Transcriptomic and Proteomic Analysis Reveals Mechanisms of Patulin-Induced Cell Toxicity in Human Embryonic Kidney Cells

toxins

Article Transcriptomic and Proteomic Analysis Reveals Mechanisms of Patulin-Induced Cell Toxicity in Human Embryonic Kidney Cells

Nianfa Han 1, Ruilin Luo 2, Jiayu Liu 2, Tianmin Guo 2, Jiayu Feng 2 and Xiaoli Peng 2,*

1 Beijing Jihua Biotechnology Services Co., LTD, Beijing 102200, China; nianfa.han@gene-van.com 2 College of Food Science and Engineering, Northwest A&F University, Yangling 712100, China; [email protected] (R.L.); [email protected] (J.L.); [email protected] (T.G.); [email protected] (J.F.) * Correspondence: [email protected]

Received: 17 September 2020; Accepted: 26 October 2020; Published: 29 October 2020

Abstract: Patulin (PAT) is a natural mycotoxin that commonly contaminates fruits and fruit-based products. Previous work indicated that PAT-induced apoptosis in which reactive oxygen species (ROS) are involved in human embryonic kidney (HEK293) cells. To uncover novel aspects of the possible mechanism of PAT nephrotoxicity, the transcriptome and proteome proﬁles were investigated using the digital gene expression (DGE) and isobaric tags for relative and absolute quantitation (iTRAQ) proteomic approaches. A total of 127 genes and 85 proteins were found to express diﬀerentially in response to 5 µM PAT for 10 h in HEK293 cells. The most dramatic changes of expression were noticed with genes or proteins related to apoptosis, oxidative phosphorylation ribosome and cell cycle. Especially, the activation of caspase 3, UQCR11, active transport form and endocytosis appeared to be crucial in PAT kidney cytotoxicity. PAT also seemed to be associated with cancer and neuropathic disease as pathways associated with carcinogenesis, Alzheimer’s disease and Parkinson’s disease were induced. Overall, this study served to uncover overall insights associated with signaling pathway that modulated the PAT toxicity mechanism.

Keywords: patulin; renal cytotoxicity; HEK293 cells; transcriptomic; proteomic; apoptosis

Key Contribution: We clearly conclude that integrated measurement of changes in protein and transcript abundance will provide new insights into signaling transduction that modulated OTA nephrotoxicity mechanism.

1. Introduction Patulin (PAT) is a highly toxic secondary metabolite produced by Aspergillus sp. and Penicillium sp., it can be commonly found in rotten fruits and crops as well as their derivative products [1,2]. At present, controlling PAT contamination remains a huge challenge. The accumulation of PAT presents a food safely risk and health hazard to human. PAT can cause edema, hemorrhage, immune dysfunction and exhibits nephrotoxic, hepatoxic, carcinogenic, neurotoxic and genotoxic properties [3,4]. A variety of mechanisms have been conducted for how PAT works, including forming covalent adducts with essential sulfhydryl groups [5], leading to mitochondrial dysfunction and endoplasmic reticulum stress [6], and the induction of excessive reactive oxygen species (ROS), which induce oxidative DNA damage and membrane peroxidation [7–9]. Moreover, mitogen-activated protein kinase and P53, Bcl2 family members as well as autophagy were also reported involved in PAT toxicology [10,11].

Toxins 2020, 12, 681; doi:10.3390/toxins12110681 www.mdpi.com/journal/toxins Toxins 2020, 12, x FOR PEER REVIEW 2 of 13 Toxins 2020, 12, 681 2 of 13 Understanding the precise toxin mechanism at molecular level is essential to predict potential deleteriousUnderstanding effects and the to precise develop toxin suitable mechanism countermeasures at molecular to levelimprove is essential human to health. predict To potential obtain a deleteriouscomprehensive effects view and of to developthe mechanism suitable of countermeasures PAT nephrotoxicity, to improve here humanwe simultaneously health. To obtain used atranscriptomic comprehensive and view proteomic of the mechanismapproaches to of clarify PAT nephrotoxicity, the complex pathological here we simultaneously changes in human used transcriptomicembryonic kidney and cells. proteomic It was found approaches that (i) to a numb clarifyer theof pro-apoptotic complex pathological genes and changes positive inregulation human embryonicof apoptosis kidney proteins cells. were It was up-reg foundulated; that (i)(ii) a complex number III of pro-apoptoticof the mitochondrial genes and respiratory positive chain regulation gene ofwas apoptosis enhanced, proteins while wereATP up-regulated;synthase was inhibited (ii) complex and III complex of the mitochondrial I was disordered; respiratory and (iii) chain ribosome, gene wascell enhanced,cycle, nucleotide while ATPmetabolism, synthase cell was growth, inhibited and and endocytosis, complex I wasas well disordered; as some anddisease-associated (iii) ribosome, cellgenes cycle, and nucleotide proteins, played metabolism, a relatively cell growth, large role and in endocytosis,PAT toxicology. as well as some disease-associated genes and proteins, played a relatively large role in PAT toxicology. 2. Results 2. Results 2.1. PAT Triggers Caspase-Dependent Cell Death via the Intrinsic Apoptotic Pathway 2.1. PAT Triggers Caspase-Dependent Cell Death via the Intrinsic Apoptotic Pathway Previous studies using HEK293 cells have shown PAT-induced apoptosis and the collapse of mitochondrialPrevious studies membrane using potential. HEK293 We cells further have con shownfirmed PAT-induced the inhibition apoptosis of cell viability and the collapseusing WST of mitochondrialmethods (Figure membrane 1a,b). Following potential. treatment We further with confirmed PAT, cells the showed inhibition a significant of cell viability decrease using in cellular WST methodsATP content (Figure (Figure1a,b). 1c,d). Following PAT treatmentalso induced with the PAT, release cells showed of Cyt ac significantfrom mitochondria decrease into cellular cytosol ATPpreceding content cell (Figure death1 c,d).(Figure PAT 1e,f). also Furthermore, induced the activations release of Cytof the c fromapoptotic mitochondria biomarkers, to cytosolcaspase precedingcascades, cellwere death also (Figuredetected.1e,f). A Furthermore,marked elevation activations of activities of the apoptoticof initiator biomarkers, caspase 9 caspaseand the cascades,downstream were executioner also detected. caspase A marked 3/7 was elevation observed of activities (Figure of2a,b). initiator Co-treatment caspase 9 andof caspase the downstream 3 specific executionerinhibitor Z-DEVD-FMK caspase 3/7 wasprotected observed cells (Figurefrom PAT-2a,b).triggered Co-treatment cell death of caspase(Figure 2c) 3 specific and LDH inhibitor release Z-DEVD-FMK(Figure 2d), and protected almost completely cells from PAT-triggered reversed the increase cell death of(Figure enzyme2c) activities and LDH of release capase (Figure9 and 3/72d), at and10 h almost (Figure completely 2e,f). Thus, reversed PAT thewas increase able to of trigger enzyme caspase-dependent activities of capase 9cell and death 3/7 at 10via h (Figurethe intrinsic2e,f). Thus,apoptosis PAT waspathway. able to trigger caspase-dependent cell death via the intrinsic apoptosis pathway.

Figure 1. Induction of mitochondrial pathway cell death by patulin (PAT). (a,b) Cell viability reduced byFigure PAT in1. Induction dose- and of time-dependent mitochondrial manners. pathway (cellc,d) death PAT induced by patulin LDH (PAT). release. (a,b ()e Cell,f) Cyt viability c release reduced from mitochondriaby PAT in dose- to cytosol and time-dependent after 5 µM PAT manners. exposure. ( *c,pd)< PAT0.05, induced ** p < 0.01 LDH versus release. control. (e,f) Cyt c release from mitochondria to cytosol after 5 μM PAT exposure. * p < 0.05, ** p < 0.01 versus control.

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a 300 300 b 3 h 3 h ** 10 h ** 10 h ** 200 200 ** ** ** ** ** ** 100 100 ** * * **

(U/mg protein) (U/mg * (U/mg protein) (U/mg Caspase 9 activity9 Caspase

0 activity Caspase 3/7 0 02.557.510 02.557.510 PAT concentration (μM) PAT concentration (μM) c 110 200 e 100 ## 150 ** 90 ** 100 80 ## 70 50 Cellviability (% of control) of (%

60 protein) (U/mg 0 Control FMK PAT PAT+ activity 9 Caspase Control FMK PAT PAT+ FMK FMK d 23 200 f ** ** 18 150 13

(%) 100 8 ## ##

LDH release 50 3 (U/mg protein) (U/mg

Caspase 3/7 activity 3/7Caspase 0 -2 Control FMK PAT PAT+ Control FMK PAT PAT+ FMK FMK

Figure 2. Caspase cascade pathway is involved in PAT nephrotoxicity. (a,b) The activities of initiator caspaseFigure 2. (9) Caspase and the cascade downstream pathway executioner is involved caspase in PAT (3, nephrotoxicity. 7) were elevated. (a,b) (Thec,d) activities Caspase 3of inhibitor initiator Z-DEVD-FMKcaspase (9) and protected the downstream cells from executio PAT triggeredner caspase cell damage(3, 7) were based elevated. on cell ( viabilityc,d) Caspase and LDH3 inhibitor leakage. Z- (DEVD-FMKe,f) Z-DEVD-FMK protected almost cells completely from PAT reversedtriggered the cell increase damage of based enzyme on cell activities viability of capaseand LDH 9 and leakage. 3/7 at 10(e, h.f) Z-DEVD-FMK * p < 0.05, ** p

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TheThe HomoHomo sapiens sapiens reference reference genome genome contains contains 22,748 22,748 genes genes and 94.49%and 94.49% of these of genesthese havegenes CATG have sites.CATG Finally, sites. Finally, the tag mappingthe tag mapping onto the onto Homo the sapiens Homo genome sapiens generatedgenome generated 13,016 tag-mapped 13,016 tag-mapped genes for controlgenes for group, control 13,268 group, for 13,268 PAT group. for PAT In detail,group. 127In detail, genes 127 were genes regulated were regulated at least twofold. at least Oftwofold. these genes,Of these 84 genes, showed 84 increased,showed increased, while 43 while showed 43 showed decreased decreased expressions expressions (Figure 3(Figurea), indicating 3a), indicating that a relativethat a relative extensive extensive transcriptional transcriptional reprogramming reprogramming occurred. occurred. The detail The didetailﬀerential differential expressed expressed genes weregenes presented were presented in Supplementary in Supplementary Table S2. Table S2.

Figure 3. The Gene Ontology (GO) analysis of the differentially expressed genes. (a) Number of diFigurefferentially 3. The expressed Gene Ontology genes. (GO) (b–d )analysis The assignment of the differentially of those genes expressed classified genes. by GO(a) Number functional of categories:differentially cell expressed component, genes. molecular (b–d) functionThe assignment and biology of those processing genes separately. classified by GO functional categories: cell component, molecular function and biology processing separately. The Gene Ontology (GO) annotations of cellular component, molecular function and biological processes of the differential expression genes are presented in Figure3b–d. Kyoto Encyclopedia of The Gene Ontology (GO) annotations of cellular component, molecular function and biological Genes and Genomes (KEGG) pathway enrichment analyses were enriched at Supplementary Table S3. processes of the differential expression genes are presented in Figure 3b–d. Kyoto Encyclopedia of In order to validate the quality of the DGE, six randomly selected genes were measured by real-time Genes and Genomes (KEGG) pathway enrichment analyses were enriched at Supplementary Table PCR. Quantification of the signals showed that except FIS1, the expression patterns of all other genes S3. In order to validate the quality of the DGE, six randomly selected genes were measured by real- were in accordance with the DGE results, although the ratios varied to some extent (Figure4a–g). time PCR. Quantification of the signals showed that except FIS1, the expression patterns of all other genes were in accordance with the DGE results, although the ratios varied to some extent (Figure 4a– g).

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a 6 ** 1.5 b 3 c g ** ** 4 1 2 3 ** * 1 2 0.5 R=0.87 2 0 0 0 0 h 3 h 10 h 1 0 h 3 h 10 h mRNA SLC25A6 control) of (Fold 0 h 3 h 10 h RNF34 mRNA RNF34 control)of (Fold HSPB8mRNA control) of (Fold 1 1.5 1.5 d e f 0 1 1 Log2 ratio of Log2 ratio of DGE ** ** * PCR real-time 0.5 ** ** ** ASL -1 0.5 0.5 FIS1 -2 0 0 0

ASL mRNA ASL control) of (Fold -20246810 0 h 3 h 10 h COX17 mRNA control) of (Fold 0 h 3 h 10 h FISI mRNA FISI control) of (Fold 0 h 3 h 10 h

Figure 4. The real-time PCR validation of eight selected genes in HEK293 cells treated by 5 µM PAT at Figure3 h and 4. 10The h. real-time (a) Heat shock PCR validation protein beta-8 of eight (HSPB8). selected (b) genes Solute in carrier HEK293 family cells 25 treated member by 6 5 (SLC25A6). μM PAT at(c )3 Ringh and ﬁnger 10 h. protein (a) Heat 34 (RNF34).shock protein (d) Fission, beta-8 mitochondrial(HSPB8). (b) Solute 1 (FIS1). carrier (e) Argininosuccinatelyase family 25 member 6 (SLC25A6).(ASL). (f) Cytochrome (c) Ring cfinger oxidase protein copper chaperone34 (RNF34). (COX17). (d) Beta-actinFission, mitochondrial (ACTB) was used 1 as(FIS1). an internal (e) Argininosuccinatelyasecontrol. Each bar represents (ASL). the ( meanf) Cytochrome of three independent c oxidase experiments.copper chaperone * p < 0.05,(COX17). ** p < 0.01Beta-actin versus (ACTB)control. was (g) Relationship used as an betweeninternal thecontrol. gene expressionEach bar measuredrepresentsby the DGE mean and of real-time three independent PCR, R is the experiments.Pearson correlation * p < 0.05, coeﬃ **cient. p < 0.01 versus control. (g) Relationship between the gene expression 2.3.measured Proteomic by Analysis DGE and real-time PCR, R is the Pearson correlation coefficient.

2.3. ProteomicTo obtain Analysis more information about the nature of PAT-induced toxicity, an isobaric tags for relative and absolute quantitation (iTRAQ)-based approach was pursued to quantify the global protein To obtain more information about the nature of PAT-induced toxicity, an isobaric tags for expression profiles. Changes in the abundance of proteins were measured and compared at two relative and absolute quantitation (iTRAQ)-based approach was pursued to quantify the global biological replicates. Eighty-five proteins were expressed differentially at the level of 1.2-fold, of which protein expression profiles. Changes in the abundance of proteins were measured and compared at 50 were upregulated and 35 were downregulated (Figure5a). The details of di fferential expressed two biological replicates. Eighty-five proteins were expressed differentially at the level of 1.2-fold, of proteins are presented in Supplementary Table S4. which 50 were upregulated and 35 were downregulated (Figure 5a). The details of differential The GO annotations of cellular components were classified into 11 different categories, of which expressed proteins are presented in Supplementary Table S4. 33, 23, 22 and 9 proteins were found to be differentially expressed in the cell nucleus (38.8%), cytoplasm (27.1%), mitochondria (25.9%) and plasma membrane (10.6%) (Figure5b–d). Proteins located in such organelles were necessary and pivotal in response to stress stimuli, providing defense against mycotoxins. At the functional level, most are involved in binding and enzyme activity. The enzymes appeared were most associated with hydrolase (14.1%), transcription cofactor (9.4%), transferase (9.4%) and oxidoreductase (8.2%). The blast annotations of biological processes revealed that the proteins were involved most in included cellular metabolism (58.8%), biosynthetic (23.5%), cell development (21.2%), response to stress (20%), signal transmission (15.3%) and cell communication (15.3%). KEGG pathway enrichment analysis resulted in 93 pathways, among which “apoptosis”, “oxidative phosphorylation”, “nucleic acid metabolism”, “amino acid metabolism” and “disease-associated” genes were enriched (Supplementary Table S5).

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Up-regulated Molecular function a Down-regulated c 60 50 ATPase activity 3 50 Structural molecule activity 11 40 35 Kinase activity 5 30 Transcription cofactor activity 8 20 Receptor activity 3 10 Transporter activity 3 0 Pyrophosphatase activity 6 Hydrolase activity Numbers of proteins 12 Control vs PAT Oxidoreductase activity 7 Cellular component Transferase activity 8 Endoplasmic reticulum 3 ATP binding 6 Envelope 10 RNA binding 12 b Cell junction 2 DNA binding 13 Cytoskeleton 13 Anion binding 12 Vesicle 3 Cation binding 10 Nucleus 33 Enzyme binding 9 Plasma membrane 9 Metal ion binding 9 Golgi apparatus 3 Lipid binding 2 Mitochondrial 22 Nucleic acid binding Ribosome 10 23 Cytoplasm 23 Protein binding 44 Number of proteins Number of proteins d Biology process 60 1 Cellular metabolism 2 Biosynthetic process 3 Response to stress 4 Signal transmission 5 Cell cycle 6 Cell death 50 50 7 Cell proliferation 8 Cell differentiation 9 Cell development 10 Cell communication 11 Cell migration 12 Cell activation 40 13 Cell divison 14 Cellular homeostasis 15 Transcription 16 Ion transport 17 Immune system 18 DNA repair 30 19 RNA biosynthetic 20 Protein modification 21 Homeostatic 22 Protein transport 23 Proteolysis 24 Rhythmic process 20 20 17 18 13 13

Number ofNumber proteins 11 9 10 9 8 8 10 6 5 7 6 7 5 3 22 4 2 2 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Figure 5. The Gene Ontology (GO) analysis of the differentially expressed genes. (a) The number ofFigure differentially 5. The Gene expressed Ontology genes. (GO) (b analysis–d) The of assignment the differentially of those expressed genes classified genes. ( bya) GOThe functionalnumber of categories:differentially cell expressed component, genes. molecular (b–d) functionThe assignment and biology of those processing genes separately. classified by GO functional categories: cell component, molecular function and biology processing separately. 2.4. Integrated Transcriptome and Proteome Analysis TheThe alterationsGO annotations of mRNA of cellular levels components do not always were resultclassified in similarinto 11 different alterations categories, in protein of levelswhich and33, 23, enzyme 22 and activities. 9 proteins Nevertheless, were found a to combination be differentially of mRNA expressed and protein in the expressioncell nucleus pattern (38.8%), is requiredcytoplasm to fully(27.1%), understand mitochondria the toxicity (25.9%) machinery. and plasma Hence, membrane the protein–protein (10.6%) (Figure interaction 5b–d). networks Proteins oflocated the altered in such gene organelles and protein were necessary expression and are pivota shownl in in response Figure6. to The stress presentative stimuli, providing networks defense were profiledagainst mycotoxins. according to At GO the biological functional processes, level, most molecular are involved functions, in binding or KEGG and enzyme pathways activity. and were The integratedenzymes appeared with the String were database. most associated In general, with sustained hydrolase PAT pressure(14.1%), exertedtranscription influences cofactor on apoptosis, (9.4%), oxidativetransferase phosphorylation, (9.4%) and oxidoreductase ribosome, cell (8.2%). cycle, The nucleotide blast annotations metabolism, of biolog cell growth,ical processes endocytosis revealed and disease-associatedthat the proteins were pathways. involved most in included cellular metabolism (58.8%), biosynthetic (23.5%), cell development (21.2%), response to stress (20%), signal transmission (15.3%) and cell communication (15.3%). KEGG pathway enrichment analysis resulted in 93 pathways, among which “apoptosis”, “oxidative phosphorylation”, “nucleic acid metabolism”, “amino acid metabolism” and “disease-associated” genes were enriched (Supplementary Table S5).

2.4. Integrated Transcriptome and Proteome Analysis

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The alterations of mRNA levels do not always result in similar alterations in protein levels and enzyme activities. Nevertheless, a combination of mRNA and protein expression pattern is required to fully understand the toxicity machinery. Hence, the protein–protein interaction networks of the altered gene and protein expression are shown in Figure 6. The presentative networks were profiled according to GO biological processes, molecular functions, or KEGG pathways and were integrated with the String database. In general, sustained PAT pressure exerted influences on apoptosis, Toxinsoxidative2020, 12phosphorylation,, 681 ribosome, cell cycle, nucleotide metabolism, cell growth, endocytosis7 and of 13 disease-associated pathways.

Figure 6.6. TheThe protein–protein protein–protein interaction interaction networks networks of the of altered the altered gene and gene protein andexpression protein expression according accordingto the String to database.the String The database. gene names The gene of corresponding names of corresponding proteins are displayedproteins are in the displayed networks. in the networks. DGE analysis indicated that three detected proapoptotic genes PSENEN (4.26), DENND4B (2.24) and SLC7A5DGE analysis (2.06). indicated Among that the three seven detected differentially proapoptotic expressed genes antiapoptosis PSENEN (4.26), genes, DENND4B four of them,(2.24) andHMOX1 SLC7A5 (3.2), (2.06). BCL2L21 Among (3.85), the RNF34 seven (2.58), differentially CIAPIN expressed (2.68) were antiapoptosis upregulated, whilegenes, the four other of them, three, AATFHMOX1 (0.48), (3.2), RHOC BCL2L21 (0.46) (3.85), and RNF34 CDKN2D (2.58), (0.42) CIAPIN were (2.68) dramatically were upregulated, downregulated. while Inthe iTRAQ other three, data, 10AATF in 11 (0.48), of the RHOC apoptosis-related (0.46) and CDKN2D proteins, (0.42) such aswere the dramatically positive regulation downregulated. of apoptosis In iTRAQ proteins data, J JUN 10 (2.29),in 11 of BCL10 the apoptosis-related (1.48), BCL7C (1.48), proteins, CCNA2 such (1.48), as th CASP3e positive (1.47), regulation PDCD4 (1.24),of apoptosis were upregulated. proteins J JUN (2.29),A BCL10 considerable (1.48), BCL7C fraction (1.48) of the, CCNA2 differentially (1.48), regulated CASP3 (1.47), genes PDCD4 and proteins (1.24), were upregulated. associated with oxidativeA considerable phosphorylation. fraction of The the gene differentially expressions regu oflated complex genes IIIand (UQCR11, proteins were 2.47) associated and complex with I (NDUFA4,oxidative phosphorylation. 2.45) were induced The by gene PAT exposure,expressions while of thecomplex gene expressionIII (UQCR11, of complex 2.47) and V (ATP6V1C2, complex I (NDUFA4,0.34), and all 2.45) the five were detected induced differentially by PAT regulated exposure, proteins, while ATP6the gene (0.797), expression ATP5O (0.793), of complex NDUFAF4 V (0.737)(ATP6V1C2, and NDUFA5 0.34), and (0.688), all the NDUFA6 five detected (0.647) differentially were significantly regulate inhibited.d proteins, ATP6 (0.797), ATP5O (0.793),The NDUFAF4 ribosome (0.737) and associated and NDUFA5 molecules (0.688), are NDUFA6 also known (0.647) as the were protein significantly translational inhibited. apparatus. Five DGEs (RPS9, CSTF3-AS1, LINC01623, RPS20, RPLP2) encoded ribosome proteins were induced. However, RPL11 mRNA expression and all ten differentially detected expression proteins in iTRAQ data were inhibited significantly. All seven proteins and five genes involved in cell cycle were upregulated by PAT, while three genes involved in cell cycle were downregulated. Among these proteins or genes, RNA binding protein RBM38 plays a crucial role in cell cycle arrest via its binding to p21. The transcription factor FOXO4 has been shown to cause cell cycle arrest between the G0 and S phases. The widespread cyclin Cyclin-A2 Toxins 2020, 12, 681 8 of 13

(CCNA2) exhibits a promotion function both cell cycle G1/S and G2/M transition. CCKN2D (0.46) has been shown to function as cell growth inhibitor that controls cell cycle G1 progression. PKMYT1 (0.45) plays a role in the negatively regulation of cell cycle G2/M transition. The regulation of these genes and proteins suggested that PAT disrupted the cell cycle, which might further affect the final destiny of cells under PAT stress. A considerable fraction of the regulated proteins and genes were associated with nucleotide metabolism. Two genes and four proteins related to RNA biosynthetic process were upregulated, while four genes and three proteins were downregulated. On the other hand, most of the differentially expressed genes and proteins related RNA degradation (three in four) as well as DNA damage and repair (six in seven) were increased. Moreover, two RNA transport-related genes, TBC1D17 (0.46) and POP7 (0.50) were decreased by PAT exposure. Downregulation of three transcripts of the cell growth, including CDKN2D (0.46), RHOC (0.42), and CAPRIN2 (0.32), was observed. Two senescence-related genes, DBAJA3 (2.11) and HSD17B10 (20.8), were upregulated. Notably, another cell growth gene, KAZALD1, was significantly induced. Such results generally suggested PAT induced the disorder of cell growth and promoted the aging of the cells. Four endocytosis-related genes, ULKL (3.02), GUCY1B3 (2.67), UIMC1 (2.44) and SCARB1 (2.34) were overexpressed at the mRNA level. Finally, three upregulated proteins, PIP5K1A (1.45), PARD6B (1.32) and PPP1RSP3 (1.36) were involved in endocytosis. In addition, several of the differentially expressed genes and proteins were functionally disease-associated, especially associated with carcinogenesis, Alzheimer’ disease and Parkinson’s disease. These findings provide support that PAT also seemed to be associated with cancer and neuropathic disease.

3. Discussion Here, in an attempt to uncover novel possible mechanism of PAT toxicity, exhaustive transcriptome and proteome profiles were investigated. In our research, only a few genes were altered simultaneously. The poor correlations between mRNA and protein large data sets were also reported by some comparative studies [12–14]. The possible explanations including: (1) protein abundance is regulated positively or negatively at the post-transcriptional, translational, and post-translational levels; (2) the half-life of mRNA and/or protein is quite different; (3) the intracellular location could also affected the extraction efficiency of proteins; (4) although 94.5% of the genes contain CATG sites could be recognized and cut off by NlaIII enzyme in DGE analysis, there is still part of the genes couldn0t be detected. As DGE and iTRAQ have their own limitations, a combination of mRNA and protein expression patterns are required to fully understand the mechanism of PAT toxicity. While the precise mechanism of PAT’s toxicity remain to be fully elucidated, some evidences suggested that a range of epigenetic influences involving oxidative stress, glutathione modulation and apoptosis [8,10,15–17]. Our data indicated that apoptosis is a major mechanism of PAT induced cell death. We also demonstrated that PAT was able to trigger apoptosis by disruption mitochondrial function, inhibiting the production of energy, releasing cyt c to cytosol, ultimately initiating the caspase signal pathway. PAT treatment led changes in proteins associated with apoptosis. Three proapoptotic genes and four antiapoptosis genes were upregulated. Most of the positive regulation of apoptosis proteins including CASP3 was upregulated. Besides, three antiapoptosis genes and one apoptosis protein were downregulated. Apoptosis is important for tissue homeostasis, but too much or too little apoptosis can also cause diverse effects [18]. In a cell, the balance between anti- and proapoptotic genes or proteins determines if a cell undergoes apoptosis or survives [19]. The upregulation of apoptosis executioner caspase 3, which was in line with the elevated enzyme activities, indicated the final destination of the cells under PAT stress. In fact, the activation caspase 3 has been reported in human leukemia cells [20], colorectal cancer cells [6,21] and in several organs in experimental animals [8,22,23] under PAT hazards. Toxins 2020, 12, 681 9 of 13

Our previous study also provided evidence that PAT induced overproduction of ROS [9]. ROS scavenger, GSH, blocked the cell apoptosis evoked by PAT [24]. Excessive ROS destroyed the mitochondrial membrane integrity, yet, in another aspect, mitochondria have been deemed as one of the major sites producing ROS. In the process of ATP formation, 1–5% of the electrons will be leaked out to form an electron transport chain and to produce ROS [25]. At present, most research results confirmed that complex III is the main source of ROS, followed by complex I, whereas complex II can also generate ROS slightly. In our system, the gene expressions of complex III (UQCR11) and complex I (NDUFA4) were induced, while the protein expression of complex I (NDUFAF4, NDUFA5 and NDUFA6) were significantly inhibited. Complex III catalyzes electron transport form QH2 to cyt c, and 70–80% of mitochondrial superoxide radicals arise from the QH2 to ubiquinone cycle [26–28]. Our results implied that the dysfunction of electron transport chain, especially the elevated expression of UQCR11 might be able to explain the increase of ROS. However, this requires further verification. Beyond that, Nicotinamide adenine dinucleotide phosphate (NADPH) oxidase is another site of intercellular ROS production. Maintaining a normal state of the cells requires sufficient energy. Mitochondria provide more than 15 times the ATP produced by anaerobic glycolysis [29]. In our system, the gene expression of ATP synthase (ATP6V1C2), and two differentially regulated ATP synthase proteins (ATP6, ATP5O) were significantly inhibited. Besides, the accumulation of ROS may lead to irreversible oxidative damage of mitochondrial membrane lipids and proteins, as well as reduce the mitochondrial transmembrane potential. Loss of transmembrane potential can perturb the production of ATP, due to the fact that an electrochemical gradient is needed to provide the driving force for ATP synthesis. PAT is a low-molecular-weight, highly polar and hydrophilic molecule, but the phospholipid bilayer plasma membrane as well as the organelle membrane is hydrophobic. The improved expression of endocytosis related genes and proteins indicated that this active transport form, endocytosis, might be a general pathway for PAT to enter into the cells, thus triggering the cytotoxic effects, and finally inducing cell death.

4. Conclusions This study presents evidence that PAT triggers caspase-dependent cell death via the intrinsic apoptotic pathway. A comprehensive transcriptome and proteomics analyses that combined two unbiased and high throughput techniques for the first time were used to define the mechanism of nephrotoxicity of PAT. Genes and proteins related to apoptosis, oxidative phosphorylation ribosome, cell cycle, nucleotide metabolism, cell growth and endocytosis were differentially expressed in HEK293 cells induced by PAT. Especially, caspase 3 appeared to be particularly crucial in PAT-induced cell apoptosis and the elevated expression of UQCR11 might be able to explain the increase of ROS. In addition, the activation active transport form and endocytosis, might be a general way for PAT to entry into the cells. Some disease-associated pathways associated with carcinogenesis, Alzheimer0 disease and Parkinson’s disease were also induced, which suggested that PAT also seemed to be associated with cancer and neuropathic disease. Taken together, our findings bring novel insight into the regulatory molecular mechanisms accounting for the toxic action of PAT against human kidney cells.

5. Materials and Methods

5.1. Cell Culture and PAT Treatment HEK293 cells were grown in DMEM medium supplemented with 10% fetal bovine serum (FBS), 100 U/mL of penicillin, and 100 µg/mL of streptomycin. Cells were cultured at 37 °C, in 5% CO2 and 95% saturated atmospheric humidity incubator. For treatment, PAT was dissolved in sterile water to make a stocking solution of 10 mM and then added to the serumfree medium in concentrations of 2.5, 5, 7.5 and 10 µM, and these were then incubated for 10 h. The control cells were treated with serum-free Toxins 2020, 12, 681 10 of 13 medium without PAT. The 5 µM PAT dose was selected for DGE and iTRAQ analysis. For caspase 3 speciﬁc inhibitor treatment, cells were treated with 5 µM Z-DEVD-FMK (BioVision, PaloAlto, Santa Clara, CA, USA) with or without PAT.

5.2. Measuring Cell Viability, LDH Leakage and Caspase Activities 2-(2-Mehtoxy-4-nitrophenyl)-3-(4-nitrophenyl)-5-(2,4-disulfophenyl)-2H-tetrazolium sodium salt (WST-8) is superior to methylthiazolyldiphenyl-tetrazolium bromide (MTT) for analyzing cell toxicity because dehydrogenase reduces WST-8 to soluble formazan in mitochondria, and WST-8 has little toxicity to cells [30]. The cell viability was determined using the Cell Counting Kit-8 (Beyotime, Beijing, China) according to the manufacturer’s instructions. LDH leakage was determined using corresponding LDH cytoxicity assay kit (Beyotime). The activities of caspase3/7 and caspase 9 were measured using corresponding colorimetric caspase-activity-assay kits (Beyotime). Both caspase 7 and caspase 3 appeared the DEVD-dependent protease activity, but caspase 3 shows a much higher eﬃciency in cleavaging Ac-DEVE-pNA.

5.3. Digital Gene Expression (DGE) The main reagents and instruments used for deep-sequencing profiles were the Gene Expression Sample Prep Kit (Illumina, San Diego, CA, USA), the Digital Gene Expression Tag Profiling Kit (Illumina), the Solexa Sequencing Chip (Illumina), the Cluster Station and the Illumina HiSeqTM 2000 System (Illumina). Genes were deemed to be significantly differentially expressed with FDR < 0.05 and absolute value of |log2 ratio| 1 (twofold difference). ≥ 5.4. iTRAQ Protein Profiling The flow chart of the proteomic expression of 5 µM PAT on HEK293 cells is shown in Supplementary Figure S2. Cells were rinsed with PBS three times and suspended in the lysis buffer (7 M urea, 2 M thiourea, 4% 3-((3-cholamidopropyl) dimethylammonium)-1-propanesulfonate (CHAPS), 40 mM Tris-HCl, pH 8.5, 1 mM phynylmethanesulfonyl fluoride(PMSF), 2 mM theylene diamine tetraacetic acid (EDTA) and sonicated in ice. The proteins were reduced with 10 mM dithiothreitol (DTT) at 56 ◦C for 1 h and then alkylated by 55 mM iodoacetamide (IAM) (final concentration) in the dark for 1 h. The reduced and alkylated protein mixtures were precipitated by adding 4 volume of chilled acetone × at 20 C overnight. After centrifugation at 4 C, an aliquot of supernatant was taken for determination − ◦ ◦ of protein concentration by Bradford. The protein in the supernatant were kept at 80 C for further − ◦ analysis. The detailed procedure including ITRAQ labeling, strong cation exchange (SCX) fractionation, liquid chromatography electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) analysis based on Q exactive, protein identification and quantitation. Proteins were deemed to be significantly differentially expressed with P values < 0.05 and fold changes of >1.2.

5.5. Reverse Transcript and Real-Time Quantitative PCR Analysis of mRNA Isolated mRNAs were reverse transcribed by using SuperRTcDNA Kit (CWBIO, Beijing, China). Quantitative real-time PCR analyses on the mRNAs were performed by using the SYBR Green Real-time PCR Master Mix (CWBIO) in a Bio-Rad IQTM5 Multicolor Real-Time PCR Detection System (Bio-Rad, Hercules, CA, USA). The thermal cycling program was set as follows: 95 ◦C for 10 min, followed by 40 cycles of 95 ◦C for 15 s and annealing/elongation at 60 ◦C for 1 min. The mRNA levels of target genes were normalized to the β-actin mRNA levels. The primers used in this were synthesized by Invitrogen (Shanghai, China) and the sequences were showed in Supplementary Table S6.

5.6. Western Blot Analysis of Protein Expression Cell lysates were prepared using cell lysis buﬀer for Western and immunoprecipitation (Beyotime) supplemented with 1 mM PMSF. Antidodies against Cyt c and β-actin were obtained from Proteintech Toxins 2020, 12, 681 11 of 13

(Wuhan, China). And antibody against HSPB8 (PA5-21612) was purchased from Invitrogen. The immunoreactive signals were developed using the ECL Western blotting Substrate (ThermoFisher, Waltham, MA, USA) and visualized on Omat X-ray ﬁlms (Kodak, Rochester, NY, USA). Densitometry analysis of the images obtained from X-ray ﬁlms was performed using the Image J software (v1.8.0, National Institutes of Health, Bethesda, MD, USA).

5.7. Statistical Analysis Microsoft Excel 2007 and SPSS 14.0 were used for the statistical analyses. Data were expressed as mean standard deviation (SD). Statistical significance was evaluated by Student’s t-test. When more ± than one group was compared with one control, significance was evaluated according to one-way analysis of variance (ANOVA), and Duncan’s multiple-range test was applied to identify group differences. p < 0.05 was considered statistically significant.

Supplementary Materials: The following are available online at http://www.mdpi.com/2072-6651/12/11/681/s1, Figure S1: Saturation evaluation of detected gene expression, Table S1: Statistics of categorization and abundance of DGE tags, Table S2: List of differentially expressed genes (twofold changes or greater, p < 0.05) by DEG from the human embryonic kidney cells following 5 µM PAT treatment for 10 h, Table S3: List of the enriched pathway based on KEGG analysis of the differentially expressed genes from HEK293 cells following 5 µM PAT treatment for 10 h, Table S4: Global changes of differentially expressed proteins (1.2-fold changes or greater, p < 0.05) quantified by iTRAQ from the cells following 5 µM PAT treatment for 10 h, Table S5: List of the enriched pathway based on KEGG analysis of the differentially expressed proteins from HEK293 cells following 5 µM PAT treatment for 10 h, Figure S2: Experimental design for iTRAQ labeling to explore the effect of 5 µM PAT on HEK293 cells, Table S6: Gene specific primers used in real-time quantitative PCR. Author Contributions: Conceptualization, N.H. and X.P.; methodology, N.H., J.L., T.G. and X.P.; formal analysis, N.H.; data curation, N.H., R.L., and J.F.; writing-original draft preparation, R.L. and N.H.; writing-review and editing, R.L. and X.P.; supervision, X.P.; project administration, X.P.; funding acquisition, N.H. and X.P. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by National Natural Science Foundation of China (NSFC, 31571928, 31201286), the Fundamental Research Funds for the Central Universities (2014YB021) and the Enterprise Horizontal Project of Jihua Biotechnology Services Co. LTD (2020). Conflicts of Interest: The authors declare no conflict of interest.

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