Search bioRxiv⌕ Search

Biology subjects

Soszynska-Jozwiak, M.

Publications and source records attributed to Soszynska-Jozwiak, M..

4 recordsLinked to original sources

Pan-cancer discovery of driver mutations in long noncoding RNAs reveals widespread functional rewiring of RNA regulatory elements

Most somatic mutations in cancer occur outside protein-coding genes, yet the functional impact of these mutations remains largely unknown. Long noncoding RNAs (lncRNAs) represent a major class of cancer-promoting genes whose molecular mechanisms are poorly understood. While individual driver mutations in lncRNAs have been identified, detecting such driver lncRNAs at scale requires large tumour genome cohorts. We analyse 12,631 cancer genomes from the 100,000 Genomes Project (100kGP) and identify 121 lncRNAs under positive selection across 19 cancer types. These driver lncRNAs are independently supported by functional genomic screens, germline predisposing variants, mutual exclusivity with protein-coding drivers, and independent oncogenic lncRNA catalogues. Overall, approximately two-thirds of analysed tumours harbour at least one lncRNA driver mutation. Leveraging the depth of this dataset, we demonstrate that somatic mutations preferentially target and remodel RNA-binding protein (RBP) interaction sites to potentiate oncogenic lncRNAs, including MALAT1, SNHG14 and NEAT1. From these data, we derive a model in which somatic mutations liberate oncogenic lncRNAs from repressive RNA:protein interactions. This work expands the number and nature of cancer driver genes, identifies targets for RNA-directed therapies, and demonstrates that with large tumour mutation catalogues we can dissect the molecular mechanisms of noncoding genes.

Cancer Biology↗

Nearest Neighbor Parameters for Estimating the Folding Stability of RNA Including Pseudouridine

Nearest neighbor parameters are widely used in software for estimating the conformational stability of an RNA sequence folding into a specific structure. Folding stability for RNA with canonical nucleotides A, C, G, and U has been widely studied, but the same is not true for most modified nucleotides. In this work, we present a comprehensive set of nearest neighbor parameters for estimating the folding stability of RNA including pseudouridine in helical or loop contexts. These parameters are derived from 210 optical melting experiments involving helices with pseudouridine-A and pseudouridine-G pairs and with pseudouridine in loop motifs. The experiments include sequences with pseudouridine and U in the same strand, including U-A and U-G pairs, allowing us to consider the folding stability of sequences with both U and pseudouridine. On average, pseudouridine stabilizes RNA folding compared to U in an analogous motif, although this effect is sequence-context dependent. These parameters improve the modeling of folding stability for RNA secondary structures containing pseudouridine. We demonstrate that these parameters successfully model the secondary structure change for Saccharomyces cerevisiae U2 snRNA when two additional inducible pseudouridines are present. These parameters are freely available and incorporated into the RNAstructure software package. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/725682v1_ufig1.gif" ALT="Figure 1"> View larger version (14K): org.highwire.dtl.DTLVardef@e1167aorg.highwire.dtl.DTLVardef@18ac7f0org.highwire.dtl.DTLVardef@4c909eorg.highwire.dtl.DTLVardef@aa8bca_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

RNA Folding Nearest Neighbor Parameters Including the Modification 1-Methyl-Pseudouridine

Nearest neighbor analysis is commonly used to estimate RNA folding stabilities. In this contribution, we report a set of RNA folding nearest neighbor parameters for estimating free energy change for RNA sequences including 1-methyl-pseudouridine. Development of mRNA vaccines has identified 1-methyl-pseudouridine as a key nucleobase modification for suppressing innate immune responses. However, the contributions of these modifications to RNA folding stability were unclear. Our new parameters provide helical terms for 1-methyl-pseudouridine-adenine and 1-methyl-pseudouridine-guanine base pairs. The parameters also estimate loop stabilities for loops with 1-methyl-pseudouridine or a combination of 1-methyl-pseudouridine and uridine. These parameters are derived using 208 optical melting experiments and tested against an additional 16 optical melting experiments. On average, we find that substitution of uridine with 1-methyl-pseudouridine stabilizes RNA folding, with the extent of stabilization depending on adjacent sequence. The estimation of tRNA folding ensembles for tRNA sequences with 1-methyl-pseudouridine was significantly improved using the new nearest neighbor parameters. The new nearest neighbor parameters are provided as part of the RNAstructure software package. With these parameters, the secondary structures of natural sequences with 1-methyl-pseudouridine and mRNA therapeutics fully substituted with 1-methyl-pseudouridine can be modeled.

bioinformatics↗

Secondary structure of subgenomic RNA M of SARS-CoV-2

SARS-CoV-2 belongs the Coronavirinae family. As other coronaviruses, SARS-CoV-2 is enveloped and possesses positive-sense, single-stranded RNA genome of [~] 30 kb. Genome RNA is used as the template for replication and transcription. During these processes, positive-sense genomic RNA (gRNA) and subgenomic RNAs (sgRNAs) are created. Several studies showed importance of genomic RNA secondary structure in SARS-CoV-2 replication. However, the structure of sgRNAs have remained largely unsolved so far. In this study, we performed probing of sgRNA M of SARS-CoV-2 in vitro. This is the first experimentally informed secondary structure model of sgRNA M, which presents features likely to be important in sgRNA M function. The knowledge about sgRNA M provides insights to better understand virus biology and could be used for designing new therapeutics.

molecular biology↗