Biology subjects

Ciamponi, F. E.

Publications and source records attributed to Ciamponi, F. E..

3 recordsLinked to original sources

bioRxiv2024

PRMT5 is required for full-length HTT expression by repressing multiple proximal intronic polyadenylation sites

Expansion of the CAG trinucleotide repeat tract in exon 1 of the Huntingtin (HTT) gene above a threshold of [~]36 repeats causes Huntingtons disease (HD) through the expression of a polyglutamine-expanded form of the HTT protein. This mutation triggers wide-ranging cellular and biochemical pathologies leading to cognitive, motor, and psychiatric symptoms in HD patients. As accurate splicing is required to produce the full-length HTT protein of [~]348 kDa, targeting HTT splicing with small molecule drugs is a compelling approach to lower HTT protein levels to treat HD, and splice modulators are being tested in the clinic. Here, we identify PRMT5 as a novel regulator of HTT mRNA splicing and alternative polyadenylation. PRMT5 inhibition disrupts the splicing of HTT introns 9 and 10, leading to activation of multiple proximal intronic polyadenylation sites within these introns and promoting premature termination, cleavage and polyadenylation (PCPA) of the HTT mRNA, thus lowering total HTT protein levels. We also detected increasing levels of these truncated, intron-containing HTT transcripts across a series of neuronal differentiation samples which correlated with lower PRMT5 expression. Notably, PRMT5 inhibition in glioblastoma (GBM) stem cells potently induced neuronal differentiation. We posit that PRMT5-mediated regulation of intronic polyadenylation, premature termination and cleavage of the HTT mRNA modulates HTT expression and plays an important role during embryonic development and neuronal differentiation.

neuroscience↗

bioRxiv2022

UHMK1 is a novel splicing regulatory kinase

The U2AF Homology Motif Kinase 1 (UHMK1) is the only kinase that contains the U2AF homology motif (UHM), a common protein interaction domain among splicing factors. Through this motif, UHMK1 interacts with the splicing factors SF1 and SF3B1, known to participate in the 3 splice site recognition during the early steps of spliceosome assembly. Although UHMK1 phosphorylates these splicing factors in vitro, the involvement of UHMK1 in RNA processing has not previously been demonstrated. Here, we identify novel putative substrates of this kinase and evaluate UHMK1 contribution to overall gene expression and splicing, by integrating global phosphoproteomics, RNA-seq, and bioinformatics approaches. Upon UHMK1 modulation, 163 unique phosphosites were differentially phosphorylated in 117 proteins, of which 106 are novel potential substrates of this kinase. Gene Ontology (GO) analysis showed enrichment of terms previously associated with UHMK1 function, such as mRNA splicing, cell cycle, cell division and microtubule organization. The majority of the annotated RNA-related proteins are components of the spliceosome, but are also involved in several steps of gene expression. Comprehensive analysis of splicing showed that UHMK1 affected over 200 alternative splicing events. Moreover, splicing reporter assay further supported UHMK1 function on splicing. Overall, RNA-seq data demonstrated that UHMK1 knockdown had a minor impact on gene expression and pointed to UHMK1 function in epithelial-mesenchymal transition. Finally, the functional assays demonstrated that UHMK1 modulation affects proliferation, colony formation, and migration of the cells. Taken together, our data implicate UHMK1 as a splicing regulatory kinase, connecting protein regulation through phosphorylation and gene expression in key cellular processes.

molecular biology↗

bioRxiv2018

BioFeatureFinder: Flexible, unbiased analysis of biological characteristics associated with genomic regions

BioFeatureFinder is a novel algorithm which allows analyses of many biological genomic landmarks (including alternatively spliced exons, DNA/RNA-binding protein binding sites, and gene/transcript functional elements, nucleotide content, conservation, k-mers, secondary structure) to identify distinguishing features. BFF uses a flexible underlying model that combines classical statistical tests with Big Data machine-learning strategies. The model is created using thousands of biological characteristics (features) that are used to build a feature map and interpret category labels in genomic ranges. Our results show that BFF is a reliable platform for analyzing large-scale datasets. We evaluated the RNA binding feature map of 110 eCLIP-seq datasets and were able to recover several well-known features from the literature for RNA-binding proteins; we were also able to uncover novel associations. BioFeatureFinder is available at https://github.com/kbmlab/BioFeatureFinder/.

bioinformatics↗