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Neuronal developmental disorder is a class of diseases in which there is impairment of the central nervous system and brain function. The brain in its developmental phase undergoes tremendous changes depending upon the stage and environmental factors. Neurodevelopmental disorders include abnormalities associated with cognitive, speech, reading, writing, linguistic, communication, and growth disorders with lifetime effects. Computational methods provide great potential for betterment of research and insight into the molecular mechanism of diseases. In this study, we have used four samples of microarray neuronal developmental data: control, RV (resveratrol), NGF (nerve growth factor), and RV + NGF. By using computational methods, we have identified genes that are expressed in the early stage of neuronal development and also involved in neuronal diseases. We have used MeV application to cluster the raw data using distance metric Pearson correlation coefficient. Finally, 60 genes were selected on the basis of coexpression analysis. Further pathway analysis was done using the Metascape tool, and the biological process was studied using gene ontology database. A total of 13 genes AKT1, BAD, BAX, BCL2, BDNF, CASP3, CASP8, CASP9, MYC, PIK3CD, MAPK1, MAPK10, and CYCS were identified that are common in all clusters. These genes are involved in neuronal developmental disorders and cancers like colorectal cancer, apoptosis, tuberculosis, amyotrophic lateral sclerosis (ALS), neuron death, and prostate cancer pathway. A protein-protein interaction study was done to identify proteins that belong to the same pathway. These genes can be used to design potential inhibitors against neurological disorders at the early stage of neuronal development. The microarray samples discussed in this publication are part of the data deposited in NCBI's Gene Expression Omnibus (Yadav et al., 2018) and are accessible through GEO Series (accession number GSE121261).

Till this date there is no vaccine or drug for coronavirus responsible for COVID-19 which has created a major challenge to stop the spread of disease. Repurposing of a drug could be a solution for this challenge, as many previously available drugs hold great potential to act as a drug molecule. The coronavirus mainly affects the host by binding to its specific receptor with the help of its surface glycoprotein or spike glycoprotein. Thus, interfering this interaction could be a potent mechanism to stop the viral infection and propagation. In this paper, we have discussed about the surface glycoprotein and how previously available drug could be repurposed to act as a potent inhibitor. Homology modelling and docking studies has been mainly done to identify the interaction and binding affinity of previous drugs and how they could act as a potential solution to stop the overall spread of the disease in case of pandemic like COVID-19 where unavailability of specific drug or vaccine is responsible for taking to the lives of many.

CHRNA7 is a neurodevelopmental protein involved in differentiation and neurogenesis, which is also named as nicotinic acetylcholine receptors, cholinergic receptor, nicotinic, alpha 7 (neuronal). The protein encoded by this gene forms a homo-oligomeric channel. It is a major component of brain nicotinic receptors displays that are blocked by and sensitive to alpha-bungarotoxin. Studies reports involvement of CHRNA7 protein in different neurological diseases. Non-availability of 3-dimensional (3D) structure leads the study toward structure 3D prediction along with its interaction analysis. The current paper is focused on the structure prediction through homology modeling of CHRNA7 along with binding site prediction using Schrödinger software suite. In continuation of the study, protein-protein interaction analysis is carried out by using string database. Tertiary structure along with binding sites was obtained, and visualized CHRAN7 protein have interaction with CHRNA protein family along with JAK2, AKT1, PICK1 protein that are involved in neurological disease. Structure formation analysis is an important aspect of proteomics studies. Hence, this predicted structure can be used for further advance studies and drug designing. Protein interaction analysis shows that CHRNA7 protein also interact with AKT1 protein which regulate neuronal differentiation and development, that signifies the role of CHRNA7 protein in neurological diseases.

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Introduction: Breast cancer is the swelling structures in the breast cells. After skin cancer, breast cancer is the most broadly accepted malignancy analysed in ladies in the United States. Breast cancer can happen in both males and females, yet it's undeniably more normal in ladies. Indications of mammary glands cancer may involve a ball or lumps in the breast, an alteration of breast shape, scratching of the skin, fluid coming out from areola, a recently reversed areola, or a red or textured fix of skin. In those with removed lay out of the illness, there can be bone agony, swollen lymph hubs, windedness, or yellow skin. There are certain microorganisms that are involved in breast cancer and have function thereof. The study of these microorganisms can be done through metagenomics study and comparing with normal samples. Methods: For current study pairwise metagenomics data for breast cancer sample was retrieved from ENA site. Four FASTQ files were retrieved with the Accession no PRJEB25419 and complete metagenomics analysis was done using GALAXY Server. Tools like FASTQC, Trim galore, KRAKEN2, Krona pie chart etc. were used for classification and identification of microorganisms in the samples. Results: The taxonomic and functional analysis of the metagenome of breast cancer identifies different organisms like. Actinobacteria, Bifidobacterium cuniculi, Murnine leukemia virus, Human endogenous retrovirus, Pezizomycotina. Different microbes and their percentage in the breast cancer have been predicted that are 4% of Nematocera, 2% of Brachycera, 3% of Galegeae, 2% of Nelumbo nucifera. Conclusion These results give insights into the metagenome of breast cancer that can be used for clinical perspectives. Detailed study is required to establish the association of these microbes with development and progression of breast cancer. Keywords: - Breast cancer, Metagenomics, Galaxy, ENA Database, Krona pie chart.

Brain is one of the most intricate organs in the human body that works with billions of cells. A brain tumor emerges when there is uncontrolled division of cells shaping a strange gathering of cells around or inside the cerebrum. To understand the complexity of brain function and gene expression in different regions of brain is most challenging and crucial. RNAseq techniques along with computational analysis has made this task much easier and accurate. In this current research RNAseq data of brain regions have been used to predict functional genes in different regions of brains. Differential expression of genes in different regions of brain highlights the function of genes and associated pathways in brain function. The aim of this study was to identify differentially expressed gene in the brain tumor samples. RNA-seq data was retrieved from ENA database with the accession no.- PRJNA294929. Total 5 samples were retrieved in fastq format, out of which 4 samples were of corpous callosum and 1 sample was of frontal cortex. Differentially Expressed Genes (DEG) analysis was done using Galaxy platform and R software, functional enrichment of DEGs was done using DAVID and GO databases. The RNA-seq data analysis shows the up regulation of PHGDH, TUBB4A, HSPA2, GFAP, NKX3-1, COX3 genes and three genes PHGDH, COX3 and MT3 shows significant difference in their gene expression. This result can have importance in understanding the complexity of brain transcriptomics and DEGs can be further studied to evaluate their expression in brain cells and associated diseases. Further wet lab verification is required as these genes can be used as potential drug target and can be used for drug designing for brain tumor.