🎙️ Episode 68: Indels Empower Antiviral Proteins to Achieve Functional Novelty Beyond Missense Mutations
🧬 In this episode of Base by Base, we dive into pioneering work by Tenthorey et al. (2025) in Cell Genomics that uncovers how insertion and deletion mutations—indels—can unlock evolutionary innovations in the antiviral protein TRIM5α. By applying both deep mutational scanning and a novel deep indel scanning approach to the v1 loop of human TRIM5α, the authors reveal that while no single-nucleotide missense change can confer restriction of the simian immunodeficiency virus SIVsab, a single in-frame duplication of phenylalanine at position 339 instantaneously grants potent antiviral activity against SIVsab and other lentiviruses. This discovery highlights indels as a powerful, yet often overlooked, mechanism for traversing otherwise insurmountable fitness landscapes in host–virus evolutionary arms races.
🔍 Study Highlights: In exhaustive screens, human TRIM5α variants bearing every possible missense change failed to inhibit SIVsab, underscoring the limits of point mutations. Deep indel scanning then identified three in-frame duplication variants that gained SIVsab restriction, with the F339dup alone replicating nine independent rhesus-like mutations in one step. This single amino acid duplication not only enabled defense against SIVsab but also broadened activity to HIV-1 and SIVcpz without impairing existing N-tropic murine leukemia virus restriction, demonstrating a net evolutionary gain. Comparative analysis of primate TRIM5α orthologs confirmed that naturally occurring indels—such as a two-residue insertion in rhesus monkeys and a 20-residue duplication in sabaeus monkeys—directly determine species-specific antiviral specificities.
🧠 Conclusion: By revealing that indel mutations can deliver high-risk, high-reward leaps in protein function inaccessible by missense changes alone, this work reshapes our understanding of antiviral adaptation. Indels emerge not as mere byproducts of genetic drift but as strategic evolutionary tools that enable rapid, robust innovation in host defenses.
📖 Reference: Tenthorey, J. L., del Banco, S., Ramzan, I., Klingenberg, H., Liu, C., Emerman, M., & Malik, H. S. (2025). Indels allow antiviral proteins to evolve functional novelty inaccessible by missense mutations. Cell Genomics, 5, 100818. https://doi.org/10.1016/j.xgen.2025.100818
📜 License: This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International (CC BY 4.0) license – https://creativecommons.org/licenses/by/4.0/

🎙️ Episode 67: Smarter Signals — How Multimodal AI Boosts Genetic Prediction of Heart Disease

🧬 In this episode of Base por Base, we delve into a major advancement in the intersection of artificial intelligence and cardiovascular genomics. A study by Zhou et al. (2025), published in The American Journal of Human Genetics, introduces M-REGLE (Multimodal Representation Learning for Genetic Discovery on Low-dimensional Embeddings), a novel AI framework designed to enhance genome-wide association studies (GWAS) by integrating multiple physiological waveform modalities such as ECG and PPG.

M-REGLE jointly analyzes these complementary health signals using variational autoencoders to generate low-dimensional, uncorrelated embeddings, which are then used to uncover new genetic associations. This multimodal approach allows for more effective representation of biological data than traditional unimodal models, significantly improving the power of GWAS and the accuracy of polygenic risk scores (PRS), especially for conditions like atrial fibrillation.

Compared to unimodal learning, M-REGLE identified 19.3% more loci from 12-lead ECG data and 13.0% more loci from ECG+PPG data. It also achieved superior PRS performance across several datasets, including UK Biobank, Indiana Biobank, EPIC-Norfolk, and the British Women’s Heart and Health Study. Notably, the embeddings generated by M-REGLE remained unsupervised yet were predictive of cardiovascular diseases, suggesting that the model captures meaningful physiological and pathological patterns from raw data alone.

🧠 Conclusion:
M-REGLE exemplifies the transformative potential of combining multimodal physiological data with deep generative models for genetic discovery. By capturing both shared and complementary information across modalities, this AI-driven approach opens new doors to understanding the genetic architecture of cardiovascular diseases and improving clinical prediction tools using data already available from wearable devices.

📖 Reference:
Zhou, Y., Khasentino, J., Yun, T., et al. (2025). Applying multimodal AI to physiological waveforms improves genetic prediction of cardiovascular traits. The American Journal of Human Genetics, 112, 1562–1579. https://doi.org/10.1016/j.ajhg.2025.05.015

📜 License:
This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International (CC BY 4.0) license – https://creativecommons.org/licenses/by/4.0/

🎙️ Episode 66: Mainstreaming Clinical Genetic Testing — A Conceptual Framework

🧬 In this episode of Base por Base, we delve into the consensus-based framework introduced by Mackley et al. (2025) in Genetics in Medicine, which proposes a structured approach to integrate genetic testing into non-geneticist clinical practice to meet growing demand amid a stable genetics workforce .

🔍 Study Highlights:
The authors convened a focus group of thirty-five experts representing twenty clinical genetics services across Canada to define “mainstreaming,” map the diagnostic care pathway into assessment, pre-testing, laboratory, and post-testing stages, and identify key variables influencing model selection . The framework outlines six categories of variables—patient characteristics, disease features, test complexity, clinician expertise, report design, and health-system context—that determine the appropriateness of different mainstreaming models . It describes four generalizable models—“to-test,” “to-result,” “to-navigation,” and fully mainstreamed—that reflect progressively shifting responsibilities from the genetics service to non-geneticist clinicians . Designed for adaptability, the taxonomy facilitates standardized evaluation of accessibility, sustainability, diagnostic yield, and patient satisfaction across diverse clinical settings .

🧠 Conclusion:
This conceptual framework provides a unified roadmap for designing, implementing, and evaluating mainstreaming initiatives in clinical genetics, optimizing scopes of practice while improving patient access to genomics-informed care .

📖 Reference:
Mackley MP, Richer J, Guerin A, et al. Mainstreaming of clinical genetic testing: a conceptual framework. Genetics in Medicine. 2025. https://doi.org/10.1016/j.gim.2025.101465

📜 License:
This episode is based on an open access article published under the Creative Commons Attribution 4.0 International (CC BY 4.0) license – https://creativecommons.org/licenses/by/4.0/