MyBASE: a database for genome polymorphism and gene function studies of Mycobacterium

<p>Abstract</p> <p>Background</p> <p>Mycobacterial pathogens are a major threat to humans. With the increasing availability of functional genomic data, research on mycobacterial pathogenesis and subsequent control strategies will be greatly accelerated. It has been suggested that genome polymorphisms, namely large sequence polymorphisms, can influence the pathogenicity of different mycobacterial strains. However, there is currently no database dedicated to mycobacterial genome polymorphisms with functional interpretations.</p> <p>Description</p> <p>We have developed a <b>my</b>cobacterial data<b>base </b>(MyBASE) housing genome polymorphism data and gene functions to provide the mycobacterial research community with a useful information resource and analysis platform. Whole genome comparison data produced by our lab and the novel genome polymorphisms identified were deposited into MyBASE. Extensive literature review of genome polymorphism data, mainly large sequence polymorphisms (LSPs), operon predictions and curated annotations of virulence and essentiality of mycobacterial genes are unique features of MyBASE. Large-scale genomic data integration from public resources makes MyBASE a comprehensive data warehouse useful for current research. All data is cross-linked and can be graphically viewed via a toolbox in MyBASE.</p> <p>Conclusion</p> <p>As an integrated platform focused on the collection of experimental data from our own lab and published literature, MyBASE will facilitate analysis of genome structure and polymorphisms, which will provide insight into genome evolution. Importantly, the database will also facilitate the comparison of virulence factors among various mycobacterial strains. MyBASE is freely accessible via <url>http://mybase.psych.ac.cn</url>.</p

Exploring the metabolic network of the epidemic pathogen Burkholderia cenocepacia J2315 via genome-scale reconstruction

<p>Abstract</p> <p>Background</p> <p><it>Burkholderia cenocepacia </it>is a threatening nosocomial epidemic pathogen in patients with cystic fibrosis (CF) or a compromised immune system. Its high level of antibiotic resistance is an increasing concern in treatments against its infection. Strain <it>B. cenocepacia </it>J2315 is the most infectious isolate from CF patients. There is a strong demand to reconstruct a genome-scale metabolic network of <it>B. cenocepacia </it>J2315 to systematically analyze its metabolic capabilities and its virulence traits, and to search for potential clinical therapy targets.</p> <p>Results</p> <p>We reconstructed the genome-scale metabolic network of <it>B. cenocepacia </it>J2315. An iterative reconstruction process led to the establishment of a robust model, <it>i</it>KF1028, which accounts for 1,028 genes, 859 internal reactions, and 834 metabolites. The model <it>i</it>KF1028 captures important metabolic capabilities of <it>B. cenocepacia </it>J2315 with a particular focus on the biosyntheses of key metabolic virulence factors to assist in understanding the mechanism of disease infection and identifying potential drug targets. The model was tested through BIOLOG assays. Based on the model, the genome annotation of <it>B. cenocepacia </it>J2315 was refined and 24 genes were properly re-annotated. Gene and enzyme essentiality were analyzed to provide further insights into the genome function and architecture. A total of 45 essential enzymes were identified as potential therapeutic targets.</p> <p>Conclusions</p> <p>As the first genome-scale metabolic network of <it>B. cenocepacia </it>J2315, <it>i</it>KF1028 allows a systematic study of the metabolic properties of <it>B. cenocepacia </it>and its key metabolic virulence factors affecting the CF community. The model can be used as a discovery tool to design novel drugs against diseases caused by this notorious pathogen.</p

In Silico Insights into the Symbiotic Nitrogen Fixation in Sinorhizobium meliloti via Metabolic Reconstruction

BACKGROUND: Sinorhizobium meliloti is a soil bacterium, known for its capability to establish symbiotic nitrogen fixation (SNF) with leguminous plants such as alfalfa. S. meliloti 1021 is the most extensively studied strain to understand the mechanism of SNF and further to study the legume-microbe interaction. In order to provide insight into the metabolic characteristics underlying the SNF mechanism of S. meliloti 1021, there is an increasing demand to reconstruct a metabolic network for the stage of SNF in S. meliloti 1021. RESULTS: Through an iterative reconstruction process, a metabolic network during the stage of SNF in S. meliloti 1021 was presented, named as iHZ565, which accounts for 565 genes, 503 internal reactions, and 522 metabolites. Subjected to a novelly defined objective function, the in silico predicted flux distribution was highly consistent with the in vivo evidences reported previously, which proves the robustness of the model. Based on the model, refinement of genome annotation of S. meliloti 1021 was performed and 15 genes were re-annotated properly. There were 19.8% (112) of the 565 metabolic genes included in iHZ565 predicted to be essential for efficient SNF in bacteroids under the in silico microaerobic and nutrient sharing condition. CONCLUSIONS: As the first metabolic network during the stage of SNF in S. meliloti 1021, the manually curated model iHZ565 provides an overview of the major metabolic properties of the SNF bioprocess in S. meliloti 1021. The predicted SNF-required essential genes will facilitate understanding of the key functions in SNF and help identify key genes and design experiments for further validation. The model iHZ565 can be used as a knowledge-based framework for better understanding the symbiotic relationship between rhizobia and legumes, ultimately, uncovering the mechanism of nitrogen fixation in bacteroids and providing new strategies to efficiently improve biological nitrogen fixation

Network-assisted analysis of primary Sjogren's syndrome GWAS data in Han Chinese

Primary Sjogren's syndrome (pSS) is a complex autoimmune disorder. So far, genetic research in pSS has lagged far behind and the underlying biological mechanism is unclear. Further exploring existing genome-wide association study (GWAS) data is urgently expected to uncover disease-related gene combination patterns. Herein, we conducted a network-based analysis by integrating pSS GWAS in Han Chinese with a protein-protein interactions network to identify pSS candidate genes. After module detection and evaluation, 8 dense modules covering 40 genes were obtained for further functional annotation. Additional 31 MHC genes with significant gene-level P-values (sigMHC-gene) were also remained. The combined module genes and sigMHC-genes, a total of 71 genes, were denoted as pSS candidate genes. Of these pSS candidates, 14 genes had been reported to be associated with any of pSS, RA, and SLE, including STAT4, GTF2I, HLA-DPB1, HLA-DRB1, PTTG1, HLA-DQB1, MBL2, TAP2, CFLAR, NFKBIE, HLA-DRA, APOM, HLA-DQA2 and NOTCH4. This is the first report of the network-assisted analysis for pSS GWAS data to explore combined gene patterns associated with pSS. Our study suggests that network-assisted analysis is a useful approach to gaining further insights into the biology of associated genes and providing important clues for future research into pSS etiology

Network-Based Analysis of Schizophrenia Genome-Wide Association Data to Detect the Joint Functional Association Signals.

Schizophrenia is a common psychiatric disorder with high heritability and complex genetic architecture. Genome-wide association studies (GWAS) have identified several significant loci associated with schizophrenia. However, the explained heritability is still low. Growing evidence has shown schizophrenia is attributable to multiple genes with moderate effects. In-depth mining and integration of GWAS data is urgently expected to uncover disease-related gene combination patterns. Network-based analysis is a promising strategy to better interpret GWAS to identify disease-related network modules. We performed a network-based analysis on three independent schizophrenia GWASs by using a refined analysis framework, which included a more accurate gene P-value calculation, dynamic network module searching algorithm and detailed functional analysis for the obtained modules genes. The result generated 79 modules including 238 genes, which form a highly connected subnetwork with more statistical significance than expected by chance. The result validated several reported disease genes, such as MAD1L1, MCC, SDCCAG8, VAT1L, MAPK14, MYH9 and FXYD6, and also obtained several novel candidate genes and gene-gene interactions. Pathway enrichment analysis of the module genes suggested they were enriched in several neural and immune system related pathways/GO terms, such as neurotrophin signaling pathway, synaptosome, regulation of protein ubiquitination, and antigen processing and presentation. Further crosstalk analysis revealed these pathways/GO terms were cooperated with each other, and identified several important genes, which might play vital roles to connect these functions. Our network-based analysis of schizophrenia GWASs will facilitate the understanding of genetic mechanisms of schizophrenia

Identification of key genes and pathways for Alzheimer’s disease via combined analysis of genome-wide expression profiling in the hippocampus

In this study, combined analysis of expression profiling in the hippocampus of 76 patients with Alzheimer’s disease (AD) and 40 healthy controls was performed. The effects of covariates (including age, gender, postmortem interval, and batch effect) were controlled, and differentially expressed genes (DEGs) were identified using a linear mixed-effects model. To explore the biological processes, functional pathway enrichment and protein-protein interaction (PPI) network analyses were performed on the DEGs. The extended genes with PPI to the DEGs were obtained. Finally, the DEGs and the extended genes were ranked using the convergent functional genomics method. Eighty DEGs with q\0.1, including 67 downregulated and 13 upregulated genes, were identified. In the pathway enrichment analysis, the 80 DEGs were significantly enriched in one Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, GABAergic synapses, and 22 Gene Ontology terms. These genes were mainly involved in neuron, synaptic signaling and transmission, and vesicle metabolism. These processes are all linked to the pathological features of AD, demonstrating that the GABAergic system, neurons, and synaptic function might be affected in AD. In the PPI network, 180 extended genes were obtained, and the hub gene occupied in the most central position was CDC42. After prioritizing the candidate genes, 12 genes, including five DEGs (ITGB5, RPH3A, GNAS, THY1, and SEPT6) and seven extended genes (JUN, GDI1, GNAI2, NEK6, UBE2D3, CDC42EP4, and ERCC3), were found highly relevant to the progression of AD and recognized as promising biomarkers for its early diagnosis

Protein-protein interaction network involving all merged module genes.

<p>Square nodes denote the reported genes associated with schizophrenia or bipolar disorder. The color of the node was proportioned with the <i>P</i>-value of gene. The width of the edge was proportioned with the No. of repeats of the edge in the modules. The purple edges, green edges and blue edges were interactions from MGS, Affy6 and Affy500K respectively.</p

Crosstalk analysis of enriched pathways/GO terms.

<p>Panel A shows the connections among the enriched pathways/GO terms. Blue squares are enriched KEGG pathways, green squares are their connected pathways from KEGG, red squares are enriched GO terms. Panel B shows the shared genes between the first group of enriched pathways. Panel C shows the shared genes between four groups of enriched pathways/GO terms. The genes in groups with more than one pathway/GO term were combined for the shared gene analysis.</p

Schizophrenia GWAS datasets used for this analysis.

<p>^a The controls of Cardiff UK were from WTCCC [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0133404#pone.0133404.ref069" target="_blank">69</a>].</p><p>^b The number of cases, controls and SNPs after quality control were labeled in parentheses.</p><p>Schizophrenia GWAS datasets used for this analysis.</p