Olivier Thas

The human gut is colonized by a complex microbiota with multiple benefits. Although the surface-attached, mucosal microbiota has a unique composition and potential to influence human health, it remains difficult to study in vivo. Therefore, we performed an in-depth microbial characterization (human intestinal tract chip (HITChip)) of a recently developed dynamic in vitro gut model, which simulates both luminal and mucosal gut microbes (mucosal-simulator of human intestinal microbial ecosystem (M-SHIME)). Inter-individual differences among human subjects were confirmed and microbial patterns unique for each individual were preserved in vitro. Furthermore, in correspondence with in vivo studies, Bacteroidetes and Proteobacteria were enriched in the luminal content while Firmicutes rather colonized the mucin layer, with Clostridium cluster XIVa accounting for almost 60% of the mucin-adhered microbiota. Of the many acetate and/or lactate-converting butyrate producers within this cluster, Roseburia intestinalis and Eubacterium rectale most specifically colonized mucins. These 16S rRNA gene-based results were confirmed at a functional level as butyryl-CoA:acetate-CoA transferase gene sequences belonged to different species in the luminal as opposed to the mucin-adhered microbiota, with Roseburia species governing the mucosal butyrate production. Correspondingly, the simulated mucosal environment induced a shift from acetate towards butyrate. As not only inter-individual differences were preserved but also because compared with conventional models, washout of relevant mucin-adhered microbes was avoided, simulating the mucosal gut microbiota represents a breakthrough in modeling and mechanistically studying the human intestinal microbiome in health and disease. Finally, as mucosal butyrate producers produce butyrate close to the epithelium, they may enhance butyrate bioavailability, which could be useful in treating diseases, such as inflammatory bowel disease.

RATIONALE: Chronic obstructive pulmonary disease (COPD) is characterized by progressive inflammation in the airways and lungs combined with disturbed homeostatic functions of pulmonary cells. MicroRNAs (miRNAs) have the ability to regulate these processes by interfering with gene transcription and translation. OBJECTIVES: We aimed to identify miRNA expression in induced sputum and examined whether the expression of miRNAs differed between patients with COPD and subjects without airflow limitation. METHODS: Expression of 627 miRNAs was evaluated in induced sputum supernatant of 32 subjects by stem-loop reverse transcription-quantitative polymerase chain reaction. Differentially expressed miRNAs were validated in an independent replication cohort of 41 subjects. Enrichment of miRNA target genes was identified by in silico analysis. Protein expression of target genes was determined by ELISA. MEASUREMENTS AND MAIN RESULTS: Thirty-four miRNAs were differentially expressed between never-smokers and current smokers without airflow limitation in the screening cohort. Eight miRNAs were expressed at a significantly lower level in current-smoking patients with COPD compared with never-smokers without airflow limitation. Reduced expression of let-7c and miR-125b in patients with COPD compared with healthy subjects was confirmed in the validation cohort. Target genes of let-7c were significantly enriched in the sputum of patients with severe COPD. The concentration of tumor necrosis factor receptor type II (TNFR-II, implicated in COPD pathogenesis and a predicted target gene of let-7c) was inversely correlated with the sputum levels of let-7c . CONCLUSIONS: let-7c is significantly reduced in the sputum of currently smoking patients with COPD and is associated with increased expression of TNFR-II.

OBJECTIVE: This study aimed to identify the oncogenes at 20q involved in colorectal adenoma to carcinoma progression by measuring the effect of 20q gain on mRNA expression of genes in this amplicon. METHODS: Segmentation of DNA copy number changes on 20q was performed by array CGH (comparative genomic hybridisation) in 34 non-progressed colorectal adenomas, 41 progressed adenomas (ie, adenomas that present a focus of cancer) and 33 adenocarcinomas. Moreover, a robust analysis of altered expression of genes in these segments was performed by microarray analysis in 37 adenomas and 31 adenocarcinomas. Protein expression was evaluated by immunohistochemistry on tissue microarrays. RESULTS: The genes C20orf24, AURKA, RNPC1, TH1L, ADRM1, C20orf20 and TCFL5, mapping at 20q, were significantly overexpressed in carcinomas compared with adenomas as a consequence of copy number gain of 20q. CONCLUSION: This approach revealed C20orf24, AURKA, RNPC1, TH1L, ADRM1, C20orf20 and TCFL5 genes to be important in chromosomal instability-related adenoma to carcinoma progression. These genes therefore may serve as highly specific biomarkers for colorectal cancer with potential clinical applications.

High-throughput sequencing technologies allow easy characterization of the human microbiome, but the statistical methods to analyze microbiome data are still in their infancy. Differential abundance methods aim at detecting associations between the abundances of bacterial species and subject grouping factors. The results of such methods are important to identify the microbiome as a prognostic or diagnostic biomarker or to demonstrate efficacy of prodrug or antibiotic drugs. Because of a lack of benchmarking studies in the microbiome field, no consensus exists on the performance of the statistical methods. We have compared a large number of popular methods through extensive parametric and nonparametric simulation as well as real data shuffling algorithms. The results are consistent over the different approaches and all point to an alarming excess of false discoveries. This raises great doubts about the reliability of discoveries in past studies and imperils reproducibility of microbiome experiments. To further improve method benchmarking, we introduce a new simulation tool that allows to generate correlated count data following any univariate count distribution; the correlation structure may be inferred from real data. Most simulation studies discard the correlation between species, but our results indicate that this correlation can negatively affect the performance of statistical methods.

BACKGROUND: The authors compared the behavior of two calculations of electroencephalographic spectral entropy, state entropy (SE) and response entropy (RE), with the A-Line ARX Index (AAI) and the Bispectral Index (BIS) and as measures of anesthetic drug effect. They compared the measures for baseline variability, burst suppression, and prediction probability. They also developed pharmacodynamic models relating SE, RE, AAI, and BIS to the calculated propofol effect-site concentration (Ceprop). METHODS: With institutional review board approval, the authors studied 10 patients. All patients received 50 mg/min propofol until either burst suppression greater than 80% or mean arterial pressure less than 50 mmHg was observed. SE, RE, AAI, and BIS were continuously recorded. Ceprop was calculated from the propofol infusion profile. Baseline variability, prediction of burst suppression, prediction probability, and Spearman rank correlation were calculated for SE, RE, AAI, and BIS. The relations between Ceprop and the electroencephalographic measures of drug effect were estimated using nonlinear mixed effect modeling. RESULTS: Baseline variability was lowest when using SE and RE. Burst suppression was most accurately detected by spectral entropy. Prediction probability and individualized Spearman rank correlation were highest for BIS and lowest for SE. Nonlinear mixed effect modeling generated reasonable models relating all four measures to Ceprop. CONCLUSIONS: Compared with BIS and AAI, both SE and RE seem to be useful electroencephalographic measures of anesthetic drug effect, with low baseline variability and accurate burst suppression prediction. The ability of the measures to predict Ceprop was best for BIS.