Biology subjects

Toikumo, S.

Publications and source records attributed to Toikumo, S..

3 recordsLinked to original sources

bioRxiv2024

Identification of SLC45A4 as a pain gene encoding a neuronal polyamine transporter.

Polyamines are regulatory metabolites with key roles in transcription, translation, cell signalling and autophagy1. They are implicated in multiple neurological disorders including stroke, epilepsy and neurodegeneration and can regulate neuronal excitability through interactions with ion channels2. Polyamines have been linked to pain showing altered levels in human persistent pain states and modulation of pain behaviour in animal models3. However, the systems governing polyamine transport within the nervous system remain unclear. In undertaking a Genome Wide Association Study (GWAS) of chronic pain intensity in the UK-Biobank we found significant association with variants mapping to the SLC45A4 gene locus. In the mouse nervous system SLC45A4 expression is enriched in all sensory neuron sub-types within the dorsal root ganglion including nociceptors. Cell-based assays show that SLC45A4 is a selective plasma membrane polyamine transporter, whilst the cryo-EM structure reveals a novel regulatory domain and basis for polyamine recognition. Mice lacking SLC45A4 show normal mechanosensitivity but reduced sensitivity to noxious heat and algogen induced tonic pain that is associated with reduced excitability of peptidergic nociceptors. Our findings thus establish a role for neuronal polyamine transport in pain perception and identify a new target for therapeutic intervention in pain treatment.

neuroscience↗

bioRxiv2024

A single-nucleus transcriptomic atlas of medium spiny neurons in the rat nucleus accumbens

Neural processing of rewarding stimuli involves several distinct regions, including the nucleus accumbens (NAc). The majority of NAc neurons are GABAergic projection neurons known as medium spiny neurons (MSNs). MSNs are broadly defined by dopamine receptor expression, but evidence suggests that a wider array of subtypes exist. To study MSN heterogeneity, we analyzed single-nucleus RNA sequencing data from the largest available rat NAc dataset. Analysis of 48,040 NAc MSN nuclei identified major populations belonging to the striosome and matrix compartments. Integration with mouse and human data indicated consistency across species and disease-relevance scoring using genome-wide association study results revealed potentially differential roles for MSN populations in substance use disorders. Additional high-resolution clustering identified 34 transcriptomically distinct subtypes of MSNs definable by a limited number of marker genes. Together, these data demonstrate the diversity of MSNs in the NAc and provide a basis for more targeted genetic manipulation of specific populations.

genetics↗

bioRxiv2022

Multi-trait genome-wide association analyses leveraging alcohol use disorder findings identify novel loci for smoking behaviors in the Million Veteran Program

Smoking behaviors and alcohol use disorder (AUD), moderately heritable traits, commonly co-occur in the general population. Single-trait genome-wide association studies (GWAS) have identified multiple loci for smoking and AUD. However, GWASs that have aimed to identify loci contributing to comorbid smoking and AUD have used small samples and thus have not been highly informative. Applying multi-trait analysis of GWASs (MTAG), we conducted a joint GWAS of smoking and AUD with data from the Million Veteran Program (N=318,694). By leveraging GWAS summary statistics for AUD, MTAG identified 21 genome-wide significant (GWS) loci associated with smoking initiation and 18 loci associated with smoking cessation compared to 16 and 8 loci, respectively, identified by single-trait GWAS. The novel loci for smoking behaviors identified by MTAG included those previously associated with psychiatric or substance use traits. Colocalization analysis identified 10 loci shared by AUD and smoking status traits, all of which achieved GWS in MTAG, including variants on SIX3, NCAM1, and near DRD2. Functional annotation of the MTAG variants highlighted biologically important regions on ZBTB20, DRD2, PPP6C, and GCKR that contribute to smoking behaviors. In contrast, MTAG of smoking behaviors and alcohol consumption (AC) did not enhance discovery compared with single-trait GWAS for smoking behaviors. We conclude that using MTAG to augment the power of GWAS enables the identification of novel genetic variants for commonly comorbid phenotypes, providing new insights into their pleiotropic effects on smoking behavior and AUD.

genetics↗