Biology subjects

Caldas, I. V.

Publications and source records attributed to Caldas, I. V..

3 recordsLinked to original sources

bioRxiv2023

The Drosophila melanogaster Y-linked gene, WDY, is required for sperm to swim in the female reproductive tract.

Unique patterns of inheritance and selection on Y chromosomes lead to the evolution of specialized gene functions. Yet characterizing the function of genes on Y chromosomes is notoriously difficult. We report CRISPR mutants in Drosophila of the Y-linked gene, WDY, which is required for male fertility. WDY mutants produce mature sperm with beating tails that can be transferred to females but fail to enter the female sperm storage organs. We demonstrate that the sperm tails of WDY mutants beat approximately half as fast as wild-type sperms and that the mutant sperm do not propel themselves within the male ejaculatory duct or female reproductive tract (RT). These specific motility defects likely cause the sperm storage defect and sterility of the mutants. Regional and genotype-dependent differences in sperm motility suggest that sperm tail beating and propulsion do not always correlate. Furthermore, we find significant differences in the hydrophobicity of key residues of a putative calcium-binding domain between orthologs of WDY that are Y-linked and those that are autosomal. Given that WDY appears to be evolving under positive selection, our results suggest that WDYs functional evolution coincides with its transition from autosomal to Y-linked in Drosophila melanogaster and its most closely related species. Finally, we show that mutants for another Y-linked gene, PRY, also show a sperm storage defect that may explain their subfertility. In contrast to WDY, PRY mutants do swim in the female RT, suggesting they are defective in yet another mode of motility, navigation, or a necessary interaction with the female RT. Overall, we provide direct evidence for the long-held presumption that protein-coding genes on the Drosophila Y regulate sperm motility.

genetics↗

bioRxiv2023

Adaptations to water stress and pastoralism in the Turkana of northwest Kenya

The Turkana people inhabit arid regions of east Africa--where temperatures are high and water is scarce--and they practice subsistence pastoralism, such that their diet is primarily composed of animal products. Working with Turkana communities, we sequenced 367 genomes and identified 8 regions putatively involved in adaptation to water stress and pastoralism. One of these regions includes a putative enhancer for STC1--a kidney-expressed gene involved in the response to dehydration and the metabolism of purine-rich foods such as red meat. We show that STC1 is induced by antidiuretic hormone in humans, is associated with urea levels in the Turkana themselves, and is under strong selection in this population (s[~]0.041). This work highlights that partnerships with subsistence-level groups can lead to new models of human physiology with biomedical relevance.

evolutionary biology↗

bioRxiv2022

Inference of selective sweep parameters through supervised learning

A selective sweep occurs when positive selection drives an initially rare allele to high population frequency. In nature, the precise parameters of a sweep are seldom known: How strong was positive selection? Did the sweep involve only a single adaptive allele (hard sweep) or were multiple adaptive alleles at the locus sweeping at the same time (soft sweep)? If the sweep was soft, did these alleles originate from recurrent new mutations (RNM) or from standing genetic variation (SGV)? Here, we present a method based on supervised machine learning to infer such parameters from the patterns of genetic variation observed around a given sweep locus. Our method is trained on sweep data simulated with SLiM, a fast and flexible framework that allows us to generate training data across a wide spectrum of evolutionary scenarios and can be tailored towards the specific population of interest. Inferences are based on summary statistics describing patterns of nucleotide diversity, haplotype structure, and linkage disequilibrium, which are estimated across systematically varying genomic window sizes to capture sweeps across a wide range of selection strengths. We show that our method can accurately infer selection coefficients in the range 0.01 < s < 100 and classify sweep types between hard sweeps, RNM soft sweeps, and SGV soft sweeps with accuracy 69 % to 95 % depending on sweep strength. We also show that the method infers the correct sweep types at three empirical loci known to be associated with the recent evolution of pesticide resistance in Drosophila melanogaster. Our study demonstrates the power of machine learning for inferring sweep parameters from present-day genotyping samples, opening the door to a better understanding of the modes of adaptive evolution in nature. Author summaryAdaptation often involves the rapid spread of a beneficial genetic variant through the population in a process called a selective sweep. Here, we develop a method based on machine learning that can infer the strength of selection driving such a sweep, and distinguish whether it involved only a single adaptive variant (a so-called hard sweep) or several adaptive variants of independent origin that were simultaneously rising in frequency at the same genomic position (a so-called soft selective sweep). Our machine learning method is trained on simulated data and only requires data sampled from a single population at a single point in time. To address the challenge of simulating realistic datasets for training, we explore the behavior of the method under a variety of testing scenarios, including scenarios where the history of the population of interest was misspecified. Finally, to illustrate the accuracy of our method, we apply it to three known sweep loci that have contributed to the evolution of pesticide resistance in Drosophila melanogaster.

evolutionary biology↗