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1. RNA polymerase II dynamics and mRNA stability feedback scale mRNA amounts with cell size

   https://doi.org/10.1016/j.cell.2023.10.012  

   Swaffer, Matthew P.; Marinov, Georgi K.; Zheng, Huan; Fuentes Valenzuela, Lucas; Tsui, Crystal Yee; Jones, Andrew W.; Greenwood, Jessica; Kundaje, Anshul; Greenleaf, William J.; Reyes-Lamothe, Rodrigo; et al (November 2023, Cell)
2. RNA polymerase II dynamics and mRNA stability feedback determine mRNA scaling with cell size

   https://doi.org/10.1101/2021.09.20.461005  

   Swaffer, M.P.; Marinov, G.K.; Zheng, H.; Jones, A.W.; Greenwood, J.; Kundaje, A.; Snijders, A.P.; Greenleaf, W.J.; Reyes-Lamothe, R.; Skotheim, J.M. (September 2021, bioRxiv)

   null (Ed.)

   A defining feature of cellular growth is that protein and mRNA amounts scale with cell size so that concentrations remain approximately constant, thereby ensuring similar reaction rates and efficient biosynthesis. A key component of this biosynthetic scaling is the scaling of mRNA amounts with cell size, which occurs even among cells with the same DNA template copy number. Here, we identify RNA polymerase II as a major limiting factor increasing transcription with cell size. Other components of the transcriptional machinery are only minimally limiting and the chromatin environment is largely invariant with size. However, RNA polymerase II activity does not increase in direct proportion to cell size, inconsistent with previously proposed DNA-titration models. Instead, our data support a dynamic equilibrium model where the rate of polymerase loading is proportional to the unengaged nucleoplasmic polymerase concentration. This sublinear transcriptional increase is then balanced by a compensatory increase in mRNA stability as cells get larger. Taken together, our results show how limiting RNA polymerase II and feedback on mRNA stability work in concert to ensure the precise scaling of mRNA amounts across the physiological cell size range. 

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3. Platform-integrated mRNA isoform quantification

   https://doi.org/10.1093/bioinformatics/btz932  

   Sun, Jiao; Chang, Jae-Woong; Zhang, Teng; Yong, Jeongsik; Kuang, Rui; Zhang, Wei; Gorodkin, ed., Jan (December 2019, Bioinformatics)

   Abstract MotivationAccurate estimation of transcript isoform abundance is critical for downstream transcriptome analyses and can lead to precise molecular mechanisms for understanding complex human diseases, like cancer. Simplex mRNA Sequencing (RNA-Seq) based isoform quantification approaches are facing the challenges of inherent sampling bias and unidentifiable read origins. A large-scale experiment shows that the consistency between RNA-Seq and other mRNA quantification platforms is relatively low at the isoform level compared to the gene level. In this project, we developed a platform-integrated model for transcript quantification (IntMTQ) to improve the performance of RNA-Seq on isoform expression estimation. IntMTQ, which benefits from the mRNA expressions reported by the other platforms, provides more precise RNA-Seq-based isoform quantification and leads to more accurate molecular signatures for disease phenotype prediction. ResultsIn the experiments to assess the quality of isoform expression estimated by IntMTQ, we designed three tasks for clustering and classification of 46 cancer cell lines with four different mRNA quantification platforms, including newly developed NanoString’s nCounter technology. The results demonstrate that the isoform expressions learned by IntMTQ consistently provide more and better molecular features for downstream analyses compared with five baseline algorithms which consider RNA-Seq data only. An independent RT-qPCR experiment on seven genes in twelve cancer cell lines showed that the IntMTQ improved overall transcript quantification. The platform-integrated algorithms could be applied to large-scale cancer studies, such as The Cancer Genome Atlas (TCGA), with both RNA-Seq and array-based platforms available. Availability and implementationSource code is available at: https://github.com/CompbioLabUcf/IntMTQ. Supplementary informationSupplementary data are available at Bioinformatics online. 

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4. Functional Proximity across an mRNA

   https://doi.org/10.1021/acs.biochem.5c00340  

   Hatfield, Breanne M; Weidmann, Chase A; McCutchin, Christina A; Irving, Patrick S; Nelson, Irma M; Felder, Simon; Weeks, Kevin M (August 2025, Biochemistry)
5. mRNA-carrying lipid nanoparticles that induce lysosomal rupture activate NLRP3 inflammasome and reduce mRNA transfection efficiency

   https://doi.org/10.1039/D2BM00883A  

   Forster III, James; Nandi, Dipika; Kulkarni, Ashish (September 2022, Biomaterials Science)

   In the last several years, countless developments have been made to engineer more efficient and potent mRNA lipid nanoparticle vaccines, culminating in the rapid development of effective mRNA vaccines against COVID-19. However, despite these advancements and materials approaches, there is still a lack of understanding of the resultant immunogenicity of mRNA lipid nanoparticles. Therefore, a more mechanistic, design-driven approach needs to be taken to determine which biophysical characteristics, especially related to changes in lipid compositions, drive nanoparticle immunogenicity. Here, we synthesized a panel of six mRNA lipid nanoparticle formulations, varying the concentrations of different lipid components and systematically studied their effect on NLRP3 inflammasome activation; a key intracellular protein complex that controls various inflammatory responses. Initial experiments aimed to determine differences in nanoparticle activation of NLRP3 inflammasomes by IL-1β ELISA, which unveiled that nanoparticles with high concentrations of ionizable lipid DLin-MC3-DMA in tandem with high cationic lipid DPTAP and low cholesterol concentration induced the greatest activation of the NLRP3 inflammasome. These results were further corroborated by the measurement of ASC specks indicative of NLRP3 complex assembly, as well as cleaved gasdermin-D and caspase-1 expression indicating complex activation. We also uncovered these activation profiles to be mechanistically correlated primarily with lysosomal rupturing caused by the delayed membrane disruption capabilities of ionizable lipids until the lysosomal stage, as well as by mitochondrial reactive oxygen species (ROS) production and calcium influx for some of the particles. Therefore, we report that the specific, combined effects of each lipid type, most notably ionizable, cationic lipids, and cholesterol, is a crucial mRNA lipid nanoparticle characteristic that varies the endo/lysosomal rupture capabilities of the formulation and activate NLRP3 inflammasomes in a lysosomal rupture dependent manner. These results provide a more concrete understanding of mRNA lipid Nanoparticle-Associated Molecular Patterns for the activation of molecular-level immune responses and provide new lipid composition design considerations for future mRNA-delivery approaches. 

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