Biology subjects

So, H.-C.

Publications and source records attributed to So, H.-C..

9 recordsLinked to original sources

bioRxiv2019

Zonation-dependent single-endothelial cell transcriptomic changes in the aged brain

With advances in single-cell genomics, molecular signatures of cells comprising the brain vasculature are revealed in unprecedented detail1,2, yet the ageing-associated cell subtype transcriptomic changes which may contribute to neurovascular dysfunction in neurodegenerative diseases3-7 remain elusive. Here, we performed single-cell transcriptomic profiling of brain endothelial cells (EC) in young adult and aged mice to characterize their ageing-associated genome-wide expression changes. We identified zonation-dependent transcriptomic changes in aged brain EC subtypes, with capillary ECs exhibiting the most transcriptomic alterations. Pathway enrichment analysis revealed altered immune/cytokine signaling in ECs of all vascular segments, while functional changes impacting the blood-brain barrier (BBB) and glucose/energy metabolism were most prominently implicated in ECs of the capillary bed - the primary site where ECs and other neurovascular unit (NVU) cell types closely interact and coordinate to regulate BBB and cerebral blood flow in health and diseased conditions8-17. Furthermore, an overrepresentation of Alzheimers disease (AD)-associated genes identified from GWAS studies was evident among the human orthologs of differentially expressed genes of aged capillary ECs but not other EC subtypes. Importantly, for numerous EC-enriched differentially expressed genes with important functional roles at the BBB and/or association with AD, we found concordant expression changes in human aged or AD brains. Finally, we demonstrated that treatment with exenatide, a glucagon-like peptide-1 receptor (GLP-1R) agonist, strongly reverses transcriptomic changes in ECs and largely reduces BBB leakage in the aged brain. Thus, our study provides insights into detailed transcriptomic alterations underlying brain EC ageing that are complex with subtype specificity yet amenable to pharmacological interventions.

neuroscience↗

bioRxiv2019

Uncovering complex disease subtypes by integrating clinical data and imputed transcriptome from genome-wide association studies: Applications in psychiatry and cardiovascular medicine

Classifying patients into clinically and biologically homogenous subgroups will facilitate the understanding of disease pathophysiology and development of more targeted prevention and intervention strategies. Traditionally, disease subtyping is based on clinical characteristics alone, however disease subtypes identified by such an approach may not conform exactly to the underlying biological mechanisms. Very few studies have integrated genomic profiles (such as those from GWAS) with clinical symptoms for disease subtyping.\n\nIn this study, we proposed a novel analytic framework capable of finding subgroups of complex diseases by leveraging both GWAS-predicted gene expression levels and clinical data by a multi-view bicluster analysis. This approach connects SNPs to genes via their effects on expression, hence the analysis is more biologically relevant and interpretable than a pure SNP-based analysis. Transcriptome of different tissues can also be readily modelled. We also proposed various new evaluation or validation metrics, such as a newly modified prediction strength measure to assess generalization of clustering performance. The proposed framework was applied to derive subtypes for schizophrenia, and to stratify subjects into different levels of cardiometabolic risks.\n\nOur framework was able to subtype schizophrenia patients with diverse prognosis and treatment response. We also applied the framework to the Northern Finland Cohort (NFBC) 1966 dataset, and identified high- and low cardiometabolic risk subgroups in a gender-stratified analysis. Our results suggest a more data-driven and biologically-informed approach to defining metabolic syndrome. The prediction strength was over 80%, suggesting that the cluster model generalizes well to new datasets. Moreover, we found that the genes blindly selected by the cluster algorithm are significantly enriched for known susceptibility genes discovered in GWAS of schizophrenia and cardiovascular diseases, providing further support to the validity of our approach. The proposed framework may be applied to any complex diseases, and opens up a new approach to patient stratification.

genetics↗

bioRxiv2019

Genome-wide copy number variation-, validation- and screening study implicates a novel copy number polymorphism associated with suicide attempts in major depressive disorder

BackgroundThe genetic basis of suicide attempts (SA) remained unclear, especially for the copy number variations (CNVs) involved. The present study aimed to identify the susceptibility variants associated with SA among major depressive disorder (MDD) patients in Chinese, covering both single-nucleotide polymorphisms and CNVs. MethodsWe conducted GWAS on MDD patients with or without SA and top results were tested in a replication study. A genome-wide CNV study was performed. Subsequently, a validation assay using the qRT-PCR technology was performed to confirm the existence of the associated CNV and then applied to the entire cohort to examine the association. ResultsIn CNV analysis, we found that the global rate of CNV was higher in SA compared to non-SA subjects (p=0.023). The genome-wide CNV study revealed a SA-associated CNV region that achieved genome-wide significance (corrected p-value=0.014). The associated CNV was successfully validated and identified to be a common variant in this cohort and its deletion rate was higher in suicide attempters (OR=2.05). Based on the GTEx database, genetic variants that probe this CNV was significantly associated with the expression level of ZNF33B in two brain regions (p-value<4.2e-05). Besides, there was a significant interaction between neuroticism and the CNV in affecting suicidal risk; the CNV showed a significant effect (OR=2.58) in subjects with high neuroticism only. ConclusionsWe identified a new common CNV that may be involved in the etiology of SA. These findings imply an important role of common CNVs in the etiology of SA, which suggests a new promising avenue for investigating the genetic architecture of SA.

genetics↗

bioRxiv2018

Causal relationships between blood lipids and depression phenotypes: A Mendelian randomization analysis

BackgroundThe etiology of depression remains poorly understood. Changes in blood lipid levels were reported to be associated with depression and suicide, however study findings were mixed. MethodsWe performed a two-sample Mendelian randomization (MR) analysis to investigate the causal relationship between blood lipids and depression phenotypes, based on large-scale GWAS summary statistics (N=188,577/480,359 for lipid/depression traits respectively). Five depression-related phenotypes were included, namely major depressive disorder (MDD; from PGC), depressive symptoms (DS; from SSGAC), longest duration and number of episodes of low mood, and history of deliberate self-harm (DSH)/suicide (from UK Biobank). MR was conducted with inverse-variance weighted (MR-IVW), Egger and Generalized Summary-data-based MR(GSMR) methods. ResultsThere was consistent evidence that triglyceride (TG) is causally associated with DS (MR-IVW beta for one-SD increase in TG=0.0346, 95% CI=0.0114-0.0578), supported by MR-IVW and GSMR and multiple r2 clumping thresholds. We also observed relatively consistent associations of TG with DSH/suicide (MR-Egger OR= 2.514, CI: 1.579-4.003). There was moderate evidence for positive associations of TG with MDD and the number of episodes of low mood. For HDL-c, we observed moderate evidence for causal associations with DS and MDD. LDL-c and TC did not show robust causal relationships with depression phenotypes, except for weak evidence that LDL-c is inversely related to DSH/suicide. We did not detect significant associations when depression phenotypes were treated as exposures. ConclusionsThis study provides evidence to a causal relationship between TG, and to a lesser extent, altered cholesterol levels with depression phenotypes. Further studies on its mechanistic basis and the effects of lipid-lowering therapies are warranted.

genetics↗

bioRxiv2017

Implications of de novo mutations in guiding drug discovery: A study of four neuropsychiatric disorders

Recent studies have suggested an important role of de novo mutations (DNMs) in neuropsychiatric disorders. As DNMs are not subject to elimination due to evolutionary pressure, they are likely to have greater disruptions on biological functions. While a number of sequencing studies have been performed on neuropsychiatric disorders, the implications of DNMs for drug discovery remain to be explored.\n\nIn this study, we employed a gene-set analysis approach to address this issue. Four neuropsychiatric disorders were studied, including schizophrenia (SCZ), autistic spectrum disorders (ASD), intellectual disability (ID) and epilepsy. We first identified gene-sets associated with different drugs, and analyzed whether the gene-set pertaining to each drug overlaps with DNMs more than expected by chance. We also assessed which medication classes are enriched among the prioritized drugs. We discovered that neuropsychiatric drug classes were indeed significantly enriched for DNMs of all four disorders; in particular, antipsychotics and antiepileptics were the most strongly enriched drug classes for SCZ and epilepsy respectively. Interestingly, we revealed enrichment of several unexpected drug classes, such as lipid-lowering agents for SCZ and anti-neoplastic agents. By inspecting individual hits, we also uncovered other interesting drug candidates or mechanisms (e.g. histone deacetylase inhibition and retinoid signaling) that might warrant further investigations. Taken together, this study provided evidence for the usefulness of DNMs in guiding drug discovery or repositioning.

genomics↗

bioRxiv2017

Exploring repositioning opportunities and side-effects of statins: a Mendelian randomization study of HMG-CoA reductase inhibition with 55 complex traits

Statin is one of the most commonly prescribed medications worldwide. Besides reduction of cardiovascular risks, statins have been proposed for the prevention or treatment of other disorders, but results from clinical studies are mixed. There are also controversies concerning the adverse effects caused by statins.\n\nIn this study we employed a Mendelian randomization (MR) approach across a wide range of complex traits to explore repositioning opportunities and side-effects of statins. MR is analogous to a \"naturalistic\" randomized controlled trial (RCT), which is much less susceptible to confounding and reverse causation as compared to observational studies.\n\nWe employed two genetic instruments (rs12916 and rs17238484) in the HMGCR gene which have been shown to provide reliable estimates of the risk of statins on type 2 diabetes and weight gain. We observed in the single- and joint-SNP analysis that low density lipoprotein cholesterol (LDL-C) reduction from HMG-CoA reductase inhibition results in increased depressive symptoms. This finding appeared to be supported by nominally significant results of raised major depression risk in single-SNP MR analysis of rs17238484, and analyses using LDL-C as the exposure. Several other outcomes also reached nominal significance (p < 0.05) in single- or joint-SNP analyses; for example, we observed causal associations of LDL-C lowering from HMG-CoA reductase inhibition with reduced risks of schizophrenia, anorexia nervosa, Alzheimer disease, Parkinson disease, as well as increased forearm bone mineral density, sleep duration and extreme longevity (highest q-value = 0.289). We also found evidence of casual relationships of LDL-C levels with schizophrenia, anorexia, sleep duration and longevity, following the same association directions as in analyses of HMGCR variants. These findings were at least partially supported by previous clinical studies. We did not observe associations with cognitive test profiles, renal outcomes, autoimmune diseases or cancers. While MR has its limitations and our findings remain to be confirmed in further studies, this work demonstrates the potential of a phenome-wide approach to reveal novel therapeutic indications and unknown drug side-effects.

genetics↗

bioRxiv2017

Genetic Overlap Of Depression With Cardiometabolic Diseases And Implications For Drug Repurposing For Comorbidities

Numerous studies have suggested associations between depression and cardiometabolic abnormalities or diseases, such as coronary artery disease and type 2 diabetes. However, little is known about the mechanism underlying this comorbidity, and whether the relationship differs by depression subtypes. Using the polygenic risk score (PRS) approach and linkage disequilibrium (LD) score regression, we investigated the genetic overlap of various depression-related phenotypes with a comprehensive panel of 20 cardiometabolic traits. GWAS results for major depressive disorder (MDD) were taken from the PGC and CONVERGE studies, with the latter focusing on severe melancholic depression. GWAS results on general depressive symptoms (DS) and neuroticism were also included. We also identified the shared genetic variants and inferred enriched pathways. In addition, we looked for drugs over-represented among the top shared genes, with an aim to finding repositioning opportunities for comorbidities.\n\nWe found significant polygenic sharing between MDD, DS and neuroticism with various cardiometabolic traits. In general, positive polygenic associations with CV risks were observed for most depression phenotypes except MDD-CONVERGE. Counterintuitively, PRS representing severe melancholic depression was associated with reduced CV risks. Enrichment analyses of shared SNPs revealed many interesting pathways, such as those related to inflammation, that underlie the comorbidity of depressive and cardiometabolic traits. Using a gene-set analysis approach, we also revealed a number of repositioning candidates, some of which were supported by prior studies, such as bupropion and glutathione. Our study highlights shared genetic bases of depression with cardiometabolic traits, and suggests the associations vary by depression subtypes. To our knowledge, this is the also first study to make use of human genomic data to guide drug discovery or repositioning for comorbid disorders.

genomics↗

bioRxiv2017

Translating GWAS Findings Into Therapies For Depression And Anxiety Disorders: Drug Repositioning Using Gene-Set Analyses And Testing For Enrichment Of Psychiatric Drug Classes

Depression and anxiety disorders are the first and sixth leading cause of disability worldwide according to latest reports from the World Health Organization. Despite their high prevalence and the significant disability resulted, there are limited advances in new drug development. On the other hand, the advent of genome-wide association studies (GWAS) has greatly improved our understanding of the genetic basis underlying psychiatric disorders.\n\nIn this work we employed gene-set analyses of GWAS summary statistics for drug repositioning. We explored five related GWAS datasets, including two on major depressive disorder (MDD-PGC and MDD-CONVERGE, with the latter focusing on severe melancholic depression), one on anxiety disorders, and two on depressive symptoms and neuroticism in the population. We extracted gene-sets associated with each drug from DSigDB and examined their association with each GWAS phenotype. We also performed repositioning analyses on meta-analyzed GWAS data, integrating evidence from all related phenotypes.\n\nImportantly, we showed that the repositioning hits are generally enriched for known psychiatric medications or those considered in clinical trials, except for MDD-PGC. Enrichment was seen for antidepressants and anxiolytics but also for antipsychotics. We also revealed new candidates or drug classes for repositioning, some of which were supported by experimental or clinical studies. For example, the top repositioning hit using meta-analyzed p-values was fendiline, which was shown to produce antidepressant-like effects in mouse models by inhibition of acid sphingomyelinase and reducing ceramide levels. Taken together, our findings suggest that human genomic data such as GWAS are useful in guiding drug discoveries for depression and anxiety disorders.

genetics↗

bioRxiv2016

When GWAS meets the Connectivity Map: drug repositioning for seven psychiatric disorders

Our knowledge of disease genetics has advanced rapidly during the past decade, with the advent of high-throughput genotyping technologies such as genome-wide association studies (GWAS). However, few methodologies were developed and systemic studies performed to identify novel drug candidates utilizing GWAS data. In this study we focus on drug repositioning, which is a cost-effective approach to shorten the developmental process of new therapies. We proposed a novel framework of drug repositioning by comparing GWAS-imputed transcriptome with drug expression profiles from the Connectivity Map. The approach was applied to 7 psychiatric disorders. We discovered a number of novel repositioning candidates, many of which are supported by preclinical or clinical evidence. We found that the predicted drugs are significantly enriched for known psychiatric medications, or therapies considered in clinical trials. For example, drugs repurposed for schizophrenia are strongly enriched for antipsychotics (p = 4.69E-06), while those repurposed for bipolar disorder are enriched for antipsychotics (p = 2.26E-07) and antidepressants (p = 1.17E-05). These findings provide support to the usefulness of GWAS signals in guiding drug discoveries and the validity of our approach in drug repositioning. We also present manually curated lists of top repositioning candidates for each disorder, which we believe will serve as a useful resource for researchers.

genomics↗