
Abhirami ThumsiIndian Institute of Science | IISC · Department of Molecular Reproduction Development and Genetics
Abhirami Thumsi
Integrated Master of Science- Biological Sciences
About
4
Publications
328
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
5
Citations
Citations since 2017
Introduction
Additional affiliations
Education
July 2012 - June 2017
Publications
Publications (4)
Rheumatoid Arthritis (RA) is a chronic debilitating disease characterized by auto-immune reaction towards self-antigen such as collagen type II. In this study, we investigated the impact of exponentially decreasing levels of antigen exposure on pro-inflammatory T-cell responses in the collagen-induced arthritis (CIA) mouse model. Using a controlled...
Inhibition of glycolysis in immune cells and cancer cells diminishes their activity, and thus combining immunotherapies with glycolytic inhibitors is challenging. Herein, a strategy is presented where glycolysis is inhibited in cancer cells using PFK15 (inhibitor of PFKFB3, rate-limiting step in glycolysis), while simultaneously glycolysis and func...
Covalent organic framework (COF) crystalline biomaterials have great potential for drug delivery since they can load large amounts of small molecules (e.g. metabolites) and release them in a controlled manner, as compared to their amorphous counterparts. Herein, we screened different metabolites for their ability to modulate T cell responses in vit...
Although different metabolic pathways have been associated with distinct macrophage phenotypes, the field of utilizing metabolites to modulate macrophage phenotype is in a nascent stage. In this report, we developed microparticles based on polymerization of alpha-ketoglutarate (a Krebs cycle metabolite), with or without encapsulation of spermine (a...
Questions
Question (1)
We have the RNAseq data of oral cancer(OSCC) tumor samples without matched normal. The filtered reads were of high base quality and more than 95% of base matching was considered for mutation calling. We have found several mutations in these reads (with reference to hg38).
For a particular position, we have 2% of reads having mutations. For others, we have less than 2% of the reads showing mutations. On what basis do we validate the functionality of the mutation? Is there a specific range (number of reads having the mutation) which implies that the mutation has a consequence on the function.
Any reply will be appreciated.
Thanks.