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Tiruvadi-Krishnan, S.

Publications and source records attributed to Tiruvadi-Krishnan, S..

2 recordsLinked to original sources

Chemokine receptor activity is differentially regulated by membrane cholesterol

Cholesterol is a key membrane component that regulates G protein-coupled receptor (GPCR) function, yet its molecular mechanisms remain unclear. Here, we combine chemical extraction of membrane sterols with functional signaling assays and single-molecule fluorescence resonance energy transfer (smFRET) to define how cholesterol controls activation of chemokine receptors. Reduction of membrane cholesterol in mammalian cells selectively decreased constitutive and agonist-induced signaling across CXCR1, CXCR2, CXCR4, while it activated ACKR3, and did not affect CXCR3, revealing receptor-specific dependence on membrane sterols. Mechanistically, cholesterol regulation partly required the conserved class A GPCR residue Trp4.50 and shifted agonist-bound CXCR4 toward active conformational states, providing a molecular explanation for its functional effects. In contrast, replenishment with oxidized cholesterol species failed to restore receptor activity, distinguishing cholesterol from oxysterols as modulators of receptor activation. Our findings identify cholesterol as an allosteric regulator of chemokine receptors and suggest that oxysterols may reshape inflammatory signaling by selectively modulating GPCR activity.

biochemistry↗

To bin or not to bin: analyzing single-cell growth data

Collection of high-throughput data has become prevalent in biology. Large datasets allow the use of statistical constructs such as binning and linear regression to quantify relationships between variables and hypothesize underlying biological mechanisms based on it. We discuss several such examples in relation to single-cell data and cellular growth. In particular, we show instances where what appears to be ordinary use of these statistical methods leads to incorrect conclusions such as growth being non-exponential as opposed to exponential and vice versa. We propose that the data analysis and its interpretation should be done in the context of a generative model, if possible. In this way, the statistical methods can be validated either analytically or against synthetic data generated via the use of the model, leading to a consistent method for inferring biological mechanisms from data. On applying the validated methods of data analysis to infer cellular growth on our experimental data, we find the growth of length in E. coli to be non-exponential. Our analysis shows that in the later stages of the cell cycle the growth rate is faster than exponential.

microbiology↗