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Hongyu Zhao

Publications and source records attributed to Hongyu Zhao.

At least 19 recordsLinked to original sources
PubMed2026

Single-cell transcriptomic atlas of Alzheimer's disease middle temporal gyrus reveals region, cell type, and sex specificity of gene expression with novel genetic risk for MERTK in female.

BackgroundAlzheimer's disease (AD), the most common age-related neurodegenerative disease, is closely associated with both amyloid-β plaque and neuroinflammation. Two thirds of AD patients are female, and they have a higher disease risk; women with AD have more extensive brain histological changes than men along with more severe cognitive symptoms and neurodegeneration.ObjectiveThis study aimed to determine how sex difference induces structural brain changes and molecular cell vulnerabilities in AD, with a focus on identifying sex-specific transcriptional alterations and genetic risk factors.MethodsWe performed single nucleus RNA sequencing on postmortem brains from individuals with AD and age- and sex-matched controls, focusing on the middle temporal gyrus, a cortical brain region strongly affected by the disease, and integrated single nucleus RNA sequencing results with genome-wide association study (GWAS) data using cell type-specific enrichment and generalized gene-set analysis approaches. The analysis pipeline is provided with threshold information.ResultsWe identified a selectively vulnerable subpopulation of layer 2/3 excitatory neurons that were RORB-negative and CDH9-expressing in both males and females. Disease-associated, but sex-independent, reactive astrocyte signatures were also present. In clear contrast, the microglia signatures of AD brains differed between males and females. Integrating single cell transcriptomic data with results from GWAS, we identified MERTK genetic variation as a candidate novel risk factor for AD selectively in females.ConclusionsTaken together, our single cell atlas of middle temporal gyrus revealed a unique cellular-level view of sex-specific transcriptional changes in AD, illuminating GWAS identification of sex-specific AD genes. These data serve as a rich resource for interrogation of the molecular and cellular basis of AD.

Alzheimer's disease↗
PubMed2025

High MGMT expression identifies aggressive colorectal cancer with distinct genomic features and immune evasion properties.

INTRODUCTION: The epigenetic silencing of O6-methylguanine DNA methyltransferase (MGMT) is associated with reduced DNA repair capacity, carcinogenesis and increased sensitivity to alkylating chemotherapy. However, the biological role and clinical significance of MGMT overexpression in cancer remains poorly understood. METHODS: Using multiplexed quantitative immunofluorescence we measured the localized levels of MGMT protein, γH2AX and CD8+ T cells in multiple retrospective colorectal cancer (CRC) cohorts. Genomic and transcriptomic features of selected cases were also studied with whole exome DNA sequencing and genome-wide methylation analysis. MGMT-methylated human CRC cells SW620 were transfected with an MGMT-containing plasmid and co-cultured with allogeneic peripheral blood mononuclear cells. RESULTS: A subset of CRCs showed MGMT protein upregulation associated with lower γH2AX, reduced CD8+ tumor infiltrating lymphocytes (TILs), mismatch repair proficient (pMMR) status and shorter survival. CD8+ TILs were more distant from MGMT-expressing cells than MGMT-negative cells and the MGMT promoter methylation status did not highly correlate with MGMT protein levels in CRC. In genomic/transcriptomic analysis, high MGMT expression was associated with a lower nonsynonymous somatic mutational burden, higher transition-to-transversion mutation ratio, increased deleterious TP53 variants and distinct transcriptomic profiles. The exogenous expression of MGMT in SW620 CRC cells reduced the number of spontaneous nonsynonymous mutations, reproduced mutational features of MGMT-high CRC and limited the in vitro T-cell-mediated killing of malignant cells induced by proinflammatory cytokines in tumor/immune cell co-cultures. CONCLUSIONS: MGMT overexpression identifies a previously undescribed subset of CRCs with distinct biological and clinical properties including reduced mutagenesis, adaptive immune evasion, predominantly pMMR phenotype and aggressive clinical course. Direct, quantitative assessment of MGMT protein expression using spatially resolved analysis is more reliable than inference of MGMT expression by promoter methylation status in CRC.

Humans↗
PubMed2025

Leveraging local ancestry and cross-ancestry genetic architecture to improve genetic prediction of complex traits in admixed populations.

The broader application of polygenic risk score (PRS) is hindered by the limited transferability of PRS developed in Europeans to non-European populations. While many statistical methods have been developed to improve the performance of PRS in non-European populations, most of them focused on discrete genetic ancestry clusters and did not consider admixed individuals. Admixed individuals pose a unique challenge for PRS calculation due to the complexity of local ancestry and cross-ancestry effect sizes. Here, we present a statistical method called SDPR_admix for calculating PRS in admixed individuals. SDPR_admix characterizes the joint distribution of the effect sizes of a genetic variant with two ancestries to be both zero, ancestry enriched, or shared with correlation. SDPR_admix outperformed other methods in simulations and improved the prediction of real traits in European-African admixed individuals in UK Biobank when trained on the Population Architecture using Genomics and Epidemiology (PAGE) dataset (N = 13,000). Deployment of SDPR_admix on All of Us (N = 52,000) further increased the prediction accuracy by approximately 5-fold on average compared with training on PAGE. This enhancement was achieved with manageable computational time and cost, demonstrating the feasibility of training PRS models on large-scale All of Us data. We provided several examples demonstrating that both ancestral-enriched and shared effects, as included in the SDPR_admix prediction model, are helpful for improving polygenic prediction in admixed populations. We also applied SDPR_admix to construct PRS for admixed Americans with mixture of European and Amerindigenous ancestries and showed that SDPR_admix overall outperformed other methods.

Humans↗
PubMed2025

Robust pleiotropy-decomposed polygenic scores identify distinct contributions to elevated coronary artery disease polygenic risk.

BACKGROUND: Polygenic risk score (PRS) have proved to offer robust risk prediction for coronary artery disease (CAD). However, the global CAD PRS summarizes the joint effects of all the markers in the genome, masking potential genetic heterogeneity that may be important for disease interpretation and targeted interventions. METHODS: Using summary-level data, we identified 43 significant CAD-related traits based on genetic correlations, and further classified them into eight pleiotropy clusters based on their biological functions. We then partitioned the genome into 2,353 near-independent regions. Variants in each region were assigned to the trait most genetically similar to CAD, and then were labeled with the corresponding pleiotropy cluster. We grouped variants without labels into a ninth, non-specific cluster. The Pleiotropy Decomposed (PD) PRSs for each of the nine clusters were calculated using variants assigned to each cluster for 407,903 samples of European ancestry from the UK Biobank (UKBB). RESULTS: We decomposed the CAD PRS into nine PD-PRSs and further stratified individuals with high CAD-PRS into nine subgroups. Each PD-PRS accounted for a higher proportion of the global CAD-PRS within its corresponding subgroup than in the remaining subjects with high CAD-PRS (e.g., 25.2% (0.07) vs. 10.06% (0.07) for lipids-PD-PRS). Additionally, these subgroups showed distinct clinical features. For example, in the lipids-related subgroup, lipoprotein(a) and LDL-cholesterol levels were 67.5% and 18.3% higher, respectively, compared to the remaining high-risk individuals. Furthermore, significant interactions were observed between blood pressure and BP PD-PRS, and between current smoking and respiratory system PD-PRS. CONCLUSION: Our findings suggest that PD-PRSs may reveal substantial genetic and phenotypic heterogeneity among individuals with high CAD-PRS. The unique PD-PRS compositions of each individual can highlight the relative importance of different pleiotropic regions.

Humans↗
PubMed2023

Single-cell transcriptomic atlas of Alzheimer's disease middle temporal gyrus reveals region, cell type and sex specificity of gene expression with novel genetic risk for MERTK in female.

Alzheimer's disease, the most common age-related neurodegenerative disease, is closely associated with both amyloid-ß plaque and neuroinflammation. Two thirds of Alzheimer's disease patients are females and they have a higher disease risk. Moreover, women with Alzheimer's disease have more extensive brain histological changes than men along with more severe cognitive symptoms and neurodegeneration. To identify how sex difference induces structural brain changes, we performed unbiased massively parallel single nucleus RNA sequencing on Alzheimer's disease and control brains focusing on the middle temporal gyrus, a brain region strongly affected by the disease but not previously studied with these methods. We identified a subpopulation of selectively vulnerable layer 2/3 excitatory neurons that that were RORB-negative and CDH9-expressing. This vulnerability differs from that reported for other brain regions, but there was no detectable difference between male and female patterns in middle temporal gyrus samples. Disease-associated, but sex-independent, reactive astrocyte signatures were also present. In clear contrast, the microglia signatures of diseased brains differed between males and females. Combining single cell transcriptomic data with results from genome-wide association studies (GWAS), we identified MERTK genetic variation as a risk factor for Alzheimer's disease selectively in females. Taken together, our single cell dataset revealed a unique cellular-level view of sex-specific transcriptional changes in Alzheimer's disease, illuminating GWAS identification of sex-specific Alzheimer's risk genes. These data serve as a rich resource for interrogation of the molecular and cellular basis of Alzheimer's disease.

Journal Article↗
PubMed2006

A biphasic pattern of gene expression during mouse retina development.

BACKGROUND: Between embryonic day 12 and postnatal day 21, six major neuronal and one glia cell type are generated from multipotential progenitors in a characteristic sequence during mouse retina development. We investigated expression patterns of retina transcripts during the major embryonic and postnatal developmental stages to provide a systematic view of normal mouse retina development, RESULTS: A tissue-specific cDNA microarray was generated using a set of sequence non-redundant EST clones collected from mouse retina. Eleven stages of mouse retina, from embryonic day 12.5 (El2.5) to postnatal day 21 (PN21), were collected for RNA isolation. Non-amplified RNAs were labeled for microarray experiments and three sets of data were analyzed for significance, hierarchical relationships, and functional clustering. Six individual gene expression clusters were identified based on expression patterns of transcripts through retina development. Two developmental phases were clearly divided with postnatal day 5 (PN5) as a separate cluster. Among 4,180 transcripts that changed significantly during development, approximately 2/3 of the genes were expressed at high levels up until PN5 and then declined whereas the other 1/3 of the genes increased expression from PN5 and remained at the higher levels until at least PN21. Less than 1% of the genes observed showed a peak of expression between the two phases. Among the later increased population, only about 40% genes are correlated with rod photoreceptors, indicating that multiple cell types contributed to gene expression in this phase. Within the same functional classes, however, different gene populations were expressed in distinct developmental phases. A correlation coefficient analysis of gene expression during retina development between previous SAGE studies and this study was also carried out. CONCLUSION: This study provides a complementary genome-wide view of common gene dynamics and a broad molecular classification of mouse retina development. Different genes in the same functional clusters are expressed in the different developmental stages, suggesting that cells might change gene expression profiles from differentiation to maturation stages. We propose that large-scale changes in gene regulation during development are necessary for the final maturation and function of the retina.

Animals↗
PubMed2006

Hemodynamic effects of potent and selective JNK inhibitors in anesthetized rats: implication for targeting protein kinases in metabolic diseases.

The hemodynamic effects of a series of potent and selective 4-aminopyridine carboxamide-based pan-JNK inhibitors were assessed in an anesthetized rat model. The effects of these agents on mean arterial pressure, heart rate, cardiac contractility, and peripheral vascular resistance are described, and the implication for targeting protein kinases in metabolic diseases is discussed.

Aminopyridines↗
PubMed2006

Discovery and pharmacological evaluation of growth hormone secretagogue receptor antagonists.

The discovery and pharmacological evaluation of potent, selective, and orally bioavailable growth hormone secretagogue receptor (GHS-R) antagonists are reported. Previously, 2,4-diaminopyrimidine-based GHS-R antagonists reported from our laboratories have been shown to be dihydrofolate reductase (DHFR) inhibitors. By comparing the X-ray crystal structure of DHFR docked with our GHS-R antagonists and GHS-R modeling, we designed and synthesized a series of potent and DHFR selective GHS-R antagonists with good pharmacokinetic (PK) profiles. An amide derivative 13d (Ca2+ flux IC50 = 188 nM, [brain]/[plasma] = 0.97 @ 8 h in rat) showed a 10% decrease in 24 h food intake in rats, and over 5% body weight reduction after 14-day oral treatment in diet-induced obese (DIO) mice. In comparison, a urea derivative 14c (Ca2+ flux IC50 = 7 nM, [brain]/[plasma] = 0.0 in DIO) failed to show significant effect on food intake in the acute feeding DIO model. These observations demonstrated for the first time that peripheral GHS-R blockage with small molecule GHS-R antagonists might not be sufficient for suppressing appetite and inducing body weight reduction.

Administration, Oral↗
PubMed2006

Discovery of potent, highly selective, and orally bioavailable pyridine carboxamide c-Jun NH2-terminal kinase inhibitors.

C-Jun NH2 terminal kinases (JNKs) are important cell signaling enzymes. JNK1 plays a central role in linking obesity and insulin resistance. JNK2 and JNK3 may be involved in inflammatory and neurological disorders, respectively. Small-molecule JNK inhibitors could be valuable tools to study the therapeutic benefits of inhibiting these enzymes and as leads for potential drugs targeting JNKs. In this report, we disclose a series of potent and highly selective JNK inhibitors with good pharmacokinetic profiles.

Administration, Oral↗
PubMed2006

Pathway analysis using random forests classification and regression.

MOTIVATION: Although numerous methods have been developed to better capture biological information from microarray data, commonly used single gene-based methods neglect interactions among genes and leave room for other novel approaches. For example, most classification and regression methods for microarray data are based on the whole set of genes and have not made use of pathway information. Pathway-based analysis in microarray studies may lead to more informative and relevant knowledge for biological researchers. RESULTS: In this paper, we describe a pathway-based classification and regression method using Random Forests to analyze gene expression data. The proposed methods allow researchers to rank important pathways from externally available databases, discover important genes, find pathway-based outlying cases and make full use of a continuous outcome variable in the regression setting. We also compared Random Forests with other machine learning methods using several datasets and found that Random Forests classification error rates were either the lowest or the second-lowest. By combining pathway information and novel statistical methods, this procedure represents a promising computational strategy in dissecting pathways and can provide biological insight into the study of microarray data. AVAILABILITY: Source code written in R is available from http://bioinformatics.med.yale.edu/pathway-analysis/rf.htm.

Algorithms↗
PubMed2006

PSMIX: an R package for population structure inference via maximum likelihood method.

BACKGROUND: Inference of population stratification and individual admixture from genetic markers is an integrative part of a study in diverse situations, such as association mapping and evolutionary studies. Bayesian methods have been proposed for population stratification and admixture inference using multilocus genotypes and widely used in practice. However, these Bayesian methods demand intensive computation resources and may run into convergence problem in Markov Chain Monte Carlo based posterior samplings. RESULTS: We have developed PSMIX, an R package based on maximum likelihood method using expectation-maximization algorithm, for inference of population stratification and individual admixture. CONCLUSION: Compared with software based on Bayesian methods (e.g., STRUCTURE), PSMIX has similar accuracy, but more efficient computations.PSMIX and its supplemental documents are freely available at http://bioinformatics.med.yale.edu/PSMIX.

Algorithms↗
PubMed2006

Aminopyridine-based c-Jun N-terminal kinase inhibitors with cellular activity and minimal cross-kinase activity.

The c-Jun N-terminal kinases (JNK-1, -2, and -3) are members of the mitogen activated protein (MAP) kinase family of enzymes. They are activated in response to certain cytokines, as well as by cellular stresses including chemotoxins, peroxides, and irradiation. They have been implicated in the pathology of a variety of different diseases with an inflammatory component including asthma, stroke, Alzheimer's disease, and type 2 diabetes mellitus. In this work, high-throughput screening identified a JNK inhibitor with an excellent kinase selectivity profile. Using X-ray crystallography and biochemical screening to guide our lead optimization, we prepared compounds with inhibitory potencies in the low-double-digit nanomolar range, activity in whole cells, and pharmacokinetics suitable for in vivo use. The new compounds were over 1,000-fold selective for JNK-1 and -2 over other MAP kinases including ERK2, p38alpha, and p38delta and showed little inhibitory activity against a panel of 74 kinases.

Aminopyridines↗
PubMed2006

Bayesian error analysis model for reconstructing transcriptional regulatory networks.

Transcription regulation is a fundamental biological process, and extensive efforts have been made to dissect its mechanisms through direct biological experiments and regulation modeling based on physical-chemical principles and mathematical formulations. Despite these efforts, transcription regulation is yet not well understood because of its complexity and limitations in biological experiments. Recent advances in high throughput technologies have provided substantial amounts and diverse types of genomic data that reveal valuable information on transcription regulation, including DNA sequence data, protein-DNA binding data, microarray gene expression data, and others. In this article, we propose a Bayesian error analysis model to integrate protein-DNA binding data and gene expression data to reconstruct transcriptional regulatory networks. There are two unique aspects to this proposed model. First, transcription is modeled as a set of biochemical reactions, and a linear system model with clear biological interpretation is developed. Second, measurement errors in both protein-DNA binding data and gene expression data are explicitly considered in a Bayesian hierarchical model framework. Model parameters are inferred through Markov chain Monte Carlo. The usefulness of this approach is demonstrated through its application to infer transcriptional regulatory networks in the yeast cell cycle.

Algorithms↗
PubMed2006

2,4-diaminopyrimidine derivatives as potent growth hormone secretagogue receptor antagonists.

Ghrelin, a gut-derived orexigenic hormone, is an endogenous ligand of the growth hormone secretagogue receptor (GHS-R). Centrally administered ghrelin has been shown to cause hunger and increase food intake in rodents. Inhibition of ghrelin actions with ghrelin antibody, peptidyl GHS-R antagonists, and antisense oligonucleosides resulted in weight loss and food intake decrease in rodents. Here we report the effects of GHS-R antagonists, some of which were potent, selective, and orally bioavailable. A structure-activity relationship study led to the discovery of 8a, which was effective in decreasing food intake and body weight in several acute rat studies.

Animals↗
PubMed2006

Two-stage designs in case-control association analysis.

DNA pooling is a cost-effective approach for collecting information on marker allele frequency in genetic studies. It is often suggested as a screening tool to identify a subset of candidate markers from a very large number of markers to be followed up by more accurate and informative individual genotyping. In this article, we investigate several statistical properties and design issues related to this two-stage design, including the selection of the candidate markers for second-stage analysis, statistical power of this design, and the probability that truly disease-associated markers are ranked among the top after second-stage analysis. We have derived analytical results on the proportion of markers to be selected for second-stage analysis. For example, to detect disease-associated markers with an allele frequency difference of 0.05 between the cases and controls through an initial sample of 1000 cases and 1000 controls, our results suggest that when the measurement errors are small (0.005), approximately 3% of the markers should be selected. For the statistical power to identify disease-associated markers, we find that the measurement errors associated with DNA pooling have little effect on its power. This is in contrast to the one-stage pooling scheme where measurement errors may have large effect on statistical power. As for the probability that the disease-associated markers are ranked among the top in the second stage, we show that there is a high probability that at least one disease-associated marker is ranked among the top when the allele frequency differences between the cases and controls are not <0.05 for reasonably large sample sizes, even though the errors associated with DNA pooling in the first stage are not small. Therefore, the two-stage design with DNA pooling as a screening tool offers an efficient strategy in genomewide association studies, even when the measurement errors associated with DNA pooling are nonnegligible. For any disease model, we find that all the statistical results essentially depend on the population allele frequency and the allele frequency differences between the cases and controls at the disease-associated markers. The general conclusions hold whether the second stage uses an entirely independent sample or includes both the samples used in the first stage and an independent set of samples.

Algorithms↗
PubMed2006

Genomewide linkage scan for opioid dependence and related traits.

Risk of opioid dependence is genetically influenced. We recruited a sample of 393 small nuclear families (including 250 full-sib and 46 half-sib pairs), each with at least one individual with opioid dependence. Subjects underwent a detailed evaluation of substance dependence-related traits. As planned a priori to reduce heterogeneity, we used cluster analytic methods to identify opioid dependence-related symptom clusters, which were shown to be heritable. We then completed a genomewide linkage scan (with 409 markers) for the opioid-dependence diagnosis and for the two cluster-defined phenotypes represented by >250 families: the heavy-opioid-use cluster and the non-opioid-use cluster. Further exploratory analyses were completed for the other cluster-defined phenotypes. The statistically strongest results were seen with the cluster-defined traits. For the heavy-opioid-use cluster, we observed a LOD score of 3.06 on chromosome 17 (empirical pointwise P = .0002) for European American (EA) and African American (AA) subjects combined, and, for the non-opioid-use cluster, we observed a LOD score of 3.46 elsewhere on chromosome 17 (empirical pointwise P = .00002, uncorrected for multiple traits studied) for EA subjects only. We also identified a possible linkage (LOD score 2.43) of opioid dependence with chromosome 2 markers for the AA subjects. These results are an initial step in identifying genes for opioid dependence on the basis of a genomewide investigation (i.e., a study not conditioned on prior physiological candidate-gene hypotheses).

Adult↗
PubMed2006

Optimization of 2,4-diaminopyrimidines as GHS-R antagonists: side chain exploration.

The synthesis and structure-activity relationships of the 4- and 6-substituents of 2,4-diaminopyrimidine-based growth hormone secretagogue receptor (GHS-R) antagonists are described. Diaminopyrimidines with 6-norbornenyl (4n) and 6-tetrahydrofuranyl (4p) substitutents were found to exhibit potent GHS-R antagonism and good selectivity (approximately 1000-fold) against dihydrofolate reductase.

Pyrimidines↗