Biology subjects

Bolger, M.

Publications and source records attributed to Bolger, M..

3 recordsLinked to original sources

bioRxiv2025

Opioid receptor distribution in the claustrum-dorsal endopiriform complex

The claustrum and dorsal endopiriform regions form a thin subcortical sheet reciprocally connected to the neocortex that is enriched in opioid receptors. The precise cellular distribution of opioid receptors within this region remains unclear. Using multiplexed fluorescence in situ hybridization (mFISH) and hierarchical cluster analysis, we mapped the expression of mu (Oprm), delta (Oprd), kappa (Oprk), and ORL1 (Oprl) opioid receptor genes at single-cell resolution in the mouse claustrum-dorsal endopiriform. Among 2,269 neurons analyzed, six transcriptional clusters were identified: three excitatory (CLA, CLA/OPRK, cortical excitatory) and three inhibitory (SST, PVALB, broad inhibitory). Oprk expression was uniquely restricted to excitatory claustrum neurons and particularly enriched in the CLA/OPRK cluster which was comprised largely of Synpr+ claustrum core projection cells. In contrast, Oprd, Oprm, and Oprl genes were more broadly distributed across both excitatory and inhibitory populations, with Oprd enriched in parvalbumin (Pvalb) inhibitory neurons and Oprl in somatostatin (Sst) inhibitory neurons. Spatial mapping confirmed that Oprk expressing cells were concentrated within the claustrum core, whereas other receptor subtypes extended across the entire claustrum and into adjacent cortical regions. A comparable distribution of opioid receptors was observed in the neighbouring dorsal endopiriform. Analysis of a publicly available single cell sequencing dataset of the macaque claustrum also revealed a similar receptor distribution, where Oprk expression was enriched within a subset of excitatory projection neurons, and Oprm,Oprd, and Oprl expressed widely in both inhibitory and excitatory cells. Together, these findings demonstrate an evolutionarily conserved, cell-type-specific organization of opioid receptor expression in the claustrum-dorsal endopiriform. These results indicate that kappa opioid receptor expression is a defining molecular feature of excitatory claustrum projection neurons and suggest distinct roles for other opioid receptors in modulating inhibitory and cortical circuits.

neuroscience↗

bioRxiv2025

Genome Sequencing of Chinese yam (Dioscorea polystachya): analysis of PEBP gene family diversity and identification of a potential tuber inducing factor.

Phosphatidylethanolamine-binding proteins (PEBPs) are a class of ancient plant proteins involved in flowering, seed development and storage organ formation. The FLOWERING LOCUS T subgroup was shown to be involved in the initiation of flowering and tuberization. We aimed to characterize the PEBP family in the tuber crop Chinese yam (Dioscorea polystachya) and to identify FLOWERING LOCUS T (FT) homologs potentially involved in Chinese yam tuber formation. Based on a de novo genome assembly of Chinese yam, we investigated members of the PEBP family in silico. Expression analysis of FT homologs in yam tuber tissue revealed DpFT3a as a candidate for a tuber inducing FT. We then performed functional characterization of DpFTs by localization and interaction studies, as well as constitutive overexpression in Arabidopsis thaliana and Solanum tuberosum. Analysis of the genome annotations revealed members of the PEBP-family in Chinese yams. Identified PEBPs were subjected to phylogenetic analysis and transcriptomic studies. Of the identified 9 FT homologs, DpFT3a, which was strongly expressed in yam tubers, was found to cause a severe tuberizing phenotype in transgenic potato (Solanum tuberosum cv. Desiree), indicating a role of DpFT3a in storage organ formation. We identified a Chinese yam specific FT homolog and provide strong indication for a role of this FT in tuber formation in yam as well as its potential to induce tuberous structures in potato. Our results complement the current knowledge on PEBP proteins in plants.

plant biology↗

bioRxiv2023

Helixer--de novo Prediction of Primary Eukaryotic Gene Models Combining Deep Learning and a Hidden Markov Model.

AO_SCPLOWBSTRACTC_SCPLOWGene structural annotation is a critical step in obtaining biological knowledge from genome sequences yet remains a major challenge in genomics projects. Current de novo Hidden Markov Models are limited in their capacity to model biological complexity; while current pipelines are resource-intensive and their results vary in quality with the available extrinsic data. Here, we build on our previous work in applying Deep Learning to gene calling to make a fully applicable, fast and user friendly tool for predicting primary gene models from DNA sequence alone. The quality is state-of-the-art, with predictions scoring closer by most measures to the references than to predictions from other de novo tools. Helixers predictions can be used as is or could be integrated in pipelines to boost quality further. Moreover, there is substantial potential for further improvements and advancements in gene calling with Deep Learning. Helixer is open source and available at https://github.com/weberlab-hhu/Helixer A web interface is available at https://www.plabipd.de/helixer_main.html

bioinformatics↗