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Abstract: Calculation of density maps from atomic models is essential for structural studies using crystallography and electron cryo-microscopy (cryoEM). These maps serve various purposes, including atomic model building, refinement, visualization, and validation. However, accurately comparing model-calculated maps to experimental data poses challenges, particularly because the resolution of cryoEM experimental maps varies across the map. Traditional crystallography methods generate finite-resolution maps with uniform resolution throughout the unit cell volume, while most modern software in cryoEM employ Gaussian-like functions to generate these maps, which does not adequately account for atomic model parameters and resolution. Recent work by Urzhumtsev & Lunin (2022, IUCr Journal, 9, 728-734) introduces a novel method for computing atomic model maps that incorporate local resolution and can be expressed as analytically differentiable functions of all atomic parameters. This approach enhances the accuracy of matching atomic models to experimental maps. In this paper, we detail the implementation of this method in CCTBX and Phenix.

Abstract: Direct Coupling Analysis has been instrumental over the past decade in leveraging evolutionary information and advancing our understanding of biomolecular structure and function. Here, we introduce sparse extensions of this method that explicitly incorporate structural information. StructureDCA focuses on physically relevant interactions by selectively retaining couplings between residues in spatial contact, and StructureDCA[RSA] additionally incorporates per-residue relative solvent accessibility. These models outperform state-of-the-art approaches in describing mutational landscapes, as they more effectively integrate structural context. Moreover, their sparse formulation enables orders-of-magnitude improvements in computational efficiency while preserving interpretability, providing a powerful framework for gaining mechanistic insights into mutation effects and advancing protein design. The StructureDCA models are available as a user-friendly Python package via the PyPI repository. The source code is freely accessible at https://github.com/3BioCompBio/StructureDCA, which also includes a Colab Notebook interface.

Abstract: Resolving fine-grained cell subtypes in single-cell RNA sequencing (scRNA-seq) data remains challenging, as their subtle transcriptional differences are often obscured by technical noise and data sparsity. Here, we present scMagnifier, a consensus clustering framework that leverages gene regulatory network (GRN)-informed in silico perturbations to amplify subtle transcriptional differences and uncover latent cell subpopulations. scMagnifier perturbs candidate transcription factors (TFs), propagates perturbation effects through cluster-specific GRNs to simulate post-perturbation expression profiles, and integrates clustering results across multiple perturbations into stable subtype assignments. Additionally, scMagnifier introduces regulatory perturbation consensus UMAP (rpcUMAP), a perturbation-aware visualization that provides clearer separation between cell subtypes and guides the selection of the optimal number of clusters. In both single-batch and multi-batch benchmarks, scMagnifier consistently improves the resolution and accuracy of fine-grained cell type identification. Notably, when integrated with spatial clustering methods such as STAGATE, scMagnifier is compatible with spatial transcriptomics workflows and effectively reveals tumor cell subtypes and their spatial organization in ovarian cancer.

Abstract: TP53 mutations occur in 80-90% of triple-negative breast cancers (TNBCs) and drive genomic instability and metastatic progression. Poly (ADP-ribose) polymerase (PARP) is critical for DNA repair and replication fork stability. How oncogenic signaling influences PARP function to sustain proliferation during replication stress remains unclear. Mutant p53 (mtp53) R273H associates tightly with chromatin, forms complexes with PARP, and enhances PARP recruitment to replication forks. The C-terminal region of mtp53 mediates mtp53-PARP and mtp53-Poly (ADP-ribose) (PAR) interactions that facilitate S phase progression. The PARP inhibitor talazoparib (TAL) combined with the alkylating agent temozolomide (TMZ) produces synergistic cytotoxicity selectively in mtp53, but not wild-type p53 (wtp53), breast cancer cells and organoids. Herein we evaluated the mechanism of mtp53-associated cell death and tested if this could translate to a preclinical xenograft model. We found that TMZ+TAL treatment induced elevated cleaved PARP and {gamma}H2AX and reduced the metastasis-promoting oncoprotein MDMX. In orthotopic xenografts expressing mtp53 R273H, but not wtp53, combination therapy significantly decreased circulating tumor cells (CTCs) and lung metastases. Transcriptomic profiling of tumors from combination treated animals demonstrated downregulation of MDMX, VEGF, and NF-{kappa}B, consistent with the observed suppression of CTCs and lung metastasis, and increased {gamma}H2AX, indicative of replication stress in mtp53 xenografts. Inhibition of metastasis was also observed in mtp53 R273H WHIM25 and p53-undetectable WHIM6 TNBC patient-derived xenografts (PDX). The mtp53 C-terminal domain (347-393) demonstrated a critical tumor promoting function, as CRISPR-mediated deletion impaired replication fork progression, tumor growth, and metastatic dissemination. DNA fiber combing showed that expression of full-length mtp53 R273H, but not C-terminal deleted {Delta}347-393, supported sustained single-stranded DNA gaps (ssGAPs) following Poly (ADP-ribose) glycohydrolase (PARG) inhibition. These findings support that mtp53 uses C-terminal amino acids to exploit PARP to enable replication stress adaptation and that mtp53 is a predictive biomarker for combined PARP inhibitor and DNA damaging therapies targeting TNBC.

Abstract: The RUNX1 transcription factor mediates cell-type specific gene expression. RUNX1 suppression and perturbations are recurrently associated with breast tumor initiation and progression. However, the mechanisms governing the dual roles of RUNX1 in sustaining the mammary epithelial phenotype while epigenetically suppressing initiation of cancer-compromised gene expression are poorly understood. To address this, we used the power of degron-mediated acute, selective, and complete RUNX1 ablation in human mammary epithelial cells. RUNX1 mediates promoter and distal enhancer-driven expression of a gene cohort. Dynamic epigenomic responsiveness upon RUNX1 ablation reveals a rapid and selective decrease in chromatin accessibility and H3K27ac at RUNX1-bound enhancers, but not promoters. While differentially initiated and expressed genes contacted by RUNX1-bound enhancers are enriched in pathways involved in epithelial maintenance and stemness, genes with RUNX1-promoter occupancy support DNA damage responsiveness. Modified cell morphology, metabolic control, increased breast cancer stemness, plasticity, anchorage-independent survival, chemoresistance, and perturbed DNA damage reactivity are observed upon RUNX1 ablation. Together, these findings define RUNX1 as an epigenetic tumor suppressor that maintains epithelial cell state by preserving enhancer activity and preventing gene expression associated with hallmarks of cancer.