Cocultivation of Chinese prescription and intestine microbiota: SJZD alleviated the major symptoms of IBS-D subjects by tuning neurotransmitter metabolism.

Objective: Diarrhea-predominant irritable bowel syndrome (IBS-D) is a recurrent and common disease featuring dysbiotic intestinal microbiota, with limited treatments. Si-Jun-Zi Decoction (SJZD), a classic Chinese prescription, has been extensively used for IBS-D. This work aimed to explore the ex vivo interactions of SJZD and IBS-D's intestinal microbiota. Methods: Five samples of intestinal microbiota collected from IBS-D volunteers and five age-matched healthy controls were recruited from the Affiliated Hospital, Chengdu University of Traditional Chinese Medicine (TCM). A representative mixture of intestinal microbiota was composed of an equal proportion of these fecal samples. To simulate the clinical interaction, this microbiota was cocultivated with SJZD at clinical dosage in an anaerobic incubator at 37°C for 35 h. Microbiota and metabolic alterations were assessed by 16S rRNA gene sequencing in the V3/V4 regions and a nontargeted metabolome platform, respectively. Results: After being cocultivated with SJZD, the dysbiotic intestine microbiota from IBS-D subjects was largely restored to those of the healthy controls. A total of 624 differentially expressed metabolites were detected by nontargeted metabolomics, of which 16 biomarkers were identified. These metabolites were then enriched into 11 pathways by KEGG, particularly those involved in neurotransmitter metabolism responses for the major symptom of IBS-D. Correlation analysis of bacterial metabolites demonstrated a synergistic pattern of neurotransmitter metabolism between Streptococcus and E. Shigella. Conclusion: SJZD rescued the dysbiotic intestinal microbiota and ameliorated the dysfunctional neurotransmitter metabolism involved in IBS-D's major symptoms.

Hyperoxia combined with the B-cell antagonist rituximab led to intestinal dysbiosis in neonatal mice.

The adverse factors impacting the intestinal microbiota of newborns remain to be elucidated. We put forward a hypothesis that hyperoxia in combination with rituximab exhibits a synergistic effect that interferes with neonatal intestinal microbiota. Six C57BL/6J mice, aged 12 weeks and pregnant 18 days, were purchased. Their pups were breastfed and raised under a 75% oxygen or conventional environment. Low- (20 mg/kg) and high-dose (40 mg/kg) rituximab were intraperitoneally administered. Fecal genomic DNA was extracted and sequenced by a 16S rRNA platform. Severe intestinal dysbiosis in newborns were observed, whereas mild dysbiosis was caused by inducing hyperoxia alone, confirming the synergistic interference of the combination of hyperoxia and B-cell antagonist (rituximab) in neonatal intestinal microbiota disruption. Slight dysbiosis was observed in the intestinal microbiota of dams, indicating their much robust ability to confront hyperoxic conditions. The abundance of Akkermansia muciniphila was significantly and extensively altered in both pups and dams after being subjecting them to hyperoxic conditions with or without rituximab administration. In conclusion, this work demonstrated that the synergistic effect of hyperoxia and rituximab led to severe intestinal dysbiosis in newborns. More studies are recommended to explore the precise regulatory mode between hyperoxia and rituximab in intestinal microbiota.

Suicide and suicide attempts after subthalamic nucleus stimulation in Parkinson's disease: a systematic review and meta-analysis.

BACKGROUND: Deep-brain stimulation is a well-established, effective treatment for patients with advanced Parkinson's disease. Recent studies examining rates of suicide attempts and suicides after deep-brain stimulation in the bilateral subthalamic nucleus have reported varying results. Using this systematic review and meta-analysis, we aim to obtain a comprehensive understanding of suicidality in Parkinson's patients after subthalamic nucleus deep brain stimulation. METHODS: We systematically examined Medline, PubMed, Web of Science, and Embase databases to identify studies published before November 2019 that measured rates of suicidality in Parkinson's patients who underwent subthalamic nucleus stimulation. A meta-analysis of the data from the included studies was conducted using Stata 12.0. RESULTS: A total of 18 studies met the eligibility criteria of this study. We found that the pooled rate of suicidal ideation was 4% (95% CI 0.00-7.2%, range 2-17%). The pooled rate of suicide attempts was 1% (95% CI 1.0-2.0%), while the pooled rate of suicide was 1% (95% CI 0.0-1.0%). CONCLUSIONS: Our findings indicate a relatively high rate of suicidality among Parkinson's patients after subthalamic nucleus deep-brain stimulation. It is important for clinicians to carefully monitor psychiatric disorders, especially suicidal ideation and suicide attempts, in Parkinson's patients before and after subthalamic nucleus deep-brain stimulation.

A genome-wide association study of early spontaneous preterm delivery.

Preterm birth is the leading cause of infant morbidity and mortality. Despite extensive research, the genetic contributions to spontaneous preterm birth (SPTB) are not well understood. Term controls were matched with cases by race/ethnicity, maternal age, and parity prior to recruitment. Genotyping was performed using Affymetrix SNP Array 6.0 assays. Statistical analyses utilized PLINK to compare allele occurrence rates between case and control groups, and incorporated quality control and multiple-testing adjustments. We analyzed DNA samples from mother-infant pairs from early SPTB cases (20(0/7)-33(6/7) weeks, 959 women and 979 neonates) and term delivery controls (39(0/7)-41(6/7) weeks, 960 women and 985 neonates). For validation purposes, we included an independent validation cohort consisting of early SPTB cases (293 mothers and 243 infants) and term controls (200 mothers and 149 infants). Clustering analysis revealed no population stratification. Multiple maternal SNPs were identified with association P-values between 10×10(-5) and 10×10(-6). The most significant maternal SNP was rs17053026 on chromosome 3 with an odds ratio (OR) 0.44 with a P-value of 1.0×10(-6). Two neonatal SNPs reached the genome-wide significance threshold, including rs17527054 on chromosome 6p22 with a P-value of 2.7×10(-12) and rs3777722 on chromosome 6q27 with a P-value of 1.4×10(-10). However, we could not replicate these findings after adjusting for multiple comparisons in a validation cohort. This is the first report of a genome-wide case-control study to identify single nucleotide polymorphisms (SNPs) that correlate with SPTB.

Maternal serum serpin B7 is associated with early spontaneous preterm birth.

OBJECTIVE: We sought to identify serum biomarkers of early spontaneous preterm birth (SPTB) using semiquantitative proteomic analyses. STUDY DESIGN: This was a nested case-control study of pregnant women with previous SPTB. Maternal serum was collected at 19-24 and 28-32 weeks' gestation, and analyzed by liquid chromatography-multiple reaction monitoring/mass spectrometry. Targeted and shotgun proteomics identified 31 candidate proteins that were differentially expressed in pooled serum samples from spontaneous preterm (cases [<34 weeks]) and term (controls) deliveries. Candidate protein expression was compared in individual serum samples between cases and controls matched by age and race groups, and clinical site. Protein expression was verified by Western blot in the placenta and fetal membranes from cases and controls. RESULTS: Serum samples were available for 35 cases and 35 controls at 19-24 weeks, and 16 cases and 16 controls at 28-32 weeks. One protein, serpin B7, yielded serum concentrations that differed between cases and controls. The mean concentration of serpin B7 at 28-32 weeks was 1.5-fold higher in women with subsequent preterm deliveries compared to controls; there was no difference at 19-24 weeks. Higher levels of serpin B7 at both gestational age windows were associated with a shorter interval to delivery, and higher levels of serpin B7 in samples from 28-32 weeks were associated with a lower gestational age at delivery. Western blotting identified serpin B7 protein in placenta, amnion, and chorion from cases and controls. CONCLUSION: Targeted and shotgun serum proteomics analyses associated 1 protein, serpin B7, with early SPTB. Our results require validation in other cohorts and analysis of the possible mechanistic role of serpin B7 in parturition.

Whole-genome detection of disease-associated deletions or excess homozygosity in a case-control study of rheumatoid arthritis.

Unlike genome-wide association studies, few comprehensive studies of copy number variation's contribution to complex human disease susceptibility have been performed. Copy number variations are abundant in humans and represent one of the least well-studied classes of genetic variants; in addition, known rheumatoid arthritis susceptibility loci explain only a portion of familial clustering. Therefore, we performed a genome-wide study of association between deletion or excess homozygosity and rheumatoid arthritis using high-density 550 K SNP genotype data from a genome-wide association study. We used a genome-wide statistical method that we recently developed to test each contiguous SNP locus between 868 cases and 1194 controls to detect excess homozygosity or deletion variants that influence susceptibility. Our method is designed to detect statistically significant evidence of deletions or homozygosity at individual SNPs for SNP-by-SNP analyses and to combine the information among neighboring SNPs for cluster analyses. In addition to successfully detecting the known deletion variants on major histocompatibility complex, we identified 4.3 and 28 kb clusters on chromosomes 10p and 13q, respectively, which were significant at a Bonferroni-type-corrected 0.05 nominal significant level. Independently, we performed analyses using PennCNV, an algorithm for identifying and cataloging copy numbers for individuals based on a hidden Markov model, and identified cases and controls that had chromosomal segments with copy number <2. Using Fisher's exact test for comparing the numbers of cases and controls with copy number <2 per SNP, we identified 26 significant SNPs (protective; more controls than cases) aggregating on chromosome 14 with P-values <10(-8).

Simulating realistic genomic data with rare variants.

Increasing evidence suggests that rare and generally deleterious genetic variants might have a strong impact on disease risks of not only Mendelian disease, but also many common diseases. However, identifying such rare variants remains challenging, and novel statistical methods and bioinformatic software must be developed. Hence, we have to extensively evaluate various methods under reasonable genetic models. Although there are abundant genomic data, they are not most helpful for the evaluation of the methods because the disease mechanism is unknown. Thus, it is imperative that we simulate genomic data that mimic the real data containing rare variants and that enable us to impose a known disease penetrance model. Although resampling simulation methods have shown their advantages in computational efficiency and in preserving important properties such as linkage disequilibrium (LD) and allele frequency, they still have limitations as we demonstrated. We propose an algorithm that combines a regression-based imputation with resampling to simulate genetic data with both rare and common variants. Logistic regression model was employed to fit the relationship between a rare variant and its nearby common variants in the 1000 Genomes Project data and then applied to the real data to fill in one rare variant at a time using the fitted logistic model based on common variants. Individuals then were simulated using the real data with imputed rare variants. We compared our method with existing simulators and demonstrated that our method performed well in retaining the real sample properties, such as LD and minor allele frequency, qualitatively.

Genome-wide association study identifies novel loci predisposing to cutaneous melanoma.

We performed a multistage genome-wide association study of melanoma. In a discovery cohort of 1804 melanoma cases and 1026 controls, we identified loci at chromosomes 15q13.1 (HERC2/OCA2 region) and 16q24.3 (MC1R) regions that reached genome-wide significance within this study and also found strong evidence for genetic effects on susceptibility to melanoma from markers on chromosome 9p21.3 in the p16/ARF region and on chromosome 1q21.3 (ARNT/LASS2/ANXA9 region). The most significant single-nucleotide polymorphisms (SNPs) in the 15q13.1 locus (rs1129038 and rs12913832) lie within a genomic region that has profound effects on eye and skin color; notably, 50% of variability in eye color is associated with variation in the SNP rs12913832. Because eye and skin colors vary across European populations, we further evaluated the associations of the significant SNPs after carefully adjusting for European substructure. We also evaluated the top 10 most significant SNPs by using data from three other genome-wide scans. Additional in silico data provided replication of the findings from the most significant region on chromosome 1q21.3 rs7412746 (P = 6 × 10(-10)). Together, these data identified several candidate genes for additional studies to identify causal variants predisposing to increased risk for developing melanoma.

Genome-wide algorithm for detecting CNV associations with diseases.

BACKGROUND: SNP genotyping arrays have been developed to characterize single-nucleotide polymorphisms (SNPs) and DNA copy number variations (CNVs). Nonparametric and model-based statistical algorithms have been developed to detect CNVs from SNP data using the marker intensities. However, these algorithms lack specificity to detect small CNVs owing to the high false positive rate when calling CNVs based on the intensity values. Therefore, the resulting association tests lack power even if the CNVs affecting disease risk are common. An alternative procedure called PennCNV uses information from both the marker intensities as well as the genotypes and therefore has increased sensitivity. RESULTS: By using the hidden Markov model (HMM) implemented in PennCNV to derive the probabilities of different copy number states which we subsequently used in a logistic regression model, we developed a new genome-wide algorithm to detect CNV associations with diseases. We compared this new method with association test applied to the most probable copy number state for each individual that is provided by PennCNV after it performs an initial HMM analysis followed by application of the Viterbi algorithm, which removes information about copy number probabilities. In one of our simulation studies, we showed that for large CNVs (number of SNPs ≥ 10), the association tests based on PennCNV calls gave more significant results, but the new algorithm retained high power. For small CNVs (number of SNPs <10), the logistic algorithm provided smaller average p-values (e.g., p = 7.54e - 17 when relative risk RR = 3.0) in all the scenarios and could capture signals that PennCNV did not (e.g., p = 0.020 when RR = 3.0). From a second set of simulations, we showed that the new algorithm is more powerful in detecting disease associations with small CNVs (number of SNPs ranging from 3 to 5) under different penetrance models (e.g., when RR = 3.0, for relatively weak signals, power = 0.8030 comparing to 0.2879 obtained from the association tests based on PennCNV calls). The new method was implemented in software GWCNV. It is freely available at http://gwcnv.sourceforge.net, distributed under a GPL license. CONCLUSIONS: We conclude that the new algorithm is more sensitive and can be more powerful in detecting CNV associations with diseases than the existing HMM algorithm, especially when the CNV association signal is weak and a limited number of SNPs are located in the CNV.

Stochastic model of an influenza epidemic with drug resistance.

A continuous-time Markov chain (CTMC) model is formulated for an influenza epidemic with drug resistance. This stochastic model is based on an influenza epidemic model, expressed in terms of a system of ordinary differential equations (ODE), developed by Stilianakis, N.I., Perelson, A.S., Hayden, F.G., [1998. Emergence of drug resistance during an influenza epidemic: insights from a mathematical model. J. Inf. Dis. 177, 863-873]. Three different treatments-chemoprophylaxis, treatment after exposure but before symptoms, and treatment after symptoms appear, are considered. The basic reproduction number, R(0), is calculated for the deterministic-model under different treatment strategies. It is shown that chemoprophylaxis always reduces the basic reproduction number. In addition, numerical simulations illustrate that the basic reproduction number is generally reduced with realistic treatment rates. Comparisons are made among the different models and the different treatment strategies with respect to the number of infected individuals during an outbreak. The final size distribution is computed for the CTMC model and, in some cases, it is shown to have a bimodal distribution corresponding to two situations: when there is no outbreak and when an outbreak occurs. Given an outbreak occurs, the total number of cases for the CTMC model is in good agreement with the ODE model. The greatest number of drug resistant cases occurs if treatment is delayed or if only symptomatic individuals are treated.