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Occurrence of non- (NCAC) species that are associated with elevated MIC values and therapeutic failures are increasing. As a result, timely and accurate means of identification to the species level is becoming an essential part of diagnostic practices in clinical settings. In this study, 301 clinically isolated yeast strains recovered from various anatomical sites [Blood ( = 145), other sites ( = 156)] were used to assess the accuracy and practicality of API 20C AUX and 21-plex PCR compared to MALDI-TOF MS and large subunit rDNA (LSU rDNA). MALDI-TOF MS correctly identified 98.33% of yeast isolates, 100% of top five species, 95.7% of rare yeast species, while 1.3% of isolates were misidentified. API 20C AUX correctly identified 83.7% of yeast isolates, 97.2% of top five species, 61.8% of rare yeast species, while 16.2% of yeast isolates were misidentified. The 21-plex PCR, accurately identified 87.3% of yeast isolates, 100% of top five species, 72% of rare yeast species, but it misidentified 1.3% of rare yeast species while 9.9% of whole yeast isolates were not identified. The combination of rapidity of 21-plex PCR and comprehensiveness of API 20C AUX, led to correct identification of 92% of included yeast isolates. Due to expensiveness of MALDI-TOF MS and sequencing, this combination strategy could be the most accurate and inexpensive alternative identification strategy for developing countries. Moreover, by the advent and development of cost-effective, reliable, and rapid PCR machines that cost 130 US dollars, 21-plex could be integrated in routine laboratories of developing and resource-limited countries to specifically identify 95% causative agents of yeast-related infections in human. Databases of MALDI-TOF MS, API 20C AUX, and the number of target species identified by 21-plex require further improvement to keep up with the diverse spectrum of yeast species.

ObjectivePatients with primary sclerosing cholangitis (PSC) were previously shown to display a bacterial gut dysbiosis but fungal microbiota has never been examined in these patients. The aim of this study was to assess the fungal gut microbiota in patients with PSC.DesignWe analysed the faecal microbiota of patients with PSC and concomitant IBD (n=27), patients with PSC and no IBD (n=22), patients with IBD and no PSC (n=33) and healthy subjects (n=30). Bacterial and fungal composition of the faecal microbiota was determined using 16S and ITS2 sequencing, respectively.ResultsWe found that patients with PSC harboured bacterial dysbiosis characterised by a decreased biodiversity, an altered composition and a decreased correlation network density. These alterations of the microbiota were associated with PSC, independently of IBD status. For the first time, we showed that patients with PSC displayed a fungal gut dysbiosis, characterised by a relative increase in biodiversity and an altered composition. Notably, we observed an increased proportion of Exophiala and a decreased proportion of Saccharomyces cerevisiae. Compared with patients with IBD and healthy subjects, the gut microbiota of patients with PSC exhibited a strong disruption in bacteria-fungi correlation network, suggesting an alteration in the interkingdom crosstalk.ConclusionThis study demonstrates that bacteria and fungi contribute to gut dysbiosis in PSC.

Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has emerged as a rapid, highly accurate, and cost-effective method for routine identification of a wide range of microorganisms. We carried out a side by side comparative evaluation of the performance of Bruker Biotyper versus VITEK MS for identification of a large and diverse collection of microorganisms. Most difficult and/or unusual microorganisms, as well as commonly encountered microorganisms were selected, including Gram-positive and negative bacteria, mycobacteria, actinomycetes, yeasts and filamentous fungi. Six hundred forty two strains representing 159 genera and 441 species from clinical specimens previously identified at the Laboratoire de santé publique du Québec (LSPQ) by reference methods were retrospectively chosen for the study. They included 254 Gram-positive bacteria, 167 Gram-negative bacteria, 109 mycobacteria and aerobic actinomycetes and 112 yeasts and moulds. MALDI-TOF MS analyses were performed on both systems according to the manufacturer's instructions. Of the 642 strains tested, the name of the genus and / or species of 572 strains were referenced in the Bruker database while 406 were present in the VITEK MS IVD database. The Biotyper correctly identified 494 (86.4%) of the strains, while the VITEK MS correctly identified 362 (92.3%) of the strains (excluding 14 mycobacteria that were not tested). Of the 70 strains not present in the Bruker database at the species level, the Biotyper correctly identified 10 (14.3%) to the genus level and 2 (2.9%) to the complex/group level. For 52 (74.2%) strains, we obtained no identification, and an incorrect identification was given for 6 (8.6%) strains. Of the 178 strains not present in the VITEK MS IVD database at the species level (excluding 71 untested mycobacteria and actinomycetes), the VITEK MS correctly identified 12 (6.8%) of the strains each to the genus and to the complex/group level. For 97 (54.5%) strains, no identification was given and for 69 (38.7%) strains, an incorrect identification was obtained. Our study demonstrates that both systems gave a high level (above 85%) of correct identification for a wide range of microorganisms. However, VITEK MS gave more misidentification when the microorganism analysed was not present in the database, compared to Bruker Biotyper. This should be taken into account when this technology is used alone for microorganism identification in a public health laboratory, where isolates received are often difficult to identify and/or unusual microorganisms.

The aim of our study was to assess the detection of circulating DNA from the most common species of Mucorales for early diagnosis of mucormycosis in at-risk patients. We retrospectively evaluated a combination of 3 quantitative polymerase chain reaction (qPCR) assays using hydrolysis probes targeting Mucor/Rhizopus, Lichtheimia (formerly Absidia), and Rhizomucor for circulating Mucorales detection. Serial serum samples from 10 patients diagnosed with proven mucormycosis (2-9 samples per patient) were analyzed. No cross-reactivity was detected in the 3 qPCR assays using 19 reference strains of opportunistic fungi, and the limit of detection ranged from 3.7 to 15 femtograms/10 µL, depending on the species. DNA from Mucorales was detected in the serum of 9 of 10 patients between 68 and 3 days before mucormycosis diagnosis was confirmed by histopathological examination and/or positive culture. All the qPCR results were concordant with culture and/or PCR-based identification of the causing agents in tissue (Lichtheimia species, Rhizomucor species, and Mucor/Rhizopus species in 4, 3, and 2 patients, respectively). Quantitative PCR was negative in only 1 patient with proven disseminated mucormycosis caused by Lichtheimia species. Our study suggests that using specific qPCR targeting several species of Mucorales according to local ecology to screen at-risk patients could be useful in a clinical setting. The cost and efficacy of this strategy should be evaluated. However, given the human and economic cost of mucormycosis and the need for rapid diagnosis to initiate prompt directed antifungal therapy, this strategy could be highly attractive.

Background Fungi are among the most abundant and diverse organisms on Earth. However, a substantial amount of the species diversity, relationships, habitats, and life strategies of these microorganisms remain to be discovered and characterized. One important factor hindering progress is the difficulty in correctly identifying fungi. Morphological and molecular characteristics have been applied in such tasks. Later, DNA barcoding has emerged as a new method for the rapid and reliable identification of species. The nrITS region is considered the universal barcode of Fungi, and the ITS1 and ITS2 sub-regions have been applied as metabarcoding markers. In this study, we performed a large-scale analysis of all the available Basidiomycota sequences from GenBank. We carried out a rigorous trimming of the initial dataset based in methodological principals of DNA Barcoding. Two different approaches (PCI and barcode gap) were used to determine the performance of the complete ITS region and sub-regions. Results For most of the Basidiomycota genera, the three genomic markers performed similarly, i.e., when one was considered a good marker for the identification of a genus, the others were also; the same results were observed when the performance was insufficient. However, based on barcode gap analyses, we identified genomic markers that had a superior identification performance than the others and genomic markers that were not indicated for the identification of some genera. Notably, neither the complete ITS nor the sub-regions were useful in identifying 11 of the 113 Basidiomycota genera. The complex phylogenetic relationships and the presence of cryptic species in some genera are possible explanations of this limitation and are discussed. Conclusions Knowledge regarding the efficiency and limitations of the barcode markers that are currently used for the identification of organisms is crucial because it benefits research in many areas. Our study provides information that may guide researchers in choosing the most suitable genomic markers for identifying Basidiomycota species.

A combination of Sanger and 454 sequences of small subunit rRNA loci were used to interrogate microbial diversity in the bovine rumen of 12 cows consuming a forage diet. Observed bacterial species richness, based on the V1-V3 region of the 16S rRNA gene, was between 1,903 to 2,432 species-level operational taxonomic units (OTUs) when 5,520 reads were sampled per animal. Eighty percent of species-level OTUs were dominated by members of the order Clostridiales, Bacteroidales, Erysipelotrichales and unclassified TM7. Abundance of Prevotella species varied widely among the 12 animals. Archaeal species richness, also based on 16S rRNA, was between 8 and 13 OTUs, representing 5 genera. The majority of archaeal OTUs (84%) found in this study were previously observed in public databases with only two new OTUs discovered. Observed rumen fungal species richness, based on the 18S rRNA gene, was between 21 and 40 OTUs with 98.4-99.9% of OTUs represented by more than one read, using Good's coverage. Examination of the fungal community identified numerous novel groups. Prevotella and Tannerella were overrepresented in the liquid fraction of the rumen while Butyrivibrio and Blautia were significantly overrepresented in the solid fraction of the rumen. No statistical difference was observed between the liquid and solid fractions in biodiversity of archaea and fungi. The survey of microbial communities and analysis of cross-domain correlations suggested there is a far greater extent of microbial diversity in the bovine rumen than previously appreciated, and that next generation sequencing technologies promise to reveal novel species, interactions and pathways that can be studied further in order to better understand how rumen microbial community structure and function affects ruminant feed efficiency, biofuel production, and environmental impact.

Fungal research is experiencing a new wave of methodological improvements that most probably will boost mycology as profoundly as molecular phylogeny has done during the last 15 years. Especially the next generation sequencing technologies can be expected to have a tremendous effect on fungal biodiversity and ecology research. In order to realise the full potential of these exciting techniques by accelerating biodiversity assessments, identification procedures of fungi need to be adapted to the emerging demands of modern large-scale ecological studies. But how should fungal species be identified in the near future? While the answer might seem trivial to most microbiologists, taxonomists working with fungi may have other views. In the present review, we will analyse the state of the art of the so-called barcoding initiatives in the light of fungi, and we will seek to evaluate emerging trends in the field. We will furthermore demonstrate that the usability of DNA barcoding as a major tool for identification of fungi largely depends on the development of high-quality sequence databases that are thoroughly curated by taxonomists and systematists.

Fifty four Trichoderma strains were isolated from soil samples collected from garlic and onion crops in eight different sites in Brazil and were identified using phylogenetic analysis based on combined ITS region, tef1-α, cal, act and rpb2 sequences. The genetic variability of the recovered Trichoderma species was analysed by AFLP and their phenotypic variability determined using MALDI-TOF. The strain clusters from both typing techniques coincided with the taxonomic determinations made from phylogenetic analysis. The phylogenetic analysis showed the occurrence of Trichoderma asperellum, Trichoderma asperelloides, Trichoderma afroharzianum, Trichoderma hamatum, Trichoderma lentiforme, Trichoderma koningiopsis, Trichoderma longibrachiatum and Trichoderma erinaceum, in the soil samples. We also identified and describe two new Trichoderma species, both in the harzianum clade of section Pachybasium, which we have named Trichoderma azevedoi sp. nov. and Trichoderma peberdyi sp. nov. The examined strains of both T. azevedoi (three strains) and T. peberdyi (12 strains) display significant genotypic and phenotypic variability, but form monophyletic clades with strong bootstrap and posterior probability support and are morphologically distinct from their respective most closely related species.

There are approximately 1 million cryptococcal infections per year among HIV+ individuals, resulting in nearly 625,000 deaths. Cryptococcus neoformans and Cryptococcus gattii are the two most common species that cause human cryptococcosis. These two species of Cryptococcus have differences in pathogenicity, diagnosis, and treatment. Cryptococcal infections are usually difficult to identify because of their slow growth in vitro. In addition, the long detection cycle of Cryptococcus in clinical specimens makes the diagnosis of Cryptococcal infections difficult. Here, we used positively charged silver nanoparticles (AgNPs + ) as a substrate to distinguish between C. neoformans and C. gattii in clinical specimens directly via surface-enhanced Raman scattering (SERS) and spectral analysis. The AgNPs + self-assembled on the surface of the fungal cell wall via electrostatic aggregation, leading to enhanced SERS signals that were better than the standard substrate negatively charged silver nanoparticles (AgNPs). The SERS spectra could also be used as a sample database in the multivariate analysis via orthogonal partial least-squares discriminant analysis. This novel SERS detection method can clearly distinguish between the two Cryptococcus species using principal component analysis. The accuracy of the training data and test data was 100% after a tenfold crossover validation.

The internal transcribed spacer (ITS) DNA marker is routinely used for fungal identification but gives a clear result for only three out of four powdery mildew samples. A search for new markers indicates that some genes offer enhanced identification in comparison with ITS. Others fail due to amplification and sequencing difficulties and lack of informative variability. Powdery mildews (Ascomycota, Erysiphales) are biotrophic, fungal plant pathogens that commonly occur worldwide on a wide range of host plants. They are unsightly and greatly reduce the vigor of their hosts and have major impacts on crop and other cultivated plants. Species within this order are straightforward to spot, but difficult to identify. A citizen science scheme was run in 2013-2016 in the UK to gather a wide array of samples on which identification methods could be developed. Current techniques for identification and phylogenetic reconstruction show scope for improvement. In this paper, we review genes used in other fungal groups for discrimination at species level. Working protocols for amplification and sequencing of seven genes (actin, β-tubulin, calmodulin,Chs, elongation factor 1-α [EF1-α], Mcm7, and Tsr1) are developed with varying success; Mcm7 proves to be the most useful at differentiation between closely related, phylogenetically young powdery mildew species for phylogenetic reconstruction when used separately and in tandem with ITS. We therefore propose this as the most appropriate candidate gene to be used commonly in powdery mildew diagnostics alongside the ITS; furthermore, this could be transferred to similarly troublesome fungal clades.