Blood-based microRNA (miRNA) signatures as biomarkers have been reported for various pathologies, including cancer, neurological disorders, cardiovascular diseases, and also infections. The regulatory mechanism behind respective miRNA patterns is only partially understood. Moreover,‘‘preserved’’ miRNAs, i.e., miRNAs that are not dysregulated in any disease, and their biological impact have been explored to a very limited extent. We set out to systematically determine their role in regulatory networks by defining groups of highly-dysregulated miRNAs that contribute to a disease signature as opposed to preserved housekeeping miRNAs. We further determined preferential targets and pathways of both dysregulated and preserved miRNAs by computing multi-layer networks, which were compared between housekeeping and dysregulated miRNAs. Of 848 miRNAs examined across 1049 blood samples, 8 potential housekeepers showed very limited expression variations, while 20 miRNAs showed highly-dysregulated expression throughout the investigated blood samples. Our approach provides important insights into miRNAs and their role in regulatory networks. The methodology can be applied to systematically investigate the differences in target genes and pathways of arbitrary miRNA sets.

In many research disciplines, hypothesis tests are applied to evaluate whether findings are statistically significant or could be explained by chance. The Wilcoxon–Mann–Whitney (WMW) test is among the most popular hypothesis tests in medicine and life science to analyze if two groups of samples are equally distributed. This nonparametric statistical homogeneity test is commonly applied in molecular diagnosis. Generally, the solution of the WMW test takes a high combinatorial effort for large sample cohorts containing a significant number of ties. Hence, P value is frequently approximated by a normal distribution. We developed EDISON-WMW, a new approach to calcu-late the exact permutation of the two-tailed unpaired WMW test without any corrections required and allowing for ties. The method relies on dynamic programing to solve the combinatorial problem of the WMW test efficiently. Beyond a straightforward implementation of the algorithm, we pre-sented different optimization strategies and developed a parallel solution. Using our program, the exact P value for large cohorts containing more than 1000 samples with ties can be calculated within minutes. We demonstrate the performance of this novel approach on randomly-generated data, benchmark it against 13 other commonly-applied approaches and moreover evaluate molec-ular biomarkers for lung carcinoma and chronic obstructive pulmonary disease (COPD). We found that approximated P values were generally higher than the exact solution provided by EDISONWMW. Importantly, the algorithm can also be applied to high-throughput omics datasets, where hundreds or thousands of features are included. To provide easy access to the multi-threaded version of EDISON-WMW, a web-based solution of our algorithm is freely available at http:// www.ccb.uni-saarland.de/software/wtest/.

The composition of the gut microbiota is linked to multiple diseases,including Parkin-son's disease(PD).Abundance of bacteria producing short-chain fatty acids(SCFAs)and fecal SCFA concentrations are reduced in PD.SCFAs exert various beneficial functions in humans.In the interventional,monocentric,open-label clinical trial"Effects of Resistant Starch on Bowel Habits,Short Chain Fatty Acids and Gut Microbiota in Parkinson's Disease"(RESISTA-PD;ID:NCT02784145),we aimed at altering fecal SCFAs by an 8-week prebiotic intervention with resistant starch(RS).We enrolled 87 subjects in three study-arms:32 PD patients received RS(PD+RS),30 control subjects received RS,and 25 PD patients received solely dietary instructions.We performed paired-end 100 bp length metagenomic sequencing of fecal samples using the BGISEQ platform at an average of 9.9 GB.RS was well-tolerated.In the PD+RS group,fecal butyrate concentrations increased significantly,and fecal calprotectin concentrations dropped significantly after 8 weeks of RS intervention.Clinically,we observed a reduction in non-motor symptom load in the PD+RS group.The reference-based analysis of metagenomes highlighted stable alpha-diversity and beta-diversity across the three groups,including bacteria producing SCFAs.Reference-free analysis suggested punctual,yet pronounced differences in the metagenomic signature in the PD+RS group.RESISTA-PD highlights that a prebiotic treatment with RS is safe and well-tolerated in PD.The stable alpha-diversity and beta-diversity alongside altered fecal butyrate and calprotectin concentrations call for long-term studies,also investigating whether RS is able to modify the clinical course of PD.

High-quality DNA extraction is a crucial step in metagenomic studies.Bias by different isolation kits impairs the comparison across datasets.A trending topic is,however,the analysis of multiple metagenomes from the same patients to draw a holistic picture of microbiota associated with diseases.We thus collected bile,stool,saliva,plaque,sputum,and conjunctival swab samples and performed DNA extraction with three commercial kits.For each combination of the specimen type and DNA extraction kit,20-gigabase(Gb)metagenomic data were generated using short-read sequencing.While profiles of the specimen types showed close proximity to each other,we observed notable differences in the alpha diversity and composition of the microbiota depending on the DNA extraction kits.No kit outperformed all selected kits on every specimen.We reached consistently good results using the Qiagen QiAamp DNA Microbiome Kit.Depending on the specimen,our data indicate that over 10 Gb of sequencing data are required to achieve sufficient resolution,but DNA-based identification is superior to identification by mass spectrometry.Finally,long-read nanopore sequencing confirmed the results(correlation coefficient＞0.98).Our results thus suggest using a strategy with only one kit for studies aiming for a direct comparison of multiple microbiotas from the same patients.

Blood-borne small non-coding (sncRNAs) are among the prominent candidates for blood-based diagnostic tests. Often, high-throughput approaches are applied to discover biomarker signatures. These have to be validated in larger cohorts and evaluated by adequate statistical learning approaches. Previously, we published high-throughput sequencing based microRNA (miRNA) signatures in Alzheimer's disease (AD) patients in the United States (US) and Germany. Here, we determined abundance levels of 21 known circulating miRNAs in 465 individuals encompassing AD patients and controls by RT-qPCR. We computed models to assess the relation between miRNA expression and phenotypes, gender, age, or disease severity (Mini-Mental State Examination; MMSE). Of the 21 miRNAs, expression levels of 20 miRNAs were consistently de-regulated in the US and German cohorts. 18 miRNAs were significantly correlated with neurodegeneration (Benjamini-Hochberg adjusted P < 0.05) with highest significance for miR-532-5p (Benjamini- Hochberg adjusted P = 4.8 × 10 -30). Machine learning models reached an area under the curve (AUC) value of 87.6％ in differentiating AD patients from controls. Further, ten miRNAs were significantly correlated with MMSE, in particular miR-26a/26b-5p (adjusted P = 0.0002). Interestingly, the miRNAs with lower abundance in AD were enriched in monocytes and T-helper cells, while those up-regulated in AD were enriched in serum, exosomes, cytotoxic t-cells, and B-cells. Our study represents the next important step in translational research for a miRNA-based AD test.

Emerging antibiotic resistance is a major global health threat. The analysis of nucleic acid sequences linked to susceptibility phenotypes facilitates the study of genetic antibiotic resistance determinants to inform molecular diagnostics and drug development. We collected genetic data (11,087 newly-sequenced whole genomes) and culture-based resistance profiles (10,991 out of the 11,087 isolates comprehensively tested against 22 antibiotics in total) of clinical isolates including 18 main species spanning a time period of 30 years. Species and drug specific resistance patterns were observed including increased resistance rates for Acinetobacter baumannii to carbapenems and for Escherichia coli to fluoroquinolones. Species-level pan-genomes were constructed to reflect the genetic repertoire of the respective species, including conserved essential genes and known resistance factors. Integrating phenotypes and genotypes through species-level pan-genomes allowed to infer gene-drug resistance associations using statistical testing. The isolate collection and the analysis results have been integrated into GEAR-base, a resource available for academic research use free of charge at https://gear-base.com.
Acinetobacter baumannii ; genetics ; isolation & purification ; Bacteria ; genetics ; isolation & purification ; Cell Culture Techniques ; methods ; Drug Resistance, Microbial ; genetics ; Escherichia coli ; genetics ; isolation & purification ; Genome, Bacterial ; Genotype ; Humans ; Internet ; Microbial Sensitivity Tests ; Phenotype ; Whole Genome Sequencing

Chronic obstructive pulmonary disease (COPD) significantly increases the risk of developing cancer. Biomarker studies frequently follow a case-control set-up in which patients diagnosed with a disease are compared to controls. Longitudinal cohort studies such as the COPD-centered German COPD and SYstemic consequences-COmorbidities NETwork (COSYCONET) study provide the patient and biomaterial base for discovering predictive molecular markers. We asked whether microRNA (miRNA) profiles in blood collected from COPD patients prior to a tumor diagnosis could support an early diagnosis of tumor development independent of the tumor type. From 2741 participants of COSYCONET diagnosed with COPD, we selected 534 individuals including 33 patients who developed cancer during the follow-up period of 54 months and 501 patients who did not develop cancer, but had similar age, gender and smoking history. Genome-wide miRNA profiles were generated and evaluated using machine learning techniques. For patients developing cancer we identified nine miRNAs with significantly decreased abundance (two-tailed unpaired t-test adjusted for multiple testing P < 0.05), including members of the miR-320 family. The identified miRNAs regulate different cancer-related pathways including the MAPK pathway (P = 2.3 × 10). We also observed the impact of confounding factors on the generated miRNA profiles, underlining the value of our matched analysis. For selected miRNAs, qRT-PCR analysis was applied to validate the results. In conclusion, we identified several miRNAs in blood of COPD patients, which could serve as candidates for biomarkers to help identify COPD patients at risk of developing cancer.
Aged ; Biomarkers, Tumor ; genetics ; Cohort Studies ; Female ; Gene Expression Profiling ; Genome, Human ; Humans ; Male ; MicroRNAs ; genetics ; Neoplasms ; diagnosis ; etiology ; genetics ; Prognosis ; Pulmonary Disease, Chronic Obstructive ; complications