Time-dependent metabolomics uncover dynamic metabolic adaptions in MCF-7 cells exposed to bisphenol A

Haoduo Zhao, Min Liu, Junjie Yang, Yuyang Chen, Mingliang Fang

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Front. Environ. Sci. Eng. ›› 2023, Vol. 17 ›› Issue (1) : 4. DOI: 10.1007/s11783-023-1604-5
RESEARCH ARTICLE
RESEARCH ARTICLE

Time-dependent metabolomics uncover dynamic metabolic adaptions in MCF-7 cells exposed to bisphenol A

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Highlights

● Metabolomic temporal profiling of cells exposed to xenobiotics.

● Global metabolome dysregulation patterns with time-resolved landscapes.

● Synchronized regulation behavior and specific dysregulation sensitivity.

● Temporal metabolic adaptions indicated cellular emphasis transition.

Abstract

The biochemical consequences induced by xenobiotic stress are featured in dose-response and time-resolved landscapes. Understanding the dynamic process of cellular adaptations is crucial in conducting the risk assessment for chemical exposure. As one of the most phenotype-related omics, metabolome in response to environmental stress can vary from seconds to days. Up to now, very few dynamic metabolomics studies have been conducted to provide time-dependent mechanistic interpretations in understanding xenobiotics-induced cellular adaptations. This study aims to explore the time-resolved metabolite dysregulation manner and dynamically perturbed biological functions in MCF-7 cells exposed to bisphenol A (BPA), a well-known endocrine-disrupting chemical. By sampling at 11 time points from several minutes to hours, thirty seven significantly dysregulated metabolites were identified, ranging from amino acids, fatty acids, carboxylic acids and nucleoside phosphate compounds. The metabolites in different pathways basically showed distinct time-resolved changing patterns, while those within the common class or same pathways showed similar and synchronized dysregulation behaviors. The pathway enrichment analysis suggested that purine metabolism, pyrimidine metabolism, aminoacyl-tRNA biosynthesis as well as glutamine/glutamate (GABA) metabolism pathways were heavily disturbed. As exposure event continued, MCF-7 cells went through multiple sequential metabolic adaptations from cell proliferation to energy metabolism, which indicated an enhancing cellular requirement for elevated energy homeostasis, oxidative stress response and ER-α mediated cell growth. We further focused on the time-dependent metabolite dysregulation behavior in purine and pyrimidine metabolism, and identified the impaired glycolysis and oxidative phosphorylation by redox imbalance. Lastly, we established a restricted cubic spline-based model to fit and predict metabolite’s full range dysregulation cartography, with metabolite’ sensitivity comparisons retrieved and novel biomarkers suggested. Overall, the results indicated that 8 h BPA exposure leaded to global dynamic metabolome adaptions including amino acid, nucleoside and sugar metabolism disorders, and the dysregulated metabolites with interfered pathways at different stages are of significant temporal distinctions.

Graphical abstract

Keywords

Metabolomics / BPA / MCF-7 / Temporal profiling / Metabolic adaption / Dysregulation correlation

Cite this article

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Haoduo Zhao, Min Liu, Junjie Yang, Yuyang Chen, Mingliang Fang. Time-dependent metabolomics uncover dynamic metabolic adaptions in MCF-7 cells exposed to bisphenol A. Front. Environ. Sci. Eng., 2023, 17(1): 4 https://doi.org/10.1007/s11783-023-1604-5

References

[1]
AcevedoN, DavisB, SchaeberleC M, SonnenscheinC, SotoA M. (2013). Perinatally administered bisphenol a as a potential mammary gland carcinogen in rats. Environmental Health Perspectives, 121( 9): 1040– 1046
CrossRef Pubmed Google scholar
[2]
Aghajanpour-MirS M, ZabihiE, Akhavan-NiakiH, KeyhaniE, BagherizadehI, BiglariS, BehjatiF. (2016). The genotoxic and cytotoxic effects of bisphenol-A (BPA) in MCF-7 cell line and amniocytes. International Journal of Molecular and Cellular Medicine, 5( 1): 19– 29
[3]
AllmanE, PainterH, SamraJ, CarrasquillaM, LlinásM ( 2016). Metabolomic profiling of the malaria box reveals antimalarial target pathways. Antimicrobial Agents and Chemotherapy, 60, AAC. 01224– 01216.
[4]
Alonso-MagdalenaP, MorimotoS, RipollC, FuentesE, NadalA. (2006). The estrogenic effect of bisphenol A disrupts pancreatic beta-cell function in vivo and induces insulin resistance. Environmental Health Perspectives, 114( 1): 106– 112
CrossRef Pubmed Google scholar
[5]
Alonso-MagdalenaP, RoperoA B, SorianoS, García-ArévaloM, RipollC, FuentesE, QuesadaI, NadalÁ. (2012). Bisphenol-A acts as a potent estrogen via non-classical estrogen triggered pathways. Molecular and Cellular Endocrinology, 355( 2): 201– 207
CrossRef Pubmed Google scholar
[6]
AzevedoL F, PortoDechandt C R, Cristinade Souza Rocha C, HornosCarneiro M F, AlbericiL C, BarbosaF Jr. (2019). Long-term exposure to bisphenol A or S promotes glucose intolerance and changes hepatic mitochondrial metabolism in male Wistar rats. Food and Chemical Toxicology, 132 : 110694
CrossRef Pubmed Google scholar
[7]
BeyerB A, FangM, SadrianB, Montenegro-BurkeJ R, PlaistedW C, KokB P C, SaezE, KondoT, SiuzdakG, LairsonL L. (2018). Metabolomics-based discovery of a metabolite that enhances oligodendrocyte maturation. Nature Chemical Biology, 14( 1): 22– 28
CrossRef Pubmed Google scholar
[8]
BlomA, EkmanE, JohannissonA, NorrgrenL, PesonenM. (1998). Effects of xenoestrogenic environmental pollutants on the proliferation of a human breast cancer cell line (MCF-7). Archives of Environmental Contamination and Toxicology, 34( 3): 306– 310
CrossRef Pubmed Google scholar
[9]
BrandK A, HermfisseU. (1997). Aerobic glycolysis by proliferating cells: a protective strategy against reactive oxygen species. FASEB J, 11( 5): 388– 395
CrossRef Pubmed Google scholar
[10]
BrodskyA N, OdenwelderD C, HarcumS W. (2019). High extracellular lactate causes reductive carboxylation in breast tissue cell lines grown under normoxic conditions. PLoS One, 14( 6): e0213419
CrossRef Pubmed Google scholar
[11]
ChenM, ZhouK, ChenX, QiaoS, HuY, XuB, XuB, HanX, TangR, MaoZ, DongC, WuD, WangY, WangS, ZhouZ, XiaY, WangX. (2014). Metabolomic analysis reveals metabolic changes caused by bisphenol-A in rats. Toxicological Sciences, 138( 2): 256– 267
CrossRef Pubmed Google scholar
[12]
ChenR, MiasG I, Li-Pook-ThanJ, JiangL, LamH Y, ChenR, MiriamiE, KarczewskiK J, HariharanM, DeweyF E. . (2012). Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell, 148( 6): 1293– 1307
CrossRef Pubmed Google scholar
[13]
CobboldS, McConvilleM. (2019). Determining the mode of action of antimalarial drugs using time-resolved lc-ms-based metabolite profiling. Methods in Molecular Biology, 1859 : 225– 239
[14]
CobboldS A, ChuaH H, NijagalB, CreekD J, RalphS A, McConvilleM J. (2016). Metabolic dysregulation induced in plasmodium falciparum by dihydroartemisinin and other front-line antimalarial drugs. The Journal of Infectious Diseases, 213( 2): 276– 286
CrossRef Pubmed Google scholar
[15]
CostelloZ, MartinH G. (2018). A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data. NPJ Systems Biology and Applications, 4( 1): 19
CrossRef Pubmed Google scholar
[16]
CowleyG S, WeirB A, VazquezF, TamayoP, ScottJ A, RusinS, East-SeletskyA, AliL D, GerathW F, PantelS E. . (2014). Parallel genome-scale loss of function screens in 216 cancer cell lines for the identification of context-specific genetic dependencies. Scientific Data, 1( 1): 140035
CrossRef Pubmed Google scholar
[17]
DesquilbetL, MariottiF. (2010). Dose-response analyses using restricted cubic spline functions in public health research. Statistics in Medicine, 29( 9): 1037– 1057
CrossRef Pubmed Google scholar
[18]
DuanY, LiF, LiY, TangY, KongX, FengZ, AnthonyT G, WatfordM, HouY, WuG. . (2016). The role of leucine and its metabolites in protein and energy metabolism. Amino Acids, 48( 1): 41– 51
CrossRef Pubmed Google scholar
[19]
EnginA B, EnginA. (2021). The effect of environmental bisphenol-A exposure on breast cancer associated with obesity. Environmental Toxicology and Pharmacology, 81 : 103544
CrossRef Pubmed Google scholar
[20]
FanX, HouT, JiaJ, TangK, WeiX, WangZ. (2020). Discrepant dose responses of bisphenol-A on oxidative stress and DNA methylation in grass carp ovary cells. Chemosphere, 248 : 126110
CrossRef Pubmed Google scholar
[21]
FangM, IvanisevicJ, BentonH P, JohnsonC H, PattiG J, HoangL T, UritboonthaiW, KurczyM E, SiuzdakG. (2015). Thermal degradation of small molecules: a global metabolomic investigation. Analytical Chemistry, 87( 21): 10935– 10941
CrossRef Pubmed Google scholar
[22]
FuQ, ScheideggerA, LaczkoE, HollenderJ. (2021). Metabolomic profiling and toxicokinetics modeling to assess the effects of the pharmaceutical diclofenac in the aquatic invertebrate Hyalella azteca. Environmental Science & Technology, 55( 12): 7920– 7929
CrossRef Pubmed Google scholar
[23]
GengS, MisraB B, ArmasE, HuhmanD V, AlbornH T, SumnerL W, ChenS. (2016). Jasmonate-mediated stomatal closure under elevated CO2 revealed by time-resolved metabolomics. Plant Journal, 88( 6): 947– 962
CrossRef Pubmed Google scholar
[24]
GouldJ C, LeonardL S, ManessS C, WagnerB L, ConnerK, ZacharewskiT, SafeS, McDonnellD P, GaidoK W. (1998). Bisphenol A interacts with the estrogen receptor alpha in a distinct manner from estradiol. Molecular and Cellular Endocrinology, 142( 1–2): 203– 214
CrossRef Pubmed Google scholar
[25]
GuoW, ShiZ, ZengT, HeY, CaiZ, ZhangJ. (2022). Metabolic study of aristolochic acid I-exposed mice liver by atmospheric pressure matrix-assisted laser desorption/ionization mass spectrometry imaging and machine learning. Talanta, 241 : 123261
CrossRef Pubmed Google scholar
[26]
HalamaA, AyeM M, DarghamS R, KulinskiM, SuhreK, AtkinS L. (2019). Metabolomics of dynamic changes in insulin resistance before and after exercise in PCOS. Frontiers in Endocrinology (Lausanne), 10 : 116
CrossRef Google scholar
[27]
HowdeshellK L, HotchkissA K, ThayerK A, VandenberghJ G, vom SaalF S. (1999). Exposure to bisphenol A advances puberty. Nature, 401( 6755): 763– 764
CrossRef Pubmed Google scholar
[28]
HuangS S Y, BenskinJ P, VeldhoenN, ChandramouliB, ButlerH, HelbingC C, CosgroveJ R ( 2017). A multi-omic approach to elucidate low-dose effects of xenobiotics in Zebrafish (Danio rerio) larvae. Aquatic Toxicology (Amsterdam, Netherlands), 182: 102– 112
Pubmed
[29]
InoueK, RitzB, BrentG A, EbrahimiR, RheeC M, LeungA M. (2020). Association of subclinical hypothyroidism and cardiovascular disease with mortality. JAMA Network Open, 3( 2): e1920745
CrossRef Pubmed Google scholar
[30]
JainM, NilssonR, SharmaS, MadhusudhanN, KitamiT, SouzaA L, KafriR, KirschnerM W, ClishC B, MoothaV K. (2012). Metabolite profiling identifies a key role for glycine in rapid cancer cell proliferation. Science, 336( 6084): 1040– 1044
CrossRef Pubmed Google scholar
[31]
JiaS, LiC, FangM, Marques Dos SantosM, SnyderS A. (2022). Non-targeted metabolomics revealing the effects of bisphenol analogues on human liver cancer cells. Chemosphere, 297 : 134088
CrossRef Pubmed Google scholar
[32]
JohannesenC D L, LangstedA, MortensenM B, NordestgaardB G. (2020). Association between low density lipoprotein and all cause and cause specific mortality in Denmark: prospective cohort study. BMJ (Clinical Research Ed.), 371 : m4266
CrossRef Pubmed Google scholar
[33]
KalkhofS, DautelF, LoguercioS, BaumannS, TrumpS, JungnickelH, OttoW, RudzokS, PotratzS, LuchA, LehmannI, BeyerA, von BergenM. (2015). Pathway and time-resolved benzo[a]pyrene toxicity on Hepa1c1c7 cells at toxic and subtoxic exposure. Journal of Proteome Research, 14( 1): 164– 182
CrossRef Pubmed Google scholar
[34]
KennedyL, SandhuJ K, HarperM E, Cuperlovic-CulfM. (2020). Role of glutathione in cancer: from mechanisms to therapies. Biomolecules, 10( 10): 1429
CrossRef Pubmed Google scholar
[35]
KerkhofsM H P M, HaijesH A, WillemsenA M, van GassenK L I, van der HamM, GerritsJ, de Sain-van der VeldenM G M, PrinsenH C M T, van DeutekomH W M, van HasseltP M, Verhoeven-DuifN M, JansJ J M. (2020). Cross-omics: integrating genomics with metabolomics in clinical diagnostics. Metabolites, 10( 5): 206
CrossRef Pubmed Google scholar
[36]
KimH, ChoiJ, KimT, LokanathN K, HaS C, SuhS W, HwangH Y, KimK K. (2010). Structural basis for the reaction mechanism of UDP-glucose pyrophosphorylase. Molecules and Cells, 29( 4): 397– 405
CrossRef Pubmed Google scholar
[37]
KowalskiG M, DeSouza D P, BurchM L, HamleyS, KloehnJ, SelathuraiA, TullD, O’CallaghanS, McConvilleM J, BruceC R. (2015). Application of dynamic metabolomics to examine in vivo skeletal muscle glucose metabolism in the chronically high-fat fed mouse. Biochemical and Biophysical Research Communications, 462( 1): 27– 32
CrossRef Pubmed Google scholar
[38]
KrycerJ R, YugiK, HirayamaA, FazakerleyD J, QuekL E, ScalzoR, OhnoS, HodsonM P, IkedaS, ShojiF. . (2017). Dynamic metabolomics reveals that insulin primes the adipocyte for glucose metabolism. Cell Reports, 21( 12): 3536– 3547
CrossRef Pubmed Google scholar
[39]
KunzN, CammE J, SommE, LodygenskyG, DarbreS, AubertM L, HüppiP S, SizonenkoS V, GruetterR. (2011). Developmental and metabolic brain alterations in rats exposed to bisphenol A during gestation and lactation. International Journal of Developmental Neuroscience, 29( 1): 37– 43
CrossRef Pubmed Google scholar
[40]
LaiY, LiuC W, YangY, HsiaoY C, RuH, LuK. (2021). High-coverage metabolomics uncovers microbiota-driven biochemical landscape of interorgan transport and gut-brain communication in mice. Nature Communications, 12( 1): 6000
CrossRef Pubmed Google scholar
[41]
LeeH J, JedrychowskiM P, VinayagamA, WuN, Shyh-ChangN, HuY, Min-WenC, MooreJ K, AsaraJ M, LyssiotisC A, PerrimonN, GygiS P, CantleyL C, KirschnerM W. (2017). Proteomic and metabolomic characterization of a mammalian cellular transition from quiescence to proliferation. Cell Reports, 20( 3): 721– 736
CrossRef Pubmed Google scholar
[42]
LiL, Hoefsloot H, GraafA, AcarE, SmildeA ( 2021). Exploring dynamic metabolomics data with multiway data analysis: a simulation study. BMC Bioinformatics 23, 31 ( 2022)
[43]
LiangL, RasmussenM H, PieningB, ShenX, ChenS, RostH, MelbyeM. (2020). Metabolic dynamics and prediction of gestational age and time to delivery in pregnant women. Cell, 181( 7): 1680– 1692
CrossRef Google scholar
[44]
LinkH, FuhrerT, GerosaL, ZamboniN, SauerU. (2015). Real-time metabolome profiling of the metabolic switch between starvation and growth. Nature Methods, 12( 11): 1091– 1097
CrossRef Pubmed Google scholar
[45]
LinkH, KochanowskiK, SauerU. (2013). Systematic identification of allosteric protein-metabolite interactions that control enzyme activity in vivo. Nature Biotechnology, 31( 4): 357– 361
CrossRef Pubmed Google scholar
[46]
LiuM, JiaS, DongT, ZhaoF, XuT, YangQ, GongJ, FangM. (2020). Metabolomic and transcriptomic analysis of MCF-7 cells exposed to 23 chemicals at human-relevant levels: estimation of individual chemical contribution to effects. Environmental Health Perspectives, 128( 12): 127008
CrossRef Pubmed Google scholar
[47]
LiuM, JiangJ, ZhengJ, HuanT, GaoB, FeiX, WangY, FangM. (2021). RTP: one effective platform to probe reactive compound transformation products and its applications for a reactive plasticizer BADGE. Environmental Science & Technology, 55( 23): 16034– 16043
CrossRef Pubmed Google scholar
[48]
LuH, ChenH, TangX, YangQ, ZhangH, ChenY Q, ChenW. (2020). Time-resolved multi-omics analysis reveals the role of nutrient stress-induced resource reallocation for TAG accumulation in oleaginous fungus Mortierella alpina. Biotechnology for Biofuels, 13( 1): 116
CrossRef Pubmed Google scholar
[49]
LuanH, ZhaoH, LiJ, ZhouY, FangJ, LiuH, LiY, XiaW, XuS, CaiZ. (2021). Machine learning for investigation on endocrine-disrupting chemicals with gestational age and delivery time in a longitudinal cohort. Research (Wash D C), 2021 : 1
CrossRef Pubmed Google scholar
[50]
LvY, WangX, LiX, XuG, BaiY, WuJ, PiaoY, ShiY, XiangR, WangL. (2020). Nucleotide de novo synthesis increases breast cancer stemness and metastasis via cGMP-PKG-MAPK signaling pathway. PLoS Biology, 18( 11): e3000872
CrossRef Pubmed Google scholar
[51]
MazatJ P, RansacS. (2019). The fate of glutamine in human metabolism: the interplay with glucose in Proliferating cells. Metabolites, 9( 5): 81
CrossRef Pubmed Google scholar
[52]
MeliR, MonnoloA, AnnunziataC, PirozziC, FerranteM C. (2020). Oxidative stress and BPA toxicity: an antioxidant approach for male and female reproductive dysfunction. Antioxidants (Basel), 9( 5): 405
CrossRef Pubmed Google scholar
[53]
MetalloC M, Vander HeidenM G. (2013). Understanding metabolic regulation and its influence on cell physiology. Molecular Cell, 49( 3): 388– 398
CrossRef Pubmed Google scholar
[54]
MoffattB A, AshiharaH. (2002). Purine and pyrimidine nucleotide synthesis and metabolism. Arabidopsis Book, 1 : 0018
CrossRef Pubmed Google scholar
[55]
MoreiraJ D, HamrazM, AbolhassaniM, BiganE, PérèsS, PaulevéL, NogueiraM L, SteyaertJ M, SchwartzL. (2016). The redox status of cancer cells supports mechanisms behind the warburg effect. Metabolites, 6( 4): 33
CrossRef Pubmed Google scholar
[56]
Moreno-SánchezR, SaavedraE, Rodríguez-EnríquezS, Olín-Sandoval V. (2008). Metabolic control analysis: a tool for designing strategies to manipulate metabolic pathways. Journal of Biomedicine & Biotechnology, 2008 : 597913
CrossRef Pubmed Google scholar
[57]
NyamundandaG, GormleyI C, BrennanL. (2014). A dynamic probabilistic principal components model for the analysis of longitudinal metabolomics data. Applied Statistics, 63( 5): 763– 782
CrossRef Google scholar
[58]
Ortiz-VillanuevaE, Navarro-MartínL, JaumotJ, BenaventeF, Sanz-NebotV, PiñaB, TaulerR ( 2017). Metabolic disruption of Zebrafish ( Danio rerio) embryos by bisphenol A: an integrated metabolomic and transcriptomic approach . Environmental Pollution, 231( Pt 1): 22– 36
Pubmed
[59]
OwenJ B, ButterfieldD A ( 2010). Measurement of oxidized/reduced glutathione ratio. Methods in Molecular Biology (Clifton, N.J.), 648: 269– 277
Pubmed
[60]
PengB, ZhaoH, KeerthisingheT P, YuY, Chen D, HuangY, FangM ( 2022). Gut microbial metabolite p-cresol alters biotransformation of bisphenol A: Enzyme competition or gene induction? Journal of Hazardous Materials, 426: 128093
[61]
PetroffO A C ( 2007). Metabolic Biopsy of the Brain. In: S. G. Waxman, ed. Molecular Neurology, 77– 100. San Diego: Academic Press
[62]
PotratzS, TarnowP, JungnickelH, BaumannS, von BergenM, TralauT, LuchA. (2017). Combination of metabolomics with cellular assays reveals new biomarkers and mechanistic insights on xenoestrogenic exposures in MCF-7 cells. Chemical Research in Toxicology, 30( 4): 883– 892
CrossRef Pubmed Google scholar
[63]
QuéméneurL, GerlandL M, FlacherM, FfrenchM, RevillardJ P, GenestierL ( 2003). Differential control of cell cycle, proliferation, and survival of primary T lymphocytes by purine and pyrimidine nucleotides. Journal of Immunology (Baltimore, Md.: 1950), 170( 10): 4986– 4995
Pubmed
[64]
RinschenM M, IvanisevicJ, GieraM, SiuzdakG. (2019). Identification of bioactive metabolites using activity metabolomics. Nature Reviews. Molecular Cell Biology, 20( 6): 353– 367
CrossRef Pubmed Google scholar
[65]
SchymanskiE L, JeonJ, GuldeR, FennerK, RuffM, SingerH P, HollenderJ. (2014). Identifying small molecules via high resolution mass spectrometry: communicating confidence. Environmental Science & Technology, 48( 4): 2097– 2098
CrossRef Pubmed Google scholar
[66]
SlominskiA, ZmijewskiM, PawelekJ. (2012). L-tyrosine and L-dihydroxyphenylalanine as hormone-like regulators of melanocyte functions. Pigment Cell & Melanoma Research, 25( 1): 14– 27
[67]
SmildeA K, WesterhuisJ A, HoefslootH C, BijlsmaS, RubinghC M, VisD J, JellemaR H, PijlH, RoelfsemaF, van der GreefJ. (2010). Dynamic metabolomic data analysis: a tutorial review. Metabolomics, 6( 1): 3– 17
CrossRef Pubmed Google scholar
[68]
SmithC A, O’MailleG, WantE J, QinC, TraugerS A, BrandonT R, CustodioD E, AbagyanR, SiuzdakG. (2005). METLIN: a metabolite mass spectral database. Therapeutic Drug Monitoring, 27( 6): 747– 751
CrossRef Google scholar
[69]
SmithC A, WantE J, O’MailleG, AbagyanR, SiuzdakG. (2006). XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Analytical Chemistry, 78( 3): 779– 787
CrossRef Pubmed Google scholar
[70]
SpégelP, SharoykoV V, GoehringI, DanielssonA P, MalmgrenS, NagornyC L, AnderssonL E, KoeckT, SharpG W, StraubS G, WollheimC B, MulderH. (2013). Time-resolved metabolomics analysis of β-cells implicates the pentose phosphate pathway in the control of insulin release. Biochemical Journal, 450( 3): 595– 605
CrossRef Pubmed Google scholar
[71]
SweeneyK J, SwarbrickA, SutherlandR L, MusgroveE A. (1998). Lack of relationship between CDK activity and G1 cyclin expression in breast cancer cells. Oncogene, 16( 22): 2865– 2878
CrossRef Pubmed Google scholar
[72]
TugizimanaF, Djami-TchatchouA T, FahrmannJ F, SteenkampP A, PiaterL A, DuberyI A. (2019). Time-resolved decoding of metabolic signatures of in vitro growth of the hemibiotrophic pathogen Colletotrichum sublineolum. Scientific Reports, 9( 1): 3290
CrossRef Pubmed Google scholar
[73]
Vahdati HassaniF, AbnousK, MehriS, JafarianA, Birner-GruenbergerR, Yazdian RobatiR, HosseinzadehH. (2018). Proteomics and phosphoproteomics analysis of liver in male rats exposed to bisphenol A: Mechanism of hepatotoxicity and biomarker discovery. Food and Chemical Toxicology, 112 : 26– 38
CrossRef Pubmed Google scholar
[74]
WestP R, WeirA M, SmithA M, DonleyE L, CezarG G. (2010). Predicting human developmental toxicity of pharmaceuticals using human embryonic stem cells and metabolomics. Toxicology and Applied Pharmacology, 247( 1): 18– 27
CrossRef Pubmed Google scholar
[75]
WuG, FangY Z, YangS, LuptonJ R, TurnerN D. (2004). Glutathione metabolism and its implications for health. J Nutr, 134( 3): 489– 492
CrossRef Pubmed Google scholar
[76]
WuJ, Jin Z, ZhengH, YanL J ( 2016). Sources and implications of NADH/NAD+ redox imbalance in diabetes and its complications . Diabetes, Metabolic Syndrome and Obesity, 9: 145– 153
Pubmed
[77]
WuJ, WangF, XieG, CaiZ. (2022). Mass spectrometric determination of N7-HPTE-dG and N7-HPTE-Gua in mammalian cells and mice exposed to methoxychlor, an emergent persistent organic pollutant. Journal of Hazardous Materials, 432 : 128741
CrossRef Pubmed Google scholar
[78]
XuT, ChenL, LimY T, ZhaoH, ChenH, ChenM W, HuanT, HuangY, SobotaR M, FangM. (2021a). System biology-guided chemical proteomics to discover protein targets of monoethylhexyl phthalate in regulating cell cycle. Environmental Science & Technology, 55( 3): 1842– 1851
CrossRef Pubmed Google scholar
[79]
XuT, LimY T, ChenL, ZhaoH, LowJ H, XiaY, SobotaR M, FangM. (2020). A novel mechanism of monoethylhexyl phthalate in lipid accumulation via inhibiting fatty acid beta-oxidation on hepatic cells. Environmental Science & Technology, 54( 24): 15925– 15934
CrossRef Pubmed Google scholar
[80]
XuT, ZhaoH, WangM, ChowA, FangM. (2021b). Metabolomics and in Silico docking-directed discovery of small-molecule enzyme targets. Analytical Chemistry, 93( 6): 3072– 3081
CrossRef Pubmed Google scholar
[81]
XuX, WangL, ZangQ, LiS, LiL, WangZ, HeJ, QiangB, HanW, ZhangR, PengX, AblizZ. (2021). Rewiring of purine metabolism in response to acidosis stress in glioma stem cells. Cell Death & Disease, 12( 3): 277
CrossRef Pubmed Google scholar
[82]
YanesO, ClarkJ, WongD M, PattiG J, Sánchez-RuizA, BentonH P, TraugerS A, DespontsC, DingS, SiuzdakG. (2010). Metabolic oxidation regulates embryonic stem cell differentiation. Nature Chemical Biology, 6( 6): 411– 417
CrossRef Pubmed Google scholar
[83]
YinJ, RenW, HuangX, DengJ, LiT, YinY. (2018). Potential mechanisms connecting purine metabolism and cancer therapy. Frontiers in Immunology, 9 : 1697
CrossRef Pubmed Google scholar
[84]
YuanC, ZhangY, LiuY, Zhang T, WangZ ( 2016). Enhanced GSH synthesis by bisphenol A exposure promoted DNA methylation process in the testes of adult rare minnow Gobiocypris rarus . Aquatic Toxicology (Amsterdam, Netherlands), 178: 99– 105
CrossRef Pubmed Google scholar
[85]
YueS, YuJ, KongY, ChenH, MaoM, JiC, ShaoS, ZhuJ, GuJ, ZhaoM. (2019). Metabolomic modulations of HepG2 cells exposed to bisphenol analogues. Environment International, 129 : 59– 67
CrossRef Pubmed Google scholar
[86]
ZamboniN, FendtS M, RühlM, SauerU. (2009). 13C-based metabolic flux analysis. Nature Protocols, 4( 6): 878– 892
CrossRef Pubmed Google scholar
[87]
ZampieriM, SekarK, ZamboniN, SauerU. (2017). Frontiers of high-throughput metabolomics. Current Opinion in Chemical Biology, 36 : 15– 23
CrossRef Pubmed Google scholar
[88]
ZengT, LiangY, DaiQ, Tian J, ChenJ, LeiB, Yang Z, CaiZ ( 2022). Application of machine learning algorithms to screen potential biomarkers under cadmium exposure based on human urine metabolic profiles. Chinese Chemical Letters,
CrossRef Google scholar
[89]
ZhangW, ZhouL, YinP, WangJ, LuX, WangX, ChenJ, LinX, XuG. (2015). A weighted relative difference accumulation algorithm for dynamic metabolomics data: long-term elevated bile acids are risk factors for hepatocellular carcinoma. Scientific Reports, 5( 1): 8984
CrossRef Pubmed Google scholar
[90]
ZhaoF, LiL, ChenY, HuangY, KeerthisingheT P, ChowA, DongT, JiaS, XingS, WarthB, HuanT, FangM. (2021). Risk-based chemical ranking and generating a prioritized human exposome database. Environmental Health Perspectives, 129( 4): 047014
CrossRef Pubmed Google scholar
[91]
ZhaoH, LiuM, LvY, FangM. (2022). Dose-response metabolomics and pathway sensitivity to map molecular cartography of bisphenol A exposure. Environment International, 158 : 106893
CrossRef Pubmed Google scholar
[92]
ZimmerB M, BaryckiJ J, SimpsonM A. (2021). Integration of sugar metabolism and proteoglycan synthesis by UDP-glucose dehydrogenase. Journal of Histochemistry and Cytochemistry, 69( 1): 13– 23
CrossRef Pubmed Google scholar

Acknowledgements

This work is supported by Singapore Ministry of Education Academic Research Fund Tier 1 (No. 04MNP000567C120) and Startup Grant of Fudan University (No. JIH 1829010Y).

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Supplementary material is available in the online version of this article at https://doi.org/10.1007/s11783-023-1604-5 and is accessible for authorized users.

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