SNP-based and haplotype-based genome-wide association on drug dependence in Han Chinese.

BACKGROUND: Drug addiction is a serious problem worldwide and is influenced by genetic factors. The present study aimed to investigate the association between genetics and drug addiction among Han Chinese. METHODS: A total of 1000 Chinese users of illicit drugs and 9693 healthy controls were enrolled and underwent single nucleotide polymorphism (SNP)-based and haplotype-based association analyses via whole-genome genotyping. RESULTS: Both single-SNP and haplotype tests revealed associations between illicit drug use and several immune-related genes in the major histocompatibility complex (MHC) region (SNP association: log10BF = 15.135, p = 1.054e-18; haplotype association: log10BF = 20.925, p = 2.065e-24). These genes may affect the risk of drug addiction via modulation of the neuroimmune system. The single-SNP test exclusively reported genome-wide significant associations between rs3782886 (SNP association: log10BF = 8.726, p = 4.842e-11) in BRAP and rs671 (SNP association: log10BF = 7.406, p = 9.333e-10) in ALDH2 and drug addiction. The haplotype test exclusively reported a genome-wide significant association (haplotype association: log10BF = 7.607, p = 3.342e-11) between a region with allelic heterogeneity on chromosome 22 and drug addiction, which may be involved in the pathway of vitamin B12 transport and metabolism, indicating a causal link between lower vitamin B12 levels and methamphetamine addiction. CONCLUSIONS: These findings provide new insights into risk-modeling and the prevention and treatment of methamphetamine and heroin dependence, which may further contribute to potential novel therapeutic approaches.

Application of wearable devices for monitoring cardiometabolic dysfunction under the exposome paradigm.

Environmental factors, including chemical/physical pollutants, as well as lifestyle and psychological factors, contribute greatly to the pathways leading to cardiometabolic diseases with a heavy disease burden and economic loss. The concept of exposomes provides a novel paradigm for combining all exposure characteristics to evaluate disease risk. A solution-like exposome requires technological support to provide continuous data to monitor vital signs and detect abnormal fluctuations. Wearable devices allow people to conveniently monitor signals during their daily routines. These new technologies empower users to more actively prevent and manage cardiometabolic disease by reviewing risk factors of the disease, especially lifestyle factors, such as sleeping time, screen time, and mental health condition. Devices with multiple sensors can monitor electrocardiography data, oxygen saturation, intraocular pressure, respiratory rate, and heart rate to enhance the exposome study and provide precise suggestions for disease prevention and management.

The association between oral and gut microbiota in male patients with alcohol dependence.

Introduction: The relationship between oral and gut microbiota in alcohol dependence (AD) is not well understood, particularly the effects of oral microbiota on the intestinal microbiota. The current study aimed to explore the association between oral and gut microbiota in AD to clarify whether oral microbiota could ectopically colonize into the gut. Methods: 16S rRNA sequence libraries were used to compare oral and gut microbial profiles in persons with AD and healthy controls (HC). Source Tracker and NetShift were used to identify bacteria responsible for ectopic colonization and indicate the driver function of ectopic colonization bacteria. Results: The α-diversity of oral microbiota and intestinal microbiota was significantly decreased in persons with AD (all p < 0.05). Principal coordinate analysis indicated greater similarity between oral and gut microbiota in persons with AD than that in HC, and oral-gut overlaps in microbiota were found for 9 genera in persons with AD relative to only 3 genera in HC. The contribution ratio of oral microbiota to intestinal microbiota composition in AD is 5.26% based on Source Tracker，and the AD with ectopic colonization showed the daily maximum standard drinks, red blood cell counts, hemoglobin content, and PACS scores decreasing (all p < 0.05). Discussion: Results highlight the connection between oral-gut microbiota in AD and suggest novel potential mechanistic possibilities.

[Identifying Depressive Disorder With Sleep Electroencephalogram Data: A Study Based on Deep Learning].

Objective: To explore the effectiveness of using deep learning network combined Vision Transformer (ViT) and Transformer to identify patients with depressive disorder on the basis of their sleep electroencephalogram (EEG) signals. Methods: The sleep EEG signals of 28 patients with depressive disorder and 37 normal controls were preprocessed. Then, the signals were converted into image format and the feature information on frequency domain and spatial domain was retained. After that, the images were transmitted to the ViT-Transformer coding network for deep learning of the EEG signal characteristics of the rapid eye movement (REM) sleep and non-rapid eye movement (NREM) sleep in patients with depressive disorder and those in normal controls, respectively, and to identify patients with depressive disorder. Results: Based on the ViT-Transformer network, after examining different EEG frequencies, we found that the combination of delta, theta, and beta waves produced better results in identifying depressive disorder. Among the different EEG frequencies, EEG signal features of delta-theta-beta combination waves in REM sleep achieved 92.8% accuracy and 93.8% precision for identifying depression, with the recall rate of patients with depression being 84.7%, and the F0.5 value being 0.917±0.074. When using the delta-theta-beta combination EEG signal features in NREM sleep to identify depressive disorder, the accuracy was 91.7%, the precision was 90.8%, the recall rate was 85.2%, and the F0.5 value was 0.914±0.062. In addition, through visualization of the sleep EEG of different sleep stages for the whole night, it was found that classification errors usually occurred during transition to a different sleep stage. Conclusion: Using the deep learning ViT-Transformer network, we found that the EEG signal features in REM sleep based on delta-theta-beta combination waves showed better effect in identifying depressive disorder.

Diurnal changes of the oral microbiome in patients with alcohol dependence.

Background: Saliva secretion and oral microbiota change in rhythm with our biological clock. Dysbiosis of the oral microbiome and alcohol consumption have a two-way interactive impact, but little is known about whether the oral microbiome undergoes diurnal changes in composition and function during the daytime in patients with alcohol dependence (AD). Methods: The impact of alcohol consumption on the diurnal salivary microbiome was examined in a case-control study of 32 AD patients and 21 healthy control (HC) subjects. We tested the changes in microbial composition and individual taxon abundance by 16S rRNA gene sequencing. Results: The present study is the first report showing that alcohol consumption enhanced the richness of the salivary microbiome and lowered the evenness. The composition of the oral microbiota changed significantly in alcohol-dependent patients. Additionally, certain genera were enriched in the AD group, including Actinomyces, Leptotrichia, Sphaerochaeta and Cyanobacteria, all of which have pathogenic effects on the host. There is a correlation between liver enzymes and oral microbiota. KEGG function analysis also showed obvious alterations during the daytime. Conclusion: Alcohol drinking influences diurnal changes in the oral microbiota, leading to flora disturbance and related functional impairment. In particular, the diurnal changes of the oral microbiota may open avenues for potential interventions that can relieve the detrimental consequences of AD.

Inhibitory Effects of Carbazomycin B Produced by Streptomyces roseoverticillatus 63 Against Xanthomonas oryzae pv. oryzae.

The present manuscript highlights the potential role of Streptomyces roseoverticillatus 63 (Sr-63) against Xanthomonas oryzae pv. oryzae (Xoo), which is the cause of a disastrous bacterial leaf blight disease with rice worldwide. The disease suppression was achieved under greenhouse conditions. A foliar spray of the fermentation broth of Sr-63 significantly reduced the leaf blight symptoms with rice in Xoo inoculated rice plants. Furthermore, we observed that the carbazomycin B, isolated from the fermentation broth of Sr-63, was demonstrated to have antibacterial activity against Xoo with a minimum inhibitory concentration (MIC) of 8 µg mL-1. The results indicated that carbzomycin B hampered the membrane formation of Xoo, reduced the production of xanthomonadin and extracellular polymeric substance (EPS). The fourier transform infrared spectroscopic (FT-IR) indicated that carbazomycin B changed the components of the cell membrane, then caused a change of the cell surface hydrophobicity of Xoo. Scanning electron microscopy revealed that the Xoo cells treated with carbazomycin B exhibited apparent structural deformation. The results also indicated that carbazomycin B had a negative impact on the metabolism of Xoo, carbazomycin B reduced the activity of malate dehydrogenase (MDH) activity and suppressed the protein expression of Xoo. Overall, our data suggests that Streptomyces roseoverticillatus 63 is a promising biocontrol agent that could be used to combat the bacterial leaf blight diseases of rice.

Transplantation of fecal microbiota from patients with alcoholism induces anxiety/depression behaviors and decreases brain mGluR1/PKC ε levels in mouse.

Recent studies have revealed that the gut microbiota participates in the psychiatric behavior changes in disorders associated with alcohol. But it still remains unknown whether alcoholism is involved in changes in gut microbiota and its underlying mechanism is also not clear. Here, we tested the gut microbiota of patients with alcoholism and conducted fecal microbiota transplantation (FMT) from patients with alcoholism to C57BL/6J mice whose gut microbiota had been sharply suppressed with antibiotics (ABX). Then we evaluated their alcohol preference degree, anxiety, and depression-like behaviors and social interaction behaviors, together with molecular changes in the medial prefrontal cortex (mPFC) and nucleus accumbens (NAc). Our data indicated that the gut microbiota of patients with alcoholism was drastically different from those of the healthy adults. The abundance of p_Firmicutes was significantly increased whereas p_Bacteroidetes was decreased. Compared to mice transplanted with fecal microbiota from healthy male adults, the mice accepting fecal microbiota from patients with alcoholism showed (a) anxiety-like and depression-like behaviors, (b) decreased social interaction behaviors, (c) spontaneous alcohol preference, and (d) decreased brain-derived neurotrophic factor (BDNF), alpha 1 subunit of GABA type A receptor (α1GABAA R) in mPFC and decreased metabotropic glutamate receptors 1 (mGluR1), protein kinase C (PKC) ε in NAc. Overall, our results suggest that fecal microbiota from patients with alcoholism did induce a status like alcohol dependence in C57BL/6J mice. The decreased expression of BDNF, α1GABAA R, and mGluR1/ PKC ε may be the underlying mechanism.

Polymorphisms in MicroRNAs are associated with survival in non-small cell lung cancer.

BACKGROUND: MicroRNAs (miRNA) play important roles in the regulation of eukaryotic gene expression and are involved in human carcinogenesis. Single-nucleotide polymorphisms (SNP) in miRNA sequence may alter miRNA functions in gene regulation, which, in turn, may affect cancer risk and disease progression. METHODS: We conducted an analysis of associations of 142 miRNA SNPs with non-small cell lung cancer (NSCLC) survival using data from a genome-wide association study (GWAS) in a Caucasian population from the Massachusetts General Hospital (Boston, MA) including 452 early-stage and 526 late-stage NSCLC cases. Replication analyses were further performed in two external populations, one Caucasian cohort from The University of Texas MD Anderson Cancer Center (Houston, TX) and one Han Chinese cohort from Nanjing, China. RESULTS: We identified seven significant SNPs in the discovery set. Results from the independent Caucasian cohort demonstrated that the C allele of rs2042253 (hsa-miRNA-5197) was significantly associated with decreased risk for death among the patients with late-stage NSCLC (discovery set: HR, 0.80; P = 0.007; validation set: HR, 0.86; P = 0.035; combined analysis: HR, 0.87; P = 0.007). CONCLUSIONS: These findings provide evidence that some miRNA SNPs are associated with NSCLC survival and can be used as predictive biomarkers. IMPACT: This study provided an estimate of outcome probability for survival experience of patients with NSCLC, which demonstrates that genetic factors, as well as classic nongenetic factors, may be used to predict individual outcome.