Sexual dimorphism has been a burning issue in recent past, in recent years? genotypic approach and incorporation of high throughput methods have revealed further insights related to sexually biased genes. Apart from primary and secondary sexual characters, brain is the most extensively studied part
Spatial temporal expression of aging related genes in aging mouse brain
Sexual dimorphism has been a burning issue in recent past, in recent years’ genotypic approach and incorporation of high throughput methods have revealed further insights related to sexually biased genes. Apart from primary and secondary sexual characters, brain is the most extensively studied part of human and other vertebrate model organism’s body. But sexually biased studies related to aging are still limited, so here by we used 4 old and young mouse brain regions, olfactory bulb (OB), hypothalamus (HT), hippocampus (HC), and cerebellum (CB) to compare for differentially expressed genes (mRNAs) and non-coding genes (lnRNAs) in male and female. We observed striking quantitative difference in OB where M/F sex biased ratio was higher in male for both mRNAs and lncRNAs, whereas in other three regions HT, HC, and CB higher in female. Subsequent analyses expressional biased, GO, evolutionary and co expression network analysis also revealed that there is evident sexual dimorphism in mRNAs and lncRNA expression in male and female mouse brain regions.
The aim of the objective is to explore the gene expression in different brain regions to reveal the transcriptomic variations in mouse brain regions during aging.
we have already done RNA-seq data analysis using the Bioinformatics approach, now we have some candidate genes. We want to validate the expression of these computationally predicted genes using RT-qPCR technique.
This study will provide a comprehensive understanding of gene expression and response in various mouse brain regions during aging, which will step forward to investigate the gene expression in neurodegenerative disease. Further, that gene can be used for computer-aided drug design to develop potential drug candidates in future
RT-qPCR results
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| RT-qPCR kit | Equipment | 2 | 34000 | 68000 |
| Total in (Rs) | 68000 |
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