Biology subjects

Parker, D.

Publications and source records attributed to Parker, D..

3 recordsLinked to original sources

bioRxiv2018

Genes involved in the convergent evolution of asexuality in stick insects

Asexual reproduction has evolved repeatedly from sexual ancestors across a wide range of taxa. While the costs and benefits associated with asexuality have received considerable attention, the molecular changes underpinning the evolution of asexual reproduction remain relatively unexplored. In particular, it is completely unknown whether the repeated evolution of asexual phenotypes involves similar molecular changes, as previous studies have focused on changes occurring in single lineages. Here we investigate the extent of convergent gene expression changes across five independent transitions to asexuality in stick insects. We compared gene expression of asexual females to females of close sexual relatives in whole-bodies, reproductive tracts, and legs. We identified a striking amount of convergent gene expression change (up to 8% of genes), greatly exceeding that expected by chance. Convergent changes were also tissue-specific, and most likely driven by selection for functional changes. Genes showing convergent changes in the reproductive tract were associated with meiotic spindle formation and centrosome organization. These genes are particularly interesting as they can influence the production of unreduced eggs, a key barrier to asexual reproduction. Changes in legs and whole-bodies were likely involved in female sexual trait decay, with enrichment in terms such as sperm-storage and pigmentation. By identifying changes occurring across multiple independent transitions to asexuality, our results provide a rare insight into the molecular basis of asexual phenotypes and suggest that the evolutionary path to asexuality is highly constrained, requiring repeated changes to the same key genes.

evolutionary biology↗

bioRxiv2017

Multifactorial Dynamics of White Matter Connectivity During Adolescence

Studying developmental changes in white matter connectivity is critical for understanding neurobiological substrates of cognition, learning, and neuropsychiatric disorders. This becomes especially important during adolescence when a rapid expansion of the behavioral repertoire occurs. Several factors such as brain geometry, genetic expression profiles, and higher level architectural specifications such as the presence of segregated modules have been associated with the observed organization of white matter connections. However, we lack understanding of the extent to which such factors jointly describe the brain network organization, nor have insights into how their contribution changes developmentally. We constructed a multifactorial model of white matter connectivity using Bayesian network analysis and tested it with diffusion imaging data from a large community sample. We investigated contributions of multiple factors in explaining observed connectivity, including architectural specifications, which promote a modular yet integrative organization, and brains geometric and genetic features. Our results demonstrated that the initially dominant geometric and genetic factors become less influential with age, whereas the effect of architectural specifications increases. The identified structural modules are associated with well-known functional systems, and the level of association increases with age. This integrative analysis provides a computational characterization of the normative evolution of structural connectivity during adolescence.

neuroscience↗

bioRxiv2017

Harmonization Of Multi-Site Diffusion Tensor Imaging Data

Diffusion tensor imaging (DTI) is a well-established magnetic resonance imaging (MRI) technique used for studying microstructural changes in the white matter. As with many other imaging modalities, DTI images suffer from technical between-scanner variation that hinders comparisons of images across imaging sites, scanners and over time. Using fractional anisotropy (FA) and mean diffusivity (MD) maps of 205 healthy participants acquired on two different scanners, we show that the DTI measurements are highly site-specific, highlighting the need of correcting for site effects before performing downstream statistical analyses. We first show evidence that combining DTI data from multiple sites, without harmonization, is counter-productive and negatively impacts the inference. Then, we propose and compare several harmonization approaches for DTI data, and show that ComBat, a popular batch-effect correction tool used in genomics, performs best at modeling and removing the unwanted inter-site variability in FA and MD maps. Using age as a biological phenotype of interest, we show that ComBat both preserves biological variability and removes the unwanted variation introduced by site. Finally, we assess the different harmonization methods in the presence of different levels of confounding between site and age, in addition to test robustness to small sample size studies.

neuroscience↗