1
|
Orsatti FL, de Queiroz Freitas AC, Borges AVBE, Santato AS, de Oliveira Assumpção C, Souza MVC, da Silva MV, Orsatti CL. Unveiling the role of exercise in modulating plasma heat shock protein 27 levels: insights for exercise immunology and cardiovascular health. Mol Cell Biochem 2024:10.1007/s11010-024-05089-8. [PMID: 39172352 DOI: 10.1007/s11010-024-05089-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Accepted: 08/05/2024] [Indexed: 08/23/2024]
Abstract
Cardiovascular disease is one of the leading causes of mortality worldwide, primarily driven by atherosclerosis, a chronic inflammatory condition contributing significantly to fatalities. Various biological determinants affecting cardiovascular health across different age and sex groups have been identified. In this context, recent attention has focused on the potential therapeutic and preventive role of increasing circulating levels of heat shock protein 27 (plasma HSP27) in combating atherosclerosis. Plasma HSP27 is recognized for its protective function in inflammatory atherogenesis, offering promising avenues for intervention and management strategies against this prevalent cardiovascular ailment. Exercise has emerged as a pivotal strategy in preventing and managing cardiovascular disease, with literature indicating an increase in plasma HSP27 levels post-exercise. However, there is limited understanding of the impact of exercise on the release of HSP27 into circulation. Clarifying these aspects is crucial for understanding the role of exercise in modulating plasma HSP27 levels and its potential implications for cardiovascular health across diverse populations. Therefore, this review aims to establish a more comprehensive understanding of the relationship between plasma HSP27 and exercise.
Collapse
Affiliation(s)
- Fábio Lera Orsatti
- Exercise Biology Laboratory (BioEx), Department of Sport Science, Health Science Institute, Federal University of Triangulo Mineiro (UFTM), Av. Frei Paulino, 30, Uberaba, MG, 38025-180, Brazil.
| | - Augusto Corrêa de Queiroz Freitas
- Exercise Biology Laboratory (BioEx), Department of Sport Science, Health Science Institute, Federal University of Triangulo Mineiro (UFTM), Av. Frei Paulino, 30, Uberaba, MG, 38025-180, Brazil
| | - Anna Victória Bernardes E Borges
- Department of Microbiology, Immunology, And Parasitology, Institute of Biological and Natural Sciences of Federal University of Triangulo Mineiro, Uberaba, MG, 38025-350, Brazil
| | - Alexia Souza Santato
- Exercise Biology Laboratory (BioEx), Department of Sport Science, Health Science Institute, Federal University of Triangulo Mineiro (UFTM), Av. Frei Paulino, 30, Uberaba, MG, 38025-180, Brazil
| | - Claudio de Oliveira Assumpção
- Exercise Biology Laboratory (BioEx), Department of Sport Science, Health Science Institute, Federal University of Triangulo Mineiro (UFTM), Av. Frei Paulino, 30, Uberaba, MG, 38025-180, Brazil
| | - Markus Vinicius Campos Souza
- Exercise Biology Laboratory (BioEx), Department of Sport Science, Health Science Institute, Federal University of Triangulo Mineiro (UFTM), Av. Frei Paulino, 30, Uberaba, MG, 38025-180, Brazil
| | - Marcos Vinicius da Silva
- Department of Microbiology, Immunology, And Parasitology, Institute of Biological and Natural Sciences of Federal University of Triangulo Mineiro, Uberaba, MG, 38025-350, Brazil
| | | |
Collapse
|
2
|
Muhamad SN, How V, Lim FL, Md Akim A, Karuppiah K, Mohd Shabri NSA. Assessment of heat stress contributing factors in the indoor environment among vulnerable populations in Klang Valley using principal component analysis (PCA). Sci Rep 2024; 14:16265. [PMID: 39009671 PMCID: PMC11251149 DOI: 10.1038/s41598-024-67110-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Accepted: 07/08/2024] [Indexed: 07/17/2024] Open
Abstract
Rising global temperatures can lead to heat waves, which in turn can pose health risks to the community. However, a notable gap remains in highlighting the primary contributing factors that amplify heat-health risk among vulnerable populations. This study aims to evaluate the precedence of heat stress contributing factors in urban and rural vulnerable populations living in hot and humid tropical regions. A comparative cross-sectional study was conducted, involving 108 respondents from urban and rural areas in Klang Valley, Malaysia, using a face-to-face interview and a validated questionnaire. Data was analyzed using the principal component analysis, categorizing factors into exposure, sensitivity, and adaptive capacity indicators. In urban areas, five principal components (PCs) explained 64.3% of variability, with primary factors being sensitivity (health morbidity, medicine intake, increased age), adaptive capacity (outdoor occupation type, lack of ceiling, longer residency duration), and exposure (lower ceiling height, increased building age). In rural, five PCs explained 71.5% of variability, with primary factors being exposure (lack of ceiling, high thermal conductivity roof material, increased building age, shorter residency duration), sensitivity (health morbidity, medicine intake, increased age), and adaptive capacity (female, non-smoking, higher BMI). The order of heat-health vulnerability indicators was sensitivity > adaptive capacity > exposure for urban areas, and exposure > sensitivity > adaptive capacity for rural areas. This study demonstrated a different pattern of leading contributors to heat stress between urban and rural vulnerable populations.
Collapse
Affiliation(s)
- Siti Nurfahirah Muhamad
- Department of Environmental and Occupational Health, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
| | - Vivien How
- Department of Environmental and Occupational Health, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia.
| | - Fang Lee Lim
- Department of Environmental Engineering, Faculty of Engineering and Green Technology (FEGT), Universiti Tunku Abdul Rahman, Kampar, Perak, Malaysia
| | - Abdah Md Akim
- Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
| | - Karmegam Karuppiah
- Department of Environmental and Occupational Health, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
| | - Nur Shabrina Azreen Mohd Shabri
- Department of Environmental and Occupational Health, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
| |
Collapse
|
3
|
McCormick JJ, Meade RD, King KE, Akerman AP, Notley SR, Kirby NV, Sigal RJ, Kenny GP. Effect of daylong exposure to indoor overheating on autophagy and the cellular stress response in older adults. Appl Physiol Nutr Metab 2024; 49:855-867. [PMID: 38394645 DOI: 10.1139/apnm-2023-0361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2024]
Abstract
To protect vulnerable populations during heat waves, public health agencies recommend maintaining indoor air temperature below ∼24-28 °C. While we recently demonstrated that maintaining indoor temperatures ≤26 °C mitigates the development of hyperthermia and cardiovascular strain in older adults, the cellular consequences of prolonged indoor heat stress are poorly understood. We therefore evaluated the cellular stress response in 16 adults (six females) aged 66-78 years during 8 h rest in ambient conditions simulating homes maintained at 22 °C (control) and 26 °C (indoor temperature upper limit proposed by health agencies), as well as non-air-conditioned domiciles during hot weather and heat waves (31 and 36 °C, respectively; all 45% relative humidity). Western blot analysis was used to assess changes in proteins associated with the cellular stress response (autophagy, apoptosis, acute inflammation, and heat shock proteins) in peripheral blood mononuclear cells harvested prior to and following exposure. Following 8 h exposure, no cellular stress response-related proteins differed significantly between the 26 and 22 °C conditions (all, P ≥ 0.056). By contrast, autophagy-related proteins were elevated following exposure to 31 °C (p62: 1.5-fold; P = 0.003) and 36 °C (LC3-II, LC3-II/I, p62; all ≥2.0-fold; P ≤ 0.002) compared to 22 °C. These responses were accompanied by elevations in apoptotic signaling in the 31 and 36 °C conditions (cleaved-caspase-3: 1.8-fold and 3.7-fold, respectively; P ≤ 0.002). Furthermore, HSP90 was significantly reduced in the 36 °C compared to 22 °C condition (0.7-fold; P = 0.014). Our findings show that older adults experience considerable cellular stress during prolonged exposure to elevated ambient temperatures and support recommendations to maintain indoor temperatures ≤26 °C to prevent physiological strain in heat-vulnerable persons.
Collapse
Affiliation(s)
- James J McCormick
- Human and Environmental Physiology Research Unit, School of Human Kinetics, University of Ottawa, Ottawa, Canada
| | - Robert D Meade
- Human and Environmental Physiology Research Unit, School of Human Kinetics, University of Ottawa, Ottawa, Canada
- Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Kelli E King
- Human and Environmental Physiology Research Unit, School of Human Kinetics, University of Ottawa, Ottawa, Canada
| | - Ashley P Akerman
- Human and Environmental Physiology Research Unit, School of Human Kinetics, University of Ottawa, Ottawa, Canada
| | - Sean R Notley
- Human and Environmental Physiology Research Unit, School of Human Kinetics, University of Ottawa, Ottawa, Canada
| | - Nathalie V Kirby
- Human and Environmental Physiology Research Unit, School of Human Kinetics, University of Ottawa, Ottawa, Canada
| | - Ronald J Sigal
- Human and Environmental Physiology Research Unit, School of Human Kinetics, University of Ottawa, Ottawa, Canada
- Departments of Medicine, Cardiac Sciences and Community Health Sciences, Faculties of Medicine and Kinesiology, University of Calgary, Calgary, AB, Canada
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada
| | - Glen P Kenny
- Human and Environmental Physiology Research Unit, School of Human Kinetics, University of Ottawa, Ottawa, Canada
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada
| |
Collapse
|
4
|
Emani PS, Liu JJ, Clarke D, Jensen M, Warrell J, Gupta C, Meng R, Lee CY, Xu S, Dursun C, Lou S, Chen Y, Chu Z, Galeev T, Hwang A, Li Y, Ni P, Zhou X, Bakken TE, Bendl J, Bicks L, Chatterjee T, Cheng L, Cheng Y, Dai Y, Duan Z, Flaherty M, Fullard JF, Gancz M, Garrido-Martín D, Gaynor-Gillett S, Grundman J, Hawken N, Henry E, Hoffman GE, Huang A, Jiang Y, Jin T, Jorstad NL, Kawaguchi R, Khullar S, Liu J, Liu J, Liu S, Ma S, Margolis M, Mazariegos S, Moore J, Moran JR, Nguyen E, Phalke N, Pjanic M, Pratt H, Quintero D, Rajagopalan AS, Riesenmy TR, Shedd N, Shi M, Spector M, Terwilliger R, Travaglini KJ, Wamsley B, Wang G, Xia Y, Xiao S, Yang AC, Zheng S, Gandal MJ, Lee D, Lein ES, Roussos P, Sestan N, Weng Z, White KP, Won H, Girgenti MJ, Zhang J, Wang D, Geschwind D, Gerstein M. Single-cell genomics and regulatory networks for 388 human brains. Science 2024; 384:eadi5199. [PMID: 38781369 PMCID: PMC11365579 DOI: 10.1126/science.adi5199] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 04/05/2024] [Indexed: 05/25/2024]
Abstract
Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multiomics datasets into a resource comprising >2.8 million nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550,000 cell type-specific regulatory elements and >1.4 million single-cell expression quantitative trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types.
Collapse
Affiliation(s)
- Prashant S Emani
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Jason J Liu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Declan Clarke
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Matthew Jensen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Jonathan Warrell
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Chirag Gupta
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Ran Meng
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Che Yu Lee
- Department of Computer Science, University of California, Irvine, CA 92697, USA
| | - Siwei Xu
- Department of Computer Science, University of California, Irvine, CA 92697, USA
| | - Cagatay Dursun
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Shaoke Lou
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Yuhang Chen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Zhiyuan Chu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
| | - Timur Galeev
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Ahyeon Hwang
- Department of Computer Science, University of California, Irvine, CA 92697, USA
- Mathematical, Computational and Systems Biology, University of California, Irvine, CA 92697, USA
| | - Yunyang Li
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
- Department of Computer Science, Yale University, New Haven, CT 06520, USA
| | - Pengyu Ni
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Xiao Zhou
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | | | - Jaroslav Bendl
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Lucy Bicks
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Tanima Chatterjee
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | | | - Yuyan Cheng
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
- Department of Ophthalmology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yi Dai
- Department of Computer Science, University of California, Irvine, CA 92697, USA
| | - Ziheng Duan
- Department of Computer Science, University of California, Irvine, CA 92697, USA
| | | | - John F Fullard
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Michael Gancz
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Diego Garrido-Martín
- Department of Genetics, Microbiology and Statistics, Universitat de Barcelona, Barcelona 08028, Spain
| | - Sophia Gaynor-Gillett
- Tempus Labs, Chicago, IL 60654, USA
- Department of Biology, Cornell College, Mount Vernon, IA 52314, USA
| | - Jennifer Grundman
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Natalie Hawken
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Ella Henry
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Gabriel E Hoffman
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research Education and Clinical Center, James J. Peters VA Medical Center, Bronx, NY 10468, USA
- Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center, Bronx, NY 10468, USA
| | - Ao Huang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
| | - Yunzhe Jiang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Ting Jin
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA
| | | | - Riki Kawaguchi
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
- Center for Autism Research and Treatment, Semel Institute, University of California, Los Angeles, CA 90095, USA
| | - Saniya Khullar
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Jianyin Liu
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Junhao Liu
- Department of Computer Science, University of California, Irvine, CA 92697, USA
| | - Shuang Liu
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Shaojie Ma
- Department of Neuroscience, Yale University, New Haven, CT 06510, USA
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | | | - Samantha Mazariegos
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Jill Moore
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA 01605, USA
| | | | - Eric Nguyen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Nishigandha Phalke
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA 01605, USA
| | - Milos Pjanic
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Henry Pratt
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA 01605, USA
| | - Diana Quintero
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | | | - Tiernon R Riesenmy
- Department of Statistics and Data Science, Yale University, New Haven, CT 06520, USA
| | - Nicole Shedd
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA 01605, USA
| | | | | | - Rosemarie Terwilliger
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06520, USA
| | | | - Brie Wamsley
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Gaoyuan Wang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Yan Xia
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Shaohua Xiao
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Andrew C Yang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Suchen Zheng
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Michael J Gandal
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles CA, 90095, USA
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Donghoon Lee
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA 98109, USA
- Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98195, USA
| | - Panos Roussos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research Education and Clinical Center, James J. Peters VA Medical Center, Bronx, NY 10468, USA
- Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center, Bronx, NY 10468, USA
| | - Nenad Sestan
- Department of Neuroscience, Yale University, New Haven, CT 06510, USA
| | - Zhiping Weng
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA 01605, USA
| | - Kevin P White
- Yong Loo Lin School of Medicine, National University of Singapore, 117597 Singapore
| | - Hyejung Won
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Matthew J Girgenti
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06520, USA
- Wu Tsai Institute, Yale University, New Haven, CT 06520, USA
- Clinical Neuroscience Division, National Center for Posttraumatic Stress Disorder, Veterans Affairs Connecticut Healthcare System, West Haven, CT 06516, USA
| | - Jing Zhang
- Department of Computer Science, University of California, Irvine, CA 92697, USA
| | - Daifeng Wang
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Daniel Geschwind
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
- Center for Autism Research and Treatment, Semel Institute, University of California, Los Angeles, CA 90095, USA
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Institute for Precision Health, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Mark Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
- Department of Computer Science, Yale University, New Haven, CT 06520, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT 06520, USA
- Department of Biomedical Informatics & Data Science, Yale University, New Haven, CT 06520, USA
| |
Collapse
|
5
|
Wang G, Li Y, Liu J, Zhang Q, Cai W, Li X. Heat shock protein-related diagnostic signature and molecular subtypes in ankylosing spondylitis: new pathogenesis insights. Int J Hyperthermia 2024; 41:2336149. [PMID: 38679420 DOI: 10.1080/02656736.2024.2336149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 03/25/2024] [Indexed: 05/01/2024] Open
Abstract
Heat shock proteins (HSP) have been associated with a range of persistent inflammatory disorders; however, little research has been conducted on the involvement of HSP in the development of ankylosing spondylitis (AS). The research aims to identify a diagnostic signature based on HSP-related genes and determine the molecular subtypes of AS. We gathered the transcriptional data of patients with AS from the GSE73754 dataset and conducted a literature search for HSP-related genes (HRGs). The logistic regression model was utilized for the identification of hub HRGs associated with AS. Subsequently, these HRGs were employed in the construction of a nomogram prediction model. We employed a consensus clustering approach to identify novel molecular subgroups. Subsequently, we conducted functional analyses, encompassing GO, KEGG, and GSEA, to elucidate the underlying mechanisms between these subgroups. To assess the immunological landscape, we employed the xCell algorithm. Through logistic regression analysis, the four core HRGs (CCT2, HSPA6, DNAJB14, and DNAJC5) were confirmed as potential biomarkers for AS. Subsequent stratification revealed two distinct molecular phenotypes, designated as Cluster 1 and Cluster 2. Notably, Cluster 2 was characterized by the upregulation of pathways pertinent to immune response and inflammation. Our research suggests that the CCT2, HSPA6, DNAJB14, and DNAJC5 exhibit potential as effective blood-based diagnostic biomarkers for AS. These findings contribute to a deeper comprehension of the underlying mechanisms involved in the development of AS and offer potential targets for personalized therapeutic interventions.
Collapse
Affiliation(s)
- Geqiang Wang
- Department of Orthopaedics and Traumatology III, The First Affiliated Hospital of Heilongjiang University of Traditional Chinese Medicine, China
| | - Yongji Li
- Department of Orthopaedics and Traumatology I, The Second Affiliated Hospital of Heilongjiang University of Traditional Chinese Medicine, China
| | - Jiaxing Liu
- Department of Orthopaedics and Traumatology III, The First Affiliated Hospital of Heilongjiang University of Traditional Chinese Medicine, China
| | - Qian Zhang
- Department of Orthopaedics and Traumatology III, The First Affiliated Hospital of Heilongjiang University of Traditional Chinese Medicine, China
| | - Weixin Cai
- Department of Orthopaedics and Traumatology III, The First Affiliated Hospital of Heilongjiang University of Traditional Chinese Medicine, China
| | - Xiaodong Li
- Department of Orthopaedics, The Third Affiliated Hospital of Heilongjiang University of Traditional Chinese Medicine, China
| |
Collapse
|
6
|
Murakami A. Impact of hormesis to deepen our understanding of the mechanisms underlying the bioactivities of polyphenols. Curr Opin Biotechnol 2024; 86:103074. [PMID: 38325232 DOI: 10.1016/j.copbio.2024.103074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 01/15/2024] [Accepted: 01/16/2024] [Indexed: 02/09/2024]
Abstract
Cells, organs, and the whole body are continuously exposed to various types of stressors, including oxidative stress, protein denaturation, hypoxia, energy starvation, and pathogen insults. Hormesis is an adaptive phenomenon in which a stressor induces cellular stress responses at low or moderate doses, while catastrophic damage is manifested at high doses. Polyphenols, as xenobiotic phytochemicals, exhibit stress responses in animal cells, as demonstrated in cellular and rodent models. In this review article, the author highlighted several molecular mechanisms underlying different types of stress adaptation and hormetic phenomena induced by bioactive polyphenols to substantially understand how and why those phytochemicals function in biological systems.
Collapse
Affiliation(s)
- Akira Murakami
- Department of Food Science and Nutrition, School of Human Science and Environment, University of Hyogo, 1-1-12, Shinzaike-Honcho, Himeji, Hyogo 670-0092, Japan.
| |
Collapse
|
7
|
Emani PS, Liu JJ, Clarke D, Jensen M, Warrell J, Gupta C, Meng R, Lee CY, Xu S, Dursun C, Lou S, Chen Y, Chu Z, Galeev T, Hwang A, Li Y, Ni P, Zhou X, Bakken TE, Bendl J, Bicks L, Chatterjee T, Cheng L, Cheng Y, Dai Y, Duan Z, Flaherty M, Fullard JF, Gancz M, Garrido-Martín D, Gaynor-Gillett S, Grundman J, Hawken N, Henry E, Hoffman GE, Huang A, Jiang Y, Jin T, Jorstad NL, Kawaguchi R, Khullar S, Liu J, Liu J, Liu S, Ma S, Margolis M, Mazariegos S, Moore J, Moran JR, Nguyen E, Phalke N, Pjanic M, Pratt H, Quintero D, Rajagopalan AS, Riesenmy TR, Shedd N, Shi M, Spector M, Terwilliger R, Travaglini KJ, Wamsley B, Wang G, Xia Y, Xiao S, Yang AC, Zheng S, Gandal MJ, Lee D, Lein ES, Roussos P, Sestan N, Weng Z, White KP, Won H, Girgenti MJ, Zhang J, Wang D, Geschwind D, Gerstein M. Single-cell genomics and regulatory networks for 388 human brains. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.18.585576. [PMID: 38562822 PMCID: PMC10983939 DOI: 10.1101/2024.03.18.585576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet, little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multi-omics datasets into a resource comprising >2.8M nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550K cell-type-specific regulatory elements and >1.4M single-cell expression-quantitative-trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types.
Collapse
Affiliation(s)
- Prashant S Emani
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Jason J Liu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Declan Clarke
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Matthew Jensen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Jonathan Warrell
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Chirag Gupta
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Ran Meng
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Che Yu Lee
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
| | - Siwei Xu
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
| | - Cagatay Dursun
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Shaoke Lou
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Yuhang Chen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Zhiyuan Chu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
| | - Timur Galeev
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Ahyeon Hwang
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
- Mathematical, Computational and Systems Biology, University of California, Irvine, CA, 92697, USA
| | - Yunyang Li
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
- Department of Computer Science, Yale University, New Haven, CT, 06520, USA
| | - Pengyu Ni
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Xiao Zhou
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | | | - Jaroslav Bendl
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Lucy Bicks
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Tanima Chatterjee
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | | | - Yuyan Cheng
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
- Department of Opthalmology, Perlman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Yi Dai
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
| | - Ziheng Duan
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
| | | | - John F Fullard
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Michael Gancz
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Diego Garrido-Martín
- Department of Genetics, Microbiology and Statistics, Universitat de Barcelona, Barcelona, 08028, Spain
| | - Sophia Gaynor-Gillett
- Tempus Labs, Inc., Chicago, IL, 60654, USA
- Department of Biology, Cornell College, Mount Vernon, IA, 52314, USA
| | - Jennifer Grundman
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Natalie Hawken
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Ella Henry
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Gabriel E Hoffman
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Mental Illness Research Education and Clinical Center, James J. Peters VA Medical Center, Bronx, NY, 10468, USA
- Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center, Bronx, NY, 10468, USA
| | - Ao Huang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
| | - Yunzhe Jiang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Ting Jin
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | | | - Riki Kawaguchi
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
- Center for Autism Research and Treatment, Semel Institute, University of California, Los Angeles, CA, 90095, USA
| | - Saniya Khullar
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Jianyin Liu
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Junhao Liu
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
| | - Shuang Liu
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Shaojie Ma
- Department of Neuroscience, Yale University, New Haven, CT, 06510, USA
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Michael Margolis
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Samantha Mazariegos
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Jill Moore
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA, 01605, USA
| | | | - Eric Nguyen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Nishigandha Phalke
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA, 01605, USA
| | - Milos Pjanic
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Henry Pratt
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA, 01605, USA
| | - Diana Quintero
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | | | - Tiernon R Riesenmy
- Department of Statistics & Data Science, Yale University, New Haven, CT, 06520, USA
| | - Nicole Shedd
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA, 01605, USA
| | - Manman Shi
- Tempus Labs, Inc., Chicago, IL, 60654, USA
| | | | - Rosemarie Terwilliger
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06520, USA
| | | | - Brie Wamsley
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Gaoyuan Wang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Yan Xia
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Shaohua Xiao
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Andrew C Yang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Suchen Zheng
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Michael J Gandal
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Donghoon Lee
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA, 98109, USA
- Department of Neurological Surgery, University of Washington, Seattle, WA, 98195, USA
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, 98195, USA
| | - Panos Roussos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Mental Illness Research Education and Clinical Center, James J. Peters VA Medical Center, Bronx, NY, 10468, USA
- Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center, Bronx, NY, 10468, USA
| | - Nenad Sestan
- Department of Neuroscience, Yale University, New Haven, CT, 06510, USA
| | - Zhiping Weng
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA, 01605, USA
| | - Kevin P White
- Yong Loo Lin School of Medicine, National University of Singapore, 117597, Singapore
| | - Hyejung Won
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Matthew J Girgenti
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06520, USA
- Wu Tsai Institute, Yale University, New Haven, CT, 06520, USA
- Clinical Neuroscience Division, National Center for Posttraumatic Stress Disorder, Veterans Affairs Connecticut Healthcare System, West Haven, CT, 06516, USA
| | - Jing Zhang
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
| | - Daifeng Wang
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Daniel Geschwind
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
- Center for Autism Research and Treatment, Semel Institute, University of California, Los Angeles, CA, 90095, USA
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Institute for Precision Health, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Mark Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
- Department of Computer Science, Yale University, New Haven, CT, 06520, USA
- Department of Statistics & Data Science, Yale University, New Haven, CT, 06520, USA
- Department of Biomedical Informatics & Data Science, Yale University, New Haven, CT, 06520, USA
| |
Collapse
|
8
|
Rubio-Tomás T, Alegre-Cortés E, Lionaki E, Fuentes JM, Tavernarakis N. Heat shock and thermotolerance in Caenorhabditis elegans: An overview of laboratory techniques. Methods Cell Biol 2024; 185:1-17. [PMID: 38556443 DOI: 10.1016/bs.mcb.2024.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/02/2024]
Abstract
The soil nematode worm Caenorhabditis elegans is a simple and well-established model for the study of many biological processes. Heat shock and thermotolerance assays have been developed for this nematode, and have been used to decipher the molecular relationships between thermal stress and aging, among others. Nevertheless, a systematic and methodological comparison of the different approaches and tools utilized is lacking in the literature. Here, we aim to provide a comprehensive summary of the most commonly used strategies for carrying out heat shock and thermotolerance assays that have been reported, highlighting specific readouts and scientific questions that can be addressed. Furthermore, we offer examples of thermotolerance assays performed with wild type nematodes, that can serve as a gauge of the animal survival under diverse conditions of stress.
Collapse
Affiliation(s)
- Teresa Rubio-Tomás
- Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, Heraklion, Greece
| | - Eva Alegre-Cortés
- Universidad de Extremadura, Departamento de Bioquímica y Biología Molecular y Genética, Facultad de Enfermería y Terapia Ocupacional, Cáceres, Spain; Instituto Universitario de Investigación Biosanitaria de Extremadura (INUBE), Cáceres, Spain
| | - Eirini Lionaki
- Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, Heraklion, Greece
| | - José M Fuentes
- Universidad de Extremadura, Departamento de Bioquímica y Biología Molecular y Genética, Facultad de Enfermería y Terapia Ocupacional, Cáceres, Spain; Instituto Universitario de Investigación Biosanitaria de Extremadura (INUBE), Cáceres, Spain; Centro de Investigación Biomédica en Red en Enfermedades Neurodegenerativas-Instituto de Salud Carlos III (CIBER-CIBERNED-ISCIII), Madrid, Spain.
| | - Nektarios Tavernarakis
- Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, Heraklion, Greece; Division of Basic Sciences, School of Medicine, University of Crete, Heraklion, Greece.
| |
Collapse
|
9
|
Omidi A, Nazifi S, Rasekh M, Zare N. Heat-shock proteins, oxidative stress, and antioxidants in one-humped camels. Trop Anim Health Prod 2023; 56:29. [PMID: 38158433 DOI: 10.1007/s11250-023-03876-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 12/15/2023] [Indexed: 01/03/2024]
Abstract
One-humped camels (Camelus dromedarius) exhibit remarkable adaptability to harsh desert environments through various physiological adaptations. This study aimed to assess variations and reference values of Heat-shock proteins (HSPs), physiological parameters, mineral concentrations, total antioxidant capacity (TAC), and malondialdehyde (MDA) in 90 healthy female one-humped camels from Zabol's outskirts in Iran. The objective was to understand how these camels adapt to heat stress. Blood samples were collected from camels located at five geographical regions and analyzed using standard kits and methods. Reference intervals for heat-shock protein 30 (HSP30), heat-shock protein 40 (HSP40), heat-shock protein 70 (HSP70), and heat-shock protein 90 (HSP90) were determined using the reference value advisor (RVA). The study found significant differences among different regions for HSPs (P < 0.05), MDA (P = 0.021), and TAC (P = 0.042) levels, indicating variations in adaptation mechanisms. However, no notable differences were observed for other measured parameters between these regions. There were no significant differences observed in the evaluated parameters between the age categories of > 36 months and < 36 months. The positive correlation between HSPs and MDA levels (ranging from 0.754 to 0.884) suggests that the synthesis of HSPs is triggered as a response to oxidative stress caused by an imbalance between the production of reactive oxygen species (ROS) and the body's antioxidant defenses. This oxidative stress, in turn, is a consequence of thermal stress. Additionally, the study reveals a negative association between TAC and HSP levels (ranging from - 0.660 to - 0.820), emphasizing the role of antioxidants in mitigating heat stress. The findings of this research offer compelling support for the critical role that HSPs play in protecting cells from heat-induced damage. Additionally, the presence of higher levels of HSPs in regions with more severe climate conditions serves as evidence of camels' adaptation to heat stress. These findings emphasize the substantial impact of environmental factors on HSP production and further reinforce the crucial role of HSPs in bolstering the resilience of camels. Further research is needed to explore HSP expression and mechanisms to effectively manage and enhance camel resilience in extreme temperatures.
Collapse
Affiliation(s)
- Arash Omidi
- Department of Animal Health Management, School of Veterinary Medicine, Shiraz University, Shiraz, Iran.
| | - Saeed Nazifi
- Department of Clinical Sciences, School of Veterinary Medicine, Shiraz University, Shiraz, Iran
| | - Mehdi Rasekh
- Department of Clinical Sciences, School of Veterinary Medicine, Zabol University, Zabol, Iran
| | - Nima Zare
- School of Veterinary Medicine, Shiraz University, Shiraz, Iran
| |
Collapse
|
10
|
Ungvari A, Gulej R, Csik B, Mukli P, Negri S, Tarantini S, Yabluchanskiy A, Benyo Z, Csiszar A, Ungvari Z. The Role of Methionine-Rich Diet in Unhealthy Cerebrovascular and Brain Aging: Mechanisms and Implications for Cognitive Impairment. Nutrients 2023; 15:4662. [PMID: 37960316 PMCID: PMC10650229 DOI: 10.3390/nu15214662] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 10/28/2023] [Accepted: 10/31/2023] [Indexed: 11/15/2023] Open
Abstract
As aging societies in the western world face a growing prevalence of vascular cognitive impairment and Alzheimer's disease (AD), understanding their underlying causes and associated risk factors becomes increasingly critical. A salient concern in the western dietary context is the high consumption of methionine-rich foods such as red meat. The present review delves into the impact of this methionine-heavy diet and the resultant hyperhomocysteinemia on accelerated cerebrovascular and brain aging, emphasizing their potential roles in cognitive impairment. Through a comprehensive exploration of existing evidence, a link between high methionine intake and hyperhomocysteinemia and oxidative stress, mitochondrial dysfunction, inflammation, and accelerated epigenetic aging is drawn. Moreover, the microvascular determinants of cognitive deterioration, including endothelial dysfunction, reduced cerebral blood flow, microvascular rarefaction, impaired neurovascular coupling, and blood-brain barrier (BBB) disruption, are explored. The mechanisms by which excessive methionine consumption and hyperhomocysteinemia might drive cerebromicrovascular and brain aging processes are elucidated. By presenting an intricate understanding of the relationships among methionine-rich diets, hyperhomocysteinemia, cerebrovascular and brain aging, and cognitive impairment, avenues for future research and potential therapeutic interventions are suggested.
Collapse
Affiliation(s)
- Anna Ungvari
- Department of Public Health, Semmelweis University, 1089 Budapest, Hungary
| | - Rafal Gulej
- Vascular Cognitive Impairment, Neurodegeneration and Healthy Brain Aging Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA; (R.G.); (B.C.); (P.M.); (S.N.); (S.T.); (A.Y.); (A.C.); (Z.U.)
- Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
| | - Boglarka Csik
- Vascular Cognitive Impairment, Neurodegeneration and Healthy Brain Aging Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA; (R.G.); (B.C.); (P.M.); (S.N.); (S.T.); (A.Y.); (A.C.); (Z.U.)
- Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
- International Training Program in Geroscience, Department of Public Health, Doctoral School of Basic and Translational Medicine, Semmelweis University, 1089 Budapest, Hungary
| | - Peter Mukli
- Vascular Cognitive Impairment, Neurodegeneration and Healthy Brain Aging Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA; (R.G.); (B.C.); (P.M.); (S.N.); (S.T.); (A.Y.); (A.C.); (Z.U.)
- Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
- International Training Program in Geroscience, Department of Public Health, Doctoral School of Basic and Translational Medicine, Semmelweis University, 1089 Budapest, Hungary
| | - Sharon Negri
- Vascular Cognitive Impairment, Neurodegeneration and Healthy Brain Aging Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA; (R.G.); (B.C.); (P.M.); (S.N.); (S.T.); (A.Y.); (A.C.); (Z.U.)
- Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
| | - Stefano Tarantini
- Vascular Cognitive Impairment, Neurodegeneration and Healthy Brain Aging Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA; (R.G.); (B.C.); (P.M.); (S.N.); (S.T.); (A.Y.); (A.C.); (Z.U.)
- Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
- International Training Program in Geroscience, Department of Public Health, Doctoral School of Basic and Translational Medicine, Semmelweis University, 1089 Budapest, Hungary
- Stephenson Cancer Center, University of Oklahoma, Oklahoma City, OK 73104, USA
- Department of Health Promotion Sciences, College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
| | - Andriy Yabluchanskiy
- Vascular Cognitive Impairment, Neurodegeneration and Healthy Brain Aging Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA; (R.G.); (B.C.); (P.M.); (S.N.); (S.T.); (A.Y.); (A.C.); (Z.U.)
- Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
- International Training Program in Geroscience, Department of Public Health, Doctoral School of Basic and Translational Medicine, Semmelweis University, 1089 Budapest, Hungary
- Stephenson Cancer Center, University of Oklahoma, Oklahoma City, OK 73104, USA
- Department of Health Promotion Sciences, College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
| | - Zoltan Benyo
- Institute of Translational Medicine, Semmelweis University, 1094 Budapest, Hungary;
- Cerebrovascular and Neurocognitive Disorders Research Group, Eötvös Loránd Research Network, Semmelweis University, 1094 Budapest, Hungary
| | - Anna Csiszar
- Vascular Cognitive Impairment, Neurodegeneration and Healthy Brain Aging Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA; (R.G.); (B.C.); (P.M.); (S.N.); (S.T.); (A.Y.); (A.C.); (Z.U.)
- Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
- Stephenson Cancer Center, University of Oklahoma, Oklahoma City, OK 73104, USA
- Department of Health Promotion Sciences, College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
- International Training Program in Geroscience, Department of Translational Medicine, Doctoral School of Basic and Translational Medicine, Semmelweis University, 1089 Budapest, Hungary
| | - Zoltan Ungvari
- Vascular Cognitive Impairment, Neurodegeneration and Healthy Brain Aging Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA; (R.G.); (B.C.); (P.M.); (S.N.); (S.T.); (A.Y.); (A.C.); (Z.U.)
- Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
- International Training Program in Geroscience, Department of Public Health, Doctoral School of Basic and Translational Medicine, Semmelweis University, 1089 Budapest, Hungary
- Stephenson Cancer Center, University of Oklahoma, Oklahoma City, OK 73104, USA
- Department of Health Promotion Sciences, College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
| |
Collapse
|
11
|
Feng Q, Xia W, Feng Z, Tan Y, Zhang Y, Liu D, Zhang G. The accelerated organ senescence and proteotoxicity in thyrotoxicosis mice. J Cell Physiol 2023; 238:2481-2498. [PMID: 37750538 DOI: 10.1002/jcp.31108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 07/25/2023] [Accepted: 07/27/2023] [Indexed: 09/27/2023]
Abstract
The mechanism of aging has always been the focus of research, because aging is related to disease susceptibility and seriously affects people's quality of life. The diseases also accelerate the aging process, especially the pathological changes of substantive organs, such as cardiac hypertrophy, severely shortened lifespan. So, lesions in organs are both a consequence and a cause of aging. However, the disease in a given organ is not in isolation but is a systemic problem. Our previous study found that thyrotoxicosis mice model has aging characteristics including immunosenescence, lipotoxicity, malnutrition. But all these characteristics will lead to organ senescence, therefore, this study continued to study the aging changes of important organs such as heart, liver, and kidney in thyrotoxicosis mice using tandem mass tags (TMT) proteomics method. The results showed that the excess thyroxine led to cardiac hypertrophy. In the liver, the ability to synthesize functional proteins, detoxify, and metabolism were declined. The effect on the kidney was the decreased ability of detoxify and metabolism. The main finding of the present study was that the acceleration of organ senescence by excess thyroxine was due to proteotoxicity. The shared cause of proteotoxicity in the three organs included the intensify of oxidative phosphorylation, the redundancy production of ribosomes, and the lack of splicing and ubiquitin proteasome system function. Totally, proteotoxicity was another parallel between thyrotoxicosis and aging in addition to lipotoxicity. Our research provided a convenient and appropriate animal model for exploring aging mechanism and antiaging drugs.
Collapse
Affiliation(s)
- Qin Feng
- State Key Laboratory of Integration and Innovation of Classic Formula and Modern Chinese Medicine, Lunan Pharmaceutical Group Co., Ltd., Linyi, Shandong, China
| | - Wenkai Xia
- State Key Laboratory of Integration and Innovation of Classic Formula and Modern Chinese Medicine, Lunan Pharmaceutical Group Co., Ltd., Linyi, Shandong, China
| | - Zhong Feng
- State Key Laboratory of Integration and Innovation of Classic Formula and Modern Chinese Medicine, Lunan Pharmaceutical Group Co., Ltd., Linyi, Shandong, China
- School of Pharmaceutical Sciences, Sun Yat-sen University, Shenzhen, China
| | - Yujun Tan
- State Key Laboratory of Integration and Innovation of Classic Formula and Modern Chinese Medicine, Lunan Pharmaceutical Group Co., Ltd., Linyi, Shandong, China
| | - Yongxia Zhang
- State Key Laboratory of Integration and Innovation of Classic Formula and Modern Chinese Medicine, Lunan Pharmaceutical Group Co., Ltd., Linyi, Shandong, China
| | - Deshan Liu
- Department of Traditional Chinese Medicine, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Guimin Zhang
- State Key Laboratory of Integration and Innovation of Classic Formula and Modern Chinese Medicine, Lunan Pharmaceutical Group Co., Ltd., Linyi, Shandong, China
| |
Collapse
|
12
|
Poznyak AV, Orekhova VA, Sukhorukov VN, Khotina VA, Popov MA, Orekhov AN. Atheroprotective Aspects of Heat Shock Proteins. Int J Mol Sci 2023; 24:11750. [PMID: 37511509 PMCID: PMC10380699 DOI: 10.3390/ijms241411750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 07/17/2023] [Accepted: 07/19/2023] [Indexed: 07/30/2023] Open
Abstract
Atherosclerosis is a major global health problem. Being a harbinger of a large number of cardiovascular diseases, it ultimately leads to morbidity and mortality. At the same time, effective measures for the prevention and treatment of atherosclerosis have not been developed, to date. All available therapeutic options have a number of limitations. To understand the mechanisms behind the triggering and development of atherosclerosis, a deeper understanding of molecular interactions is needed. Heat shock proteins are important for the normal functioning of cells, actively helping cells adapt to gradual changes in the environment and survive in deadly conditions. Moreover, multiple HSP families play various roles in the progression of cardiovascular disorders. Some heat shock proteins have been shown to have antiatherosclerotic effects, while the role of others remains unclear. In this review, we considered certain aspects of the antiatherosclerotic activity of a number of heat shock proteins.
Collapse
Affiliation(s)
- Anastasia V Poznyak
- Institute for Atherosclerosis Research, Osennyaya 4-1-207, 121609 Moscow, Russia
| | - Varvara A Orekhova
- Institute for Atherosclerosis Research, Osennyaya 4-1-207, 121609 Moscow, Russia
| | - Vasily N Sukhorukov
- Institute for Atherosclerosis Research, Osennyaya 4-1-207, 121609 Moscow, Russia
| | - Victoria A Khotina
- Institute of General Pathology and Pathophysiology, 8, Baltiyskaya St., 125315 Moscow, Russia
| | - Mikhail A Popov
- Department of Cardiac Surgery, Moscow Regional Research and Clinical Institute (MONIKI), 61/2, Shchepkin St., 129110 Moscow, Russia
| | - Alexander N Orekhov
- Institute for Atherosclerosis Research, Osennyaya 4-1-207, 121609 Moscow, Russia
| |
Collapse
|
13
|
Li J, Li D, Chen Y, Chen W, Xu J, Gao L. Gut Microbiota and Aging: Traditional Chinese Medicine and Modern Medicine. Clin Interv Aging 2023; 18:963-986. [PMID: 37351381 PMCID: PMC10284159 DOI: 10.2147/cia.s414714] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 06/08/2023] [Indexed: 06/24/2023] Open
Abstract
The changing composition of gut microbiota, much like aging, accompanies people throughout their lives, and the inextricable relationship between both has recently attracted extensive attention as well. Modern medical research has revealed that a series of changes in gut microbiota are involved in the aging process of organisms, which may be because gut microbiota modulates aging-related changes related to innate immunity and cognitive function. At present, there is no definite and effective method to delay aging. However, Nobel laureate Tu Youyou's research on artemisinin has inspired researchers to study the importance of Traditional Chinese Medicine (TCM). TCM, as an ancient alternative medicine, has unique advantages in preventive health care and in treating diseases as it already has formed an independent understanding of the aging system. TCM practitioners believe that the mechanism of aging is mainly deficiency, and pathological states such as blood stasis, qi stagnation and phlegm coagulation can exacerbate the process of aging, which involves a series of organs, including the brain, kidney, heart, liver and spleen. Our current understanding of aging has led us to realise that TCM can indeed make some beneficial changes, such as the improvement of cognitive impairment. However, due to the multi-component and multi-target nature of TCM, the exploration of its mechanism of action has become extremely complex. While analysing the relationship between gut microbiota and aging, this review explores the similarities and differences in treatment methods and mechanisms between TCM and Modern Medicine, in order to explore a new approach that combines TCM and Modern Medicine to regulate gut microbiota, improve immunity and delay aging.
Collapse
Affiliation(s)
- Jinfan Li
- Department of First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 250000, People’s Republic of China
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education, Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, People’s Republic of China
| | - Dong Li
- Department of Diabetes, Licheng District Hospital of Traditional Chinese Medicine, Jinan, Shandong, 250100, People’s Republic of China
| | - Yajie Chen
- Department of Rehabilitation and Health Care, Jinan Vocational College of Nursing, Jinan, Shandong, 250100, People’s Republic of China
| | - Wenbin Chen
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education, Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, People’s Republic of China
- Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Jinan, Shandong, 250021, People’s Republic of China
- Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong, 250021, People’s Republic of China
| | - Jin Xu
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education, Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, People’s Republic of China
- Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Jinan, Shandong, 250021, People’s Republic of China
- Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong, 250021, People’s Republic of China
| | - Ling Gao
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education, Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, People’s Republic of China
- Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Jinan, Shandong, 250021, People’s Republic of China
- Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong, 250021, People’s Republic of China
| |
Collapse
|
14
|
Shikhevich S, Chadaeva I, Khandaev B, Kozhemyakina R, Zolotareva K, Kazachek A, Oshchepkov D, Bogomolov A, Klimova NV, Ivanisenko VA, Demenkov P, Mustafin Z, Markel A, Savinkova L, Kolchanov NA, Kozlov V, Ponomarenko M. Differentially Expressed Genes and Molecular Susceptibility to Human Age-Related Diseases. Int J Mol Sci 2023; 24:ijms24043996. [PMID: 36835409 PMCID: PMC9966505 DOI: 10.3390/ijms24043996] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 02/02/2023] [Accepted: 02/13/2023] [Indexed: 02/18/2023] Open
Abstract
Mainstream transcriptome profiling of susceptibility versus resistance to age-related diseases (ARDs) is focused on differentially expressed genes (DEGs) specific to gender, age, and pathogeneses. This approach fits in well with predictive, preventive, personalized, participatory medicine and helps understand how, why, when, and what ARDs one can develop depending on their genetic background. Within this mainstream paradigm, we wanted to find out whether the known ARD-linked DEGs available in PubMed can reveal a molecular marker that will serve the purpose in anyone's any tissue at any time. We sequenced the periaqueductal gray (PAG) transcriptome of tame versus aggressive rats, identified rat-behavior-related DEGs, and compared them with their known homologous animal ARD-linked DEGs. This analysis yielded statistically significant correlations between behavior-related and ARD-susceptibility-related fold changes (log2 values) in the expression of these DEG homologs. We found principal components, PC1 and PC2, corresponding to the half-sum and the half-difference of these log2 values, respectively. With the DEGs linked to ARD susceptibility and ARD resistance in humans used as controls, we verified these principal components. This yielded only one statistically significant common molecular marker for ARDs: an excess of Fcγ receptor IIb suppressing immune cell hyperactivation.
Collapse
Affiliation(s)
- Svetlana Shikhevich
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences (SB RAS), Novosibirsk 630090, Russia
| | - Irina Chadaeva
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences (SB RAS), Novosibirsk 630090, Russia
| | - Bato Khandaev
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences (SB RAS), Novosibirsk 630090, Russia
- The Natural Sciences Department, Novosibirsk State University, Novosibirsk 630090, Russia
| | - Rimma Kozhemyakina
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences (SB RAS), Novosibirsk 630090, Russia
| | - Karina Zolotareva
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences (SB RAS), Novosibirsk 630090, Russia
- The Natural Sciences Department, Novosibirsk State University, Novosibirsk 630090, Russia
| | - Anna Kazachek
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences (SB RAS), Novosibirsk 630090, Russia
- The Natural Sciences Department, Novosibirsk State University, Novosibirsk 630090, Russia
| | - Dmitry Oshchepkov
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences (SB RAS), Novosibirsk 630090, Russia
- The Natural Sciences Department, Novosibirsk State University, Novosibirsk 630090, Russia
| | - Anton Bogomolov
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences (SB RAS), Novosibirsk 630090, Russia
- The Natural Sciences Department, Novosibirsk State University, Novosibirsk 630090, Russia
| | - Natalya V. Klimova
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences (SB RAS), Novosibirsk 630090, Russia
| | - Vladimir A. Ivanisenko
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences (SB RAS), Novosibirsk 630090, Russia
- The Natural Sciences Department, Novosibirsk State University, Novosibirsk 630090, Russia
| | - Pavel Demenkov
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences (SB RAS), Novosibirsk 630090, Russia
| | - Zakhar Mustafin
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences (SB RAS), Novosibirsk 630090, Russia
- The Natural Sciences Department, Novosibirsk State University, Novosibirsk 630090, Russia
| | - Arcady Markel
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences (SB RAS), Novosibirsk 630090, Russia
- The Natural Sciences Department, Novosibirsk State University, Novosibirsk 630090, Russia
| | - Ludmila Savinkova
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences (SB RAS), Novosibirsk 630090, Russia
| | - Nikolay A. Kolchanov
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences (SB RAS), Novosibirsk 630090, Russia
- The Natural Sciences Department, Novosibirsk State University, Novosibirsk 630090, Russia
| | - Vladimir Kozlov
- Research Institute of Fundamental and Clinical Immunology (RIFCI) SB RAS, Novosibirsk 630099, Russia
| | - Mikhail Ponomarenko
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences (SB RAS), Novosibirsk 630090, Russia
- Correspondence: ; Tel.: +7-(383)-363-4963 (ext. 1311)
| |
Collapse
|
15
|
Bi J, Wen M, Guo X, Dai H, He Y, Shu Z. Ozone reduces lifespan and alters gene expression profiles in Rhyzopertha dominica (Fabricius). 3 Biotech 2022; 12:345. [PMID: 36386568 PMCID: PMC9646687 DOI: 10.1007/s13205-022-03397-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 10/12/2022] [Indexed: 11/11/2022] Open
Abstract
Rhyzopertha dominica is one of the most important stored grain pests that seriously damage rice and wheat. At present, the method of controlling stored grain pests mainly relies on insecticide fumigation. However, the excessive use of pesticides not only leaves pesticide residues, with harmful effects on human health and the environment, but also induces insect resistance. Ozone is a strong oxidant with the characteristics of easy decomposition and without residue. Although ozone has been widely used in the food industry in recent years, research on the control of stored grain pests is limited. In this research, we used ozone treatment to control R. dominica adults and explore the molecular mechanisms that affect them. Here, we found that ozone treatment on R. dominica adults could decrease life span and increase malondialdehyde (MDA) content, as well as reduce activity of total superoxide dismutase (SOD) and catalase (CAT). Using RNA-seq technology, we identified 641 genes that were differentially expressed between ozone-treated and control R. dominica adults [fold-change of ≥ 2 (q-value < 5%)]. When comparing ozone treatment with control R. dominica adults, 330 genes were significantly upregulated and 311 were downregulated. RT-qPCR confirmed that 11 genes were differentially expressed in ozone-treated and control R. dominica adults. These genes were involved in insect cuticle protein and antioxidant system. This research showed that ozone treatment could reduce the lifespan of R. dominica through antioxidant system. It is an environmentally benign method for the control of stored grain pests and has great development potential.
Collapse
Affiliation(s)
- Jie Bi
- School of Food Science and Engineering, Wuhan Polytechnic University, Wuhan, 430023 People’s Republic of China
| | - Mingming Wen
- School of Food Science and Engineering, Wuhan Polytechnic University, Wuhan, 430023 People’s Republic of China
| | - Xuguang Guo
- The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Huang Dai
- School of Food Science and Engineering, Wuhan Polytechnic University, Wuhan, 430023 People’s Republic of China
| | - Yanping He
- School of Food Science and Engineering, Wuhan Polytechnic University, Wuhan, 430023 People’s Republic of China
| | - Zaixi Shu
- School of Food Science and Engineering, Wuhan Polytechnic University, Wuhan, 430023 People’s Republic of China
| |
Collapse
|
16
|
Kaszubowska L, Foerster J, Kmieć Z. NKT-like (CD3 + CD56+) cells differ from T cells in expression level of cellular protective proteins and sensitivity to stimulation in the process of ageing. Immun Ageing 2022; 19:18. [PMID: 35410272 PMCID: PMC8996639 DOI: 10.1186/s12979-022-00274-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Accepted: 04/01/2022] [Indexed: 12/13/2022]
Abstract
BACKGROUND NKT-like cells are T lymphocytes coexpressing several NK cell-associated receptors. They are effector lymphocytes of innate and adaptive immunity, and their number increases with age. The study aimed to analyze the expression of cellular protective proteins, i.e. sirtuin 1 (SIRT1), heat shock protein 70 (HSP70) and manganese superoxide dismutase (SOD2) in NKT-like and T cells of the young ('young', 31 subjects, age range 19-24 years), seniors aged under 85 ('old'; 30 subjects, age range 65-84 years) and seniors aged over 85 ('oldest', 24 subjects, age range 85-94 years). Both NKT-like and T cells were cultured for 48 h and stimulated with IL-2, LPS and PMA with ionomycin and compared with unstimulated control cells. RESULTS The oldest seniors varied from the other age groups by significantly increased expression of SIRT1 and HSP70 in both NKT-like and T cells observed in both stimulated and nonstimulated cells. The analyzed lymphocyte populations of the oldest revealed not only the highest expression of these proteins but also insensitivity to all types of applied stimulation. When NKT-like cells were compared to T cells, higher expression of the studied protective proteins was observed in both stimulated and unstimulated NKT-like cells. Neither CD3 + CD56+ nor CD3+ cells revealed elevated expression of SOD2, and these cells responded to stimulation until very advanced age. T cells revealed higher sensitivity to stimulation with IL-2 regarding SIRT1 and HSP70 expression. NKT-like cells were more sensitive to stimulation with PMA and ionomycin concerning the expression of these proteins. IL-2 did not induce a significant increase in SOD2 expression in the studied age groups. CONCLUSIONS The oldest seniors developed an adaptive stress response in both T and NKT-like cells regarding the expression of SIRT1 and HSP70, which was increased and insensitive to further stimulation in contrast to SOD2, which showed a more inducible pattern of expression. CD3 + CD56+ cells exhibited higher expression of cellular protective proteins than CD3+ cells in both stimulated and control, nonstimulated cells. NKT-like and T cells showed a distinct sensitivity to the applied stimulatory factors in the respective age groups.
Collapse
Affiliation(s)
- Lucyna Kaszubowska
- Department of Histology, Medical University of Gdańsk, Dębinki 1, 80-211, Gdańsk, Poland.
| | - Jerzy Foerster
- Department of Social and Clinical Gerontology, Medical University of Gdańsk, Dębinki 1, 80-211, Gdańsk, Poland
| | - Zbigniew Kmieć
- Department of Histology, Medical University of Gdańsk, Dębinki 1, 80-211, Gdańsk, Poland
| |
Collapse
|
17
|
A New Role of Acute Phase Proteins: Local Production Is an Ancient, General Stress-Response System of Mammalian Cells. Int J Mol Sci 2022; 23:ijms23062972. [PMID: 35328392 PMCID: PMC8954921 DOI: 10.3390/ijms23062972] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 02/20/2022] [Accepted: 03/03/2022] [Indexed: 02/06/2023] Open
Abstract
The prevailing general view of acute-phase proteins (APPs) is that they are produced by the liver in response to the stress of the body as part of a systemic acute-phase response. We demonstrated a coordinated, local production of these proteins upon cell stress by the stressed cells. The local, stress-induced APP production has been demonstrated in different tissues (kidney, breast cancer) and with different stressors (hypoxia, fibrosis and electromagnetic heat). Thus, this local acute-phase response (APR) seems to be a universal mechanism. APP production is an ancient defense mechanism observed in nematodes and fruit flies as well. Local APP production at the tissue level is also supported by sporadic literature data for single proteins; however, the complex, coordinated, local appearance of this stress response has been first demonstrated only recently. Although a number of literature data are available for the local production of single acute-phase proteins, their interpretation as a local, coordinated stress response is new. A better understanding of the role of APPs in cellular stress response may also be of diagnostic/prognostic and therapeutic significance.
Collapse
|