Validating the results of candida.app and Solu Platform

Abstract

Candida auris is an emerging health threat, and better genomic analysis tool are needed for its surveillance. Candida.app is a free, user-friendly tool for genomic analysis of C. auris isolates. This validation study shows that Solu and candida.app results are accurate for genomıc characterization of Candida auris.

Introduction

Candida auris is an emerging fungal pathogen that has spread rapidly since its first discovery in 2009. At least five distinct clades have been reported, suggesting its simultaneous emerge in different geographic locations. [1-3]

Due to its recent emergence, it can be difficult to trace Candida auris transmissions or even identify the species with conventional phenotypic and molecular methods [4]. Genomic surveillance is a promising method for tracking this emerging pathogen, but the industry lacks user-friendly analysis tools.

To bridge this gap, we created the Solu Platform for user-friendly genomics analysis. Some of the platform's tools are now freely accessible through the candida.app website.

Methods

The workflows of Solu Platform and candida.app are described in the below flowchart.

The platform accepts sample uploads as raw reads or assembled genomes. Raw read uploads are quality-checked with FastQC [5] and assembled using Shovill [6]. Assembly quality is validated using QUAST [7] for both upload formats.

The platform identifies a sample’s species and clade using the Bactinspector [8] species identification tool, with a database that is augmented with a fungal refseq database and clade-level references for Candida auris.

Anti-fungal resistance mutations are searched with AmrFinderPlus [9], using a custom database that is curated for Candida auris. The database is based on AfrBase [10], and contains 16 point mutations shown in the table below.

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The platform computes pairwise distances between each sample using the reference-free SNP comparison tool SKA [11].

Validation

Validation methodology

We performed a preliminary validation for the workflow by comparing its results to those from two previously published Candida auris studies, Lockhart et al. (2017) and Spruijtenburg et al. (2022). The validation dataset included 18 Candida auris samples representing all five clades.

The samples were retrieved from ENA and uploaded to Solu Platform through its drag-and-drop interface. Samples were uploaded as either assemblies or raw reads, depending on their availability at ENA. The analyses on Solu Platform started automatically after upload, and the results were available within minutes.

Species, clade and AFR mutation analysis

The below table shows the results for the validation samples. The AFR mutations written in black were present in both results, those in red only in the reference articles, and those in green only in Solu Platform results. All clade results matched those reported in the reference papers.

All 18 samples were correctly identified as Candida auris, and assigned into the same clade as in the reference studies.

The platform identified the same AFR as the reference articles with a few exceptions. Spruijtenburg et al. described two point mutations, ERG11_I466L and Tac1b_D559G, that were not in the platform’s AFR database and, thus, no present in the results. Both of these genes have only hypothesized contributions towards antifungal resistance [3].

The results from Solu Platform also included several mutations that were not included in the reference publications.

As a conclusion, the platform seems to accurately detect the species and clade of Candida auris samples and find the most relevant AFR-conferring mutations.

Pairwise SNP distances

The heatmap below shows the pairwise SNP distance between the samples.

The pairwise SNP differences between samples were notably higher than those reported in the reference publication from Lockhart et al. For example, Solu Platform identified a 127-SNP difference between two clade IV isolates, B11245 and B11243, while the reference article reported all samples from clade IV to be within 16 SNPs of each other. A difference of this magnitude was to be expected since SKA analyzes SNPs from any part of the genomes while Lockhart et al. used a much more conservative reference-alignment-based method for detecting SNPs.

Despite the different SNP calling methods, both results lead to the same conclusions regarding phylogeny. In the results of Solu Platform, the distances within each clade were significantly smaller (64-1,048 SNPs) than the between-clade distances (24,000-102,000 SNPs). This was similar to the findings in previous studies where within-clade distances were <100 SNPs and different clades were tens of thousands SNPs apart [2,3].

As a conclusion, the SNP distance analysis leads to the same conclusions as those from previously published studies, but the different SNP calling methods need to be interpreted using different SNP thresholds.

Discussion

Candida auris is an rapidly emerging health threat, and genomic analysis is needed for its effective surveillance. We provide candida.app as a free, user-friendly tool for analyzing the species and resistome from WGS sequencing data. The tool demonstrated accurate results in this preliminary validation of a 18-sample dataset.

We welcome all feedback and collaboration from the community, as we continue developing both candida.app and the Solu Platform.

Work with Solu

If you're interested in working with Solu for analyzing Candida genomes, there are a few steps you can take. You can read about opportunities to sequencing and/or analysis on our website. You can also reach out to our team directly for more information and to discuss your specific research needs.

References

1. Du H, Bing J, Hu T, Ennis CL, Nobile CJ, Huang G. Candida auris: Epidemiology, biology, antifungal resistance, and virulence. Xue C, editor. PLoS Pathog. 2020 Oct 22;16(10):e1008921.
2. Lockhart SR, Etienne KA, Vallabhaneni S, Farooqi J, Chowdhary A, Govender NP, et al. Simultaneous Emergence of Multidrug-Resistant Candida auris on 3 Continents Confirmed by Whole-Genome Sequencing and Epidemiological Analyses. CLINID. 2017 Jan 15;64(2):134–40.
3. Spruijtenburg B, Badali H, Abastabar M, Mirhendi H, Khodavaisy S, Sharifisooraki J, et al. Confirmation of fifth Candida auris clade by whole genome sequencing. Emerging Microbes & Infections. 2022 Dec 31;11(1):2405–11.
4. Jeffery-Smith A, Taori SK, Schelenz S, Jeffery K, Johnson EM, Borman A, et al. Candida auris: a Review of the Literature. Clin Microbiol Rev. 2018 Jan;31(1):e00029-17.
5. Andrews S. FastQC. Available from: https://github.com/s-andrews/FastQC
6. Seemann T. Shovill. Available from: https://github.com/tseemann/shovill
7. Gurevich A, Saveliev V, Vyahhi N, Tesler G. QUAST: quality assessment tool for genome assemblies. Bioinformatics. 2013 Apr 15;29(8):1072–5.
8. Underwood A. Bactinspector. 2020. BactInspector. Available from: https://gitlab.com/antunderwood/bactinspector
9. Feldgarden M, Brover V, Gonzalez-Escalona N, Frye JG, Haendiges J, Haft DH, et al. AMRFinderPlus and the Reference Gene Catalog facilitate examination of the genomic links among antimicrobial resistance, stress response, and virulence. Sci Rep. 2021 Jun 16;11(1):12728.
10. Jain A, Singhal N, Kumar M. AFRbase: a database of protein mutations responsible for antifungal resistance. Martelli PL, editor. Bioinformatics. 2023 Nov 1;39(11):btad677.
11. Harris SR. SKA: Split Kmer Analysis Toolkit for Bacterial Genomic Epidemiology. Genomics; 2018 Oct. Available from: http://biorxiv.org/lookup/doi/10.1101/453142
12. Healey KR, Kordalewska M, Jiménez Ortigosa C, Singh A, Berrío I, Chowdhary A, et al. Limited ERG11 Mutations Identified in Isolates of Candida auris Directly Contribute to Reduced Azole Susceptibility. Antimicrob Agents Chemother. 2018 Oct;62(10):e01427-18.

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