Wang, Liguo

Abstract DNMT3A and TET2 are epigenetic regulator genes commonly mutated in age-related clonal hematopoiesis (CH). Despite having opposed epigenetic functions, these mutations are associated with increased all-cause mortality and a low risk for progression to hematologic neoplasms. Although individual impacts on the epigenome have been described using different model systems, the phenotypic complexity in humans remains to be elucidated. Here, we make use of a natural inflammatory response occurring during coronavirus disease 2019 (COVID-19), to understand the association of these mutations with inflammatory morbidity (acute respiratory distress syndrome [ARDS]) and mortality. We demonstrate the age-independent, negative impact of DNMT3A mutant (DNMT3Amt) CH on COVID-19–related ARDS and mortality. Using single-cell proteogenomics we show that DNMT3A mutations involve myeloid and lymphoid lineage cells. Using single-cell multiomics sequencing, we identify cell-specific gene expression changes associated with DNMT3A mutations, along with significant epigenomic deregulation affecting enhancer accessibility, resulting in overexpression of interleukin-32 (IL-32), a proinflammatory cytokine that can result in inflammasome activation in monocytes and macrophages. Finally, we show with single-cell resolution that the loss of function of DNMT3A is directly associated with increased chromatin accessibility in mutant cells. Hence, we demonstrate the negative prognostic impact of DNMT3Amt CH on COVID-19–related ARDS and mortality. DNMT3Amt CH in the context of COVID-19, was associated with inflammatory transcriptional priming, resulting in overexpression of IL32. This overexpression was secondary to increased chromatic accessibility, specific to DNMT3Amt CH cells. DNMT3Amt CH can thus serve as a potential biomarker for adverse outcomes in COVID-19.

Abstract An accurate assessment of p53’s functional statuses is critical for cancer genomic medicine. However, there is a significant challenge in identifying tumors with non-mutational p53 inactivation which is not detectable through DNA sequencing. These undetected cases are often misclassified as p53-normal, leading to inaccurate prognosis and downstream association analyses. To address this issue, we built the support vector machine (SVM) models to systematically reassess p53’s functional statuses in TP53 wild-type (TP53WT) tumors from multiple The Cancer Genome Atlas (TCGA) cohorts. Cross-validation demonstrated the good performance of the SVM models with a mean area under the receiver operating characteristic curve (AUROC) of 0.9822, precision of 0.9747, and recall of 0.9784. Our study revealed that a significant proportion (87%–99%) of TP53WT tumors actually had compromised p53 function. Additional analyses uncovered that these genetically intact but functionally impaired (termed as predictively reduced function of p53 or TP53WT-pRF) tumors exhibited genomic and pathophysiologic features akin to TP53-mutant tumors: heightened genomic instability and elevated levels of hypoxia. Clinically, patients with TP53WT-pRF tumors experienced significantly shortened overall survival or progression-free survival compared to those with predictively normal function of p53 (TP53WT-pN) tumors, and these patients also displayed increased sensitivity to platinum-based chemotherapy and radiation therapy.

Abstract Objective To develop machine learning models using patient and migraine features that can predict treatment responses to commonly used migraine preventive medications. Background Currently, there is no accurate way to predict response to migraine preventive medications, and the standard trial‐and‐error approach is inefficient. Methods In this cohort study, we analyzed data from the Mayo Clinic Headache database prospectively collected from 2001 to December 2023. Adult patients with migraine completed questionnaires during their initial headache consultation to record detailed clinical features and then at each follow‐up to track preventive medication changes and monthly headache days. We included patients treated with at least one of the following migraine preventive medications: topiramate, beta‐blockers (propranolol, metoprolol, atenolol, nadolol, timolol), tricyclic antidepressants (amitriptyline, nortriptyline), verapamil, gabapentin, onabotulinumtoxinA, and calcitonin gene‐related peptide (CGRP) monoclonal antibodies (mAbs) (erenumab, fremanezumab, galcanezumab, eptinezumab). We pre‐trained a deep neural network, “TabNet,” using 145 variables, then employed TabNet‐embedded data to construct prediction models for each medication to predict binary outcomes (responder vs. non‐responder). A treatment responder was defined as having at least a 30% reduction in monthly headache days from baseline. All model performances were evaluated, and metrics were reported in the held‐out test set (train 85%, test 15%). SHapley Additive exPlanations (SHAP) were conducted to determine variable importance. Results Our final analysis included 4260 patients. The responder rate for each medication ranged from 28.7% to 34.9%, and the mean time to treatment outcome for each medication ranged from 151.3 to 209.5 days. The CGRP mAb prediction model achieved a high area under the receiver operating characteristics curve (AUC) of 0.825 (95% confidence interval [CI] 0.726, 0.920) and an accuracy of 0.80 (95% CI 0.70, 0.88). The AUCs of prediction models for beta‐blockers, tricyclic antidepressants, topiramate, verapamil, gabapentin, and onabotulinumtoxinA were: 0.664 (95% CI 0.579, 0.745), 0.611 (95% CI 0.562, 0.682), 0.605 (95% CI 0.520, 0.688), 0.673 (95% CI 0.569, 0.724), 0.628 (0.533, 0.661), and 0.581 (95% CI 0.550, 0.632), respectively. Baseline monthly headache days, age, body mass index (BMI), duration of migraine attacks, responses to previous medication trials, cranial autonomic symptoms, family history of headache, and migraine attack triggers were among the most important variables across all models. A variable could have different contributions; for example, lower BMI predicts responsiveness to CGRP mAbs and beta‐blockers, while higher BMI predicts responsiveness to onabotulinumtoxinA, topiramate, and gabapentin. Conclusion We developed an accurate prediction model for CGRP mAbs treatment response, leveraging detailed migraine features gathered from a headache questionnaire before starting treatment. Employing the same methods, the model performances for other medications were less impressive, though similar to the machine learning models reported in the literature for other diseases. This may be due to CGRP mAbs being migraine‐specific. Incorporating medical comorbidities, genomic, and imaging factors might enhance the model performance. We demonstrated that migraine characteristics are important in predicting treatment responses and identified the most crucial predictors for each of the seven types of preventive medications. Our results suggest that precision migraine treatment is feasible.

Background and Aims: Disruption of the epigenome is a hallmark of human disease, including liver cirrhosis and HCC. While genetic heterogeneity is an established effector of pathologic phenotypes, epigenetic heterogeneity is less well understood. Environmental exposures alter the liver-specific DNA methylation landscape and influence the onset of liver cancer. Given that currently available treatments are unable to target frequently mutated genes in HCC, there is an unmet need for novel therapeutics to prevent or reverse liver damage leading to hepatic tumorigenesis, which the epigenome may provide. Approach and Results: We performed genome-wide profiling of DNA methylation, copy number, and gene expression from multiple liver regions from 31 patients with liver disease to examine their crosstalk and define the individual and combinatorial contributions of these processes to liver disease progression. We identified epigenetic heterogeneity hotspots that are conserved across patients. Elevated epigenetic heterogeneity is associated with increased gene expression heterogeneity. Cirrhotic regions comprise 2 distinct cohorts—one exclusively epigenetic, and the other where epigenetic and copy number variations collaborate. Epigenetic heterogeneity hotspots are enriched for genes central to liver function (eg, HNF1A) and known tumor suppressors (eg, RASSF1A). These hotspots encompass genes including ACSL1, ACSL5, MAT1A, and ELFN1, which have phenotypic effects in functional screens, supporting their relevance to hepatocarcinogenesis. Moreover, epigenetic heterogeneity hotspots are linked to clinical measures of outcome. Conclusions: Substantial epigenetic heterogeneity arises early in liver disease development, targeting key pathways in the progression and initiation of both cirrhosis and HCC. Integration of epigenetic and transcriptional heterogeneity unveils putative epigenetic regulators of hepatocarcinogenesis.

Abstract Background DNMT3A and TET2 are epigenetic regulator genes commonly mutated in age-related clonal hematopoiesis (CH). Despite having opposing epigenetic functions, these mutations are associated with increased all-cause mortality and a low risk for progression to hematological neoplasms. While individual impacts on the epigenome have been described using different model systems, the phenotypic complexity in humans remains to be elucidated. Results Here we make use of a natural inflammatory response occurring during coronavirus disease 2019 (COVID-19), to understand the association of these mutations with inflammatory morbidity and mortality. We demonstrate the age-independent, negative impact of DNMT3A mutant CH on COVID-19-related cytokine release severity and mortality. Using single cell proteogenomics we show that DNMT3A mutations involve myeloid and lymphoid cells. Using single cell multiomics sequencing, we identify cell-specific gene expression changes associated with DNMT3A mutations, along with significant epigenomic deregulation affecting enhancer accessibility, resulting in overexpression of IL32, a proinflammatory cytokine that can result in inflammasome activation in monocytes and macrophages. Finally, we show with single cell resolution that the loss of function of DNMT3A is directly associated with increased chromatin accessibility in mutant cells. Conclusions We demonstrate the negative prognostic impact of DNMT3Amt CH on COVID-19 related inflammatory morbidity and mortality. DNMT3Amt CH involves myeloid and lymphoid cells and in the context of COVID-19, was associated with inflammatory transcriptional priming, resulting in overexpression of IL32. This overexpression was secondary to increased chromatic accessibility, specific to DNMT3Amt CH cells. DNMT3Amt CH can serve as a potential biomarker for adverse inflammatory outcomes.

Introduction: Clonal dendritic cell (DC) aggregates in chronic myelomonocytic leukemia (CMML) are seen in approximately 20-30% patients at diagnosis. Previously, these were thought to be comprised of CD123+ plasmacytoid DCs but our data has suggested that these are heterogenous and comprise of myeloid DCs, monocytes, and myeloid-derived suppressor cells. Furthermore, these aggregates uniformly express immune checkpoints such as indoleamine 2,3-dioxygenase-1 (IDO), associate with an IDO-specific systemic impact (increased trytophan catabolism), and expanded regulatory T cell population in CMML, suggesting a role in inducing immune tolerance. DC aggregates are also associated with increased risk of acute myeloid leukemia (AML) transformation and resistance to hypomethylating agents, highlighting that they are important for disease progression in CMML (Mangaonkar A et al. Blood Adv 2020). We performed this study to decipher the architecture of a CMML DC aggregate to understand the complex inter-cellular interactions, and assess their impact on the adaptive immune system. Methods: In this study, we used archival fresh-frozen paraffin-embedded (FFPE) bone marrow biopsy specimens obtained from CMML patients, and isolated monocytes (CD14+ magnetic bead sorting) and lymphocytes (CD14-ve fraction) from untreated CMML and age-matched normal control (NC) peripheral blood mononuclear cells (PBMCs). DCs were cultured from monocytes using a commercial protocol. Spatial architecture of the CMML BM microenvironment was interrogated through multiplex imaging of FFPE samples by co-detection by indexing (CODEX) using 30 markers (CCR6, CD107a, CD11c, CD20, CD21, CD31, CD3e, CD4, CD44, CD45, CD45RO CD56, CD68, CD8, E-Cadherin, FoxP3, HLA-DR, CD14, CD16, CD11b, CD33, CD15, CD66b, CD123, CD303, CD34, CD163 and IDO). Single-cell transcriptomic signatures were analyzed using the 10X single cell Fixed RNA profiling platform, and validated using bulk RNA seq (Illumina stranded mRNA prep) from isolated and cultured cells. Impact of DCs on lymphocytes was assessed via autologous mixed DC-T culture experiments. Results: BM FFPE samples from 5 CMML cases (3 with DC aggregates) were included for multiplex imaging via CODEX. Results confirm the significant interactions of CD8+ and CD4+ T cells with CD123+/HLA-DR+ DCs within a CMML DC aggregate (Fig. 1A). Upon quantitative assessment of cell-cell interactions, IDO+/CD8+T ( P=0.006), IDO+/MDSC ( P=0.005), and IDO+/macrophage ( P=0.001) interactions were significantly increased in CMML samples with versus without DC aggregates . Specific findings were validated using single-stain immunohistochemistry and multiplex immunoflourescence staining. Next, in order to assess transcriptomic signatures within BM at the single-cell level, we chose two cases; CMML (BM aspirate blast% 1, WBC 49.6 x 10 9/L, ASXL1/GATA2/SETBP1/SRSF2 mutations) and AML transformed from CMML (BM aspirate blast% 81, WBC 152 x 10 9/L, NPM1/DNMT3A/TET2 mutations). Approximately, 10,000 cells were obtained and analyzed with cell type annotation through human PBMC reference (Azimuth). Results show an expanded regulatory T cell population in AML compared to CMML (UMAP plot, Fig. 1B), suggestive of immune tolerance. In order to validate findings and perform functional experiments, we used sorted monocytes and cutured DCs from CMML and NC. DC phenotype was confirmed both through visual inspection and flow cytometry, and clonality was confirmed through Sanger sequencing (for previously identified mutations). Differential gene expression analysis through bulk RNAseq showed significant differences between CMML and NC lymphocytes with immunoinhibitory receptor, LILRB2 (log2fold change=7.05, adjusted P=0.0002) being one of the top five significantly upregulated genes, further confirming immune tolerance. Finally, mixed autologous DC-lymphocyte reaction (1:4 ratio) experiments demonstrated that DCs induce T cell tolerance in CMML (expansion of exhausted PD1/TIM3/LAG3+ CD8 T cells) when compared to NC. Conclusion: Clonal dendritic cell aggregates within the CMML bone marrow microenviroment interact with T cells and induce immune tolerance. This represents a novel mechanism of disease progression in CMML with potential therapeutic targets (IDO, LILRB2, and/or CD123).

Abstract Brain white matter tracts undergo structural and functional changes linked to late-life cognitive decline, but the cellular and molecular contributions to their selective vulnerability are not well defined. In naturally aged mice, we demonstrate that senescent and disease-associated microglia (DAM) phenotypes converge in hippocampus-adjacent white matter. Through gold-standard gene expression and immunolabeling combined with high-dimensional spatial mapping, we identified microglial cell fates in aged white matter characterized by aberrant morphology, microenvironment reorganization, and expression of senescence and DAM markers, including galectin 3 (GAL3/Lgals3), B-cell lymphoma 2 (Bcl2), and cyclin dependent kinase inhibitors, including Cdkn2a/p16ink4a. Pharmacogenetic or pharmacological targeting of p16ink4a or BCL2 reduced white matter GAL3+ DAM abundance and rejuvenated microglial fimbria organization. Our results demonstrate dynamic changes in microglial identity in aged white matter that can be reverted by senotherapeutic intervention to promote homeostatic maintenance in the aged brain.

Abstract Motivation Analyzing the overlap between two sets of genomic intervals is a frequent task in the field of bioinformatics. Typically, this is accomplished by counting the number (or proportion) of overlapped regions, which applies an arbitrary threshold to determine if two genomic intervals are overlapped. By making binary calls but disregarding the magnitude of the overlap, such an approach often leads to biased, non-reproducible, and incomparable results. Results We developed the cobind package, which incorporates six statistical measures: the Jaccard coefficient, Sørensen–Dice coefficient, Szymkiewicz–Simpson coefficient, collocation coefficient, pointwise mutual information (PMI), and normalized PMI. These measures allow for a quantitative assessment of the collocation strength between two sets of genomic intervals. To demonstrate the effectiveness of these methods, we applied them to analyze CTCF’s binding sites identified from ChIP-seq, cancer-specific open-chromatin regions (OCRs) identified from ATAC-seq of 17 cancer types, and oligodendrocytes-specific OCRs identified from scATAC-seq. Our results indicated that these new approaches effectively re-discover CTCF’s cofactors, as well as cancer-specific and oligodendrocytes-specific master regulators implicated in disease and cell type development. Availability and implementation The cobind package is implemented in Python and freely available at https://cobind.readthedocs.io/en/latest/.

Abstract Androgen receptor (AR) inhibition by androgen deprivation and/or antiandrogen administration is the mainstay therapy for advanced prostate cancer. However, most prostate cancers ultimately become resistant to these therapies, indicating the importance of identifying mechanisms driving resistance to improve patient outcomes. Here we demonstrated that acute treatment with the antiandrogen enzalutamide (ENZ) decreased glutathione (GSH) production, increased lipid peroxidation, and induced ferroptosis in prostate cancer cells. Consistently, meta-analysis of transcriptomic data linked the androgen-AR axis to metabolism-related biological processes, including lipid metabolism. The cystine transporter gene SLC7A11 was a key AR target, and full-length AR (AR-FL) transactivated SLC7A11 transcription by directly occupying the SLC7A11 promoter and putative enhancer regions. AR variants (AR-V) preferentially bound the SLC7A11 enhancer and upregulated SLC7A11 expression, thereby conferring resistance to ferroptosis induced by ENZ treatment. However, this effect was abolished following downregulation of AR-Vs using the dual CBP/p300 and BET inhibitor NEO2734. These findings reveal ferroptosis induction as an anticancer mechanism of antiandrogens and SLC7A11 as a direct target gene of AR-FL and AR-Vs. AR-V-mediated SLC7A11 expression represents a mechanism coupling ferroptosis resistance to prostate cancer progression. Significance: Upregulation of SLC7A11 can be induced by androgen receptor variants to inhibit antiandrogen-induced prostate cancer cell ferroptosis and to drive castration resistance in prostate cancer.