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

A novel comparative genomics analysis for common drug and vaccine targets in Corynebacterium pseudotuberculosis and other CMN group of human pathogens

Caseous lymphadenitis is a chronic goat and sheep disease caused by Corynebacterium pseudotuberculosis (Cp) that accounts for a huge economic loss worldwide. Proper vaccination or medication is not available because of the lack of understanding of molecular biology of the pathogen. In a recent approach, four Cp (CpFrc41, Cp1002, CpC231, and CpI-19) genomes were sequenced to elucidate the molecular pathology of the bacteria. In this study, using these four genome sequences along with other eight genomes (total 12 genomes) and a novel subtractive genomics approach (first time ever applied to a veterinary pathogen), we identified potential conserved common drug and vaccine targets of these four Cp strains along with other Corybacterium, Mycobacterium and Nocardia (CMN) group of human pathogens (Corynebacterium diphtheriae and Mycobacterium tuberculosis) considering goat, sheep, bovine, horse, and human as the most affected hosts. The minimal genome of Cp1002 was found to consist of 724 genes, and 20 conserved common targets (to all Cp strains as well as CMN group of pathogens) from various metabolic pathways (13 from host-pathogen common and seven from pathogen's unique pathways) are potential targets irrespective of all hosts considered. ubiA from host-pathogen common pathway and an ABC-like transporter from unique pathways may serve dual (drug and vaccine) targets. Two Corynebacterium-specific (mscL and resB) and one broad-spectrum (rpmB) novel targets were also identified. Strain-specific targets are also discussed. Six important targets were subjected to virtual screening, and one compound was found to be potent enough to render two targets (cdc and nrdL). We are currently validating all identified targets and lead compounds.

Mapping the protein interaction network in methicillin-resistant Staphylococcus aureus

Mortality attributable to infection with methicillin-resistant Staphylococcus aureus (MRSA) has now overtaken the death rate for AIDS in the United States, and advances in research are urgently needed to address this challenge. We report the results of the systematic identification of protein-protein interactions for the hospital-acquired strain MRSA-252. Using a high-throughput pull-down strategy combined with quantitative proteomics to distinguish specific from nonspecific interactors, we identified 13,219 interactions involving 608 MRSA proteins. Consecutive analyses revealed that this protein interaction network (PIN) exhibits scale-free organization with the characteristic presence of highly connected hub proteins. When clinical and experimental antimicrobial targets were queried in the network, they were generally found to occupy peripheral positions in the PIN with relatively few interacting partners. In contrast, the hub proteins identified in this MRSA PIN that are essential for network integrity and stability have largely been overlooked as drug targets. Thus, this empirical MRSA-252 PIN provides a rich source for identifying critical proteins essential for network stability, many of which can be considered as prospective antimicrobial drug targets.

Synthetic biology: From the first synthetic cell to see its current situation and future development

Synthetic biology is an emerging field, which, since its birth, has shown great value and potential in many fields including medicine, energy, environment and agriculture. It is also important for the study of the origin and evolution of life. Since the publication of the first synthetic cell in May, 2010, synthetic biology again attracts high attention and leads to extensive discussions all over the world. There have been a number of researches and achievements on synthetic biology in the United States and European countries. While in China, so far there is no systematic research on synthetic biology. In order to promote the development of this new discipline in China, we organized this review to systematically introduce the concept and research content of synthetic biology, summarize the achievements, and investigate the current situation in both China and abroad. We also analyzed the opportunities and challenges in synthetic biology, and looked forward to the future development of synthetic biology, especially its future development in China.

COMODO: an adaptive coclustering strategy to identify conserved coexpression modules between organisms

Increasingly large-scale expression compendia for different species are becoming available. By exploiting the modularity of the coexpression network, these compendia can be used to identify biological processes for which the expression behavior is conserved over different species. However, comparing module networks across species is not trivial. The definition of a biologically meaningful module is not a fixed one and changing the distance threshold that defines the degree of coexpression gives rise to different modules. As a result when comparing modules across species, many different partially overlapping conserved module pairs across species exist and deciding which pair is most relevant is hard. Therefore, we developed a method referred to as conserved modules across organisms (COMODO) that uses an objective selection criterium to identify conserved expression modules between two species. The method uses as input microarray data and a gene homology map and provides as output pairs of conserved modules and searches for the pair of modules for which the number of sharing homologs is statistically most significant relative to the size of the linked modules. To demonstrate its principle, we applied COMODO to study coexpression conservation between the two well-studied bacteria Escherichia coli and Bacillus subtilis. COMODO is available at: http://homes.esat.kuleuven.be/∼kmarchal/Supplementary_Information_Zarrineh_2010/comodo/index.html.

Correlating traits of gene retention, sequence divergence, duplicability and essentiality in vertebrates, arthropods, and fungi

Delineating ancestral gene relations among a large set of sequenced eukaryotic genomes allowed us to rigorously examine links between evolutionary and functional traits. We classified 86% of over 1.36 million protein-coding genes from 40 vertebrates, 23 arthropods, and 32 fungi into orthologous groups and linked over 90% of them to Gene Ontology or InterPro annotations. Quantifying properties of ortholog phyletic retention, copy-number variation, and sequence conservation, we examined correlations with gene essentiality and functional traits. More than half of vertebrate, arthropod, and fungal orthologs are universally present across each lineage. These universal orthologs are preferentially distributed in groups with almost all single-copy or all multicopy genes, and sequence evolution of the predominantly single-copy orthologous groups is markedly more constrained. Essential genes from representative model organisms, Mus musculus, Drosophila melanogaster, and Saccharomyces cerevisiae, are significantly enriched in universal orthologs within each lineage, and essential-gene-containing groups consistently exhibit greater sequence conservation than those without. This study of eukaryotic gene repertoire evolution identifies shared fundamental principles and highlights lineage-specific features, it also confirms that essential genes are highly retained and conclusively supports the "knockout-rate prediction" of stronger constraints on essential gene sequence evolution. However, the distinction between sequence conservation of single- versus multicopy orthologs is quantitatively more prominent than between orthologous groups with and without essential genes. The previously underappreciated difference in the tolerance of gene duplications and contrasting evolutionary modes of "single-copy control" versus "multicopy license" may reflect a major evolutionary mechanism that allows extended exploration of gene sequence space.

The pseudogenes of Mycobacterium leprae reveal the functional relevance of gene order within operons

Almost 50 years following the discovery of the prokaryotic operon, the functional relevance of gene order within operons remains unclear. In this work, we take advantage of the eroded genome of Mycobacterium leprae to add evidence supporting the notion that functionally less important genes have a tendency to be located at the end of its operons. M. leprae’s genome includes 1133 pseudogenes and 1614 protein-coding genes and can be compared with the close genome of M. tuberculosis. Assuming M. leprae’s pseudogenes to represent dispensable genes, we have studied the position of these pseudogenes in the operons of M. leprae and of their orthologs in M. tuberculosis. We observed that both tend to be located in the 3′ (downstream) half of the operon (P-values of 0.03 and 0.18, respectively). Analysis of pseudogenes in all available prokaryotic genomes confirms this trend (P-value of 7.1 × 10⁻⁷). In a complementary analysis, we found a significant tendency for essential genes to be located at the 5′ (upstream) half of the operon (P-value of 0.006). Our work provides an indication that, in prokarya, functionally less important genes have a tendency to be located at the end of operons, while more relevant genes tend to be located toward operon starts.

Structure of an essential bacterial protein YeaZ (TM0874) from Thermotoga maritima at 2.5 Å resolution

YeaZ is involved in a protein network that is essential for bacteria. The crystal structure of YeaZ from Thermotoga maritima was determined to 2.5 Å resolution. Although this protein belongs to a family of ancient actin-like ATPases, it appears that it has lost the ability to bind ATP since it lacks some key structural features that are important for interaction with ATP. A conserved surface was identified, supporting its role in the formation of protein complexes.

Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis

Prediction of metabolic changes that result from genetic or environmental perturbations has several important applications, including diagnosing metabolic disorders and discovering novel drug targets. A cardinal challenge in obtaining accurate predictions is the integration of transcriptional regulatory networks with the corresponding metabolic network. We propose a method called probabilistic regulation of metabolism (PROM) that achieves this synthesis and enables straightforward, automated, and quantitative integration of high-throughput data into constraint-based modeling, making it an ideal tool for constructing genome-scale regulatory-metabolic network models for less-studied organisms. PROM introduces probabilities to represent gene states and gene-transcription factor interactions. By using PROM, we constructed an integrated regulatory-metabolic network for the model organism, Escherichia coli, and demonstrated that our method based on automated inference is more accurate and comprehensive than the current state of the art, which is based on manual curation of literature. After validating the approach, we used PROM to build a genome-scale integrated metabolic-regulatory model for Mycobacterium tuberculosis, a critically important human pathogen. This study incorporated data from more than 1,300 microarrays, 2,000 transcription factor-target interactions regulating 3,300 metabolic reactions, and 1,905 KO phenotypes for E. coli and M. tuberculosis. PROM identified KO phenotypes with accuracies as high as 95%, and predicted growth rates quantitatively with correlation of 0.95. Importantly, PROM represents the successful integration of a top-down reconstructed, statistically inferred regulatory network with a bottom-up reconstructed, biochemically detailed metabolic network, bridging two important classes of systems biology models that are rarely combined quantitatively.

Essential biological processes of an emerging pathogen: DNA replication, transcription, and cell division in Acinetobacter spp

Within the last 15 years, members of the bacterial genus Acinetobacter have risen from relative obscurity to be among the most important sources of hospital-acquired infections. The driving force for this has been the remarkable ability of these organisms to acquire antibiotic resistance determinants, with some strains now showing resistance to every antibiotic in clinical use. There is an urgent need for new antibacterial compounds to combat the threat imposed by Acinetobacter spp. and other intractable bacterial pathogens. The essential processes of chromosomal DNA replication, transcription, and cell division are attractive targets for the rational design of antimicrobial drugs. The goal of this review is to examine the wealth of genome sequence and gene knockout data now available for Acinetobacter spp., highlighting those aspects of essential systems that are most suitable as drug targets. Acinetobacter spp. show several key differences from other pathogenic gammaproteobacteria, particularly in global stress response pathways. The involvement of these pathways in short- and long-term antibiotic survival suggests that Acinetobacter spp. cope with antibiotic-induced stress differently from other microorganisms.

PROFESS: a PROtein function, evolution, structure and sequence database

The proliferation of biological databases and the easy access enabled by the Internet is having a beneficial impact on biological sciences and transforming the way research is conducted. There are approximately 1100 molecular biology databases dispersed throughout the Internet. To assist in the functional, structural and evolutionary analysis of the abundant number of novel proteins continually identified from whole-genome sequencing, we introduce the PROFESS (PROtein Function, Evolution, Structure and Sequence) database. Our database is designed to be versatile and expandable and will not confine analysis to a pre-existing set of data relationships. A fundamental component of this approach is the development of an intuitive query system that incorporates a variety of similarity functions capable of generating data relationships not conceived during the creation of the database. The utility of PROFESS is demonstrated by the analysis of the structural drift of homologous proteins and the identification of potential pancreatic cancer therapeutic targets based on the observation of protein-protein interaction networks. Database URL: http://cse.unl.edu/~profess/

mGenomeSubtractor: a web-based tool for parallel in silico subtractive hybridization analysis of multiple bacterial genomes

mGenomeSubtractor performs an mpiBLAST-based comparison of reference bacterial genomes against multiple user-selected genomes for investigation of strain variable accessory regions. With parallel computing architecture, mGenomeSubtractor is able to run rapid BLAST searches of the segmented reference genome against multiple subject genomes at the DNA or amino acid level within a minute. In addition to comparison of protein coding sequences, the highly flexible sliding window-based genome fragmentation approach offered can be used to identify short unique sequences within or between genes. mGenomeSubtractor provides powerful schematic outputs for exploration of identified core and accessory regions, including searches against databases of mobile genetic elements, virulence factors or bacterial essential genes, examination of G+C content and binucleotide distribution bias, and integrated primer design tools. mGenomeSubtractor also allows for the ready definition of species-specific gene pools based on available genomes. Pan-genomic arrays can be easily developed using the efficient oligonucleotide design tool. This simple high-throughput in silico ‘subtractive hybridization’ analytical tool will support the rapidly escalating number of comparative bacterial genomics studies aimed at defining genomic biomarkers of evolutionary lineage, phenotype, pathotype, environmental adaptation and/or disease-association of diverse bacterial species. mGenomeSubtractor is freely available to all users without any login requirement at: http://bioinfo-mml.sjtu.edu.cn/mGS/.

Use of genome-scale metabolic models for understanding microbial physiology

The exploitation of microorganisms in industrial, medical, food and environmental biotechnology requires a comprehensive understanding of their physiology. The availability of genome sequences and accumulation of high-throughput data allows gaining understanding of microbial physiology at the systems level, and genome-scale metabolic models represent a valuable framework for integrative analysis of metabolism of microorganisms. Genome-scale metabolic models are reconstructed based on a combination of genome sequence information and detailed biochemical information, and these reconstructed models can be used for analyzing and simulating the operation of metabolism in response to different stimuli. Here we discuss the requirement for having detailed physiological insight in order to exploit microorganisms for production of fuels, chemicals and pharmaceuticals. We further describe the reconstruction process of genome-scale metabolic models and different algorithms that can be used to apply these models to gain improved insight into microbial physiology.

Functionality of essential genes drives gene strand-bias in bacterial genomes

Essential genes, indispensable genes for an organism's survival, encode functions that are considered a foundation of life. Based on those experimentally determined for 10 bacteria, we find that essential genes are more preferentially situated at the leading strand than at the lagging strand, for all the 10 genomes studied, confirming previous findings based on either smaller datasets or putatively assigned ones by homology search. Furthermore, we find that rather than all essential genes, only those with the COG functional category of information storage and process (J, K and L), and subcategories D (cell cycle control), M (cell wall biogenesis), O (posttranslational modification), C (energy production and conversion), G (carbohydrate transport and metabolism), E (amino acid transport and metabolism) and F (nucleotide transport and metabolism) are preferentially situated at the leading strand. In contrast, the strand-bias for essential genes in other COG functional subcategories is not statistically significant. These results suggest that the remarkable strand-bias of the distribution of essential genes is mainly relevant to the aforementioned functionalities, which, therefore, likely play a key role in shaping the gene strand-bias in bacterial genomes.

Essential molecular functions associated with the circular code evolution

A circular code is a set of trinucleotides allowing the reading frames in genes to be retrieved locally, i.e. anywhere in genes and in particular without start codons, and automatically with a window of few nucleotides. In 1996, a common circular code, called X, was identified in large populations of eukaryotic and prokaryotic genes. Hence, it is believed to be an ancestral structural property of genes. A new computational approach based on comparative genomics is developed to identify essential molecular functions associated with circular codes. It is based on a quantitative and sensitive statistical method (FPTF) to identify three permuted trinucleotide sets in the three frames of genes, a flower automaton algorithm to determine if a trinucleotide set is a circular code or not, and an integrated Gene Ontology and Taxonomy (iGOT) database. By carrying out automatic circular code analyses on a huge number of gene populations where each population is associated with a particular molecular function, it identifies 266 gene populations having circular codes close to X. Surprisingly, their molecular functions include 98% of those covered by the essential genes of the DEG database (Database of Essential Genes). Furthermore, three trinucleotides GTG, AAG and GCG, replacing three trinucleotides of the code X and called "evolutionary" trinucleotides, significantly occur in these 266 gene populations. Finally, a new method developed to analyse and quantify the stability of a set of trinucleotides demonstrates that these evolutionary trinucleotides are associated with a significant increase of the stability of the common circular code X. Indeed, its stability increases from the 1502th rank to the 16th rank after the replacement of the three evolutionary trinucleotides among 9920 possible trinucleotide replacement sets.

Synthetic biology: tools to design, build, and optimize cellular processes

The general central dogma frames the emergent properties of life, which make biology both necessary and difficult to engineer. In a process engineering paradigm, each biological process stream and process unit is heavily influenced by regulatory interactions and interactions with the surrounding environment. Synthetic biology is developing the tools and methods that will increase control over these interactions, eventually resulting in an integrative synthetic biology that will allow ground-up cellular optimization. In this review, we attempt to contextualize the areas of synthetic biology into three tiers: (1) the process units and associated streams of the central dogma, (2) the intrinsic regulatory mechanisms, and (3) the extrinsic physical and chemical environment. Efforts at each of these three tiers attempt to control cellular systems and take advantage of emerging tools and approaches. Ultimately, it will be possible to integrate these approaches and realize the vision of integrative synthetic biology when cells are completely rewired for biotechnological goals. This review will highlight progress towards this goal as well as areas requiring further research.

Computational prediction of essential genes in an unculturable endosymbiotic bacterium, Wolbachia of Brugia malayi

BACKGROUND

Wolbachia (wBm) is an obligate endosymbiotic bacterium of Brugia malayi, a parasitic filarial nematode of humans and one of the causative agents of lymphatic filariasis. There is a pressing need for new drugs against filarial parasites, such as B. malayi. As wBm is required for B. malayi development and fertility, targeting wBm is a promising approach. However, the lifecycle of neither B. malayi nor wBm can be maintained in vitro. To facilitate selection of potential drug targets we computationally ranked the wBm genome based on confidence that a particular gene is essential for the survival of the bacterium.

RESULTS

wBm protein sequences were aligned using BLAST to the Database of Essential Genes (DEG) version 5.2, a collection of 5,260 experimentally identified essential genes in 15 bacterial strains. A confidence score, the Multiple Hit Score (MHS), was developed to predict each wBm gene's essentiality based on the top alignments to essential genes in each bacterial strain. This method was validated using a jackknife methodology to test the ability to recover known essential genes in a control genome. A second estimation of essentiality, the Gene Conservation Score (GCS), was calculated on the basis of phyletic conservation of genes across Wolbachia's parent order Rickettsiales. Clusters of orthologous genes were predicted within the 27 currently available complete genomes. Druggability of wBm proteins was predicted by alignment to a database of protein targets of known compounds.

CONCLUSION

Ranking wBm genes by either MHS or GCS predicts and prioritizes potentially essential genes. Comparison of the MHS to GCS produces quadrants representing four types of predictions: those with high confidence of essentiality by both methods (245 genes), those highly conserved across Rickettsiales (299 genes), those similar to distant essential genes (8 genes), and those with low confidence of essentiality (253 genes). These data facilitate selection of wBm genes for entry into drug design pipelines.

Steps toward broad-spectrum therapeutics: discovering virulence-associated genes present in diverse human pathogens

BACKGROUND

New and improved antimicrobial countermeasures are urgently needed to counteract increased resistance to existing antimicrobial treatments and to combat currently untreatable or new emerging infectious diseases. We demonstrate that computational comparative genomics, together with experimental screening, can identify potential generic (i.e., conserved across multiple pathogen species) and novel virulence-associated genes that may serve as targets for broad-spectrum countermeasures.

RESULTS

Using phylogenetic profiles of protein clusters from completed microbial genome sequences, we identified seventeen protein candidates that are common to diverse human pathogens and absent or uncommon in non-pathogens. Mutants of 13 of these candidates were successfully generated in Yersinia pseudotuberculosis and the potential role of the proteins in virulence was assayed in an animal model. Six candidate proteins are suggested to be involved in the virulence of Y. pseudotuberculosis, none of which have previously been implicated in the virulence of Y. pseudotuberculosis and three have no record of involvement in the virulence of any bacteria.

CONCLUSION

This work demonstrates a strategy for the identification of potential virulence factors that are conserved across a number of human pathogenic bacterial species, confirming the usefulness of this tool.

Genome subtraction for novel target definition in Salmonella typhi

Large genomic sequencing projects of pathogens as well as human genome leads to immense genomic and proteomic data which would be very beneficial for the novel target identification in pathogens. Subtractive genomic approach is one of the most useful strategies helpful in identification of potential targets. The approach works by subtracting the genes or proteins homologous to both host and the pathogen and identify those set of gene or proteins which are essential for the pathogen and are exclusively present in the pathogen. Subtractive genomic approach is employed to identify novel target in salmonella typhi. The pathogen has 4718 proteins out of which 300 are found to be essential (" indispensable to support cellular life") in the pathogen with no human homolog. Metabolic pathway analyses of these 300 essential proteins revealed that 149 proteins are exclusively involved in several metabolic pathway of S. typhi. 8 metabolic pathways are found to be present exclusively in the pathogen comprising of 27 enzymes unique to the pathogen. Thus, these 27 proteins may serve as prospective drug targets. Sub-cellular localization prediction of the 300 essential proteins was done which reveals that 11 proteins lie on the outer membrane of the pathogen which could be probable vaccine candidates.

NeMeSys: a biological resource for narrowing the gap between sequence and function in the human pathogen Neisseria meningitidis

BACKGROUND

Genome sequences, now available for most pathogens, hold promise for the rational design of new therapies. However, biological resources for genome-scale identification of gene function (notably genes involved in pathogenesis) and/or genes essential for cell viability, which are necessary to achieve this goal, are often sorely lacking. This holds true for Neisseria meningitidis, one of the most feared human bacterial pathogens that causes meningitis and septicemia.

RESULTS

By determining and manually annotating the complete genome sequence of a serogroup C clinical isolate of N. meningitidis (strain 8013) and assembling a library of defined mutants in up to 60% of its non-essential genes, we have created NeMeSys, a biological resource for Neisseria meningitidis systematic functional analysis. To further enhance the versatility of this toolbox, we have manually (re)annotated eight publicly available Neisseria genome sequences and stored all these data in a publicly accessible online database. The potential of NeMeSys for narrowing the gap between sequence and function is illustrated in several ways, notably by performing a functional genomics analysis of the biogenesis of type IV pili, one of the most widespread virulence factors in bacteria, and by identifying through comparative genomics a complete biochemical pathway (for sulfur metabolism) that may potentially be important for nasopharyngeal colonization.

CONCLUSIONS

By improving our capacity to understand gene function in an important human pathogen, NeMeSys is expected to contribute to the ongoing efforts aimed at understanding a prokaryotic cell comprehensively and eventually to the design of new therapies.

Acinetobacter baylyi ADP1 as a model for metabolic system biology

Information produced by the annotation of an 'average bacterial genome' can be separated into three parts. One-third represents what we know, another third what we think we know, and the last third what we know we do not know. Knowledge of metabolism is also described by this three thirds rule. Understanding how a cell operates will require a better knowledge of the two ignored thirds of its parts. Moreover, metabolism needs to be further investigated using organisms whose life styles are different from those of model organisms. In this short review, we present Acinetobacter baylyi ADP1 as an environmental model especially suitable for large-scale genetic manipulation. Resources have been constructed in the past few years that can form the basis for diverse metabolic studies: the genome sequence, a single gene mutant collection, and a genome-scale metabolic model.