In silico characterization of structural and functional impact of the deleterious SNPs on FSHR gene

FSHR is an important gene which plays a major role in the development of secondary sex characteristics and influences the female reproductive cycle by regulating the Follicle Stimulating Hormone. Though this gene and its protein are extensively studied, no attempts have been made yet to methodically analyze the variants in this gene. One of the chief objectives during the analysis of human genetic variation is to distinguish between the Single Nucleotide Polymorphisms (SNPs) that are functionally neutral from those that contribute to the disorder. To predict the possible impact of SNPs on the FSHR structure and function, data were obtained from NCBI (dbSNP and dbVar) and validated manually. Various bioinformatics tools were used to predict the alterations at transcriptional, post transcriptional stages and protein interaction. Around 38 variants reported by NCBI Variation Viewer were sorted by SIFT and 14 of them were reported damaging, 13 were reported to be either benign or damaging by PROVEAN and Panther. From these 13 SNPs, the most damaging (11 SNPs) were modeled using Pymol and the energy difference between the native and mutated structure was calculated by Swiss PDB – Viewer. Based on our analysis, we have reported potential candidate SNPs for the FSHR gene involved in the regulation of ovarian pathophysiology

In silico characterization of structural and functional impact of the deleterious SNPs on FSHR gene

492-499FSHR is an important gene which plays a major role in the development of secondary sex characteristics and influences the female reproductive cycle by regulating the Follicle Stimulating Hormone. Though this gene and its protein are extensively studied, no attempts have been made yet to methodically analyze the variants in this gene. One of the chief objectives during the analysis of human genetic variation is to distinguish between the Single Nucleotide Polymorphisms (SNPs) that are functionally neutral from those that contribute to the disorder. To predict the possible impact of SNPs on the FSHR structure and function, data were obtained from NCBI (dbSNP and dbVar) and validated manually. Various bioinformatics tools were used to predict the alterations at transcriptional, post transcriptional stages and protein interaction. Around 38 variants reported by NCBI Variation Viewer were sorted by SIFT and 14 of them were reported damaging, 13 were reported to be either benign or damaging by PROVEAN and Panther. From these 13 SNPs, the most damaging (11 SNPs) were modeled using Pymol and the energy difference between the native and mutated structure was calculated by Swiss PDB – Viewer. Based on our analysis, we have reported potential candidate SNPs for the FSHR gene involved in the regulation of ovarian pathophysiology

In silico analysis of functional non-synonymous and intronic variants found in a polycystic ovarian syndrome (PCOS) candidate gene: DENND1A

The major thrust of our study confers to the identification of non-synonymous and intronic variants of the DENND1A gene via in silico methods and determination of its effect on the structural integrity of the protein. The outcome identifies potential disease -causing SNPs. The pathogenic variants of DENND1A were deduced via in silico analysis using various tools that include SIFT, PolyPhen-2, PROVEAN, SNP & GO, and PANTHER. The intronic variants were analysed using RegulomeDB. The 3D protein structure was obtained using the SWISS PDB modeler and validated by Ramachandran plots and QMEAN server. The effect on the stability of the protein structure caused by the SNPs was evaluated on the PYMOL and SWISS model platform. The functional changes caused by the SNPs were analysed in silico with I Mutant and Mutation Taster. The post-translational modifications were also predicted. STRING database was used for screening the protein interaction network. The SNPs rs2479106 and rs10986105 on the splice sites were found to be pathogenic for PCOS. The amino acid changes V179G and P331L were found to be disease-causing but the disease association with PCOS is yet to be validated

In silico analysis of functional non-synonymous and intronic variants found in a polycystic ovarian syndrome (PCOS) candidate gene: DENND1A

584-593The major thrust of our study confers to the identification of non-synonymous and intronic variants of the DENND1A gene via in silico methods and determination of its effect on the structural integrity of the protein. The outcome identifies potential disease -causing SNPs. The pathogenic variants of DENND1A were deduced via in silico analysis using various tools that include SIFT, PolyPhen-2, PROVEAN, SNP & GO, and PANTHER. The intronic variants were analysed using RegulomeDB. The 3D protein structure was obtained using the SWISS PDB modeler and validated by Ramachandran plots and QMEAN server. The effect on the stability of the protein structure caused by the SNPs was evaluated on the PYMOL and SWISS model platform. The functional changes caused by the SNPs were analysed in silico with I Mutant and Mutation Taster. The post-translational modifications were also predicted. STRING database was used for screening the protein interaction network. The SNPs rs2479106 and rs10986105 on the splice sites were found to be pathogenic for PCOS. The amino acid changes V179G and P331L were found to be disease-causing but the disease association with PCOS is yet to be validated

Plant growth promoting rhizobacteria Bacillus subtilis RR4 isolated from rice rhizosphere induces malic acid biosynthesis in rice roots

Malic acid, one of the major organic acid exudates from roots, plays a significant role in the chemotaxis of beneficial bacteria to the plantâ s rhizosphere. In this study, the effect of a plant growth promoting rhizobacterium, Bacillus subtilis RR4, on the synthesis and exudation of malic acid (MA) from roots is demonstrated in rice. To test the chemotactic ability of RR4 towards MA, capillary chemotaxis assay was performed which revealed a positive response (relative chemotactic ratio of 6.15 with 10 ÂľM MA) and with increasing concentrations of MA, an elevated chemotactic response was observed. qPCR performed to analyze the influence of RR4 on the MA biosynthetic gene, malate synthase (OsMS) and the transporter, aluminium-activated malate transporter (OsALMT) demonstrated significant differential expression with 1.8 and -0.58 fold changes, respectively, in RR4 treated roots. The gene expression pattern of OsMS confirmed with that of HPLC data which showed elevated MA levels in roots (1.52 fold) while the levels of MA in root exudates were not altered significantly although expression of OsALMT was reduced. Our results demonstrate that B. subtilis RR4 is chemotactic to malic acid and can induce biosynthesis of MA in rice roots.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Implementing an intensive care registry in India: Preliminary results of the case-mix program and an opportunity for quality improvement and research

Background: The epidemiology of critical illness in India is distinct from high-income countries. However, limited data exist on resource availability, staffing patterns, case-mix and outcomes from critical illness. Critical care registries, by enabling a continual evaluation of service provision, epidemiology, resource availability and quality, can bridge these gaps in information. In January 2019, we established the Indian Registry of IntenSive care to map capacity and describe case-mix and outcomes. In this report, we describe the implementation process, preliminary results, opportunities for improvement, challenges and future directions. Methods: All adult and paediatric ICUs in India were eligible to join if they committed to entering data for ICU admissions. Data are collected by a designated representative through the electronic data collection platform of the registry. IRIS hosts data on a secure cloud-based server and access to the data is restricted to designated personnel and is protected with standard firewall and a valid secure socket layer (SSL) certificate. Each participating ICU owns and has access to its own data. All participating units have access to de-identified network-wide aggregate data which enables benchmarking and comparison. Results: The registry currently includes 14 adult and 1 paediatric ICU in the network (232 adult ICU beds and 9 paediatric ICU beds). There have been 8721 patient encounters with a mean age of 56.9 (SD 18.9); 61.4% of patients were male and admissions to participating ICUs were predominantly unplanned (87.5%). At admission, most patients (61.5%) received antibiotics, 17.3% needed vasopressors, and 23.7% were mechanically ventilated. Mortality for the entire cohort was 9%. Data availability for demographics, clinical parameters, and indicators of admission severity was greater than 95%. Conclusions: IRIS represents a successful model for the continual evaluation of critical illness epidemiology in India and provides a framework for the deployment of multi-centre quality improvement and context-relevant clinical research