Critical for brain development, neurodevelopmental and network disorders, the GABRA1 gene encodes for the α1 subunit, an abundantly and developmentally expressed subunit of heteropentameric gamma-aminobutyric acid A receptors (GABAARs) mediating primary inhibition in the brain. Mutations of the GABAAR subunit genes including GABRA1 gene are associated with epilepsy, a group of syndromes, characterized by unprovoked seizures and diagnosed by integrative approach, that involves genetic testing. Despite the diagnostic use of genetic testing, a large fraction of the GABAAR subunit gene variants including the variants of GABRA1 gene is not known in terms of their molecular consequence, a challenge for precision and personalized medicine. Addressing this, one hundred thirty-seven GABRA1 gene variants of unknown clinical significance have been extracted from the ClinVar database and computationally analyzed for pathogenicity. Eight variants (L49H, P59L, W97R, D99G, G152S, V270G, T294R, P305L) are predicted as pathogenic and mapped to the α1 subunit\'s extracellular domain (ECD), transmembrane domains (TMDs) and extracellular linker. This is followed by the integration with relevant data for cellular pathology and severity of the epilepsy syndromes retrieved from the literature. Our results suggest that the pathogenic variants in the ECD of GABRA1 (L49H, P59L, W97R, D99G, G152S) will probably manifest decreased surface expression and reduced current with mild epilepsy phenotypes while V270G, T294R in the TMDs and P305L in the linker between the second and the third TMDs will likely cause reduced cell current with severe epilepsy phenotypes. The results presented in this study provides insights for clinical genetics and wet lab experimentation.

Cell-cell interactions (CCIs) are essential for multicellular organisms to coordinate biological processes and functions. One classical type of CCI interaction is between secreted ligands and cell surface receptors, i.e. ligand-receptor (LR) interactions. With the recent development of single-cell technologies, a large amount of single-cell ribonucleic acid (RNA) sequencing (scRNA-Seq) data has become widely available. This data availability motivated the single-cell-resolution study of CCIs, particularly LR-based CCIs. Dozens of computational methods and tools have been developed to predict CCIs by identifying LR-based CCIs. Many of these tools have been theoretically reviewed. However, there is little study on current LR-based CCI prediction tools regarding their performance and running results on public scRNA-Seq datasets. In this work, to fill this gap, we tested and compared nine of the most recent computational tools for LR-based CCI prediction. We used 15 well-studied scRNA-Seq samples that correspond to approximately 100K single cells under different experimental conditions for testing and comparison. Besides briefing the methodology used in these nine tools, we summarized the similarities and differences of these tools in terms of both LR prediction and CCI inference between cell types. We provided insight into using these tools to make meaningful discoveries in understanding cell communications.

BACKGROUND: Cysteine S-sulfenylation is a major type of dynamic post-translational modification of the protein that plays an important role in regulating many biological processes in both of prokaryotic and eukaryotic species. To understand the function of S-sulfenylated proteins, identification of S-sulfenylation sites is an essential step. Due to numerous restrictions of experimental methods, computational prediction of the potential S-sulfenylation sites becomes popular. In this review, we discuss the recent development and challenges in protein S-sulfenylation site prediction from the available datasets, algorithms and accessible services. We also demonstrate the encountered limitation and future perspective of the computational prediction tools.
CONCLUSIONS: The development of S-sulfenylation site prediction and their application is an emerging field of protein bioinformatics research. Accurate predictors are expected to identify general and species-specific S-sulfenylation sites when more experimental annotation data are available. Combining experimental and computational technologies will definitely accelerate an understanding of protein S-sulfenylation, discovering regulatory networks in living organisms.

Establishing genetic basis of Idiopathic generalized epilepsies (IGE) is challenging because of their complex inheritance pattern and genetic heterogeneity. Kir4.1 inwardly rectifying channel (KCNJ10) is one of the independent genes reported to be associated with seizure susceptibility. In the current study we have performed a comprehensive in silico analysis of genetic variants in KCNJ10 gene at functional and structural level along with a case-control analysis for the association of rs1130183 (R271C) polymorphism in Indian patients with IGE. Age and sex matched 108 epileptic patients and normal healthy controls were examined. Genotyping of KCNJ10rs1130183 variation was performed using PCR-RFLP method. The risk association was determined by using odds ratio and 95% confidence interval. Functional effects of non-synonymous SNPs (nsSNPs) in KCNJ10 gene were analyzed using SIFT PolyPhen-2, I-Mutant 2.0, PANTHER and FASTSNP. Subsequently, homology modeling of protein three dimensional (3D) structures was performed using Modeller tool (9.10v) and compared the native protein with mutant for assessment of structure and stability. SIFT, PolyPhen-2, I-Mutant 2.0 and PANTHER collectively showed rs1130183, rs1130182 and rs137853073 SNPs inKCNJ10 gene affect protein structure and function. There was a considerable variation in the Root Mean Square Deviation (RMSD) value between the native and mutant structure (1.17Ǻ). Association analysis indicate KCNJ10rs1130183 did not contribute to risk of seizure susceptibility in Indian patients with IGE (OR- 0.38; 95%CI, 0.07-2.05) and T allele frequency (0.02%) was in concordance with dbSNP reports. This study identifies potential SNPs that may contribute to seizure susceptibility and further studies with the selected SNPs in larger number of samples and their functional analysis is required for understanding the variants of KCNJ10 with seizure susceptibility.