An in silico model of LINE-1-mediated neoplastic evolution
| Autor: | Jack LeBien, Joel Atallah, Gerald McCollam |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Statistics and Probability
In silico Population Retrotransposon Computational biology Biology medicine.disease_cause Biochemistry Somatic evolution in cancer Genome Evolution Molecular 03 medical and health sciences 0302 clinical medicine medicine Humans Computer Simulation education Molecular Biology Gene Alleles 030304 developmental biology 0303 health sciences education.field_of_study Computer Science Applications Computational Mathematics Long Interspersed Nucleotide Elements Computational Theory and Mathematics Tumor progression DNA Transposable Elements Carcinogenesis 030217 neurology & neurosurgery |
| Zdroj: | Bioinformatics (Oxford, England). 36(14) |
| ISSN: | 1367-4811 |
| Popis: | Motivation Recent research has uncovered roles for transposable elements (TEs) in multiple evolutionary processes, ranging from somatic evolution in cancer to putatively adaptive germline evolution across species. Most models of TE population dynamics, however, have not incorporated actual genome sequence data. The effect of site integration preferences of specific TEs on evolutionary outcomes and the effects of different selection regimes on TE dynamics in a specific genome are unknown. We present a stochastic model of LINE-1 (L1) transposition in human cancer. This system was chosen because the transposition of L1 elements is well understood, the population dynamics of cancer tumors has been modeled extensively, and the role of L1 elements in cancer progression has garnered interest in recent years. Results Our model predicts that L1 retrotransposition (RT) can play either advantageous or deleterious roles in tumor progression, depending on the initial lesion size, L1 insertion rate and tumor driver genes. Small changes in the RT rate or set of driver tumor-suppressor genes (TSGs) were observed to alter the dynamics of tumorigenesis. We found high variation in the density of L1 target sites across human protein-coding genes. We also present an analysis, across three cancer types, of the frequency of homozygous TSG disruption in wild-type hosts compared to those with an inherited driver allele. Availability and implementation Source code is available at https://github.com/atallah-lab/neoplastic-evolution. Contact jlebien@uno.edu Supplementary information Supplementary data are available at Bioinformatics online. |
| Databáze: | OpenAIRE |
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