Biology subjects

Chandrasekaran, V.

Publications and source records attributed to Chandrasekaran, V..

3 recordsLinked to original sources

bioRxiv2021

Chromatin binding of survivin regulates glucose metabolism in the IFN-g producing CD4+T cells

Interferon-gamma (IFN{gamma}) producing T cells develop metabolic adaptation required for their effector functions in tumour biology, autoimmunity and antiviral defence. Using sorted CD4+ cells we demonstrated that glycolytic switch and high glucose uptake in IFN{gamma}-producing cells was associated with survivin expression. Inhibition of survivin restored glycolysis by upregulating the transcription of phosphofructokinase PFKFB3 and reducing glucose uptake. Integration of the whole-genome sequencing of the chromatin immunoprecipitated with survivin with transcription changes in CD4+ cells after survivin inhibition revealed co-localization of survivin, IRF1 and SMAD3 in the regulatory elements paired to the differentially expressed genes. Western blot demonstrated direct binding of survivin to IRF1 and SMAD3. Functionally, inhibition of survivin repressed IFN{gamma} signalling and activated SMAD3-dependent protein remodelling, which resulted in the effector-to-memory transition of CD4+ cells. These findings demonstrate the key role of survivin in IFN{gamma}-dependent metabolic adaptation and identify survivin inhibition as an attractive strategy to counteract these effects.

immunology↗

bioRxiv2021

Visualising formation of the ribosomal active site in mitochondria

Ribosome assembly is an essential and complex process that is regulated at each step by specific biogenesis factors. Using cryo-electron microscopy, we identify and order major steps in the formation of the highly conserved peptidyl transferase centre (PTC) and tRNA binding sites in the large subunit of the human mitochondrial ribosome (mitoribosome). The conserved GTPase GTPBP7 regulates the folding and incorporation of core 16S ribosomal RNA (rRNA) helices and the ribosomal protein bL36m, and ensures that the PTC base U3039 has been 2'-O-methylated. Additionally, GTPBP7 binds the RNA methyltransferase NSUN4 and MTERF4, which facilitate earlier steps by sequestering H68-71 of the 16S rRNA and allowing biogenesis factors to access the maturing PTC. Consistent with the central role of NSUN4*MTERF4 and GTPBP7 during mitoribosome biogenesis, in vivo mutagenesis designed to disrupt binding of their Caenorhabditis elegans orthologs to the large subunit potently activates mitochondrial stress responses and results in severely reduced viability, developmental delays and sterility. Next-generation RNA sequencing reveals widespread gene expression changes in these mutant animals that are indicative of mitochondrial stress response activation. We also answer the long-standing question of why NSUN4 but not its enzymatic activity, is indispensable for mitochondrial protein synthesis in metazoans.

biochemistry↗

bioRxiv2021

Mitochondrial Phenotypes Distinguish Pathogenic MFN2 Mutations by Pooled Functional Genomics Screen

Most human genetic variation is classified as VUS - variants of uncertain significance. While advances in genome editing have allowed innovation in pooled screening platforms, many screens deal with relatively simple readouts (viability, fluorescence) and cannot identify the complex cellular phenotypes that underlie most human diseases. In this paper, we present a generalizable functional genomics platform that combines high-content imaging, machine learning, and microraft isolation in a new method termed "Raft-Seq". We highlight the efficacy of our platform by showing its ability to distinguish pathogenic point mutations of the mitochondrial regulator MFN2, even when the cellular phenotype is subtle. We also show that our platform achieves its efficacy using multiple cellular features, which can be configured on-the-fly. Raft-Seq enables a new way to perform pooled screening on sets of mutations in biologically relevant cells, with the ability to physically capture any cell with a perturbed phenotype and expand it clonally, directly from the primary screen. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=121 SRC="FIGDIR/small/434746v2_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@c774b4org.highwire.dtl.DTLVardef@bee63corg.highwire.dtl.DTLVardef@fd7099org.highwire.dtl.DTLVardef@eb7d87_HPS_FORMAT_FIGEXP M_FIG C_FIG Here, we address the need to evaluate the impact of numerous genetic variants. This manuscript depicts the methods of using machine learning on a biologically relevant phenotype to predict specific point mutations, followed by physically capturing those mutated cells.

genomics↗