BackgroundH. sapiens-M. tuberculosis H37Rv protein-protein interaction (PPI) data are essential for understanding the infection mechanism of the formidable pathogen M. tuberculosis H37Rv. Computational prediction is an important strategy to fill the gap in experimental H. sapiens-M. tuberculosis H37Rv PPI data. Homology-based prediction is frequently used in predicting both intra-species and inter-species PPIs. However, some limitations are not properly resolved in several published works that predict eukaryote-prokaryote inter-species PPIs using intra-species template PPIs.ResultsWe develop a stringent homology-based prediction approach by taking into account (i) differences between eukaryotic and prokaryotic proteins and (ii) differences between inter-species and intra-species PPI interfaces. We compare our stringent homology-based approach to a conventional homology-based approach for predicting host-pathogen PPIs, based on cellular compartment distribution analysis, disease gene list enrichment analysis, pathway enrichment analysis and functional category enrichment analysis. These analyses support the validity of our prediction result, and clearly show that our approach has better performance in predicting H. sapiens-M. tuberculosis H37Rv PPIs. Using our stringent homology-based approach, we have predicted a set of highly plausible H. sapiens-M. tuberculosis H37Rv PPIs which might be useful for many of related studies. Based on our analysis of the H. sapiens-M. tuberculosis H37Rv PPI network predicted by our stringent homology-based approach, we have discovered several interesting properties which are reported here for the first time. We find that both host proteins and pathogen proteins involved in the host-pathogen PPIs tend to be hubs in their own intra-species PPI network. Also, both host and pathogen proteins involved in host-pathogen PPIs tend to have longer primary sequence, tend to have more domains, tend to be more hydrophilic, etc. And the protein domains from both host and pathogen proteins involved in host-pathogen PPIs tend to have lower charge, and tend to be more hydrophilic.ConclusionsOur stringent homology-based prediction approach provides a better strategy in predicting PPIs between eukaryotic hosts and prokaryotic pathogens than a conventional homology-based approach. The properties we have observed from the predicted H. sapiens-M. tuberculosis H37Rv PPI network are useful for understanding inter-species host-pathogen PPI networks and provide novel insights for host-pathogen interaction studies.ReviewersThis article was reviewed by Michael Gromiha, Narayanaswamy Srinivasan and Thomas Dandekar.

BackgroundH. sapiens-M. tuberculosis H37Rv protein-protein interaction (PPI) data are very important information to illuminate the infection mechanism of M. tuberculosis H37Rv. But current H. sapiens-M. tuberculosis H37Rv PPI data are very scarce. This seriously limits the study of the interaction between this important pathogen and its host H. sapiens. Computational prediction of H. sapiens-M. tuberculosis H37Rv PPIs is an important strategy to fill in the gap. Domain-domain interaction (DDI) based prediction is one of the frequently used computational approaches in predicting both intra-species and inter-species PPIs. However, the performance of DDI-based host-pathogen PPI prediction has been rather limited.ResultsWe develop a stringent DDI-based prediction approach with emphasis on (i) differences between the specific domain sequences on annotated regions of proteins under the same domain ID and (ii) calculation of the interaction strength of predicted PPIs based on the interacting residues in their interaction interfaces.We compare our stringent DDI-based approach to a conventional DDI-based approach for predicting PPIs based on gold standard intra-species PPIs and coherent informative Gene Ontology terms assessment. The assessment results show that our stringent DDI-based approach achieves much better performance in predicting PPIs than the conventional approach. Using our stringent DDI-based approach, we have predicted a small set of reliable H. sapiens-M. tuberculosis H37Rv PPIs which could be very useful for a variety of related studies.We also analyze the H. sapiens-M. tuberculosis H37Rv PPIs predicted by our stringent DDI-based approach using cellular compartment distribution analysis, functional category enrichment analysis and pathway enrichment analysis. The analyses support the validity of our prediction result. Also, based on an analysis of the H. sapiens-M. tuberculosis H37Rv PPI network predicted by our stringent DDI-based approach, we have discovered some important properties of domains involved in host-pathogen PPIs. We find that both host and pathogen proteins involved in host-pathogen PPIs tend to have more domains than proteins involved in intra-species PPIs, and these domains have more interaction partners than domains on proteins involved in intra-species PPI.ConclusionsThe stringent DDI-based prediction approach reported in this work provides a stringent strategy for predicting host-pathogen PPIs. It also performs better than a conventional DDI-based approach in predicting PPIs. We have predicted a small set of accurate H. sapiens-M. tuberculosis H37Rv PPIs which could be very useful for a variety of related studies.

BackgroundPrenatal inorganic arsenic (iAs) exposure is associated with pregnancy outcomes. Maternal capabilities of arsenic biotransformation and elimination may influence the susceptibility of arsenic toxicity. Therefore, we examined the determinants of arsenic metabolism of pregnant women in Bangladesh who are exposed to high levels of arsenic.MethodsIn a prospective birth cohort, we followed 1613 pregnant women in Bangladesh and collected urine samples at two prenatal visits: one at 4–16 weeks, and the second at 21–37 weeks of pregnancy. We measured major arsenic species in urine, including iAs (iAs%) and methylated forms. The proportions of each species over the sum of all arsenic species were used as biomarkers of arsenic methylation efficiency. We examined the difference in arsenic methylation using a paired t-test between first and second visits. Using linear regression, we examined determinants of arsenic metabolism, including age, BMI at enrollment, education, financial provider income, arsenic exposure level, and dietary folate and protein intake, adjusted for daily energy intake.ResultsComparing visit 2 to visit 1, iAs% decreased 1.1% (p <  0.01), and creatinine-adjusted urinary arsenic level (U-As) increased 21% (95% CI: 15, 26%; p <  0.01). Drinking water arsenic concentration was positively associated with iAs% at both visits. When restricted to participants with higher adjusted urinary arsenic levels (adjusted U-As > 50 μg/g-creatinine) gestational age at measurement was strongly associated with DMA% (β = 0.38, p <  0.01) only at visit 1. Additionally, DMA% was negatively associated with daily protein intake (β = − 0.02, p <  0.01) at visit 1, adjusting for total energy intake and other covariates.ConclusionsOur findings indicate that arsenic metabolism and adjusted U-As level increase during pregnancy. We have identified determinants of arsenic methylation efficiency at visit 1.

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