Computational Molecular Biology 2024, Vol.14, No.5, 202-210 http://bioscipublisher.com/index.php/cmb 203 genetic networks, the evolutionary pathways they follow, and their contributions to the development of new traits. We will discuss the significance of these processes in understanding the broader principles of evolutionary biology and developmental genetics, providing valuable insights for ongoing research and future directions in the recruitment of new genes. 2 Origin and Evolution of New Genes 2.1 Gene duplication and divergence Gene duplication is a well-established mechanism for the creation of new genes. It involves the copying of an existing gene, which can then diverge and acquire new functions. This process is a major source of genetic novelty and has been extensively studied across various species. In Drosophila, tandem gene duplication has generated approximately 80% of nascent duplicates limited to single species, while dispersed duplicates (Fang, 2024), which are more likely to be retained and functional, account for 44.1% of new genes shared by multiple species. Similarly, in yeast and flies, duplicated genes exhibit high turnover rates at the species level but show stability in deeper evolutionary branches, indicating their significant role in long-term evolutionary processes (Montañés et al., 2023). 2.2 Gene fusion and recombination Gene fusion and recombination are other important mechanisms contributing to the origin of new genes. These processes involve the merging of different genomic sequences to form chimeric genes with novel functions. In the Drosophila melanogaster species complex, approximately 30% of new genes have recruited various genomic sequences to form chimeric structures, highlighting the importance of structural innovation in gene fixation (Prabh and Roedelsperger, 2022). Additionally, genomic parasites and messenger RNAs of ancestral genes can be co-opted to form new genes, further expanding the repertoire of genetic diversity (Kaessmann, 2010). 2.3 De novo gene birth De novo gene birth is the process by which new genes evolve from previously non-genic sequences. This mechanism has gained increasing attention as a significant source of genetic innovation. Studies have shown that de novo genes can arise from noncoding DNA and gradually integrate into cellular networks, although their prevalence and functional significance are still under investigation (Moyers and Zhang, 2016). In mammals, comparative genomics has identified thousands of new transcriptional events in humans and chimpanzees, some of which show evidence of protein translation and natural selection, suggesting their potential functionality. In yeasts, de novo genes preferentially emerge in GC-rich intergenic regions and recombination hotspots, indicating that meiotic recombination may facilitate their origination (Vakirlis et al., 2018). The evolutionary dynamics of de novo genes differ from those of duplicated genes. De novo candidates are typically shorter, show less expression, and are overrepresented on sex chromosomes. They also exhibit higher attrition rates and weaker evolutionary constraints, reflecting their rapid turnover and evolutionary lability (Prabh and Roedelsperger, 2022). Despite these differences, both de novo and duplicated genes share similarities in their initial evolutionary phases, such as low sequence constraints and high turnover rates. 3 Patterns of Developmental Recruitment of New Genes 3.1 Spatiotemporal expression patterns Spatiotemporal expression patterns are crucial for understanding how new genes are recruited during development. These patterns refer to the specific times and locations where genes are expressed, which can significantly influence developmental processes. The study of transcription factors has shown that they regulate gene expression in specific spatiotemporal domains, leading to robust developmental outcomes despite environmental and genetic variations. Additionally, the formation of spatiotemporal patterns is essential in various biological phenomena, such as embryogenesis and neural network formation, where gene regulatory networks (GRNs) play a pivotal role (Roy et al., 2022). The integration of spatial and temporal datasets, as demonstrated in the sea anemone Nematostella vectensis, further highlights the importance of these patterns in identifying potential gene interactions and regulatory networks (Abdol et al., 2017).
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