Heterogeneity of Islet-Infiltrating IL-21+ CD4 T Cells in a Mouse Model of Type 1 Diabetes.

IL-21 is essential for type 1 diabetes (T1D) development in the NOD mouse model. IL-21-expressing CD4 T cells are present in pancreatic islets where they contribute to T1D progression. However, little is known about their phenotype and differentiation states. To fill this gap, we generated, to our knowledge, a novel IL-21 reporter NOD strain to further characterize IL-21+ CD4 T cells in T1D. IL-21+ CD4 T cells accumulate in pancreatic islets and recognize ß cell Ags. Single-cell RNA sequencing revealed that CD4 T effector cells in islets actively express IL-21 and they are highly diabetogenic despite expressing multiple inhibitory molecules, including PD-1 and LAG3. Islet IL-21+ CD4 T cells segregate into four phenotypically and transcriptionally distinct differentiation states, that is, less differentiated early effectors, T follicular helper (Tfh)-like cells, and two Th1 subsets. Trajectory analysis predicts that early effectors differentiate into both Tfh-like and terminal Th1 cells. We further demonstrated that intrinsic IL-27 signaling controls the differentiation of islet IL-21+ CD4 T cells, contributing to their helper function. Collectively, our study reveals the heterogeneity of islet-infiltrating IL-21+ CD4 T cells and indicates that both Tfh-like and Th1 subsets produce IL-21 throughout their differentiation process, highlighting the important sources of IL-21 in T1D pathogenesis.

Cellular Senescence Contributes to the Progression of Hyperoxic Bronchopulmonary Dysplasia.

Oxidative stress, inflammation, and endoplasmic reticulum (ER) stress sequentially occur in bronchopulmonary dysplasia (BPD), and all result in DNA damage. When DNA damage becomes irreparable, tumor suppressors increase, followed by apoptosis or senescence. Although cellular senescence contributes to wound healing, its persistence inhibits growth. Therefore, we hypothesized that cellular senescence contributes to BPD progression. Human autopsy lungs were obtained. Sprague-Dawley rat pups exposed to 95% oxygen between Postnatal Day 1 (P1) and P10 were used as the BPD phenotype. N-acetyl-lysyltyrosylcysteine-amide (KYC), tauroursodeoxycholic acid (TUDCA), and Foxo4 dri were administered intraperitoneally to mitigate myeloperoxidase oxidant generation, ER stress, and cellular senescence, respectively. Lungs were examined by histology, transcriptomics, and immunoblotting. Cellular senescence increased in rat and human BPD lungs, as evidenced by increased oxidative DNA damage, tumor suppressors, GL-13 stain, and inflammatory cytokines with decreased cell proliferation and lamin B expression. Cellular senescence-related transcripts in BPD rat lungs were enriched at P10 and P21. Single-cell RNA sequencing showed increased cellular senescence in several cell types, including type 2 alveolar cells. In addition, Foxo4-p53 binding increased in BPD rat lungs. Daily TUDCA or KYC, administered intraperitoneally, effectively decreased cellular senescence, improved alveolar complexity, and partially maintained the numbers of type 2 alveolar cells. Foxo4 dri administered at P4, P6, P8, and P10 led to outcomes similar to TUDCA and KYC. Our data suggest that cellular senescence plays an essential role in BPD after initial inducement by hyperoxia. Reducing myeloperoxidase toxic oxidant production, ER stress, and attenuating cellular senescence are potential therapeutic strategies for halting BPD progression.

Self-Renewing Islet TCF1+ CD8 T Cells Undergo IL-27-Controlled Differentiation to Become TCF1- Terminal Effectors during the Progression of Type 1 Diabetes.

In type 1 diabetes (T1D) autoreactive CD8 T cells infiltrate pancreatic islets and destroy insulin-producing ß cells. Progression to T1D onset is a chronic process, which suggests that the effector activity of ß-cell autoreactive CD8 T cells needs to be maintained throughout the course of disease development. The mechanism that sustains diabetogenic CD8 T cell effectors during the course of T1D progression has not been completely defined. Here we used single-cell RNA sequencing to gain further insight into the phenotypic complexity of islet-infiltrating CD8 T cells in NOD mice. We identified two functionally distinct subsets of activated CD8 T cells, CD44highTCF1+CXCR6- and CD44highTCF1-CXCR6+, in islets of prediabetic NOD mice. Compared with CD44highTCF1+CXCR6- CD8 T cells, the CD44highTCF1-CXCR6+ subset expressed higher levels of inhibitory and cytotoxic molecules and was more prone to apoptosis. Adoptive cell transfer experiments revealed that CD44highTCF1+CXCR6- CD8 T cells, through continuous generation of the CD44highTCF1-CXCR6+ subset, were more capable than the latter population to promote insulitis and the development of T1D. We further showed that direct IL-27 signaling in CD8 T cells promoted the generation of terminal effectors from the CD44highTCF1+CXCR6- population. These results indicate that islet CD44highTCF1+CXCR6- CD8 T cells are a progenitor-like subset with self-renewing capacity, and, under an IL-27-controlled mechanism, they differentiate into the CD44highTCF1-CXCR6+ terminal effector population. Our study provides new insight into the sustainability of the CD8 T cell response in the pathogenesis of T1D.

Sex-specific interactive effect of melamine and DEHP on a marker of early kidney damage in Taiwanese adults: A national population-based study from the Taiwan Biobank.

Taiwan had the high incidence of chronic kidney disease (CKD) worldwide. Our objective was to examine associations between daily exposure of phthalates and melamine, two common nephrotoxins, and kidney damage risk in a well-established nationwide cohort. Study subjects were from Taiwan Biobank (TWB) with existing data of questionnaire and biochemical examinations. Average daily intake (ADI) levels of melamine and seven parental phthalates, including DEHP (di-2-ethylhexylphthalate), DiBP (Dibutyl phthalate), DnBP (Di-n-butyl phthalate), BBzP (Butyl benzyl phthalate), DEP (Diethyl phthalate), and DMP (Dimethyl phthalate) were estimated using a creatinine excretion-based model from urine melamine and 10 phthalate metabolites. Urine microalbumin to creatinine ratio (ACR) was used to represent for the outcome of kidney damage. Two statistical strategies were used: First, a weighted quantile sum (WQS) regression model to select the most important exposure variables of ADI levels of phthalates and melamine associated with ACR; Second, to examine effects of those most important exposure variables on ACR in multivariable linear regression models. In total, 1153 eligible adults were left for analyses. Of them, 591 (51.3%) and 562 (48.7%) were men and women, respectively, with a median age of 49 years old. By WQS, a significant and positive association was found between ADI of melamine and phthalates and ACR (ß = 0.14, p = 0.002). ADI levels of melamine had the highest weight (0.57), followed by DEHP (0.13). Next, examining the two most important exposures in association with ACR, we found that the higher the melamine and DEHP intakes, the higher the ACR levels were found. An interaction effect was also found between melamine and DEHP intakes on urine ACR (p = 0.015). This result was more prominent in men (p = 0.008) than in women (p = 0.651). Environmental co-exposure of melamine and DEHP can potentially affect ACR in the community-dwelling Taiwanese adult population.

MethylSeqDesign: a framework for Methyl-Seq genome-wide power calculation and study design issues.

Bisulfite DNA methylation sequencing (Methyl-Seq) becomes one of the most important technologies to study methylation level difference at a genome-wide scale. Due to the complexity and large scale of methyl-Seq data, power calculation and study design method have not been developed. Here, we propose a "MethylSeqDesign" framework for power calculation and study design of Methyl-Seq experiments by utilizing information from pilot data. Differential methylation analysis is based on a beta-binomial model. Power calculation is achieved using mixture model fitting of p-values from pilot data and a parametric bootstrap procedure. To circumvent the issue of existing tens of millions of methylation sites, we focus on the inference of pre-specified targeted regions. The performance of the method was evaluated with simulations. Two real examples are analyzed to illustrate our method. An R package "MethylSeqDesign" to implement this method is publicly available.

Older molecular brain age in severe mental illness.

Psychiatric disorders are associated with accelerated aging and enhanced risk for neurodegenerative disorders. Brain aging is associated with molecular, cellular, and structural changes that are robust on the group level, yet show substantial inter-individual variability. Here we assessed deviations in gene expression from normal age-dependent trajectories, and tested their validity as predictors of risk for major mental illnesses and neurodegenerative disorders. We performed large-scale gene expression and genotype analyses in postmortem samples of two frontal cortical brain regions from 214 control subjects aged 20-90 years. Individual estimates of "molecular age" were derived from age-dependent genes, identified by robust regression analysis. Deviation from chronological age was defined as "delta age". Genetic variants associated with deviations from normal gene expression patterns were identified by expression quantitative trait loci (cis-eQTL) of age-dependent genes or genome-wide association study (GWAS) on delta age, combined into distinct polygenic risk scores (PRScis-eQTL and PRSGWAS), and tested for predicting brain disorders or pathology in independent postmortem expression datasets and clinical cohorts. In these validation datasets, molecular ages, defined by 68 and 76 age-related genes for two brain regions respectively, were positively correlated with chronological ages (r = 0.88/0.91), elevated in bipolar disorder (BP) and schizophrenia (SCZ), and unchanged in major depressive disorder (MDD). Exploratory analyses in independent clinical datasets show that PRSs were associated with SCZ and MDD diagnostics, and with cognition in SCZ and pathology in Alzheimer's disease (AD). These results suggest that older molecular brain aging is a common feature of severe mental illnesses and neurodegeneration.

Simultaneous estimation of cluster number and feature sparsity in high-dimensional cluster analysis.

Estimating the number of clusters (K) is a critical and often difficult task in cluster analysis. Many methods have been proposed to estimate K, including some top performers using resampling approach. When performing cluster analysis in high-dimensional data, simultaneous clustering and feature selection is needed for improved interpretation and performance. To our knowledge, little has been studied for simultaneous estimation of K and feature sparsity parameter in a high-dimensional exploratory cluster analysis. In this paper, we propose a resampling method to bridge this gap and evaluate its performance under the sparse K-means clustering framework. The proposed target function balances between sensitivity and specificity of clustering evaluation of pairwise subjects from clustering of full and subsampled data. Through extensive simulations, the method performs among the best over classical methods in estimating K in low-dimensional data. For high-dimensional simulation data, it also shows superior performance to simultaneously estimate K and feature sparsity parameter. Finally, we evaluated the methods in four microarray, two RNA-seq, one SNP, and two nonomics datasets. The proposed method achieves better clustering accuracy with fewer selected predictive genes in almost all real applications.

MetaOmics: analysis pipeline and browser-based software suite for transcriptomic meta-analysis.

SUMMARY: The rapid advances of omics technologies have generated abundant genomic data in public repositories and effective analytical approaches are critical to fully decipher biological knowledge inside these data. Meta-analysis combines multiple studies of a related hypothesis to improve statistical power, accuracy and reproducibility beyond individual study analysis. To date, many transcriptomic meta-analysis methods have been developed, yet few thoughtful guidelines exist. Here, we introduce a comprehensive analytical pipeline and browser-based software suite, called MetaOmics, to meta-analyze multiple transcriptomic studies for various biological purposes, including quality control, differential expression analysis, pathway enrichment analysis, differential co-expression network analysis, prediction, clustering and dimension reduction. The pipeline includes many public as well as >10 in-house transcriptomic meta-analytic methods with data-driven and biological-aim-driven strategies, hands-on protocols, an intuitive user interface and step-by-step instructions. AVAILABILITY AND IMPLEMENTATION: MetaOmics is freely available at https://github.com/metaOmics/metaOmics. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Temporal and Spatial Post-Transcriptional Regulation of Zebrafish tie1 mRNA by Long Noncoding RNA During Brain Vascular Assembly.

OBJECTIVE: Tie1 (tyrosine kinase containing immunoglobulin and epidermal growth factor homology 1), an endothelial and hematopoietic cell-specific receptor tyrosine kinase, is an important regulator of angiogenesis and critical for maintaining vascular integrity. The post-transcriptional regulation of tie1 mRNA expression is not understood, but it might partly explain Tie1's differential expression pattern in endothelium. Following up on our previous work that identified natural antisense transcripts from the tie1 locus-tie1 antisense (tie1AS), which regulates tie1 mRNA levels in zebrafish-we attempted to identify the mechanism of this regulation. APPROACH AND RESULTS: Through in vitro and in vivo ribonucleoprotein binding studies, we demonstrated that tie1AS long noncoding RNA interacts with an RNA binding protein-embryonic lethal and abnormal vision Drosophila-like 1 (Elavl1)-that regulates tie1 mRNA levels. When we disrupted the interaction between tie1AS and Elavl1 by using constitutively active antisense morpholino oligonucleotides or photoactivatable morpholino oligonucleotides, tie1 mRNA levels increased between 26 and 31 hours post-fertilization, particularly in the head. This increase correlated with dilation of primordial midbrain channels, smaller eyes, and reduced ventricular space. We also observed these phenotypes when we used CRISPR (clustered regularly interspaced short palindromic repeats)-mediated CRISPRi (CRISPR-mediated interference) to knock down tie1AS. Treatment of the morpholino oligonucleotide-injected embryos with a small molecule that decreased tie1 mRNA levels rescued all 3 abnormal phenotypes. CONCLUSIONS: We identified a novel mode of temporal and spatial post-transcriptional regulation of tie1 mRNA. It involves long noncoding RNA, tie1AS, and Elavl1 (an interactor of tie1AS).

MetaKTSP: a meta-analytic top scoring pair method for robust cross-study validation of omics prediction analysis.

MOTIVATION: Supervised machine learning is widely applied to transcriptomic data to predict disease diagnosis, prognosis or survival. Robust and interpretable classifiers with high accuracy are usually favored for their clinical and translational potential. The top scoring pair (TSP) algorithm is an example that applies a simple rank-based algorithm to identify rank-altered gene pairs for classifier construction. Although many classification methods perform well in cross-validation of single expression profile, the performance usually greatly reduces in cross-study validation (i.e. the prediction model is established in the training study and applied to an independent test study) for all machine learning methods, including TSP. The failure of cross-study validation has largely diminished the potential translational and clinical values of the models. The purpose of this article is to develop a meta-analytic top scoring pair (MetaKTSP) framework that combines multiple transcriptomic studies and generates a robust prediction model applicable to independent test studies. RESULTS: We proposed two frameworks, by averaging TSP scores or by combining P-values from individual studies, to select the top gene pairs for model construction. We applied the proposed methods in simulated data sets and three large-scale real applications in breast cancer, idiopathic pulmonary fibrosis and pan-cancer methylation. The result showed superior performance of cross-study validation accuracy and biomarker selection for the new meta-analytic framework. In conclusion, combining multiple omics data sets in the public domain increases robustness and accuracy of the classification model that will ultimately improve disease understanding and clinical treatment decisions to benefit patients. AVAILABILITY AND IMPLEMENTATION: An R package MetaKTSP is available online. (http://tsenglab.biostat.pitt.edu/software.htm). CONTACT: ctseng@pitt.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Enhanced Molecular Aging in Late-Life Depression: the Senescent-Associated Secretory Phenotype.

OBJECTIVE: This study aims to investigate whether a systemic molecular pattern associated with aging (senescent-associated secretory phenotype [SASP]) is elevated in adults with late-life depression (LLD), compared with never-depressed elderly comparison participants. DESIGN: Cross-sectional study. PARTICIPANTS: We included 111 older adults (80 with LLD and 31 comparison participants) in this study. MEASUREMENT: A panel of 22 SASP-related proteins was extracted from a previous multiplex protein panel performed in these participants. We conducted a principal component analysis to create the SASP index based on individual weights of each of protein. RESULTS: Participants with LLD showed a significantly increased SASP index compared with comparison participants, after controlling for age, depressive symptoms, medical comorbidity (CIRS-G) scores, sex, and cognitive performance (F(1,98) = 7.3, p = 0.008). Correlation analyses revealed that the SASP index was positively correlated with age (r = 0.2, p = 0.03) and CIRS score (r = 0.27, p = 0.005), and negatively correlated with information processing speed (r = -0.34, p = 0.001), executive function (r = -0.27, p = 0.004) and global cognitive performance (r = -0.28, p = 0.007). CONCLUSIONS: To the best of our knowledge, this is the first study to show that a set of proteins (i.e., SASP index) primarily associated with cellular aging is abnormally regulated and elevated in LLD. These results suggest that individuals with LLD display enhanced aging-related molecular patterns that are associated with higher medical comorbidity and worse cognitive function. Finally, we provide a set of proteins that can serve as potential therapeutic targets and biomarkers to monitor the effects of therapeutic or preventative interventions in LLD.

Age-dependent increase in Kalirin-9 and Kalirin-12 transcripts in human orbitofrontal cortex.

KALRN (KAL) is a Rho GEF that is highly involved in regulation of the actin cytoskeleton within dendrites. There are several isoforms of the protein that arise from differential splicing of KALRN's 66 exons. KAL isoforms have different functions in development. For example, overexpression of the KAL9 and KAL12 isoforms induce dendritic elongation in early development. However, in mature neurons KAL9 overexpression reduces dendritic length, a phenotype also observed in normal human ageing. We therefore hypothesized that KAL9 would have increased expression with age, and undertook to evaluate the expression of individual KALRN exons throughout the adult lifespan. Postmortem human brain grey matter from Brodmann's area (BA) 11 and BA47 derived from a cohort of 209 individuals without psychiatric or neurodegenerative disease, ranging in age from 16 to 91 years, were analysed for KALRN expression by Affymetrix exon array. Analysis of the exon array data in an isoform-specific manner, as well as confirmatory isoform-specific qPCR studies, indicated that the longer KAL9 and KAL12 isoforms demonstrated a statistically significant, but modest, increase with age. The small magnitude of the age effect suggests that inter-individual factors other than age likely contribute to a higher degree to KAL9 and KAL12 expression. In contrast to KAL9 and KAL12, global KALRN expression did not increase with age. Our work suggests that global measures of KALRN gene expression may be misleading and future studies should focus on isoform-specific quantification.

SMART: Statistical Metabolomics Analysis-An R Tool.

Metabolomics data provide unprecedented opportunities to decipher metabolic mechanisms by analyzing hundreds to thousands of metabolites. Data quality concerns and complex batch effects in metabolomics must be appropriately addressed through statistical analysis. This study developed an integrated analysis tool for metabolomics studies to streamline the complete analysis flow from initial data preprocessing to downstream association analysis. We developed Statistical Metabolomics Analysis-An R Tool (SMART), which can analyze input files with different formats, visually represent various types of data features, implement peak alignment and annotation, conduct quality control for samples and peaks, explore batch effects, and perform association analysis. A pharmacometabolomics study of antihypertensive medication was conducted and data were analyzed using SMART. Neuromedin N was identified as a metabolite significantly associated with angiotensin-converting-enzyme inhibitors in our metabolome-wide association analysis (p = 1.56 × 10(-4) in an analysis of covariance (ANCOVA) with an adjustment for unknown latent groups and p = 1.02 × 10(-4) in an ANCOVA with an adjustment for hidden substructures). This endogenous neuropeptide is highly related to neurotensin and neuromedin U, which are involved in blood pressure regulation and smooth muscle contraction. The SMART software, a user guide, and example data can be downloaded from http://www.stat.sinica.edu.tw/hsinchou/metabolomics/SMART.htm .

Applying genome-wide gene-based expression quantitative trait locus mapping to study population ancestry and pharmacogenetics.

BACKGROUND: Gene-based analysis has become popular in genomic research because of its appealing biological and statistical properties compared with those of a single-locus analysis. However, only a few, if any, studies have discussed a mapping of expression quantitative trait loci (eQTL) in a gene-based framework. Neither study has discussed ancestry-informative eQTL nor investigated their roles in pharmacogenetics by integrating single nucleotide polymorphism (SNP)-based eQTL (s-eQTL) and gene-based eQTL (g-eQTL). RESULTS: In this g-eQTL mapping study, the transcript expression levels of genes (transcript-level genes; T-genes) were correlated with the SNPs of genes (sequence-level genes; S-genes) by using a method of gene-based partial least squares (PLS). Ancestry-informative transcripts were identified using a rank-score-based multivariate association test, and ancestry-informative eQTL were identified using Fisher's exact test. Furthermore, key ancestry-predictive eQTL were selected in a flexible discriminant analysis. We analyzed SNPs and gene expression of 210 independent people of African-, Asian- and European-descent. We identified numerous cis- and trans-acting g-eQTL and s-eQTL for each population by using PLS. We observed ancestry information enriched in eQTL. Furthermore, we identified 2 ancestry-informative eQTL associated with adverse drug reactions and/or drug response. Rs1045642, located on MDR1, is an ancestry-informative eQTL (P = 2.13E-13, using Fisher's exact test) associated with adverse drug reactions to amitriptyline and nortriptyline and drug responses to morphine. Rs20455, located in KIF6, is an ancestry-informative eQTL (P = 2.76E-23, using Fisher's exact test) associated with the response to statin drugs (e.g., pravastatin and atorvastatin). The ancestry-informative eQTL of drug biotransformation genes were also observed; cross-population cis-acting expression regulators included SPG7, TAP2, SLC7A7, and CYP4F2. Finally, we also identified key ancestry-predictive eQTL and established classification models with promising training and testing accuracies in separating samples from close populations. CONCLUSIONS: In summary, we developed a gene-based PLS procedure and a SAS macro for identifying g-eQTL and s-eQTL. We established data archives of eQTL for global populations. The program and data archives are accessible at http://www.stat.sinica.edu.tw/hsinchou/genetics/eQTL/HapMapII.htm. Finally, the results from our investigations regarding the interrelationship between eQTL, ancestry information, and pharmacodynamics provide rich resources for future eQTL studies and practical applications in population genetics and medical genetics.

Using a centered general linear model for detection of interactions among biomarkers.

The dummy variable based general linear model (gLM) is commonly used to model categorical factors and their interactions. However, the main factors and their interactions in a general linear model are often correlated even when the factors are independently distributed. Alternatively, the classical two-way factorial analysis of variance (ANOVA) model can avoid the correlation between the main factors and their interactions when the main factors are independent. But the ANOVA model is hardly applicable to a regular linear regression model especially in the presence of other covariates due to constraints on its model parameters. In this study, a centered general linear model (cgLM) is proposed for modeling interactions between categorical factors based on their centered dummy variables. We show that the cgLM can avoid the correlation between the main factors and their interactions as the ANOVA model when the main factors are independent. Meanwhile, similar to gLM, it can be used in regular regression and fitted conveniently using the standard least square approach by choosing appropriate baselines to avoid constraints on its model parameters. The potential advantage of cgLM over gLM for detection of interactions in model building procedures is also illustrated and compared via a simulation study. Finally, the cgLM is applied to a postmortem brain gene expression data set.

Meta-Analytic Gene-Clustering Algorithm for Integrating Multi-Omics and Multi-Study Data.

Gene pathways and gene-regulatory networks are used to describe the causal relationship between genes, based on biological experiments. However, many genes are still to be studied to define novel pathways. To address this, a gene-clustering algorithm has been used to group correlated genes together, based on the similarity of their gene expression level. The existing methods cluster genes based on only one type of omics data, which ignores the information from other types. A large sample size is required to achieve an accurate clustering structure for thousands of genes, which can be challenging due to the cost of multi-omics data. Meta-analysis has been used to aggregate the data from multiple studies and improve the analysis results. We propose a computationally efficient meta-analytic gene-clustering algorithm that combines multi-omics datasets from multiple studies, using the fixed effects linear models and a modified weighted correlation network analysis framework. The simulation study shows that the proposed method outperforms existing single omic-based clustering approaches when multi-omics data and/or multiple studies are available. A real data example demonstrates that our meta-analytic method outperforms single-study based methods.

Modifiable vascular risk factors contribute to stroke in 1080 NOTCH3 R544C carriers in Taiwan Biobank.

BACKGROUND AND AIM: Previous studies have suggested cardiovascular risk factors increase the risk of not only common sporadic stroke but also of stroke in patients with monogenic stroke disorders including CADASIL. We investigated the effects of the NOTCH3 Arg544Cys (R544C) variant and associated vascular risk factors on stroke in the Taiwanese population. METHODS: This study was conducted using data from the Taiwan Biobank, consisting of at least 130,000 Han Chinese participants. The genotype was derived from customized genome-wide arrays for 650,000 to 750,000 single-nucleotide polymorphisms (SNPs). Individuals with NOTCH3 R544C were subsequently matched with noncarriers based on the propensity score at a 1:10 ratio by demographic and cardiovascular risk factors. The odds ratio (OR) for stroke or other phenotypes in NOTCH3 R544C carriers and matched noncarriers was then calculated. Univariate and multivariate regression analyses were performed on cardiovascular risk factors in NOTCH3 R544C carriers with and without stroke. The polygenic risk score (PRS) model, adopted from the UK Biobank, was then applied to evaluate the role of NOTCH3 R544C in stroke. RESULTS: From the 114,282 participants with both genotype and questionnaire results, 1080 (0.95%) harbored the pathogenic NOTCH3 R544C variant. When compared to the matched controls (n = 10,800), the carriers presented with a history of stroke (OR: 2.52, 95% confidence interval (CI) (1.45, 4.37)), dementia (OR: 30.1, 95% CI (3.13, 289.43)), and sibling history of stroke (OR: 2.48, 95% CI (1.85, 3.34)) phenotypes. The risk of stroke increased with every 10-year increase in age (p = 0.006, Cochran-Mantel-Haenszel test). Among NOTCH3 R544C carriers, 16 (1.3%) of the 1080 carriers with a stroke history were older, male, and more likely to have hypertension, diabetes, dyslipidemia, and a family history of stroke. In the stepwise multivariate analysis, hypertension (OR: 11.28, 95% CI (3.54, 43.3)) and diabetes mellitus (OR: 4.10, 95% CI (1.31, 12.4)) were independently associated with stroke. Harboring the NOTCH3 R544C variant in the Taiwan Biobank is comparable with a 6.74 standard deviations increase in individual's polygenic risk score for stroke. CONCLUSION: While the NOTCH3 R544C variant alone increased the risk of stroke, modifiable vascular risk factors also played a role in the occurrence of stroke in Taiwanese community-dwelling individuals carrying the NOTCH3 variant.

Monocytes in type 1 diabetes families exhibit high cytolytic activity and subset abundances that correlate with clinical progression.

Monocytes are immune regulators implicated in the pathogenesis of type 1 diabetes (T1D), an autoimmune disease that targets insulin-producing pancreatic ß cells. We determined that monocytes of recent onset (RO) T1D patients and their healthy siblings express proinflammatory/cytolytic transcriptomes and hypersecrete cytokines in response to lipopolysaccharide exposure compared to unrelated healthy controls (uHCs). Flow cytometry measured elevated circulating abundances of intermediate monocytes and >2-fold more CD14+CD16+HLADR+KLRD1+PRF1+ NK-like monocytes among patients with ROT1D compared to uHC. The intermediate to nonclassical monocyte ratio among ROT1D patients correlated with the decline in functional ß cell mass during the first 24 months after onset. Among sibling nonprogressors, temporal decreases were measured in the intermediate to nonclassical monocyte ratio and NK-like monocyte abundances; these changes coincided with increases in activated regulatory T cells. In contrast, these monocyte populations exhibited stability among T1D progressors. This study associates heightened monocyte proinflammatory/cytolytic activity with T1D susceptibility and progression and offers insight to the age-dependent decline in T1D susceptibility.

IL-13 promotes functional recovery after myocardial infarction via direct signaling to macrophages.

There is great interest in identifying signaling pathways that promote cardiac repair after myocardial infarction (MI). Prior studies suggest a beneficial role for IL-13 signaling in neonatal heart regeneration; however, the cell types mediating cardiac regeneration and the extent of IL-13 signaling in the adult heart after injury are unknown. We identified an abundant source of IL-13 and the related cytokine, IL-4, in neonatal cardiac type 2 innate lymphoid cells, but this phenomenon declined precipitously in adult hearts. Moreover, IL-13 receptor deletion in macrophages impaired cardiac function and resulted in larger scars early after neonatal MI. By using a combination of recombinant IL-13 administration and cell-specific IL-13 receptor genetic deletion models, we found that IL-13 signaling specifically to macrophages mediated cardiac functional recovery after MI in adult mice. Single transcriptomics revealed a subpopulation of cardiac macrophages in response to IL-13 administration. These IL-13-induced macrophages were highly efferocytotic and were identified by high IL-1R2 expression. Collectively, we elucidated a strongly proreparative role for IL-13 signaling directly to macrophages following cardiac injury. While this pathway is active in proregenerative neonatal stages, reactivation of macrophage IL-13 signaling is required to promote cardiac functional recovery in adults.

Estrogen-related receptor alpha promotes thyroid tumor cell survival via a tumor subtype-specific regulation of target gene networks.

Mortalin (encoded by HSPA9) is a mitochondrial chaperone often overexpressed in cancer through as-yet-unknown mechanisms. By searching different RNA-sequencing datasets, we found that ESRRA is a transcription factor highly correlated with HSPA9 in thyroid cancer, especially in follicular, but not C cell-originated, tumors. Consistent with this correlation, ESRRA depletion decreased mortalin expression only in follicular thyroid tumor cells. Further, ESRRA expression and activity were relatively high in thyroid tumors with oncocytic characteristics, wherein ESRRA and mortalin exhibited relatively high functional overlap. Mechanistically, ESRRA directly regulated HSPA9 transcription through a novel ESRRA-responsive element located upstream of the HSPA9 promoter. Physiologically, ESRRA depletion suppressed thyroid tumor cell survival via caspase-dependent apoptosis, which ectopic mortalin expression substantially abrogated. ESRRA depletion also effectively suppressed tumor growth and mortalin expression in the xenografts of oncocytic or ESRRA-overexpressing human thyroid tumor cells in mice. Notably, our Bioinformatics analyses of patient data revealed two ESRRA target gene clusters that contrast oncocytic-like and anaplastic features of follicular thyroid tumors. These findings suggest that ESRRA is a tumor-specific regulator of mortalin expression, the ESRRA-mortalin axis has higher significance in tumors with oncocytic characteristics, and ESRRA target gene networks can refine molecular classification of thyroid cancer.