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Abstract The gut microbiome, recognized as a critical component in the development of chronic diseases and aging processes, constitutes a promising approach for predicting host health status. Previous research has underscored the potential of microbiome-based predictions, and the rapid advancements of machine learning techniques have introduced new opportunities for exploiting microbiome data. To predict various host nonhealthy conditions, this study proposed an integrated machine learning-based estimation pipeline of Gut Age Index (GAI) by establishing a health aging baseline with the gut microbiome data from healthy individuals. We assessed the performance of GAI pipeline on two extensive cohorts - the Guangdong Gut Microbiome Project (GGMP) and the American Gut Project (AGP). In the GGMP cohort, for 20 common chronic diseases such as metabolic syndrome, obesity, and cardiovascular diseases, the proposed GAI achieved a balanced accuracy, ranging from 66 to 75%, with the prediction performance for atherosclerosis being the highest. In the AGP cohort, the balanced accuracy of GAI ranged from 58 to 72% for 10 diseases. Based on the results from these two datasets, we conclude that our proposed approach in this study can be used to predict individual health status, which offers the potential for scalable, cost-effective, and personalized health insights.

The Artificial Intelligence (AI) technology holds immense potential in the realm of automated diagnosis for Major Depressive Disorder (MDD), yet it is not without potential shortcomings. This paper systematically reviews the research progresses of integrating AI technology with depression diagnosis and provides a comprehensive analysis of existing research findings. In this context, we observe that the knowledge-driven first-generation of depression diagnosis methods could only address deterministic issues in structured information, with the selection of depression-related features directly influencing identification outcomes. The data-driven second-generation of depression diagnosis methods achieved automatic learning of features but required substantial high-quality clinical data, and the results were often obtained solely from the black-box models which lack sufficient explainability. In an effort to overcome the limitations of the preceding approaches, the third-generation of depression diagnosis methods combined the strengths of knowledge-driven and data-driven approaches. Through the fusion of information, the diagnostic accuracy is greatly enhanced, but the interpretability remains relatively weak. In order to enhance interpretability and introduce diagnostic criteria, this paper offers a new approach using Large Language Models (LLMs) as AI agents for assisting the depression diagnosis. Finally, we also discuss the potential advantages and challenges associated with this approach. This newly proposed innovative approach has the potential to offer new perspectives and solutions in the diagnosis of depression.

Summary: Alcohol dependence is a heritable disorder, yet its genetic basis and underlying mechanisms remain poorly understood, especially in Chinese population. In this study, we conducted gene-based and transcript-based association tests and found a significant association between ARVCF expression in the cortex and hippocampus of the brain and alcohol use in a cohort of 1,329 individuals with Chinese ancestry. Further analysis using the effective-median-based Mendelian randomization framework for inferring the causal genes (EMIC) revealed a causal relationship between ARVCF expression in the frontal cortex and alcohol use. Moreover, leveraging extensive European alcohol dependence data, our gene association tests and EMIC analysis showed that ARVCF expression in the nucleus accumbens was significantly associated with alcohol dependence. Finally, animal studies indicated that Arvcf knockout mice lacked conditioned place preference for alcohol. Together, our combined human genetic and animal studies indicate that ARVCF plays a crucial role in alcohol dependence.

Introduction: As one of the common psychiatric diseases, depression poses serious threats to human health. Although many genes have been nominated for depression, few of them were investigated in details at the molecular level. Objectives: To demonstrate Frizzled class receptor 6 (FZD6) functions in depression through disrupting Wnt/β-catenin signal pathway. Methods: The FZD6 edited cell line and mouse model were generated by using CRISPR/Cas9 technique. The expression of key genes and proteins in Wnt/β-catenin pathway was determined by qRT-PCR and Western blotting, respectively. Animal behavioral tests, including open field test (OFT), elevated plus maze test (EPM), forced swimming test (FST), tail suspension test (TST), and sucrose preference test (SPT), were employed to determine anxiety- and depressive-like behaviors. Immunofluorescent staining was used to assess cell proliferation in the hippocampus of mouse brain. Results: Among patients with depression, FZD6, one of the receptors of Wnt ligand, was significantly decreased. In CRISPR/Cas9-based FZD6 knockdown cells, we showed that FZD6 plays a significant role in regulating expression of genes involved in Wnt/β-catenin pathway. Subsequently behavioral studies on Fzd6 knockdown mice (with a 5-nucleotide deletion; Fzd6-Δ5) revealed significant changes in depressive symptoms, including increased immobility duration in FST, less preference of sucrose in SPT, reduction of distance traveled in OFT, and decreased time spent in open arms in EPM. Immunofluorescent staining showed decreased cell proliferation in the hippocampus of Fzd6-Δ5 mice with reduced number of Ki67+ and PCNA+ cells.Moreover, decreased Gsk3β mRNA expression, phosphorylated GSK3β, and cytoplasmic β-catenin in the hippocampus of Fzd6-Δ5 mice provided further evidence supporting the role of Fzd6 in depression. Conclusion: Together, above findings proved the significant role of FZD6 in depression through its effect on hippocampal cell proliferation and its ability to regulate canonical Wnt/β-catenin pathway.

BackgroundCurrently, the prevalence of allergic rhinitis (AR) remains high and there is a great need to develop better and safer ways to alleviate AR symptoms. The Lactobacillus plantarum GUANKE probiotic was reported as an immunomodulator through maintaining Th1/Th2 balance. This study aimed to determine the efficacy of GUANKE in AR subjects.MethodsAdults aged from 18 to 60 years old and previously suffered from AR were recruited and received GUANKE probiotics treatment for 4 weeks. The questionnaires of Total nasal symptom scores (TNSS), total non-nasal symptom score (TNNSS), and rhinitis control assessment test (RCAT) were used to assess the effectiveness before and after treatment. The serum allergen-specific IgE and cytokines were also determined at baseline and after 4 weeks of probiotics administration.ResultsThe results showed that TNSS and TNNSS were significantly reduced and the RCAT score was significantly increased compared to baseline. The sub-symptom score of rhinorrhea, itching, sneezing, and tearing in each questionnaire also showed significant changes, and the serum IgE level was markedly decreased. We further measured inflammatory-related proteins in serum and found that a total of 20 proteins (6 upregulated and 14 downregulated) were significantly changed compared to baseline, including IL-4, IL-7, IL-20, IL-33, CXCL1, CXCL5, CXCL6, CXCL11, CCL4, CCL23, TGF-alpha, LAP-TGF-beta-1, MMP-1, MMP-10, AXIN1, NT-3, OSM, SCF, CD6, and NRTN. Enrichment analysis showed that these significantly altered proteins were mainly enriched in cytokine and chemokine-related signaling pathways.ConclusionTaken together, this study demonstrated the Lactobacillus plantarum GUANKE can serve as an effective immunobiotic for the treatment of AR, which is realized through maintaining the Th1/Th2 balance by modulating the functions of various cytokines and chemokines.

Summary: Although numerous susceptibility loci are nominated for nicotine dependence (ND), no report showed any association of ARVCF with ND. Through genome-wide sequencing analysis, we first identified genetic variants associated nominally with ND and then replicated them in an independent sample. Of the six replicated variants, rs148582811 in ARVCF located in the enhancer-associated marker peak is attractive. The effective-median-based Mendelian randomization analysis indicated that ARVCF is a causal gene for ND. RNA-seq analysis detected decreased ARVCF expression in smokers compared to nonsmokers. Luciferase reporter assays indicated that rs148582811 and its located DNA fragment allele-specifically regulated ARVCF expression. Immunoprecipitation analysis revealed that transcription factor X-ray repair cross-complementing protein 5 (XRCC5) bound to the DNA fragment containing rs148582811 and allele-specifically regulated ARVCF expression at the mRNA and protein levels. With the Arvcf knockout mouse model, we showed that Arvcf deletion not only impairs hippocampus-dependent learning and memory, but also alleviated nicotine-induced memory deficits.

BackgroundsTobacco smoking is an important risk factor for coronary artery disease (CAD), but the genetic mechanisms linking smoking to CAD remain largely unknown.MethodsWe analyzed summary data from the genome-wide association study (GWAS) of the UK Biobank for CAD, plasma lipid concentrations (n = 184,305), and smoking (n = 337,030) using different biostatistical methods, which included LD score regression and Mendelian randomization (MR).ResultsWe identified SNPs shared by CAD and at least one smoking behavior, the genes where these SNPs are located were found to be significantly enriched in the processes related to lipoprotein metabolic, chylomicron-mediated lipid transport, lipid digestion, mobilization, and transport. The MR analysis revealed a positive correlation between smoking cessation and decreased risk for CAD when smoking cessation was considered as exposure (p = 0.001), and a negative correlation between the increased risk for CAD and smoking cessation when CAD was considered as exposure (p = 2.95E-08). This analysis further indicated that genetic liability for smoking cessation increased the risk of CAD.ConclusionThese findings inform the concomitant conditions of CAD and smoking and support the idea that genetic liabilities for smoking behaviors are strongly associated with the risk of CAD.

Summary: Background: Hepatitis B virus (HBV) infection is one of the main causes of hepatocellular carcinoma (HCC). The relationship between HBV infection and the host genome as well as their underlying mechanisms remain largely unknown. Methods: In this study, we performed a whole-genome exon sequencing analysis of 300 sib-pairs of Chinese HBV-infected families with the goal of identifying variants and genes involved in HBV infection. A site-direct mutant plasmid was used to investigate the function of SNP rs76438938 in KNG1. The functional and mechanical studies of KNG1 were conducted with in vitro liver cell lines and a hydrodynamic injection model in vivo. The impact of KNG1 on HBV infection therapy was determined in hepatocytes treated with IFN-α/λ1. Findings: Our whole-exon association study of 300 families with hepatitis B infection found that SNP rs76438938 in KNG1 significantly increased the risk for HBV infection, and the rs76438938-T allele was found to promote HBV replication by increasing the stability of KNG1 mRNA. By competitively binding HSP90A with MAVS, KNG1 can inhibit the expression of types I and III IFNs by promoting MAVS lysosomal degradation. Such suppression of IFN expression and promotion of HBV replication by Kng1 were further demonstrated with an animal model in vivo. Lastly, we showed that the rs76438938-C allele can improve the therapeutic effect of IFN-α and -λ1 in HBV infection. Interpretation: This study identified a SNP, rs76438938, in a newly discovered host gene, KNG1, for its involvement in HBV infection and treatment effect through modulating the cellular antiviral process. Funding: This study was supported in part by the Independent Task of State Key Laboratory for Diagnosis and Treatment of Infectious Diseases of the First Affiliated Hospital of Zhejiang University, the China Precision Medicine Initiative (2016YFC0906300), and the Research Center for Air Pollution and Health of Zhejiang University.

Abstract Immunotherapy utilizing chimeric antigen receptor T cell (CAR-T) therapy holds promise for hematologic malignancies, however, response rates and associated immune-related adverse effects widely vary among patients. Here we show, by comparing diversity and composition of the gut microbiome during different CAR-T therapeutic phases in the clinical trial ChiCTR1800017404, that the gut flora characteristically differs among patients and according to treatment stages, and might also reflect patient response to therapy in relapsed/refractory multiple myeloma (MM; n = 43), acute lympholastic leukemia (ALL; n = 23) and non-Hodgkin lymphoma (NHL; n = 12). We observe significant temporal differences in diversity and abundance of Bifidobacterium, Prevotella, Sutterella, and Collinsella between MM patients in complete remission (n = 24) and those in partial remission (n = 11). Furthermore, we find that patients with severe cytokine release syndrome present with higher abundance of Bifidobacterium, Leuconostoc, Stenotrophomonas, and Staphylococcus, which is reproducible in an independent cohort of 38 MM patients. This study has important implications for understanding the biological role of the microbiome in CAR-T treatment responsiveness of hematologic malignancy patients, and may guide therapeutic intervention to increase efficacy. The success rate of CAR-T cell therapy is high in blood cancers, yet individual patient characteristics might reduce therapeutic benefit. Here we show that therapeutic response in MM, ALL and NHL, and occurrence of severe cytokine release syndrome in multiple myeloma are associated with specific gut microbiome alterations.

IntroductionSubmassive hepatic necrosis (SMHN, defined as necrosis of 15–90% of the entire liver on explant) is a likely characteristic pathological feature of ACLF in patients with hepatitis B cirrhosis. We aimed to comprehensively explore microbiome and bile acids patterns across enterhepatic circulation and build well-performing machine learning models to predict SMHN status.MethodsBased on the presence or absence of SMHN, 17 patients with HBV-related end-stage liver disease who received liver transplantation were eligible for inclusion. Serum, portal venous blood, and stool samples were collected for comparing differences of BA spectra and gut microbiome and their interactions. We adopted the random forest algorithm with recursive feature elimination (RF-RFE) to predict SMHN status.ResultsBy comparing total BA spectrum between SMHN (−) and SMHN (+) patients, significant changes were detected only in fecal (P = 0.015). Compared with the SMHN (+) group, the SMHN (−) group showed that UDCA, 7-KLCA, 3-DHCA, 7-KDCA, ISOLCA and α-MCA in feces, r-MCA, 7-KLCA and 7-KDCA in serum, γ-MCA and 7-KLCA in portal vein were enriched, and TUDCA in feces was depleted. PCoA analysis showed significantly distinct overall microbial composition in two groups (P = 0.026). Co-abundance analysis showed that bacterial species formed strong and broad relationships with BAs. Among them, Parabacteroides distasonis had the highest node degree. We further identified a combinatorial marker panel with a high AUC of 0.92.DiscussionOur study demonstrated the changes and interactions of intestinal microbiome and BAs during enterohepatic circulation in ACLF patients with SMHN. In addition, we identified a combinatorial marker panel as non-invasive biomarkers to distinguish the SMHN status with high AUC.

Although various susceptibility genes have been revealed to influence tobacco smoking, the underlying regulatory mechanisms between genetic variants and smoking are poorly understood. In this study, we investigated cis-expression quantitative trait loci (cis-eQTLs) and methylation quantitative trait loci (mQTLs) for 56 candidate smoking-linked genes using the BrainCloud cohort samples. An eQTL was revealed to significantly affect EGLN2 expression in the European sample and two mQTLs were respectively detected in CpG sites in NRXN1 and CYP2A7. Interestingly, we found for the first time that the minor allele of the single nucleotide polymorphism (SNP) rs3745277 located in CYP2A7P1 (downstream of CYP2B6) significantly decreased methylation at the CpG site for CYP2A7 (cg25427638; P = 5.31 × 10–7), reduced expression of CYP2B6 (P = 0.03), and lowered the percentage of smokers (8.8% vs. 42.3%; Odds Ratio (OR) = 0.14, 95% Confidence Interval (CI): 0.02–0.62; P = 4.47 × 10–3) in a dominant way for the same cohort sample. Taken together, our findings resulted from analyzing genetic variation, DNA methylation, mRNA expression, and smoking status together using the same participants revealed a regulatory mechanism linking mQTLs to the smoking phenotype. Moreover, we demonstrated the presence of different regulatory effects of low-frequency and common variants on mRNA expression and DNA methylation.

Although numerous susceptibility loci for depression have been identified in recent years, their biological function and molecular mechanism remain largely unknown. By using an exome-wide association study for depressive symptoms assessed by the Center for Epidemiological Studies Depression (CES-D) score, we discovered a novel missense single nucleotide polymorphism (SNP), rs61753730 (Q152E), located in the fourth exon of the frizzled class receptor 6 gene (FZD6), which is a potential causal variant and is significantly associated with the CES-D score. Computer-based in silico analysis revealed that the protein configuration and stability, as well as the secondary structure of FZD6 differed greatly between the wild-type (WT) and Q152E mutant. We further found that rs61753730 significantly affected the luciferase activity and expression of FZD6 in an allele-specific way. Finally, we generated Fzd6-knockin (Fzd6-KI) mice with rs61753730 mutation using the CRISPR/Cas9 genome editing system and found that these mice presented greater immobility in the forced swimming test, less preference for sucrose in the sucrose preference test, as well as decreased center entries, center time, and distance traveled in the open filed test compared with WT mice after exposed to chronic social defeat stress. These results indicate the involvement of rs61753730 in depression. Taken together, our findings demonstrate that SNP rs61753730 is a novel functional variant and plays an important role in depressive symptoms.

The intestinal microbiota regulate the brain function of the host through the production of a myriad of metabolites and are associated with various neurological diseases. Understanding the intestinal microbiome of patients with prolonged disorders of consciousness (DoC) is important for the evaluation and treatment of the disease. To investigate the differences in the intestinal microbiome and short-chain fatty acids (SCFAs) among patients in a vegetative state (VS), a minimally conscious state (MCS), and emerged from MCS (EMCS), as well as the influence of antibiotics on these patients, 16S ribosomal RNA (16S rRNA) sequencing and targeted lipidomics were performed on fecal samples from patients; in addition, analysis of the electroencephalogram (EEG) signals was performed to evaluate the brain function of these patients. The results showed that the intestinal microbiome of the three groups differed greatly, and some microbial communities showed a reduced production of SCFAs in VS patients compared to the other two groups. Moreover, reduced microbial communities and five major SCFAs, along with attenuated brain functional connectivity, were observed in MCS patients who were treated with antibiotics compared to those who did not receive antibiotic treatment, but not in the other pairwise comparisons. Finally, three genus-level microbiota—Faecailbacterium, Enterococcus, and Methanobrevibacter—were considered as potential biomarkers to distinguish MCS from VS patients, with high accuracy both in the discovery and validation cohorts. Together, our findings improved the understanding of patients with prolonged DoC from the intestinal microbiome perspective and provided a new reference for the exploration of therapeutic targets.

Recently, emerging evidence has indicated that aberrant enhancers, especially super-enhancers, play pivotal roles in the transcriptional reprogramming of multiple cancers, including hepatocellular carcinoma (HCC). In this study, we performed integrative analyses of ChIP-seq, RNA-seq, and whole-genome bisulfite sequencing (WGBS) data to identify intergenic differentially expressed enhancers (DEEs) and genic differentially methylated enhancers (DMEs), along with their associated differentially expressed genes (DEE/DME-DEGs), both of which were also identified in independent cohorts and further confirmed by HiC data. Functional enrichment and prognostic model construction were conducted to explore the functions and clinical significance of the identified enhancer aberrations. We identified a total of 2,051 aberrant enhancer-associated DEGs (AE-DEGs), which were highly concurrent in multiple HCC datasets. The enrichment results indicated the significant overrepresentations of crucial biological processes and pathways implicated in cancer among these AE-DEGs. A six AE-DEG-based prognostic signature, whose ability to predict the overall survival of HCC was superior to that of both clinical phenotypes and previously published similar prognostic signatures, was established and validated in TCGA-LIHC and ICGC-LIRI cohorts, respectively. In summary, our integrative analysis depicted a landscape of aberrant enhancers and associated transcriptional dysregulation in HCC and established an aberrant enhancer-derived prognostic signature with excellent predictive accuracy, which might be beneficial for the future development of epigenetic therapy for HCC.

DNA methylation is a key transcription regulator, whose aberration was ubiquitous and important in most cancers including hepatocellular carcinoma (HCC). Whole-genome bisulfite sequencing (WGBS) was conducted for comparison of DNA methylation in tumor and adjacent tissues from 33 HCC patients, accompanying RNA-seq to determine differentially methylated region-associated, differentially expressed genes (DMR-DEGs), which were independently replicated in the TCGA-LIHC cohort and experimentally validated via 5-aza-2-deoxycytidine (5-azadC) demethylation. A total of 9,867,700 CpG sites showed significantly differential methylation in HCC. Integrations of mRNA-seq, histone ChIP-seq, and WGBS data identified 611 high-confidence DMR-DEGs. Enrichment analysis demonstrated activation of multiple molecular pathways related to cell cycle and DNA repair, accompanying repression of several critical metabolism pathways such as tyrosine and monocarboxylic acid metabolism. In TCGA-LIHC, we replicated about 53% of identified DMR-DEGs and highlighted the prognostic significance of combinations of methylation and expression of nine DMR-DEGs, which were more efficient prognostic biomarkers than considering either type of data alone. Finally, we validated 22/23 (95.7%) DMR-DEGs in 5-azadC-treated LO2 and/or HepG2 cells. In conclusion, integration of epigenome and transcriptome data depicted activation of multiple pivotal cell cycle-related pathways and repression of several metabolic pathways triggered by aberrant DNA methylation of promoters and enhancers in HCC.

Cigarette smoke is a representative source of toxic chemical exposures to humans, and the adverse consequences of cigarette smoking are mediated by its effect on both neuronal and immune–inflammatory systems. Cigarette smoking also is a major risk factor for intestinal disorders, such as Crohn’s disease and peptic ulcer. On the other hand, cigarette smoking is protective against developing ulcerative colitis. The effects of cigarette smoking on intestinal disorders include changes in intestinal irrigation and microbiome, increases in permeability of the mucosa, and impaired mucosal immune responses. However, the underlying mechanism linking cigarette smoking with intestinal microbiota dysbiosis is largely unknown. In this communication, we first review the current knowledge about the mechanistic interaction between cigarette smoke and intestinal microbiota dysbiosis, which include the likely actions of nicotine, aldehydes, polycyclic aromatic hydrocarbons, heavy metals, volatile organic compounds and toxic gases, and then reveal the potential mechanisms of the lung–gut cross talk and skin-gut cross talk in regulating the balance of intestinal microbiota and the interrelation of intestinal microbiota dysbiosis and systemic disorders.

BackgroundMajor depressive disorder (MDD) is a global health challenge that impacts the quality of patients’ lives severely. The disorder can manifest in many forms with different combinations of symptoms, which makes its clinical diagnosis difficult. Robust biomarkers are greatly needed to improve diagnosis and to understand the etiology of the disease. The main purpose of this study was to create a predictive model for MDD diagnosis based on peripheral blood transcriptomes.Materials and MethodsWe collected nine RNA expression datasets for MDD patients and healthy samples from the Gene Expression Omnibus database. After a series of quality control and heterogeneity tests, 302 samples from six studies were deemed suitable for the study. R package “MetaOmics” was applied for systematic meta-analysis of genome-wide expression data. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic effectiveness of individual genes. To obtain a better diagnostic model, we also adopted the support vector machine (SVM), random forest (RF), k-nearest neighbors (kNN), and naive Bayesian (NB) tools for modeling, with the RF method being used for feature selection.ResultsOur analysis revealed six differentially expressed genes (AKR1C3, ARG1, KLRB1, MAFG, TPST1, and WWC3) with a false discovery rate (FDR) < 0.05 between MDD patients and control subjects. We then evaluated the diagnostic ability of these genes individually. With single gene prediction, we achieved a corresponding area under the curve (AUC) value of 0.63 ± 0.04, 0.67 ± 0.07, 0.70 ± 0.11, 0.64 ± 0.08, 0.68 ± 0.07, and 0.62 ± 0.09, respectively, for these genes. Next, we constructed the classifiers of SVM, RF, kNN, and NB with an AUC of 0.84 ± 0.09, 0.81 ± 0.10, 0.73 ± 0.11, and 0.83 ± 0.09, respectively, in validation datasets, suggesting that the SVM classifier might be superior for constructing an MDD diagnostic model. The final SVM classifier including 70 feature genes was capable of distinguishing MDD samples from healthy controls and yielded an AUC of 0.78 in an independent dataset.ConclusionThis study provides new insights into potential biomarkers through meta-analysis of GEO data. Constructing different machine learning models based on these biomarkers could be a valuable approach for diagnosing MDD in clinical practice.

BackgroundSolute carrier family 1 member 5 (SLC1A5) is a major glutamine transporter and plays a key role in tumor growth. The main objectives of this study were to visualize the prognostic landscape of SLC1A5 in multiple cancers and determine the relations between SLC1A5 expression and tumor immunity.MethodsSLC1A5 expression and its effect on tumor prognosis were analyzed using multiple online tools Oncomine, Gene Expression Profiling Interactive Analysis, PrognoScan, and Kaplan-Meier plotter with their own datasets as well as the data from The Cancer Genome Atlas. The correlations between SLC1A5 and tumor immune infiltrates were determined via TIMER.ResultsSLC1A5 expression was significantly higher in several types of cancers, including hepatocellular carcinoma (HCC), compared with corresponding normal tissues. High SLC1A5 expression correlated with poor overall survival and with disease-free survival related to alcohol consumption. Moreover, SLC1A5 expression correlated positively with the numbers of tumor-infiltrating B cells, CD4+ T and CD8+ T cells, macrophages, neutrophils, and dendritic cells in HCC and in lower-grade glioma (LGG). Also, SLC1A5 expression showed strong correlations with diverse immune marker sets in HCC and LGG, indicating its role in regulating tumor immunity.ConclusionsSLC1A5 represents a useful prognostic biomarker in multiple cancers, and its expression correlates highly with tumor immune-cell infiltration, especially in HCC and LGG.

Smoking is a complex behavior with a heritability as high as 50%. Given such a large genetic contribution, it provides an opportunity to prevent those individuals who are susceptible to smoking dependence from ever starting to smoke by predicting their inherited predisposition with their genomic profiles. Although previous studies have identified many susceptibility variants for smoking, they have limited power to predict smoking behavior. We applied the support vector machine (SVM) and random forest (RF) methods to build prediction models for smoking behavior. We first used 1,431 smokers and 1,503 non-smokers of African origin for model building with a 10-fold cross-validation and then tested the prediction models on an independent dataset consisting of 213 smokers and 224 non-smokers. The SVM model with 500 top single nucleotide polymorphisms (SNPs) selected using logistic regression (p

Abstract Smoking is a well-documented risk factor in various cancers, especially lung cancer. In the current study, we tested the hypothesis that abnormal DNAm loci associated with smoking are enriched in genes and pathways that convey a risk of cancer by determining whether smoking-related methylated genes led to enrichment in cancer-related pathways. We analyzed two sets of smoking-related methylated genes from 28 studies originating from blood and buccal samples. By analyzing 320 methylated genes from 26 studies on blood samples (N = 17,675), we found 57 enriched pathways associated with different types of cancer (FDR

Abstract This study aims to characterize the gut microbiota in depressed patients with bipolar disorder (BD) compared with healthy controls (HCs), to examine the effects of quetiapine treatment on the microbiota, and to explore the potential of microbiota as a biomarker for BD diagnosis and treatment outcome. Analysis of 16S‐ribosomal RNA gene sequences reveals that gut microbial composition and diversity are significantly different between BD patients and HCs. Phylum Bacteroidetes and Firmicutes are the predominant bacterial communities in BD patients and HCs, respectively. Lower levels of butyrate‐producing bacteria are observed in untreated patients. Microbial composition changes following quetiapine treatment in BD patients. Notably, 30 microbial markers are identified on a random forest model and achieve an area under the curve (AUC) of 0.81 between untreated patients and HCs. Ten microbial markers are identified with the AUC of 0.93 between responder and nonresponder patients. This study characterizes the gut microbiota in BD and is the first to evaluate microbial changes following quetiapine monotherapy. Gut microbiota‐based biomarkers may be helpful in BD diagnosis and predicting treatment outcome, which need further validations.

Background: Gallbladder cancer (GBC) is a rare and aggressive malignancy of the biliary tract with a dismal survival rate. Effective biomarkers and therapeutic targets are urgently needed.Methods: We analyzed gene expression profiles of GBC to identify differentially expressed genes (DEGs) and then used these DEGs to identify functional module biomarkers based on protein functional interaction (FI) networks. We further evaluated the module-gene protein expression and clinical significance with immunohistochemistry staining (IHC) in a tissue microarray (TMA) from 80 GBC samples.Results: Five functional modules were identified. Module 0 included classical cancer signaling pathways, such as Ras and PI3K-Akt; and modules 1–4 included genes associated with muscle cells, fibrinogen, extracellular matrix, and integrins, respectively. We validated the expression of LIFR, PIK3R1, and MMP12, which were hubs or functional nodes in modules. Compared with paired peritumoural tissues, we found that the expression of LIFR (P = 0.002) and PIK3R1 (P = 0.046) proteins were significantly downregulated, and MMP12 (P = 0.006) was significantly upregulated. Further prognostic analysis showed that patients with low expression of LIFR had shorter overall survival than those with high expression (log-rank test P = 0.028), the same trend as for PIK3R1 (P = 0.053) and MMP12 (P = 0.006). Multivariate analysis indicated that expression of MMP12 protein (hazard ratio [HR] = 0.429; 95% confidence interval [CI] 0.198, 0.930; P = 0.032) was one of the significant independent prognostic factors for overall survival.Conclusions: We found a highly reliable FI network, which revealed LIFR, PIK3R1, and MMP12 as novel prognostic biomarker candidates for GBC. These findings could accelerate biomarker discovery and therapeutic development in this cancer.

Background: Several studies have revealed significant associations between single nucleotide polymorphisms (SNPs) in the cannabinoid receptor 1 (CNR1) gene and a broad spectrum of psychiatric disorders such as major depressive disorder (MDD), attention deficit hyperactivity disorder (ADHD), and schizophrenia. Personality traits that are highly related to susceptibility to these conditions have been associated with the CNR1 variants in subjects of Caucasian origin. However, there are no reported studies regarding the effects of CNR1 polymorphisms on personality traits in the African-American (AA) population.Methods: We performed an imputation-based association analysis for 26 CNR1 variants with five dimensions of personality in 3,046 AAs.Results: SNPs rs806372 and rs2180619 showed a significant association with extraversion after Bonferroni correction for multiple testing (p < 0.0019). Further, several extraversion-associated SNPs were significantly associated with conscientiousness, agreeableness, and openness. SNP priority score analysis indicated that SNPs rs806368, rs806371, and rs2180619 play a role in the modulation of personality and psychiatric conditions.Conclusion:CNR1 is important in determining personality traits in the AA population.

AimsAlthough it is known that there is a high smoking prevalence among Chinese, key issues such as social and environmental factors impacting smoking initiation and persistence, the percentage of smokers considered nicotine dependence (ND), and the availability and use of ND treatments have rarely been investigated.MethodsTo address these issues, from 2012 to 2014, we conducted a large-scale study in the Zhejiang and Shanxi provinces of China using the Fagerström Test for Nicotine Dependence and other validated questionnaires.ResultsOf the 17,057 subjects, consisting of 13,476 males and 3,581 females aged 15 years or older, the prevalence of male smoking was 66.1% [95% confidence interval (CI) 65.5%, 66.9%] and that of female smoking was 3.2% (95% CI 3.0%, 3.8%). Among males, 25.8% (95% CI 25.0%, 26.5%) were low-to-moderate ND, and 11.8% (95% CI 11.2%, 12.3%) were high ND (H-ND), persons who have significant difficulty quitting without treatment. The degrees of ND were related to age, extent of education, and annual family income. The social–environmental factors examined conveyed a higher risk for smoking initiation, which is particularly true for the influence of smoking by friends. Furthermore, current smokers had a significantly higher risk of suffering respiratory and digestive symptoms.ConclusionThese data not only show a high smoking prevalence in Chinese men but also reveal that a relatively large number of smokers are H-ND. Considering that few Chinese smokers seek ND treatment, a comprehensive smoking prevention and treatment program designed specifically for Chinese is greatly needed.

BackgroundTopiramate (TPM) is suggested to be a promising medication for treatment of methamphetamine (METH) dependence, but the molecular basis remains to be elucidated.MethodsAmong 140 METH-dependent participants randomly assigned to receive either TPM (N = 69) or placebo (N = 71) in a previously conducted randomized controlled trial, 50 TPM- and 49 placebo-treated participants had a total 212 RNA samples available at baseline, week 8, and week 12 time points. Following our primary analysis of gene expression data, we reanalyzed the microarray expression data based on a latent class analysis of binary secondary outcomes during weeks 1–12 that provided a classification of 21 responders and 31 non-responders with consistent responses at both time points.ResultsBased on secondary outcomes, 1,381, 576, 905, and 711 differentially expressed genes at nominal P values

AimsPersonality characteristics are linked to nicotine dependence (ND). It remains unclear whether these factors differ across African-American (AA) and European-American (EA) male and female smokers. This study was aimed to determine the relationship between personality traits and smoking status, as well as the degree of ND, in AA and EA male and female samples.MethodsA total of 5,040 participants (AA: N = 3,737, female = 54.31%; EA: N = 1,313, female = 64.51%) were included in this study, with 2,474 smokers and 2,566 non-smokers. The measures used in this study included five dimensions of personality by the NEO-personality inventory-revised (neuroticism, extraversion, agreeableness, openness to experience, and conscientiousness), Fagerström test for ND, and drive subscale of the ND Syndrome Scale (NDSS).ResultsIn the AA sample, neuroticism was significantly associated with a higher risk of smoking [odds ratio (OR) = 1.057; 95% confidence interval (CI) 1.032, 1.083; p