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Interaction between tumor cells and immune cells in the tumor microenvironment is important in cancer development. Immune cells interact with the tumor cells to shape this process. Here, we use single-cell RNA sequencing analysis to delineate the immune landscape and tumor heterogeneity in a cohort of patients with HBV-associated human hepatocellular carcinoma (HCC). We found that tumor-associated macrophages suppress tumor T cell infiltration and TIGIT-NECTIN2 interaction regulates the immunosuppressive environment. The cell state transition of immune cells towards a more immunosuppressive and exhaustive status exemplifies the overall cancer-promoting immunocellular landscape. Furthermore, the heterogeneity of global molecular profiles reveals co-existence of intra-tumoral and inter-tumoral heterogeneity, but is more apparent in the latter. This analysis of the immunosuppressive landscape and intercellular interactions provides mechanistic information for the design of efficacious immune-oncology treatments in hepatocellular carcinoma. Different cells of the tumour microenvironment in HBV-associated human hepatocellular carcinoma (HCC) interact to promote tumour immunity. Here the authors use single cell RNA sequencing to identify TIGIT-NECTIN2 as a pathway by which tumour-associated macrophages reduce intratumour T cell activation.

BackgroundDepression and cardiovascular disease (CVD) are associated with each other but their relationship remains unclear. We aim to determine whether genetic predisposition to depression are causally linked to CVD [including coronary artery disease (CAD), myocardial infarction (MI), stroke and atrial fibrillation (AF)].MethodsUsing summary statistics from the largest genome-wide association studies (GWAS) or GWAS meta-analysis of depression (primary analysis: n = 500 199), broad depression (help-seeking behavior for problems with nerves, anxiety, tension or depression; secondary analysis: n = 322 580), CAD (n = 184 305), MI (n = 171 875), stroke (n = 446 696) and AF (n = 1 030 836), genetic correlation was tested between two depression phenotypes and CVD [MI, stroke and AF (not CAD as its correlation was previously confirmed)]. Causality was inferred between correlated traits by Mendelian Randomization analyses.ResultsBoth depression phenotypes were genetically correlated with MI (depression: rG = 0.169; p = 9.03 × 10−9; broad depression: rG = 0.123; p = 1 × 10−4) and AF (depression: rG = 0.112; p = 7.80 × 10−6; broad depression: rG = 0.126; p = 3.62 × 10−6). Genetically doubling the odds of depression was causally associated with increased risk of CAD (OR = 1.099; 95% CI 1.031–1.170; p = 0.004) and MI (OR = 1.146; 95% CI 1.070–1.228; p = 1.05 × 10−4). Adjustment for blood lipid levels/smoking status attenuated the causality between depression and CAD/MI. Null causal association was observed for CVD on depression. A similar pattern of results was observed in the secondary analysis for broad depression.ConclusionsGenetic predisposition to depression may have positive causal roles on CAD/MI. Genetic susceptibility to self-awareness of mood problems may be a strong causal risk factor of CAD/MI. Blood lipid levels and smoking may potentially mediate the causal pathway. Prevention and early diagnosis of depression are important in the management of CAD/MI.

Schizophrenia (SCZ) is a debilitating neuropsychiatric disorder with high heritability and complex inheritance. In the past decade, successful identification of numerous susceptibility loci has provided useful insights into the molecular etiology of SCZ. However, applications of these findings to clinical classification and diagnosis, risk prediction, or intervention for SCZ have been limited, and elucidating the underlying genomic and molecular mechanisms of SCZ is still challenging. More recently, multiple Omics technologies – genomics, transcriptomics, epigenomics, proteomics, metabolomics, connectomics, and gut microbiomics – have all been applied to examine different aspects of SCZ pathogenesis. Integration of multi-Omics data has thus emerged as an approach to provide a more comprehensive view of biological complexity, which is vital to enable translation into assessments and interventions of clinical benefit to individuals with SCZ. In this review, we provide a broad survey of the single-omics studies of SCZ, summarize the advantages and challenges of different Omics technologies, and then focus on studies in which multiple omics data are integrated to unravel the complex pathophysiology of SCZ. We believe that integration of multi-Omics technologies would provide a roadmap to create a more comprehensive picture of interactions involved in the complex pathogenesis of SCZ, constitute a rich resource for elucidating the potential molecular mechanisms of the illness, and eventually improve clinical assessments and interventions of SCZ to address clinical translational questions from bench to bedside.

Chronic hepatitis B virus (HBV) infection is strongly associated with the initiation and development of hepatocellular carcinoma (HCC). However, the genetic alterations and pathogenesis mechanisms remain significantly unexplored, especially for HBV-induced metabolic reprogramming. Analysis of integration breakpoints in HBV-positive HCC samples revealed the preferential clustering pattern within the 3′-end of X gene in the HBV genome, leading to the production of C-terminal truncated X protein (Ct-HBx). In this study, we not only characterized the oncogenic role of two Ct-HBx (HBx-120 and HBx-134) via in vitro and in vivo functional assays but also deciphered their underlying molecular mechanisms. Gene expression profiling by transcriptome sequencing identified potential targets of Ct-HBx and novel malignant hallmarks such as glycolysis, cell cycle, and m-TORC1 signaling in Ct-HBx-expressing cells. TXNIP, a well-established regulator of glucose metabolism, was shown to be downregulated by Ct-HBx and play a pivotal role in Ct-HBx-mediated HCC progression. Suppression of TXNIP is frequently observed in HCC patients with Ct-HBx expression and significantly ( P  = 0.015) correlated to a poorer prognosis. Re-introduction of TXNIP attenuated the metabolic reprogramming induced by the Ct-HBx and inhibited the tumor growth in the mice model. Further study suggested that Ct-HBx could downregulate TXNIP via a transcriptional repressor nuclear factor of activated T cells 2 (NFACT2). Collectively, our findings indicate that TXNIP plays a critical role in Ct-HBx-mediated hepatocarcinogenesis, serving as a novel therapeutic strategy in HCC treatment.

Cardiovascular diseases represent a major health issue in patients with schizophrenia (SCZ) and bipolar disorder (BD), but the exact nature of cardiometabolic (CM) abnormalities involved and the underlying mechanisms remain unclear. Psychiatric medications are known risk factors, but it is unclear whether there is a connection between the disorders (SCZ/BD) themselves and CM abnormalities. Using polygenic risk scores and linkage disequilibrium score regression, we investigated the shared genetic bases of SCZ and BD with 28 CM traits. We performed Mendelian randomization (MR) to elucidate causal relationships between the two groups of disorders. The analysis was based on large-scale meta-analyses of genome-wide association studies. We also identified the potential shared genetic variants and inferred the pathways involved. We found tentative polygenic associations of SCZ with glucose metabolism abnormalities, adverse adipokine profiles, increased waist-to-hip ratio and visceral adiposity (false discovery rate or FDR<0.05). However, there was an inverse association with body mass index. For BD, we observed several polygenic associations with favorable CM profiles at FDR<0.05. MR analysis showed that SCZ may be causally linked to raised triglyceride and that lower fasting glucose may be linked to BD. We also identified numerous single nucleotide polymorphisms and pathways shared between SCZ/BD with CM traits, some of which are related to inflammation or the immune system. Our findings suggest that SCZ patients may be genetically predisposed to several CM abnormalities independent of medication side effects. On the other hand, CM abnormalities in BD may be more likely to be secondary. However, the findings require further validation.

Genome-wide association studies (GWAS) are commonly employed to study the genetic basis of complex traits/diseases, and a key question is how much heritability could be explained by all single nucleotide polymorphisms (SNPs) in GWAS. One widely used approach that relies on summary statistics only is linkage disequilibrium score regression (LDSC); however, this approach requires certain assumptions about the effects of SNPs (e.g., all SNPs contribute to heritability and each SNP contributes equal variance). More flexible modeling methods may be useful. We previously developed an approach recovering the “true” effect sizes from a set of observed z-statistics with an empirical Bayes approach, using only summary statistics. However, methods for standard error (SE) estimation are not available yet, limiting the interpretation of our results and the applicability of the approach. In this study, we developed several resampling-based approaches to estimate the SE of SNP-based heritability, including two jackknife and three parametric bootstrap methods. The resampling procedures are performed at the SNP level as it is most common to estimate heritability from GWAS summary statistics alone. Simulations showed that the delete-d-jackknife and parametric bootstrap approaches provide good estimates of the SE. In particular, the parametric bootstrap approaches yield the lowest root-mean-squared-error (RMSE) of the true SE. We also explored various methods for constructing confidence intervals (CIs). In addition, we applied our method to estimate the SNP-based heritability of 12 immune-related traits (levels of cytokines and growth factors) to shed light on their genetic architecture. We also implemented the methods to compute the sum of heritability explained and the corresponding SE in an R package SumVg. In conclusion, SumVg may provide a useful alternative tool for calculating SNP heritability and estimating SE/CI, which does not rely on distributional assumptions of SNP effects.

Second-generation antipsychotics (SGAs) cause metabolic side effects. However, patients' metabolic profiles were influenced by time-invariant and time-varying confounders. Real-world evidence on the long-term, dynamic effects of SGAs (e.g. different treatment sequences) are limited. We employed advanced causal inference methods to evaluate the metabolic impact of SGAs in a naturalistic cohort. We followed 696 Chinese patients with schizophrenia-spectrum disorders receiving SGAs. Longitudinal targeted maximum likelihood estimation (LTMLE) was used to estimate the average treatment effects (ATEs) of continuous SGA treatment versus 'no treatment' on metabolic outcomes, including total cholesterol (TC), high-density lipoprotein (HDL), low-density lipoprotein (LDL), triglyceride (TG), fasting glucose (FG), and body mass index (BMI), over 6-18 months at 3-month intervals. LTMLE accounted for time-invariant and time-varying confounders. Post-SGA discontinuation side effects were also assessed. The ATEs of continuous SGA treatment on BMI and TG showed an inverted U-shaped pattern, peaking at 12 months and declining afterwards. Similar patterns were observed for TC and LDL, albeit the ATEs peaked at 15 months. For FG and HDL, the ATEs peaked at ~6 months. The adverse impact of SGAs on BMI persisted even after medication discontinuation, yet other metabolic parameters did not show such lingering side effects. Clozapine and olanzapine exhibited greater metabolic side effects compared to other SGAs. Our real-world study suggests that metabolic side effects may stabilize with prolonged continuous treatment. Clozapine and olanzapine confer higher cardiometabolic risks than other SGAs. The side effects of SGAs on BMI may persist after drug discontinuation. These insights may guide antipsychotic choice and improve management of metabolic side effects.

Purpose Data on mental disorder prevalence and health service utilization required to inform healthcare management and planning are lacking in Hong Kong. The current study determined the prevalence of common mental disorders (CMD), and examined the patterns of mental health service utilization and associated factors. Methods We analyzed data from the Hong Kong Mental Morbidity Survey (HKMMS) of 5,719 Chinese adults aged 16–75 years in the general Hong Kong population, using the Chinese Revised Clinical Interview Schedule (CIS-R). Results The weighted prevalence estimate for any past-week CMD was 13.3 %, with mixed anxiety and depressive disorder being the most frequent diagnoses. CMD was positively associated with female gender, being divorced or separated, alcohol misuse, substance dependence, lack of regular physical exercise, and a family history of mental disorder. Among individuals with CMD, only 26 % had consulted mental health services in the past year; less than 10 % consulted general practitioners or family physicians. Lack of mental health service usage was significantly more likely in men and those with lower educational attainment. Conclusions Apart from attention to psychosocial risks, health and lifestyle factors are important considerations for mental health promotion. Service utilization for individuals with CMD in Hong Kong remains suboptimal, and would be enhanced by strengthening community primary care.

Retinal structural and functional changes in humans can be manifestations of different physiological or pathological conditions. Retinal imaging is the only way to directly inspect blood vessels and their pathological changes throughout the whole body non-invasively. Various quantitative analysis metrics have been used to measure the abnormalities of retinal microvasculature in the context of different retinal, cerebral and systemic disorders. Recently developed optical coherence tomography angiography (OCTA) is a non-invasive imaging tool that allows high-resolution three-dimensional mapping of the retinal microvasculature. The identification of retinal biomarkers from OCTA images could facilitate clinical investigation in various scenarios. We provide a framework for extracting computational retinal microvasculature biomarkers (CRMBs) from OCTA images through a knowledge-driven computerized automatic analytical system. Our method allows for improved identification of the foveal avascular zone (FAZ) and introduces a novel definition of vessel dispersion in the macular region. Furthermore, retinal large vessels and capillaries of the superficial and deep plexus can be differentiated, correlating with retinal pathology. The diagnostic value of OCTA CRMBs was demonstrated by a cross-sectional study with 30 healthy subjects and 43 retinal vein occlusion (RVO) patients, which identified strong correlations between OCTA CRMBs and retinal function in RVO patients. These OCTA CRMBs generated through this “all-in-one” pipeline may provide clinicians with insights about disease severity, treatment response and prognosis, aiding in the management and early detection of various disorders.

There have been few studies performed to examine the pathophysiological differences between different types of psychosis, such as between delusional disorder (DD) and schizophrenia (SZ). Notably, despite the different clinical characteristics of DD and schizophrenia (SZ), antipsychotics are deemed equally effective pharmaceutical treatments for both conditions. In this context, dopamine dysregulation may be transdiagnostic of the pathophysiology of psychotic disorders such as DD and SZ. In this study, an examination is made of the dopamine synthesis capacity (DSC) of patients with SZ, DD, other psychotic disorders, and the DSC of healthy subjects. Fifty-four subjects were recruited to the study, comprising 35 subjects with first-episode psychosis (11 DD, 12 SZ, 12 other psychotic disorders) and 19 healthy controls. All received an 18F-DOPA positron emission tomography (PET)/magnetic resonance (MR) scan to measure DSC (Kocc;30–60 value) within 1 month of starting antipsychotic treatment. Clinical assessments were also made, which included Positive and Negative Syndrome Scale (PANSS) measurements. The mean Kocc;30–60 was significantly greater in the caudate region of subjects in the DD group (ES = 0.83, corrected p = 0.048), the SZ group (ES = 1.40, corrected p = 0.003) and the other psychotic disorder group (ES = 1.34, corrected p = 0.0045), compared to that of the control group. These data indicate that DD, SZ, and other psychotic disorders have similar dysregulated mechanisms of dopamine synthesis, which supports the utility of abnormal dopamine synthesis in transdiagnoses of these psychotic conditions.