Gene expression is governed by dynamic switches between repressive and activating transcriptional states. Among the molecules mediating these transitions, chromatin readers and transcription factors play pivotal roles. However, how they assemble with regulatory machineries to enable crosstalk between gene repression and activation remains unknown. Here, we use an integrative structural dynamics approach - combining cryo-EM, crosslinking mass spectrometry, fragment-resolved protein interactome mapping and crystallography - to show how the dual-role chromatin reader Cti6 and transcription factors Ash1 and Ume6 engage the Sin3 deacetylase complex, a major regulatory hub in eukaryotes. We find that Cti6 competes with Ash1 to drive its dynamic recruitment to a shared peripheral module, while Ume6 engages the Sin3 scaffold through a defined, minimal interface. Using high-throughput mutational scanning, we reveal deleterious and gain-of-function mutations in Sin3, identifying evolutionarily conserved residues essential for anchoring transcription factors. Together, these results provide structural and functional insights into how dual-role regulators engage the central Sin3 complex, revealing subtle assembly principles that may facilitate crosstalk between gene repression and activation. They also establish an integrative multidisciplinary framework to dissect the dynamics of macromolecular assemblies across biological systems.

Abstract Purpose: Enzymatic pockets, such as those found in histone deacetylases (HDACs), have long served as attractive targets for drug discovery. However, conventional HDAC inhibitors often lack selectivity and cause systemic toxicity due to paralog redundancy and their incorporation into multi-subunit transcriptional regulatory complexes. To identify more selective modulators, we performed an unbiased yeast genetic screen of ∼52,000 compounds by interrogating the activity of the conserved HDAC/Rpd3L complex. Follow-up mechanistic studies uncovered hits that do not directly inhibit HDAC catalytic activity but instead modulate repression through alternative mechanisms. We subsequently evaluated the lead compound, E6R, in human neuroblastoma cells and mouse xenografts, benchmarking against the enzymatic inhibitor TSA in vitro and Vorinostat (SAHA) in vivo. These studies demonstrate that E6R, a first-in-class non-enzymatic SIN3-HDAC modulator, achieves comparable anti-tumor efficacy with far greater selectivity and minimal global transcriptional disruption. Methods: E6R was evaluated in yeast and SK-N-BE(2)-C neuroblastoma cells using bulk and single-cell RNA-seq (Seq-Well S3), SIN3A ChIP-seq, viability and invasion assays, and mouse xenografts. Results: In yeast, E6R disrupts Sin3/Rpd3L-dependent transcriptional repression without inhibiting HDAC catalytic activity. In human neuroblastoma cells, E6R produced anti-tumor activity comparable to TSA. Transcriptomically, E6R modulated ∼14-fold fewer genes than TSA and caused minimal global perturbation. Interestingly, E6R selectively activated stress- and senescence-associated programs governed by the ATF4-driven integrated stress response (ISR), including GDF15, DDIT3, ATF3, and FGF21, while inducing minimal off-target effects. SIN3A ChIP-seq revealed promoter-proximal loss of SIN3A binding at several ISR loci, most notably GDF15 (∼55 bp upstream of the TSS), consistent with direct de-repression through dissociation of the SIN3-HDAC complex. Although both compounds shared repression of E2F, MYC, and glycolytic targets and activation of p53, TNFα/NF-κB, and apoptotic signaling, E6R induced a distinct stress-adaptive state through an HDAC-independent mechanism. Functionally, E6R significantly reduced neuroblastoma cell invasion and tumor growth with limited cytotoxicity. In vivo, E6R inhibited neuroblastoma xenograft growth comparably to Vorinostat, supporting non-enzymatic HDAC modulation as a therapeutic alternative. Conclusions: Together, these data strongly suggest that E6R is a selective, non-enzymatic SIN3-HDAC modulator that reprograms chromatin from a repressive to a stress-adaptive, anti-proliferative state, offering a mechanistically distinct and potentially safer framework for HDAC-targeted cancer therapy. Citation Format: Olivia Debnath, Julien Olivet, Soon Gang Choi, Yasmine Bramerloo, Jeremy Blavier, TINA O'GRADY, Florent Laval, Vladimir V. Botchkarev, Bin Hu, Anthony C. Varca, Jonathan Bruyr, Samira Ibrahim, Tasneem Jivanjee, Joshua D. Bromley, Sarah K. Nyquist, Natalia Calonghi, Alessandra Stefan, Alejandro Hochkoeppler, Maria Francesca Baietti, Eleonora Leucci, Michael A. Calderwood, Tong Hao, Alex K. Shalek, David E. Hill, Sara J. Buhrlage, Sirano Dhe-Paganon, Franck Dequiedt, Jean Claude Twizere, Marc Vidal. Targeting non-enzymatic HDAC-mediated repression reveals a selective stress-adaptive mechanism for cancer therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 6776.

Genotype-phenotype relationships are mediated through intricate networks of physical and functional interactions among macromolecules. Knowledge of the interactome is vital to understand and model genetics and cellular biology. Recent advances in accurately predicting tertiary protein structures using artificial intelligence (AI) approaches such as AlphaFold1 have revived the vision that the protein-protein interactome might be fully predictable through computational modeling of quaternary structures. Here we present a comprehensive experimental framework to systematically assess the impact of AI-driven interactome predictions for yeast2 and human3. We find that the quality of high-confidence predictions is on par with established experimental approaches. However, in proteome-wide screening, the tested AI approaches underperform in the discovery of strictly novel protein-protein interactions (PPIs) compared to experimental reference interactome maps. In particular, the yeast interactome map described here identifies >40-fold more novel PPIs than its AI counterpart. Strikingly, AlphaFold provides structural models for a substantial number of experimentally identified PPIs missed by the virtual screens. Our results suggest that, at this stage, the main contribution of AI predictions is to provide quaternary structure models for experimentally identified PPIs.

Parkinson's disease (PD) is a progressive neurodegenerative disorder lacking disease-modifying therapies, and its management is limited by the absence of accessible biomarkers for disease progression and treatment response. We implemented an ultra-deep plasma proteomics workflow integrating Mag-Net extracellular vesicle enrichment with Orbitrap Astral mass spectrometry to profile longitudinal samples from PD patients. This approach quantified 6,481 plasma proteins, an unprecedented depth in PD studies, revealing distinct signatures associated with disease duration and dopaminergic therapy exposure. Candidate biomarkers were validated in an independent cohort using ELISA, demonstrating predictive utility in AI-driven models. To uncover mechanistic drivers, we intersected proteomic data with our new genome-wide overexpression screens for regulators of alpha-synuclein pre-formed fibril uptake, identifying MFN2, PSMD4, and EIF4G1 as major hubs that link systemic proteomic changes to mitochondrial dynamics and proteostasis. Additionally, a meta-analysis of brain transcriptomes responsive to Levodopa (L-DOPA) treatment identified 42 candidate genes, including NDUFS4, GNAS, TSC1, and NTS, some of which are targets of approved therapeutics. Finally, an integrative network analysis revealed that key pathological hubs, such as IFNG and PLAT, are targets of approved pharmacological agents. Overall, these findings provide a systems-level resource for PD biomarker discovery and reveal druggable pathways for precision medicine strategies aimed at improving therapeutic outcomes. ### Competing Interest Statement The authors have declared no competing interest. Korea Health Industry Development Institute National Research Foundation, RS-2024-00411768, RS-2024-00445180 Montreal General Hospital, https://ror.org/04gbhgc79 Aune Foundation McGill University, https://ror.org/01pxwe438

Cancer drug resistance remains a major barrier to durable treatment success, often leading to relapse despite advances in precision oncology. While combination therapies are being increasingly investigated, such as chemotherapy with small molecule inhibitors, predicting drug response and identifying rational drug combinations based on resistance mechanisms remain major challenges. Therefore, a proteome-wide, single-gene overexpression screening platform is essential for guiding rational therapy selection. Here, we present BOGO (Bxb1-landing pad human ORFeome-integrated system for a proteome-wide Gene Overexpression), a robust, scalable, and reproducible screening platform that enables single-copy, site-specific integration and overexpression of ~19,000 human open across cancer cell models. Using BOGO, we identified drug-specific response drivers for 16 chemotherapeutic agents and integrated clinical datasets to uncover proliferation and resistance-associated genes with prognostic potential. Drug response similarity networks revealed both shared and unique mechanisms, highlighting key pathways such as autophagy, apoptosis, and Wnt signaling, and notable resistance-associated genes including BCL2, POLD2, and TRADD. In particular, we proposed a synergistic combination of the BCL2 family inhibitor ABT-263 (Navitoclax®) and the DNA analog TAS-102 (Lonsurf®), which revealed that lysosomal modulation is a key mechanism driving DNA analog resistance. This combination therapy selectively enhanced cytotoxicity in colorectal and pancreatic cancer cells in vitro, and demonstrated therapeutic benefit in vivo in both cell line-derived xenograft (CDX) and patient-derived xenograft (PDX) models. Together, these findings establish BOGO as a powerful gene overexpression perturbation platform for systematically identifying chemoresistance and chemosensitization drivers, and for discovering rational combination therapies. Its scalability and reproducibility position BOGO as a broadly applicable tool for functional genomics and therapeutic discovery beyond cancer resistance.

Cancer systems biology seeks to understand how cancer arises as a system of interconnected molecules, cells, and tissues, with the goal of understanding, predicting, and controlling the disease. In the last decade, the field has rapidly grown as advances in experimental, computational, and analytic technologies have improved our ability to capture and recapitulate the complexities of cancer at multiple scales. However, the field's promise to understand how specific molecular changes give rise to altered cancer outcomes remains incompletely fulfilled. Fortunately, an opportunity exists to accelerate progress by better coordinating modeling and data-gathering efforts across the cancer systems biology community. This will create the foundation for building accurate, multiscale cancer models that can better predict and identify improved therapeutic interventions. Here, we outline some of the current challenges in cancer systems biology research, how they can be addressed, and actions that the community can take to accelerate progress in the field. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI .

Most human transcription factor (TF) genes encode multiple protein isoforms differing in DNA-binding domains, effector domains, or other protein regions. The global extent to which this results in functional differences between isoforms remains unknown. Here, we systematically compared 693 isoforms of 246 TF genes, assessing DNA binding, protein binding, transcriptional activation, subcellular localization, and condensate formation. Relative to reference isoforms, two-thirds of alternative TF isoforms exhibit differences in one or more molecular activities, which often could not be predicted from sequence. We observed two primary categories of alternative TF isoforms: "rewirers" and "negative regulators," both of which were associated with differentiation and cancer. Our results support a model wherein the relative expression levels of, and interactions involving, TF isoforms add an understudied layer of complexity to gene regulatory networks, demonstrating the importance of isoform-aware characterization of TF functions and providing a rich resource for further studies.

Protein-protein interactions (PPIs) offer great opportunities to expand the druggable proteome and therapeutically tackle various diseases, but remain challenging targets for drug discovery. Here, we provide a comprehensive pipeline that combines experimental and computational tools to identify and validate PPI targets and perform early-stage drug discovery. We have developed a machine learning approach that prioritizes interactions by analyzing quantitative data from binary PPI assays and AlphaFold-Multimer predictions. Using the quantitative assay LuTHy together with our machine learning algorithm, we identified high-confidence interactions among SARS-CoV-2 proteins for which we predicted three-dimensional structures using AlphaFold Multimer. We employed VirtualFlow to target the contact interface of the NSP10-NSP16 SARS-CoV-2 methyltransferase complex by ultra-large virtual drug screening. Thereby, we identified a compound that binds to NSP10 and inhibits its interaction with NSP16, while also disrupting the methyltransferase activity of the complex, and SARS-CoV-2 replication. Overall, this pipeline will help to prioritize PPI targets to accelerate the discovery of early-stage drug candidates targeting protein complexes and pathways.

Cooperativity and antagonism between transcription factors (TFs) can drastically modify their binding to regulatory DNA elements. While mapping these relationships between TFs is important for understanding their context-specific functions, existing approaches either rely on DNA binding motif predictions, interrogate one TF at a time, or study individual TFs in parallel. Here, we introduce paired yeast one-hybrid (pY1H) assays to detect cooperativity and antagonism across hundreds of TF-pairs at DNA regions of interest. We provide evidence that a wide variety of TFs are subject to modulation by other TFs in a DNA region-specific manner. We also demonstrate that TF-TF relationships are often affected by alternative isoform usage and identify cooperativity and antagonism between human TFs and viral proteins from human papillomaviruses, Epstein-Barr virus, and other viruses. Altogether, pY1H assays provide a broadly applicable framework to study how different functional relationships affect protein occupancy at regulatory DNA regions.

Generating reference maps of interactome networks illuminates genetic studies by providing a protein-centric approach to finding new components of existing pathways, complexes, and processes. We apply state-of-the-art methods to identify binary protein-protein interactions (PPIs) for Drosophila melanogaster . Four all-by-all yeast two-hybrid (Y2H) screens of > 10,000 Drosophila proteins result in the ‘FlyBi’ dataset of 8723 PPIs among 2939 proteins. Testing subsets of data from FlyBi and previous PPI studies using an orthogonal assay allows for normalization of data quality; subsequent integration of FlyBi and previous data results in an expanded binary Drosophila reference interaction network, DroRI, comprising 17,232 interactions among 6511 proteins. We use FlyBi data to generate an autophagy network, then validate in vivo using autophagy-related assays. The deformed wings ( dwg ) gene encodes a protein that is both a regulator and a target of autophagy. Altogether, these resources provide a foundation for building new hypotheses regarding protein networks and function.

Supplementary Methods from MECP2 Is a Frequently Amplified Oncogene with a Novel Epigenetic Mechanism That Mimics the Role of Activated RAS in Malignancy

Supplementary Table S1. List of validated hits from N minus RAS screen. Supplementary Table S2. Amplicons in Human Cancer (from Broad Institute TCGA Copy Number Portal). Supplementary Table S3. Cancer cell lines used in current study with their MECP2 expression and RAS/MAPK/PI3K pathways mutation status (based on COSMIC, canSAR, ATCC, and CCLE databases).

Supplementary Figure S1. The Amplification of the MECP2 Gene Drives Its Expression, and the MECP2 Gene on the Active X Chromosome is Preferentially Amplified, Related to Figure 1. Supplementary Figure S2. MECP2 Overexpression Allows Soft Agar Growth of N minus RAS Cells in Two Different Types of Human Breast Epithelial Cells, and MECP2 Splicing Isoforms Differ In Their Ability to Confer Anchorage Independent Growth, Related to Figure 2A. Supplementary Figure S3: MECP2 Overexpression Allows Two Different Types of N minus RAS Breast Epithelial Cells To Grow As Tumor Xenograft In Nude Mice, Related to Figure 2E, 2F. Supplementary Figure S4. The MECP2 e2 Short Splicing Isoform Allows Sustained Activation of the MAPK Pathway after Prolonged Starvation in Minimal Medium without Growth Factors, Related to Figure 4A-C. Supplementary Figure S5. Both MECP2 Isoforms Activate the PI3K Pathway, Related to Figure 4D. Supplementary Figure S6. Additional human cancer cell lines with high level of MECP2 are growth-inhibited upon inhibition of MECP2 expression, Related to Figure 4E.

Widespread sequencing has yielded thousands of missense variants predicted or confirmed as disease-causing. This creates a new bottleneck: determining the functional impact of each variant - largely a painstaking, customized process undertaken one or a few genes or variants at a time. Here, we established a high-throughput imaging platform to assay the impact of coding variation on protein localization, evaluating 3,547 missense variants of over 1,000 genes and phenotypes. We discovered that mislocalization is a common consequence of coding variation, affecting about one-sixth of all pathogenic missense variants, all cellular compartments, and recessive and dominant disorders alike. Mislocalization is primarily driven by effects on protein stability and membrane insertion rather than disruptions of trafficking signals or specific interactions. Furthermore, mislocalization patterns help explain pleiotropy and disease severity and provide insights on variants of unknown significance. Our publicly available resource will likely accelerate the understanding of coding variation in human diseases.

ABSTRACT Enzymatic pockets such as those of histone deacetylases (HDACs) are among the most favored targets for drug development. However, enzymatic inhibitors often exhibit low selectivity and high toxicity due to targeting multiple enzyme paralogs, which are often involved in distinct multisubunit complexes. Here, we report the discovery and characterization of a non-enzymatic small molecule inhibitor of HDAC transcriptional repression functions with comparable anti-tumor activity to the enzymatic HDAC inhibitor Vorinostat, and anti-psychedelic activity of an HDAC2 knockout in vivo . We highlight that these phenotypes are achieved while modulating the expression of 20- and 80-fold fewer genes than enzymatic and genetic inhibition in the respective models. Thus, by achieving the same biological outcomes as established therapeutics while impacting a dramatically smaller number of genes, inhibitors of protein-protein interactions can offer important advantages in improving the selectivity of epigenetic modulators. GRAPHICAL ABSTRACT

Understanding the mechanisms of coronavirus disease 2019 (COVID-19) disease severity to efficiently design therapies for emerging virus variants remains an urgent challenge of the ongoing pandemic. Infection and immune reactions are mediated by direct contacts between viral molecules and the host proteome, and the vast majority of these virus–host contacts (the ‘contactome’) have not been identified. Here, we present a systematic contactome map of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with the human host encompassing more than 200 binary virus–host and intraviral protein–protein interactions. We find that host proteins genetically associated with comorbidities of severe illness and long COVID are enriched in SARS-CoV-2 targeted network communities. Evaluating contactome-derived hypotheses, we demonstrate that viral NSP14 activates nuclear factor κB (NF-κB)-dependent transcription, even in the presence of cytokine signaling. Moreover, for several tested host proteins, genetic knock-down substantially reduces viral replication. Additionally, we show for USP25 that this effect is phenocopied by the small-molecule inhibitor AZ1. Our results connect viral proteins to human genetic architecture for COVID-19 severity and offer potential therapeutic targets.

Protein-protein interactions (PPIs) are essential in understanding numerous aspects of protein function. Here, we significantly scaled and modified analyses of the recently developed all-vs-all sequencing (AVA-Seq) approach using a gold-standard human protein interaction set (hsPRS-v2) containing 98 proteins. Binary interaction analyses recovered 20 of 47 (43%) binary PPIs from this positive reference set (PRS), comparing favorably with other methods. However, the increase of 20x in the interaction search space for AVA-Seq analysis in this manuscript resulted in numerous changes to the method required for future use in genome-wide interaction studies. We show that standard sequencing analysis methods must be modified to consider the possible recovery of thousands of positives among millions of tested interactions in a single sequencing run. The PRS data were used to optimize data scaling, auto-activator removal, rank interaction features (such as orientation and unique fragment pairs), and statistical cutoffs. Using these modifications to the method, AVA-Seq recovered >500 known and novel PPIs, including interactions between wild-type fragments of tumor protein p53 and minichromosome maintenance complex proteins 2 and 5 (MCM2 and MCM5) that could be of interest in human disease.

Protein-protein interactions (PPIs) are important in understanding numerous aspects of protein function. Here, the recently developed all-vs-all sequencing (AVA-Seq) approach to determine protein-protein interactions was tested on a gold-standard human protein interaction set (hsPRS-v2). Initially, these data were interpreted strictly from a binary PPI perspective to compare AVA-Seq to other binary PPI methods tested on the same hsPRS-v2. AVA-Seq recovered 20 of 47 (43%) binary PPIs from this reference set comparing favorably with other methods. The same experimental data allowed for the determination of >500 known and novel PPIs including interactions between wildtype fragments of tumor protein p53 and minichromosomal maintenance complex proteins 2, and 5 (MCM2 and MCM5) that could be of interest in human disease. Additional results gave a better understanding of why interactions might be missed using AVA-Seq and aide future PPI experimental design for maximum recovery of information.

An amendment to this paper has been published and can be accessed via the original article.

SummaryHundreds of different protein complexes that perform important functions across all cellular processes, collectively comprising the “complexome” of an organism, have been identified1. However, less is known about the fraction of the interactome that exists outside the complexome, in the “outer-complexome”. To investigate features of “inner”- versus outer-complexome organisation in yeast, we generated a high-quality atlas of binary protein-protein interactions (PPIs), combining three previous maps2–4and a new reference all-by-all binary interactome map. A greater proportion of interactions in our map are in the outer-complexome, in comparison to those found by affinity purification followed by mass spectrometry5–7or in literature curated datasets8–11. In addition, recent advances in deep learning predictions of PPI structures12mirror the existing experimentally resolved structures in being largely focused on the inner complexome and missing most interactions in the outer-complexome. Our new PPI network suggests that the outer-complexome contains considerably more PPIs than the inner-complexome, and integration with functional similarity networks13–15reveals that interactions in the inner-complexome are highly detectable and correspond to pairs of proteins with high functional similarity, while proteins connected by more transient, harder-to-detect interactions in the outer-complexome, exhibit higher functional heterogeneity.