Male ruff sandpipers engaging in the act of mating typically fall into one of three groups, with variations in how aggressive they are and how showy their plumage is, among other factors. Now, a new study reports a single gene – HSD17B2 – drives these dramatic differences among male ruff sandpiper morphs. The findings show how evolutionary changes in a single gene's structure, sequence, and regulation can drive significant diversity within a single species. The androgen testosterone plays a key role in male reproductive development. It influences a range of physical traits, such as body size and ornamentation. It also influences social behaviors, including those related to courtship. Testosterone levels vary considerably between individuals, and a portion of this variation is thought to be genetic. However, more work is needed to characterize how genetic variants are linked to testosterone concentrations and different reproductive phenotypes. Jasmine Loveland and colleagues investigated testosterone production and metabolism in the ruff (Calidris pugnax), a shorebird renowned for its striking variation in male physical traits and reproductive behaviors. Ruffs exhibit three distinct male mating morphs. "Independent" males exhibit elaborate plumage and aggressively defend display territories to attract mates. "Satellite" males are less ornate and less aggressive, displaying alongside dominant independents to opportunistically secure mates. In contrast, "Faeder" males resemble females in size and appearance, lacking the showy plumage of other morphs, which enables them to blend in, avoid male aggression, and mate covertly. Independents have high circulating testosterone levels but low levels of androstenedione, a less potent androgen. The nonaggressive satellite and faeder males show the reverse pattern. Prior studies linked these morphs to a supergene containing around 100 genes. Loveland et al. focused on HSD17B2, a gene within the supergene, and discovered that evolutionary changes in this gene lead to increased production of highly active enzymes in low-testosterone ruff morphs. These enzymes convert testosterone to androstenedione at a faster rate, lowering testosterone levels in the bloodstream. The tissue-specific activity of HSD17B2 allows these morphs to keep testosterone levels high in the testes for reproduction while limiting its effects elsewhere, supporting ruffs’ unique mating behaviors and traits. “The findings of Loveland et al. point to what may be an emerging “rule of life” for hormone-mediated traits or possibly all complex traits – in evolution, each problem has many possible solutions,” writes Kimberly Rosvall in a related Perspective.

In a new study involving whole-genome data, researchers present “CASTER,” a tool that uses arrangements in DNA sequences known as site patterns to infer “species trees,” which are diagrams that depict the evolutionary relationships among species. The tool, which performs with exceptional accuracy and scalability and overcomes the limitations of traditional phylogenetic methods, offers transformative potential for evolutionary research. The growing availability of genomic data has revitalized efforts to construct precise species trees and model gene tree variations. However, the methodology for utilizing genome-wide data lags behind data availability. While traditional methods struggle with incomplete lineage sorting (ILS), and two-step approaches face computational hurdles, emerging site-based methods address ILS but are hampered by scalability and accuracy issues. In response, Chao Zhang and colleagues developed CASTER (Coalescence-aware Alignment-based Species Tree Estimator), a novel approach that directly infers species trees from whole-genome alignments. Through extensive simulations and analyses of previously studied genomic datasets, including for birds and mammals, Zhang et al. demonstrate that CASTER is typically faster and more accurate than other state-of-the-art methods in analyzing hundreds of recombining genomes with precise phylogenic inference. However, despite its promise, the authors note CASTER’s limitations, including the absence of branch lengths and reliance on specific evolutionary model assumptions. Future developments aim to address these theoretical and practical challenges, expanding the tool’s applicability to broader data types and more complex biological scenarios.

 

For reporters interested in research integrity issues, author Siavash Mir Arabbaygi notes, “Our field has made considerable strides in ensuring (almost) all tools used in practice are open source (including the method introduced in this paper and all other methods introduced by our lab). Top journals in the field also do a great job of nudging authors to provide data in public repositories such as Dryad, Zenodo, and FigShare. Authors provide data at various levels of detail, partially due to the effort needed to export large datasets and partially because of several limitations that public repositories have on the size of the provided data. Unlike some other fields, phylogenetics research has been very open and generous in terms of data sharing.”

Rising carbon dioxide levels affect more than just the climate; they also affect the chemistry of the oceans. When saltwater absorbs carbon dioxide, it becomes acidic, which alters the aquatic animal ecosystem. But how exactly does ocean acidification impact animals whose genetic makeup can shift depending on environmental cues? A study published in ACS’ Environmental Science & Technology addresses this question through the “eyes” of oysters.

Rakesh Jain PhD, Director of the E.L. Steele Laboratories for Tumor Biology at Massachusetts General Hospital and A. Werk Cook Professor of Radiation Oncology at Harvard Medical School, is senior author of a new study in PNAS, Targeting EPHB2/ABL1 Restores Anti-Tumor Immunity in Preclinical Models of Ependymoma.

Our bodies are made up of around 75 billion cells. But what function does each individual cell perform and how greatly do a healthy person’s cells differ from those of someone with a disease? To draw conclusions, enormous quantities of data must be analyzed and interpreted. For this purpose, machine learning methods are applied. Researchers at the Technical University of Munich (TUM) and Helmholtz Munich have now tested self-supervised learning as a promising approach for testing 20 million cells or more.