Citations

Abstract Immune checkpoint proteins such as programmed death-ligand 1 (PD-L1) retain general immune homeostasis and self-tolerance. PD-L1 binds to programmed cell death protein 1 (PD-1) and inhibits T cell activation or protects tumor cells from T cell-mediated killing. PD-L1 has recently been shown to be up-regulated in invasive lung fibroblasts by p53 and focal adhesion kinase (FAK), any potential role of transforming growth factor-beta (TGFβ) in PD-L1 action is unknown in fibrosis. TGFβ is a mysterious protein with various roles in healthy tissue homeostasis/development as well as the development or progression of cancer, fibrotic disorders, and immune modulation, et al. In the current study, we showed that TGFβ induces the expression of the immunoinhibitory molecule PD-L1 in human and murine fibroblasts in a Smad2/3- and YAP/TAZ-dependent manner. Furthermore, PD-L1 knockdown decreased the TGFβ-dependent induction of extracellular matrix proteins, including collagen Ia1 (colIa1) and alpha-smooth muscle actin (α-SMA), and cell migration/wound healing. In addition to an endogenous role for PD-L1 in profibrotic TGFβ signaling, TGFβ stimulated-human lung fibroblast-derived PD-L1 into extracellular vesicles (EVs) capable of inhibiting T-cell proliferation in response to T cell receptor stimulation and mediating fibroblast cell migration. These findings provide new insights and potential targets for a variety of fibrotic and malignant diseases.

Abstract Type 2 immune cells and eosinophils are transiently present in lung tissue not only in pathology but also during normal postnatal development. However, the lung developmental processes underlying postnatal airway recruitment of eosinophils after birth remain unexplored. We determined that in mice, mature eosinophils are recruited to the lung during P3–14, which corresponds to the primary septation/alveolarization phase of lung development. Developmental eosinophils peaked during P10–14 and exhibited Siglec-Fmed/highCD11c−/low phenotypes, similar to allergic asthma models. By interrogating the lung transcriptome and proteome during peak eosinophil recruitment in postnatal development, we identified markers that functionally capture mesenchymal-epithelial interface establishment (Nes, Smo, Wnt5a, Nog) and provisional extracellular matrix (ECM) deposition (Tnc, Postn, Spon2, Thbs2) as key lung morphogenetic events associating with eosinophils. We identifiedTenascin-C (TNC) as one of the key ECM markers in the lung epithelial-mesenchymal interface at the RNA and protein levels, consistently associating with eosinophils in development and disease. By RNA-seq analysis, naïve murine eosinophils cultured with TNC-enriched ECM significantly induced expression of Siglec-F, CD11c, eosinophil peroxidase, and other markers typical for activated eosinophils in development and allergic inflammatory responses. TNC knockout mice had an altered eosinophil recruitment profile in development. Collectively, our results indicate that lung morphogenetic processes associated with heightened Type 2 immunity are not merely tissue “background” but specifically guide immune cells both in development and pathology.

Background: Algorithmic application of the 2019 Society of Cardiovascular Angiography and Intervention (SCAI) shock stages effectively stratifies mortality risk for patients with cardiogenic shock. However, clinician assessment of SCAI staging may differ. Moreover, the implications of the 2022 SCAI criteria update remain incompletely defined. Methods: The Critical Care Cardiology Trials Network is a multicenter registry of cardiac intensive care units (CICUs). Between 2019 and 2021, participating centers (n=32) contributed at least a 2-month snapshot of consecutive medical CICU admissions. In-hospital mortality was assessed across 3 separate staging methods: clinician assessment, Critical Care Cardiology Trials Network algorithmic application of the 2019 SCAI criteria, and a revision of the Critical Care Cardiology Trials Network application using the 2022 SCAI criteria. Results: Of 9612 admissions, 1340 (13.9%) presented with cardiogenic shock with in-hospital mortality of 35.2%. Both clinician and algorithm-based staging using the 2019 SCAI criteria identified a stepwise gradient of mortality risk (stage C–E: 19.0% to 83.7% and 14.6% to 52.2%, respectively; P trend <0.001 for each). Clinician assignment of SCAI stages identified higher risk patients compared with algorithm-based assignment (stage D: 49.9% versus 29.3%; stage E: 83.7% versus 52.2%). Algorithmic application of the 2022 SCAI criteria, with incorporation of the vasoactive-inotropic score, more closely approximated clinician staging (mortality for stage C-E: 21.9% to 70.5%; P trend <0.001). Conclusions: Both clinician and algorithm-based application of the 2019 SCAI stages identify a stepwise gradient of mortality risk, although clinician-staging may better allocate higher risk patients into advanced SCAI stages. Updated algorithmic staging using the 2022 SCAI criteria and vasoactive-inotropic score further refines risk stratification.

Abstract Understanding the function of non-coding genomic sequence variants represents a challenge for biomedicine. Many diseases are products of gene-by-environment interactions with complex mechanisms. This study addresses these themes by mechanistic characterization of non-coding variants that influence gene expression only after drug or hormone exposure. Using glucocorticoid signaling as a model system, we integrated genomic, transcriptomic, and epigenomic approaches to unravel mechanisms by which variant function could be revealed by hormones or drugs. Specifically, we identified cis-regulatory elements and 3D interactions underlying ligand-dependent associations between variants and gene expression. One-quarter of the glucocorticoid-modulated variants that we identified had already been associated with clinical phenotypes. However, their affected genes were ‘unmasked’ only after glucocorticoid exposure and often with function relevant to the disease phenotypes. These diseases involved glucocorticoids as risk factors or therapeutic agents and included autoimmunity, metabolic and mood disorders, osteoporosis and cancer. For example, we identified a novel breast cancer risk gene, MAST4, with expression that was repressed by glucocorticoids in cells carrying the risk genotype, repression that correlated with MAST4 expression in breast cancer and treatment outcomes. These observations provide a mechanistic framework for understanding non-coding genetic variant-chemical environment interactions and their role in disease risk and drug response.

AbstractAlternative polyadenylation (APA) is a common genetic regulatory mechanism that generates distinct 3′ ends for RNA transcripts. Changes in APA have been associated with multiple biological processes and disease phenotypes. However, the role of hormones and their drug analogs in APA remains largely unknown. In this study, we investigated transcriptome‐wide the impact of glucocorticoids on APA in 30 human B‐lymphoblastoid cell lines. We found that glucocorticoids could regulate APA for a subset of genes, possibly by changing the expression of 142 RNA‐binding proteins, some with known APA‐regulating properties. Interestingly, genes with glucocorticoid‐mediated APA were enriched in viral translation‐related pathways, while genes with glucocorticoid‐mediated expression were enriched in interferon and interleukin pathways, suggesting that glucocorticoid‐mediated APA might result in functional consequences distinct from gene expression. For example, glucocorticoids, a pharmacotherapy for severe COVID‐19, were found to change the APA but not the expression of LY6E, an important antiviral inhibitor in coronavirus diseases. Glucocorticoid‐mediated APA was also cell‐type‐specific, suggesting an action of glucocorticoids that may be unique to immune regulation. We also observed evidence for genotype‐dependent glucocorticoid‐mediated APA (referred to as pharmacogenomic‐alterative polyadenylation quantitative trait loci), providing potential functional mechanisms for a series of common genetic variants that had previously been associated with immune disorders, but without a clear mechanism. In summary, this study reports a series of observations regarding the impact of glucocorticoids on APA, raising the possibility that this mechanism might have implications for both disease pathophysiology and drug therapy.

Aims Increased LV wall thickness is frequently encountered in transthoracic echocardiography (TTE). While accurate and early diagnosis is clinically important, given the differences in available therapeutic options and prognosis, an extensive workup is often required to establish the diagnosis. We propose the first echo-based, automated deep learning model with a fusion architecture to facilitate the evaluation and diagnosis of increased left ventricular (LV) wall thickness. Methods and Results Patients with an established diagnosis of increased LV wall thickness (hypertrophic cardiomyopathy (HCM), cardiac amyloidosis (CA), and hypertensive heart disease (HTN)/others) between 1/2015 to 11/2019 at Mayo Clinic Arizona were identified. The cohort was divided into 80%/10%/10% for training, validation, and testing sets, respectively. Six baseline TTE views were used to optimize a pre-trained InceptionResnetV2 model. Each model output was used to train a meta-learner under a fusion architecture. Model performance was assessed by multiclass area under the receiver operating characteristic curve (AUROC). A total of 586 patients were used for the final analysis (194 HCM, 201 CA, and 191 HTN/others). The mean age was 55.0 years, and 57.8% were male. Among the individual view-dependent models, the apical 4 chamber model had the best performance (AUROC: HCM: 0.94, CA: 0.73, and HTN/other: 0.87). The final fusion model outperformed all the view-dependent models (AUROC: CA: 0.90, HCM: 0.93, and HTN/other: 0.92). Conclusion The echo-based InceptionResnetV2 fusion model can accurately classify the main etiologies of increased LV wall thickness and can facilitate the process of diagnosis and workup.

Abstract With aging, somatic mutations in hematopoietic stem and progenitor cells (HSPC) can give rise to clonal hematopoiesis of indeterminate potential (CHIP), a premalignant state associated with diverse age-related diseases. Here we report the largest multi-ancestry genome-wide analysis of CHIP to date (N = 1,018,305), including individuals of African (N = 85,978), Admixed American (N = 191,371), East Asian (N = 13,532), European (N = 694,015), and South Asian (N = 13,193) ancestry. Multi-ancestry meta-analyses identified 72 genome-wide significant loci, including 44 novel associations implicating genes such as AFF1 , ATF7IP , ATP8B4 , BCL2 , CEBPA , CYRIA , DNM2 , ELF1 , NKX2-3 , PIK3CB , PRDM16 , RPN1 , TERC , and TRIM4 . Notably, variants at MECOM and PHF20L1 showed opposite allelic effects between DNMT3A - and non- DNMT3A -driven CHIP, highlighting driver-specific germline influences. Implicated loci converge on pathways regulating telomere maintenance, cell-cycle control, hematopoietic transcription, DNA damage response and immune signaling. These findings support a model in which germline variation both expands the HSPC pool and biases clonal selection in a driver-dependent manner, providing a mechanistic basis for inter-individual heterogeneity in CHIP. Phenome-wide association analyses further linked CHIP to hematologic, neoplastic, and circulatory traits, with enrichment across hematopoietic and non-hematopoietic cell types. Together, this work expands the genomic and phenomic landscape of CHIP and reveals germline–somatic interactions that shape clonal evolution during aging.

Background: With the improvement in outcomes for acute coronary syndrome (ACS), the practice of routine admission to cardiac intensive care units (CICUs) is evolving. We aimed to describe the epidemiology of patients with ACS admitted to contemporary CICUs. Methods: Using the CCCTN (Critical Care Cardiology Trials Network) Registry for consecutive medical CICU admissions across 26 advanced CICUs in North America between 2017 and 2020, we identified patients with a primary diagnosis of ACS at CICU admission and compared patient characteristics, resource utilization, and outcomes to patients admitted with a non-ACS diagnosis and across sub-populations of patients with ACS, including by indication for CICU admission. Results: Of 10 118 CICU admissions, 29.4% (n=2978) were for a primary diagnosis of ACS, with significant interhospital variability (range, 13.4%–56.6%). Compared with patients admitted with a diagnosis other than ACS, patients with ACS had fewer comorbidities, lower acute severity of illness with less utilization of advanced CICU therapies (41.3% versus 66.1%, P <0.0001), and lower CICU mortality (5.4% versus 9.9%, P <0.0001). Monitoring alone, without another CICU indication at the time of admission, was the most frequent admission indication in patients with ACS (53.8%); less common indications in patients with ACS included respiratory insufficiency, shock, or the need for vasoactive therapy. Of patients with ACS admitted for monitoring alone, 94.8% did not subsequently require advanced intensive care unit therapies and had a low CICU length of stay (1.5 days [0.9–2.4] versus 2.6 [1.4–5.1], P <0.0001) and CICU mortality (0.6% versus 11.0%, P <0.0001), compared with patients with ACS with an admission indication beyond monitoring. Conclusions: In a registry of tertiary care CICUs, ACS represent ≈1/3 of all admissions with significant variability across hospitals. More than half of the ACS admissions to the CICU were for routine monitoring alone, with a low rate of complications and mortality. This observation highlights an opportunity for prospective studies to refine triage strategies for lower risk patients with ACS.