🎙️ 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/

🎙️ Episode 65: Uncovering Hidden Splice Defects — Genome Sequencing and Group-Enrichment in Marfan Syndrome

🧬 In this episode of Base por Base, we delve into a groundbreaking study by Walker et al. (2025) in Genetics in Medicine that leverages whole-genome sequencing from the 100,000 Genomes Project alongside advanced in silico prediction and targeted RNA assays to reveal the contribution of non-canonical FBN1 splice variants to undiagnosed Marfan syndrome.

🔍 Key Highlights of the Study:
The authors systematically screened over 78,000 genomes, identifying 20 ultra-rare FBN1 variants in 23 families that lie beyond the ±8-base canonical splice regions; enrichment analysis showed these deep intronic and pseudoexon-creating variants account for approximately 3% of Familial Thoracic Aortic Aneurysm Disease cases previously lacking a molecular diagnosis; 70% of the variants were predicted to generate novel pseudoexons or extend exons, often introducing premature termination codons; experimental confirmation via RT-PCR, minigene constructs, and limited RNA-seq validated splicing aberrations for 16 of the 20 variants; replication in UK Biobank participants coded for Marfan syndrome supported a significant enrichment of predicted splice defects; these findings underscore the power of integrating genome sequencing, SpliceAI predictions, and bespoke RNA testing to uncover cryptic splice mutations that standard clinical assays may miss .

🧠 Conclusion:
This work inaugurates a new paradigm in Marfan diagnostics, demonstrating that systematic intronic analysis and confirmatory RNA assays can lift the veil on cryptic splice variants, thereby enhancing diagnostic yield and guiding more precise surveillance strategies for individuals at risk.

📖 Reference:
Walker S., Bunyan D.J., Thomas H.B., et al. (2025). Utility of genome sequencing and group-enrichment to support splice variant interpretation in Marfan syndrome. Genetics in Medicine. https://doi.org/10.1016/j.gim.2025.101477

📜 License:
This episode is based on a journal pre-proof published by Elsevier Inc. on behalf of the American College of Medical Genetics and Genomics. All rights reserved.

🎙️ Episode 64: Pisces — A Multi-Modal Framework for Predicting Drug Combination Synergy

🧬 In this episode of Base by Base, we delve into a groundbreaking methodological advance published by Xu et al. (2025) in Cell Genomics, which introduces Pisces, a novel machine-learning framework that overcomes data sparsity in drug combination studies by generating multiple modality-specific views for each drug pair. By projecting eight complementary data modalities—ranging from molecular structures and SMILES strings to drug targets, side-effect profiles, and ontological annotations—into a unified embedding space, Pisces expands the effective size of existing datasets up to sixty-four-fold, enabling robust synergy prediction even when some modalities are missing .

🔍 Highlights of the study: The Pisces framework creates independent augmented instances for each modality pair, bypassing the need for imputation; a tailored aggregator then selects the most informative predictions via a noisy-label ResNet module, excluding low-quality augmentations; across benchmarks, Pisces achieves state-of-the-art performance on cell-line-based and patient-derived xenograft synergy prediction tasks, significantly outperforming five leading approaches; it generalizes to never-before-seen drug combinations and cell lines, and even extends to predicting drug–drug interactions with high accuracy; interpretability analyses uncovered a breast cancer–sensitive pathway that correlates with improved survival in TCGA patients; finally, all code and datasets are openly accessible, accelerating future discoveries .

🧠 Conclusion: By integrating diverse drug modalities and leveraging data augmentation, Pisces inaugurates a new paradigm in computational pharmacology, offering a versatile and interpretable tool for discovering effective combination therapies and anticipating adverse interactions in clinical contexts.

📖 Reference:
Xu, H., Lin, J., Woicik, A., Liu, Z., Ma, J., Zhang, S., Poon, H., Wang, L., & Wang, S. (2025). Pisces: A multi-modal data augmentation approach for drug combination synergy prediction. Cell Genomics, 5, 100892. https://doi.org/10.1016/j.xgen.2025.100892

📜 License: This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International License (CC BY 4.0).