Citations

ABSTRACT Genes involved in the regulation of chromatin structure are frequently disrupted in cancer, contributing to an aberrant transcriptome and phenotypic plasticity. Yet, therapeutics targeting mutant forms of chromatin-modifying enzymes have yielded only modest clinical utility, underscoring the difficulty of targeting the epigenomic underpinnings of aberrant gene regulatory networks. Here, we sought to identify novel epigenetic vulnerabilities in diffuse large B-cell lymphoma (DLBCL). Through phenotypic screens and biochemical analysis, we demonstrated that inhibition of the H3K9 demethylases KDM4A and KDM4C elicits potent, subtype-agnostic cytotoxicity by antagonizing transcriptional networks associated with B-cell identity and epigenetically rewiring heterochromatin. KDM4 demethylases associated with the KRAB zinc finger ZNF587, and their enzymatic inhibition led to DNA replication stress and DNA damage-induced cGAS-STING activation. Broad surveys of transcriptional data from patients also revealed KDM4 family dysregulation in several other cancer types. To explore this potential therapeutic avenue, we performed high-throughput small molecule screens with H3K9me3 nucleosome substrates and identified novel KDM4 demethylase inhibitors. AI-guided protein-ligand binding predictions suggested diverse modes of action for various small molecule hits. Our findings underscore the relevance of targeting fundamental transcriptional and epigenetic mechanisms for anti-cancer therapy. HIGHLIGHTS Phenotypic screens identified JIB-04 as a potent anti-cancer agent for multiple subtypes of diffuse large B-cell lymphoma JIB-04 binds and inhibits KDM4 demethylases resulting in epigenomic rewiring of heterochromatin KDM4 demethylases cooperate with KRAB zinc fingers to limit DNA replication stress, and KDM4 inhibition instigates DNA-damage and cGAS-STING activation in several human cancers High-throughput small molecule screens with semi-synthetic nucleosome substrates and AI-guided molecular docking simulations identify novel KDM4 inhibitors

AbstractAutoimmune pancreatitis (AIP) is identified as a severe chronic immune‐related disorder in pancreas, including two subtypes. In this study, pancreatic lesions in patients diagnosed as either type 1 AIP or type 2 AIP are examined, and these patients’ peripheral blood at single‐cell level. Furthermore, flow cytometry, immunofluorescence, and functional assays are performed to verify the identified cell subtypes. In type 1 AIP, there is a notable increase in the amount of B cells and plasma cells, and IgG4+ plasma cells are key pathogenic cells of AIP. The differentiation path of naïve‐stage B cells into IgG4+ produced plasma cells is observed, and an increased amount of T helper cells and T follicular helper (Tfh) cells. This study also reveals that HIF‐1α, an activated transcriptional factor, can directly bind to promoter site of NAMPT, promoting higher levels of visfatin production in HIF1A+ classical monocytes. Pancreatic stellate cells can be activated by extracellular visfatin and promote the development of fibrotic response in pancreatic lesions across both AIP subtypes. The current findings shed light on the exploration of dynamic alterations in peripheral blood cells and cell subgroups in pancreatic lesions of AIP, while elucidating a pathogenic cell subset and potential fibrosis mechanism of AIP.

ABSTRACT Understanding clinical trajectories of sepsis patients is crucial for prognostication, resource planning, and to inform digital twin models of critical illness. This study aims to identify common clinical trajectories based on dynamic assessment of cardiorespiratory support using a validated electronic health record data that covers retrospective cohort of 19,177 patients with sepsis admitted to intensive care units (ICUs) of Mayo Clinic Hospitals over 8-year period. Patient trajectories were modeled from ICU admission up to 14 days using an unsupervised machine learning two-stage clustering method based on cardiorespiratory support in ICU and hospital discharge status. Of 19,177 patients, 42% were female with a median age of 65 (interquartile range [IQR], 55–76) years, The Acute Physiology, Age, and Chronic Health Evaluation III score of 70 (IQR, 56–87), hospital length of stay (LOS) of 7 (IQR, 4–12) days, and ICU LOS of 2 (IQR, 1–4) days. Four distinct trajectories were identified: fast recovery (27% with a mortality rate of 3.5% and median hospital LOS of 3 (IQR, 2–15) days), slow recovery (62% with a mortality rate of 3.6% and hospital LOS of 8 (IQR, 6–13) days), fast decline (4% with a mortality rate of 99.7% and hospital LOS of 1 (IQR, 0–1) day), and delayed decline (7% with a mortality rate of 97.9% and hospital LOS of 5 (IQR, 3–8) days). Distinct trajectories remained robust and were distinguished by Charlson Comorbidity Index, The Acute Physiology, Age, and Chronic Health Evaluation III scores, as well as day 1 and day 3 SOFA ( P < 0.001 ANOVA). These findings provide a foundation for developing prediction models and digital twin decision support tools, improving both shared decision making and resource planning.