Integrating Culture-based Antibiotic Resistance Profiles with Whole-genome Sequencing Data for 11,087 Clinical Isolates / 基因组蛋白质组与生物信息学报·英文版

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.

Genome-wide MicroRNA Expression Profiles in COPD: Early Predictors for Cancer Development / 基因组蛋白质组与生物信息学报·英文版

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.