380: Prime-SGE maps drug-resistance variants at scale

380: Prime-SGE maps drug-resistance variants at scale

Abadie FMC et al., Cell Genomics - Abadie et al. present prime‑SGE, a pooled prime‑editing framework that installs thousands of precise point mutations across multiple oncogenes and identifies drug‑resistance variants by sequencing integrated pegRNAs after positive‑selection with kinase inhibitors. The method resolved known resistance mutations (e.g., EGFR C797S, KRAS G12 variants), uncovered less-characterized candidates, compared resistance landscapes across covalent and non‑covalent EGFR inhibitors, and validated resistant edits in vivo. Key terms: prime editing, drug resistance, EGFR, KRAS, multiplex screening. Study Highlights: Prime‑SGE uses libraries of barcoded pegRNAs/epegRNAs delivered at low MOI into PEmax‑expressing, MLH1‑knockout cell lines to program thousands of point mutations and read out variant abundance by sequencing integrated guides after drug selection. In pooled screens across eight oncogenes and three EGFR inhibitors, prime‑SGE recovered established resistance mutations (EGFR C797S, KRAS G12 variants) and identified less-characterized hits (e.g., EGFR Q791, Y801). Distinct resistance landscapes emerged for covalent versus non‑covalent EGFR inhibitors, and barcodes showed many resistant clones arose from independent editing events. Prime‑edited resistant cells formed tumors in osimertinib-treated xenografts, demonstrating in vivo relevance. Conclusion: Prime‑SGE enables scalable, positive‑selection profiling of thousands of precise point mutations across the genome to identify and compare drug‑resistance variants, though sensitivity is limited by variable prime editing efficiency. The approach can prioritize resistance variants for follow-up and inform inhibitor development and choice. Music: Enjoy the music based on this article at the end of the episode. Article title: A multiplex, prime editing framework for identifying drug resistance variants at scale First author: Abadie FMC Journal: Cell Genomics DOI: 10.1016/j.xgen.2026.101167 Reference: Abadie FMC, Suiter CC, Smith NT, et al. A multiplex, prime editing framework for identifying drug resistance variants at scale. Cell Genomics. 2026;6:101167. doi:10.1016/j.xgen.2026.101167 License: This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International License (CC BY 4.0) – https://creativecommons.org/licenses/by/4.0/ Support: Base by Base – Stripe donations: https://donate.stripe.com/7sY4gz71B2sN3RWac5gEg00 Official website https://basebybase.com On PaperCast Base by Base you'll discover the latest in genomics, functional genomics, structural genomics, and proteomics. Episode link: https://basebybase.com/episodes/prime-sge-drug-resistance-variants QC: This episode was checked against the original article PDF and publication metadata for the episode release published on 2026-05-29. QC Scope: - article metadata and core scientific claims from the narration - excludes analogies, intro/outro, and music - transcript coverage: Audited the transcript's sections describing Prime-SGE concept, the scale of edits, key resistance mutations (EGFR C797S, KRAS G12 variants, Q791, Y801), inhibitor contexts (osimertinib, sunvozertinib, CH7233163), in vivo xenograft validation, and limitations (editing efficiency, false negatives). QC Summary: - factual score: 10/10 - metadata score: 10/10 - supported core claims: 8 - claims flagged for review: 0 - metadata checks passed: 4 - metadata issues found: 0 Metadata Audited: - article_doi - article_title - article_journal - license Factual Items Audited: - Prime-SGE enables multiplexed installation of thousands of precise edits across multiple genes with readout by integrated pegRNA barcodes - Large-scale screens used 3,825 epegRNAs programming 1,220 single-nucleotide mutations across seven oncogenes and three TKIs - Resistance mutations include EGFR C797S and KRAS G12 variants; novel mutations such as EGFR Q791 and Y801 described - KRAS G12 variants (G12C, G12S, G12V, G12D, etc.) confer resistance to osimertinib, sunvozertinib, and CH7233163 - Distinct resistance landscapes emerge for covalent (osimertinib, sunvozertinib) versus non-covalent (CH7233163) inhibitors - In vivo validation shows prime-edited cells (e.g., C797S pool) form tumors under osimertinib treatment in xenografts QC result: Pass.

379: Long reads reveal hidden structural and repeat variation in autism

Mortazavi M et al., Cell Genomics - PaperCast Base by Base discusses a long-read whole-genome sequencing study of 267 individuals from 63 families that increased detection of structural variants and tandem repeats, resolved complex rearrangements, linked repeat expansions to methylation at FMR1, and estimated rare-variant contributions to ASD heritability. Key terms: long-read sequencing, structural variants, tandem repeats, autism, methylation. Study Highlights: The authors performed long-read WGS (PacBio HiFi and Oxford Nanopore) on 267 individuals and integrated calls with prior short-read data, increasing detection of gene-disrupting SVs by 33% and TRs by 38%. They discovered novel exonic de novo and somatic-mosaic SVs and characterized a previously undescribed class of nested DUP-DEL complex rearrangements. Joint phasing and methylation analysis identified deletions affecting imprinted genes (e.g., ADNP2) and showed that intermediate FMR1 CGG expansions (35–54 repeats) associate with allele-specific hypermethylation. Burden and heritability analyses indicate rare SVs, TRs, and damaging SNVs together explain a measurable fraction of ASD heritability, though power is limited by sample size. Conclusion: Long-read WGS uncovers substantial previously hidden structural and repeat variation and enables combined phased genetic and methylation analysis to improve functional interpretation in ASD, but larger cohorts and deeper coverage are needed to refine associations and heritability estimates. Music: Enjoy the music based on this article at the end of the episode. Article title: Long-read genome sequencing improves detection and functional interpretation of structural and repeat variants in autism First author: Mortazavi M Journal: Cell Genomics DOI: 10.1016/j.xgen.2026.101186 Reference: Mortazavi M., Guevara J., Diaz J., et al., 2026. Long-read genome sequencing improves detection and functional interpretation of structural and repeat variants in autism. Cell Genomics 6, 101186. https://doi.org/10.1016/j.xgen.2026.101186 License: This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International License (CC BY 4.0) – https://creativecommons.org/licenses/by/4.0/ Support: Base by Base – Stripe donations: https://donate.stripe.com/7sY4gz71B2sN3RWac5gEg00 Official website https://basebybase.com On PaperCast Base by Base you'll discover the latest in genomics, functional genomics, structural genomics, and proteomics. Episode link: https://basebybase.com/episodes/long-read-wgs-autism-structural-repeat-variants QC: This episode was checked against the original article PDF and publication metadata for the episode release published on 2026-05-27. QC Scope: - article metadata and core scientific claims from the narration - excludes analogies, intro/outro, and music - transcript coverage: Substantively audited portions describing LR-WGS methodology, SV/TR detection gains, mosaic and de novo SVs (STK33), large balanced rearrangements, nested DUP-DEL SVs, imprinting (ADNP2), FMR1 gray-zone methylation, ASD heritability, and study limitations/future directions. - transcript topics: LR-WGS methods and methylation calling; Structural variants and tandem repeats detection gains; Mosaic and de novo SVs (STK33) and functional impact; Complex DUP-DEL rearrangements; Imprinted genes and ADNP2; FMR1 CGG repeats and methylation, XCI independence QC Summary: - factual score: 10/10 - metadata score: 10/10 - supported core claims: 7 - claims flagged for review: 0 - metadata checks passed: 4 - metadata issues found: 0 Metadata Audited: - article_doi - article_title - article_journal - license Factual Items Audited: - Cohort: LR-WGS performed on 267 individuals from 63 ASD families (including 117 offspring, 76 cases, 41 controls; 126 parents). - Sequencing platforms: PacBio HiFi and Oxford Nanopore (ONT); mean read lengths ~11.3 kb for HiFi and ~5.6 kb for ONT. - LR-WGS boosted detection of SVs by 33% and TRs by 38% compared to SR-WGS. - Out of ~44,000 SVs detected (non-TR SVs), ~16,488 were novel LR-WGS-specific variants; ~7,084 were SR-WGS-specific. - TR regions: LR-WGS genotyped ~98% of annotated TR regions; many TR regions not accessible by SR-WGS. - Mosaic STK33 duplication identified, maternal haplotype, present in ~50% of cells, with an in-frame 66 amino acid duplication. QC result: Pass.

378: Dominant-negative PSMB8 variants stall immunoproteasome assembly

Wijngaard R et al., The American Journal of Human Genetics - Researchers describe seven individuals with monoallelic PSMB8 missense variants that impair immunoproteasome assembly, causing early-onset immunodeficiency and variable systemic inflammation via a dominant-negative mechanism. Key terms: PSMB8, immunoproteasome, PRAAS-ID, immunodeficiency, proteasome assembly. Study Highlights: Seven individuals from five families carrying distinct monoallelic PSMB8 variants presented with neonatal-onset immunodeficiency, B cell lymphopenia, hypogammaglobulinemia, and variable inflammatory disease. Structural modeling predicted destabilization of proteasome interfaces, and complexome profiling plus native assays showed reduced fully assembled immunoproteasomes with accumulation of a ∼440-kDa assembly intermediate. Mutant PSMB8 precursors accumulated, incorporation into 20S/26S complexes was reduced, immunoproteasome-specific activity decreased, and integrated stress response genes were induced. These data support a shared dominant-negative mechanism disrupting immunoproteasome biogenesis and immune signaling. Conclusion: Monoallelic PSMB8 missense variants impair incorporation of β5i into assembling immunoproteasomes, stalling biogenesis, reducing immunoproteasome abundance and activity, and producing clinically variable immunodeficiency with systemic inflammation consistent with PRAAS-ID. Music: Enjoy the music based on this article at the end of the episode. Article title: Monoallelic PSMB8 variants cause PRAAS with immunodeficiency through impaired immunoproteasome assembly First author: Wijngaard R Journal: The American Journal of Human Genetics DOI: 10.1016/j.ajhg.2026.04.015 Reference: Wijngaard R., van der Made C.I., Kalkan Uçar S., et al. Monoallelic PSMB8 variants cause PRAAS with immunodeficiency through impaired immunoproteasome assembly. Am J Hum Genet. 2026;113:1–19. doi:10.1016/j.ajhg.2026.04.015 License: This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International License (CC BY 4.0) – https://creativecommons.org/licenses/by/4.0/ Support: Base by Base – Stripe donations: https://donate.stripe.com/7sY4gz71B2sN3RWac5gEg00 Official website https://basebybase.com On PaperCast Base by Base you'll discover the latest in genomics, functional genomics, structural genomics, and proteomics. Episode link: https://basebybase.com/episodes/monoallelic-psmb8-praas-id-immunoproteasome-assembly QC: This episode was checked against the original article PDF and publication metadata for the episode release published on 2026-05-26. QC Scope: - article metadata and core scientific claims from the narration - excludes analogies, intro/outro, and music - transcript coverage: Substantive audit of immunoproteasome biology, dominant-negative mechanism of monoallelic PSMB8 variants, complexome profiling findings (440-kDa assembly intermediate, reduced IP abundance), functional consequences (IP activity reduction, ISR activation), and clinical implications described in the transcript. - transcript topics: Immunoproteasome structure and SP/IP distinction; Dominant-negative PSMB8 variants and mechanism; Complexome profiling methodology and IP assembly intermediates; Impaired IP biogenesis and 440-kDa intermediate; ISR activation and immune signaling effects; Clinical features: B cell lymphopenia, hypogammaglobulinemia, leukocyte inclusions QC Summary: - factual score: 10/10 - metadata score: 10/10 - supported core claims: 6 - claims flagged for review: 0 - metadata checks passed: 4 - metadata issues found: 0 Metadata Audited: - article_doi - article_title - article_journal - license Factual Items Audited: - Monoallelic PSMB8 variants cause PRAAS-ID via dominant-negative impairment of immunoproteasome assembly - Mutant PSMB8 subunits are inefficiently incorporated into immunoproteasomes, leading to defective IP biogenesis and reduced IP activity - Complexome profiling reveals accumulation of a ~440-kDa IP assembly intermediate with assembly factors - The 440-kDa intermediate is catalytically inactive despite partial assembly - Leukocyte vacuolization and inclusions observed in blood/bone marrow - B cell lymphopenia and hypogammaglobulinemia, frequently requiring IVIG QC result: Pass.

377: ProteomeLM — proteome-scale language modeling for interactomes and essential genes

Malbranke C et al., Proceedings of the National Academy of Sciences (PNAS) - ProteomeLM is a transformer-based language model trained on complete proteomes that produces contextualized protein embeddings and attention signals which recover protein–protein interactions unsupervised and support supervised PPI and gene essentiality prediction across diverse taxa. Key terms: proteome language model, protein–protein interactions, gene essentiality, ProteomeLM, deep learning. Study Highlights: ProteomeLM was trained on ~32,000 proteomes using ESM‑C embeddings and a custom polar loss to reconstruct masked protein embeddings in proteome context. Its attention heads encode protein–protein interactions without supervision and distinguish direct physical binding, complex membership, and broader functional associations. As a fast first-pass filter it outperforms amino-acid coevolution (DCA) in recall while reducing compute by orders of magnitude. Downstream supervised models—ProteomeLM-PPI and ProteomeLM-Ess—achieve state-of-the-art cross-species PPI prediction and strong gene essentiality prediction that generalizes to held-out and synthetic minimal genomes. Conclusion: Representing proteins in whole-proteome context yields interpretable attention signals that capture functional and physical relationships, enabling rapid, accurate interactome screening and improved gene essentiality prediction across the tree of life. Music: Enjoy the music based on this article at the end of the episode. Article title: ProteomeLM: A proteome-scale language model enables accurate and rapid prediction of protein–protein interactions and gene essentiality across taxa First author: Malbranke C Journal: Proceedings of the National Academy of Sciences (PNAS) DOI: 10.1073/pnas.2524201123 Reference: Malbranke C, Zalaffi GP, Bitbol A-F. ProteomeLM: A proteome-scale language model enabling accurate and rapid prediction of protein–protein interactions and gene essentiality across taxa. Proc Natl Acad Sci U S A. 2026;123:e2524201123. doi:10.1073/pnas.2524201123 License: This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International License (CC BY 4.0) – https://creativecommons.org/licenses/by/4.0/ Support: Base by Base – Stripe donations: https://donate.stripe.com/7sY4gz71B2sN3RWac5gEg00 Official website https://basebybase.com On PaperCast Base by Base you'll discover the latest in genomics, functional genomics, structural genomics, and proteomics. Episode link: https://basebybase.com/episodes/proteomelm-interactomes-essentiality QC: This episode was checked against the original article PDF and publication metadata for the episode release published on 2026-05-26. QC Scope: - article metadata and core scientific claims from the narration - excludes analogies, intro/outro, and music - transcript coverage: Audited substantive scientific content in transcript: ProteomeLM architecture, functional encoding, polar loss, unsupervised PPI via attention, speed/screening benefits, supervised PPI (ProteomeLM-PPI), gene essentiality predictions (ProteomeLM-Ess), and cross-species/minimal cells. - transcript topics: ProteomeLM architecture and training on whole proteomes; Functional encoding using orthology (OrthoDB); Polar loss and avoiding reliance on coarse functional encoding; Attention coefficients encoding protein-protein interactions (PPI) in unsupervised manner; Unsupervised PPI detection and protein complex membership; Speed and scalability of whole-interactome screening vs DCA QC Summary: - factual score: 10/10 - metadata score: 10/10 - supported core claims: 6 - claims flagged for review: 0 - metadata checks passed: 4 - metadata issues found: 0 Metadata Audited: - article_doi - article_title - article_journal - license Factual Items Audited: - ProteomeLM trained on ~32,000 annotated proteomes spanning the tree of life and uses a functional encoding via orthologous groups (OrthoDB). - ProteomeLM’s attention coefficients encode PPI without supervision (no interaction labels during training). - ProteomeLM enables fast whole-interactome screening and is substantially faster than DCA (up to six orders of magnitude); inference under 10 minutes per proteome on a single GPU. - Unsupervised PPI performance in Escherichia coli: a single attention head (head7, layer3) achieves AUC = 0.92. - ProteomeLM can distinguish direct interactions, same-complex interactions, and genetic associations; ribosome and TRiC/CCT complex analyses yield high AUC (>= 0.99 for some tests). - ProteomeLM-PPI achieves state-of-the-art supervised PPI predictions across species; ProteomeLM-Ess predicts gene essentiality; best reported AUC = 0.93 with layer-8 embeddings from QC result: Pass.

376: Pfh1's Balancing Act: Unwinding, Rewinding, and the Role of Mitochondrial SSB

Ortiz-Rodríguez M et al., Proceedings of the National Academy of Sciences (PNAS) - Single-molecule optical tweezers and fluorescence reveal how the S. pombe Pif1-family helicase Pfh1 alternates ATP-dependent unwinding and ATP-modulated rewinding at replication-fork-like substrates, and how mitochondrial SSB spRim1 tunes those activities. Key terms: Pfh1 helicase, Pif1-family, DNA unwinding, spRim1, single-molecule. Study Highlights: Using single-molecule optical tweezers and fluorescence, the authors show Pfh1 performs ATP-dependent unwinding–rewinding cycles with an intrinsic ~20–22 bp processivity. Contacts with the translocating strand modulate apparent ATP affinity while engagement of the displaced strand limits maximum unwinding velocity. The mitochondrial SSB spRim1 binds the displaced strand, disrupts those contacts, and increases unwinding and rewinding velocities. Rewinding is ATP-dependent and proceeds via a sliding-back mechanism rather than strand switching. Conclusion: Pfh1 balances unwinding and rewinding through coordinated ATP-dependent interactions with both fork strands; binding of spRim1 to the displaced strand disrupts inhibitory helicase–strand contacts and accelerates fork dynamics, providing a mechanistic framework for how Pif1-family helicases promote replication fork progression without disrupting replisome organization. Music: Enjoy the music based on this article at the end of the episode. Article title: Regulation of Pfh1 helicase activity by nucleic acid interactions and mitochondrial SSB First author: Ortiz-Rodríguez M Journal: Proceedings of the National Academy of Sciences (PNAS) DOI: 10.1073/pnas.2602528123 Reference: Ortiz-Rodríguez M, Singh SP, Cao-García FJ, Galletto R, Ibarra B. Regulation of Pfh1 helicase activity by nucleic acid interactions and mitochondrial SSB. PNAS. 2026;123(21):e2602528123. doi:10.1073/pnas.2602528123 License: This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International License (CC BY 4.0) – https://creativecommons.org/licenses/by/4.0/ Support: Base by Base – Stripe donations: https://donate.stripe.com/7sY4gz71B2sN3RWac5gEg00 Official website https://basebybase.com On PaperCast Base by Base you'll discover the latest in genomics, functional genomics, structural genomics, and proteomics. Episode link: https://basebybase.com/episodes/pfh1-helicase-unwinding-rewinding QC: This episode was checked against the original article PDF and publication metadata for the episode release published on 2026-05-26. QC Scope: - article metadata and core scientific claims from the narration - excludes analogies, intro/outro, and music - transcript coverage: Audited the transcript sections describing Pfh1 unwinding/rewinding cycles, force and ATP dependencies, spRim1 modulation, DNA fork vs RNA–DNA fork experiments, and the proposed sliding-back mechanism and its biological relevance. - transcript topics: Pfh1 helicase function and 5'-3' directionality; Unwinding–rewinding cycles and ~20 bp processivity; ATP concentration and force dependencies (Km(f), Vmax); Role of spRim1 in DNA fork unwinding/rewinding; RNA–DNA fork experiments and strand-switching debate; Rewinding mechanism and ATP hydrolysis effects QC Summary: - factual score: 10/10 - metadata score: 10/10 - supported core claims: 7 - claims flagged for review: 0 - metadata checks passed: 4 - metadata issues found: 0 Metadata Audited: - article_doi - article_title - article_journal - license Factual Items Audited: - PfH1 operates via unwinding–rewinding cycles with coordination between fork strands - Intrinsic unwinding processivity is ~20 bp (not extending beyond ~22 bp per burst) - Unwinding velocity increases with ATP and is modulated by force via Km(f) and Vmax - spRim1 binds displaced strand, increasing unwinding velocity on the DNA fork to ~115 bp/s and shortening inter-burst recovery - spRim1 accelerates rewinding on the DNA fork; on RNA–DNA forks, spRim1 does not stimulate unwinding/rewinding - RNA–DNA fork experiments support a sliding-back mechanism over strand-switching QC result: Pass.