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dos Santos, F. F.

Publications and source records attributed to dos Santos, F. F..

3 recordsLinked to original sources

Processed pseudogenes as dynamic substrates of vertebrate genome evolution

Pseudogenes, gene copies presumed nonfunctional, are widespread products of genome evolution, yet their retention under selection and functional significance across vertebrate diversity remain poorly understood. Here, by analyzing 244 high-quality, chromosome-scale genomes from the Vertebrate Genomes Project spanning seven major vertebrate lineages, we show that the most abundant class of pseudogenes, processed pseudogenes (retrocopies), is a dynamic substrate for evolutionary innovation rather than an inert relic. Retrocopy abundance varies by more than an order of magnitude across lineages, closely tracks autonomous retrotransposon content, and a considerable fraction retains intact open reading frames under purifying selection. We establish a two-stage model in which a conserved formation bias toward highly expressed housekeeping genes is followed by lineage-specific selective filtering that shapes distinct functional repertoires. Testing this model, we show that the mammalian X chromosome exports retrocopies to autosomes at significantly elevated rates enriched for functionally constrained copies, establishing meiotic sex chromosome inactivation as the selective driver. Furthermore, tumor suppressor gene retrocopies accumulate preferentially over oncogene retrocopies in large-bodied and long-lived mammalian lineages, identifying retrocopy-mediated tumor suppressor dosage expansion as a previously unrecognized genomic correlate of Petos paradox. Beyond cancer-related dynamics, retrocopy abundance itself correlates with key mammalian life-history traits, including brain mass, generation length, and reproductive timing, which suggests that retrocopy turnover is broadly coupled to organismal pace-of-life. These findings recast retrocopies as a major axis of vertebrate genome evolution and provide a comprehensive resource for studying gene duplicate innovation.

genomics↗

FREDDIE: A comprehensive tool for detecting exonization of retrotransposable elements in short and long RNA sequencing data

BackgroundTransposable elements (TEs) constitute a significant portion of mammalian genomes, accounting for about 50% of the total DNA. Intragenic TEs are of particular interest as they are co-transcribed with their host genes in pre-mRNA, potentially leading to the formation of novel chimeric transcripts and the exonization of TEs. The abundance of RNA sequencing data currently available offers a unique opportunity to explore transcriptomic variations. However, a significant limitation is the capability of existing computational tools. Here, we introduce FREDDIE, an innovative algorithm designed to detect the exonization of retrotransposable elements using RNA-seq data. FREDDIE can process short and long RNA sequencing data, assemble and quantify transcripts, evaluate coding potential, and identify protein domains in chimeric transcripts involving exonized TEs and retrocopies. ResultsTo demonstrate the efficacy of FREDDIE, we analyzed and validated TE exonization in two human cancer cell lines, K562 and U251. We have identified 322 chimeric transcripts, of which 126 were from K562, and 196 were from U251. Among these chimeric transcripts, there were 35 that showed similar exonization patterns and host genes. These transcripts involve protein-coding genes of the host and exonization of LINE-1 (L1), Alu elements, and retrocopies of coding genes. We have selected some candidates and validated them experimentally through RT-PCR. The validation rate for these candidates was 70%, later confirmed by long-read sequencing. Additionally, we applied FREDDIE to analyze TE exonization across 157 glioblastoma samples, identifying 1,010 chimeric transcripts. The majority of these transcripts involved the exonization of Alu elements (69.8%), followed by L1 (20.6%) and retrocopies (9.6%). Notably, we discovered a highly expressed L1 exonization within the ROS gene, resulting in a truncated open reading frame (ORF) with the deletion of two protein domains. ConclusionsFREDDIE is an efficient and user-friendly tool for identifying chimeric transcripts that involve exonization of intragenic TEs. Overall, FREDDIE enables comprehensive investigations into the contributions of TEs to transcriptome evolution, variation, and disease-associated abnormalities, and it operates effectively on standard computing systems. FREDDIE is publicly available: https://github.com/galantelab/freddie

bioinformatics↗

Reboot: a straightforward approach to identify genes and splicing isoforms associated with cancer patient prognosis

Nowadays, the massive amount of data generated by modern sequencing technologies provides an unprecedented opportunity to find genes associated with cancer patient prognosis, connecting basic and translational research. However, treating high dimensionality of gene expression data and integrating it with clinical variables are major challenges to carry out these analyses. Here, we present Reboot, an original and efficient algorithm to find genes and splicing isoforms associated with cancer patient survival, disease progression, or other clinical endpoints. Reboot innovates by using a multivariate strategy with penalized Cox regression (LASSO method) combined with a bootstrap approach, in addition to statistical tests for supporting the findings, which are automatically plotted. Applying Reboot on data from 154 glioblastoma patients, we identified a three-gene signature (IKBIP, OSMR, PODNL1) whose increased derived risk score was significantly associated with worse patients prognosis, even in conjunction with other well-established clinical parameters. Similarly, Reboot was able to find a seven-splicing isoforms signature (CENPF-201; MLKL-202; NUP54-201; MCF2L-201; TFDP1-207; BBS1-206; HTT-202) related to worse overall survival in 177 pancreatic adenocarcinoma patients with elevated risk scores after uni- and multivariate analyses. In summary, Reboot is an efficient, intuitive, and straightforward way for finding genes or splicing isoforms (transcripts) signatures relevant to patient prognosis, which can democratize this kind of analysis and shed light on still under-investigated sets of cancer-related genes. Reboot effectively runs on either servers or personal computers and it is freely available at github.com/galantelab/reboot.

bioinformatics↗