Protein synthesis is a vital process that is highly regulated at the initiation step of translation. Eukaryotic 5' transcript leaders (TLs) contain a variety of cis-acting features that influence translation and messenger RNA stability. However, the relative influences of these features in natural TLs are poorly characterized. To address this, we used massively parallel reporter assays (MPRAs) to quantify RNA levels, ribosome loading, and protein levels from 11,027 natural yeast TLs in vivo and systematically compared the relative impacts of their sequence features on gene expression. We found that yeast TLs influence gene expression over two orders of magnitude. While a leaky scanning model using Kozak contexts (-4 to +1 around the AUG start) and upstream AUGs (uAUGs) explained half of the variance in expression across TLs, the addition of other features explained ∼80% of gene expression variation. Our analyses detected key cis-acting sequence features, quantified their effects in vivo, and compared their roles to motifs reported from an in vitro study of ribosome recruitment. In addition, our work quantitated the effects of alternative transcription start site usage on gene expression in yeast. Thus, our study provides new quantitative insights into the roles of TL cis-acting sequences in regulating gene expression.
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http://dx.doi.org/10.1093/nar/gkaf165 | 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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