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Systems Biology Approaches for the Prediction of Possible Role of Chlamydia pneumoniae Proteins in the Etiology of Lung Cancer.

ABSTRACT: Accumulating evidence has recently supported the association of bacterial infection with the growth and development of cancers, particularly in organs that are constantly exposed to bacteria such as the lungs, colon, cervical cancer etc. Our in silico study on the proteome of Chlamydia pneumoniae suggests an unprecedented idea of the etiology of lung cancer and have revealed that the infection of C. pneumoniae is associated with lung cancer development and growth. It is reasonable to assume that C. pneumoniae transports its proteins within host-intracellular organelles during infection, where they may work with host-cell proteome. The current study was performed for the prediction of nuclear targeting protein of C. pneumoniae in the host cell using bioinformatics predictors including ExPASy pI/Mw tool, nuclear localization signal (NLS) mapper, balanced sub cellular localization predictor (BaCeILo), and Hum-mPLoc 2.0. We predicted 47/1112 nuclear-targeting proteins of C. pneumoniae connected with several possible alterations in host replication and transcription during intracellular infection. These nuclear-targeting proteins may direct to competitive interactions of host and C. pneumoniae proteins with the availability of same substrate and may be involved as etiological agents in the growth and development of lung cancer. These novel findings are expected to access in better understanding of lung cancer etiology and identifying molecular targets for therapy.

SUBMITTER: Khan S

PROVIDER: S-EPMC4752481 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Systems Biology Approaches for the Prediction of Possible Role of Chlamydia pneumoniae Proteins in the Etiology of Lung Cancer.

Khan Shahanavaj S Imran Ahamad A Khan Abdul Arif AA Abul Kalam Mohd M Alshamsan Aws A

PloS one 20160212 2

Accumulating evidence has recently supported the association of bacterial infection with the growth and development of cancers, particularly in organs that are constantly exposed to bacteria such as the lungs, colon, cervical cancer etc. Our in silico study on the proteome of Chlamydia pneumoniae suggests an unprecedented idea of the etiology of lung cancer and have revealed that the infection of C. pneumoniae is associated with lung cancer development and growth. It is reasonable to assume that ...[more]

PMID: 26871581

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Project description:ObjectivesIncorporation of genetic factors in psychosocial/perioperative models for predicting chronic postsurgical pain (CPSP) is key for personalization of analgesia. However, single variant associations with CPSP have small effect sizes, making polygenic risk assessment important. Unfortunately, pediatric CPSP studies are not sufficiently powered for unbiased genome wide association (GWAS). We previously leveraged systems biology to identify candidate genes associated with CPSP. The goal of this study was to use systems biology prioritized gene enrichment to generate polygenic risk scores (PRS) for improved prediction of CPSP in a prospectively enrolled clinical cohort.MethodsIn a prospectively recruited cohort of 171 adolescents (14.5 ± 1.8 years, 75.4% female) undergoing spine fusion, we collected data about anesthesia/surgical factors, childhood anxiety sensitivity (CASI), acute pain/opioid use, pain outcomes 6-12 months post-surgery and blood (for DNA extraction/genotyping). We previously prioritized candidate genes using computational approaches based on similarity for functional annotations with a literature-derived "training set." In this study, we tested ranked deciles of 1336 prioritized genes for increased representation of variants associated with CPSP, compared to 10,000 randomly selected control sets. Penalized regression (LASSO) was used to select final variants from enriched variant sets for calculation of PRS. PRS incorporated regression models were compared with previously published non-genetic models for predictive accuracy.ResultsIncidence of CPSP in the prospective cohort was 40.4%. 33,104 case and 252,590 control variants were included for association analyses. The smallest gene set enriched for CPSP had 80/1010 variants associated with CPSP (p < 0.05), significantly higher than in 10,000 randomly selected control sets (p = 0.0004). LASSO selected 20 variants for calculating weighted PRS. Model adjusted for covariates including PRS had AUROC of 0.96 (95% CI: 0.92-0.99) for CPSP prediction, compared to 0.70 (95% CI: 0.59-0.82) for non-genetic model (p < 0.001). Odds ratios and positive regression coefficients for the final model were internally validated using bootstrapping: PRS [OR 1.98 (95% CI: 1.21-3.22); β 0.68 (95% CI: 0.19-0.74)] and CASI [OR 1.33 (95% CI: 1.03-1.72); β 0.29 (0.03-0.38)].DiscussionSystems biology guided PRS improved predictive accuracy of CPSP risk in a pediatric cohort. They have potential to serve as biomarkers to guide risk stratification and tailored prevention. Findings highlight systems biology approaches for deriving PRS for phenotypes in cohorts less amenable to large scale GWAS.

Dataset Information

Systems Biology Approaches for the Prediction of Possible Role of Chlamydia pneumoniae Proteins in the Etiology of Lung Cancer.

Publications

Systems Biology Approaches for the Prediction of Possible Role of Chlamydia pneumoniae Proteins in the Etiology of Lung Cancer.

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