Abstract
Background
Candida albicans is a fungal pathogen causing human infections. Here we investigated differential gene expression patterns and functional enrichment in C. albicans strains grown under different conditions.
Methods
A systematic GEO database search identified 239 “Candida albicans” datasets, of which 14 were selected after rigorous criteria application. Retrieval of raw sequencing data from the ENA database was accompanied by essential metadata extraction from dataset descriptions and original articles. Pre-processing via the tailored nf-core pipeline for C. albicans involved alignment, gene/transcript quantification, and diverse quality control measures. Quality assessment via PCA and DESeq2 identified significant genes (FDR < = 0.05, log2-fold change > = 1 or <= -1), while topGO conducted GO term enrichment analysis. Exclusions were made based on data quality and strain relevance, resulting in the selection of seven datasets from the SC5314 strain background for in-depth investigation.
Results
The meta-analysis of seven selected studies unveiled a substantial number of genes exhibiting significant up-regulation (24,689) and down-regulation (18,074). These differentially expressed genes were further categorized into 2,497 significantly up-regulated and 2,573 significantly down-regulated Gene Ontology (GO) IDs. GO term enrichment analysis clustered these terms into distinct groups, providing insights into the functional implications. Three target gene lists were compiled based on previous studies, focusing on central metabolism, ion homeostasis, and pathogenicity. Frequency analysis revealed genes with higher occurrence within the identified GO clusters, suggesting their potential as antifungal targets. Notably, the genes TPS2, TPS1, RIM21, PRA1, SAP4, and SAP6 exhibited higher frequencies within the clusters. Through frequency analysis within the GO clusters, several key genes emerged as potential targets for antifungal therapies. These include RSP5, GLC7, SOD2, SOD5, SOD1, SOD6, SOD4, SOD3, and RIM101 which exhibited higher occurrence within the identified clusters.
Conclusion
This comprehensive study significantly advances our understanding of the dynamic nature of gene expression in C. albicans. The identification of genes with enhanced potential as antifungal drug targets underpins their value for future interventions. The highlighted genes, including TPS2, TPS1, RIM21, PRA1, SAP4, SAP6, RSP5, GLC7, SOD2, SOD5, SOD1, SOD6, SOD4, SOD3, and RIM101, hold promise for the development of targeted antifungal therapies.
Similar content being viewed by others
Background
Every year, 150 million people are infected with fungal pathogens which results in almost 1.7 million deaths yearly [1, 2]. A significant number of these infections are caused by Candida species, with C. albicans ranking as the fourth most prevalent cause of hospital-acquired bloodstream infections [3, 4]. In 2009, another Candida species, C. auris, was described for the first time and now, this species is causing infections everywhere in the world [5]. A major problem with this species is that most isolates are resistant to fluconazole and a large percentage of the isolates is also resistant towards echinocandins and polyenes [6, 7]. In many cases multidrug resistance is observed.
Candida albicans, an opportunistic human fungal pathogen, is frequently found in the human microbiome as a commensal in the gastrointestinal tract, oral cavity, and vaginal tract [8, 9]. However, this typical harmless species has the capacity to cause several types of infections depending on the host niche. It can cause mucosal infections in immune-competent persons, with vaginal infections as a typical example, but it can also cause systemic infections in immune-compromised patients, where different organs will be colonized, resulting to the death of the patient in many cases [3, 10,11,12,13]. The number of infections caused by C. albicans is also increasing because of the advancement of medical care in hospitals. More and more patients receive various types of implants, such as catheters, valves, hips, that are all suitable substrates for these fungal cells to attach to and to form a biofilm. The extracellular matrix produced by biofilm cells is known to sequester antifungal drugs resulting in tolerance towards these drugs. This barrier can even be further increased in multispecies biofilms [14,15,16,17,18,19]. Further, the increase in the use of broad-spectrum antibiotics is also increasing the incidence of fungal infections [11, 20, 21]. Finally, the increase in patients with diabetes or those receiving transplants also have a higher chance of getting a fungal infection [22].
C. albicans is a species that exhibits a great degree of adaptability, as it can grow in a variety of environmental conditions with varying nutrition availability, or differences in temperature, pH, osmolarity, and oxygen availability [23]. This flexibility is partly caused by the pleiotrophic morphologies that this species can grow in. Apart from growing as a budding yeast, they can also grow in the form of pseudohyphae and true hyphae, the morphology mostly associated with an infection [3, 24]. Hyphal cells can infiltrate tissues and cause damage [25,26,27,28] and they are important for the development of biofilms [29,30,31,32,33,34]. Several virulence factors are also associated with the hyphal morphology. These include the expression of different adhesin proteins, hydrolytic enzymes and the production of the candidalysin toxin, which can intercalate in the host cell membrane [8, 35,36,37].
Apart from understanding virulence factors, recognizing crucial predisposing elements for candidiasis development is also essential. Talapko et al. [38] reviewed factors like neutropenia, immunosuppression, diabetes, age, as well as those linked to patient care such as extended antimicrobial therapy, prolonged hospital stays, catheter use, and surgeries.
Candidiasis treatment options are limited to five chemical groups - polyenes, echinocandins, azoles, pyrimidine analogues, and allylamines, of which the first three are mainly used to treat systemic infections [39]. Azoles target ergosterol biosynthesis, polyenes bind to sterols in the membrane and echinocandins target cell wall biosynthesis. A major problem with these drugs, apart from being either fungistatic or toxic, is the rapid development of drug resistance. This is mainly a problem for the azoles and echinocandins [40,41,42] and this results in high mortality rates which can go up to 60% for C. albicans [43]. Bhattacharya et al. and Srivastava et al. [44, 45] reviewed the molecular mechanisms associated with antifungal resistance, including overexpression of transporters, alterations in cell wall/ergosterol production, and mutations in regulatory transcription factors. Further investigation into these mechanisms might aid in identifying resistant strains, discovering fresh drug targets, and restraining drug resistance progression.
The growing need for new antifungal agents stems from increased infections caused by resistant fungi and emerging strains, resulting in recent approvals of new antifungal drugs in 2021 and 2022 [2, 46, 47]. Novel antifungal drugs that are active on Candida sp. are fosmanogepix (interferes with mannoprotein production in the cell wall by inhibiting Gwt1), ibrexafungerp and rezafungin (a triterpenoid and novel echinocandin that both inhibit the β-(1,3)-glucan biosynthesis) and oteseconazole (novel tetrazole inhibiting ergosterol biosynthesis and used to treat vaginal infections) [48,49,50].
A major problem for the development of novel antifungal drugs is the fact that both fungi and humans are eukaryotes and phylogenetically seen, fungi are closely related to animals. This makes it difficult to identify enzymes or metabolic pathways that are specific for fungi and where inhibitors would not affect host cells. Whereas in the past, only essential proteins were targeted, now there is also more effort to target virulence factors, that are not necessarily essential for growth, but that are essential for virulence. There may be less pressure to develop resistance against such drugs [51].
The rapid advancements of omics technologies have resulted in a strong increase in the abundance of genomic data in public repositories. The use of “omics” techniques to study the biology of C. albicans has been significantly increased by the sequencing and annotation of the common laboratory strain, SC5314 [52,53,54]. Key features in C. albicans are being examined using a variety of post-genomic approaches, such as comparative genomics, transcriptional profiling, and the construction of a full gene deletion collection [55].
For various biological purposes, large volumes of transcriptomic data have been generated using microarray or RNA sequencing technologies and are publicly available. Despite the biological and technical variability among samples in a given study, and the differences between studies, it is possible to systematically assemble and integrate the available gene expression data with the available meta-information on the different experimental conditions and workflows, in order to overcome unwanted variation as well as to increase the statistical power [56]. Such RNA-seq meta-analysis enables the generation of more complete data sets, the identification of individual research biases or shortcomings, the collection of the most reliable data, and the discovery of new trends and relationships [57].
In recent years, the integration of high-throughput sequencing and bioinformatics analysis data have revolutionized our understanding of genetic regulation, particularly in the context of C. albians infections. These approaches have enabled the identification of differentially expressed genes (DEGs) and the exploration of affected biological processes, molecular functions, and cellular components [58, 59]. Moreover, the utilization of Gene Ontology (GO) terms has proven invaluable in annotating gene product functions and predicting phenotypic outcomes [56, 60, 61]. Building on this progress, our work aims to further elucidate the distinct molecular characteristics exhibited by the identified GO terms under normal versus environmental stress conditions. By clustering these terms, we aimed to uncover a set of genes sensitive to various stress conditions, thereby identifying potential antifungal targets in C. albicans.
In this study, we compiled and analysed a comprehensive RNA-seq gene expression dataset through a meta-analysis approach. We integrated transcriptomics data from seven studies to extract the gene expression information under different conditions. This allowed us to reveal the presence of specific genes with high frequency within the up and down-regulated gene ontology clusters. Notably, these key genes involved in central metabolism, ion homeostasis, and pathogenicity emerged as potential targets for anti-fungal interventions.
Overall, our study deciphers the molecular mechanisms of C. albicans and identifies promising targets for the development of novel antifungal strategies by integrating gene expression information from multiple studies.
Methods
Data retrieval and pre-processing
A comprehensive search was conducted on the Gene Expression Omnibus (GEO) database [62] using the keyword “Candida albicans” resulting in the identification of 239 datasets. Following a rigorous selection process as shown in Figs. 1 and 14 datasets were retained for downstream analysis. Raw sequencing data for these selected datasets were downloaded from the European Nucleotide Archive (ENA) database [63] according to the accession numbers. Corresponding meta-information was extracted from the description of the data sets and the original articles, including genotype and treatment conditions, sample preparation and sequencing information.
The nf-core pipeline [64] was employed for pre-processing the raw data with configurations for C. albicans. The reference genome obtained from EnsemblFungi was converted to a GTF file and provided as input to the pipeline. Quality control (QC) reports and read counts were generated by the pipeline. In brief, the workflow processes raw data from FastQ inputs with FastQC [65], reads are then aligned with STAR [66], producing gene counts (featureCounts, StringTie) or transcripts (Salmon, tximport) and conducting comprehensive quality control on the results (RSeQC, Qualimap, dupRadar, Preseq, edgeR, MultiQC).
Quality assessment and differential expression analysis
To assess the quality of the pre-processed data, principal component analysis (PCA) was performed using prompt function in the R environment with the log2-transformed counts as input. Subsequently, differentially expressed genes were identified using the DESeq2 package [67]. The obtained p-values are corrected for multiple testing using the Benjamini and Hochberg method using default setting in DESeq2. Genes were considered statistically significant if they exhibited a false discovery rate (FDR) of < = 0.05 and a log2-fold change > = 1 or <= -1. The GO term enrichment analysis was performed using topGO [68, 69]. GSE55819 [70] was excluded from further analysis due to low quality results. Additionally, GSE38298 [71], involving clinical isolates, and GSE103674 [72], focusing on white-opaque strains, were excluded from subsequent analyses. Moreover, GSE37682 [73], GSE75124 [74], GSE86540 [75], and GSE45141 [76] datasets were excluded as they utilized strains derived from the SC5314 background (CAF4-2, BWP17, SN152). Consequently, the following datasets (GSE87832, GSE102039, GSE64659, GSE99902, GSE73409, GSE99767, GSE49310), all based on SC5314 strain background, were chosen for further investigation. The details of these seven studies are provided in Table 1.
Results
Study-specific clustering of gene expression patterns unveiled by principal component analysis (PCA)
The seven selected studies provide a comprehensive insight into C. albicans’ responses to a range of stimuli, including nutrient fluctuations, growth conditions, stressors, and pH variations (Additional Table 1). Through rigorous RNA-seq analyses, these investigations unveil how gene expression patterns change under different conditions, shedding light on the intricate molecular mechanisms underlying C. albicans’ adaptability.
The principal component analysis (PCA) was performed on the gene counts from these seven RNA-seq studies encompassing 46 different conditions (Fig. 2). The aim of the analysis was to identify patterns and relationships among the conditions based on their gene expression profiles. The results of the PCA revealed distinct clustering patterns among the conditions from each study. Specifically, conditions within the same study tended to cluster together, indicating similarity in gene expression profiles.
Among the studies, conditions from GSE99767 [77] and GSE49310 [78] datasets exhibited close proximity and almost clustered together. Both studies, conducted by the same research group, investigated responses to acidic conditions and weak acid environments. Similarly, conditions from GSE87832 [79] and GSE73409 [80] datasets were also found to cluster close to each other. Both studies explored responses to specific treatments (casamino acids, glutamate, ⍺-ketoglutarate, and hydrogen peroxide), which suggests that there may share biological processes or pathways activated in response to these treatments. Furthermore, conditions from GSE73409 [80] and GSE64659 [81] datasets also clustered together. Both studies examined responses to different treatments (hydrogen peroxide and N-Acetylglucosamine (GlcNAc)), indicating potential shared regulatory mechanisms in response to these stressors. On the other hand, conditions from GSE99902 [82] and GSE102039 [83] datasets appeared to be unique and distinct from the other five experiments. Azadmanesh et al. [82] focused on the impact of various growth media, while Tao et al. [83] investigated responses to different air and CO2 environments. These distinct experimental conditions likely led to divergent gene expression patterns, indicating that these studies had different gene expression profiles compared to the other studies included in the analysis.
Overall, the PCA analysis of the gene counts from the seven RNA sequencing studies revealed clear clustering patterns, with conditions from the same study tending to group together. These findings provide valuable insights into the relationships and similarities among the different experimental conditions investigated in the RNA sequencing studies.
Differential gene expression analysis for individual studies
Differential expression analysis was performed using DESeq2 to identify genes that exhibited significant up- or down-regulation in C. albicans samples under different conditions for each study, respectively (Table 2). The details of the specific comparisons, including the conditions, fold changes, and statistical significance, are provided in Additional Table 2.
In this comprehensive analysis, we investigated the differential gene expression patterns in C. albicans across seven distinct studies employing DESeq2 comparisons. These studies encompassed a wide array of growth conditions, providing a diverse set of experimental settings for transcriptomic profiling. Figure 3 portrays a series of Venn diagrams, each pertaining to a specific study among the seven selected RNA-seq investigations.
Danhof et al. [79] examined the response C. albicans strain SC5314 to various substrates, including glucose, casamino acids, glutamate, and α -ketoglutarate, over a five-hour period. A total of 4217 DEGs were identified. Among these, 727 genes exhibited overlapping expression profiles across the three comparisons in this study. Tao et al. [83] delved into the total RNA profiles of both wild-type and SFL2 deletion strains under different environmental conditions, including air and a 5% CO2 atmosphere, over a span of 22 h. 1408 DEGs were identified and only one gene was differentially expressed in all comparisons.
Similarly, Du et al. [81] exposed C. albicans SC5314 cells to GlcNAc for either 5 or 24 h and identified 711 genes that were differentially expressed. Azadmanesh et al. [82] examined cells grown in various liquid and solid media conditions, including FBS, LEE, RMPI, YPD, and Spider. 3187 DEGs were identified. Notably, 17 genes demonstrated overlapping expression patterns, including the noteworthy gene SOD5 associated with oxidative stress response. Tscherner et al. [80] conducted a transcriptome analysis of wild type, hat1∆, cac2∆, and rtt109∆ deletion strains both before and after treatment with hydrogen peroxide. A total of 13,146 DEGs were identified, and of these, 871 genes demonstrated overlapping expression profiles.
Finally, Cottier et al. [77] performed a multifaceted exploration involving two strains (SC5314 and mig1∆), two media types (YPD and YPMaltose), and three different acid conditions (no acid, acetic acid, butyric acid). Similarly, Cottier et al. [78] employed a robust experimental design, analysing the transcriptional profiles of wild-type C. albicans SC5314 under six distinct conditions, comprising both control and weak acid environments. This investigation included multiple time points and replicates, allowing for a thorough exploration of the genetic landscape in response to different weak organic acids. A total of 1970 DEGs were identified, and among them, 10 genes demonstrated overlapping expression patterns across the various weak acid environments. Notably, CZF1, CTA4, and ZCF39, genes associated with zinc homeostasis, were among the overlapping DEGs, adding further significance to these findings. These diverse experimental setups provide a rich resource for understanding the transcriptional responses of C. albicans under various physiological conditions.
GO enrichment analysis
To gain further insights into the functional implications of these differentially expressed genes, GO enrichment analysis was performed using the topGo package [68], with the significantly up-regulated and down-regulated GO terms separately. Terms with a node size of > = 5 were included, and the significance of over-represented terms was assessed using the Fisher’s exact test with a threshold of FDR < 0.01. A total of 2,496 significantly up-regulated GO terms and 2,573 significantly down-regulated GO terms were identified. Among the up-regulated GO terms, 907 GO term IDs were found to be unique (after removal of duplications), indicating distinct biological processes associated with these genes. Similarly, for the down-regulated GO terms, 812 GO term IDs were identified as unique (after removal of duplications), representing specific functional categories associated with the down-regulated genes.
As shown in Fig. 4, the simplifyEnrichment method [84] was then applied to cluster the significantly up-regulated and down-regulated GO terms based on the semantic similarity, which facilitated the identification of distinct functional modules or pathways within the enriched GO terms. As a result, 69 distinct clusters were identified for the up-regulated GO terms, and 68 distinct clusters were found for the down-regulated GO terms. We chose the top 4 clusters with the highest GO ID numbers for further analysis, as displayed in Table 3.
Analysis of target gene lists in relation to up- and down-regulated GO clusters
Based on previous studies, three target gene lists related to potential antifungal targets in C. albicans were compiled, which can be categorized into three functional categories: central metabolism, ion homeostasis, and pathogenicity. The first gene list, comprising 24 genes associated with central metabolism, was derived from the review paper by Wijnants et al. [51]. Additionally, genes related to the glycerol pathway were incorporated into this list. The second gene list, containing 17 genes involved in ion homeostasis, was extracted from the study by Li et al. [85]. Finally, the third gene list consisted of 27 genes associated with pathogenicity, as reported by Ahmed et al. [86].
To evaluate the relevance of these target genes within the identified GO clusters, their frequency within the up and down-regulated GO term clusters was determined using g:Convert at g:Profiler. As summarized in Table 4, it showed that the specific genes in these three compiled gene lists exhibited a higher occurrence within each cluster, indicating their potential importance as antifungal targets. The complete target genes list and their frequency in the selected clusters are provided in Additiona Table 3.
Within the central metabolism category, the genes TPS2 and TPS1 were found to have a frequency of 4 and 3, respectively, among the up-regulated GO clusters. Similarly, these genes exhibited the same frequency within the down-regulated GO clusters. In C. albicans, the TPS1 gene encodes trehalose-6-phosphate synthase (Tps1), a crucial enzyme responsible for initiating trehalose biosynthesis. Disruption of the TPS1 gene results in reduced cell viability during oxidative stress, defective hyphal transition at 37 °C, and decreased infectivity [87,88,89,90,91]. While trehalose-6P phosphatase (Tps2) is the enzyme that converts trehalose-6P into trehalose, and contributes to C. albicans’ cell viability, virulence, and susceptibility to macrophage phagocytosis [90, 92,93,94].
For the ion homeostasis category, several genes showed notable occurrence in both up and down-regulated GO clusters. RIM21, RIM9, PRA1, ZRT1, CSR1, MID1, and CCH1 were among the genes with the highest frequencies in both categories. PRA1, ZRT1, and CSR1 genes are involved in Zinc homeostasis. Disruption of ZRT1 leads to growth defects in zinc-limited environments [95]. PRA1 and ZRT1 are upregulated during infection to facilitate zinc uptake [96]. Pra1 works through its ability to scavenge host zinc and it is also involved in endothelial damage, as its deletion results in shorter hyphae formation [95].
Csr1 plays a role in proliferation, hyphae formation, and biofilm maturation [97]. Deletion of CSR1 inhibits the expression of the hypha-related gene HWP1, that also plays an important role during biofilm formation and for interaction with host cells [97,98,99,100]. Cch1 and Mid1 function within the Ca2+ cell survival (CCS) pathway in C. albicans, playing crucial roles in the fungus’s viability and virulence, with mutants lacking these proteins displaying decreased virulence and heightened sensitivity to azoles [101,102,103].
In the pathogenicity category, genes CPH1, SAP4, SAP6, SAP5, SAP9, and SAP10 were identified with higher occurrence within both up and down-regulated GO clusters. SAP4, SAP6, SAP5, SAP9, and SAP10 encode secreted aspartyl proteases, which are one of the three primary extracellular hydrolytic enzymes secreted by Candida species and the most clinically relevant [104]. Sap enzymes play a vital role in various biological activities, including hyphae formation and adherence [105, 106]. The transcription factor Cph1 plays a role in various cellular processes, including phenotypic white-opaque switching for mating and filament formation [107]. Cph1 is known to regulate genes involved in cell wall construction, such as chitin synthase genes and those related to hyphal development [108, 109].
These results highlight the potential relevance of the target genes in the context of the identified GO clusters, emphasizing their potential as important antifungal targets in C. albicans.
Frequency analysis of genes in up- and down-regulated GO clusters
To gain further insights into the functional relevance of the identified up- and down-regulated Gene Ontology (GO) clusters in C. albicans, we conducted a frequency analysis to determine the most frequently occurring genes within these clusters. In Table 5, we present the highest ranked genes identified within the up-regulated GO clusters. Notably, the E3 ubiquitin-protein ligase gene (RSP5) appears in two clusters (3 and 1) with frequencies of 97 and 41, respectively. In S. cerevisiae, Rsp5 functions in the ubiquitin-dependent endocytosis of plasma membrane proteins [110]. The exact physiological function of Rsp5 in C. albicans is still not fully understood. To fully comprehend the role and significance of Rsp5 in C. albicans, further research is required [111].
Additionally, the Serine/threonine-protein phosphatase gene, GLC7, and the Superoxide dismutase genes SOD2, SOD5, SOD1, SOD6, SOD4, and SOD3 demonstrate high occurrence in the up-regulated clusters.
Table 6 displays the genes with the highest frequency identified within the down-regulated GO clusters. These genes, along with their descriptions and frequencies, highlight the most frequently occurring genes associated with down-regulated biological processes. The ATP-dependent RNA helicase gene (C7_03400C_A) exhibits the highest frequency with 103 occurrences in the down-regulated clusters.
Other identified genes include C3_06710W_A (not annotated), RIM101 (pH-response transcription factor), and C4_01950W_A (F-actin-capping protein subunit) with frequencies of 37, 15, and 41, respectively. C7_03400C_A is the ortholog of S. cerevisiae MTR4, a gene encoding an ATP-dependent RNA helicase that plays a role in RNA processing [112, 113]. According to the Candida Genome Database (CGD), C3_06710W is annotated as a protein of unknown function. However, it is regulated by Sef1, Sfu1, and Hap43, underscoring a potential role in iron homeostasis. In S. cerevisiae, VHC1 is the ortholog of C3_06710W. Further research is required to determine the precise function and regulatory mechanisms of C3_06710W/VHC1 in Candida species. C4_01950W_B is annotated as a putative F-actin-capping protein subunit beta, and it is suggested to be potentially essential as homozygous deletion strains could not be obtained using the UAU1 method. In S. cerevisiae, CAP2 is the ortholog of C4_01950W_B. The F-actin-capping protein subunit beta is likely involved in regulating actin filament dynamics and cytoskeletal organization. Further studies are needed to understand the specific role of C4_01950W_B/CAP2 in Candida species.
Discussion
Candida albicans: challenges and antifungal target identification
C. albicans, a versatile opportunistic fungal pathogen, poses significant challenges in the clinical management of candidiasis. The rise in immunocompromised patients and the widespread use of broad-spectrum antibiotics have led to an increased incidence of C. albicans infections [11, 20]. Understanding the pathogenicity mechanisms of C. albicans is pivotal for developing effective antifungal therapies and diagnostics [114].
The ability of C. albicans to transition between different morphological states, such as yeast and hyphal cells, or between the epigenetic white and opaque phases, contribute to its pathogenicity and ability to form biofilms of which the extracellular matrix acts as a protective shield against host defenses and antifungal treatments [26, 30, 115, 116]. Specific gene families governing adhesion, proteolysis, and defense against reactive oxygen species play significant roles depending on the infection site [8, 117]. Moreover, the metabolic flexibility of the fungus, allowing it to thrive in diverse environments contributes to reduced susceptibility to antifungals and aids in evading host defenses [118].
A promising approach in the quest for innovative and potent antifungals is the strategic targeting of fungal virulence mechanisms. Disrupting these traits holds significant potential for the advancement of novel therapeutic strategies [119, 120]. The intricate process of transcriptional regulation plays a pivotal role in these adaptive mechanisms. As such, transcriptomic studies conducted throughout the course of an infection, or in conditions that mimic this process, offer invaluable insights. Through transcriptomics analysis, we gain the ability to elucidate the essential pathways required for fungal adaptation within the host environment [114, 121, 122].
Our transcriptomic meta-analysis revealed genes with high frequency in the selected GO clusters, most of which are associated with different virulence factors, as shown in Fig. 5. Importantly, some of these genes have already been suggested as potential antifungal targets.
In our investigation, we introduced an innovative approach that utilizes the wealth of publicly accessible RNA sequencing data for a thorough meta-analysis. This method is designed to extract valuable insights from extensive transcriptomic datasets, providing a systematic analysis that complements experimental studies. By unveiling new perspectives and potential targets, this approach helps to understand complex biological systems better and offers guidance for future experimental inquiries.
Key genes associated with central metabolism, ion homeostasis and pathogenicity
Our meta-analysis revealed several key genes that showed associations with central metabolism, Ion homeostasis and pathogenicity in C. albicans.
Central metabolism and trehalose synthesis
Within the central metabolism category, the genes TPS2 and TPS1 were identified with high frequencies in both up and down-regulated clusters. These genes encode the primary enzymes involved in the synthesis of trehalose, a stress protectant molecule that plays a crucial role in the response to various environmental stresses [123, 124]. That these two genes clearly come out of our analysis supports our approach as they were previously already linked to virulence in a number of pathogenic fungi [90, 125,126,127].
The absence of Tps1 and Tps2 in mammals, coupled with their pivotal roles in cell viability and virulence, positions them as promising targets for novel antifungal medications [90, 128].
Miao et al. [129] explored the complex structural characteristics of Tps2 enzymes across different catalytic phases, elucidating the mechanisms behind substrate recognition and phosphate removal. Significantly, the similarities in both structure and function of Tps2 proteins across various pathogenic fungi, including C. albicans, underscore their specialization for the trehalose pathway, offering the potential for tailored enzyme inhibitors to minimize unforeseen side effects. Additionally, their findings indicate that minor alterations to Tps2’s catalytic pocket might disrupt fungal resistance to drug binding while hindering the conversion of trehalose-6-phosphate (T6P) into trehalose [129]. This disruption could potentially yield highly potent antifungal drugs by preventing the production of trehalose from T6P, consequently causing the accumulation of toxic T6P [90].
T6P itself has shown promise as an inhibitor of Tps1, paving the way for the development of T6P analogs as potential antifungal agents [128]. Similarly, the development of specific inhibitors against Tps2 can aid in the discovery of novel antifungals against C. albicans infections and those caused by other human fungal pathogens [90, 125,126,127].
Zinc homeostasis
The genes PRA1, ZRT1, and CSR1 exhibited high frequencies in both up and down-regulated clusters in the ion homeostasis category. These genes play crucial roles in regulating zinc homeostasis, which significantly impacts various aspects of C. albicans’ biology and pathogenesis. C. albicans has developed mechanisms to tightly control intracellular zinc levels because of the toxicity of excess zinc [130].
Targeting zinc homeostasis shows potential for developing antifungal drugs. Zinc-attenuating compounds, such as ZAC307 and ZAC989, have been identified and demonstrated antifungal activity by chelating zinc in vitro [131]. These compounds have also shown efficacy in murine fungal infection models, indicating their potential as a novel class of antifungal agents [131].
Calcium homeostasis
MID1 and CCH1 are two other genes that showed high frequency in the ion homeostasis category. In C. albicans, Cch1 and Mid1 are essential components of the Ca2+ cell survival (CCS) pathway, which regulates calcium homeostasis [102]. Disruption of this pathway can result in growth problems and cell death in eukaryotic organisms [132]. These proteins, Cch1 and Mid1, are homologous to the catalytic and regulatory subunits of mammalian voltage-gated calcium channels, respectively [133, 134].
SAP genes and CPH1: keys to C. albicans pathogenicity
In the pathogenicity category, genes SAP4, SAP6, SAP5, SAP9, SAP10, and CPH1 were consistently present with high frequencies in both up and down-regulated clusters. Secreted Aspartyl Proteases (SAPs) are crucial for C. albicans to establish infections. They enable the fungus to degrade host tissues, adhere to mucosal surfaces, and form biofilms, all of which contribute to their pathogenicity [135,136,137]. Recent research by Dhanasekaran, et al. [138] explored the potential of bioactive components from medicinal herbs as inhibitors of Sap enzymes. The study identified hesperidin and vitexin as promising candidates based on their drug-likeness, safety, and their ability to interact with the catalytic site of the Sap5 enzyme.
Recent research by Wagner et al. [139] revealed that the transcription factor Cph1 plays a key role in the unmasking of immunogenic elements, such as ß (1,3)-glucan and chitin, in C. albicans. This unmasking process mediated by Cph1 triggers immune responses, including the activation of immune cells like macrophages and neutrophils, ultimately leading to enhanced fungal clearance during infection [139]. Understanding the mechanisms underlying Cph1-mediated unmasking provides valuable insights into host-fungus interactions and potential therapeutic strategies for combating C. albicans infections.
Exploring potential antifungal targets in C. albicans
Through frequency analysis, several key genes were found to be frequently occurring in the up/down regulated clusters, which include RSP5, GLC7, SOD2, SOD5, SOD1, SOD6, SOD4, SOD3, C7_03400C_A, RIM101, C3_06710W and C4_01950W_A.
RSP5 and GLC7
Rsp5, an E3 ubiquitin ligase, is a central player in temperature sensing, coordination of the heat shock response, and regulation of sugar transporters, all of which significantly impact Candida’s pathogenicity and its ability to adapt to different environments, as indicated by previous studies [111, 140]. However, the precise physiological functions of Rsp5 in C. albicans remain incompletely understood and further research is necessary to gain a comprehensive understanding of its role and its significance [111].
GLC7, the gene encoding the Serine/threonine-protein phosphatase Glc7, functions in various cellular processes in C. albicans, including modulating cell morphology, regulating cell cycle progression, facilitating DNA damage response, and enhancing stress resistance [141, 142].
Role of SOD genes
The SOD gene family in C. albicans plays a key role in protecting the fungus against oxidative stress, enabling its survival within the host environment [143].
Bink et al. [144] reported that the efficacy of miconazole against C. albicans biofilms can be improved by utilizing Sod inhibitors. These results underscore the important role played by Sod’s in the formation of miconazole-tolerant persister cells in C. albicans biofilms, primarily through their ROS detoxifying activity. Earlier studies have also implicated Sod4 and Sod5 in ROS detoxification in C. albicans [145, 146]. The active-site structure and copper binding characteristics of Sod5, a copper-only enzyme, significantly differ from those of Cu/Zn-SODs found in animal hosts. These distinctions highlight the potential of targeting Cu-only Sod’s as a viable approach for future antifungal drug design [147, 148].
Targeting the Rim101 pH response pathway
Activation of the Rim101 pathway leads to the expression of specific genes involved in various cellular processes and virulence factors, including growth, iron metabolism, cell wall structure, yeast-to-hypha transition, adhesion, and biofilm formation [149]. Disruption of the RIM101 gene in C. albicans leads to increased susceptibility to both echinocandins and azoles, indicating the involvement of the Rim pathway in tolerance and/or resistance to these antifungal drugs [149,150,151].
Targeting the Rim101 pH response pathway, which is specific to fungi and conserved among fungal species, holds promise for the development of novel antifungal strategies. Combining the targeting of the Rim pathway with existing antifungal drugs may represent a potent approach in combating C. albicans infections [149,150,151].
Incorporating the findings of the meta-analysis in relation to the original studies
Our meta-analysis uncovered several areas of agreement and divergence between our findings and the original studies, shedding light on the intricate regulatory mechanisms governing C. albicans’ response to specific stressors.
In the study conducted by Cottier et al. [77], the investigation centered around the genetic and molecular responses of C. albicans to acetic and butyric acids, with an emphasis on the influence of glucose availability and the MIG1 gene on weak organic acid (WOA) sensitivity. The study identified a set of six genes, including GLG2, ALD6, FDH1, and HGT16, consistently upregulated under conditions of heightened WOA sensitivity. Intriguingly, our meta-analysis did not highlight any of these genes as significant in the context of C. albicans stress responses. This discrepancy suggests that while these genes play a role in WOA sensitivity, their contribution might not be as universal as initially proposed. Similarly, in Cottier et al’s study [78], the investigation delved into the responses of C. albicans to WOAs produced by bacteria present in the human host. The study identified a core transcriptional response encompassing genes associated with iron homeostasis, carboxylic acid metabolism, and ribosome biogenesis. However, none of these genes emerged as prominent candidates in our meta-analysis, indicating that the regulatory pathways governing WOA responses might vary across different experimental conditions.
Our meta-analysis also connected findings from Danhof et al. [79], who explored genetic factors influencing medium alkalinization during growth on ⍺-ketoglutarate, with those of Cottier et al. [77]. The study highlighted the role of the CPH1 gene in this process. Interestingly, our analysis supported this finding, particularly in dataset GSE99767 [77], where variations in carbon sources led to the upregulation of CPH1. This convergence of results underlines the robustness of the CPH1 gene’s involvement in C. albicans’ responses to specific stressors.
Furthermore, Tscherner et al. [80] focused on the role of histone acetyltransferases (HATs) in oxidative stress resistance. Although the study revealed a set of oxidative stress-related genes, our meta-analysis did not identify these specific genes as central components of C. albicans’ stress response network. However, the study highlighted SOD genes, particularly Sod4 and Sod5, as essential for survival upon phagocytosis. Interestingly, our meta-analysis did identify these SOD genes as potential antifungal targets, reinforcing their significance in C. albicans’ interactions with the host immune system.
In conclusion, our meta-analysis provides a comprehensive overview of the transcriptional responses of C. albicans to various stressors. While there are instances of agreement with the original studies, such as the role of CPH1 and SOD genes, there are also instances of non-convergence, as seen with genes identified in [77] and [78]. These disparities emphasize the context-dependent nature of C. albicans’ responses to stressors and highlight the need for a nuanced understanding of the regulatory mechanisms governing these responses. Our findings not only contribute to deciphering the intricate stress response network of C. albicans but also provide insights into potential avenues for future research into antifungal strategies targeting specific stress-responsive genes.
Limitations and future directions
The findings from our meta-analysis highlight the potential of these identified genes as targets for the development of novel antifungal therapies against C. albicans. By focusing on key genes involved in central metabolism, ion homeostasis, and pathogenicity, we can potentially disrupt critical cellular processes and enhance the efficacy of antifungal treatments.
Moreover, the meta-analysis approach employed in this study demonstrates the power and benefits of integrating and analyzing large-scale publicly available transcriptomic data. As expected, variations in experimental conditions, sample sizes, and data preprocessing methods across different studies can introduce potential biases and heterogeneity. In this study, the RNA-seq data were analyzed with a unified bioinformatics pipeline, which reduced the biases introduced by software.
With gene expression data from different conditions, it provides a comprehensive overview of the transcriptomic landscape of C. albicans, highlighting the interconnection of different biological processes and pathways. By examining the collective gene expression changes across multiple studies, we can gain a more holistic understanding of C. albicans’ response to stress, its adaptation mechanisms, and its virulence factors. This information can guide future research efforts and aid in the development of targeted interventions. It is important to acknowledge the limitations of our study. While meta-analysis provides valuable insights, it relies on the availability and quality of publicly accessible transcriptomic data. Therefore, cautious interpretation of the results is necessary, and additional experimental validations are needed to confirm the functional significance of the identified target genes.
Conclusion
In conclusion, our meta-analysis of transcriptomic data in C. albicans revealed key genes associated with antifungal resistance and pathogenicity. Genes involved in central metabolism, ion homeostasis, and pathogenicity emerged as potential targets for therapeutic interventions. The findings underscore the importance of integrated data analysis approaches and highlight the benefits of meta-analysis in uncovering novel insights and potential therapeutic avenues. Further research and experimental validation are warranted to elucidate the precise roles of these genes and their potential as targets for antifungal strategies.
Data availability
Not applicable.
Abbreviations
- CCS:
-
Ca2 + cell survival
- CGD:
-
Candida genome Database
- DEGs:
-
Differentially expressed genes
- ENA:
-
European Nucleotide Archive
- FDR:
-
False Discovery Rate
- GEO:
-
Gene Expression Omnibus
- GO:
-
Gene Ontology
- HATs:
-
Histone acetyltransferases
- GlcNAc:
-
N-Acetylglucosamine
- PCC:
-
Pearson correlation coefficient
- PCA:
-
Principal component analysis
- QC:
-
Quality control
- SAPs:
-
Secreted aspartyl proteases
- SOD:
-
Superoxide dismutase
- TPM:
-
Transcripts per million
- T6P:
-
Trehalose-6-phosphate
- Tps:
-
Trehalose-6-phosphate synthase
- Tps2:
-
Trehalose-6-phosphate phosphatase
- WOA:
-
Weak Organic Acid
References
Bongomin F, Gago S, Oladele RO, Denning DW. Global and multi-national prevalence of fungal diseases-Estimate Precision. J fungi (Basel Switzerland). 2017;3(4):57.
Kainz K, Bauer MA, Madeo F, Carmona-Gutierrez D. Fungal infections in humans: the silent crisis. Microb cell (Graz Austria). 2020;7(6):143–5.
Brown GD, Denning DW, Gow NA, Levitz SM, Netea MG, White TC. Hidden killers: human fungal infections. Sci Transl Med. 2012;4(165):165rv13.
Robbins N, Wright GD, Cowen LE. Antifungal drugs: the current armamentarium and development of New agents. Microbiol Spect. 2016;4(5). https://doi.org/10.1128/microbiolspec.funk-0002-2016.
Desoubeaux G, Coste AT, Imbert C, Hennequin C. Overview about Candida Auris: what’s up 12 years after its first description. J De Mycol Medicale. 2022;32:101248.
Carolus H, Pierson S, Munoz JF, Subotić A, Cruz RB, Cuomo CA, et al. Genome-wide analysis of experimentally evolved Candida Auris reveals multiple mechanisms of multidrug resistance. mBio. 2021;12:e03333–20.
Fisher MC, Alastruey-Izquierdo A, Berman J, Bicanic T, Bignell EM, Bowyer P, et al. Tackling the emergence threat of antifungal resistance to human health. Nat Rev Microbiol. 2022;20:557–71.
Calderone RA, Fonzi WA. Virulence factors of Candida albicans. Trends Microbiol. 2001;9(7):327–35.
Desai JV, Mitchell AP, Andes DR. Fungal biofilms, drug resistance, and recurrent infection. Cold Spring Harbor Perspect Med. 2014;4(10):a019729.
Cassone A, Cauda R. Candida and candidiasis in HIV-infected patients: where commensalism, opportunistic behavior and frank pathogenicity lose their borders. AIDS. 2012;26(12):1457–72.
Colombo AL, Perfect J, DiNubile M, Bartizal K, Motyl M, Hicks P, et al. Global distribution and outcomes for Candida species causing invasive candidiasis: results from an international randomized double-blind study of caspofungin versus amphotericin B for the treatment of invasive candidiasis. Eur J Clin Microbiol Infect Dis. 2003;22(8):470–4.
Imam N, Carpenter CC, Mayer KH, Fisher A, Stein M, Danforth SB. Hierarchical pattern of mucosal candida infections in HIV-seropositive women. Am J Med. 1990;89(2):142–6.
McCullough MJ, Ross BC, Reade PC. Candida albicans: a review of its history, taxonomy, epidemiology, virulence attributes, and methods of strain differentiation. Int J Oral Maxillofac Surg. 1996;25(2):136–44.
Balish E, Phillips AW. Growth and virulence of Candida albicans after oral inoculation in the chick with a monoflora of either Escherichia coli or Streptococcus faecalis. J Bacteriol. 1966;91(5):1744–9.
Carolus H, Van Dyck K, Van Dijck P. Candida albicans and Staphylococcus Species: A Threatening Twosome. Frontiers in microbiology. 2019;10:2162.
Mathé L, Van Dijck P. Recent insights into Candida albicans biofilm resistance mechanisms. Curr Genet. 2013;59(4):251–64.
Rustchenko EP, Howard DH, Sherman F. Variation in assimilating functions occurs in spontaneous Candida albicans mutants having chromosomal alterations. Microbiology. 1997;143(Pt 5):1765–78.
Shoham S, Marr KA. Invasive fungal infections in solid organ transplant recipients. Future Microbiol. 2012;7(5):639–55.
Weissman Z, Berdicevsky I, Cavari BZ, Kornitzer D. The high copper tolerance of Candida albicans is mediated by a P-type ATPase. Proc Natl Acad Sci USA. 2000;97(7):3520–5.
Bouza E, Muñoz P. Epidemiology of candidemia in intensive care units. Int J Antimicrob Agents. 2008;32(Suppl 2):87–91.
Fukushima C, Shimoda T, Kawano T, Tomari S, Mitsuta K, Obase Y, et al. Effects of amphotericin B gargles on oral colonization of Candida albicans in asthmatic patients on steroid inhalation therapy. Respir Int Rev Thorac Dis. 2001;68(5):465–70.
Razzaghi-Abyaneh M, Sadeghi G, Zeinali E, Alirezaee M, Shams-Ghahfarokhi M, Amani A, et al. Species distribution and antifungal susceptibility of Candida spp. isolated from superficial candidiasis in outpatients in Iran. J De Mycol Medicale. 2014;24(2):e43–50.
Paramythiotou E, Frantzeskaki F, Flevari A, Armaganidis A, Dimopoulos G. Invasive fungal infections in the ICU: how to approach, how to treat. Molecules. 2014;19(1):1085–119.
Lu Y, Su C, Liu H. Candida albicans hyphal initiation and elongation. Trends Microbiol. 2014;22(12):707–14.
Liu Y, Solis NV, Heilmann CJ, Phan QT, Mitchell AP, Klis FM, et al. Role of retrograde trafficking in stress response, host cell interactions, and virulence of Candida albicans. Eukaryot Cell. 2014;13(2):279–87.
Sudbery P, Gow N, Berman J. The distinct morphogenic states of Candida albicans. Trends Microbiol. 2004;12(7):317–24.
Uppuluri P, Pierce CG, López-Ribot JL. Candida albicans biofilm formation and its clinical consequences. Future Microbiol. 2009;4(10):1235–7.
Whiteway M, Bachewich C. Morphogenesis in Candida albicans. Annu Rev Microbiol. 2007;61:529–53.
Bonhomme J, d’Enfert C. Candida albicans biofilms: building a heterogeneous, drug-tolerant environment. Curr Opin Microbiol. 2013;16(4):398–403.
Chandra J, Kuhn DM, Mukherjee PK, Hoyer LL, McCormick T, Ghannoum MA. Biofilm formation by the fungal pathogen Candida albicans: development, architecture, and drug resistance. J Bacteriol. 2001;183(18):5385–94.
Desai JV, Mitchell AP. Candida albicans Biofilm Development and its genetic control. Microbiol Spectr. 2015;3(3). https://doi.org/10.1128/microbiolspec.MB-0005-2014.
López D, Vlamakis H, Kolter R, Biofilms. Cold Spring Harb Perspect Biol. 2010;2(7):a000398.
López-Ribot JL. Candida albicans biofilms: more than filamentation. Curr Biology: CB. 2005;15(12):R453–5.
Mayer FL, Wilson D, Hube B. Candida albicans pathogenicity mechanisms. Virulence. 2013;4(2):119–28.
Almeida RS, Brunke S, Albrecht A, Thewes S, Laue M, Edwards JE, et al. The hyphal-associated adhesin and invasin Als3 of Candida albicans mediates iron acquisition from host ferritin. PLoS Pathog. 2008;4(11):e1000217.
Aoki W, Kitahara N, Miura N, Morisaka H, Yamamoto Y, Kuroda K, et al. Comprehensive characterization of secreted aspartic proteases encoded by a virulence gene family in Candida albicans. J BioChem. 2011;150(4):431–8.
Moyes DL, Wilson D, Richardson JP, Mogavero S, Tang SX, Wernecke J, et al. Candidalysin is a fungal peptide toxin critical for mucosal infection. Nature. 2016;532(7597):64–8.
Talapko J, Juzbašić M, Matijević T, Pustijanac E, Bekić S, Kotris I, et al. Candida albicans-the virulence factors and clinical manifestations of infection. J fungi (Basel Switzerland). 2021;7(2):79.
Johnson MD. Antifungal in clinical use and the pipeline. Infect Dis Clin North Am. 2021;35:341–71.
Baldim JL, de Alcântara BGV, Domingos ODS, Soares MG, Caldas IS, Novaes RD, et al. The correlation between Chemical Structures and antioxidant, prooxidant, and Antitrypanosomatid properties of flavonoids. Oxidative Med Cell Longev. 2017;2017:3789856.
Basenko EY, Pulman JA, Shanmugasundram A, Harb OS, Crouch K, Starns D et al. FungiDB: an Integrated Bioinformatic Resource for Fungi and Oomycetes. J fungi (Basel Switzerland). 2018;4(1).
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinf (Oxford England). 2014;30(15):2114–20.
Alqahtani FM, Handy ST, Sutton CL, Farone MB. Combining genome-wide gene expression analysis (RNA-seq) and a gene editing platform (CRISPR-Cas9) to uncover the selectively pro-oxidant activity of Aurone compounds against Candida albicans. Front Microbiol. 2021;12:708267.
Bhattacharya S, Sae-Tia S, Fries BC. Candidiasis and mechanisms of Antifungal Resistance. Antibiotics. 2020;9(6):312.
Srivastava V, Singla RK, Dubey AK. Emerging virulence, Drug Resistance and Future anti-fungal drugs for Candida Pathogens. Curr Top Med Chem. 2018;18(9):759–78.
Campoy S, Adrio JL, Antifungals. Biochem Pharmacol. 2017;133:86–96.
Teixeira MM, Carvalho DT, Sousa E, Pinto E. New Antifungal agents with Azole Moieties. Pharmaceuticals (Basel Switzerland). 2022;15(11):1427.
Stover KR, Hawkins BK, Keck JM, Barber KE, Cretella DA. Antifungal resistance, combinations and pipeline: oh my! Drugs Context. 2023;12:2023–7.
Hoenigl M, Sprute R, Egger M, Arastehfar A, Cornely OA, Krause R, et al. The antifungal pipeline: fosmanogepix, ibrexafungerp, olorofim, opelconazole, and rezafungin. Drugs. 2021;81:1703–29.
Vandecruys P, Baldewijns S, Sillen M, Van Genechten W, Van Dijck P. Oteseconazole: a long-awaited diversification of the antifungal arsenal to manage recurrent vulvovaginal candidiasis (RVVC). Expert Rev Anti Infect Ther. 2023;21:799–812.
Wijnants S, Vreys J, Van Dijck P. Interesting antifungal drug targets in the central metabolism of Candida albicans. Trends Pharmacol Sci. 2022;43(1):69–79.
Braun BR, van Het Hoog M, d’Enfert C, Martchenko M, Dungan J, Kuo A, et al. A human-curated annotation of the Candida albicans genome. PLoS Genet. 2005;1(1):36–57.
Butler G, Rasmussen MD, Lin MF, Santos MA, Sakthikumar S, Munro CA, et al. Evolution of pathogenicity and sexual reproduction in eight Candida genomes. Nature. 2009;459(7247):657–62.
Jones T, Federspiel NA, Chibana H, Dungan J, Kalman S, Magee BB, et al. The diploid genome sequence of Candida albicans. Proc Natl Acad Sci USA. 2004;101(19):7329–34.
Robbins N, Ketela T, Kim SH, Cowen LE. Chemical-genetic approaches for exploring mode of action of antifungal compounds in the fungal pathogen Candida albicans. Methods Mol Biol. 2023;2658:145–65.
Ma T, Huo Z, Kuo A, Zhu L, Fang Z, Zeng X, et al. MetaOmics: analysis pipeline and browser-based software suite for transcriptomic meta-analysis. Bioinf (Oxford England). 2018;35(9):1597–9.
van Wijk KJ, Friso G, Walther D, Schulze WX. Meta-analysis of Arabidopsis thaliana Phospho-Proteomics Data reveals compartmentalization of Phosphorylation motifs. Plant Cell. 2014;26(6):2367–89.
Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene Ontology: tool for the unification of biology. Nat Genet. 2000;25(1):25–9.
Oliveira AP, Patil KR, Nielsen J. Architecture of transcriptional regulatory circuits is knitted over the topology of bio-molecular interaction networks. BMC Syst Biol. 2008;2(1):17.
Chen L, Zhang YH, Wang S, Zhang Y, Huang T, Cai YD. Prediction and analysis of essential genes using the enrichments of gene ontology and KEGG pathways. PLoS ONE. 2017;12(9):e0184129.
Yu MK, Kramer M, Dutkowski J, Srivas R, Licon K, Kreisberg J, et al. Translation of genotype to phenotype by a hierarchy of cell subsystems. Cell Syst. 2016;2(2):77–88.
Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, et al. NCBI GEO: archive for functional genomics data sets–update. Nucleic Acids Res. 2013;41(Database issue):D991–5.
Leinonen R, Akhtar R, Birney E, Bower L, Cerdeno-Tárraga A, Cheng Y, et al. Eur Nucleotide Archive Nucleic Acids Res. 2011;39(Database issue):D28–31.
Ewels PA, Peltzer A, Fillinger S, Patel H, Alneberg J, Wilm A, et al. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol. 2020;38(3):276–8.
Andrews S. FastQC: a quality control tool for high throughput sequence data. Cambridge, United Kingdom: In.: Babraham Bioinformatics, Babraham Institute; 2010.
Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinf (Oxford England). 2013;29(1):15–21.
Love MI, Huber W, Anders S. Moderated estimation of Fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550.
Alexa A, Rahnenführer J. Gene set enrichment analysis with topGO. Bioconductor Improv. 2009;27:1–26.
Alexa A, Rahnenführer J, Lengauer T. Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. Bioinf (Oxford England). 2006;22(13):1600–7.
Puri S, Lai WK, Rizzo JM, Buck MJ, Edgerton M. Iron-responsive chromatin remodelling and MAPK signalling enhance adhesion in Candida albicans. Mol Microbiol. 2014;93(2):291–305.
Dhamgaye S, Bernard M, Lelandais G, Sismeiro O, Lemoine S, Coppee JY, et al. RNA sequencing revealed novel actors of the acquisition of drug resistance in Candida albicans. BMC Genomics. 2012;13:396.
Scaduto CM, Kabrawala S, Thomson GJ, Scheving W, Ly A, Anderson MZ, et al. Epigenetic control of pheromone MAPK signaling determines sexual fecundity in Candida albicans. Proc Natl Acad Sci USA. 2017;114(52):13780–5.
Vandeputte P, Pradervand S, Ischer F, Coste AT, Ferrari S, Harshman K, et al. Identification and functional characterization of Rca1, a transcription factor involved in both antifungal susceptibility and host response in Candida albicans. Eukaryot Cell. 2012;11(7):916–31.
Lohse MB, Johnson AD. Identification and characterization of Wor4, a New Transcriptional Regulator of White-Opaque switching. (Bethesda). 2016;G3(3):721–9.
Hollomon JM, Grahl N, Willger SD, Koeppen K, Hogan DA. Global role of cyclic AMP Signaling in pH-Dependent responses in Candida albicans. mSphere. 2016;1(6):e00283–16.
Desai JV, Bruno VM, Ganguly S, Stamper RJ, Mitchell KF, Solis N, et al. Regulatory role of glycerol in Candida albicans biofilm formation. mBio. 2013;4(2):e00637–12.
Cottier F, Tan ASM, Yurieva M, Liao W, Lum J, Poidinger M, et al. The transcriptional response of Candida albicans to weak organic acids, carbon source, and MIG1 inactivation unveils a role for HGT16 in mediating the fungistatic effect of acetic acid. G3 (Bethesda). 2017;7(11):3597–604.
Cottier F, Tan AS, Chen J, Lum J, Zolezzi F, Poidinger M, et al. The transcriptional stress response of Candida albicans to weak organic acids. G3 (Bethesda). 2015;5(4):497–505.
Danhof HA, Vylkova S, Vesely EM, Ford AE, Gonzalez-Garay M, Lorenz MC. Robust extracellular pH modulation by Candida albicans during growth in carboxylic acids. MBio. 2016;7(6):e01646–16.
Tscherner M, Zwolanek F, Jenull S, Sedlazeck FJ, Petryshyn A, Frohner IE, et al. The Candida albicans histone acetyltransferase Hat1 regulates stress resistance and virulence via distinct chromatin Assembly pathways. PLoS Pathog. 2015;11(10):e1005218.
Du H, Guan G, Li X, Gulati M, Tao L, Cao C, et al. N-Acetylglucosamine-Induced cell death in Candida albicans and its implications for adaptive mechanisms of nutrient sensing in yeasts. mBio. 2015;6(5):e01376–15.
Azadmanesh J, Gowen AM, Creger PE, Schafer ND, Blankenship JR. Filamentation involves two overlapping, but distinct, programs of Filamentation in the pathogenic Fungus Candida albicans. G3 (Bethesda). 2017;7(11):3797–808.
Tao L, Zhang Y, Fan S, Nobile CJ, Guan G, Huang G. Integration of the tricarboxylic acid (TCA) cycle with cAMP signaling and Sfl2 pathways in the regulation of CO2 sensing and hyphal development in Candida albicans. PLoS Genet. 2017;13(8):e1006949.
Gu Z, Hübschmann D, SimplifyEnrichment:. A Bioconductor Package for Clustering and Visualizing Functional Enrichment results. Genom Proteom Bioinform. 2022;21(1):190–202.
Li Y, Sun L, Lu C, Gong Y, Li M, Sun S. Promising antifungal targets against Candida albicans based on Ion Homeostasis. Front Cell Infect Microbiol. 2018;8:286.
Ahmed N, Mahmood MS, Ullah MA, Araf Y, Rahaman TI, Moin AT, et al. COVID-19-Associated Candidiasis: possible Patho-Mechanism, predisposing factors, and Prevention Strategies. Curr Microbiol. 2022;79(5):127.
Alvarez-Peral FJ, Zaragoza O, Pedreno Y, Argüelles JC. Protective role of trehalose during severe oxidative stress caused by hydrogen peroxide and the adaptive oxidative stress response in Candida albicans. Microbiology. 2002;148(Pt 8):2599–606.
Zaragoza O, Blazquez MA, Gancedo C. Disruption of the Candida albicans TPS1 gene encoding trehalose-6-phosphate synthase impairs formation of hyphae and decreases infectivity. J Bacteriol. 1998;180(15):3809–15.
Martínez-Esparza M, Aguinaga A, González-Párraga P, García-Peñarrubia P, Jouault T, Argüelles JC. Role of trehalose in resistance to macrophage killing: study with a tps1/tps1 trehalose-deficient mutant of Candida albicans. Clin Microbiol Infect. 2007;13(4):384–94.
Thammahong A, Puttikamonkul S, Perfect JR, Brennan RG, Cramer RA. Central Role of the Trehalose Biosynthesis Pathway in the pathogenesis of human fungal infections: opportunities and challenges for Therapeutic Development. Microbiol Mol Biology Reviews: MMBR. 2017;81(2):e00053–16.
Zaragoza O, González-Párraga P, Pedreño Y, Alvarez-Peral FJ, Argüelles JC. Trehalose accumulation induced during the oxidative stress response is independent of TPS1 mRNA levels in Candida albicans. Int Microbiol. 2003;6(2):121–5.
Maidan MM, De Rop L, Relloso M, Diez-Orejas R, Thevelein JM, Van Dijck P. Combined inactivation of the Candida albicans GPR1 and TPS2 genes results in avirulence in a mouse model for systemic infection. Infect Immun. 2008;76(4):1686–94.
Martínez-Esparza M, Martínez-Vicente E, González-Párraga P, Ros JM, García-Peñarrubia P, Argüelles JC. Role of trehalose-6P phosphatase (TPS2) in stress tolerance and resistance to macrophage killing in Candida albicans. Int J Med Microbiology: IJMM. 2009;299(6):453–64.
Van Dijck P, De Rop L, Szlufcik K, Van Ael E, Thevelein JM. Disruption of the Candida albicans TPS2 gene encoding trehalose-6-phosphate phosphatase decreases infectivity without affecting hypha formation. Infect Immun. 2002;70(4):1772–82.
Citiulo F, Jacobsen ID, Miramón P, Schild L, Brunke S, Zipfel P, et al. Candida albicans scavenges host zinc via Pra1 during endothelial invasion. PLoS Pathog. 2012;8(6):e1002777.
Xu W, Solis NV, Ehrlich RL, Woolford CA, Filler SG, Mitchell AP. Activation and alliance of regulatory pathways in C. Albicans during mammalian infection. PLoS Biol. 2015;13(2):e1002076.
Kim MJ, Kil M, Jung JH, Kim J. Roles of zinc-responsive transcription factor Csr1 in filamentous growth of the pathogenic yeast Candida albicans. J Microbiol Biotechnol. 2008;18(2):242–7.
Staab JF, Datta K, Rhee P. Niche-specific requirement for hyphal wall protein 1 in virulence of Candida albicans. PLoS ONE. 2013;8(11):e80842.
Nobile CJ, Schneider HA, Nett JE, Sheppard DC, Filler SG, Andes DR, et al. Complementary adhesin function in C. albicans formation. Curr Biology: CB. 2008;18(14):1017–24.
Staab JF, Bradway SD, Fidel PL, Sundstrom P. Adhesive and mammalian transglutaminase substrate properties of Candida albicans Hwp1. Sci (New York NY). 1999;283(5407):1535–8.
Roberts SK, McAinsh M, Widdicks L. Cch1p mediates Ca2 + influx to protect Saccharomyces cerevisiae against eugenol toxicity. PLoS ONE. 2012;7(9):e43989.
Wang H, Lu G, Yang B, Wang F, Yu Q, Xu N, et al. Effect of CCH1 or MID1 gene disruption on drug tolerance and pathogenesis of Candida albicans. Sheng Wu Gong Cheng Xue bao = Chinese. J Biotechnol. 2012;28(6):726–36.
Yu Q, Wang H, Cheng X, Xu N, Ding X, Xing L, et al. Roles of Cch1 and Mid1 in morphogenesis, oxidative stress response and virulence in Candida albicans. Mycopathologia. 2012;174(5–6):359–69.
Naglik JR, Challacombe SJ, Hube B. Candida albicans secreted aspartyl proteinases in virulence and pathogenesis. Microbiol Mol Biology Reviews: MMBR. 2003;67(3):400–28.
Liu H. Transcriptional control of dimorphism in Candida albicans. Curr Opin Microbiol. 2001;4(6):728–35.
Sundstrom P. Adhesion in Candida Spp. Cell Microbiol. 2002;4(8):461–9.
Ramírez-Zavala B, Weyler M, Gildor T, Schmauch C, Kornitzer D, Arkowitz R, et al. Activation of the Cph1-dependent MAP kinase signaling pathway induces white-opaque switching in Candida albicans. PLoS Pathog. 2013;9(10):e1003696.
Huang H, Harcus D, Whiteway M. Transcript profiling of a MAP kinase pathway in C. Albicans. Microbiol Res. 2008;163(4):380–93.
Maiti P, Ghorai P, Ghosh S, Kamthan M, Tyagi RK, Datta A. Mapping of functional domains and characterization of the transcription factor Cph1 that mediate morphogenesis in Candida albicans. Fungal Genet Biology: FG B. 2015;83:45–57.
Becuwe M, Léon S. Integrated control of transporter endocytosis and recycling by the arrestin-related protein Rod1 and the ubiquitin ligase Rsp5. eLife. 2014;3:e03307.
Hanumantha Rao K, Roy K, Paul S, Ghosh S. N-acetylglucosamine transporter, Ngt1, undergoes sugar-responsive endosomal trafficking in Candida albicans. Mol Microbiol. 2022;117(2):429–49.
Bernstein KA, Granneman S, Lee AV, Manickam S, Baserga SJ. Comprehensive mutational analysis of yeast DEXD/H box RNA helicases involved in large ribosomal subunit biogenesis. Mol Cell Biol. 2006;26(4):1195–208.
Skrzypek MS, Binkley J, Binkley G, Miyasato SR, Simison M, Sherlock G. The Candida Genome Database (CGD): incorporation of Assembly 22, systematic identifiers and visualization of high throughput sequencing data. Nucleic Acids Res. 2017;45(D1):D592–D6.
Amorim-Vaz S, Tran Vdu T, Pradervand S, Pagni M, Coste AT, Sanglard D. RNA Enrichment Method for Quantitative Transcriptional Analysis of Pathogens in Vivo Applied to the Fungus Candida albicans. mBio. 2015;6(5):e00942–15.
Perry AM, Hernday AD, Nobile CJ. Unraveling how Candida albicans forms sexual biofilms. J fungi (Basel Switzerland). 2020;6(1):14.
Kim J, Sudbery P. Candida albicans, a major human fungal pathogen. J Microbiol. 2011;49(2):171–7.
Poulain D. Candida albicans, plasticity and pathogenesis. Crit Rev Microbiol. 2015;41(2):208–17.
Padder SA, Ramzan A, Tahir I, Rehman RU, Shah AH. Metabolic flexibility and extensive adaptability governing multiple drug resistance and enhanced virulence in Candida albicans. Crit Rev Microbiol. 2022;48(1):1–20.
Clatworthy AE, Pierson E, Hung DT. Targeting virulence: a new paradigm for antimicrobial therapy. Nat Chem Biol. 2007;3(9):541–8.
Gauwerky K, Borelli C, Korting HC. Targeting virulence: a new paradigm for antifungals. Drug Discovery Today. 2009;14(3–4):214–22.
Schrevens S, Durandau E, Tran VDT, Sanglard D, Using. Vivo transcriptomics and RNA enrichment to identify genes involved in virulence of Candida Glabrata. Virulence. 2022;13(1):1285–303.
Zhu G-D, Xie L-M, Su J-W, Cao X-J, Yin X, Li Y-P, et al. Identification of differentially expressed genes and signaling pathways with Candida infection by bioinformatics analysis. Eur J Med Res. 2022;27(1):43.
Cabib E, Leloir LF. The biosynthesis of trehalose phosphate. J Biol Chem. 1958;231(1):259–75.
Leloir LF, Cabib E. The enzymatic synthesis of trehalose phosphate. J Am Chem Soc. 1953;75(21):5445–6.
Al-Bader N, Vanier G, Liu H, Gravelat FN, Urb M, Hoareau CM, et al. Role of trehalose biosynthesis in Aspergillus fumigatus development, stress response, and virulence. Infect Immun. 2010;78(7):3007–18.
Fillinger S, Chaveroche MK, van Dijck P, de Vries R, Ruijter G, Thevelein J, et al. Trehalose is required for the acquisition of tolerance to a variety of stresses in the filamentous fungus aspergillus nidulans. Microbiology. 2001;147(Pt 7):1851–62.
Petzold EW, Himmelreich U, Mylonakis E, Rude T, Toffaletti D, Cox GM, et al. Characterization and regulation of the trehalose synthesis pathway and its importance in the pathogenicity of Cryptococcus neoformans. Infect Immun. 2006;74(10):5877–87.
Magalhães RS, De Lima KC, de Almeida DS, De Mesquita JF, Eleutherio EC. Trehalose-6-Phosphate as a potential lead candidate for the development of Tps1 inhibitors: insights from the Trehalose Biosynthesis pathway in diverse yeast species. Appl Biochem Biotechnol. 2017;181(3):914–24.
Miao Y, Tenor JL, Toffaletti DL, Washington EJ, Liu J, Shadrick WR, et al. Structures of trehalose-6-phosphate phosphatase from pathogenic fungi reveal the mechanisms of substrate recognition and catalysis. Proc Natl Acad Sci USA. 2016;113(26):7148–53.
Łoboda D, Rowińska-Żyrek M. Zinc binding sites in Pra1, a zincophore from Candida albicans. Dalton Trans. 2017;46(40):13695–703.
Cohrt KAO, Marín L, Kjellerup L, Clausen JD, Dalby-Brown W, Calera JA et al. Novel Zinc-Attenuating Compounds as Potent Broad-Spectrum Antifungal Agents with In Vitro and In Vivo Efficacy. Antimicrobial agents and chemotherapy. 2018;62(5):e02024-17.
Liu S, Hou Y, Liu W, Lu C, Wang W, Sun S. Components of the calcium-calcineurin signaling pathway in fungal cells and their potential as antifungal targets. Eukaryot Cell. 2015;14(4):324–34.
Locke EG, Bonilla M, Liang L, Takita Y, Cunningham KW. A homolog of voltage-gated Ca2+ channels stimulated by depletion of secretory Ca2+ in yeast. Mol Cell Biol. 2000;20(18):6686–94.
Viladevall L, Serrano R, Ruiz A, Domenech G, Giraldo J, Barceló A, et al. Characterization of the calcium-mediated response to alkaline stress in Saccharomyces cerevisiae. J Biol Chem. 2004;279(42):43614–24.
De Bernardis F, Chiani P, Ciccozzi M, Pellegrini G, Ceddia T, D’Offizzi G, et al. Elevated aspartic proteinase secretion and experimental pathogenicity of Candida albicans isolates from oral cavities of subjects infected with human immunodeficiency virus. Infect Immun. 1996;64(2):466–71.
De Bernardis F, Sullivan PA, Cassone A. Aspartyl proteinases of Candida albicans and their role in pathogenicity. Med Mycol. 2001;39(4):303–13.
Joo MY, Shin JH, Jang H-C, Song ES, Kee SJ, Shin MG, et al. Expression of SAP5 and SAP9 in Candida albicans biofilms: comparison of bloodstream isolates with isolates from other sources. Med Mycol. 2013;51(8):892–6.
Dhanasekaran S, Pushparaj SP, Sundar MS, Jeyabalan S, Devi RV. Revealing anti-fungal potential of plant-derived bioactive therapeutics in targeting secreted aspartyl proteinase (SAP) of Candida albicans: a molecular dynamics approach. J Biomol Struct Dyn. 2023;April:1–15.
Wagner AS, Hancock TJ, Lumsdaine SW, Kauffman SJ, Mangrum MM, Phillips EK, et al. Activation of Cph1 causes ß(1,3)-glucan unmasking in Candida albicans and attenuates virulence in mice in a neutrophil-dependent manner. PLoS Pathog. 2021;17(8):e1009839.
Leach MD, Cowen LE. Membrane fluidity and temperature sensing are coupled via circuitry comprised of Ole1, Rsp5, and Hsf1 in Candida albicans. Eukaryot Cell. 2014;13(8):1077–84.
Hu K, Li W, Wang H, Chen K, Wang Y, Sang J. Shp1, a regulator of protein phosphatase 1 Glc7, has important roles in cell morphogenesis, cell cycle progression and DNA damage response in Candida albicans. Volume 49. Fungal genetics and biology: FG & B; 2012. pp. 433–42. 6.
Yao G, Wan J, Mu C, Liu Q, Wang Y, Sang J. Sds22 participates in Glc7 mediated Rad53 dephosphorylation in MMS-induced DNA damage in Candida albicans. Fungal Genet Biology: FG B. 2016;93:50–61.
Schatzman SS, Culotta VC. Chemical Warfare at the Microorganismal Level: a closer look at the Superoxide dismutase enzymes of pathogens. ACS Infect Dis. 2018;4(6):893–903.
Bink A, Vandenbosch D, Coenye T, Nelis H, Cammue BP, Thevissen K. Superoxide dismutases are involved in Candida albicans biofilm persistence against miconazole. Antimicrob Agents Chemother. 2011;55(9):4033–7.
Frohner IE, Bourgeois C, Yatsyk K, Majer O, Kuchler K. Candida albicans cell surface superoxide dismutases degrade host-derived reactive oxygen species to escape innate immune surveillance. Mol Microbiol. 2009;71(1):240–52.
Martchenko M, Alarco AM, Harcus D, Whiteway M. Superoxide dismutases in Candida albicans: transcriptional regulation and functional characterization of the hyphal-induced SOD5 gene. Mol Biol Cell. 2004;15(2):456–67.
Gleason JE, Galaleldeen A, Peterson RL, Taylor AB, Holloway SP, Waninger-Saroni J, et al. Candida albicans SOD5 represents the prototype of an unprecedented class of Cu-only superoxide dismutases required for pathogen defense. Proc Natl Acad Sci USA. 2014;111(16):5866–71.
Schatzman SS, Peterson RL, Teka M, He B, Cabelli DE, Cormack BP, et al. Copper-only superoxide dismutase enzymes and iron starvation stress in Candida fungal pathogens. J Biol Chem. 2020;295(2):570–83.
Cornet M, Gaillardin C. pH signaling in human fungal pathogens: a new target for antifungal strategies. Eukaryot Cell. 2014;13(3):342–52.
Davis D, Edwards JEJ, Mitchell AP, Ibrahim AS. Candida albicans RIM101 pH response pathway is required for host-pathogen interactions. Infect Immun. 2000;68(10):5953–9. https://doi.org/10.1128/iai.68.10.5953-5959.2000.
Davis D, Wilson RB, Mitchell AP. RIM101-dependent and-independent pathways govern pH responses in Candida albicans. Mol Cell Biol. 2000;20(3):971–8. https://doi.org/10.1128/mcb.20.3.971-978.2000.
Acknowledgements
We express our gratitude to the Schlumberger Foundation for their generous support of Zeinab Hefny through the Faculty for the Future (FFTF) PhD Scholarship. Additionally, we acknowledge the support of KU Leuven for providing her with the PhD Junior Mobility Grant (JUMO). We also convey our sincere thanks to Mihail Anton, John Hellgren and Dimitra Lappa for their invaluable assistance in configuring the nf-core RNA-seq analysis pipeline.
Funding
This work was supported by a grant from the Schlumberger foundation to ZH and by a grant from the Fund for Scientific Research Flanders (grant# G0B1820N). We would like to thank the research council of KU Leuven for providing a PhD Junior Mobility Grant to ZH (JUMO/16/045).
Author information
Authors and Affiliations
Contributions
Funding acquisition and supervision were handled by J.N. and P.V.D. The research was conceptualized and designed by Z.H., J.N., and P.V.D. Z.H. conducted data collection and performed the research. Data analysis was carried out by Z.H., B.J., and I.E. The original draft of the manuscript was written by Z.H. All authors, including Z.H., B.J., I.E., J.N., and P.V.D., contributed to the editing of the manuscript. The final version of the paper was approved by all authors.
Corresponding authors
Ethics declarations
Declarations
Not applicable.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
Not applicable.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
12866_2024_3213_MOESM3_ESM.xlsx
Supplementary Material 3. Additional Table .xlsx contains the complete target gene lists and their frequency in the selected GO clusters
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
About this article
Cite this article
Hefny, Z.A., Ji, B., Elsemman, I.E. et al. Transcriptomic meta-analysis to identify potential antifungal targets in Candida albicans. BMC Microbiol 24, 66 (2024). https://doi.org/10.1186/s12866-024-03213-8
Received:
Accepted:
Published:
DOI: https://doi.org/10.1186/s12866-024-03213-8