INTRODUCTION
Fungal infections affect in excess of a billion people, resulting in approximately 11.5 million life-threatening infections and more than 1.5 million deaths annually (1).
Candida biofilms represent an important clinical entity associated with adaptive resistance to many antifungals and are linked to excess morbidity and mortality (9–11). Although
RESULTS
Candida auris biofilms exhibit temporal antifungal resistance.
Mature
FIG 1
Candida auris transcriptome assembly.
Given the temporal patterns of biofilm-associated resistance, we undertook a transcriptional profiling approach to understand the mechanisms governing antifungal biofilm resistance (Fig. 2). Sequencing of samples using Illumina HiSeq produced around 414 million single-end reads of 50-bp length. Following processing, the number of reads was reduced by 3 million through trimming and quality control stages. All sequenced sample reads were then assembled into an ~11.5-Mb transcriptome which consisted of 5,889 identified Trinity transcripts and 5,848 genes based on the longest isoform of transcripts. At least half of the assembled sequenced bases were found on contigs of a length of 3,488 bp (N50) (Table 1). The completeness and quality of the
FIG 2
Bioinformatic pipeline for
TABLE 1
Summarized statistics for transcriptome assembly of
| Category | Value |
|---|---|
| No. of reads | |
| Total | 414,364,539 |
| After trimming | 411,626,529 |
| Total no. of assembled bases | 11,593,681 |
| GC content, % | 45.35 |
| Total no. by Trinity | |
| “Genes” | 5,848 |
| “Transcripts” | 5,889 |
| Contig (bp) | |
| N50 | 3,488 |
| Median | 1,308 |
| Avg | 1,983 |
| No. of reads aligned (%) | |
| 1 time | 393,124,946 (95.51) |
| >1 time | 9,368,727 (2.28) |
| Overall | 402,493,673 (97.78) |
| Functional annotation, no. of transcripts | |
| Swiss-Prot matches, BLASTx | 3,200 |
| Swiss-Prot unique proteins, BLASTx | 3,176 |
| Swiss-Prot matches, BLASTp | 3,041 |
| Swiss-Prot unique proteins, BLASTp | 3,019 |
| TMHMM | 701 |
| SignalP | 202 |
| Gene Ontology | 3,085 |
| KEGG | 2,889 |
TABLE 2
Assessment of
| % genes | Ascomycota | Saccharomyceta | Saccharomycetales |
|---|---|---|---|
| Complete | 94 | 91.4 | 91.7 |
| Complete single copy | 93.4 | 90.5 | 90.9 |
| Complete duplicated | 0.6 | 0.9 | 0.8 |
| Fragmented | 3.4 | 4.8 | 4.6 |
| Missing | 2.6 | 3.8 | 3.7 |
| Total no. of genes | 1,315 | 1,759 | 1,711 |
Identification by sequence homology searches with BLASTx function yielded annotation of 54% of Trinity transcripts and 54% of unique “genes.” Identification of protein sequences with BLASTp, against TransDecoder-identified open reading frames (ORFs) and potential coding sequences, gave functional annotation matches with 51% of the transcripts and 41% of unique “genes” (Table 1). The presence of known signal peptides, functional protein domains, and protein topology was discerned by searches against the SignalP and TMHMM databases, respectively. Of the predicted proteins, 202 sequences were predicted to have signal peptides and 701 transmembrane protein topologies were predicted.
Additional annotation was performed via the software BLAST2GO, which obtains BLAST hits that are used to retrieve and map gene ontology (GO) and KEGG terms. It also utilizes InterProScan, which acquires functional annotation of protein sequences from EBI’s InterPro databases (https://www.ebi.ac.uk/interpro/). These databases are a consortium of online databases that include PANTHER, Pfam, and SUPERFAMILY (20). Both the Trinotate and BLAST2GO annotation files are supplied as Data Set S1 in the supplemental material.
BLAST2GO searches were performed with a fungus taxonomical filter, which annotated 1,157 genes with BLAST and an additional 4,365 genes from the InterPro databases. InterPro and BLAST-derived GO terms were merged to give a total of 9,504 GO annotations assigned to 2,479 genes. These annotations were distributed among three main GO categories, biological process (3,633, 38%), cellular component (3,116, 33%), and molecular function (2,755, 29%) (Fig. S1). InterProScan was able to classify Trinity transcripts according to superfamilies based on known structures. The best-represented superfamilies were the P-loop-containing nucleoside triphosphate hydrolase (236 genes), the major facilitator superfamily (MFS) (113 genes), Armadillo-type fold (102 genes), and protein kinase-like superfamily (90 genes) (Fig. S2). From annotation against the available databases, there were 6 major enzyme classes represented, which included hydrolyases (290 genes), transferases (150 genes), oxidoreductases (88 genes), ligases (21 genes), lyases (22 genes), and isomerases (15 genes) (Fig. S3).
DE and functional annotation of
Differential expression (DE) analysis was performed to investigate the transcriptional changes observed with biofilm development. Multivariate analysis by principal-component analysis (PCA) demonstrates variance between the different time points; 0 h shows the greatest variance from the other biofilm time points. In addition, there is also some variance between biofilms at 4, 12, and 24 h (Fig. 3A). DE analysis demonstrated that 791 and 464 genes were upregulated in biofilm formation and planktonic cells, respectively, with a minimum 2-fold change (Fig. 3A). Phase-dependent differential expression of these upregulated genes is illustrated in the Venn diagram in Fig. 3B, with the downregulated genes shown in Fig. 3C; individual genes are described in Data Set S2. Of these biofilm-upregulated genes, selected genes involved in antifungal resistance and biofilm-associated mechanisms are listed in Table 3. Glycosylphosphatidylinositol (GPI)-anchored cell wall genes, including IFF4, CSA1, PGA26, and PGA52, were upregulated at all time points of biofilm formation, highlighting their potential role within cellular adhesion (Table 3). Two further adhesins, HYR3 and ALS5, were also shown to be upregulated but only in mature biofilms (Table 3). As the biofilm developed into intermediate and mature stages, a number of genes encoding efflux pumps were upregulated, including RDC3, SNQ2, CDR1, and YHD3. In addition, MDR1 was shown to be upregulated at the 24-h time point (Table 3). To understand the functional processes related to differentially expressed genes, a cutoff of 2-fold upregulation (adjusted P value of <0.05) was used for gene ontology (GO) analysis comparing planktonic cells to 24-h biofilms. The 278 differentially expressed genes were assigned to 28 GO terms with an overenrichment P value of <0.05, comprising 13 biological processes, 9 cellular components, and 6 molecular functions, and contained a number of differentially expressed functional categories (Fig. 4A). Included within these GO terms were transmembrane transport, within which several ATP-binding cassette (ABC) and major facilitator superfamily (MFS) transporters were highly upregulated in
FIG 3
Quality control and differential expression analysis of
TABLE 3
Upregulated biofilm- and resistance-associated genes
| Gene | Function | Fold change compared to | ||
|---|---|---|---|---|
| 4 h | 12 h | 24 h | ||
| IFF4 | Adhesion | 2.29 | 5.01 | 3.62 |
| PGA26 | Adhesion | 2.02 | 3.90 | 2.55 |
| PGA52 | Adhesion | 2.22 | 2.38 | 2.42 |
| CSA1 | Adhesion | 3.87 | 6.47 | 6.43 |
| PGA7 | Adhesion | 3.94 | 4.82 | |
| HYR3 | Adhesion | 2.06 | ||
| ALS5 | Adhesion | 3.82 | ||
| RDC3 | Efflux pump | 4.29 | 3.91 | |
| SNQ2 | Efflux pump | 2.63 | 3.42 | |
| CDR1 | Efflux pump | 2.30 | 3.19 | |
| YHD3 | Efflux pump | 2.14 | 2.15 | |
| MDR1 | Efflux pump | 2.3 | ||
| KRE6 | Extracellular matrix | 3.92 | 3.09 | |
| EXG | Extracellular matrix | 2.69 | 2.26 | |
| SAP5 | Hydrolytic enzyme | 2.19 | ||
| PLB3 | Hydrolytic enzyme | 2.13 | ||
FIG 4
Functional annotation of differentially expressed genes reveals upregulation of drug transporters. Gene distribution of significantly upregulated
Efflux pumps play a primary role in antifungal resistance in
Transcriptional analysis and function annotation revealed a significant upregulation of a number of drug efflux pumps, from both ABC and MFS transporters. To confirm the role of these membrane proteins within biofilms, we assessed efflux pump activity. Both 12- and 24-h biofilms exhibited increased efflux compared to planktonic cells, with 4-h biofilms below the detectable limit of the assay. Efflux from 12-h biofilms was 2.21-fold (P < 0.05) greater than that from planktonic cells, with a 2.38-fold increase shown in 24-h biofilms (P < 0.005). No statistical differences were observed between 12- and 24 h-biofilms (Fig. 5). Interestingly, efflux pump activity is shown to be constitutively expressed within biofilms, with no induction observed in response to azole antifungals (Fig. S4).
FIG 5
Efflux pump activity is increased in
Given the increased activity of efflux pumps in biofilms, we then assessed the contribution of these transporters to fluconazole sensitivity (Table 4). When biofilms were incubated for 12 h in the presence of fluconazole, the sessile MIC50 (SMIC50) ranged between 32 and >128 µg/ml. However, when also grown in the presence of fluconazole and an efflux pump inhibitor (EPI), the SMIC50 ranged between 2 and 16 µg/ml for all isolates, ranging from a 4- to 16-fold increase in susceptibility. The same trend was observed for 24-h biofilms, with the SMIC50 range between 64 and >128 µg/ml for fluconazole-only treatment, with 2- to 8-fold reductions observed with coincubation with the EPI (SMIC50, 8 to 64 µg/ml).
TABLE 4
Inhibition of efflux pumps increases azole susceptibility
| Isolate no. | Fluconazole SMIC50 (μg/ml) at time: | |||||
|---|---|---|---|---|---|---|
| 12 h | 24 h | |||||
| With | Without | Fold | With | Without | Fold | |
| NCPF8971 | 16 | 64 | 4 | 16 | >128 | ≥8 |
| NCPF8973 | 2 | 32 | 16 | 8 | 64 | 8 |
| NCPF8984 | 16 | >128 | ≥8 | 64 | >128 | ≥2 |
| NCPF8990 | 8 | 32 | 4 | 16 | 64 | 4 |
a
EPI, efflux pump inhibitor.
DISCUSSION
The rapid and simultaneous emergence of the pathogenic yeast
FIG 6
Formation and development of
To investigate this, we undertook an RNA sequencing-based approach for the analysis of
The initiation of biofilm formation depends on an initial adherence phase of colonization of a specific surface before subsequent proliferation to promote disease. A number of GPI-linked cell wall proteins were upregulated at the early biofilm time point, highlighting their role in the initial adherence stage. In
In
One of the most defining characteristics of biofilms is their recalcitrance to antimicrobial agents. As described in other Candida species, biofilm-associated drug resistance comprises a number of different mechanisms that coordinate with one another through the various phases of biofilm development (33). An underlying mechanism across Candida spp. is the upregulation of efflux pumps within biofilm-associated cells (34–36). Planktonically,
A further key mechanism of Candida biofilm resistance is the formation of the ECM, which functions to provide stability and sequestration of drugs from the biofilm, as well as protection from environmental stressors (40). Recent studies have now identified that various Candida spp. conserve a constitutive polysaccharide backbone that functions to impede antifungal delivery, and yet the composition of the ECM varies between species (41, 42). Although its composition remains unknown, it could be hypothesized that
Given the alarming global emergence of antifungal resistance, the requirement for new antifungals is pivotal (46). Drug efficacy and development have plateaued in recent years, yet an encouraging number of molecules remain within the antifungal pipeline (47, 48). Several studies have assessed the positive efficacy of novel compounds, including APX001, CD101, SCY078, and ceragenins, against
Given that we can now genetically manipulate this pathogenic yeast (55, 56), future work analyzing the functional roles and processes of specific genes and proteins will further enhance our understanding of biofilm-associated pathogenicity and resistance. Unraveling the key factors that regulate the transcriptional network that exists for
MATERIALS AND METHODS
Microbial growth and standardization.
Four
Characterization of biofilm formation.
Isolates were standardized as described above and grown for 4, 12, and 24 h at 37°C. Following growth, biofilms were washed with phosphate-buffered saline (PBS; Sigma, Dorset, United Kingdom), and biomass was quantified using the crystal violet assay, as previously described (59). In addition, biofilm composition was analyzed using propidium monoazide (PMA) quantitative PCR (qPCR), a method able to differentiate live cells from a population (60). Samples were prepared as previously described (60), before sonication in 1 ml of PBS at 35 kHz for 10 min in an ultrasonic water bath to remove and disaggregate the biofilm (61). After sonication, samples were incubated in the dark with 50 µM PMA (Cambridge BioScience, Cambridge, United Kingdom) for 10 min to allow uptake of the dye. All samples were then exposed for 5 min to a 650-W halogen light before DNA was extracted using the QIAamp DNA minikit, per the manufacturer’s protocol (Qiagen, Crawley, United Kingdom). One microliter of extracted DNA was then added to a master mix containing Fast SYBR Green master mix, RNase-free water, and 10 µM
Biofilm visualization.
Biofilms were standardized and grown on Thermanox coverslips (Fisher Scientific, Loughborough, United Kingdom) as described above. At selected time points, biofilms were washed with PBS before processing for scanning electron microscopy (SEM). Biofilms were fixed in 2% paraformaldehyde, 2% glutaraldehyde, 0.15 M sodium cacodylate, and 0.15% (wt/vol) alcian blue, before being processed as previously described (59). Biofilms were then sputter coated in gold before being viewed under a JEOL-JSM-6400 microscope.
Planktonic and sessile susceptibility testing.
Planktonic MICs (pMICs) were determined visually using the Clinical and Laboratory Standards Institute M27-A3 broth microdilution method (63). Standardized cells were treated with serial 2-fold dilutions of miconazole nitrate (0.25 to 128 mg/liter), micafungin (0.25 to 128 mg/liter), and amphotericin B (0.063 to 32 mg/liter). In addition, biofilms were grown for 4, 12, and 24 h as described above before treatment with the same concentrations as planktonic cells. Sessile MICs (sMICs) were determined using the XTT [2,3-bis(2-methoxy-4-nitro-5-sulfophenyl)-2H-tetrazolium-5-carboxanilide salt] metabolic reduction assay (64). The sMIC was calculated as the concentration leading to 80% reduction in XTT colorimetric readings in comparison to an untreated positive control.
RNA extraction and sequencing analysis.
Following biofilm characterization,
Transcriptome annotation and differential expression analysis.
Raw fastq reads were quality controlled using Trim Galore v0.4.5 (https://github.com/FelixKrueger/TrimGalore) to remove Illumina adapters and trim reads with a quality score lower than 20. Reads were then aligned to the RefSeq genome sequence B8441 using HISAT2 (66). The aligned reads were then coordinate sorted, and SAM files were converted to BAM before all aligned reads were merged using SAMtools (38). The resulting aligned reads were assembled de novo using genome-guided Trinity v2.5.1 (66). The completed transcriptome was assessed by using the contig length distribution metrics (N50), percentage of annotation, and the third-party Benchmarking Universal Single-Copy Orthologs (BUSCO) v3 assessment program (http://busco.ezlab.org/). Annotation of candidate open reading frames (ORFs), identified with TransDecoder v5.0.2 (http://transdecoder.sourceforge.net/), was then performed using the Trinotate v3.1.0 package (https://trinotate.github.io/). Trinotate performs functional annotation of transcriptomes from the UniProt Swiss-Prot database via homology searches with the Basic Local Alignment Search Tool (BLAST) functions BLASTp for protein queries and BLASTx for nucleotide queries. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) EggNOG identifiers were also inferred from the Swiss-Prot protein database. BLAST2GO annotation was additionally performed, which also relies upon BLAST but includes the annotation from European Bioinformatics Institute (EBI) InterPro databases. The extraction through to the annotation is summarized in Fig. 2. The reference transcriptome created by Trinity was used to create an index, and the trimmed reads were then counted and annotated against this index using Kallisto gene abundance quantification software. Gene abundance files for each sample replicate were then imported into R for differential analysis based upon the DESeq2 package. All additional statistics, analysis, and visualization were produced within R.
Temporal efflux pump activity and inhibition.
The efflux pump activity of planktonic and sessile cells was assessed using the alanine β-naphthylamine (Ala-Nap) fluorescent assay as previously described (38). For planktonic assessment, four
Statistical analysis.
Graph production, data distribution, and statistical analysis were carried out using GraphPad Prism (version 8; La Jolla, CA) and R Studio (version 1.1). For efflux pump activity experiments, data were normalized before Student’s t test was used to compare samples. Statistical significance was achieved if P was <0.05.
Data availability.
Raw data files are deposited under accession no. PRJNA477447.
b Institute of Healthcare, Policy and Practise, University of the West of Scotland, Paisley, United Kingdom
c National Mycology Reference Laboratory, Public Health England South-West, Bristol, United Kingdom
d Mycology Reference Centre Manchester, University Hospital of South Manchester & University of Manchester, Manchester Academic Health Sciences Centre, Faculty of Biology, Medicine and Health, Division of Infection, Immunity and Respiratory Medicine, Manchester, United Kingdom
e ESCMID Study Group for Biofilms (ESGB) ‡
Carnegie Mellon University
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Abstract
ABSTRACT
IMPORTANCE Fungal infections represent an important cause of human morbidity and mortality, particularly if the fungi adhere to and grow on both biological and inanimate surfaces as communities of cells (biofilms). Recently, a previously unrecognized yeast,
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