Recent advances in management of cryptococcal meningitis: commentary

Cryptococcal meningitis remains a substantial health burden with high morbidity, particularly in developing countries. Antifungal treatment regimens are guided by host factors, severity of illness (including presence of complications), and causative cryptococcal species. Recent clinical studies indicate the need for rapidly fungicidal induction therapy regimens using amphotericin B in combination with flucytosine for optimal outcomes. Maintenance therapy with fluconazole is necessary until recovery of immune function. Cryptococcus gattii meningitis requires prolonged induction/eradication therapy. Prompt control of raised intracranial pressure or hydrocephalus is essential. Clinicians should be vigilant for immune restoration-like features. Adjuvant surgery, corticosteroids, and/or recombinant interferon-gamma may be required for large cryptococcomas, cerebral edema, or refractory infection

Defining Reference Sequences for Nocardia Species by Similarity and Clustering Analyses of 16S rRNA Gene Sequence Data

The intra- and inter-species genetic diversity of bacteria and the absence of 'reference', or the most representative, sequences of individual species present a significant challenge for sequence-based identification. The aims of this study were to determine the utility, and compare the performance of several clustering and classification algorithms to identify the species of 364 sequences of 16S rRNA gene with a defined species in GenBank, and 110 sequences of 16S rRNA gene with no defined species, all within the genus Nocardia. A total of 364 16S rRNA gene sequences of Nocardia species were studied. In addition, 110 16S rRNA gene sequences assigned only to the Nocardia genus level at the time of submission to GenBank were used for machine learning classification experiments. Different clustering algorithms were compared with a novel algorithm or the linear mapping (LM) of the distance matrix. Principal Components Analysis was used for the dimensionality reduction and visualization. Results: The LM algorithm achieved the highest performance and classified the set of 364 16S rRNA sequences into 80 clusters, the majority of which (83.52%) corresponded with the original species. The most representative 16S rRNA sequences for individual Nocardia species have been identified as 'centroids' in respective clusters from which the distances to all other sequences were minimized; 110 16S rRNA gene sequences with identifications recorded only at the genus level were classified using machine learning methods. Simple kNN machine learning demonstrated the highest performance and classified Nocardia species sequences with an accuracy of 92.7% and a mean frequency of 0.578

Linear normalised hash function for clustering gene sequences and identifying reference sequences from multiple sequence alignments

The aim of this study was to develop a method that would identify the cluster centroids and the optimal number of clusters for a given sensitivity level and could work equally well for the different sequence datasets. A novel method that combines the linear mapping hash function and multiple sequence alignment (MSA) was developed. This method takes advantage of the already sorted by similarity sequences from the MSA output, and identifies the optimal number of clusters, clusters cut-offs, and clusters centroids that can represent reference gene vouchers for the different species. The linear mapping hash function can map an already ordered by similarity distance matrix to indices to reveal gaps in the values around which the optimal cut-offs of the different clusters can be identified. The method was evaluated using sets of closely related (16S rRNA gene sequences of Nocardia species) and highly variable (VP1 genomic region of Enterovirus 71) sequences and outperformed existing unsupervised machine learning clustering methods and dimensionality reduction methods. This method does not require prior knowledge of the number of clusters or the distance between clusters, handles clusters of different sizes and shapes, and scales linearly with the dataset. The combination of MSA with the linear mapping hash function is a computationally efficient way of gene sequence clustering and can be a valuable tool for the assessment of similarity, clustering of different microbial genomes, identifying reference sequences, and for the study of evolution of bacteria and viruses

Whole Genome Sequencing of Australian Candida glabrata Isolates Reveals Genetic Diversity and Novel Sequence Types

Candida glabrata is a pathogen with reduced susceptibility to azoles and echinocandins. Analysis by traditional multilocus sequence typing (MLST) has recognized an increasing number of sequence types (STs), which vary with geography. Little is known about STs of C. glabrata in Australia. Here, we utilized whole genome sequencing (WGS) to study the genetic diversity of 51 Australian C. glabrata isolates and sought associations between STs over two time periods (2002–2004, 2010–2017), and with susceptibility to fluconazole by principal component analysis (PCA). Antifungal susceptibility was determined using Sensititre YeastOneTM Y010 methodology and WGS performed on the NextSeq 500 platform (Illumina) with in silico MLST STs inferred by WGS data. Single nucleotide polymorphisms (SNPs) in genes linked to echinocandin, azole and 5-fluorocytosine resistance were analyzed. Of 51 isolates, WGS identified 18 distinct STs including four novel STs (ST123, ST124, ST126, and ST127). Four STs accounted for 49% of isolates (ST3, 15.7%; ST83, 13.7%; ST7, 9.8%; ST26, 9.8%). Split-tree network analysis resolved isolates to terminal branches; many of these comprised multiple isolates from disparate geographic settings but four branches contained Australian isolates only. ST3 isolates were common in Europe, United States and now Australia, whilst ST8 and ST19, relatively frequent in the United States, were rare/absent amongst our isolates. There was no association between ST distribution (genomic similarity) and the two time periods or with fluconazole susceptibility. WGS identified mutations in the FKS1 (S629P) and FKS2 (S663P) genes in three, and one, echinocandin-resistant isolate(s), respectively. Both mutations confer phenotypic drug resistance. Twenty-five percent (13/51) of isolates were fluconazole-resistant (MIC ≥ 64 μg/ml) of which 9 (18%) had non wild-type MICs to voriconazole and posaconazole. Multiple SNPs were present in genes linked to azole resistance such as CgPDR1 and CgCDR1, as well as several in MSH2; however, SNPs occurred in both azole-susceptible and azole-resistant isolates. Although no particular SNP in these genes was definitively associated with resistance, azole-resistant/non-wild type isolates had a propensity to harbor SNPs resulting in amino acid substitutions in Pdr1 beyond the first 250 amino acid positions. The presence of SNPs may be markers of STs. Our study shows the value of WGS for high-resolution sequence typing of C. glabrata, discovery of novel STs and potential to monitor trends in genetic diversity. WGS assessment for echinocandin resistance augments phenotypic susceptibility testing