Mucosal Profiling of Pediatric-Onset Colitis and IBD Reveals Common Pathogenics and Therapeutic Pathways.

Pediatric-onset colitis and inflammatory bowel disease (IBD) have significant effects on the growth of infants and children, but the etiopathogenesis underlying disease subtypes remains incompletely understood. Here, we report single-cell clustering, immune phenotyping, and risk gene analysis for children with undifferentiated colitis, Crohn's disease, and ulcerative colitis. We demonstrate disease-specific characteristics, as well as common pathogenesis marked by impaired cyclic AMP (cAMP)-response signaling. Specifically, infiltration of PDE4B- and TNF-expressing macrophages, decreased abundance of CD39-expressing intraepithelial T cells, and platelet aggregation and release of 5-hydroxytryptamine at the colonic mucosae were common in colitis and IBD patients. Targeting these pathways by using the phosphodiesterase inhibitor dipyridamole restored immune homeostasis and improved colitis symptoms in a pilot study. In summary, comprehensive analysis of the colonic mucosae has uncovered common pathogenesis and therapeutic targets for children with colitis and IBD.

Identification of a molecular network regulated by multiple ASD high risk genes.

Genetic sequencing has identified high-confidence ASD risk genes with loss-of-function mutations. How the haploinsufficiency of distinct ASD risk genes causes ASD remains to be elucidated. In this study, we examined the role of four top-ranking ASD risk genes, ADNP, KDM6B, CHD2, and MED13, in gene expression regulation. ChIP-seq analysis reveals that gene targets with the binding of these ASD risk genes at promoters are enriched in RNA processing and DNA repair. Many of these targets are found in ASD gene database (SFARI), and are involved in transcription regulation and chromatin remodeling. Common gene targets of these ASD risk genes include a network of high confidence ASD genes associated with gene expression regulation, such as CTNNB1 and SMARCA4. We further directly examined the transcriptional impact of the deficiency of these ASD risk genes. Our mRNA profiling with qPCR assays in cells with the knockdown of Adnp, Kdm6b, Chd2 or Med13 has revealed an intricate pattern of their cross-regulation, as well as their influence on the expression of other ASD genes. In addition, some synaptic genes, such as Snap25 and Nrxn1, are strongly regulated by deficiency of the four ASD risk genes, which could be through the direct binding at promoters or indirectly through the targets like Ctnnb1 or Smarca4. The identification of convergent and divergent gene targets that are regulated by multiple ASD risk genes will help to understand the molecular mechanisms underlying common and unique phenotypes associated with haploinsufficiency of ASD-associated genes.

CellGO: a novel deep learning-based framework and webserver for cell-type-specific gene function interpretation.

Interpreting the function of genes and gene sets identified from omics experiments remains a challenge, as current pathway analysis tools often fail to consider the critical biological context, such as tissue or cell-type specificity. To address this limitation, we introduced CellGO. CellGO tackles this challenge by leveraging the visible neural network (VNN) and single-cell gene expressions to mimic cell-type-specific signaling propagation along the Gene Ontology tree within a cell. This design enables a novel scoring system to calculate the cell-type-specific gene-pathway paired active scores, based on which, CellGO is able to identify cell-type-specific active pathways associated with single genes. In addition, by aggregating the activities of single genes, CellGO extends its capability to identify cell-type-specific active pathways for a given gene set. To enhance biological interpretation, CellGO offers additional features, including the identification of significantly active cell types and driver genes and community analysis of pathways. To validate its performance, CellGO was assessed using a gene set comprising mixed cell-type markers, confirming its ability to discern active pathways across distinct cell types. Subsequent benchmarking analyses demonstrated CellGO's superiority in effectively identifying cell types and their corresponding cell-type-specific pathways affected by gene knockouts, using either single genes or sets of genes differentially expressed between knockout and control samples. Moreover, CellGO demonstrated its ability to infer cell-type-specific pathogenesis for disease risk genes. Accessible as a Python package, CellGO also provides a user-friendly web interface, making it a versatile and accessible tool for researchers in the field.

Network-based analysis revealed significant interactions between risk genes of severe COVID-19 and host genes interacted with SARS-CoV-2 proteins.

Whether risk genes of severe coronavirus disease 2019 (COVID-19) from genome-wide association study could play their regulatory roles by interacting with host genes that were interacted with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) proteins was worthy of exploration. In this study, we implemented a network-based approach by developing a user-friendly software Network Calculator (https://github.com/Haoxiang-Qi/Network-Calculator.git). By using Network Calculator, we identified a network composed of 13 risk genes and 28 SARS-CoV-2 interacted host genes that had the highest network proximity with each other, with a hub gene HNRNPK identified. Among these genes, 14 of them were identified to be differentially expressed in RNA-seq data from severe COVID-19 cases. Besides, by expression enrichment analysis in single-cell RNA-seq data, compared with mild COVID-19, these genes were significantly enriched in macrophage, T cell and epithelial cell for severe COVID-19. Meanwhile, 74 pathways were significantly enriched. Our analysis provided insights for the underlying genetic etiology of severe COVID-19 from the perspective of network biology.

Altered HCAR3 expression may underlying the blunted niacin responses of the psychiatric disorders and the risk of schizophrenia.

AIM: Blunted niacin response (BNR) was an endophenotype of schizophrenia, but the underlying mechanism remains unclarified. The objective of this study was to verify whether genes associated with BNR pathway constitute the genetic basis and the pathological mechanism of BNR phenotypic psychiatric patients. METHODS: Two independent sample sets consisting of 971 subjects were enrolled in this study. A total of 62 variants were genotyped in the discovery set, then the related variants were verified in the verification set. The published PGC GWAS data were used to validate the associations between the variants and psychiatry disorders. RT-PCR analysis, eQTL data, and Dual-Luciferase Reporter experiment were used to investigate the potential molecular mechanisms of the variants underlying BNR. RESULTS: The results showed that two SNPs, rs56959712 in HCAR2 and rs2454721 in HCAR3 were significantly associated with niacin response. The risk allele T of rs2454721 could affect the niacin responses of psychiatric patients through elevated HCAR3 gene expression. These two genes, especially HCAR3, were significantly associated with the risk of schizophrenia, as identified in this study and verified using the published GWAS data. CONCLUSION: HCAR3 is a novel schizophrenia susceptibility gene which is significantly associated with blunted niacin response in schizophrenia. In-depth investigation of HCAR3 is of great significance for uncovering the pathogenesis and propose new therapeutic targets for psychiatric disorders, especially for the BNR subgroup patients.

TREM2 signalling as a multifaceted player in brain homoeostasis and a potential target for Alzheimer's disease treatment.

Triggering receptor expressed on myeloid cells-2 (TREM2) has crucial roles in microglial physiology, differentiation, metabolism and survival. Genome-wide association studies (GWAS) show that genetic mutations of the TREM2 increase the risk of late-onset Alzheimer's disease (AD) by two to four times, disrupting the microglial function in reducing the progression of the disease. Accumulating data show that TREM2 function in AD is related primarily to the clearance of soluble and insoluble amyloid beta (Aß42) aggregates from the brain. TREM2 also ameliorates the pathological effects of activated microglia on neuronal tau pathology, demonstrating its protective anti-inflammatory effects. However, since the excessive activation of TREM2 signalling can inhibit pro-inflammatory reactions and suppress the role of microglia in immune surveillance, at the late stages of the disease, it might promote immune tolerance, which is detrimental. The contradictory effects of TREM2 mutations on brain amyloidopathy and tauopathy in multiple mouse models, as well as studies revealing various effects of TREM2 overexpression, complicate the understanding of the role that TREM2 plays in AD aetiopathogenesis. In this review, we summarize the latest developments regarding the significance of TREM2 signalling in the stability of microglial pro- and anti-inflammatory activations and propose the mechanisms that should be targeted in the future to treat AD.

Alterations in gut microbiota and host transcriptome of patients with coronary artery disease.

BACKGROUND: Coronary artery disease (CAD) is a widespread heart condition caused by atherosclerosis and influences millions of people worldwide. Early detection of CAD is challenging due to the lack of specific biomarkers. The gut microbiota and host-microbiota interactions have been well documented to affect human health. However, investigation that reveals the role of gut microbes in CAD is still limited. This study aims to uncover the synergistic effects of host genes and gut microbes associated with CAD through integrative genomic analyses. RESULTS: Herein, we collected 52 fecal and 50 blood samples from CAD patients and matched controls, and performed amplicon and transcriptomic sequencing on these samples, respectively. By comparing CAD patients with health controls, we found that dysregulated gut microbes were significantly associated with CAD. By leveraging the Random Forest method, we found that combining 20 bacteria and 30 gene biomarkers could distinguish CAD patients from health controls with a high performance (AUC = 0.92). We observed that there existed prominent associations of gut microbes with several clinical indices relevant to heart functions. Integration analysis revealed that CAD-relevant gut microbe genus Fusicatenibacter was associated with expression of CAD-risk genes, such as GBP2, MLKL, and CPR65, which is in line with previous evidence (Tang et al., Nat Rev Cardiol 16:137-154, 2019; Kummen et al., J Am Coll Cardiol 71:1184-1186, 2018). In addition, the upregulation of immune-related pathways in CAD patients were identified to be primarily associated with higher abundance of genus Blautia, Eubacterium, Fusicatenibacter, and Monoglobus. CONCLUSIONS: Our results highlight that dysregulated gut microbes contribute risk to CAD by interacting with host genes. These identified microbes and interacted risk genes may have high potentials as biomarkers for CAD.

New "drugs and targets" in the GWAS era of bipolar disorder.

OBJECTIVE: Due to the phenotypic heterogeneity and etiological complexity of bipolar disorder (BD), many patients do not respond well to the current medications, and developing novel effective treatment is necessary. Whether any BD genome-wide association study (GWAS) risk genes were targets of existing drugs or novel drugs that can be repurposed in the clinical treatment of BD is a hot topic in the GWAS era of BD. METHODS: A list of 425 protein-coding BD risk genes was distilled through the BD GWAS, and 4479 protein-coding druggable targets were retrieved from the druggable genome. The overlapped genes/targets were subjected to further analyses in DrugBank, Pharos, and DGIdb datasets in terms of their FDA status, mechanism of action and primary indication, to identify their potential for repurposing. RESULTS: We identified 58 BD GWAS risk genes grouped as the druggable targets, and several genes were given higher priority. These BD risk genes were targets of antipsychotics, antidepressants, antiepileptics, calcium channel antagonists, as well as anxiolytics and analgesics, either existing clinically-approved drugs for BD or the drugs than can be repurposed for treatment of BD in the future. Those genes were also likely relevant to BD pathophysiology, as many of them encode ion channel, ion transporter or neurotransmitter receptor, or the mice manipulating those genes are likely to mimic the phenotypes manifest in BD patients. CONCLUSIONS: This study identifies several targets that may facilitate the discovery of novel treatments in BD, and implies the value of conducting GWAS into clinical translation.

Neurodevelopmental disturbances in schizophrenia: evidence from genetic and environmental factors.

Since more than 3 decades, schizophrenia (SZ) has been regarded as a neurodevelopmental disorder. The neurodevelopmental hypothesis proposes that SZ is associated with genetic and environmental risk factors, which influence connectivity in neuronal circuits during vulnerable developmental periods. We carried out a non-systematic review of genetic/environmental factors that increase SZ risk in light of its neurodevelopmental hypothesis. We also reviewed the potential impact of SZ-related environmental and genetic risk factors on grey and white matter pathology and brain function based on magnetic resonance imaging and post-mortem studies. Finally, we reviewed studies that have used patient-derived neuronal models to gain knowledge of the role of genetic and environmental factors in early developmental stages. Taken together, these studies indicate that a variety of environmental factors may interact with genetic risk factors during the pre- or postnatal period and/or during adolescence to induce symptoms of SZ in early adulthood. These risk factors induce disturbances of macro- and microconnectivity in brain regions involving the prefrontal, temporal and parietal cortices and the hippocampus. On the molecular and cellular level, a disturbed synaptic plasticity, loss of oligodendrocytes and impaired myelination have been shown in brain regions of SZ patients. These cellular/histological phenotypes are related to environmental risk factors such as obstetric complications, maternal infections and childhood trauma and genetic risk factors identified in recent genome-wide association studies. SZ-related genetic risk may contribute to active processes interfering with synaptic plasticity in the adult brain. Advances in stem cell technologies are providing promising mechanistic insights into how SZ risk factors impact the developing brain. Further research is needed to understand the timing of the different complex biological processes taking place as a result of the interplay between genetic and environmental factors.

Investigating the role of lipid genes in liver disease using fatty liver models of alcohol and high fat in zebrafish (Danio rerio).

BACKGROUND: Accumulation of lipid in the liver is the first hallmark of both alcohol-related liver disease (ALD) and non-alcohol-related fatty liver disease (NAFLD). Recent studies indicate that specific mutations in lipid genes confer risk and might influence disease progression to irreversible liver cirrhosis. This study aimed to understand the function/s of lipid risk genes driving disease development in zebrafish genetic models of alcohol-related and non-alcohol-related fatty liver. METHODS: We used zebrafish larvae to investigate the effect of alcohol and high fat to model fatty liver and tested the utility of this model to study lipid risk gene functions. CRISPR/Cas9 gene editing was used to create knockdowns in 5 days post-fertilisation zebrafish larvae for the available orthologs of human cirrhosis risk genes (pnpla3, faf2, tm6sf2). To establish fatty liver models, larvae were exposed to ethanol and a high-fat diet (HFD) consisting of chicken egg yolk. Changes in morphology (imaging), survival, liver injury (biochemical tests, histopathology), gene expression (qPCR) and lipid accumulation (dye-specific live imaging) were analysed across treatment groups to test the functions of these genes. RESULTS: Exposure of 5-day post-fertilisation (dpf) WT larvae to 2% ethanol or HFD for 48 h developed measurable hepatic steatosis. CRISPR-Cas9 genome editing depleted pnpla3, faf2 and tm6sf2 gene expression in these CRISPR knockdown larvae (crispants). Depletion significantly increased the effects of ethanol and HFD toxicity by increasing hepatic steatosis and hepatic neutrophil recruitment ≥2-fold in all three crispants. Furthermore, ethanol or HFD exposure significantly altered the expression of genes associated with ethanol metabolism (cyp2y3) and lipid metabolism-related gene expression, including atgl (triglyceride hydrolysis), axox1, echs1 (fatty acid ß-oxidation), fabp10a (transport), hmgcra (metabolism), notch1 (signalling) and srebp1 (lipid synthesis), in all three pnpla3, faf2 and tm6sf2 crispants. Nile Red staining in all three crispants revealed significantly increased lipid droplet size and triglyceride accumulation in the livers following exposure to ethanol or HFD. CONCLUSIONS: We identified roles for pnpla3, faf2 and tm6sf2 genes in triglyceride accumulation and fatty acid oxidation pathways in a zebrafish larvae model of fatty liver.

A Bayesian framework to integrate multi-level genome-scale data for Autism risk gene prioritization.

BACKGROUND: Autism spectrum disorder (ASD) is a group of complex neurodevelopment disorders with a strong genetic basis. Large scale sequencing studies have identified over one hundred ASD risk genes. Nevertheless, the vast majority of ASD risk genes remain to be discovered, as it is estimated that more than 1000 genes are likely to be involved in ASD risk. Prioritization of risk genes is an effective strategy to increase the power of identifying novel risk genes in genetics studies of ASD. As ASD risk genes are likely to exhibit distinct properties from multiple angles, we reason that integrating multiple levels of genomic data is a powerful approach to pinpoint genuine ASD risk genes. RESULTS: We present BNScore, a Bayesian model selection framework to probabilistically prioritize ASD risk genes through explicitly integrating evidence from sequencing-identified ASD genes, biological annotations, and gene functional network. We demonstrate the validity of our approach and its improved performance over existing methods by examining the resulting top candidate ASD risk genes against sets of high-confidence benchmark genes and large-scale ASD genome-wide association studies. We assess the tissue-, cell type- and development stage-specific expression properties of top prioritized genes, and find strong expression specificity in brain tissues, striatal medium spiny neurons, and fetal developmental stages. CONCLUSIONS: In summary, we show that by integrating sequencing findings, functional annotation profiles, and gene-gene functional network, our proposed BNScore provides competitive performance compared to current state-of-the-art methods in prioritizing ASD genes. Our method offers a general and flexible strategy to risk gene prioritization that can potentially be applied to other complex traits as well.

The role of Alzheimer's disease risk genes in endolysosomal pathways.

There is ample pathological and biological evidence for endo-lysosomal dysfunction in Alzheimer's disease (AD) and emerging genetic studies repeatedly implicate endo-lysosomal genes as associated with increased AD risk. The endo-lysosomal network (ELN) is essential for all cell types of the central nervous system (CNS), yet each unique cell type utilizes cellular trafficking differently (see Fig. 1). Challenges ahead involve defining the role of AD associated genes in the functionality of the endo-lysosomal network (ELN) and understanding how this impacts the cellular dysfunction that occurs in AD. This is critical to the development of new therapeutics that will impact, and potentially reverse, early disease phenotypes. Here we review some early evidence of ELN dysfunction in AD pathogenesis and discuss the role of selected AD-associated risk genes in this pathway. In particular, we review genes that have been replicated in multiple genome-wide association studies(Andrews et al., 2020; Jansen et al., 2019; Kunkle et al., 2019; Lambert et al., 2013; Marioni et al., 2018) and reviewed in(Andrews et al., 2020) that have defined roles in the endo-lysosomal network. These genes include SORL1, an AD risk gene harboring both rare and common variants associated with AD risk and a role in trafficking cargo, including APP, through the ELN; BIN1, a regulator of clathrin-mediated endocytosis whose expression correlates with Tau pathology; CD2AP, an AD risk gene with roles in endosome morphology and recycling; PICALM, a clathrin-binding protein that mediates trafficking between the trans-Golgi network and endosomes; and Ephrin Receptors, a family of receptor tyrosine kinases with AD associations and interactions with other AD risk genes. Finally, we will discuss how human cellular models can elucidate cell-type specific differences in ELN dysfunction in AD and aid in therapeutic development.

Genetic correlation between thyroid hormones and Parkinson's disease.

Parkinson's disease (PD) was reported to be connected with thyroid diseases clinically, which might be a critical clew to immune pathogenesis of PD. However, there was no further research to study the pathogenesis correlation between PD and thyroid diseases. In this study, except for investigating the difference in thyroid hormone between PD and the control group, we explored genetic correlation between thyroid and PD. We tried to find their shared molecular pathway by analyzing the effect of PD risk genes on thyroid function. Interestingly, most of those 12 meaningful SNPs we found could affect PD and thyroid function through immune mechanism, which is consistent with our original conjecture and provides significant evidence for the immune pathogenesis of PD.

Exposure to a real traffic environment impairs brain cognition in aged mice.

Traffic-related air pollution (TRAP) has been a common public health problem, which is associated with central nervous system dysfunction according to large-scale epidemiological studies. Current studies are mostly limited to artificial laboratory exposure environments and specific genetic mechanisms remain unclear. Therefore, we chose a real-world transportation environment to expose aged mice, transporting them from the laboratory to a 1-m-high dry platform inside the campus and tunnel, and the mice were exposed daily from 7 a.m. to 7 p.m. for 2, 4 and 12 weeks respectively. Compared with the control group (in campus), the memory function of mice in the experimental group (in tunnel) was significantly impaired in the Morris water maze test. TRAP exposure increased the number of activated microglia in the hippocampal DG, CA1, CA3 regions and dorsolateral prefrontal cortex (dPFC). And neuroinflammation and oxidative stress levels were up-regulated in both hippocampus and dPFC of aged mice. By screening the risk genes of Alzheimer's disease, we found the mRNA and protein levels of ABCA7 were down-regulated and those of PYK2 were up-regulated. The DNA methylation ratios increased in four CpG sites of abca7 promoter region and decreased in one CpG site of pyk2 promoter region, which were consistent with the altered expression of ABCA7 and PYK2. In conclusion, exposure to the real traffic environment impaired memory function and enhanced neuroinflammation and oxidative stress responses, which could be relevant to the altered expression and DNA methylation levels of ABCA7 and PYK2. Our work provides a new and promising understanding of the pathological mechanisms of cognitive impairment caused by traffic-related air pollution.

A qualitative study of unaffected ATM and CHEK2 carriers: How participants make meaning of 'moderate risk' genetic results in a population breast cancer screening trial.

Relatively little is known about experiences of individuals with a pathogenic variant in a moderately penetrant breast cancer gene, particularly those without a personal history of cancer. The WISDOM trial is testing a model of risk-based breast cancer screening that integrates genomic (nine genes and polygenic risk) and other risk factors. In the context of an embedded Ethical, Legal, and Social Implications (ELSI) study of WISDOM, we conducted qualitative interviews at two timepoints post-result disclosure with 22 ATM and CHEK2 carriers. Results disclosure and interview recordings were transcribed and analyzed using a grounded theory analysis framework. We found that participants minimized the significance of their results in comparison to BRCA; were surprised but not alarmed by the results in the absence of family history; did not fundamentally change their perception of their breast cancer risk despite the new genomic information; exhibited variable responses to WISDOM's screening and risk reduction recommendations; and shared test results with family but did not strongly encourage cascade testing. Participants viewed the results as having limited utility and responded accordingly. Our study offers important insights into how genetic test results for moderate-risk genes are received, understood, and acted upon in population screening context.

Characterization of gene expression patterns in mild cognitive impairment using a transcriptomics approach and neuroimaging endophenotypes.

INTRODUCTION: Identification of novel therapeutics and risk assessment in early stages of Alzheimer's disease (AD) is a crucial aspect of addressing this complex disease. We characterized gene-expression patterns at the mild cognitive impairment (MCI) stage to identify critical mRNA measures and gene clusters associated with AD pathogenesis. METHODS: We used a transcriptomics approach, integrating magnetic resonance imaging (MRI) and peripheral blood-based gene expression data using persistent homology (PH) followed by kernel-based clustering. RESULTS: We identified three clusters of genes significantly associated with diagnosis of amnestic MCI. The biological processes associated with each cluster were mitochondrial function, NF-kB signaling, and apoptosis. Cluster-level associations with cortical thickness displayed canonical AD-like patterns. Driver genes from clusters were also validated in an external dataset for prediction of amyloidosis and clinical diagnosis. DISCUSSION: We found a disease-relevant transcriptomic signature sensitive to prodromal AD and identified a subset of potential therapeutic targets associated with AD pathogenesis.

Establishment of an immune-related gene prognostic model for head and neck tumors.

This study aimed to screen the key immune-related genes (IRGs) in head and neck squamous cell carcinoma (HNSC) and construct the IRGs-related prognostic model to predict the overall survival (OS) of patients with HNSC. The RNA-seq data and clinical data were downloaded from The Cancer Genome Atlas database, and IRGs were obtained from the Immunology Database and Analysis Portal. Differentially expressed genes (DEGs) between HNSC and normal samples were identified, followed by integration with IRGs to screen differentially expressed IRGs. After univariate and multivariate proportional hazard regression analyses, an IRG-based risk model was constructed. Meanwhile, data chip of GSE65858 as the validation set to assess the predicted performance of established model. Next, univariate and multivariate Cox regression analyses were performed to identify the independent prognostic factor of HNSC, and the Nomogram model was developed to predict patient outcome. Furthermore, the correlation between immune cell infiltration and risk score was analyzed. A total of 65 differently expressed IRGs associated with prognosis of HNSC were screened, and finally a 26-gene IRG signature was identified to construct a prognostic prediction model. The AUC of ROC curve was 0.750. Survival analysis showed that patients in the high-risk group had a worse prognosis. Independent prognostic analysis showed that risk score could be considered as an independent predictor for HNSC prognosis. Nomogram assessment showed that the model had high reliability for predicting the survival of patients with HNSC in 1, 2, 3 years. Ultimately, the abundance of B cells and CD4+ T cell infiltration in HNSC showed negative correlations with risk score. Our IRG-based prognostic risk model may be used to estimate the prognosis of HNSC patients.

Single cell sequencing analysis identifies genetics-modulated ORMDL3+ cholangiocytes having higher metabolic effects on primary biliary cholangitis.

BACKGROUND: Primary biliary cholangitis (PBC) is a classical autoimmune disease, which is highly influenced by genetic determinants. Many genome-wide association studies (GWAS) have reported that numerous genetic loci were significantly associated with PBC susceptibility. However, the effects of genetic determinants on liver cells and its immune microenvironment for PBC remain unclear. RESULTS: We constructed a powerful computational framework to integrate GWAS summary statistics with scRNA-seq data to uncover genetics-modulated liver cell subpopulations for PBC. Based on our multi-omics integrative analysis, 29 risk genes including ORMDL3, GSNK2B, and DDAH2 were significantly associated with PBC susceptibility. By combining GWAS summary statistics with scRNA-seq data, we found that cholangiocytes exhibited a notable enrichment by PBC-related genetic association signals (Permuted P < 0.05). The risk gene of ORMDL3 showed the highest expression proportion in cholangiocytes than other liver cells (22.38%). The ORMDL3+ cholangiocytes have prominently higher metabolism activity score than ORMDL3- cholangiocytes (P = 1.38 × 10-15). Compared with ORMDL3- cholangiocytes, there were 77 significantly differentially expressed genes among ORMDL3+ cholangiocytes (FDR < 0.05), and these significant genes were associated with autoimmune diseases-related functional terms or pathways. The ORMDL3+ cholangiocytes exhibited relatively high communications with macrophage and monocyte. Compared with ORMDL3- cholangiocytes, the VEGF signaling pathway is specific for ORMDL3+ cholangiocytes to interact with other cell populations. CONCLUSIONS: To the best of our knowledge, this is the first study to integrate genetic information with single cell sequencing data for parsing genetics-influenced liver cells for PBC risk. We identified that ORMDL3+ cholangiocytes with higher metabolism activity play important immune-modulatory roles in the etiology of PBC.

Prediction and prioritization of autism-associated long non-coding RNAs using gene expression and sequence features.

BACKGROUND: Autism spectrum disorders (ASD) refer to a range of neurodevelopmental conditions, which are genetically complex and heterogeneous with most of the genetic risk factors also found in the unaffected general population. Although all the currently known ASD risk genes code for proteins, long non-coding RNAs (lncRNAs) as essential regulators of gene expression have been implicated in ASD. Some lncRNAs show altered expression levels in autistic brains, but their roles in ASD pathogenesis are still unclear. RESULTS: In this study, we have developed a new machine learning approach to predict candidate lncRNAs associated with ASD. Particularly, the knowledge learnt from protein-coding ASD risk genes was transferred to the prediction and prioritization of ASD-associated lncRNAs. Both developmental brain gene expression data and transcript sequence were found to contain relevant information for ASD risk gene prediction. During the pre-training phase of model construction, an autoencoder network was implemented for a representation learning of the gene expression data, and a random-forest-based feature selection was applied to the transcript-sequence-derived k-mers. Our models, including logistic regression, support vector machine and random forest, showed robust performance based on tenfold cross-validations as well as candidate prioritization with hypothetical loci. We then utilized the models to predict and prioritize a list of candidate lncRNAs, including some reported to be cis-regulators of known ASD risk genes, for further investigation. CONCLUSIONS: Our results suggest that ASD risk genes can be accurately predicted using developmental brain gene expression data and transcript sequence features, and the models may provide useful information for functional characterization of the candidate lncRNAs associated with ASD.

Anxiety risk SNPs on chromosome 2 modulate arousal in children in a fear generalization paradigm.

Alterations in fear learning/generalization are considered to be relevant mechanisms engendering the development of anxiety disorders being the most prevalent mental disorders. Although anxiety disorders almost exclusively have their first onset in childhood and adolescence, etiological research focuses on adult individuals. In this study, we evaluated findings of a recent meta-analysis of genome-wide association studies in adult anxiety disorders with significant associations of four single nucleotide polymorphisms (SNPs) in a large cohort of 347 healthy children (8-12 years) characterized for dimensional anxiety. We investigated the modulation of anxiety parameters by these SNPs in a discriminative fear conditioning and generalization paradigm in the to-date largest sample of children. Results extended findings of the meta-analysis showing a genomic locus on 2p21 to modulate anxious personality traits and arousal ratings. These SNPs might, thus, serve as susceptibility markers for a shared risk across pathological anxiety, presumably mediated by alterations in arousal.