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Liu Y, Myojin T, Li K, Kurita A, Seto M, Motoyama A, Liu X, Satoh A, Munemasa S, Murata Y, Nakamura T, Nakamura Y. A Major Intestinal Catabolite of Quercetin Glycosides, 3-Hydroxyphenylacetic Acid, Protects the Hepatocytes from the Acetaldehyde-Induced Cytotoxicity through the Enhancement of the Total Aldehyde Dehydrogenase Activity. Int J Mol Sci 2022; 23:ijms23031762. [PMID: 35163684 PMCID: PMC8836260 DOI: 10.3390/ijms23031762] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 01/31/2022] [Accepted: 02/01/2022] [Indexed: 12/20/2022] Open
Abstract
Aldehyde dehydrogenases (ALDHs) are the major enzyme superfamily for the aldehyde metabolism. Since the ALDH polymorphism leads to the accumulation of acetaldehyde, we considered that the enhancement of the liver ALDH activity by certain food ingredients could help prevent alcohol-induced chronic diseases. Here, we evaluated the modulating effects of 3-hydroxyphenylacetic acid (OPAC), the major metabolite of quercetin glycosides, on the ALDH activity and acetaldehyde-induced cytotoxicity in the cultured cell models. OPAC significantly enhanced the total ALDH activity not only in mouse hepatoma Hepa1c1c7 cells, but also in human hepatoma HepG2 cells. OPAC significantly increased not only the nuclear level of aryl hydrocarbon receptor (AhR), but also the AhR-dependent reporter gene expression, though not the nuclear factor erythroid-2-related factor 2 (Nrf2)-dependent one. The pretreatment of OPAC at the concentration required for the ALDH upregulation completely inhibited the acetaldehyde-induced cytotoxicity. Silencing AhR impaired the resistant effect of OPAC against acetaldehyde. These results strongly suggested that OPAC protects the cells from the acetaldehyde-induced cytotoxicity, mainly through the AhR-dependent and Nrf2-independent enhancement of the total ALDH activity. Our findings suggest that OPAC has a protective potential in hepatocyte models and could offer a new preventive possibility of quercetin glycosides for targeting alcohol-induced chronic diseases.
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Affiliation(s)
- Yujia Liu
- School of Biological Engineering, Dalian Polytechnic University, Dalian 116034, China;
- School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, China; (K.L.); (X.L.)
- Graduate School of Environmental and Life Science, Okayama University, Okayama 700-8530, Japan; (T.M.); (A.K.); (M.S.); (A.M.); (S.M.); (Y.M.); (T.N.)
| | - Takumi Myojin
- Graduate School of Environmental and Life Science, Okayama University, Okayama 700-8530, Japan; (T.M.); (A.K.); (M.S.); (A.M.); (S.M.); (Y.M.); (T.N.)
| | - Kexin Li
- School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, China; (K.L.); (X.L.)
- Graduate School of Environmental and Life Science, Okayama University, Okayama 700-8530, Japan; (T.M.); (A.K.); (M.S.); (A.M.); (S.M.); (Y.M.); (T.N.)
| | - Ayuki Kurita
- Graduate School of Environmental and Life Science, Okayama University, Okayama 700-8530, Japan; (T.M.); (A.K.); (M.S.); (A.M.); (S.M.); (Y.M.); (T.N.)
| | - Masayuki Seto
- Graduate School of Environmental and Life Science, Okayama University, Okayama 700-8530, Japan; (T.M.); (A.K.); (M.S.); (A.M.); (S.M.); (Y.M.); (T.N.)
| | - Ayano Motoyama
- Graduate School of Environmental and Life Science, Okayama University, Okayama 700-8530, Japan; (T.M.); (A.K.); (M.S.); (A.M.); (S.M.); (Y.M.); (T.N.)
| | - Xiaoyang Liu
- School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, China; (K.L.); (X.L.)
- Graduate School of Environmental and Life Science, Okayama University, Okayama 700-8530, Japan; (T.M.); (A.K.); (M.S.); (A.M.); (S.M.); (Y.M.); (T.N.)
| | - Ayano Satoh
- Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University, Okayama 700-8530, Japan;
| | - Shintaro Munemasa
- Graduate School of Environmental and Life Science, Okayama University, Okayama 700-8530, Japan; (T.M.); (A.K.); (M.S.); (A.M.); (S.M.); (Y.M.); (T.N.)
| | - Yoshiyuki Murata
- Graduate School of Environmental and Life Science, Okayama University, Okayama 700-8530, Japan; (T.M.); (A.K.); (M.S.); (A.M.); (S.M.); (Y.M.); (T.N.)
| | - Toshiyuki Nakamura
- Graduate School of Environmental and Life Science, Okayama University, Okayama 700-8530, Japan; (T.M.); (A.K.); (M.S.); (A.M.); (S.M.); (Y.M.); (T.N.)
| | - Yoshimasa Nakamura
- Graduate School of Environmental and Life Science, Okayama University, Okayama 700-8530, Japan; (T.M.); (A.K.); (M.S.); (A.M.); (S.M.); (Y.M.); (T.N.)
- Correspondence: ; Tel.: +81-86-251-8300; Fax: +81-86-251-8388
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Rodríguez-Zavala JS, Calleja LF, Moreno-Sánchez R, Yoval-Sánchez B. Role of Aldehyde Dehydrogenases in Physiopathological Processes. Chem Res Toxicol 2019; 32:405-420. [PMID: 30628442 DOI: 10.1021/acs.chemrestox.8b00256] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Many different diseases are associated with oxidative stress. One of the main consequences of oxidative stress at the cellular level is lipid peroxidation, from which toxic aldehydes may be generated. Below their toxicity thresholds, some aldehydes are involved in signaling processes, while others are intermediaries in the metabolism of lipids, amino acids, neurotransmitters, and carbohydrates. Some aldehydes ubiquitously distributed in the environment, such as acrolein or formaldehyde, are extremely toxic to the cell. On the other hand, aldehyde dehydrogenases (ALDHs) are able to detoxify a wide variety of aldehydes to their corresponding carboxylic acids, thus helping to protect from oxidative stress. ALDHs are located in different subcellular compartments such as cytosol, mitochondria, nucleus, and endoplasmic reticulum. The aim of this review is to analyze, and highlight, the role of different ALDH isoforms in the detoxification of aldehydes generated in processes that involve high levels of oxidative stress. The ALDH physiological relevance becomes evident by the observation that their expression and activity are enhanced in different pathologies that involve oxidative stress such as neurodegenerative disorders, cardiopathies, atherosclerosis, and cancer as well as inflammatory processes. Furthermore, ALDH mutations bring about several disorders in the cell. Thus, understanding the mechanisms by which these enzymes participate in diverse cellular processes may lead to better contend with the damage caused by toxic aldehydes in different pathologies by designing modulators and/or protocols to modify their activity or expression.
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Affiliation(s)
| | | | - Rafael Moreno-Sánchez
- Departamento de Bioquímica , Instituto Nacional de Cardiología , México 14080 , México
| | - Belem Yoval-Sánchez
- Departamento de Bioquímica , Instituto Nacional de Cardiología , México 14080 , México
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Zhang C, Lu Y, Feng Q, Wang X, Lou H, Liu J, Ning Z, Yuan K, Wang Y, Zhou Y, Deng L, Liu L, Yang Y, Li S, Ma L, Zhang Z, Jin L, Su B, Kang L, Xu S. Differentiated demographic histories and local adaptations between Sherpas and Tibetans. Genome Biol 2017; 18:115. [PMID: 28619099 PMCID: PMC5472941 DOI: 10.1186/s13059-017-1242-y] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2016] [Accepted: 05/22/2017] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND The genetic relationships reported by recent studies between Sherpas and Tibetans are controversial. To gain insights into the population history and the genetic basis of high-altitude adaptation of the two groups, we analyzed genome-wide data in 111 Sherpas (Tibet and Nepal) and 177 Tibetans (Tibet and Qinghai), together with available data from present-day human populations. RESULTS Sherpas and Tibetans show considerable genetic differences and can be distinguished as two distinct groups, even though the divergence between them (~3200-11,300 years ago) is much later than that between Han Chinese and either of the two groups (~6200-16,000 years ago). Sub-population structures exist in both Sherpas and Tibetans, corresponding to geographical or linguistic groups. Differentiation of genetic variants between Sherpas and Tibetans associated with adaptation to either high-altitude or ultraviolet radiation were identified and validated by genotyping additional Sherpa and Tibetan samples. CONCLUSIONS Our analyses indicate that both Sherpas and Tibetans are admixed populations, but the findings do not support the previous hypothesis that Tibetans derive their ancestry from Sherpas and Han Chinese. Compared to Tibetans, Sherpas show higher levels of South Asian ancestry, while Tibetans show higher levels of East Asian and Central Asian/Siberian ancestry. We propose a new model to elucidate the differentiated demographic histories and local adaptations of Sherpas and Tibetans.
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Affiliation(s)
- Chao Zhang
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai, 200031, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yan Lu
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai, 200031, China
| | - Qidi Feng
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai, 200031, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xiaoji Wang
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai, 200031, China.,University of Chinese Academy of Sciences, Beijing, 100049, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Haiyi Lou
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai, 200031, China
| | - Jiaojiao Liu
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai, 200031, China.,University of Chinese Academy of Sciences, Beijing, 100049, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Zhilin Ning
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai, 200031, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Kai Yuan
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai, 200031, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yuchen Wang
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai, 200031, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Ying Zhou
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai, 200031, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Lian Deng
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai, 200031, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Lijun Liu
- Key Laboratory for Molecular Genetic Mechanisms and Intervention Research on High Altitude Disease of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang, Shaanxi, 712082, China
| | - Yajun Yang
- State Key Laboratory of Genetic Engineering and Ministry of Education (MOE) Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, 200433, China
| | - Shilin Li
- State Key Laboratory of Genetic Engineering and Ministry of Education (MOE) Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, 200433, China
| | - Lifeng Ma
- Key Laboratory for Molecular Genetic Mechanisms and Intervention Research on High Altitude Disease of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang, Shaanxi, 712082, China
| | - Zhiying Zhang
- Key Laboratory for Molecular Genetic Mechanisms and Intervention Research on High Altitude Disease of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang, Shaanxi, 712082, China
| | - Li Jin
- State Key Laboratory of Genetic Engineering and Ministry of Education (MOE) Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, 200433, China
| | - Bing Su
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
| | - Longli Kang
- Key Laboratory for Molecular Genetic Mechanisms and Intervention Research on High Altitude Disease of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang, Shaanxi, 712082, China
| | - Shuhua Xu
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai, 200031, China. .,University of Chinese Academy of Sciences, Beijing, 100049, China. .,School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China. .,Collaborative Innovation Center of Genetics and Development, Shanghai, 200438, China.
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Aldehyde dehydrogenase 3A1 promotes multi-modality resistance and alters gene expression profile in human breast adenocarcinoma MCF-7 cells. Int J Biochem Cell Biol 2016; 77:120-128. [PMID: 27276244 DOI: 10.1016/j.biocel.2016.06.004] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2015] [Revised: 03/21/2016] [Accepted: 06/03/2016] [Indexed: 12/20/2022]
Abstract
Aldehyde dehydrogenases participate in a variety of cellular homeostatic mechanisms like metabolism, proliferation, differentiation, apoptosis, whereas recently, they have been implicated in normal and cancer cell stemness. We explored roles for ALDH3A1 in conferring resistance to chemotherapeutics/radiation/oxidative stress and whether ectopic overexpression of ALDH3A1 could lead to alterations of gene expression profile associated with cancer stem cell-like phenotype. MCF-7 cells were stably transfected either with an empty vector (mock) or human aldehyde dehydrogenase 3A1 cDNA. The expression of aldehyde dehydrogenase 3A1 in MCF-7 cells was associated with altered cell proliferation rate and enhanced cell resistance against various chemotherapeutic drugs (4-hydroxyperoxycyclophosphamide, doxorubicin, etoposide, and 5-fluorouracil). Aldehyde dehydrogenase 3A1 expression also led to increased tolerance of MCF-7 cells to gamma radiation and hydrogen peroxide-induced stress. Furthermore, aldehyde dehydrogenase 3A1-expressing MCF-7 cells exhibited gene up-regulation of cyclins A, B1, B2, and down-regulation of cyclin D1 as well as transcription factors p21, CXR4, Notch1, SOX2, SOX4, OCT4, and JAG1. When compared to mock cells, no changes were observed in mRNA levels of ABCA2 and ABCB1 protein pumps with only a minor decrease of the ABCG2 pump in the aldehyde dehydrogenase 3A1-expressing cells. Also, the adhesion molecules EpCAM and CD49F were also found to be up-regulated in aldehyde dehydrogenase 3A1expressing cells. Taken together, ALDH3A1 confers a multi-modality resistance phenotype in MCF-7 cells associated with slower growth rate, increased clonogenic capacity, and altered gene expression profile, underlining its significance in cell homeostasis.
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Arthaud L, Rokia-Mille SB, Raad H, Dombrovsky A, Prevost N, Capovilla M, Robichon A. Trade-off between toxicity and signal detection orchestrated by frequency- and density-dependent genes. PLoS One 2011; 6:e19805. [PMID: 21625551 PMCID: PMC3098255 DOI: 10.1371/journal.pone.0019805] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2010] [Accepted: 04/12/2011] [Indexed: 01/05/2023] Open
Abstract
Behaviors in insects are partly highly efficient Bayesian processes that fulfill exploratory tasks ending with the colonization of new ecological niches. The foraging (for) gene in Drosophila encodes a cGMP-dependent protein kinase (PKG). It has been extensively described as a frequency-dependent gene and its transcripts are differentially expressed between individuals, reflecting the population density context. Some for transcripts, when expressed in a population at high density for many generations, concomitantly trigger strong dispersive behavior associated with foraging activity. Moreover, genotype-by-environment interaction (GEI) analysis has highlighted a dormant role of for in energetic metabolism in a food deprivation context. In our current report, we show that alleles of for encoding different cGMP-dependent kinase isoforms influence the oxidation of aldehyde groups of aromatic molecules emitted by plants via Aldh-III and a phosphorylatable adaptor. The enhanced efficiency of oxidation of aldehyde odorants into carboxyl groups by the action of for lessens their action and toxicity, which should facilitate exploration and guidance in a complex odor environment. Our present data provide evidence that optimal foraging performance requires the fast metabolism of volatile compounds emitted by plants to avoid neurosensory saturation and that the frequency-dependent genes that trigger dispersion influence these processes.
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Affiliation(s)
- Laury Arthaud
- UMR INRA/CNRS/UNSA 6243, University of Nice Sophia Antipolis, Sophia Antipolis, France
| | - Selim Ben Rokia-Mille
- UMR INRA/CNRS/UNSA 6243, University of Nice Sophia Antipolis, Sophia Antipolis, France
| | - Hussein Raad
- UMR INRA/CNRS/UNSA 6243, University of Nice Sophia Antipolis, Sophia Antipolis, France
| | - Aviv Dombrovsky
- Agricultural Research Organization, The Volcani Center, Bet Dagan, Israel
| | - Nicolas Prevost
- UMR INRA/CNRS/UNSA 6243, University of Nice Sophia Antipolis, Sophia Antipolis, France
| | - Maria Capovilla
- Dulbecco Telethon Institute, Department of Biology and Evolution, University of Ferrara, Ferrara, Italy
| | - Alain Robichon
- UMR INRA/CNRS/UNSA 6243, University of Nice Sophia Antipolis, Sophia Antipolis, France
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Dere E, Forgacs AL, Zacharewski TR, Burgoon LD. Genome-wide computational analysis of dioxin response element location and distribution in the human, mouse, and rat genomes. Chem Res Toxicol 2011; 24:494-504. [PMID: 21370876 DOI: 10.1021/tx100328r] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
The aryl hydrocarbon receptor (AhR) mediates responses elicited by 2,3,7,8-tetrachlorodibenzo-p-dioxin by binding to dioxin response elements (DRE) containing the core consensus sequence 5'-GCGTG-3'. The human, mouse, and rat genomes were computationally searched for all DRE cores. Each core was then extended by 7 bp upstream and downstream, and matrix similarity (MS) scores for the resulting 19 bp DRE sequences were calculated using a revised position weight matrix constructed from bona fide functional DREs. In total, 72318 human, 70720 mouse, and 88651 rat high-scoring (MS ≥ 0.8437) putative DREs were identified. Gene-encoding intragenic DNA regions had ∼1.6 times more putative DREs than the noncoding intergenic DNA regions. Furthermore, the promoter region spanning ±1.5 kb of a TSS had the highest density of putative DREs within the genome. Chromosomal analysis found that the putative DRE densities of chromosomes X and Y were significantly lower than the mean chromosomal density. Interestingly, the 10 kb upstream promoter region on chromosome X of the genomes were significantly less dense than the chromosomal mean, while the same region in chromosome Y was the most dense. In addition to providing a detailed genomic map of all DRE cores in the human, mouse, and rat genomes, these data will further aid the elucidation of AhR-mediated signal transduction.
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Affiliation(s)
- Edward Dere
- Department of Biochemistry & Molecular Biology, Michigan State University, East Lansing, MI 48824, USA
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