Automated Author ProfileT., Premkumar
T., Premkumar
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 0.3 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Alzheimer’s disease (AD) is a slowly progressive neurodegenerative disease and a leading cause of dementia. We aim to identify key genes for the development of therapeutic targets and biomarkers for potential treatments for AD. Meta-analysis was performed on six microarray datasets and identified the differentially expressed genes between healthy and Alzheimer’s disease samples. Thereafter, we filtered out the common genes which were present in at least four microarray datasets for downstream analysis. We have constructed a gene-gene network for the common genes and identified six hub genes. Furthermore, we investigated the regulatory mechanisms of these hub genes by analysing their interaction with miRNAs and transcription factors. The gene ontology analysis results highlighted the enriched terms significantly associated with hub genes. Through an extensive literature survey, we found that three of the hub genes including GRIN1, SYN2, and SYT1 were critically involved in disease development. To leverage existing drugs for potential repurposing, we predicted drug-gene interaction using the drug-gene interaction database, and performed molecular docking studies. The docking results revealed that the drug compounds had strong interactions and favorable binding with selected hub genes. Lorazepam exhibits a binding energy of −7.3 kcal/mol with GRIN1, Lorediplon exhibits binding energies of −7.7 kcal/mol and −6.3 kcal/mol with the SYT1, and SYN2 respectively. In addition, 100 ns molecular dynamics simulations were carried out for the top complexes and apo protein as well. Furthermore, the MM-PBSA free energy calculations also revealed that these complexes are stable and had favorable energies. According to our study, the identified hub gene could serve as a biomarker as well as a therapeutic target for AD, and the proposed repurposed drug molecules appear to have promising efficacy in treating the disease. Communicated by Ramaswamy H. Sarma
Authors
- T., Premkumar ;
- Katta, Bhavana ;
- S., Sajitha Lulu ;
- Sundararajan, Vino
Alzheimer’s disease (AD) is a slowly progressive neurodegenerative disease and a leading cause of dementia. We aim to identify key genes for the development of therapeutic targets and biomarkers for potential treatments for AD. Meta-analysis was performed on six microarray datasets and identified the differentially expressed genes between healthy and Alzheimer’s disease samples. Thereafter, we filtered out the common genes which were present in at least four microarray datasets for downstream analysis. We have constructed a gene-gene network for the common genes and identified six hub genes. Furthermore, we investigated the regulatory mechanisms of these hub genes by analysing their interaction with miRNAs and transcription factors. The gene ontology analysis results highlighted the enriched terms significantly associated with hub genes. Through an extensive literature survey, we found that three of the hub genes including GRIN1, SYN2, and SYT1 were critically involved in disease development. To leverage existing drugs for potential repurposing, we predicted drug-gene interaction using the drug-gene interaction database, and performed molecular docking studies. The docking results revealed that the drug compounds had strong interactions and favorable binding with selected hub genes. Lorazepam exhibits a binding energy of −7.3 kcal/mol with GRIN1, Lorediplon exhibits binding energies of −7.7 kcal/mol and −6.3 kcal/mol with the SYT1, and SYN2 respectively. In addition, 100 ns molecular dynamics simulations were carried out for the top complexes and apo protein as well. Furthermore, the MM-PBSA free energy calculations also revealed that these complexes are stable and had favorable energies. According to our study, the identified hub gene could serve as a biomarker as well as a therapeutic target for AD, and the proposed repurposed drug molecules appear to have promising efficacy in treating the disease. Communicated by Ramaswamy H. Sarma
Authors
- T., Premkumar ;
- Katta, Bhavana ;
- S., Sajitha Lulu ;
- Sundararajan, Vino