# Transcriptomics Analysis ofCandida albicans Treated with Huanglian Jiedu Decoction Using RNA-seq

Authors: Qianqian Yang; Lei Gao; Maocan Tao; Zhe Chen; Xiaohong Yang; Yi Cao
Journal: Evidence-Based Complementary and Alternative Medicine
 (2016)

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## Abstract

Candida albicans is the major invasive fungal pathogen of humans, causing diseases ranging from superficial mucosal infections to disseminated, systemic infections that are often life-threatening. Resistance ofC. albicans to antifungal agents and limited antifungal agents has potentially serious implications for management of infections. As a famous multiherb prescription in China, Huanglian Jiedu Decoction (HLJJD,Orengedokuto in Japan) is efficient againstTrichophyton mentagrophytes andC. albicans. But the antifungal mechanism of HLJDD remains unclear. In this study, by using RNA-seq technique, we performed a transcriptomics analysis of gene expression changes forC. albicans under the treatment of HLJDD. A total of 6057 predicted protein-encoding genes were identified. By gene expression analysis, we obtained a total of 735 differentially expressed genes (DEGs), including 700 upregulated genes and 35 downregulated genes. Genes encoding multidrug transporters such as ABC transporter and MFS transporter were identified to be significantly upregulated. Meanwhile, by pathway enrichment analysis, we identified 26 significant pathways, in which pathways of DNA replication and transporter activity were mainly involved. These results might provide insights for the inhibition mechanism of HLJDD againstC. albicans.


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## Body

## 1. Introduction

Candida albicans is the most prevalent opportunistic fungal pathogen implicated in superficial mucosal infections as well as invasive disseminated infections, especially in immunocompromised patients [1, 2].C. albicans infections are usually treated with antifungal agents, such as azoles, echinocandins, and polyene drugs. Limited by the number of available antifungal targets, the antifungal agents still remain restricted. The azoles are the most widely used drugs for treating pathogenic fungal infections. Sterol 14α-demethylase (ERG11) is an ancestral activity of the cytochrome P450 superfamily, which is required for ergosterol biosynthesis in fungi and cholesterol biosynthesis in mammals [3]. As a key enzyme of sterol biosynthesis, Erg11 is the main target for therapeutic azole antifungal drugs [4, 5].Widespread overuse of azole drugs for decades has led to the occurrence of drug-resistant isolates [6–8]. The prolonged and repeated treatment of OPC (oropharyngeal candidiasis) in AIDS patients has resulted in an increasing frequency of therapy failures caused by the emergence of fluconazole-resistantC. albicans strains. In one study, the levels of fluconazole resistance of a series of 17 clinical isolates taken from a single HIV-infected patient who was treated with azoles over 2 years increased over 200-fold [9]. In recent years, the incidence of azole-resistant strains ofC. albicans has increased, especially the rapid emergence of fluconazole-resistant strains. In the vast majority of countries, far less than 10% ofC. albicans strains isolated from 1997 to 2001 are resistant to fluconazole [10]. But recent study in China showed that the rate of fluconazole resistance inC. albicans was almost 14.1% [11]. In USA, compared with 2008, the proportion of cases identified from 2008 to 2013 from Georgia and Maryland with fluconazole resistance decreased (GA: 8.0% to 7.1%, −10%; MD: 6.6% to 4.9%, −25%), but the proportion of cases with an isolate resistant to an echinocandin increased (GA: 1.2% to 2.9%, +147%; MD: 2.0% to 3.5%, +77%) [12]. So far, several resistance mechanisms ofC. albicans have been well characterized: alterations in the sterol biosynthesis pathway, mutations in theERG11 gene encoding the drug target enzyme, overexpression of theERG11 gene, and overexpression of genes encoding efflux pumps [13]. Resistance ofC. albicans to antifungal agents and limited antifungal agents has potentially serious implications for management of infections.As a famous multiherb prescription in China, Huanglian Jiedu Decoction (HLJJD,Orengedokuto in Japan) is an aqueous extract of 4 herbal materials,Coptidis Rhizoma,Scutellariae Radix,Phellodendri Cortex, andGardeniae Fructuswith the ratio of 3 : 2 : 2 : 3. HLJJD was first mentioned in the bookWai-Tai-Mi-Yao compiled by Wang Tao in the Tang dynasty (about 752 AD), and it has been widely used in the clinical practice in China and officially listed in the Chinese Pharmacopoeia [14]. HLJJD has been widely used in the treatment of gastrointestinal disorders, inflammation and cardiovascular diseases, and Alzheimer’s disease in China [15–17]. Modern pharmacological research also demonstrated multiple biological activities of HLJDD: decreasing levels of plasma glucose and blood lipid in type 2 diabetes mellitus [18, 19]; increasing the cerebral blood flow, inhibiting the platelet aggregation; reducing hypertension and altering the gene expression profiles of spontaneous hypertensive rats [20–23]; reducing hepatic triglyceride accumulation, restraining the preadipocyte differentiation and lipid accumulation, inhibiting the lipid peroxidation, and preventing atherosclerosis [15, 24–26]. Anti-inflammatory effects of HLJDD were also investigated in some papers [18, 27–29]. Moreover, inMugil cephalus, 1% modified HLJDD feeding for 28 days may be an optimal dose to preventLactococcus garvieae infection and could be used in aquaculture industries. In in vitro study, the modified HLJDD also activated the plasma bactericidal activities [30].Each herb of HLJDD contains many chemical components. Some papers had reported the content determinations of components contained in HLJDD [31, 32]. HLJDD contains multiple bioactive secondary metabolites, mainly including alkaloids fromCoptidis Rhizoma andPhellodendri Cortex, flavonoids fromScutellariae Radix, and terpenes fromGardeniae Fructus. There are 4 typical compounds from HLJDD: geniposide, baicalin, berberine, and baicalein [27]. Further study showed that the combination of fluconazole and baicalein or berberine produced potently synergistic action in vitro, while baicalein and berberine showed weak antifungal activity when they were tested alone [33, 34]. Our preliminary work showed that HLJDD is efficient againstTrichophyton mentagrophytesandC. albicans[35]. HLJDD showed its impressive antifungal effect by multitarget and multichannel actions, due to the multiple components. For that reason, the use of HLJDD may be more beneficial to human health in fungal infection treatment as diverse mechanisms showed complementary effects between herbs. But the antifungal mechanism of HLJDD remains unclear.RNA-seq (deep-sequencing of cDNA) has been used successfully to identify and quantify gene expression at a genome scale level under different conditions or in different cell types. Moreover, it is significantly more sensitive than microarray hybridization approaches [36]. This approach has already been used inC. albicansto generate a high-resolution map of theC. albicans transcriptome under several different environmental conditions [37]. The effect of berberine chloride onMicrosporum canis infection was analyzed by the construction of a transcriptome of theM. canis cellular responses upon berberine treatment [38]. Therefore, in this study, by using RNA-seq technique, we performed a large-scale analysis of gene expression changes whenC. albicans was exposed to HLJDD, to better understand how HLJDD inhibits the growth ofC. albicans.


## 2. Materials and Methods

### 2.1. Strain and Culture Conditions

TheC. albicans strain used in this study is SC5314 [39].C. albicans strains were routinely grown on YPD (1% yeast extract, 2% peptone, and 2% glucose) medium.


### 2.2. Preparation of the Extract of HLJDD

The herbal medicines of modified HLJDD were dried at 40°C for 24 h and then pulverized to powder using a mechanical blender. 0.5%, 1%, and 2% w/w of the powder was prepared and boiled for 30 min with 200 mL of deionized water, and the aqueous extracts were filtered through Whatman number 1 filter paper. The HLJDD residues were also boiled with another 200 mL of deionized water.


### 2.3. Determination of Sensitivity of the SC5314 Strain to HLJDD

Antifungal susceptibility testing was performed by using the CLSI M27-A3 microbroth dilution method [40]. MICs were determined after growth at 30°C for 24 h for HLJDD. MICs were read as the lowest drug concentration producing a prominent decrease in turbidity translating to 100% growth reduction compared with the drug-free control.


### 2.4. Total RNA Extraction

To identify genes in the early response ofC. albicans to HLJDD, we treated the isolate with HLJDD at 20 mg/mL, the lowest drug concentration producing a prominent decrease in turbidity translating to 100% growth reduction compared with the drug-free control determined above. To extract total RNA, the cells of SC5314 were inoculated into YPD medium and cultured at 30°C overnight. Before SC5314 were harvested for RNA extraction, the culture was treated with HLJDD at 20 mg/mL for 3 h. The untreated culture was used as the control. Total RNA was isolated according to the protocol described by Alison et al. [41].


### 2.5. RNA Sequencing and Assembling

Three independent experiments were performed for the study of either controlC. albicans orC. albicans with HLJDD treatment. Shear cDNA into 300–500 bp fragments using ultrasonic apparatus (Fisher) and purify it with Ampure beads (Agencourt, America). Library of all the samples was constructed according to the procedure of NEBNext® UltraTM RNA Library Prep Kit for Illumina (NEB, America). Sequencing library was checked with Onedrop quantitation, 2% agarose gel electrophoresis detection, and high sensitivity of DNA chip detection. Paired-end sequencing of cDNA was carried out with Illumina Hiseq TM2000. Raw data was filtered by removing reads with adaptor sequences, as well as low quality reads. Then, clean reads were obtained and mapped to reference sequences using SOAP (2.21) [42].


### 2.6. Gene Prediction and Annotation

Trinity software was used to assemble the clean reads into contigs and BLAST (2.2.23) was used to do gene prediction. Predicted sequences (e-value < 1.0e-05) were annotated with information from GenBank NR, GO, and KEGG using BLAST2GO (2.2.5). GO classification was conducted using WEGO [43].


### 2.7. Analysis of Differential Expressed Genes

The expression level for each gene is determined by the numbers of reads uniquely mapped to the specific gene and the total number of uniquely mapped reads in the sample. The gene expression level is calculated by using RPKM (Reads Per kb per Million reads) method [36]. Then, NOI seq method was applied to screen differentially expressed genes between two groups, with the threshold of significance as fold change of RPKM ≥ 3 and Probability ≥ 0.8 [44].


### 2.8. Enrichment Analysis of GO and KEGG Pathways

Enrichment analysis was performed by hypergeometric test to find significantly enriched GO terms and KEGG pathways in DEGs. False discovery rate (FDR) of pathways was calculated. The threshold of significance of pathways was set as FDR < 0.05.


### 2.9. Real-Time Quantitative Reverse Transcription- (qRT-) PCR

To evaluate the validation of RNA-seq results, we conducted quantitative real-time (RT) PCR assays for determination of expression of 8 genes. Gene expression levels were calculated using the2-ΔΔCt method [45]. For each sample, PCR amplifications with primer pair actin-F and actin-R for the quantification of expression of actin gene were performed as a reference. The experiment was repeated 3 times.


## 2.1. Strain and Culture Conditions

TheC. albicans strain used in this study is SC5314 [39].C. albicans strains were routinely grown on YPD (1% yeast extract, 2% peptone, and 2% glucose) medium.


## 2.2. Preparation of the Extract of HLJDD

The herbal medicines of modified HLJDD were dried at 40°C for 24 h and then pulverized to powder using a mechanical blender. 0.5%, 1%, and 2% w/w of the powder was prepared and boiled for 30 min with 200 mL of deionized water, and the aqueous extracts were filtered through Whatman number 1 filter paper. The HLJDD residues were also boiled with another 200 mL of deionized water.


## 2.3. Determination of Sensitivity of the SC5314 Strain to HLJDD

Antifungal susceptibility testing was performed by using the CLSI M27-A3 microbroth dilution method [40]. MICs were determined after growth at 30°C for 24 h for HLJDD. MICs were read as the lowest drug concentration producing a prominent decrease in turbidity translating to 100% growth reduction compared with the drug-free control.


## 2.4. Total RNA Extraction

To identify genes in the early response ofC. albicans to HLJDD, we treated the isolate with HLJDD at 20 mg/mL, the lowest drug concentration producing a prominent decrease in turbidity translating to 100% growth reduction compared with the drug-free control determined above. To extract total RNA, the cells of SC5314 were inoculated into YPD medium and cultured at 30°C overnight. Before SC5314 were harvested for RNA extraction, the culture was treated with HLJDD at 20 mg/mL for 3 h. The untreated culture was used as the control. Total RNA was isolated according to the protocol described by Alison et al. [41].


## 2.5. RNA Sequencing and Assembling

Three independent experiments were performed for the study of either controlC. albicans orC. albicans with HLJDD treatment. Shear cDNA into 300–500 bp fragments using ultrasonic apparatus (Fisher) and purify it with Ampure beads (Agencourt, America). Library of all the samples was constructed according to the procedure of NEBNext® UltraTM RNA Library Prep Kit for Illumina (NEB, America). Sequencing library was checked with Onedrop quantitation, 2% agarose gel electrophoresis detection, and high sensitivity of DNA chip detection. Paired-end sequencing of cDNA was carried out with Illumina Hiseq TM2000. Raw data was filtered by removing reads with adaptor sequences, as well as low quality reads. Then, clean reads were obtained and mapped to reference sequences using SOAP (2.21) [42].


## 2.6. Gene Prediction and Annotation

Trinity software was used to assemble the clean reads into contigs and BLAST (2.2.23) was used to do gene prediction. Predicted sequences (e-value < 1.0e-05) were annotated with information from GenBank NR, GO, and KEGG using BLAST2GO (2.2.5). GO classification was conducted using WEGO [43].


## 2.7. Analysis of Differential Expressed Genes

The expression level for each gene is determined by the numbers of reads uniquely mapped to the specific gene and the total number of uniquely mapped reads in the sample. The gene expression level is calculated by using RPKM (Reads Per kb per Million reads) method [36]. Then, NOI seq method was applied to screen differentially expressed genes between two groups, with the threshold of significance as fold change of RPKM ≥ 3 and Probability ≥ 0.8 [44].


## 2.8. Enrichment Analysis of GO and KEGG Pathways

Enrichment analysis was performed by hypergeometric test to find significantly enriched GO terms and KEGG pathways in DEGs. False discovery rate (FDR) of pathways was calculated. The threshold of significance of pathways was set as FDR < 0.05.


## 2.9. Real-Time Quantitative Reverse Transcription- (qRT-) PCR

To evaluate the validation of RNA-seq results, we conducted quantitative real-time (RT) PCR assays for determination of expression of 8 genes. Gene expression levels were calculated using the2-ΔΔCt method [45]. For each sample, PCR amplifications with primer pair actin-F and actin-R for the quantification of expression of actin gene were performed as a reference. The experiment was repeated 3 times.


## 3. Results

### 3.1. RNA Sequencing and Gene Prediction

Approximately 12,000,000 raw reads were obtained from each sample. After filtering by quality, about 96% clean reads were mapped. Summary of mapping result was shown in Table1. Using the longest sequence of a subgroup as the unigene as the reference sequence, we got 6057 predicted protein-encoding genes totally. The data have been submitted to NCBI under BioProject accession number PRJNA314910.Table 1
Summary of reads inC. albicans with or without HLJDD treatment.
Sample
Total Reads
Total mapped reads
Mapping percentage
Ca_CK_1
12,377,083
11,840,278
95.66%
Ca_CK_2
11,758,367
11,313,372
96.22%
Ca_CK_3
12,283,182
11,830,964
96.32%
Ca_HT_1
12,406,841
11,924,995
96.12%
Ca_HT_2
11,803,831
11,338,558
96.06%
Ca_HT_3
12,212,721
11,727,140
96.02%


### 3.2. Identification and Verification of Differentially Expressed Genes

By using the threshold of significance as fold change of RPKM ≥ 3 and Probability ≥ 0.8, we obtained a total of 735 differentially expressed genes (DEGs), including 700 upregulated genes and 35 downregulated genes (Supporting Information Table S1 in Supplementary Material available online athttp://dx.doi.org/10.1155/2016/3198249). The 20 most upregulated genes in response to HLJDD are listed in Table 2.Table 2
The 20 most upregulated genes in response to HLJDD treatment.
Standard or systematic name in CGD
ID in GenBank
Annotation
Size
log
2
⁡ ratio
Probability
C7_01060W_A
XP_720301.1
Hypothetical protein
142 aa
11.25
0.81
C5_04240C_A
XP_721977.1
Hypothetical protein
103 aa
11.21
0.80
C1_03880C_A
XP_711956.1
Hypothetical protein
120 aa
9.85
0.95
C7_01130C_A
XP_712469.1
Hypothetical protein
146 aa
9.42
0.92
PGA39
EEQ43586.1
Predicted protein
288 aa
9.32
0.85
C1_12040W_A
XP_716393.1
Hypothetical protein
143 aa
9.31
0.92
CR_01870C_A
XP_718251.1
Hypothetical protein
196 aa
8.90
0.85
CR_04980C_A
XP_711981.1
Hypothetical protein
193 aa
8.87
0.92
LIP10
XP_723508
Secretory lipase 10
465 aa
8.85
0.81
C3_01010W_A
XP_718606.1
Hypothetical protein
102 aa
8.67
0.90
C4_06150C_A
EEQ44911.1
Tat binding protein 1-interacting
175 aa
8.27
0.99
C7_03900W_A
XP_715240.1
Hypothetical protein
109 aa
7.96
0.94
CR_07970C_A
XP_714226.1
Hypothetical protein
119 aa
7.91
0.81
CR_06990W_A
XP_712676.1
Transcription activator
865 aa
7.84
0.88
C5_02090W_A
EEQ43139.1
Predicted protein
100 aa
7.84
0.84
SPO22
XP_718811.1
Meiosis specific protein
566 aa
7.73
0.96
CR_07550C_A
XP_710398.1
Hypothetical protein
101 aa
7.72
0.82
C3_02250C_A
XP_721699.1
Hypothetical protein
162 aa
7.71
0.85
C7_01060W_A
XP_718305.1
Hypothetical protein
111 aa
7.70
0.89A total of 8 genes including 7 upregulated and 1 downregulated gene from DGE libraries were selected for real-time PCR analysis to validate the DGE data. The results showed that 8 genes were demonstrated to have a consistent change for both DGE and real-time PCR while actin genes had no significant difference in real-time PCR (Supporting Information Table S2).


### 3.3. Effects of HLJDD Treatment on the Genes Involved in Sterol Biosynthesis

As the most widely used antifungal drugs, azoles can block fungal sterol biosynthesis pathway. Thus, effects of HLJDD on the genes involved in sterol biosynthesis were analyzed in detail. Expression of 23 genes involved in sterol biosynthesis was detected in the RNA-seq analysis, and expression of 8 genes showed a more than 2-fold increase afterC. albicans was treated with HLJDD; only the genes encoding sterol 24-C-methyltransferase (ERG6) and C-8 sterol isomerase (ERG2) were upregulated by more than 3 times (Table 3). None of these 23 genes was downregulated significantly (Probability > 0.8) by HLJJD.Table 3
Response to HLJDD of the genes involved in ergosterol biosynthesis.
Standard or systematic name in CGD
ID in GenBank
Annotation
log
2
⁡ ratio
Probability
ERG1
XP_711894.1
Squalene monooxygenase
2.13
0.95
ERG2
XP_718886.1
C-8 sterol isomerase
3.23
0.94
ERG3
XP_713577.1
C-5 sterol desaturase
1.84
0.94
ERG4
XP_717662.1
Sterol C-24 (28) reductase
−0.34
0.65
ERG5
XP_716933.1
Cytochrome P450 61
2.02
0.97
ERG6
XP_721588.1
Sterol 24-C-methyltransferase
3.33
0.97
ERG7
XP_722471.1
2,3-Oxidosqualene-lanosterol cyclase
−0.23
0.38
ERG8
XP_722678.1
Phosphomevalonate kinase
−0.01
0.03
ERG9
XP_714460.1
Squalene synthetase
−0.60
0.77
ERG10
XP_710124.1
Acetyl-CoA acetyltransferase IA
0.96
0.91
ERG11
XP_716761.1
Cytochrome P450 51
2.02
0.97
ERG12
XP_723305.1
Mevalonate kinase
0.97
0.85
ERG13
XP_716446.1
Hydroxymethylglutaryl-CoA synthase
2.30
0.97
MVD1/ERG19
XP_718960.1
Diphosphomevalonate decarboxylase
0.04
0.12
ERG24
XP_710205.1
Delta(14)-sterol reductase
2.53
0.93
ERG25
XP_713420.1
C-4 methylsterol oxidase
1.31
0.91
XP_722703.1
C-4 methylsterol oxidase
1.02
0.92
ERG26
XP_715564.1
C-3 sterol dehydrogenase/C-4 decarboxylase
0.23
0.39
ERG27
XP_717865.1
3-Keto sterol reductase
1.67
0.90
ERG28
XP_717865.1
Hypothetical protein
0.38
0.68
HMG1
XP_713636.1
Hydroxymethylglutaryl-CoA reductase
2.36
0.96
IDI1
XP_720295.1
Isopentenyl-diphosphate delta-isomerase
−0.09
0.24
CYB5
XP_720295.1
Cytochrome b5
−0.27
0.62


### 3.4. Effects of HLJDD Treatment on the Genes Encoding Multidrug Transporters

InC. albicans, upregulation of multidrug transporter genes is one of the well-documented mechanisms of resistance to azole antifungal agents [9, 46–48]. Two families of multidrug transporters, the ABC (ATP-binding cassette) transporter family (Cdr1p and Cdr2p) and the major facilitator superfamily (MFS, CaMdr1p), have been shown to be involved in resistance to azole antifungal agents [47, 48]. Thus, we also paid attention to the multidrug transporter genes. In genome sequences ofC. albicans, a total of 36 genes are annotated as multidrug transporters. In this study, expression of 32 genes was detected by the RNA-seq, and 7 genes were identified to be significantly upregulated more than 3 times by HLJDD treatment (Table 4), includingCDR2 (Candida Drug Resistance) from the family of ABC transporters. Cdr2 has been shown as the principal mediators of resistance to azoles due to transport phenomena [47, 48].Table 4
Response to HLJDD of the genes involved in multidrug resistance ofC. albicans.
Standard or systematic name in CGD
ID in GenBank
Annotation
log
2
⁡ ratio
Probability
CDR1
XP_723062.1
Multidrug resistance protein CDR1
2.42
0.99
CDR2
XP_723022.1
Multidrug resistance ABC transporter
5.32
0.99
CDR3
XP_441615.1
N terminal 2/3 of opaque-specific ABC transporter
0.75
0.67
CDR4
XP_717543.1
Potential ABC transporter
−2.49
0.99
ATM1
XP_712090.1
Potential mitochondrial ABC transporter similar toS. cerevisiae ATM1
0.79
0.76
HST6
XP_716101.1
Potential ABC transporter similar toS. cerevisiae STE6
5.44
0.88
MDL1
XP_718280.1
Potential ABC transporter similar toS. cerevisiae mitochondrial inner membrane MDL1
0.81
0.79
MLT1
XP_717637.1
Vacuolar multidrug resistance ABC transporter
1.75
0.93
MDR1
XP_719165.1
Major Facilitator Transporter
0.63
0.77
CR_04620C_A
XP_717510.1
MFS transporter, DHA1 family, multidrug resistance protein
4.46
0.91
SGE11
XP_715705.1
Potential MFS-MDR transporter
1.31
0.84
C1_10710C_A
XP_714012.1
MFS transporter, DHA2 family, multidrug resistance protein
5.1
0.90
C3_03070W_A
XP_720131.1
MFS transporter, DHA2 family, multidrug resistance protein
0.47
0.66
NAG4
XP_712435.1
MFS transporter, DHA1 family, multidrug resistance protein
5.83
0.77
TPO41
XP_717426.1
MFS transporter, DHA1 family, multidrug resistance protein
2.34
0.95
C6_01870C_A
XP_716705.1
MFS transporter, DHA1 family, multidrug resistance protein
2.59
0.94
NAG3
XP_712434.1
MFS transporter, DHA1 family, multidrug resistance protein
2.8
0.85
C1_10200C_A
XP_723572.1
MFS transporter, DHA1 family, multidrug resistance protein
1.23
0.91
C2_02570W_A
EEQ45693.1
MFS transporter, DHA1 family, multidrug resistance protein
1.34
0.78
TPO3
XP_723233.1
MFS transporter, DHA1 family, multidrug resistance protein
−0.95
0.85
HOL1
XP_721489.1
MFS transporter, DHA1 family, multidrug resistance protein
2.0
0.85
CR_01340W_A
XP_718285.1
MFS transporter, DHA1 family, multidrug resistance protein
3.93
0.93
HOL4
XP_712971.1
MFS transporter, DHA1 family, multidrug resistance protein
0.88
0.81
C3_03440C_A
XP_720169.1
Potential drug or polyamine transporter
3.44
0.95
TPO2
XP_715197.1
Potential drug or polyamine transporter
2.31
0.82
QDR3
XP_714342.1
Potential multidrug resistance transporter
2.33
0.87
C2_00540W_A
XP_719644.1
Potential MATE family drug/sodium antiporter
−0.28
0.56
C7_03590C_A
EEQ47129.1
Multidrug resistance protein, MATE family
0.16
0.26
C1_00830W_A
XP_718985.1
Potential MATE family drug/sodium antiporter
0.44
0.34
CR_10640W_A
XP_719407.1
Multidrug resistance protein, MATE family
3.15
0.96
QDR2
XP_714698.1
Potential quinidine/multidrug transporter
1.63
0.94
FLU1
XP_721413.1
Multidrug efflux transporter
1.76
0.91


### 3.5. Enrichment Analysis of GO and KEGG Pathways

GO and KEGG assignments were used to classify the genes in the response ofC. albicans to HLJDD. By GO classification analysis, the percentage and distribution of top-level GO terms were portrayed in the 3 categories: (A) cellular component; (B) molecular function, and (C) biological process (Figure 1). A high percentage of genes were assigned to “cell,” “cell part,” “binding,” “catalytic,” “cellular process,” and “metabolic process” (Figure 1).Figure 1
Functional categories of genes inC. albicans in response to HLJDD.By enrichment analysis, with FDR < 0.05, 23 significant GO terms and 3 significant KEGG pathways were identified (Supporting Information Table S3). These significant pathways were mainly associated with DNA replication and transporter activity. The maps with highest unigene representation were meiosis (cal04113; 23 unigenes), followed by cell cycle (cal04111; 23 unigenes), and DNA replication (cal03030; 11 unigenes).


## 3.1. RNA Sequencing and Gene Prediction

Approximately 12,000,000 raw reads were obtained from each sample. After filtering by quality, about 96% clean reads were mapped. Summary of mapping result was shown in Table1. Using the longest sequence of a subgroup as the unigene as the reference sequence, we got 6057 predicted protein-encoding genes totally. The data have been submitted to NCBI under BioProject accession number PRJNA314910.Table 1
Summary of reads inC. albicans with or without HLJDD treatment.
Sample
Total Reads
Total mapped reads
Mapping percentage
Ca_CK_1
12,377,083
11,840,278
95.66%
Ca_CK_2
11,758,367
11,313,372
96.22%
Ca_CK_3
12,283,182
11,830,964
96.32%
Ca_HT_1
12,406,841
11,924,995
96.12%
Ca_HT_2
11,803,831
11,338,558
96.06%
Ca_HT_3
12,212,721
11,727,140
96.02%


## 3.2. Identification and Verification of Differentially Expressed Genes

By using the threshold of significance as fold change of RPKM ≥ 3 and Probability ≥ 0.8, we obtained a total of 735 differentially expressed genes (DEGs), including 700 upregulated genes and 35 downregulated genes (Supporting Information Table S1 in Supplementary Material available online athttp://dx.doi.org/10.1155/2016/3198249). The 20 most upregulated genes in response to HLJDD are listed in Table 2.Table 2
The 20 most upregulated genes in response to HLJDD treatment.
Standard or systematic name in CGD
ID in GenBank
Annotation
Size
log
2
⁡ ratio
Probability
C7_01060W_A
XP_720301.1
Hypothetical protein
142 aa
11.25
0.81
C5_04240C_A
XP_721977.1
Hypothetical protein
103 aa
11.21
0.80
C1_03880C_A
XP_711956.1
Hypothetical protein
120 aa
9.85
0.95
C7_01130C_A
XP_712469.1
Hypothetical protein
146 aa
9.42
0.92
PGA39
EEQ43586.1
Predicted protein
288 aa
9.32
0.85
C1_12040W_A
XP_716393.1
Hypothetical protein
143 aa
9.31
0.92
CR_01870C_A
XP_718251.1
Hypothetical protein
196 aa
8.90
0.85
CR_04980C_A
XP_711981.1
Hypothetical protein
193 aa
8.87
0.92
LIP10
XP_723508
Secretory lipase 10
465 aa
8.85
0.81
C3_01010W_A
XP_718606.1
Hypothetical protein
102 aa
8.67
0.90
C4_06150C_A
EEQ44911.1
Tat binding protein 1-interacting
175 aa
8.27
0.99
C7_03900W_A
XP_715240.1
Hypothetical protein
109 aa
7.96
0.94
CR_07970C_A
XP_714226.1
Hypothetical protein
119 aa
7.91
0.81
CR_06990W_A
XP_712676.1
Transcription activator
865 aa
7.84
0.88
C5_02090W_A
EEQ43139.1
Predicted protein
100 aa
7.84
0.84
SPO22
XP_718811.1
Meiosis specific protein
566 aa
7.73
0.96
CR_07550C_A
XP_710398.1
Hypothetical protein
101 aa
7.72
0.82
C3_02250C_A
XP_721699.1
Hypothetical protein
162 aa
7.71
0.85
C7_01060W_A
XP_718305.1
Hypothetical protein
111 aa
7.70
0.89A total of 8 genes including 7 upregulated and 1 downregulated gene from DGE libraries were selected for real-time PCR analysis to validate the DGE data. The results showed that 8 genes were demonstrated to have a consistent change for both DGE and real-time PCR while actin genes had no significant difference in real-time PCR (Supporting Information Table S2).


## 3.3. Effects of HLJDD Treatment on the Genes Involved in Sterol Biosynthesis

As the most widely used antifungal drugs, azoles can block fungal sterol biosynthesis pathway. Thus, effects of HLJDD on the genes involved in sterol biosynthesis were analyzed in detail. Expression of 23 genes involved in sterol biosynthesis was detected in the RNA-seq analysis, and expression of 8 genes showed a more than 2-fold increase afterC. albicans was treated with HLJDD; only the genes encoding sterol 24-C-methyltransferase (ERG6) and C-8 sterol isomerase (ERG2) were upregulated by more than 3 times (Table 3). None of these 23 genes was downregulated significantly (Probability > 0.8) by HLJJD.Table 3
Response to HLJDD of the genes involved in ergosterol biosynthesis.
Standard or systematic name in CGD
ID in GenBank
Annotation
log
2
⁡ ratio
Probability
ERG1
XP_711894.1
Squalene monooxygenase
2.13
0.95
ERG2
XP_718886.1
C-8 sterol isomerase
3.23
0.94
ERG3
XP_713577.1
C-5 sterol desaturase
1.84
0.94
ERG4
XP_717662.1
Sterol C-24 (28) reductase
−0.34
0.65
ERG5
XP_716933.1
Cytochrome P450 61
2.02
0.97
ERG6
XP_721588.1
Sterol 24-C-methyltransferase
3.33
0.97
ERG7
XP_722471.1
2,3-Oxidosqualene-lanosterol cyclase
−0.23
0.38
ERG8
XP_722678.1
Phosphomevalonate kinase
−0.01
0.03
ERG9
XP_714460.1
Squalene synthetase
−0.60
0.77
ERG10
XP_710124.1
Acetyl-CoA acetyltransferase IA
0.96
0.91
ERG11
XP_716761.1
Cytochrome P450 51
2.02
0.97
ERG12
XP_723305.1
Mevalonate kinase
0.97
0.85
ERG13
XP_716446.1
Hydroxymethylglutaryl-CoA synthase
2.30
0.97
MVD1/ERG19
XP_718960.1
Diphosphomevalonate decarboxylase
0.04
0.12
ERG24
XP_710205.1
Delta(14)-sterol reductase
2.53
0.93
ERG25
XP_713420.1
C-4 methylsterol oxidase
1.31
0.91
XP_722703.1
C-4 methylsterol oxidase
1.02
0.92
ERG26
XP_715564.1
C-3 sterol dehydrogenase/C-4 decarboxylase
0.23
0.39
ERG27
XP_717865.1
3-Keto sterol reductase
1.67
0.90
ERG28
XP_717865.1
Hypothetical protein
0.38
0.68
HMG1
XP_713636.1
Hydroxymethylglutaryl-CoA reductase
2.36
0.96
IDI1
XP_720295.1
Isopentenyl-diphosphate delta-isomerase
−0.09
0.24
CYB5
XP_720295.1
Cytochrome b5
−0.27
0.62


## 3.4. Effects of HLJDD Treatment on the Genes Encoding Multidrug Transporters

InC. albicans, upregulation of multidrug transporter genes is one of the well-documented mechanisms of resistance to azole antifungal agents [9, 46–48]. Two families of multidrug transporters, the ABC (ATP-binding cassette) transporter family (Cdr1p and Cdr2p) and the major facilitator superfamily (MFS, CaMdr1p), have been shown to be involved in resistance to azole antifungal agents [47, 48]. Thus, we also paid attention to the multidrug transporter genes. In genome sequences ofC. albicans, a total of 36 genes are annotated as multidrug transporters. In this study, expression of 32 genes was detected by the RNA-seq, and 7 genes were identified to be significantly upregulated more than 3 times by HLJDD treatment (Table 4), includingCDR2 (Candida Drug Resistance) from the family of ABC transporters. Cdr2 has been shown as the principal mediators of resistance to azoles due to transport phenomena [47, 48].Table 4
Response to HLJDD of the genes involved in multidrug resistance ofC. albicans.
Standard or systematic name in CGD
ID in GenBank
Annotation
log
2
⁡ ratio
Probability
CDR1
XP_723062.1
Multidrug resistance protein CDR1
2.42
0.99
CDR2
XP_723022.1
Multidrug resistance ABC transporter
5.32
0.99
CDR3
XP_441615.1
N terminal 2/3 of opaque-specific ABC transporter
0.75
0.67
CDR4
XP_717543.1
Potential ABC transporter
−2.49
0.99
ATM1
XP_712090.1
Potential mitochondrial ABC transporter similar toS. cerevisiae ATM1
0.79
0.76
HST6
XP_716101.1
Potential ABC transporter similar toS. cerevisiae STE6
5.44
0.88
MDL1
XP_718280.1
Potential ABC transporter similar toS. cerevisiae mitochondrial inner membrane MDL1
0.81
0.79
MLT1
XP_717637.1
Vacuolar multidrug resistance ABC transporter
1.75
0.93
MDR1
XP_719165.1
Major Facilitator Transporter
0.63
0.77
CR_04620C_A
XP_717510.1
MFS transporter, DHA1 family, multidrug resistance protein
4.46
0.91
SGE11
XP_715705.1
Potential MFS-MDR transporter
1.31
0.84
C1_10710C_A
XP_714012.1
MFS transporter, DHA2 family, multidrug resistance protein
5.1
0.90
C3_03070W_A
XP_720131.1
MFS transporter, DHA2 family, multidrug resistance protein
0.47
0.66
NAG4
XP_712435.1
MFS transporter, DHA1 family, multidrug resistance protein
5.83
0.77
TPO41
XP_717426.1
MFS transporter, DHA1 family, multidrug resistance protein
2.34
0.95
C6_01870C_A
XP_716705.1
MFS transporter, DHA1 family, multidrug resistance protein
2.59
0.94
NAG3
XP_712434.1
MFS transporter, DHA1 family, multidrug resistance protein
2.8
0.85
C1_10200C_A
XP_723572.1
MFS transporter, DHA1 family, multidrug resistance protein
1.23
0.91
C2_02570W_A
EEQ45693.1
MFS transporter, DHA1 family, multidrug resistance protein
1.34
0.78
TPO3
XP_723233.1
MFS transporter, DHA1 family, multidrug resistance protein
−0.95
0.85
HOL1
XP_721489.1
MFS transporter, DHA1 family, multidrug resistance protein
2.0
0.85
CR_01340W_A
XP_718285.1
MFS transporter, DHA1 family, multidrug resistance protein
3.93
0.93
HOL4
XP_712971.1
MFS transporter, DHA1 family, multidrug resistance protein
0.88
0.81
C3_03440C_A
XP_720169.1
Potential drug or polyamine transporter
3.44
0.95
TPO2
XP_715197.1
Potential drug or polyamine transporter
2.31
0.82
QDR3
XP_714342.1
Potential multidrug resistance transporter
2.33
0.87
C2_00540W_A
XP_719644.1
Potential MATE family drug/sodium antiporter
−0.28
0.56
C7_03590C_A
EEQ47129.1
Multidrug resistance protein, MATE family
0.16
0.26
C1_00830W_A
XP_718985.1
Potential MATE family drug/sodium antiporter
0.44
0.34
CR_10640W_A
XP_719407.1
Multidrug resistance protein, MATE family
3.15
0.96
QDR2
XP_714698.1
Potential quinidine/multidrug transporter
1.63
0.94
FLU1
XP_721413.1
Multidrug efflux transporter
1.76
0.91


## 3.5. Enrichment Analysis of GO and KEGG Pathways

GO and KEGG assignments were used to classify the genes in the response ofC. albicans to HLJDD. By GO classification analysis, the percentage and distribution of top-level GO terms were portrayed in the 3 categories: (A) cellular component; (B) molecular function, and (C) biological process (Figure 1). A high percentage of genes were assigned to “cell,” “cell part,” “binding,” “catalytic,” “cellular process,” and “metabolic process” (Figure 1).Figure 1
Functional categories of genes inC. albicans in response to HLJDD.By enrichment analysis, with FDR < 0.05, 23 significant GO terms and 3 significant KEGG pathways were identified (Supporting Information Table S3). These significant pathways were mainly associated with DNA replication and transporter activity. The maps with highest unigene representation were meiosis (cal04113; 23 unigenes), followed by cell cycle (cal04111; 23 unigenes), and DNA replication (cal03030; 11 unigenes).


## 4. Discussion

C. albicans is the most prevalent opportunistic fungal pathogen causing superficial to systemic infections in immunocompromised individuals [1, 2]. The concomitant use of drugs and the lack of available drugs frequently result in the occurrence of drug-resistant isolates and strains display multidrug resistance (MDR). In search of novel fungicides, efficiency of medicinal plants against fungi has been reported, but studies on their underlying mechanisms are very few [49]. In this study, we explored a famous multiherb prescription in China, Huanglian Jiedu Decoction (HLJJD,Orengedokuto in Japan), for its antifungal potential. Our preliminary work showed that HLJDD is efficient againstTrichophyton mentagrophytesandC. albicans [35]. HLJDD showed its impressive antifungal effect by multitarget and multichannel actions, but studies on the underlying mechanisms are very few. To determine the antifungal mechanism of HLJDD againstC. albicans, we performed a large-scale analysis of gene expression changes whenC. albicans was exposed to HLJDD, to better understand how HLJDD inhibits the growth ofC. albicans. Due to the multiple components of HLJDD, it is most likely that the antifungal effect is multitarget and multichannel actions. KEGG analysis suggested that 3 cellular functions were affected inC. albicans upon HLJDD treatment, including meiosis, cell cycle, and DNA replication. Most genes (56 genes) involved in the 3 cellular functions were upregulated excepted for 1 gene, potential hexose transporter (XP_719596.1). Among these genes, Spo22 (also called Zip4) (XP_718811.1) was upregulated obviously upon HLJDD treatment. Zip4/Spo22 was shown to be a central protein of the SICs (synapsis initiation complexes), from which the polymerization of the transverse filament proceeds. InS. cerevisiae, Zip4/Spo22 was identified as a member of the ZMM group of proteins that also includes Zip1, Zip2, Zip3, Msh4, Msh5, and Mer3 which together control the formation of class I COs [50–52]. InArabidopsis thaliana, Zip4/Spo22 function in class I CO formation is conserved with budding yeast. However, mutation in AtZIP4 does not prevent synapsis, showing that both aspects of the Zip4 function (i.e., class I CO maturation and synapsis) can be uncoupled [51].Azoles are the most widely used antifungal drugs, which target on cytochrome P450 lanosterol 14α-demethylase encoded by theERG11 gene. InFusarium graminearum, using a deep serial analysis of gene expression (DeepSAGE) sequencing approach, the transcriptional response ofF. graminearum to tebuconazole (a widely used azole fungicide) was profiled. Expression of 23 genes involved in sterol biosynthesis was detected in the DeepSAGE analysis, and expression of 9 genes showed a more than 5-fold increase after the fungus was treated with tebuconazole. None of these 23 genes was downregulated by more than 5 times by tebuconazole [53]. Thus, effects of HLJDD on the genes involved in sterol biosynthesis were analyzed in detail. Expression of 23 genes involved in sterol biosynthesis was detected in the RNA-seq analysis, and expression of 8 genes showed a more than 2-fold increase after the fungus was treated with HLJDD, only the genes encoding sterol 24-C-methyltransferase (ERG6) and C-8 sterol isomerase (ERG2) were upregulated by more than 3 times (Table 3). None of these 23 genes was downregulated significantly (Probability > 0.8) by HLJJD. These results indicate that HLJDD might also affect sterol biosynthesis ofC. albicans.Overexpression of multidrug resistance efflux transporter genes in several fungi was found to be correlated with azole resistance [54]. InC. albicans, a number of efflux transporter genes have been cloned and characterized. Two families of multidrug transporters, the ABC (ATP-binding cassette) transporter family (Cdr1p and Cdr2p) and the major facilitator superfamily (MFS, CaMdr1p), have been shown to be involved in resistance to azole antifungal agents [47, 48]. Expression of 32 genes out of 36 genes annotated as multidrug transporters in genome sequences ofC. albicanswas detected by the RNA-seq sequencing. Expression of 13 genes was upregulated by more than 2 times by HLJDD; meanwhile, only 2 genes were significantly downregulated includingCDR4. In addition, expression of only 4 genes was upregulated by more than 3 times by HLJDD, includingCDR2, which plays an important role in azole resistance (Table 4). The upregulated expression of these genes may be related to efflux of HLJJD, which provides supporting evidence to previous studies on expression level.Previous study examined changes in the gene expression profile ofC. albicans following exposure to representatives of the 4 currently available classes of antifungal agents, the azoles (ketoconazole), polyenes (amphotericin B), echinocandins (caspofungin), and nucleotide analogs (5-flucytosine). And the data showed that none of the differentially regulated genes found exhibited similar changes in expression for all 4 classes of drugs. Thus, the response ofC. albicans to different drugs seems to be highly specific [55]. Ketoconazole exposure increased the expression of genes involved in lipid, fatty acid, sterol metabolism, and several genes associated with azole resistance, includingCDR1 andCDR2 [56]. It is surprising that HLJDD increased the expression of genes involved in sterol metabolism and azole resistance (CDR1 andCDR2). Considering the similarity of expression changing pattern, it is possible that HLJDD affects sterol metabolism. And further experiments are required to confirm this hypothesis.


## 5. Conclusions

In conclusion, we performed a transcriptomics analysis of gene expression changes forC. albicans under treatment of HLJDD using RNA-seq technique. Overall, a total of 6057 predicted protein-encoding genes were identified. Further gene expression analysis revealed a total of 735 differentially expressed genes (DEGs), including 700 upregulated genes and 35 downregulated genes. Intensive bioinformatics analysis identified 26 significant pathways, and DNA replication and transporter activity were mainly involved. In addition, genes encoding multidrug transporters such as ABC transporter and MFS transporter were identified to be significantly upregulated. Overall, the results from this study might provide insights in understanding of the mechanisms for the response ofC. albicansto HLJJD. Furthermore, this work demonstrates the potential utility of the RNA-seq technique in antifungal studies.



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*Source: 3198249-2016-04-06.xml*