Periodic Reporting for period 4 - MECHANOGENOMICS (Unravelling mammalian mechanosensor diversity by functional genomics)
Reporting period: 2021-03-01 to 2022-02-28
Mechanosensation refers to our ability to perceive touch, pain and proprioception through our somatosensory system, constituting with vision, olfaction and hearing the crucial senses governing our perception of surrounding environment and our social interactions. Mechanosensation relies on mechanotransduction, the signaling by which external mechanical stimuli are converted into biological signals within the cell. Molecular mechanosensors of the somatosensory system are mechanosensitive ion channels, which identification constitutes one of the most important challenges in the field of sensory transduction. Our goal was to identify molecular components of these mechanosensitive channels and to characterize their roles in touch, mechanical pain sensing and proprioception.
Patients presenting with chronic pain and sensory malfunction often complain of a heightened perception of pain to noxious mechanical stimuli (mechanical hyperalgesia) and/or pain to innocuous touch (mechanical allodynia). One strategy for developing novel analgesics is to target initial steps in the pain pathway. In this context, mechanosensitive channels are the molecular players initiating the pain response by detecting noxious stimuli. Identification of molecular components and modulators of somatosensory mechanotransduction will provide new therapeutics targets for treating touch and proprioceptive disorders and mechanical pain.
By using a functional transcriptomics approach, this project led to define the molecular repertoire of the distinct types of somatosensory mechanoreceptors that have been characterized functionally. Screening of candidate genes led to the identification of a G-protein coupled receptor involved in inflammatory mechanical pain. This gene is a promising therapeutical target for treating mechanical pain sensitization. Moreover, the dataset generated by this project constitutes an open resource available to the scientific community to explore further the cell-type-specific determinants of mechanosensory properties.
Patients presenting with chronic pain and sensory malfunction often complain of a heightened perception of pain to noxious mechanical stimuli (mechanical hyperalgesia) and/or pain to innocuous touch (mechanical allodynia). One strategy for developing novel analgesics is to target initial steps in the pain pathway. In this context, mechanosensitive channels are the molecular players initiating the pain response by detecting noxious stimuli. Identification of molecular components and modulators of somatosensory mechanotransduction will provide new therapeutics targets for treating touch and proprioceptive disorders and mechanical pain.
By using a functional transcriptomics approach, this project led to define the molecular repertoire of the distinct types of somatosensory mechanoreceptors that have been characterized functionally. Screening of candidate genes led to the identification of a G-protein coupled receptor involved in inflammatory mechanical pain. This gene is a promising therapeutical target for treating mechanical pain sensitization. Moreover, the dataset generated by this project constitutes an open resource available to the scientific community to explore further the cell-type-specific determinants of mechanosensory properties.
Previous studies have identified distinct MS currents in mouse DRG neurons reflecting the activation of distinct types of ion channels, with discrepancy between studies regarding their properties. We performed a comprehensive classification of mouse DRG MS currents through patch-clamp characterization of MS currents in large samples of neurons. The distribution analysis of current kinetics led to identify four distinct MS current types, classified as rapidly-, intermediately-, slowly- and ultra-slowly- adapting currents (RA, IA, SA, ultra-SA, respectively). If RA-currents are known to be sustained by Piezo2 channels, identities of genes involved in other current types are unknown.
We performed patch-clamp recordings of DRG neuron MS currents combined with single-cell RNA sequencing (mechano patch-seq). Two rounds of this technically demanding approach led to successfully generate the specific expression profile of 53 individual neurons for which MS currents have been characterized.
We used gene-expression analysis to sort out neurons into molecularly defined neuronal subclasses. This procedure led to classify each neuron from our sample in transcriptional clusters corresponding to the distinct sensory mechanoreceptor populations. This allowed us to assign MS current signatures to neuronal subtypes, leading to define MS current type distribution into transcriptomically defined neuronal populations. Our dataset identifies the molecular repertoire of the distinct types of somatosensory mechanoreceptors.
Data analysis by grouping neurons according to their MS current signature led to identify candidate genes involved in the distinct MS current types. Specific and selective enrichment of Piezo2 in RA current expressing neurons validates our approach. The lists of protein coding genes that are specifically enriched in neurons expressing IA-, SA- or ultra-SA MS currents were generated. By refining from 13,000 transcripts expressed per DRG neuron to only hundreds of candidates, this dataset represents a valuable resource for the identification of genes involved in the generation of MS current types. These results have been disseminated in posters and oral presentations, including FENS meeting 2020.
We investigated further on two genes claimed during the time course of this project to encode MS channels in DRG neurons, Tmem150c and Tmem120a. As our dataset contradicts published results, we performed expression analysis, siRNA functional experiments and cloning/overexpression in heterologous system of both genes. Our results invalidate published results, which although being negative data, represent a much-needed cold shower for the mechanotransduction field and the pain research scientific community.
All these results have been published at Cell Reports and RNAseq data deposited to NCBI’s Gene Expression Omnibus. The Tmem150c overexpression experiments has been published as a collaborative work at Neuron.
Over 200 candidates identified by our approach have been tested functionally by siRNA knock-down, representing a tremendous amount of work. We did not identify new constituent of MS ion channel so far, but we followed up on a promising candidate. This gene encodes an orphan GPCR. Inactivation of this gene induces a specific reduction of ultra-slowly adapting current amplitude in nociceptive neurons. Behavioral experiments shows that knock-out animals have defects in mechanical pain sensing during inflammation, highlighting this gene as a promising therapeutical target for treating mechanical pain sensitization.
Presentation of this work to the pain research scientific community will occur in the “Pain Mechanisms and Therapeutics” Conference in Verona.
We performed patch-clamp recordings of DRG neuron MS currents combined with single-cell RNA sequencing (mechano patch-seq). Two rounds of this technically demanding approach led to successfully generate the specific expression profile of 53 individual neurons for which MS currents have been characterized.
We used gene-expression analysis to sort out neurons into molecularly defined neuronal subclasses. This procedure led to classify each neuron from our sample in transcriptional clusters corresponding to the distinct sensory mechanoreceptor populations. This allowed us to assign MS current signatures to neuronal subtypes, leading to define MS current type distribution into transcriptomically defined neuronal populations. Our dataset identifies the molecular repertoire of the distinct types of somatosensory mechanoreceptors.
Data analysis by grouping neurons according to their MS current signature led to identify candidate genes involved in the distinct MS current types. Specific and selective enrichment of Piezo2 in RA current expressing neurons validates our approach. The lists of protein coding genes that are specifically enriched in neurons expressing IA-, SA- or ultra-SA MS currents were generated. By refining from 13,000 transcripts expressed per DRG neuron to only hundreds of candidates, this dataset represents a valuable resource for the identification of genes involved in the generation of MS current types. These results have been disseminated in posters and oral presentations, including FENS meeting 2020.
We investigated further on two genes claimed during the time course of this project to encode MS channels in DRG neurons, Tmem150c and Tmem120a. As our dataset contradicts published results, we performed expression analysis, siRNA functional experiments and cloning/overexpression in heterologous system of both genes. Our results invalidate published results, which although being negative data, represent a much-needed cold shower for the mechanotransduction field and the pain research scientific community.
All these results have been published at Cell Reports and RNAseq data deposited to NCBI’s Gene Expression Omnibus. The Tmem150c overexpression experiments has been published as a collaborative work at Neuron.
Over 200 candidates identified by our approach have been tested functionally by siRNA knock-down, representing a tremendous amount of work. We did not identify new constituent of MS ion channel so far, but we followed up on a promising candidate. This gene encodes an orphan GPCR. Inactivation of this gene induces a specific reduction of ultra-slowly adapting current amplitude in nociceptive neurons. Behavioral experiments shows that knock-out animals have defects in mechanical pain sensing during inflammation, highlighting this gene as a promising therapeutical target for treating mechanical pain sensitization.
Presentation of this work to the pain research scientific community will occur in the “Pain Mechanisms and Therapeutics” Conference in Verona.
Combining patch-clamp recordings of mechanosensitive currents and single-cell transcriptome sequencing (mechano patch-seq) to generate the specific expression profile of distinct populations of mouse mechanosensitive neurons was a technical challenge that we successfully achieved. Analysis of the RNAseq dataset building on the comprehensive classification of DRG MS currents we performed during the project led to the identification of candidate genes involved in distinct MS current types. By refining from 13,000 transcripts expressed per DRG neuron to only hundreds of candidates, this dataset represents a valuable open resource for the identification of specific components and/or modulators of somatosensory mechanosensitive channels. Their identification would be transformative as a scientific finding and have many clinical implications in acute, inflammatory and chronic pain. In this context, we identified a G-protein coupled receptor involved in inflammatory mechanical pain. Moreover, the conversion of mechanical force into biochemical signaling is crucial for the physiology of many additional cell types. Although our results aim to advance pain research, they could potentially impact a variety of other biological systems that may share mechanosensitive ion channels with the somatosensory system.