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Biology subjects

Bramble, M.

Publications and source records attributed to Bramble, M..

2 recordsLinked to original sources

Deep mutational scan of the pore of the cold-sensing TRPM8 channel

Members of the Transient Receptor Potential (TRP) family of ion channels have a nearly ubiquitous role in human physiology, tuning cell signaling to remarkably diverse physical and chemical stimuli. Although there is extensive structural data on TRP channels, a systematic and unbiased interrogation of structure-function relations in these proteins is required to fully elucidate their mechanisms of function. By focusing on a critical pore region of the TRPM8 channel, which is the main detector of cold and cooling agents in sensory neurons, we show how deep mutational scanning can be used in combination with the available structural data to understand how TRP channels respond to stimuli. We define a novel mechanism whereby the extracellular pore loop, which has only been resolved in structures representing desensitized states of the channel, plays an essential role in the response of TRPM8 to menthol or cold by coordinating the movement of the S6 helices that line and gate the pore, and the ion-selectivity filter that binds permeant cations. Moreover, our screen reveals sequence determinants along the S6 helices that explain how their architecture sustains gating and, together, provide strong support for a structural mechanism of TRPM8 pore opening in response to menthol and cold.

biophysics↗

Comprehensive benchmark of integrative strategies for analyzing microbiome-metabolome relationships

BackgroundThe exponential growth of high-throughput sequencing technologies was an incredible opportunity for researchers to combine various -omics within computational frameworks. Among these, metagenomics and metabolomics data have gained an increasing interest due to their involvement in many complex diseases. However, currently, no standard seems to emerge for jointly integrating both microbiome and metabolome datasets within statistical models. ResultsThus, in this paper we comprehensively benchmarked nineteen different integrative methods to untangle the complex relationships between microorganisms and metabolites. Methods evaluated in this paper cover most of the researchers goals such as global associations, data summarization, individual associations, and feature selection. Through an extensive and realistic simulation we identified best methods across questions commonly encountered by researchers. We applied the most promising methods in an application to real gut microbial datasets, unraveling complementary biological processes involved between the two omics. We also provided practical guidelines for practitioners tailored to specific scientific questions and data types. ConclusionIn summary, our work paves the way toward establishing research standards when mutually analyzing metagenomics and metabolomics data, building foundations for future methodological developments.

bioinformatics↗