Open BiologyGenomicsPeer-reviewed journal article
Terminal regions of a protein are a hotspot for low complexity regions (LCRs) and selection
Abstract
A majority of the protein-coding genes consist of low-complexity regions (LCRs) in eukaryotes. Volatile LCRs are a novel source of adaptive variation, functional diversification, and evolutionary novelty. LCRs contribute to a wide range of neurodegenerative disorders. Conversely, these regions also play a pivotal role in critical cellular functions, such as morphogenesis, signaling, and transcriptional regulation. An interplay of selection and mutation governs the composition and length of LCRs. High %GC and mutations provide length variability because of mechanisms like replication slippage. The selection is nearly neutral for expansion/contraction within the normal range and purifying above a critical length. Because of the complex dynamics between selection and mutation, we need a better understanding of the coexistence and mechanisms of the two. Our findings indicate that site-specific positive selection and LCRs prefer the terminal regions of a gene and co-occur in most of the Tetrapoda clades. Interestingly, positively selected sites (PSS) are significantly favored in LCRs in eight of the twelve clades studied. We also observed a significant favor of PSSs in the polyQ region of MAML2 in five clades. We also found that PSSs in a gene have position-specific roles. Terminal-PSS genes are enriched for adenyl nucleotide binding, while central-PSS genes are involved in glycosaminoglycan binding. Moreover, central-PSS genes mainly participate in defense responses, but terminal-PSS genes are non-specific. LCR-containing genes have a significantly higher %GC and lower {omega} (dN/dS) than genes without repeats across the Tetrapoda clade. A lower {omega} suggests that even though LCRs provide rapid functional diversity, LCR-containing genes face intense purifying selection.
- organism: multiple species
- assay: protein sequence
- research focus: phylogenetics / evolution
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 6
- FWCI
- 1.59
- Field percentile
- top 18%
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