Biology subjects

Campelo dos Santos, A. L.

Publications and source records attributed to Campelo dos Santos, A. L..

6 recordsLinked to original sources

bioRxiv2025

Adaptive deletion of functional duplicate genes in Drosophila

Gene deletion is traditionally viewed as a nonadaptive mechanism that eliminates functional redundancy, yet emerging evidence indicates that it disproportionately affects tissue-specific duplicates with unique functions. Here, we test whether gene deletion preferentially removes weakly constrained, degenerating duplicates or instead eliminates functionally active duplicates as an adaptive genome streamlining process. To identify the evolutionary and functional factors that determine which duplicates are lost, we systematically analyzed 100 gene deletion events in Drosophila by integrating sequence, expression, interaction, and structural data. We uncovered a strong bias toward the loss of younger child copies among functionally unique duplicates, whereas no such bias was observed for redundant duplicates. Contrary to expectations under relaxed constraint, deleted unique genes evolve more slowly, show higher expression, engage in more protein-protein interactions, and do not exhibit elevated structural divergence or intrinsic disorder relative to redundant duplicates. When compared with single-copy genes, deleted unique genes display similar evolutionary rates, slightly lower expression, greater network connectivity, comparable structural divergence, and lower intrinsic disorder. These patterns suggest that deletion frequently targets functionally active rather than degenerate genes. Collectively, our results support the hypothesis that gene deletion in Drosophila can represent an adaptive process that removes transiently functional duplicates, promoting genome streamlining and regulatory stability. SIGNIFICANCEGene deletion is commonly assumed to be a nonadaptive consequence of redundancy or functional decay after gene duplication. This view has shaped how gene loss is interpreted across evolutionary studies. Using 100 gene deletion events in Drosophila, we show that this assumption can be misleading. Deletion strongly favors loss of the younger child copy among functionally unique duplicate genes, whereas redundant duplicates show no such bias. Deleted unique genes also lack signatures of degeneration: they evolve slowly, show relatively high expression, participate in extensive protein-protein interaction networks, exhibit typical levels of structural divergence, and tend to be structurally ordered. Together, these results support a model in which gene deletion can selectively remove transiently functional duplicates, contributing to genome streamlining and regulatory stability. More broadly, our study reframes gene deletion as a potentially adaptive force shaping gene content evolution.

evolutionary biology↗

bioRxiv2025

Genomic insights into ancestry and infectious disease in 17th-century colonial Brazil

During urban redevelopment in the historic district of a Brazilian metropolis, archaeologists uncovered a previously undocumented 17th-century cemetery, containing the closely spaced remains of over two dozen young adult males of military age. Historical records suggest the site once housed a makeshift hospital, raising questions about the origins and causes of death of those interred, particularly given the absence of skeletal evidence for violent or fatal trauma. The current study integrates bioarchaeological, historical, and genomic data to investigate the ancestry and putative disease-related mortality of nine individuals whose remains were recovered and subsequently sequenced. Population-genetic analyses revealed strong affinities with Northern European populations, particularly from Norway, Iceland, Estonia, and Czechoslovia, consistent with their likely roles as soldiers or laborers employed by the Dutch West India Company. To explore potential causes of death, we conducted a metagenomic screening with a novel pipeline optimized for degraded DNA, which revealed widespread presence of Klebsiella pneumoniae and Mycobacterium tuberculosis pathogens across all samples. Authenticity was confirmed through post-mortem damage patterns characteristic of historical samples. These findings, together with the absence of combat trauma and the collective burial context at the site, support the hypothesis of an epidemic-related mortality event. This study contributes to the growing field of historical pathogen genomics and offers a rare genomic perspective on life, mobility, and health during a period of colonial upheaval in South America.

evolutionary biology↗

bioRxiv2025

Semi-supervised detection of natural selection with positive-unlabeled learning

Identifying genomic regions shaped by natural selection is a central goal in evolutionary ge-nomics. Existing machine learning methods for this task are typically trained using simulated genomic data labeled according to specific evolutionary scenarios. While effective in controlled settings, these models are limited by their reliance on explicit class labels. They can only detect the specific processes they were trained to recognize, making it difficult to interpret predictions for regions influenced by other evolutionary forces. This limitation is especially problematic when analyzing empirical genomes shaped by a mixture of adaptive, demographic, and ecolog-ical factors. One-vs.-rest strategies offer a potential alternative, but suffer from the inherent complexity of modeling all other evolutionary and demographic processes as a catch-all "rest" class. Here, we explore positive-unlabeled learning as a more flexible framework for detection of adaptive events. Positive-unlabeled learning is a semi-supervised approach that permits iden-tification of samples of a target class using only positive labels and an unlabeled background, without requiring explicit modeling of negative samples. To assess the utility of this approach, we focus on a binary classification setting for detecting selective sweeps (positive samples) aris-ing from positive natural selection against a mixed background composed of both unlabeled sweeps and neutrally-evolving regions. To accomplish this goal, we introduce PULSe, a method that employs only a set of labeled sweep observations for training while treating all remaining data as unlabeled. By avoiding assumptions about the composition of the background, PULSe enables robust sweep discovery in realistic genomic landscapes. We systematically evaluate its performance across a range of demographic, adaptive, and confounding contexts, including domain shift arising from misspecified demographic models, and find that PULSe delivers high performance and generalizability. To demonstrate a practical application of PULSe, we ana-lyzed European and Bengali in Bangladesh human genomes, treating the empirical genomes as unlabeled sets, and recapitulating several previously-identified sweep candidates. Our results show that PULSe provides a powerful and versatile alternative for detecting adaptive regions, with the potential to generalize across a range of genomic landscapes.

genomics↗

bioRxiv2025

Efficient detection and characterization of targets of natural selection using transfer learning

Natural selection leaves detectable patterns of altered spatial diversity within genomes, and identifying affected regions is crucial for understanding species evolution. Recently, machine learning approaches applied to raw population genomic data have been developed to uncover these adaptive signatures. Convolutional neural networks (CNNs) are particularly effective for this task, as they handle large data arrays while maintaining element correlations. However, shallow CNNs may miss complex patterns due to their limited capacity, while deep CNNs can capture these patterns but require extensive data and computational power. Transfer learning addresses these challenges by utilizing a deep CNN pre-trained on a large dataset as a feature extraction tool for downstream classification and evolutionary parameter prediction. This approach reduces extensive training data generation requirements and computational needs while maintaining high performance. In this study, we developed TrIdent, a tool that uses transfer learning to enhance detection of adaptive genomic regions from image representations of multilocus variation. We evaluated TrIdent across various genetic, demographic, and adaptive settings, in addition to unphased data and other confounding factors. TrIdent demonstrated improved detection of adaptive regions compared to recent methods using similar data representations. We further explored model interpretability through class activation maps and adapted TrIdent to infer selection parameters for identified adaptive candidates. Using whole-genome haplotype data from European and African populations, TrIdent effectively recapitulated known sweep candidates and identified novel cancer, and other disease-associated genes as potential sweeps.

evolutionary biology↗

bioRxiv2023

Genomic analyses correspond with deep persistence of peoples of Blackfoot Confederacy from glacial times

Mutually beneficial partnerships between genomics researchers and North American Indigenous Nations are rare. Here, we present one such partnership that provides insight into the peopling of the Americas and furnishes a new line of evidence that can be used to further treaty and Indigenous rights. We show that the genomics of sampled individuals from the Blackfoot Confederacy belong to a previously undescribed ancient lineage that diverged from other genomic lineages in the Americas in Late Pleistocene times. Using multiple complementary forms of knowledge, we provide a scenario for Blackfoot population history that fits with oral tradition and provides a plausible model for the evolutionary process of the peopling of the Americas. One-Sentence SummaryPreviously unknown genomic lineage in North America revealed in present-day Indigenous community and historic ancestors.

genomics↗

bioRxiv2021

The Genomic Prehistory of the Indigenous People of Uruguay

The prehistory of the people of Uruguay is greatly complicated by the dramatic and severe effects of European contact, as with most of the Americas. After the series of military campaigns that exterminated the last remnants of nomadic peoples, Uruguayan official history masked and diluted the former indigenous ethnic diversity into the narrative of a singular people that all but died out. Here we present the first whole genome sequences of the Indigenous people of the region before the arrival of Europeans, from an archaeological site in eastern Uruguay that dates from 2,000 years before present. We find a surprising connection to ancient individuals from Panama and eastern Brazil, but not to modern Amazonians. This result may be indicative of a distinct migration route into South America that may have occurred along the Atlantic coast. We also find a distinct ancestry previously undetected in South America. Though this work begins to piece together some of the demographic nuance of the region, the sequencing of ancient individuals from across Uruguay is needed to better understand the ancient prehistory and genetic diversity that existed before European contact, thereby helping to rebuild the history of the indigenous population of what is now Uruguay.

genomics↗