Expression quantitative trait loci (eQTLs) provide a key bridge between noncoding DNA sequence variants and organismal traits. The effects of eQTLs can differ among tissues, cell types, and cellular states, but these differences are obscured by gene expression measurements in bulk populations. We developed a one-pot approach to map eQTLs in by single-cell RNA sequencing (scRNA-seq) and applied it to over 100,000 single cells from three crosses. We used scRNA-seq data to genotype each cell, measure gene expression, and classify the cells by cell-cycle stage. We mapped thousands of local and distant eQTLs and identified interactions between eQTL effects and cell-cycle stages. We took advantage of single-cell expression information to identify hundreds of genes with allele-specific effects on expression noise. We used cell-cycle stage classification to map 20 loci that influence cell-cycle progression. One of these loci influenced the expression of genes involved in the mating response. We showed that the effects of this locus arise from a common variant (W82R) in the gene , which encodes a signaling protein that negatively regulates the mating pathway. The 82R allele increases mating efficiency at the cost of slower cell-cycle progression and is associated with a higher rate of outcrossing in nature. Our results provide a more granular picture of the effects of genetic variants on gene expression and downstream traits.
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http://dx.doi.org/10.7554/eLife.95566 | DOI Listing |
ACS Synth Biol
March 2025
Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, United States.
Cell-free synthetic biology biosensors have potential as effective diagnostic technologies for the detection of chemical compounds, such as toxins and human health biomarkers. They have several advantages over conventional laboratory-based diagnostic approaches, including the ability to be assembled, freeze-dried, distributed, and then used at the point of need. This makes them an attractive platform for cheap and rapid chemical detection across the globe.
View Article and Find Full Text PDFIt is known that inhibition of the endoplasmic reticulum transmembrane signaling protein (ERN1) suppresses the glioblastoma cells proliferation. The present study aims to investigate the impact of inhibition of ERN1 endoribonuclease and protein kinase activities on the , , and gene expression in U87MG glioblastoma cells with an intent to reveal the role of ERN1 signaling in the regulation of expression of these genes. The U87MG glioblastoma cells with inhibited ERN1 endoribonuclease (dnrERN1) or both enzymatic activities of ERN1 (endoribonuclease and protein kinase; dnERN1) were used.
View Article and Find Full Text PDFEndocr Regul
January 2025
1Department of Molecular Biology, Palladin Institute of Biochemistry, National Academy of Sciences of Ukraine, Kyiv, Ukraine.
For the effective growth of malignant tumors, including glioblastoma, the necessary factors involve endoplasmic reticulum (ER) stress, hypoxia, and the availability of nutrients, particularly glucose. The ER degradation enhancing alpha-mannosidase like protein 1 (EDEM1) is involved in ER-associated degradation (ERAD) targeting misfolded glycoproteins for degradation in an N-glycan-independent manner. EDEM1 was also identified as a new modulator of insulin synthesis and secretion.
View Article and Find Full Text PDFAm J Drug Alcohol Abuse
March 2025
Department of Biomedical Sciences, Joan C. Edwards School of Medicine, Marshall University, Huntington, WV, USA.
Females remain underrepresented in opioid use disorder (OUD) research, particularly regarding dorsal striatal neuroadaptations. Chaperonins seem to play a role in opioid-induced neural plasticity, yet their contribution to OUD-related changes in the dorsal striatum (DS) remains poorly understood. Given known sex differences in opioid sensitivity, it is important to determine how chaperonin expression contributes to OUD-related adaptations in females.
View Article and Find Full Text PDFBioinformatics
March 2025
Department of Computer Science, University of Turin, Torino, 10123, Italy.
Motivation: Computational models are crucial for addressing critical questions about systems evolution and deciphering system connections. The pivotal feature of making this concept recognisable from the biological and clinical community is the possibility of quickly inspecting the whole system, bearing in mind the different granularity levels of its components. This holistic view of system behaviour expands the evolution study by identifying the heterogeneous behaviours applicable, for example, to the cancer evolution study.
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