The CD300 gene cluster encodes activating and inhibitory lipid-binding innate immune receptors that, in humans and mice, play important roles in immune response, including viral entry and autoimmunity. However, their broader evolutionary and functional diversification dynamics across vertebrates remain unresolved.
Genomic and transcriptomic analyses were employed to identify CD300 orthologs and paralogs across vertebrates. Numbers and combinations of CD300 genes were quantified and cataloged. AI-based protein folding tools were used to predict changes in protein structures. Recombinant forms of CD300 proteins were generated and subjected to an unbiased lipid-binding assay to identify and quantify ligand binding differences between orthologs and paralogs.
Our analyses reveal that CD300s are present across jawed vertebrates, with a pronounced pulse of gene duplication coinciding with the origin of placental mammals. Within mammals, the evolutionary trajectory of this cluster is remarkably labile. We observe dramatic lineage-specific expansions and contractions in groups such as chiropterans (bats) and cetaceans (whale and dolphins) that correspond to major events in the evolutionary history of these groups. Structural predictions suggest that these gene duplications are accompanied by corresponding changes in protein shape, including the extracellular domains that mediate lipid binding. Ongoing lipidomics-based ligand screening supports this prediction.
Our preliminary data show divergent lipid-binding profiles across CD300 paralogs, suggesting functional novelty. Together, these results position CD300s as a tractable model for understanding how the molecular diversification of clustered immune gene families corresponds to their ligand preferences likely reflecting their evolutionary histories.
NSF IOS 2419126; NSF IOS 2419127; NSF IOS 2419128
Veterinary and Comparative Immunology (VET)
Jeffrey A. Yoder, Erin S. Baker, Ian Birchler De Allende et al.· Journal of Immunology· 0 citations
The arrival of generative artificial intelligence (GenAI) in educational settings has sparked debate over how it will transform teaching and learning. Language education is already grappling with teacher shortages, workload intensification, and shifting program viability. While conceptual work has outlined theoretical use cases, little empirical research has examined how world language teachers actually use GenAI or the challenges they encounter. In this study, we used a mixed‐methods design to survey U.S. world language teachers, followed by interviews with eight teachers who were highly proficient in GenAI implementation. Findings revealed various factors shaping GenAI adoption and challenged claims that GenAI diminishes teacher expertise or undermines critical thinking. Access to GenAI and policies for its use were uneven across school districts and grade levels. Without institutional guidance, many teachers were self‐taught, underscoring the need for targeted professional development. We discuss how, with appropriate guidance, emerging AI‐integrated pedagogical models have the potential to revitalize and sustain language education programs through strategic, teacher‐led use of GenAI.
Jue Wang, K. Davin, Scott Kissau et al.· Foreign language annals· 1 citation
Course-based undergraduate research experiences (CUREs) broaden access to authentic scientific inquiry through responsive instructor support as research problems become increasingly complex. Generative artificial intelligence (GenAI) may extend this support by providing individualized assistance that can adapt as student needs change. However, how embedding GenAI within a CURE to provide support across the research process impacts student inquiry, collaboration, and scientific reasoning remains unresolved. Here we use longitudinal qualitative data collected across three semesters of a bioinformatics and genomics CURE to show that GenAI expanded the intellectual reach of the research experience in three distinct ways. First, personalized, on-demand scaffolding allowed students to move beyond the boundaries of instructor expertise and transform their own interests into researchable inquiry, with all teams developing distinct self-directed projects rather than selecting instructor-provided topics. Second, GenAI became part of the distributed cognitive system of research teams, helping novice researchers communicate and coordinate across differentiated expertise without eliminating specialization. Third, expanded capability did not replace the need for disciplinary judgment. Students increasingly validated, revised, or rejected AI-generated contributions, such that research independence emerged through retained intellectual responsibility. Together, these findings suggest that GenAI can extend the reach of CUREs by expanding what novice researchers can investigate, how they can collaborate, and the level of responsibility they can assume while preserving human judgment central to authentic scientific inquiry.
Aditi Babar, K. Davin, A. Dornburg· 0 citations
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