Bao-Zhu Yang

Schizophrenia (SCZ) has been increasingly linked to disruptions in gut microbiota (GM) and immune system dysregulation, including the immune-inflammatory response system (IRS) and compensatory immune-regulatory system (CIRS). To investigate their causal roles and potential confounding effects, we applied genetically informed Mendelian randomization (MR) using genome-wide association study (GWAS) data from a large SCZ meta-analysis (52,017 cases and 75,889 controls), 72 GM genera, and 22 traits of IRS, CIRS, and immune cell indices of European ancestry cohorts. We followed the STROBE-MR guidelines for the MR approach. We found complex causality involving both protective and risk effects. Our analysis revealed protective causal effects of GM genera Barnesiella, Desulfovibrio, Gordonibacter, and Romboutsia (OR = 0.85-0.93, p = 0.005-0.00018), and a risk effect from Clostridium innocuum (OR = 1.09, p = 0.007). Notably, Barnesiella and Gordonibacter represent novel findings. Among immune traits, tumor necrosis factor receptor 1 (sTNF-R1) showed a protective effect (OR = 0.83, p = 0.047), whereas regulatory T cell (Treg) counts increased the risk of SCZ (OR = 1.05, p = 0.006). Multivariate MR confirmed the independent effects of Desulfovibrio, Gordonibacter, and sTNF-R1 after adjusting for other traits. The identified bacterial genera and immune measures exhibit biological relevance to the pathogenesis of SCZ. These findings suggest that specific GM and immune traits contribute independently to SCZ pathogenesis. Particularly, cumulating evidence supporting Tregs as a therapeutic target for psychosis underscores the significance of our results. Further research on their function is needed to validate the causal role of the identified gut microbes and immune mechanisms in SCZ.

This study sought to investigate the prevalence, correlates, and network structure of the manifested symptoms in gambling disorder (GD) among methamphetamine (MA) use disorder (MUD) patients in China. We interviewed 1069 patients using the Semi-Structured Assessment for Drug Dependence and Alcoholism (SSADDA), Chinese version. Besides MA and other substance use disorders, GD was also ascertained by SSADDA. Other psychiatric diagnoses were ascertained, including major depressive episodes (MDEs), antisocial personality disorder, suicide and self-harm, and environmental factors, including childhood experiences. Of 1069 participants, 711 met the DSM-5 diagnostic criteria for MUD. Among the 711 participants with MUD, 52.3% met DSM-5 diagnostic criteria for GD. We found that alcohol use together with MA, childhood violent experiences, MDEs, severe MUD, and gambling duration significantly differed between MUD participants with and without GD. In the GD-MUD network, the central symptoms were gambling preoccupation (GD1), giving up important activities (MUD6), financial trouble (GD9), and MA tolerance (MUD5). MA tolerance (MUD5) also served as a bridge symptom across the network, exhibiting substantial associations with gambling preoccupation (GD1). GD is prevalent among individuals in treatment for MUD in China. Network analysis suggests that gambling preoccupation and MA tolerance represent central features, and that MA tolerance serves as a bridge across GD and MUD.

Methamphetamine (MA) is a potent stimulant and notoriously addictive. Individuals respond to MA effects differently and thus have a varying susceptible risk of developing MA use disorder. Cumulative evidence has indicated that gut dysbiosis contributes to behavioral response to drug effects. However, the role of gut microbiota in the susceptible risk of developing MA use disorder has remained elusive. Using an MA‐induced conditioned place preference (CPP) rat model, we administrated the same dose of MA to rats, which then showed distinct preferences in drug‐related place, indicating their different responses to MA. From all of the MA‐exposed rats, the eight with the highest CPP scores were labeled as group high CPP (H‐CPP), and the eight with the lowest were labeled as group low CPP (L‐CPP). By 16S ribosomal RNA (rRNA) sequencing, we found that the gut microbiota compositions differed between H‐CPP and L‐CPP. Specifically, Akkermansia was significantly higher in H‐CPP and positively correlated with the CPP scores. Notably, H‐CPP and L‐CPP differed in the gut microbiota composition prior to the CPP training; Ruminococcus was the dominant phylotype in H‐CPP at baseline. More importantly, rats pretreated by antibiotics showed a significantly stronger MA‐induced CPP than did the controls. Our study demonstrates that the gut dysbiosis was associated with the MA‐induced CPP, indicating that the gut microbiota might be important modulators for MA‐induced behavior and vulnerability to MA use disorder.

The United States is experiencing the worst opioid overdose (OpOD) crisis in its history. We carried out a genome‐wide association study on OpOD severity among 3 477 opioid‐exposed individuals, 1 019 of whom experienced OpODs, including 2 032 European Americans (EAs) (653 overdose cases), and 1 445 African Americans (AAs) (366 overdose cases). Participants were scored 1 to 4 based on their reported overdose status and the number of times that medical treatment was required. Genome‐wide association study (GWAS) of EAs and AAs separately resulted in two genome‐wide significant (GWS) signals in AAs but none in EAs. The first signal was represented by three closely mapped variants (rs115208233, rs116181528, and rs114077267) located near mucolipin 1 (MCOLN1) and patatin‐like phospholipase domain containing 6 (PNPLA6), and the other signal was represented by rs369098800 near dead‐box helicase 18 (DDX18). There were no additional GWS signals in the trans‐population meta‐analysis, so that post‐GWAS analysis focused on these loci. In network analysis, MCOLN1 was coexpressed with PNPLA6, but only MCOLN1‐associated genes were enriched in functional categories relevant to OpOD, including calcium and cation channel activities; no enrichment was observed for PNPLA6‐associated genes. Drug repositioning analysis was carried out in the connectivity map (CMap) database for MCOLN1 (PNPLA6 was not available in CMap) and showed that the opioid agonist drug‐induced expression profile is similar to that of MCOLN1 overexpression and yielded the highest‐ranked expression profile of 83 drug classes. Thus, MCOLN1 may be a risk gene for OpOD, but replication is needed. This knowledge could be helpful in the identification of drug targets for preventing OpOD.

Alcohol dependence (AD) and nicotine dependence (ND) co‐occur frequently (AD+ND). We integrated SNP‐based, gene‐based, and protein–protein interaction network analyses to identify shared risk genes or gene subnetworks for AD+ND in African Americans (AAs, N = 2,094) and European Americans (EAs, N = 1,207). The DSM‐IV criterion counts for AD and ND were modeled as two dependent variables in a multivariate linear mixed model, and analyzed separately for the two populations. The most significant SNP was rs6579845 in EAs (p < 1.29 × 10⁻⁸) in GM2A, which encodes GM2 ganglioside activator, and is a cis‐expression quantitative locus that affects GM2A expression in blood and brain tissues. However, this SNP was not replicated in our another small sample (N = 678). We identified a subnetwork of 24 genes that contributed to the AD+ND criterion counts. In the gene‐set analysis for the subnetwork in an independent sample, the Study of Addiction: Genetics and Environment project (predominately EAs), these 24 genes as a set differed in AD+ND versus control subjects in EAs (p = .041). Functional enrichment analysis for this subnetwork revealed that the gene enrichment involved primarily nerve growth factor pathways, and cocaine and amphetamine addiction. In conclusion, we identified a genome‐wide significant variant at GM2A and a gene subnetwork underlying the genetic trait of shared AD+ND. These results increase our understanding of the shared (pleiotropic) genetic risk that underlies AD+ND.

Next generation sequencing (NGS) has been leading the genetic study of human disease into an era of unprecedented productivity. Many bioinformatics pipelines have been developed to call variants from NGS data. The performance of these pipelines depends crucially on the variant caller used and on the calling strategies implemented. We studied the performance of four prevailing callers, SAMtools, GATK, glftools and Atlas2, using single-sample and multiple-sample variant-calling strategies. Using the same aligner, BWA, we built four single-sample and three multiple-sample calling pipelines and applied the pipelines to whole exome sequencing data taken from 20 individuals. We obtained genotypes generated by Illumina Infinium HumanExome v1.1 Beadchip for validation analysis and then used Sanger sequencing as a “gold-standard” method to resolve discrepancies for selected regions of high discordance. Finally, we compared the sensitivity of three of the single-sample calling pipelines using known simulated whole genome sequence data as a gold standard. Overall, for single-sample calling, the called variants were highly consistent across callers and the pairwise overlapping rate was about 0.9. Compared with other callers, GATK had the highest rediscovery rate (0.9969) and specificity (0.99996), and the Ti/Tv ratio out of GATK was closest to the expected value of 3.02. Multiple-sample calling increased the sensitivity. Results from the simulated data suggested that GATK outperformed SAMtools and glfSingle in sensitivity, especially for low coverage data. Further, for the selected discrepant regions evaluated by Sanger sequencing, variant genotypes called by exome sequencing versus the exome array were more accurate, although the average variant sensitivity and overall genotype consistency rate were as high as 95.87% and 99.82%, respectively. In conclusion, GATK showed several advantages over other variant callers for general purpose NGS analyses. The GATK pipelines we developed perform very well.