms the classical centrality measures: Degree Centrality (DC), Information Centrality (IC), Eigenvector Centrality (EC), Subgraph Centrality (SC), Betweenness Centrality (BC), Closeness Centrality (CC), Edge Clustering Coefficient Centrality (NC), and two newly proposed essential proteins prediction methods: PeC and CoEWC. Experimental results indicate that the precision of predicting essential proteins can be improved by considering the modularity of proteins and integrating gene expression profiles with network topological features.

Virtual Screening of compounds to 1-deoxy-Dxylulose 5-phosphate reductoisomerase (DXR) from Plasmodium falciparum

The 1-deoxy-D-xylulose 5-phosphate reductoisomerase (DXR) protein (Gen Bank ID AAN37254.1) from Plasmodium falciparum is a potential drug target. Therefore, it is of interest to screen DXR against a virtual library of compounds (at the ZINC database) for potential binders as possible inhibitors. This exercise helped to choose 10 top ranking molecules with ZINC00200163 [N-(2,2di methoxy ethyl)-6-methyl-2, 3, 4, 9-tetrahydro-1H-carbazol-1-amine] a having good fit (-6.43 KJ/mol binding energy) with the target protein. Thus, ZINC00200163 is identified as a potential molecule for further comprehensive characterization and in-depth analysis.

Targeting Mycobacterium tuberculosis nucleoid-associated protein HU with structure-based inhibitors

The nucleoid-associated protein HU plays an important role in maintenance of chromosomal architecture and in global regulation of DNA transactions in bacteria. Although HU is essential for growth in Mycobacterium tuberculosis (Mtb), there have been no reported attempts to perturb HU function with small molecules. Here we report the crystal structure of the N-terminal domain of HU from Mtb. We identify a core region within the HU-DNA interface that can be targeted using stilbene derivatives. These small molecules specifically inhibit HU-DNA binding, disrupt nucleoid architecture and reduce Mtb growth. The stilbene inhibitors induce gene expression changes in Mtb that resemble those induced by HU deficiency. Our results indicate that HU is a potential target for the development of therapies against tuberculosis.

IFIM: a database of integrated fitness information for microbial genes

Knowledge of an organism’s fitness for survival is important for a complete understanding of microbial genetics and effective drug design. Current essential gene databases provide only binary essentiality data from genome-wide experiments. We therefore developed a new database that Integrates quantitative Fitness Information for Microbial genes (IFIM). The IFIM database currently contains data from 16 experiments and 2186 theoretical predictions. The highly significant correlation between the experiment-derived fitness data and our computational simulations demonstrated that the computer-generated predictions were often as reliable as the experimental data. The data in IFIM can be accessed easily, and the interface allows users to browse through the gene fitness information that it contains. IFIM is the first resource that allows easy access to fitness data of microbial genes. We believe this database will contribute to a better understanding of microbial genetics and will be useful in designing drugs to resist microbial pathogens, especially when experimental data are unavailable.

Database URL:http://cefg.uestc.edu.cn/ifim/ or http://cefg.cn/ifim/

Network-based identification of key proteins involved in apoptosis and cell cycle regulation

OBJECTIVES

Cancer cells differ from normal body cells in their ability to divide indefinitely and to evade programmed cell death. Crosstalk between apoptosis and cell cycle processes promotes balance between proliferation and death, and limits population growth and survival of cells. However, intricate relationships between them and how they are able to manipulate the fate of cancer cells still remain to be clarified. Identification of key factors involved in both apoptosis and cell cycle regulation may help to address this problem.

MATERIALS AND METHODS

Identification of such key proteins was carried out, using a series of bioinformatics methods, such as network construction and key protein identification.

RESULTS

In this study, we computationally constructed human apoptotic/cell cycle-related protein-protein interactions (PPIs) networks from five experimentally supported protein interaction databases, and further integrated these high-throughput data sets into a Naïve Bayesian model to predict protein functional connections. On the basis of modified apoptotic/cell cycle related PPI networks, we calculated and ranked all protein members involved in apoptosis and cell cycle regulation. Our results not only identified some already known key proteins such as p53, Rb, Myc and Src but also found that the proteasome, Cullin family members, kinases and transcriptional repressors play important roles in regulating apoptosis and the cell cycle. Furthermore, we found that the top 100 proteins ranked by PeC were enriched in some pathways such as those of cancer, the proteasome, the cell cycle and Wnt signalling.

CONCLUSIONS

We constructed the global human apoptotic/cell cycle related PPI network based on five online databases, and a Naïve Bayesian model. In addition, we systematically identified apoptotic/cell cycle related key proteins in cancer cells. These findings may uncover intricate relationships between apoptosis and cell cycle processes and thus provide further new clues towards future anticancer drug discovery.

Sexually transmitted diseases putative drug target database: a comprehensive database of putative drug targets of pathogens identified by comparative genomics

Objective:

Sexually transmitted diseases (STD) are the serious public health problems and also impose a financial burden on the economy. Sexually transmitted infections are cured with single or multiple antibiotics. However, in many cases the organism showed persistence even after treatment. In the current study, the set of druggable targets in STD pathogens have been identified by comparative genomics.

Materials and Methods:

The subtractive genomics scheme exploits the properties of non-homology, essentiality, membrane localization and metabolic pathway uniqueness in identifying the drug targets. To achieve the effective use of data and to understand properties of drug target under single canopy, an integrated knowledge database of drug targets in STD bacteria was created. Data for each drug targets include biochemical pathway, function, cellular localization, essentiality score and structural details.

Results:

The proteome of STD pathogens yielded 44 membrane associated proteins possessing unique metabolic pathways when subjected to the algorithm. The database can be accessed at http://biomedresearchasia.org/index.html.

Conclusion:

Diverse data merged in the common framework of this database is expected to be valuable not only for basic studies in clinical bioinformatics, but also for basic studies in immunological, biotechnological and clinical fields.

The N-terminal extension of S12 influences small ribosomal subunit assembly in Escherichia coli

The small subunit (SSU) of the ribosome of E. coli consists of a core of ribosomal RNA (rRNA) surrounded peripherally by ribosomal proteins (r-proteins). Ten of the 15 universally conserved SSU r-proteins possess nonglobular regions called extensions. The N-terminal noncanonically structured extension of S12 traverses from the solvent to intersubunit surface of the SSU and is followed by a more C-terminal globular region that is adjacent to the decoding center of the SSU. The role of the globular region in maintaining translational fidelity is well characterized, but a role for the S12 extension in SSU structure and function is unknown. We examined the effect of stepwise truncation of the extension of S12 in SSU assembly and function in vitro and in vivo. Examination of in vitro assembly in the presence of sequential N-terminal truncated variants of S12 reveals that N-terminal deletions of greater than nine amino acids exhibit decreased tRNA-binding activity and altered 16S rRNA architecture particularly in the platform of the SSU. While wild-type S12 expressed from a plasmid can rescue a genomic deletion of the essential gene for S12, rpsl; N-terminal deletions of S12 exhibit deleterious phenotypic consequences. Partial N-terminal deletions of S12 are slow growing and cold sensitive. Strains bearing these truncations as the sole copy of S12 have increased levels of free SSUs and immature 16S rRNA as compared with the wild-type S12. These differences are hallmarks of SSU biogenesis defects, indicating that the extension of S12 plays an important role in SSU assembly.

Predicting Essential Proteins Based on Weighted Degree Centrality

Essential proteins are vital for an organism's viability under a variety of conditions. There are many experimental and computational methods developed to identify essential proteins. Computational prediction of essential proteins based on the global protein-protein interaction (PPI) network is severely restricted because of the insufficiency of the PPI data, but fortunately the gene expression profiles help to make up the deficiency. In this work, Pearson correlation coefficient (PCC) is used to bridge the gap between PPI and gene expression data. Based on PCC and edge clustering coefficient (ECC), a new centrality measure, i.e., the weighted degree centrality (WDC), is developed to achieve the reliable prediction of essential proteins. WDC is employed to identify essential proteins in the yeast PPI and e-Coli networks in order to estimate its performance. For comparison, other prediction technologies are also performed to identify essential proteins. Some evaluation methods are used to analyze the results from various prediction approaches. The prediction results and comparative analyses are shown in the paper. Furthermore, the parameter λ in the method WDC will be analyzed in detail and an optimal λ value will be found. Based on the optimal λ value, the differentiation of WDC and another prediction method PeC is discussed. The analyses prove that WDC outperforms other methods including DC, BC, CC, SC, EC, IC, NC, and PeC. At the same time, the analyses also mean that it is an effective way to predict essential proteins by means of integrating different data sources.

Species-selective killing of bacteria by antimicrobial peptide-PNAs

Broad-spectrum antimicrobials kill indiscriminately, a property that can lead to negative clinical consequences and an increase in the incidence of resistance. Species-specific antimicrobials that could selectively kill pathogenic bacteria without targeting other species in the microbiome could limit these problems. The pathogen genome presents an excellent target for the development of such antimicrobials. In this study we report the design and evaluation of species-selective peptide nucleic acid (PNA) antibacterials. Selective growth inhibition of B. subtilis, E. coli, K. pnuemoniae and S. enterica serovar Typhimurium in axenic or mixed culture could be achieved with PNAs that exploit species differences in the translation initiation region of essential genes. An S. Typhimurium-specific PNA targeting ftsZ resulted in elongated cells that were not observed in E. coli, providing phenotypic evidence of the selectivity of PNA-based antimicrobials. Analysis of the genomes of E. coli and S. Typhimurium gave a conservative estimate of >150 PNA targets that could potentially discriminate between these two closely related species. This work provides a basis for the development of a new class of antimicrobial with a tuneable spectrum of activity.

Genome-wide saturation mutagenesis of Burkholderia pseudomallei K96243 predicts essential genes and novel targets for antimicrobial development

Burkholderia pseudomallei is the causative agent of melioidosis, an often fatal infectious disease for which there is no vaccine. B. pseudomallei is listed as a tier 1 select agent, and as current therapeutic options are limited due to its natural resistance to most antibiotics, the development of new antimicrobial therapies is imperative. To identify drug targets and better understand the complex B. pseudomallei genome, we sought a genome-wide approach to identify lethal gene targets. As B. pseudomallei has an unusually large genome spread over two chromosomes, an extensive screen was required to achieve a comprehensive analysis. Here we describe transposon-directed insertion site sequencing (TraDIS) of a library of over 10⁶ transposon insertion mutants, which provides the level of genome saturation required to identify essential genes. Using this technique, we have identified a set of 505 genes that are predicted to be essential in B. pseudomallei K96243. To validate our screen, three genes predicted to be essential, pyrH, accA, and sodB, and a gene predicted to be nonessential, bpss0370, were independently investigated through the generation of conditional mutants. The conditional mutants confirmed the TraDIS predictions, showing that we have generated a list of genes predicted to be essential and demonstrating that this technique can be used to analyze complex genomes and thus be more widely applied.

Burkholderia pseudomallei is a lethal human pathogen that is considered a potential bioterrorism threat and has limited treatment options due to an unusually high natural resistance to most antibiotics. We have identified a set of genes that are required for bacterial growth and thus are excellent candidates against which to develop potential novel antibiotics. To validate our approach, we constructed four mutants in which gene expression can be turned on and off conditionally to confirm that these genes are required for the bacteria to survive.

A shotgun antisense approach to the identification of novel essential genes in Pseudomonas aeruginosa

Background

Antibiotics in current use target a surprisingly small number of cellular functions: cell wall, DNA, RNA, and protein biosynthesis. Targeting of novel essential pathways is expected to play an important role in the discovery of new antibacterial agents against bacterial pathogens, such as Pseudomonas aeruginosa, that are difficult to control because of their ability to develop resistance, often multiple, to all current classes of clinical antibiotics.

Results

We aimed to identify novel essential genes in P. aeruginosa by shotgun antisense screening. This technique was developed in Staphylococcus aureus and, following a period of limited success in Gram-negative bacteria, has recently been used effectively in Escherichia coli. To also target low expressed essential genes, we included some variant steps that were expected to overcome the non-stringent regulation of the promoter carried by the expression vector used for the shotgun antisense libraries. Our antisense screenings identified 33 growth-impairing single-locus genomic inserts that allowed us to generate a list of 28 “essential-for-growth” genes: five were “classical” essential genes involved in DNA replication, transcription, translation, and cell division; seven were already reported as essential in other bacteria; and 16 were “novel” essential genes with no homologs reported to have an essential role in other bacterial species. Interestingly, the essential genes in our panel were suggested to take part in a broader range of cellular functions than those currently targeted by extant antibiotics, namely protein secretion, biosynthesis of cofactors, prosthetic groups and carriers, energy metabolism, central intermediary metabolism, transport of small molecules, translation, post-translational modification, non-ribosomal peptide synthesis, lipopolysaccharide synthesis/modification, and transcription regulation. This study also identified 43 growth-impairing inserts carrying multiple loci targeting 105 genes, of which 25 have homologs reported as essential in other bacteria. Finally, four multigenic growth-impairing inserts belonged to operons that have never been reported to play an essential role.

Conclusions

For the first time in P. aeruginosa, we applied regulated antisense RNA expression and showed the feasibility of this technology for the identification of novel essential genes.

The transcriptome landscape of Prochlorococcus MED4 and the factors for stabilizing the core genome

BACKGROUND

Gene gain and loss frequently occurs in the cyanobacterium Prochlorococcus, a phototroph that numerically dominates tropical and subtropical open oceans. However, little is known about the stabilization of its core genome, which contains approximately 1250 genes, in the context of genome streamlining. Using Prochlorococcus MED4 as a model organism, we investigated the constraints on core genome stabilization using transcriptome profiling.

RESULTS

RNA-Seq technique was used to obtain the transcriptome map of Prochlorococcus MED4, including operons, untranslated regions, non-coding RNAs, and novel genes. Genome-wide expression profiles revealed that three factors contribute to core genome stabilization. First, a negative correlation between gene expression levels and protein evolutionary rates was observed. Highly expressed genes were overrepresented in the core genome but not in the flexible genome. Gene necessity was determined as a second powerful constraint on genome evolution through functional enrichment analysis. Third, quick mRNA turnover may increase corresponding proteins' fidelity among genes that were abundantly expressed. Together, these factors influence core genome stabilization during MED4 genome evolution.

CONCLUSIONS

Gene expression, gene necessity, and mRNA turnover contribute to core genome maintenance during cyanobacterium Prochlorococcus genus evolution.

Metabolic network analysis-based identification of antimicrobial drug targets in category A bioterrorism agents

The 2001 anthrax mail attacks in the United States demonstrated the potential threat of bioterrorism, hence driving the need to develop sophisticated treatment and diagnostic protocols to counter biological warfare. Here, by performing flux balance analyses on the fully-annotated metabolic networks of multiple, whole genome-sequenced bacterial strains, we have identified a large number of metabolic enzymes as potential drug targets for each of the three Category A-designated bioterrorism agents including Bacillus anthracis, Francisella tularensis and Yersinia pestis. Nine metabolic enzymes- belonging to the coenzyme A, folate, phosphatidyl-ethanolamine and nucleic acid pathways common to all strains across the three distinct genera were identified as targets. Antimicrobial agents against some of these enzymes are available. Thus, a combination of cross species-specific antibiotics and common antimicrobials against shared targets may represent a useful combinatorial therapeutic approach against all Category A bioterrorism agents.

Genetic characterization and role in virulence of the ribonucleotide reductases of Streptococcus sanguinis

Streptococcus sanguinis is a cause of infective endocarditis and has been shown to require a manganese transporter called SsaB for virulence and O2 tolerance. Like certain other pathogens, S. sanguinis possesses aerobic class Ib (NrdEF) and anaerobic class III (NrdDG) ribonucleotide reductases (RNRs) that perform the essential function of reducing ribonucleotides to deoxyribonucleotides. The accompanying paper (Makhlynets, O., Boal, A. K., Rhodes, D. V., Kitten, T., Rosenzweig, A. C., and Stubbe, J. (2014) J. Biol. Chem. 289, 6259-6272) indicates that in the presence of O2, the S. sanguinis class Ib RNR self-assembles an essential diferric-tyrosyl radical (Fe(III)2-Y(•)) in vitro, whereas assembly of a dimanganese-tyrosyl radical (Mn(III)2-Y(•)) cofactor requires NrdI, and Mn(III)2-Y(•) is more active than Fe(III)2-Y(•) with the endogenous reducing system of NrdH and thioredoxin reductase (TrxR1). In this study, we have shown that deletion of either nrdHEKF or nrdI completely abolishes virulence in an animal model of endocarditis, whereas nrdD mutation has no effect. The nrdHEKF, nrdI, and trxR1 mutants fail to grow aerobically, whereas anaerobic growth requires nrdD. The nrdJ gene encoding an O2-independent adenosylcobalamin-cofactored RNR was introduced into the nrdHEKF, nrdI, and trxR1 mutants. Growth of the nrdHEKF and nrdI mutants in the presence of O2 was partially restored. The combined results suggest that Mn(III)2-Y(•)-cofactored NrdF is required for growth under aerobic conditions and in animals. This could explain in part why manganese is necessary for virulence and O2 tolerance in many bacterial pathogens possessing a class Ib RNR and suggests NrdF and NrdI may serve as promising new antimicrobial targets.

Protein domains of unknown function are essential in bacteria

More than 20% of all protein domains are currently annotated as “domains of unknown function” (DUFs). About 2,700 DUFs are found in bacteria compared with just over 1,500 in eukaryotes. Over 800 DUFs are shared between bacteria and eukaryotes, and about 300 of these are also present in archaea. A total of 2,786 bacterial Pfam domains even occur in animals, including 320 DUFs. Evolutionary conservation suggests that many of these DUFs are important. Here we show that 355 essential proteins in 16 model bacterial species contain 238 DUFs, most of which represent single-domain proteins, clearly establishing the biological essentiality of DUFs. We suggest that experimental research should focus on conserved and essential DUFs (eDUFs) for functional analysis given their important function and wide taxonomic distribution, including bacterial pathogens.

The functional units of proteins are domains. Typically, each domain has a distinct structure and function. Genomes encode thousands of domains, and many of the domains have no known function (domains of unknown function [DUFs]). They are often ignored as of little relevance, given that many of them are found in only a few genomes. Here we show that many DUFs are essential DUFs (eDUFs) based on their presence in essential proteins. We also show that eDUFs are often essential even if they are found in relatively few genomes. However, in general, more common DUFs are more often essential than rare DUFs.

A multi-level multi-scale approach to study essential genes in Mycobacterium tuberculosis

Background

The set of indispensable genes that are required by an organism to grow and sustain life are termed as essential genes. There is a strong interest in identification of the set of essential genes, particularly in pathogens, not only for a better understanding of the pathogen biology, but also for identifying drug targets and the minimal gene set for the organism. Essentiality is inherently a systems property and requires consideration of the system as a whole for their identification. The available experimental approaches capture some aspects but each method comes with its own limitations. Moreover, they do not explain the basis for essentiality in most cases. A powerful prediction method to recognize this gene pool including rationalization of the known essential genes in a given organism would be very useful. Here we describe a multi-level multi-scale approach to identify the essential gene pool in a deadly pathogen, Mycobacterium tuberculosis.

Results

The multi-level workflow analyses the bacterial cell by studying (a) genome-wide gene expression profiles to identify the set of genes which show consistent and significant levels of expression in multiple samples of the same condition, (b) indispensability for growth by using gene expression integrated flux balance analysis of a genome-scale metabolic model, (c) importance for maintaining the integrity and flow in a protein-protein interaction network and (d) evolutionary conservation in a set of genomes of the same ecological niche. In the gene pool identified, the functional basis for essentiality has been addressed by studying residue level conservation and the sub-structure at the ligand binding pockets, from which essential amino acid residues in that pocket have also been identified. 283 genes were identified as essential genes with high-confidence. An agreement of about 73.5% is observed with that obtained from the experimental transposon mutagenesis technique. A large proportion of the identified genes belong to the class of intermediary metabolism and respiration.

Conclusions

The multi-scale, multi-level approach described can be generally applied to other pathogens as well. The essential gene pool identified form a basis for designing experiments to probe their finer functional roles and also serve as a ready shortlist for identifying drug targets.

DEG 10, an update of the database of essential genes that includes both protein-coding genes and noncoding genomic elements

The combination of high-density transposon-mediated mutagenesis and high-throughput sequencing has led to significant advancements in research on essential genes, resulting in a dramatic increase in the number of identified prokaryotic essential genes under diverse conditions and a revised essential-gene concept that includes all essential genomic elements, rather than focusing on protein-coding genes only. DEG 10, a new release of the Database of Essential Genes (available at http://www.essentialgene.org), has been developed to accommodate these quantitative and qualitative advancements. In addition to increasing the number of bacterial and archaeal essential genes determined by genome-wide gene essentiality screens, DEG 10 also harbors essential noncoding RNAs, promoters, regulatory sequences and replication origins. These essential genomic elements are determined not only in vitro, but also in vivo, under diverse conditions including those for survival, pathogenesis and antibiotic resistance. We have developed customizable BLAST tools that allow users to perform species- and experiment-specific BLAST searches for a single gene, a list of genes, annotated or unannotated genomes. Therefore, DEG 10 includes essential genomic elements under different conditions in three domains of life, with customizable BLAST tools.

CEG: a database of essential gene clusters

Background

Essential genes are indispensable for the survival of living entities. They are the cornerstones of synthetic biology, and are potential candidate targets for antimicrobial and vaccine design.

Description

Here we describe the Cluster of Essential Genes (CEG) database, which contains clusters of orthologous essential genes. Based on the size of a cluster, users can easily decide whether an essential gene is conserved in multiple bacterial species or is species-specific. It contains the similarity value of every essential gene cluster against human proteins or genes. The CEG_Match tool is based on the CEG database, and was developed for prediction of essential genes according to function. The database is available at http://cefg.uestc.edu.cn/ceg.

Conclusions

Properties contained in the CEG database, such as cluster size, and the similarity of essential gene clusters against human proteins or genes, are very important for evolutionary research and drug design. An advantage of CEG is that it clusters essential genes based on function, and therefore decreases false positive results when predicting essential genes in comparison with using the similarity alignment method.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-14-769) contains supplementary material, which is available to authorized users.

Pathogenicity phenomena in three model systems: from network mining to emerging system-level properties

Understanding the interconnections of microbial pathogenicity phenomena, such as biofilm formation, quorum sensing and antimicrobial resistance, is a tremendous open challenge for biomedical research. Progress made by wet-lab researchers and bioinformaticians in understanding the underlying regulatory phenomena has been significant, with converging evidence from multiple high-throughput technologies. Notably, network reconstructions are already of considerable size and quality, tackling both intracellular regulation and signal mediation in microbial infection. Therefore, it stands to reason that in silico investigations would play a more active part in this research. Drug target identification and drug repurposing could take much advantage of the ability to simulate pathogen regulatory systems, host-pathogen interactions and pathogen cross-talking. Here, we review the bioinformatics resources and tools available for the study of the gram-negative bacterium Pseudomonas aeruginosa, the gram-positive bacterium Staphylococcus aureus and the fungal species Candida albicans. The choice of these three microorganisms fits the rationale of the review converging into pathogens of great clinical importance, which thrive in biofilm consortia and manifest growing antimicrobial resistance.

Integrative annotation of variants from 1092 humans: application to cancer genomics

Interpreting variants, especially noncoding ones, in the increasing number of personal genomes is challenging. We used patterns of polymorphisms in functionally annotated regions in 1092 humans to identify deleterious variants; then we experimentally validated candidates. We analyzed both coding and noncoding regions, with the former corroborating the latter. We found regions particularly sensitive to mutations ("ultrasensitive") and variants that are disruptive because of mechanistic effects on transcription-factor binding (that is, "motif-breakers"). We also found variants in regions with higher network centrality tend to be deleterious. Insertions and deletions followed a similar pattern to single-nucleotide variants, with some notable exceptions (e.g., certain deletions and enhancers). On the basis of these patterns, we developed a computational tool (FunSeq), whose application to ~90 cancer genomes reveals nearly a hundred candidate noncoding drivers.

Prediction of essential proteins based on gene expression programming

Background

Essential proteins are indispensable for cell survive. Identifying essential proteins is very important for improving our understanding the way of a cell working. There are various types of features related to the essentiality of proteins. Many methods have been proposed to combine some of them to predict essential proteins. However, it is still a big challenge for designing an effective method to predict them by integrating different features, and explaining how these selected features decide the essentiality of protein. Gene expression programming (GEP) is a learning algorithm and what it learns specifically is about relationships between variables in sets of data and then builds models to explain these relationships.

Results

In this work, we propose a GEP-based method to predict essential protein by combing some biological features and topological features. We carry out experiments on S. cerevisiae data. The experimental results show that the our method achieves better prediction performance than those methods using individual features. Moreover, our method outperforms some machine learning methods and performs as well as a method which is obtained by combining the outputs of eight machine learning methods.

Conclusions

The accuracy of predicting essential proteins can been improved by using GEP method to combine some topological features and biological features.

Clustering based on multiple biological information: approach for predicting protein complexes

Protein complexes are a cornerstone of many biological processes. Protein-protein interaction (PPI) data enable a number of computational methods for predicting protein complexes. However, the insufficiency of the PPI data significantly lowers the accuracy of computational methods. In the current work, the authors develop a novel method named clustering based on multiple biological information (CMBI) to discover protein complexes via the integration of multiple biological resources including gene expression profiles, essential protein information and PPI data. First, CMBI defines the functional similarity of each pair of interacting proteins based on the edge-clustering coefficient and the Pearson correlation coefficient. Second, CMBI selects essential proteins as seeds to build the protein complexes. A redundancy-filtering procedure is performed to eliminate redundant complexes. In addition to the essential proteins, CMBI also uses other proteins as seeds to expand protein complexes. To check the performance of CMBI, the authors compare the complexes discovered by CMBI with the ones found by other techniques by matching the predicted complexes against the reference complexes. The authors use subsequently GO::TermFinder to analyse the complexes predicted by various methods. Finally, the effect of parameters T and R is investigated. The results from GO functional enrichment and matching analyses show that CMBI performs significantly better than the state-of-the-art methods.

Geptop: a gene essentiality prediction tool for sequenced bacterial genomes based on orthology and phylogeny

Integrative genomics predictors, which score highly in predicting bacterial essential genes, would be unfeasible in most species because the data sources are limited. We developed a universal approach and tool designated Geptop, based on orthology and phylogeny, to offer gene essentiality annotations. In a series of tests, our Geptop method yielded higher area under curve (AUC) scores in the receiver operating curves than the integrative approaches. In the ten-fold cross-validations among randomly upset samples, Geptop yielded an AUC of 0.918, and in the cross-organism predictions for 19 organisms Geptop yielded AUC scores between 0.569 and 0.959. A test applied to the very recently determined essential gene dataset from the Porphyromonas gingivalis, which belongs to a phylum different with all of the above 19 bacterial genomes, gave an AUC of 0.77. Therefore, Geptop can be applied to any bacterial species whose genome has been sequenced. Compared with the essential genes uniquely identified by the lethal screening, the essential genes predicted only by Gepop are associated with more protein-protein interactions, especially in the three bacteria with lower AUC scores (<0.7). This may further illustrate the reliability and feasibility of our method in some sense. The web server and standalone version of Geptop are available at http://cefg.uestc.edu.cn/geptop/ free of charge. The tool has been run on 968 bacterial genomes and the results are accessible at the website.

Human growth is associated with distinct patterns of gene expression in evolutionarily conserved networks

Background

A co-ordinated tissue-independent gene expression profile associated with growth is present in rodent models and this is hypothesised to extend to all mammals. Growth in humans has similarities to other mammals but the return to active long bone growth in the pubertal growth spurt is a distinctly human growth event. The aim of this study was to describe gene expression and biological pathways associated with stages of growth in children and to assess tissue-independent expression patterns in relation to human growth.

Results

We conducted gene expression analysis on a library of datasets from normal children with age annotation, collated from the NCBI Gene Expression Omnibus (GEO) and EBI Arrayexpress databases. A primary data set was generated using cells of lymphoid origin from normal children; the expression of 688 genes (ANOVA false discovery rate modified p-value, q < 0.1) was associated with age, and subsets of these genes formed clusters that correlated with the phases of growth – infancy, childhood, puberty and final height. Network analysis on these clusters identified evolutionarily conserved growth pathways (NOTCH, VEGF, TGFB, WNT and glucocorticoid receptor – Hyper-geometric test, q < 0.05). The greatest degree of network ‘connectivity’ and hence functional significance was present in infancy (Wilcoxon test, p < 0.05), which then decreased through to adulthood. These observations were confirmed in a separate validation data set from lymphoid tissue. Similar biological pathways were observed to be associated with development-related gene expression in other tissues (conjunctival epithelia, temporal lobe brain tissue and bone marrow) suggesting the existence of a tissue-independent genetic program for human growth and maturation.

Conclusions

Similar evolutionarily conserved pathways have been associated with gene expression and child growth in multiple tissues. These expression profiles associate with the developmental phases of growth including the return to active long bone growth in puberty, a distinctly human event. These observations also have direct medical relevance to pathological changes that induce disease in children. Taking into account development-dependent gene expression profiles for normal children will be key to the appropriate selection of genes and pathways as potential biomarkers of disease or as drug targets.

Mycobacterium tuberculosis shikimate kinase inhibitors: design and simulation studies of the catalytic turnover

Shikimate kinase (SK) is an essential enzyme in several pathogenic bacteria and does not have any counterpart in human cells, thus making it an attractive target for the development of new antibiotics. The key interactions of the substrate and product binding and the enzyme movements that are essential for catalytic turnover of the Mycobacterium tuberculosis shikimate kinase enzyme (Mt-SK) have been investigated by structural and computational studies. Based on these studies several substrate analogs were designed and assayed. The crystal structure of Mt-SK in complex with ADP and one of the most potent inhibitors has been solved at 2.15 Å. These studies reveal that the fixation of the diaxial conformation of the C4 and C5 hydroxyl groups recognized by the enzyme or the replacement of the C3 hydroxyl group in the natural substrate by an amino group is a promising strategy for inhibition because it causes a dramatic reduction of the flexibility of the LID and shikimic acid binding domains. Molecular dynamics simulation studies showed that the product is expelled from the active site by three arginines (Arg117, Arg136, and Arg58). This finding represents a previously unknown key role of these conserved residues. These studies highlight the key role of the shikimic acid binding domain in the catalysis and provide guidance for future inhibitor designs.

Partial AUC maximization for essential gene prediction using genetic algorithms

Identifying genes indispensable for an organism‘s life and their characteristics is one of the central questions in current biological research, and hence it would be helpful to develop computational approaches towards the prediction of essential genes. The performance of a predictor is usually measured by the area under the receiver operating characteristic curve (AUC). We propose a novel method by implementing genetic algorithms to maximize the partial AUC that is restricted to a specific interval of lower false positive rate (FPR), the region relevant to follow-up experimental validation. Our predictor uses various features based on sequence information, proteinprotein interaction network topology, and gene expression profiles. A feature selection wrapper was developed to alleviate the over-fitting problem and to weigh each feature’s relevance to prediction. We evaluated our method using the proteome of budding yeast. Our implementation of genetic algorithms maximizing the partial AUC below 0.05 or 0.10 of FPR outperformed other popular classification methods. [BMB Reports 2013; 46(1): 41-46]

Transcriptional abundance is not the single force driving the evolution of bacterial proteins

Background

Despite rapid progress in understanding the mechanisms that shape the evolution of proteins, the relative importance of various factors remain to be elucidated. In this study, we have assessed the effects of 16 different biological features on the evolutionary rates (ERs) of protein-coding sequences in bacterial genomes.

Results

Our analysis of 18 bacterial species revealed new correlations between ERs and constraining factors. Previous studies have suggested that transcriptional abundance overwhelmingly constrains the evolution of yeast protein sequences. This transcriptional abundance leads to selection against misfolding or misinteractions. In this study we found that there was no single factor in determining the evolution of bacterial proteins. Not only transcriptional abundance (codon adaptation index and expression level), but also protein-protein associations (PPAs), essentiality (ESS), subcellular localization of cytoplasmic membrane (SLM), transmembrane helices (TMH) and hydropathicity score (HS) independently and significantly affected the ERs of bacterial proteins. In some species, PPA and ESS demonstrate higher correlations with ER than transcriptional abundance.

Conclusions

Different forces drive the evolution of protein sequences in yeast and bacteria. In bacteria, the constraints are involved in avoiding a build-up of toxic molecules caused by misfolding/misinteraction (transcriptional abundance), while retaining important functions (ESS, PPA) and maintaining the cell membrane (SLM, TMH and HS). Each of these independently contributes to the variation in protein evolution.

Identification of druggable targets for Acinetobacter baumannii via subtractive genomics and plausible inhibitors for MurA and MurB

Emergence of the multidrug-resistant pathogens has rendered the current therapies ineffective thereby, resulting in the need for new drugs and drug targets. The accumulating protein sequence data has initiated a drift from classical drug discovery protocols to structure-based drug designing. In the present study, in silico subtractive genomics approach was implemented to find a set of potential drug targets present in an opportunist bacterial pathogen, Acinetobacter baumannii (A. baumannii). Out of the 43 targets identified, further studies for protein model building and lead-inhibitor identification were carried out on two cell-essential targets, MurA and MurB enzymes (of A. baumannii designated as MurAAb and MurBAb) involved in the peptidoglycan biosynthesis pathway of bacteria. The homology model built for each of them was further refined and validated using various available programs like PROCHECK, Errat, ProSA energy plots, etc. Compounds showing activity against MurA and MurB enzymes of other organisms were collected from the literature and were docked into the active site of MurAAb and MurBAb enzymes. Three inhibitors namely, T6361, carbidopa, and aesculin, showed maximum Glide score, hydrogen bonding interactions with the key amino acid residues of both the enzymes and acceptable ADME properties. Furthermore, molecular dynamics simulation studies on MurAAb-T6361 and MurBAb-T6361 complexes suggested that the ligand has a high binding affinity with both the enzymes and the hydrogen bonding with the key residues were stable in the dynamic condition also. Therefore, these ligands have been propsed as dual inhibitors and promising lead compounds for the drug design against MurAAb and MurBAb enzymes.

in silico analyses of metabolic pathway and protein interaction network for identification of next gen therapeutic targets in Chlamydophila pneumoniae

Chlamydophila pneumoniae, the causative agent of chronic obstructive pulmonary disease (COPD), is presently the fifth mortality causing chronic disease in the world. The understanding of disease and treatment options are limited represents a severe concern and a need for better therapeutics. With the advancements in the field of complete genome sequencing and computational approaches development have lead to metabolic pathway analysis and protein-protein interaction network which provides vital evidence to the protein function and has been appropriate to the fields such as systems biology and drug discovery. Protein interaction network analysis allows us to predict the most potential drug targets among large number of the non-homologous proteins involved in the unique metabolic pathway. A computational comparative metabolic pathway analysis of the host H. sapiens and the pathogen C pneumoniae AR39 has been carried out at three level analyses. Firstly, metabolic pathway analysis was performed to identify unique metabolic pathways and non-homologous proteins were identified. Secondly, essentiality of the proteins was checked, where these proteins contribute to the growth and survival of the organism. Finally these proteins were further subjected to predict protein interaction networks. Among the total 65 pathways in the C pneumoniae AR39 genome 10 were identified as the unique metabolic pathways which were not found in the human host, 32 enzymes were predicted as essential and these proteins were considered for protein interaction analysis, later using various criteria's we have narrowed down to prioritize ribonucleotide-diphosphate reductase subunit beta as a potential drug target which facilitate for the successful entry into drug designing.

Construction and application of dynamic protein interaction network based on time course gene expression data

In recent years, researchers have tried to inject dynamic information into static protein interaction networks (PINs). The paper first proposes a three-sigma method to identify active time points of each protein in a cellular cycle, where three-sigma principle is used to compute an active threshold for each gene according to the characteristics of its expression curve. Then a dynamic protein interaction network (DPIN) is constructed, which includes the dynamic changes of protein interactions. To validate the efficiency of DPIN, MCL, CPM, and core attachment algorithms are applied on two different DPINs, the static PIN and the time course PIN (TC-PIN) to detect protein complexes. The performance of each algorithm on DPINs outperforms those on other networks in terms of matching with known complexes, sensitivity, specificity, f-measure, and accuracy. Furthermore, the statistics of three-sigma principle show that 23-45% proteins are active at a time point and most proteins are active in about half of cellular cycle. In addition, we find 94% essential proteins are in the group of proteins that are active at equal or great than 12 timepoints of GSE4987, which indicates the potential existence of feedback mechanisms that can stabilize the expression level of essential proteins and might provide a new insight for predicting essential proteins from dynamic protein networks.

Current concepts of genetic variability of genital mycoplasmas and their role in the development of inflammatory diseases of the urogenital system

The authors disclose current concepts of the taxonomic and morphologic characteristics of genital mycoplasmas and their role in the development of inflammatory urogenital diseases and reproductive disorders. They also discuss such issues as genetic variability of genital mycoplasmas and possible interrelation with different variants of the clinical course of inflammatory processes in the urogenital tract.

In silico synteny based comparative genomics approach for identification and characterization of novel therapeutic targets in Chlamydophila pneumoniae

Chlamydophila pneumoniae is one of the most important and well studied gram negative bacterial strain with respect to community acquired pneumonia and other respiratory diseases like Chronic obstructive pulmonary disease (COPD), Chronic asthma, Alzheimer's disease, Atherosclerosis and Multisclerosis which have a great potential to infect humans and many other mammals. According to WHO prediction, COPD is to become the third leading cause of death by 2030. Unfortunately, the molecular mechanisms leading to chronic infections are poorly understood and the difficulty in culturing C pneumoniae in experimental conditions and lack of entirely satisfactory serological methods for diagnosis is also a hurdle for drug discovery and development. We have performed an insilico synteny based comparative genomics analysis of C pneumoniae and other eight Chlamydial organisms to know the potential of C pneumoniae which cause COPD but other Chlamydial organisms lack in potential to cause COPD though some are involved in human pathogenesis. We have identified total 354 protein sequences as non-orthologous to other Chlamydial organisms, except hypothetical proteins 70 were found functional out of which 60 are non homologous to Homo sapiens proteome and among them 18 protein sequences are found to be essential for survival of the C pneumoniae based on BLASTP search against DEG database of essential genes. CELLO analysis results showed that about 80% proteins are found to be cytoplasmic, Among which 5 were found as bacterial exotoxins and 2 as bacterial endotoxins, remaining 11 proteins were found to be involved in DNA binding, RNA binding, catalytic activity, ATP binding, oxidoreductase activity, hydrolase activity and proteolysis activity. It is expected that our data will facilitate selection of C pneumoniae proteins for successful entry into drug design pipelines.

In silico prediction of drug targets in phytopathogenic Pseudomonas syringae pv. phaseolicola: charting a course for agrigenomics translation research

Pseudomonas syringae pv. phaseolicola is a major plant pathogen causing halo blight disease and has world-wide importance. The emerging post-genomics field of agrigenomics, together with the availability of whole genome sequences of a number of pathogens and host organisms, offer the promise for identification of potential drug targets using sequence comparison approaches. On the other hand, lack of gene expression data for most of the phytopathogenic microbes still remains a formidable barrier. The present study aimed at the prediction of drug targets in Pseudomonas syringae pv. phaseolicola by exploiting the knowledge of Codon Usage bias for gene expression subtractively, supported by gene expression analysis and sequence comparisons. Based on screening of the Database of Essential Genes using blastx, 158 of the total 5172 genes of P. syringae pv. phaseolicola were enlisted as vitally essential genes. Similarity search for these 158 essential genes against available host-plant sequences (Phaseolous vulgaris) led to the identification of homologues of 21 genes in the host genome, thus leaving behind a subset of 137 genes. Expression analysis of these 137 genes using RSCU(gene,) validated by microarray gene expression data suggested 22 genes had higher expression levels in the cell, and therefore their products have been identified as putative drug targets. The gene ontology analysis of these 22 genes revealed their indispensable roles in pivotal metabolic pathways of P. syringae pv. phaseolicola. Upon comparison of the sequences of these genes with other soil bacteria, we identified two genes that were unique to P. syringae pv. phaseolicola. The products of these genes can potentially be utilized for drug development so as to control the halo blight disease and thereby accelerate translation research in the nascent field of agrigenomics.

Rapid countermeasure discovery against Francisella tularensis based on a metabolic network reconstruction

In the future, we may be faced with the need to provide treatment for an emergent biological threat against which existing vaccines and drugs have limited efficacy or availability. To prepare for this eventuality, our objective was to use a metabolic network-based approach to rapidly identify potential drug targets and prospectively screen and validate novel small-molecule antimicrobials. Our target organism was the fully virulent Francisella tularensis subspecies tularensis Schu S4 strain, a highly infectious intracellular pathogen that is the causative agent of tularemia and is classified as a category A biological agent by the Centers for Disease Control and Prevention. We proceeded with a staggered computational and experimental workflow that used a strain-specific metabolic network model, homology modeling and X-ray crystallography of protein targets, and ligand- and structure-based drug design. Selected compounds were subsequently filtered based on physiological-based pharmacokinetic modeling, and we selected a final set of 40 compounds for experimental validation of antimicrobial activity. We began screening these compounds in whole bacterial cell-based assays in biosafety level 3 facilities in the 20th week of the study and completed the screens within 12 weeks. Six compounds showed significant growth inhibition of F. tularensis, and we determined their respective minimum inhibitory concentrations and mammalian cell cytotoxicities. The most promising compound had a low molecular weight, was non-toxic, and abolished bacterial growth at 13 µM, with putative activity against pantetheine-phosphate adenylyltransferase, an enzyme involved in the biosynthesis of coenzyme A, encoded by gene coaD. The novel antimicrobial compounds identified in this study serve as starting points for lead optimization, animal testing, and drug development against tularemia. Our integrated in silico/in vitro approach had an overall 15% success rate in terms of active versus tested compounds over an elapsed time period of 32 weeks, from pathogen strain identification to selection and validation of novel antimicrobial compounds.

The chemical interactome space between the human host and the genetically defined gut metabotypes

The bacteria that colonize the gastrointestinal tracts of mammals represent a highly selected microbiome that has a profound influence on human physiology by shaping the host's metabolic and immune system activity. Despite the recent advances on the biological principles that underlie microbial symbiosis in the gut of mammals, mechanistic understanding of the contributions of the gut microbiome and how variations in the metabotypes are linked to the host health are obscure. Here, we mapped the entire metabolic potential of the gut microbiome based solely on metagenomics sequencing data derived from fecal samples of 124 Europeans (healthy, obese and with inflammatory bowel disease). Interestingly, three distinct clusters of individuals with high, medium and low metabolic potential were observed. By illustrating these results in the context of bacterial population, we concluded that the abundance of the Prevotella genera is a key factor indicating a low metabolic potential. These metagenome-based metabolic signatures were used to study the interaction networks between bacteria-specific metabolites and human proteins. We found that thirty-three such metabolites interact with disease-relevant protein complexes several of which are highly expressed in cells and tissues involved in the signaling and shaping of the adaptive immune system and associated with squamous cell carcinoma and bladder cancer. From this set of metabolites, eighteen are present in DrugBank providing evidence that we carry a natural pharmacy in our guts. Furthermore, we established connections between the systemic effects of non-antibiotic drugs and the gut microbiome of relevance to drug side effects and health-care solutions.

Identification and characterization of potential therapeutic candidates in emerging human pathogen Mycobacterium abscessus: a novel hierarchical in silico approach

Mycobacterium abscessus, a non-tuberculous rapidly growing mycobacterium, is recognized as an emerging human pathogen causing a variety of infections ranging from skin and soft tissue infections to severe pulmonary infections. Lack of an optimal treatment regimen and emergence of multi-drug resistance in clinical isolates necessitate the development of better/new drugs against this pathogen. The present study aims at identification and qualitative characterization of promising drug targets in M. abscessus using a novel hierarchical in silico approach, encompassing three phases of analyses. In phase I, five sets of proteins were mined through chokepoint, plasmid, pathway, virulence factors, and resistance genes and protein network analysis. These were filtered in phase II, in order to find out promising drug target candidates through subtractive channel of analysis. The analysis resulted in 40 therapeutic candidates which are likely to be essential for the survival of the pathogen and non-homologous to host, human anti-targets, and gut flora. Many of the identified targets were found to be involved in different metabolisms (viz., amino acid, energy, carbohydrate, fatty acid, and nucleotide), xenobiotics degradation, and bacterial pathogenicity. Finally, in phase III, the candidate targets were qualitatively characterized through cellular localization, broad spectrum, interactome, functionality, and druggability analysis. The study explained their subcellular location identifying drug/vaccine targets, possibility of being broad spectrum target candidate, functional association with metabolically interacting proteins, cellular function (if hypothetical), and finally, druggable property. Outcome of the present study could facilitate the identification of novel antibacterial agents for better treatment of M. abscesses infections.

Mechanistic basis of the inhibition of type II dehydroquinase by (2S)- and (2R)-2-benzyl-3-dehydroquinic acids

The structural changes caused by the substitution of the aromatic moiety in (2S)-2-benzyl-3-dehydroquinic acids and its epimers in C2 by electron-withdrawing or electron-donating groups in type II dehydroquinase enzyme from M. tuberculosis and H. pylori has been investigated by structural and computational studies. Both compounds are reversible competitive inhibitors of this enzyme, which is essential in these pathogenic bacteria. The crystal structures of M. tuberculosis and H. pylori in complex with (2S)-2-(4-methoxy)benzyl- and (2S)-2-perfluorobenzyl-3-dehydroquinic acids have been solved at 2.0, 2.3, 2.0, and 1.9 Å, respectively. The crystal structure of M. tuberculosis in complex with (2R)-2-(benzothiophen-5-yl)methyl-3-dehydroquinic acid is also reported at 1.55 Å. These crystal structures reveal key differences in the conformation of the flexible loop of the two enzymes, a difference that depends on the presence of electron-withdrawing or electron-donating groups in the aromatic moiety of the inhibitors. This loop closes over the active site after substrate binding, and its flexibility is essential for the function of the enzyme. These differences have also been investigated by molecular dynamics simulations in an effort to understand the significant inhibition potency differences observed between some of these compounds and also to obtain more information about the possible movements of the loop. These computational studies have also allowed us to identify key structural factors of the H. pylori loop that could explain its reduced flexibility in comparison to the M. tuberculosis loop, specifically by the formation of a key salt bridge between the side chains of residues Asp18 and Arg20.

A statistical framework for improving genomic annotations of prokaryotic essential genes

Large-scale systematic analysis of gene essentiality is an important step closer toward unraveling the complex relationship between genotypes and phenotypes. Such analysis cannot be accomplished without unbiased and accurate annotations of essential genes. In current genomic databases, most of the essential gene annotations are derived from whole-genome transposon mutagenesis (TM), the most frequently used experimental approach for determining essential genes in microorganisms under defined conditions. However, there are substantial systematic biases associated with TM experiments. In this study, we developed a novel Poisson model–based statistical framework to simulate the TM insertion process and subsequently correct the experimental biases. We first quantitatively assessed the effects of major factors that potentially influence the accuracy of TM and subsequently incorporated relevant factors into the framework. Through iteratively optimizing parameters, we inferred the actual insertion events occurred and described each gene’s essentiality on probability measure. Evaluated by the definite mapping of essential gene profile in Escherichia coli, our model significantly improved the accuracy of original TM datasets, resulting in more accurate annotations of essential genes. Our method also showed encouraging results in improving subsaturation level TM datasets. To test our model’s broad applicability to other bacteria, we applied it to Pseudomonas aeruginosa PAO1 and Francisella tularensis novicida TM datasets. We validated our predictions by literature as well as allelic exchange experiments in PAO1. Our model was correct on six of the seven tested genes. Remarkably, among all three cases that our predictions contradicted the TM assignments, experimental validations supported our predictions. In summary, our method will be a promising tool in improving genomic annotations of essential genes and enabling large-scale explorations of gene essentiality. Our contribution is timely considering the rapidly increasing essential gene sets. A Webserver has been set up to provide convenient access to this tool. All results and source codes are available for download upon publication at http://research.cchmc.org/essentialgene/.

Extrachromosomal, extraordinary and essential--the plasmids of the Roseobacter clade

The alphaproteobacterial Roseobacter clade (Rhodobacterales) is one of the most important global players in carbon and sulfur cycles of marine ecosystems. The remarkable metabolic versatility of this bacterial lineage provides access to diverse habitats and correlates with a multitude of extrachromosomal elements. Four non-homologous replication systems and additional subsets of individual compatibility groups ensure the stable maintenance of up to a dozen replicons representing up to one third of the bacterial genome. This complexity presents the challenge of successful partitioning of all low copy number replicons. Based on the phenomenon of plasmid incompatibility, we developed molecular tools for target-oriented plasmid curing and could generate customized mutants lacking hundreds of genes. This approach allows one to analyze the relevance of specific replicons including so-called chromids that are known as lifestyle determinants of bacteria. Chromids are extrachromosomal elements with a chromosome-like genetic imprint (codon usage, GC content) that are essential for competitive survival in the natural habitat, whereas classical dispensable plasmids exhibit a deviating codon usage and typically contain type IV secretion systems for conjugation. The impact of horizontal plasmid transfer is exemplified by the scattered occurrence of the characteristic aerobic anoxygenic photosynthesis among the Roseobacter clade and the recently reported transfer of the 45-kb photosynthesis gene cluster to extrachromosomal elements. Conjugative transmission may be the crucial driving force for rapid adaptations and hence the ecological prosperousness of this lineage of pink bacteria.

Identification of potential targets in Staphylococcus aureus N315 using computer aided protein data analysis

Staphylococcus aureus is a gram positive bacterium, responsible for both community-acquired and hospital-acquired infection, resulting in a mortality rate of 39%. 43.2% resistance to methicilin and emerging resistance to Fluroquinolone and Oxazolidinone, have evoked the necessity of the establishment of alternative and effective therapeutic approach to treat this bacteria. In this computational study, various database and online software are used to determine some specific targets of Staphylococcus aureus N315 other than those used by Penicillin, Quinolone and Oxazolidinone. For this purpose, among 302 essential proteins, 101 nonhomologous proteins were accrued and 64 proteins which are unique in several metabolic pathways of S. aureus were isolated by using metabolic pathway analysis tools. Furthermore, 7 essentially unique enzymes involved in exclusive metabolic pathways were revealed by this research, which can be potential drug target. Along with these important enzymes, 15 non-homologous proteins located on membrane were identified, which can play a vital role as potential therapeutic targets for the future researchers.

Correlation of genomic features with dynamic modularity in the yeast interactome: a view from the structural perspective

The idea of the existence of date and party hubs in protein-protein interaction networks has been debated since it was proposed in 2004. Based on the incorporation of the information extracted from known three-dimensional structures of protein interactions, we revisited the properties associated with date and party hubs previously identified. The correlation of genomic essentiality, gene coexpression, and functional semantic similarity with date and party hubs were examined. The number of interaction interfaces associated with each hub was taken into account. The results suggested that the identification of date and party hubs based on their network connectivity and expression profiles with interaction partners may be incomplete. The number of interaction interfaces could play an important role in examining functional and topological properties associated with each hub protein. The observation is robust to the choice of degree cutoffs for hubs. Furthermore, we found that while singlish-interface hubs seem to correspond mostly to date hub, it appears that there is no significant difference between the proportions of multi-interface proteins categorized as date and as party hubs.