MHC Class II (DRB) Promoter Polymorphism and Its Role in Parasite Control among Malaria Patients.

MHC class II (MHCII) molecules are cell surface glycoproteins that play an important role to develop adaptive immune responses. MHCII-disease association is not restricted to structural variation alone but also may extend to genetic variations, which may modulate gene expression. The observed variations in class II gene expression make it possible that the association of MHCII polymorphism with diseases may relate to the level of gene expression in addition to the restriction of response to Ag. Understanding the extent of, and the mechanisms underlying, transcription factor DNA binding variation is therefore key to elucidate the molecular determinants of complex phenotypes. In this study, we investigated whether single nucleotide polymorphisms in MHCII-DRB regulatory gene may be associated with clinical outcomes of malaria in Plasmodium-infected individuals. To this end, we conducted a case-control study to compare patients who had mild malaria with those patients who had asymptomatic Plasmodium infection. It demonstrates that GTAT haplotype exerts an increased DRB transcriptional activity, resulting in higher DRB expression and subsequently perturbed Ag presentation and T cell activation, higher TLR-mediated innate immune gene expression, and Ag clearance, so low parasitemia in comparison with haplotypes other than GTAT (GTAC, GGGT). Hence, we hypothesized that DRB gene promoter polymorphism might lead to altered DRB gene expression, which could possibly affect the TLR-triggered innate immune responses in malaria patients. These genetic findings may contribute to the understanding of the pathogenesis of malaria and will facilitate the rational vaccine design for malaria.

DBPMod: a supervised learning model for computational recognition of DNA-binding proteins in model organisms.

DNA-binding proteins (DBPs) play critical roles in many biological processes, including gene expression, DNA replication, recombination and repair. Understanding the molecular mechanisms underlying these processes depends on the precise identification of DBPs. In recent times, several computational methods have been developed to identify DBPs. However, because of the generic nature of the models, these models are unable to identify species-specific DBPs with higher accuracy. Therefore, a species-specific computational model is needed to predict species-specific DBPs. In this paper, we introduce the computational DBPMod method, which makes use of a machine learning approach to identify species-specific DBPs. For prediction, both shallow learning algorithms and deep learning models were used, with shallow learning models achieving higher accuracy. Additionally, the evolutionary features outperformed sequence-derived features in terms of accuracy. Five model organisms, including Caenorhabditis elegans, Drosophila melanogaster, Escherichia coli, Homo sapiens and Mus musculus, were used to assess the performance of DBPMod. Five-fold cross-validation and independent test set analyses were used to evaluate the prediction accuracy in terms of area under receiver operating characteristic curve (auROC) and area under precision-recall curve (auPRC), which was found to be ~89-92% and ~89-95%, respectively. The comparative results demonstrate that the DBPMod outperforms 12 current state-of-the-art computational approaches in identifying the DBPs for all five model organisms. We further developed the web server of DBPMod to make it easier for researchers to detect DBPs and is publicly available at https://iasri-sg.icar.gov.in/dbpmod/. DBPMod is expected to be an invaluable tool for discovering DBPs, supplementing the current experimental and computational methods.