Archives

  • 2018-07
  • 2018-10
  • 2018-11
  • 2019-04
  • 2019-05
  • 2019-06
  • 2019-07
  • 2019-08
  • 2019-09
  • 2019-10
  • 2019-11
  • 2019-12
  • 2020-01
  • 2020-02
  • 2020-03
  • 2020-04
  • 2020-05
  • 2020-06
  • 2020-07
  • 2020-08
  • 2020-09
  • 2020-10
  • 2020-11
  • 2020-12
  • 2021-01
  • 2021-02
  • 2021-03
  • 2021-04
  • 2021-05
  • 2021-06
  • 2021-07
  • 2021-08
  • 2021-09
  • 2021-10
  • 2021-11
  • 2021-12
  • 2022-01
  • 2022-02
  • 2022-03
  • 2022-04
  • 2022-05
  • 2022-06
  • 2022-07
  • 2022-08
  • 2022-09
  • 2022-10
  • 2022-11
  • 2022-12
  • 2023-01
  • 2023-02
  • 2023-03
  • 2023-04
  • 2023-05
  • 2023-06
  • 2023-07
  • 2023-08
  • 2023-09
  • 2023-10
  • 2023-11
  • 2023-12
  • 2024-01
  • 2024-02
  • 2024-03
  • 2024-04
  • A key function of the reference AFST would be

    2024-03-06

    A key function of the reference AFST would be to detect fungal resistance, i.e. to determine which antifungal agents are likely to be clinically inactive [2]. To this end, the epidemiological cut-off value, defined as ‘the upper end of the wild-type (WT) or beginning of the non-wild-type (NWT) MIC distribution’, is useful to indicate whether or not a given fungal isolate ‘is devoid of phenotypically detectable acquired resistance mechanisms or not’ [9]. This indication is irrespective of the clinical susceptibility categorization as susceptible, intermediate (or susceptible-dose-dependent) or resistant for that isolate. As in the case of echinocandin or azole antifungal agents, clinical breakpoint MICs and epidemiological cut-offs may not agree [10], [11]. As a response to the uncertainties about the reference AFST methods, new technologies, particularly those that are nucleic-acid-based, have emerged as promising tools for the detection of antifungal resistance. Although not yet a reality, with future advances in diagnosis of fungal infections and resistance [12], the potential of rapid (<4 h to result) diagnostic approaches for antifungal resistance is plausible to ensure that a patient quickly receives appropriate antifungals. The prophecy that ‘very soon we shall not need breakpoints for phenotypic susceptibility testing as these will be replaced by genetic methods’ [13] is not so far from coming true. However, it is required that newer-generation methods (e.g. whole genome sequencing) are capable of predicting not only resistance but also susceptibility, as well as quantifying the level of resistance [14]. It should be noted that nucleic mass of cl sequence-based amplification techniques have the ability to detect mRNA transcripts of the target gene, so offering a more confident prediction of resistance phenotypes. In contrast, phenotype-centred (or semi-molecular) approaches combining a culture step with molecular analysis (i.e. by real-time PCR or matrix-assisted laser desorption–ionization time-of-flight mass spectrometry (MALDI-TOF MS)) could be very promising alternatives to the classical phenotypic AFST [15], [16].
    Nucleic acid-based diagnostic assay platforms for detection of echinocandin resistance Clinical resistance to echinocandins has been associated with a number of amino acid substitutions caused by single nucleotide polymorphisms in specific hot spot (HS) regions of the genes FKS1 (all Candida species) and FKS2 (Candida glabrata), which encode the drug target β-1,3-d-glucan synthase. These mutations reduce by 30-fold to several thousand-fold the echinocandin inhibition of the target enzyme and arise in C. glabrata and Candida albicans isolates recovered almost exclusively from patients with previous echinocandin exposure [17]. The most dominant mutation in all Candida species seems to involve codon S645, which is equivalent to S629 in C. glabrata (reviewed in ref. [10]), altering the amino acid sequence in the HS1 region. However, the degree of MIC elevation, i.e. the level of resistance, depends on the location as well as the single amino acid substitutions [3], with mutations at S629 resulting in higher echinocandin MIC values than mutations at R631 or D632 in C. glabrata[18]. It was also noted that, for the functionally equivalent mutations S629P and S663P, the mutation in the FKS2 gene (S663P) was associated with higher echinocandin MIC values than the mutation in the FKS1 gene (S629P) [18]. Nevertheless, the FKS2 S663P alteration was seen to rapidly disappear after the discontinuation of echinocandin treatment in a patient with persistent, clonal C. glabrata fungaemia [19]. Since the majority of mutations that affect C. glabrata susceptibility to echinocandins would be in the FKS1 HS1 and FKS2 HS1 [18], [20], [21] it is reasonable that these regions were chosen as the primary targets of molecular assays for resistance detection [22], [23], [24]. Until recently, DNA sequencing has been the only available method for the identification of mutations in FKS1 and FKS2, but it is impractical for most clinical microbiology laboratories because of its high costs and laboriousness. Alternatively, multiple PCR assays where each oligonucleotide probe is specific for a defined genotype could be employed, but the complex interactions occurring between oligonucleotides would make the optimization of assay conditions quite difficult. By contrast, the Luminex MagPix technology using xMAP microspheres has been developed as a platform for the establishment of multiplex assays, which permits high-throughput analysis of up to one hundred different target molecules in a single test.