[Comparison of Three Different Methods in Investigating the Molecular Epidemiology of Carbapenem-Resistant Acinetobacter baumannii Clinical Isolates]. / Karbapeneme Dirençli Acinetobacter baumannii Klinik Izolatlarinin Moleküler Epidemiyoloji Açisindan Arastirilmasinda Üç Farkli Yöntemin Karsilastirilmasi.

The aim of the study was to evaluate the relationship between carbapenem-resistant Acinetobacter baumannii isolates carrying oxacillinase-type carbapenemase genes with "international high-risk clones" (IC I, II, and III) by different molecular epidemiological methods and to statistically compare the concordance and discrimination power of the methods. Carbapenem-resistant and moderately susceptible A.baumannii isolates from non-repeating blood cultures of 72 patients were included in the study. The presence of "blaOXA-23 , blaOXA-24 , blaOXA-51 ve blaOXA-58 " genes within OXA-type carbapenemases was detected by polymerase chain reaction (PCR) method and confirmed by DNA sequence analysis. Pulsed f ield gel electrophoresis (PFGE), multilocus sequence typing (MLST) and matrix-assisted laser desorption/ ionization time- of-flight mass spectrometry (MALDI-TOF MS) analyses were performed to evaluate the clonal relations of IC I, II and III clones together with clinical isolates. In the statistical comparison of the methods, discrimination power was evaluated by Simpson index of diversity (SID) and concordance by "Wallace coefficient". All of the isolates were found to carry blaOXA-23 and blaOXA-51 genes. As a result of the bioinformatic analysis of the four isolates selected for sequence analysis; blaOXA-23 and blaOXA-51 genes were detected in the selected isolates, and the analysis of two isolates carrying blaOXA-51 gene showed 99% similarity with blaOXA-92 gene. The isolates were clustered into five pulsotypes (A, B, C, D and E) according to ≥ 85% similarity coefficient by PFGE. The isolates and RUH 875, RUH 134, LUH 5875 strains belonging to high-risk clones ICI, ICII and ICIII, respectively, were divided into five main groups [A (n= 58), B (n= 8), C (n= 4), D (n= 4) and E (n= 1)] and 10 subgroups (A1, A2, A4, A5, A6, A9, B1, B4, C3, D1) by PFGE. IC clone III (E1) and seven strains showed singleton PFGE profiles (A3, A7, A8, B2, B3, C1, C2). ICII was found in A5 subtype, ICI in C1 subtype and ICIII in E1 subtype. By PFGE subtype groups, 18 pulsotypes were determined and ST1, ST2, ST81, ST157 and ST604 sequence types were found in 20 isolates randomly selected from pulsotypes according to MLST Pasteur scheme (cpn60, fusA, gltA, pyrG, recA, rplB, rpoB). Principal component analysis (PCA) of the spectra of 72 A. baumannii isolates and ICI, ICII and ICIII clones was performed by MALDI-TOF MS. In PCA analysis, the cluster distance level was defined as 1.5 and the isolates were divided into three clusters. IC clone I, II and III together with 70 clinical isolates were grouped in one cluster, while two clinical isolates (AB083 and AB0115) formed singleton clusters. There was no significant agreement between MALDI-TOF MS; MLST and PFGE data according to Wallace coefficient. It was found that PFGE method gave significant results in terms of discrimination power with SID coefficient, MALDI-TOF MS PCA analysis had the lowest discrimination power value, and the Wallace coefficient result of PFGE and MLST was concordant. In conclusion, MALDI-TOF MS may not function as a gold standard method like PFGE and MLST for epidemiological analysis in A.baumannii species and the epidemiological typing protocols used for MALDI-TOF MS need to be improved and developed.

NMSDR: Drug repurposing approach based on transcriptome data and network module similarity.

Computational drug repurposing aims to discover new treatment regimens by analyzing approved drugs on the market. This study proposes previously approved compounds that can change the expression profile of disease-causing proteins by developing a network theory-based drug repurposing approach. The novelty of the proposed approach is an exploration of module similarity between a disease-causing network and a compound-specific interaction network; thus, such an association leads to more realistic modeling of molecular cell responses at a system biology level. The overlap of the disease network and each compound-specific network is calculated based on a shortest-path similarity of networks by accounting for all protein pairs between networks. A higher similarity score indicates a significant potential of a compound. The approach was validated for breast and lung cancers. When all compounds are sorted by their normalized-similarity scores, 36 and 16 drugs are proposed as new candidates for breast and lung cancer treatment, respectively. A literature survey on candidate compounds revealed that some of our predictions have been clinically investigated in phase II/III trials for the treatment of two cancer types. As a summary, the proposed approach has provided promising initial results by modeling biochemical cell responses in a network-level data representation.