Posters
Presentation Type
Poster
Discipline Track
Biomedical ENGR/Technology/Computation
Abstract Type
Research/Clinical
Abstract
Background: Neurodegeneration is a pathological condition where progressive loss of neuronal cells occurs in the brain. Neurodegenerative disorders have become more prevalent and by 2050, it is predicted that they will be the primary cause of ageing worldwide. Globally, the prevalence of neurodegenerative disorders like Alzheimer’s disease (AD) and Parkinson’s disease (PD) has been increasing tremendously. It has been identified that various proteins, such as APP, Tau, -synuclein, and TAR DNA-binding protein 43, are some of the main causative factors of dementia in older patients. Several studies have supported the idea that in-silico analyses using high-throughput RNA-seq data are the most efficient way to comprehend differentially expressed genes, cross-talk, and molecular pathways, which can aid in the early detection of brain cell depletion.
Methods: In-silico approach for high-throughput RNA-seq, STAR tool was used for alignment of fastq files accessed from ENA data archive, to study the differentially expressed genes (DEG). DESeq2 package from Bioconductor was used for all the commonly expressed genes. Gene ontology and pathway analysis was done using Cytoscape to study the network and PPI (Protein-Protein Interactions).
Result: According to data for AD and PD, nearly 49% of the genes are found to be expressed in both neurodegenerative disorders. The finding showed that by using Cytoscape, expressed genes for FDR > 0.05, and found ERCC1 and COX15 highly expressed genes.
Conclusions: The complex molecular foundations of these disorders are currently indescribable. In spite of diverse clinical and pathological expressions, common features have been recognized in both Neurodegenerative Disorders which provide indication of their convergence.
Academic/Professional Position
Fellow
Recommended Citation
Bharti, Asmita and Bubber, Parvesh, "In-silico approach for identifying metabolic proteins and their pathways involved in neurodegenerative disorders RNA-Seq data" (2024). Research Symposium. 122.
https://scholarworks.utrgv.edu/somrs/2023/posters/122
Included in
In-silico approach for identifying metabolic proteins and their pathways involved in neurodegenerative disorders RNA-Seq data
Background: Neurodegeneration is a pathological condition where progressive loss of neuronal cells occurs in the brain. Neurodegenerative disorders have become more prevalent and by 2050, it is predicted that they will be the primary cause of ageing worldwide. Globally, the prevalence of neurodegenerative disorders like Alzheimer’s disease (AD) and Parkinson’s disease (PD) has been increasing tremendously. It has been identified that various proteins, such as APP, Tau, -synuclein, and TAR DNA-binding protein 43, are some of the main causative factors of dementia in older patients. Several studies have supported the idea that in-silico analyses using high-throughput RNA-seq data are the most efficient way to comprehend differentially expressed genes, cross-talk, and molecular pathways, which can aid in the early detection of brain cell depletion.
Methods: In-silico approach for high-throughput RNA-seq, STAR tool was used for alignment of fastq files accessed from ENA data archive, to study the differentially expressed genes (DEG). DESeq2 package from Bioconductor was used for all the commonly expressed genes. Gene ontology and pathway analysis was done using Cytoscape to study the network and PPI (Protein-Protein Interactions).
Result: According to data for AD and PD, nearly 49% of the genes are found to be expressed in both neurodegenerative disorders. The finding showed that by using Cytoscape, expressed genes for FDR > 0.05, and found ERCC1 and COX15 highly expressed genes.
Conclusions: The complex molecular foundations of these disorders are currently indescribable. In spite of diverse clinical and pathological expressions, common features have been recognized in both Neurodegenerative Disorders which provide indication of their convergence.