No alarming resistances against the agents tested were found; however, owing to the relatively high frequency of C. glabrata with elevated fluconazole MICs, this species and, to a certain extent, C. krusei must be taken into consideration when choosing antifungal agents for calculated therapy. Etest is a reliable method for the susceptibility testing of Candida spp. and the 24 h readings of both Etest and BMD can serve as helpful preliminary results in most cases.

ABSTRACTThe increasing incidence of azole resistance inAspergillus fumigatuscausing invasive aspergillosis (IA) in immunocompromised/hematological patients emphasizes the need to improve the detection of resistance-mediatingcyp51Agene mutations from primary clinical samples, particularly as the diagnosis of invasive aspergillosis is rarely based on a positive culture yield in this group of patients. We generated primers from the unique sequence of theAspergillus fumigatus cyp51Agene to establish PCR assays with consecutive DNA sequence analysis to detect and identify theA. fumigatus cyp51Atandem repeat (TR) mutation in the promoter region and the L98H and M220 alterations directly in clinical samples. After testing of the sensitivity and specificity of the assays using serially dilutedA. fumigatusand human DNA,A. fumigatus cyp51Agene fragments of about 150 bp potentially carrying the mutations were amplified directly from primary clinical samples and subsequently DNA sequenced. The determined sensitivities of the PCR assays were 600 fg, 6 pg, and 4 pg ofA. fumigatusDNA for the TR, L98H, and M220 mutations, respectively. There was no cross-reactivity with human genomic DNA detectable. Sequencing of the PCR amplicons forA. fumigatuswild-type DNA confirmed thecyp51Awild-type sequence, and PCR products from one azole-resistantA. fumigatusisolate showed the L98H and TR mutations. The second azole-resistant isolate revealed an M220T alteration. We consider our assay to be of high epidemiological and clinical relevance to detect azole resistance and to optimize antifungal therapy in patients with IA.

Gene expression time-course experiments allow to study the dynamics of transcriptomic changes in cells exposed to different stimuli. However, most approaches for the reconstruction of gene association networks (GANs) do not propose prior-selection approaches tailored to time-course transcriptome data. Here, we present a workflow for the identification of GANs from time-course data using prior selection of genes differentially expressed over time identified by natural cubic spline regression modeling (NCSRM). The workflow comprises three major steps: 1) the identification of differentially expressed genes from time-course expression data by employing NCSRM, 2) the use of regularized dynamic partial correlation as implemented in GeneNet to infer GANs from differentially expressed genes and 3) the identification and functional characterization of the key nodes in the reconstructed networks. The approach was applied on a time-resolved transcriptome data set of radiation-perturbed cell culture models of non-tumor cells with normal and increased radiation sensitivity. NCSRM detected significantly more genes than another commonly used method for time-course transcriptome analysis (BETR). While most genes detected with BETR were also detected with NCSRM the false-detection rate of NCSRM was low (3%). The GANs reconstructed from genes detected with NCSRM showed a better overlap with the interactome network Reactome compared to GANs derived from BETR detected genes. After exposure to 1 Gy the normal sensitive cells showed only sparse response compared to cells with increased sensitivity, which exhibited a strong response mainly of genes related to the senescence pathway. After exposure to 10 Gy the response of the normal sensitive cells was mainly associated with senescence and that of cells with increased sensitivity with apoptosis. We discuss these results in a clinical context and underline the impact of senescence-associated pathways in acute radiation response of normal cells. The workflow of this novel approach is implemented in the open-source Bioconductor R-package splineTimeR.