Back

Expression of Calca gene-derived peptides in the murine taste system

Palayyan, S. R.; Siddiqui, A. H.; Sukumaran, S. K.

2026-01-20 neuroscience
10.64898/2026.01.16.700005 bioRxiv
Show abstract

The Calcitonin Related Polypeptide Alpha (Calca) gene is a source of four biologically active peptides with varied physiological roles. Alternative splicing of the Calca messenger RNA generates either prepro calcitonin gene related peptide (CGRP) or preprocalcitonin encoding transcripts. Proteolytic processing of preprocalcitonin generates procalcitonin, calcitonin and katacalcin. Calcitonin is a ligand for the G-protein coupled receptor calcitonin receptor (CALCR) while CGRP is a ligand for the CGRP receptor (CGRP1R) formed by the calcitonin receptor like receptor (CALCRL)receptor activity modifying protein 1 (RAMP1) complex. Interestingly, procalcitonin too, is a ligand for the CGRP1R where it can antagonize CGRP. CGRP expression in taste neurons has been documented and is posited to regulate taste signaling. Single cell and bulk RNASeq of taste papillae revealed that the preprocalcitonin but not the CGRP transcript is expressed in Tas1r3- expressing type II taste cells, while Calcrl (but not Calcr) and Ramp1 are expressed in stem/progenitor and type I cells in the circumvallate papillae. The CGRP1R is also expressed by fibroblasts in the lingual mesenchyme. We confirmed this expression pattern using quantitative polymerase chain reaction (qPCR), RNAScope and immunohistochemistry. qPCR of geniculate and nodose-petrosal ganglia revealed that both express Cgrp and CGRP1R subunit mRNAs, but not procalcitonin and Calcr. This interesting expression patterns suggests that procalcitonin and CGRP might reciprocally regulate the CGRP1R in taste cells and lingual fibroblasts and thereby influence taste signaling, taste cell regeneration and the taste microbiome.

Published in Chemical Senses (predicted rank #1) · training set

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.