Mapping natural selection through the drosophila melanogaster development following a multiomics data integration approach
- Autores: Martha L. Coronado
- Directores de la Tesis: Antonio Barbadilla Prados (dir. tes.), Isaac Salazar Ciudad (codir. tes.)
- Lectura: En la Universitat Autònoma de Barcelona ( España ) en 2018
- Idioma: español
- Tribunal Calificador de la Tesis: Alfredo Ruiz Panadero (presid.), Carme Segarra Robert (secret.), Marie Sémon (voc.)
- Programa de doctorado: Programa de Doctorado en Genética por la Universidad Autónoma de Barcelona
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- Resumen
Charles Darwin's theory of evolution proposes that the adaptations of organisms arise because of the process of natural selection. Natural selection leaves a characteristic footprint on the patterns of genetic variation that can be detected by means of statistical methods of genomic analysis. Today, we can infer the action of natural selection in a genome and even quantify what proportion of the incorporated genetic variants in the populations are adaptive. The genomic era has led to the paradoxical situation in which much more evidence of selection is available on the genome than on the phenotype of the organism, the primary target of natural selection.
The advent of next-generation sequencing (NGS) technologies is providing a vast amount of -omics data, especially increasing the breadth of available developmental transcriptomic series. In contrast to the genome of an organism, the transcriptome is a phenotype that varies during the lifetime and across different body parts. Studying a developmental transcriptome from a population genomic and spatio-temporal perspective is a promising approach to understand the genetic and developmental basis of the phenotypic change.
This thesis is an integrative population genomics and evolutionary biology project following a bioinformatic approach. It is performed in three sequential steps: (i) the comparison of different variations of the McDonald and Kreitman test (MKT), a method to detect recurrent positive selection on coding sequences at the molecular level, using empirical data from a North American population of D. melanogaster and simulated data, (ii) the inference of the genome features correlated with the evolutionary rate of protein-coding genes, and (iii) the integration of patterns of genomic variation with annotations of large sets of spatio-temporal developmental data (evo-dev-omics).
As a result of this approach, we have carried out two different studies integrating the patterns of genomic diversity with multiomics layers across developmental time and space. In the first study we give a global perspective on how natural selection acts during the whole life cycle of D. melanogaster, assessing whether different regimes of selection act through the developmental stages. In the second study, we draw an exhaustive map of selection acting on the complete embryo anatomy of D. melanogaster.
Taking all together, our results show that genes expressed in mid- and late-embryonic development stages exhibit the highest sequence conservation and the most complex structure: they are larger, consist of more exons and longer introns, encode a large number of isoforms and, on average, are highly expressed. Selective constraint is pervasive, particularly on the digestive and nervous systems. On the other hand, earlier stages of embryonic development are the most divergent, which seems to be due to the diminished efficiency of natural selection on maternal-effect genes. Additionally, genes expressed in these first stages have on average the shortest introns, probably due to the need for a rapid and efficient expression during the short cell cycles. Adaptation is found in the structures that also show evidence of adaptation in the adult, the immune and reproductive systems. Finally, genes that are expressed in one or a few different anatomical structures are younger and have higher rates of evolution, unlike genes that are expressed in all or almost all structures.
Population genomics is no longer a theoretical science, it has become an interdisciplinary field where bioinformatics, large functional -omics datasets, statistical and evolutionary models and emerging molecular techniques are all integrated to get a systemic view of the causes and consequences of evolution. The integration of population genomics with other phenotypic multiomics data is the necessary step to gain a global picture of how adaptation occurs in nature.
