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

New England Biolabs, 240 County Road, Ipswich, MA 01938-2723, USA.
BMC Microbiology (Impact Factor: 2.73). 11/2009; 9(1):243. DOI: 10.1186/1471-2180-9-243
Source: PubMed


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.
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.
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.

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    • "The comparison with DBD resulted in the identification of those FDA approved drugs or drug like compounds for which the experimental evidence of binding with proteins similar to the S. aureus is available. Hence, these steps helped in advancing the identification of potential drug or lead like molecules for future medicinal chemistry based approaches (Holman et al., 2009). Out of 51 prioritized targets, all belonged to the unique metabolic pathways of MRSA including two component system, β lactamase resistance, secondary bile acid biosynthesis, nitrogen metabolism, methane metabolism, peptidoglycan biosynthesis, phosphotransferase system, benzoate degradation, Dalanine metabolism, limonene, bacterial secretion system and pinene degradation. "
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    ABSTRACT: Multiple Drug Resistant (MDR) bacteria are no more inhibited by the front line antibiotics due to extreme resistance. Methicillin Resistant Staphylococcus aureus (MRSA) is one of the MDR pathogens notorious for its widespread infection around the world. The high resistance acquired by MRSA needs a serious concern and efforts should be carried out for the discovery of better therapeutics. With this aim, we designed a comparison of the metabolic pathways of the pathogen, MRSA strain 252 (MRSA252) with the human host (i.e., Homo sapiens) by using well-established in silico methods. We identified several metabolic pathways unique to MRSA (i.e., absent in the human host). Furthermore, a subtractive genomics analysis approach was applied for retrieval of proteins only from the unique metabolic pathways. Subsequently, proteins of unique MRSA pathways were compared with the host proteins. As a result, we have shortlisted few unique and essential proteins that could act as drug targets against MRSA. We further assessed the druggability potential of the shortlisted targets by comparing them with the DrugBank Database (DBD). The identified drug targets could be useful for an effective drug discovery phase. We also searched the sequences of unique as well as essential enzymes from MRSA in Protein Data Bank (PDB). We shortlisted at least 12 enzymes for which there was no corresponding deposition in PDB, reflecting that their crystal structures are yet to be solved! We selected Glutamate synthase out of those 12 enzymes owing to its participation in significant metabolic pathways of the pathogen e.g., Alanine, Aspartate, Glutamate and Nitrogen metabolism and its evident suitability as drug target among other MDR bacteria e.g., Mycobacteria. Due to the unavailability of any crystal structure of Glutamate synthase in PDB, we generated the 3D structure by homology modeling. The modeled structure was validated by multiple analysis tools. The active site of Glutamate synthase was identified by not only superimposing the template structure (PDB ID: 1E0A) over each other but also by the Parallel-ProBiS algorithm. The identified active site was further validated by cross-docking the co-crystallized ligand (2-oxoglutaric acid; AKG) of PDB ID: 1LLW. It was concluded that the comparative metabolic in silico analysis together with structure-based methods provides an effective approach for the identification of novel antibiotic targets against MRSA. Copyright © 2014 Elsevier B.V. All rights reserved.
    Gene 11/2014; 556(2). DOI:10.1016/j.gene.2014.11.056 · 2.14 Impact Factor
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    • "Several prediction models have been developed in silico to identify essential genes. Among these models, the simplest one is prediction of essential genes based on the known essentiality of homologous genes [18-21]. Although these prediction models show high confidence levels, they still have two limitations: first, the conserved orthologs between species only account for a small portion of the genome [22] and, second, the orthologs, especially in distantly related species, often show variations in gene regulations and functions [6,23], which lead to potential diversity in gene essentiality. "
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    ABSTRACT: Determination of the minimum gene set for cellular life is one of the central goals in biology. Genome-wide essential gene identification has progressed rapidly in certain bacterial species; however, it remains difficult to achieve in most eukaryotic species. Several computational models have recently been developed to integrate gene features and used as alternatives to transfer gene essentiality annotations between organisms. We first collected features that were widely used by previous predictive models and assessed the relationships between gene features and gene essentiality using a stepwise regression model. We found two issues that could significantly reduce model accuracy: (i) the effect of multicollinearity among gene features and (ii) the diverse and even contrasting correlations between gene features and gene essentiality existing within and among different species. To address these issues, we developed a novel model called feature-based weighted Naive Bayes model (FWM), which is based on Naive Bayes classifiers, logistic regression, and genetic algorithm. The proposed model assesses features and filters out the effects of multicollinearity and diversity. The performance of FWM was compared with other popular models, such as support vector machine, Naive Bayes model, and logistic regression model, by applying FWM to reciprocally predict essential genes among and within 21 species. Our results showed that FWM significantly improves the accuracy and robustness of essential gene prediction. FWM can remarkably improve the accuracy of essential gene prediction and may be used as an alternative method for other classification work. This method can contribute substantially to the knowledge of the minimum gene sets required for living organisms and the discovery of new drug targets.
    BMC Genomics 12/2013; 14(1):910. DOI:10.1186/1471-2164-14-910 · 3.99 Impact Factor
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    • "In order to target focused library screening to drugs with predicted activity against Wolbachia, a bioinformatic analysis of predicted essential genes was undertaken. An essentiality score for each predicted gene of wBm was determined by two separate approaches (Holman et al. 2009). The first method compared each gene to entries in DEG (the Database of Essential Genes), a collection of *5000 experimentally identified essential genes from 15 different bacterial species, to predict essential genes that are mostly conserved across the bacterial domain. "
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    Parasitology 07/2013; 141(1):1-9. DOI:10.1017/S0031182013001108 · 2.56 Impact Factor
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