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Mascellani Bergo A, Leiss K, Havlik J. Twenty Years of 1H NMR Plant Metabolomics: A Way Forward toward Assessment of Plant Metabolites for Constitutive and Inducible Defenses to Biotic Stress. J Agric Food Chem 2024; 72:8332-8346. [PMID: 38501393 DOI: 10.1021/acs.jafc.3c09362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/20/2024]
Abstract
Metabolomics has become an important tool in elucidating the complex relationship between a plant genotype and phenotype. For over 20 years, nuclear magnetic resonance (NMR) spectroscopy has been known for its robustness, quantitative capabilities, simplicity, and cost-efficiency. 1H NMR is the method of choice for analyzing a broad range of relatively abundant metabolites, which can be used for both capturing the plant chemical profile at one point in time and understanding the pathways that underpin plant defense. This systematic Review explores how 1H NMR-based plant metabolomics has contributed to understanding the role of various compounds in plant responses to biotic stress, focusing on both primary and secondary metabolites. It clarifies the challenges and advantages of using 1H NMR in plant metabolomics, interprets common trends observed, and suggests guidelines for method development and establishing standard procedures.
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Affiliation(s)
- Anna Mascellani Bergo
- Department of Food Science, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, 16500 Prague, Czechia
| | - Kirsten Leiss
- Business Unit Greenhouse Horticulture, Wageningen University & Research, 2665MV Bleiswijk, Netherlands
| | - Jaroslav Havlik
- Department of Food Science, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, 16500 Prague, Czechia
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Nikulkova M, Abdrabou W, Carlton JM, Idaghdour Y. Exploiting integrative metabolomics to study host-parasite interactions in Plasmodium infections. Trends Parasitol 2024; 40:313-323. [PMID: 38508901 PMCID: PMC10994734 DOI: 10.1016/j.pt.2024.02.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 02/15/2024] [Accepted: 02/16/2024] [Indexed: 03/22/2024]
Abstract
Despite years of research, malaria remains a significant global health burden, with poor diagnostic tests and increasing antimalarial drug resistance challenging diagnosis and treatment. While 'single-omics'-based approaches have been instrumental in gaining insight into the biology and pathogenicity of the Plasmodium parasite and its interaction with the human host, a more comprehensive understanding of malaria pathogenesis can be achieved through 'multi-omics' approaches. Integrative methods, which combine metabolomics, lipidomics, transcriptomics, and genomics datasets, offer a holistic systems biology approach to studying malaria. This review highlights recent advances, future directions, and challenges involved in using integrative metabolomics approaches to interrogate the interactions between Plasmodium and the human host, paving the way towards targeted antimalaria therapeutics and control intervention methods.
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Affiliation(s)
- Maria Nikulkova
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY 11101, USA; Johns Hopkins Malaria Research Institute, Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Wael Abdrabou
- Program in Biology, Division of Science and Mathematics, New York University, Abu Dhabi, United Arab Emirates
| | - Jane M Carlton
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY 11101, USA; Johns Hopkins Malaria Research Institute, Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA.
| | - Youssef Idaghdour
- Program in Biology, Division of Science and Mathematics, New York University, Abu Dhabi, United Arab Emirates
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Zeng Q, Du ZQ. Advances in the discovery of genetic elements underlying longissimus dorsi muscle growth and development in the pig. Anim Genet 2023; 54:709-720. [PMID: 37796678 DOI: 10.1111/age.13365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 07/08/2023] [Accepted: 07/08/2023] [Indexed: 10/07/2023]
Abstract
As a major source of protein in human diets, pig meat plays a crucial role in ensuring global food security. Key determinants of meat production refer to the chemical and physical compositions or characteristics of muscle fibers, such as the number, hypertrophy potential, fiber-type conversion and intramuscular fat deposition. However, the growth and formation of muscle fibers comprises a complex process under spatio-temporal regulation, that is, the intermingled and concomitant proliferation, differentiation, migration and fusion of myoblasts. Recently, with the fast and continuous development of next-generation sequencing technology, the integration of quantitative trait loci mapping with genome-wide association studies (GWAS) has greatly helped animal geneticists to discover and explore thousands of functional or causal genetic elements underlying muscle growth and development. However, owing to the underlying complex molecular mechanisms, challenges to in-depth understanding and utilization remain, and the cost of large-scale sequencing, which requires integrated analyses of high-throughput omics data, is high. In this review, we mainly elaborate on research advances in integrative analyses (e.g. GWAS, omics) for identifying functional genes or genomic elements for longissimus dorsi muscle growth and development for different pig breeds, describing several successful transcriptome analyses and functional genomics cases, in an attempt to provide some perspective on the future functional annotation of genetic elements for muscle growth and development in pigs.
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Affiliation(s)
- Qingjie Zeng
- College of Animal Science and Technology, Jiangxi Agricultural University, Nanchang, Jiangxi, China
| | - Zhi-Qiang Du
- College of Animal Science, Yangtze University, Jingzhou, Hubei, China
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Vishweswaraiah S, Yilmaz A, Saiyed N, Khalid A, Koladiya PR, Pan X, Macias S, Robinson AC, Mann D, Green BD, Kerševičiūte I, Gordevičius J, Radhakrishna U, Graham SF. Integrative Analysis Unveils the Correlation of Aminoacyl-tRNA Biosynthesis Metabolites with the Methylation of the SEPSECS Gene in Huntington's Disease Brain Tissue. Genes (Basel) 2023; 14:1752. [PMID: 37761892 PMCID: PMC10530570 DOI: 10.3390/genes14091752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 08/28/2023] [Accepted: 08/30/2023] [Indexed: 09/29/2023] Open
Abstract
The impact of environmental factors on epigenetic changes is well established, and cellular function is determined not only by the genome but also by interacting partners such as metabolites. Given the significant impact of metabolism on disease progression, exploring the interaction between the metabolome and epigenome may offer new insights into Huntington's disease (HD) diagnosis and treatment. Using fourteen post-mortem HD cases and fourteen control subjects, we performed metabolomic profiling of human postmortem brain tissue (striatum and frontal lobe), and we performed DNA methylome profiling using the same frontal lobe tissue. Along with finding several perturbed metabolites and differentially methylated loci, Aminoacyl-tRNA biosynthesis (adj p-value = 0.0098) was the most significantly perturbed metabolic pathway with which two CpGs of the SEPSECS gene were correlated. This study improves our understanding of molecular biomarker connections and, importantly, increases our knowledge of metabolic alterations driving HD progression.
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Affiliation(s)
- Sangeetha Vishweswaraiah
- Department of Obstetrics and Gynecology, Corewell Health William Beaumont University Hospital, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA; (S.V.); (U.R.)
| | - Ali Yilmaz
- Metabolomics Department, Corewell Health Research Institute, 3811 W. 13 Mile Road, Royal Oak, MI 48073, USA; (A.Y.); (N.S.); (A.K.); (P.R.K.)
| | - Nazia Saiyed
- Metabolomics Department, Corewell Health Research Institute, 3811 W. 13 Mile Road, Royal Oak, MI 48073, USA; (A.Y.); (N.S.); (A.K.); (P.R.K.)
| | - Abdullah Khalid
- Metabolomics Department, Corewell Health Research Institute, 3811 W. 13 Mile Road, Royal Oak, MI 48073, USA; (A.Y.); (N.S.); (A.K.); (P.R.K.)
| | - Purvesh R. Koladiya
- Metabolomics Department, Corewell Health Research Institute, 3811 W. 13 Mile Road, Royal Oak, MI 48073, USA; (A.Y.); (N.S.); (A.K.); (P.R.K.)
| | - Xiaobei Pan
- Advanced Asset Technology Centre, Institute for Global Food Security, Queen’s University Belfast, Belfast BT9 5DL, UK; (X.P.); (S.M.); (B.D.G.)
| | - Shirin Macias
- Advanced Asset Technology Centre, Institute for Global Food Security, Queen’s University Belfast, Belfast BT9 5DL, UK; (X.P.); (S.M.); (B.D.G.)
| | - Andrew C. Robinson
- Faculty of Biology, Medicine and Health, School of Biological Sciences, Division of Neuroscience, The University of Manchester, Salford Royal Hospital, Salford M6 8HD, UK; (A.C.R.); (D.M.)
| | - David Mann
- Faculty of Biology, Medicine and Health, School of Biological Sciences, Division of Neuroscience, The University of Manchester, Salford Royal Hospital, Salford M6 8HD, UK; (A.C.R.); (D.M.)
| | - Brian D. Green
- Advanced Asset Technology Centre, Institute for Global Food Security, Queen’s University Belfast, Belfast BT9 5DL, UK; (X.P.); (S.M.); (B.D.G.)
| | - Ieva Kerševičiūte
- VUGENE, LLC, 625 Kenmoor Ave Suite 301 PMB 96578, Grand Rapids, MI 49546, USA; (I.K.); (J.G.)
| | - Juozas Gordevičius
- VUGENE, LLC, 625 Kenmoor Ave Suite 301 PMB 96578, Grand Rapids, MI 49546, USA; (I.K.); (J.G.)
| | - Uppala Radhakrishna
- Department of Obstetrics and Gynecology, Corewell Health William Beaumont University Hospital, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA; (S.V.); (U.R.)
| | - Stewart F. Graham
- Department of Obstetrics and Gynecology, Corewell Health William Beaumont University Hospital, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA; (S.V.); (U.R.)
- Metabolomics Department, Corewell Health Research Institute, 3811 W. 13 Mile Road, Royal Oak, MI 48073, USA; (A.Y.); (N.S.); (A.K.); (P.R.K.)
- Department of Obstetrics and Gynecology, Oakland University-William Beaumont School of Medicine, Rochester, MI 48309, USA
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Joshi AD, Rahnavard A, Kachroo P, Mendez KM, Lawrence W, Julián-Serrano S, Hua X, Fuller H, Sinnott-Armstrong N, Tabung FK, Shutta KH, Raffield LM, Darst BF. An epidemiological introduction to human metabolomic investigations. Trends Endocrinol Metab 2023; 34:505-525. [PMID: 37468430 PMCID: PMC10527234 DOI: 10.1016/j.tem.2023.06.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 06/17/2023] [Accepted: 06/19/2023] [Indexed: 07/21/2023]
Abstract
Metabolomics holds great promise for uncovering insights around biological processes impacting disease in human epidemiological studies. Metabolites can be measured across biological samples, including plasma, serum, saliva, urine, stool, and whole organs and tissues, offering a means to characterize metabolic processes relevant to disease etiology and traits of interest. Metabolomic epidemiology studies face unique challenges, such as identifying metabolites from targeted and untargeted assays, defining standards for quality control, harmonizing results across platforms that often capture different metabolites, and developing statistical methods for high-dimensional and correlated metabolomic data. In this review, we introduce metabolomic epidemiology to the broader scientific community, discuss opportunities and challenges presented by these studies, and highlight emerging innovations that hold promise to uncover new biological insights.
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Affiliation(s)
- Amit D Joshi
- Clinical & Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Ali Rahnavard
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Priyadarshini Kachroo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Kevin M Mendez
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Wayne Lawrence
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Sachelly Julián-Serrano
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA; Department of Public Health, University of Massachusetts Lowell, Lowell, MA, USA
| | - Xinwei Hua
- Clinical & Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA; Department of Cardiology, Peking University Third Hospital, Beijing, China
| | - Harriett Fuller
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Nasa Sinnott-Armstrong
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Fred K Tabung
- The Ohio State University College of Medicine and Comprehensive Cancer Center, Columbus, OH, USA
| | - Katherine H Shutta
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Burcu F Darst
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
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Pai MGJ, Biswas D, Verma A, Srivastava S. A proteome-level view of brain tumors for a better understanding of novel diagnosis, prognosis, and therapy. Expert Rev Proteomics 2023; 20:381-395. [PMID: 37970632 DOI: 10.1080/14789450.2023.2283498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 11/01/2023] [Indexed: 11/17/2023]
Abstract
INTRODUCTION Brain tumors are complex and heterogeneous malignancies with significant challenges in diagnosis, prognosis, and therapy. Proteomics, the large-scale study of proteins and their functions, has emerged as a powerful tool to comprehensively investigate the molecular mechanisms underlying brain tumor regulation. AREAS COVERED This review explores brain tumors from a proteomic standpoint, highlighting recent progress and insights gained through proteomic methods. It delves into the proteomic techniques employed and underscores potential biomarkers for early detection, prognosis, and treatment planning. Recent PubMed Central proteomic studies (2017-present) are discussed, summarizing findings on altered protein expression, post-translational changes, and protein interactions. This sheds light on brain tumor signaling pathways and their significance in innovative therapeutic approaches. EXPERT OPINION Proteomics offers immense potential for revolutionizing brain tumor diagnosis and therapy. To unlock its full benefits, further translational research is crucial. Combining proteomics with other omics data enhances our grasp of brain tumors. Validating and translating proteomic biomarkers are vital for better patient results. Challenges include tumor complexity, lack of curated proteomic databases, and the need for collaboration between researchers and clinicians. Overcoming these challenges requires investment in technology, data sharing, and translational research.
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Affiliation(s)
- Medha Gayathri J Pai
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Deeptarup Biswas
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Ayushi Verma
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Sanjeeva Srivastava
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, India
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Swain A, Pan A. Protein Therapeutic Target Candidates Against Acinetobacter baumannii, a Pathogen of Concern to Planetary Health: A Network-Based Integrative Omics Drug Discovery Approach. OMICS 2023; 27:62-74. [PMID: 36735546 DOI: 10.1089/omi.2022.0180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Acinetobacter baumannii, an opportunistic gram-negative pathogen responsible for several nosocomial infections, has developed resistance to various antibiotics. Proteins involved in the two-component system (TCS), virulence, and antibiotic resistance (AR), help this pathogen in regulating antibiotic susceptibility and virulence mechanisms. The present study reports a network-based integrative omics approach to drug discovery to identify key regulatory proteins as therapeutic candidates against A. baumannii. We collected data on the TCS, virulence, and AR proteins from various databases (P2CS, VFDB, ARDB, and PAIDB), which were subjected to network, host-pathogen, and gene expression data analysis. Network analysis identified 43 hubs, and 10 proteins were found to be interacting with human proteins associated with vital pathways. Of the 53 (43 + 10) pathogen proteins, 46 had no orthologs in the human host. Twelve proteins, namely, RpfC, Wzc, OmpR, EnvZ, BfmS, PilG, histidine kinase, ABC 3 transport family protein, outer membrane porin OprD family, CsuD, Pgm, and LpxA, were differentially expressed in the resistant strain. We propose these proteins as key regulators that warrant evaluation as therapeutic target candidates in the future. Furthermore, structure prediction of ABC 3 transport family protein was performed as a case study. The findings from this study are poised to facilitate and inform drug discovery and development against A. baumannii.
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Affiliation(s)
- Aishwarya Swain
- Department of Bioinformatics, School of Life Sciences, Pondicherry University, Puducherry, India
| | - Archana Pan
- Department of Bioinformatics, School of Life Sciences, Pondicherry University, Puducherry, India
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Cominetti O, Agarwal S, Oller-Moreno S. Editorial: Advances in methods and tools for multi-omics data analysis. Front Mol Biosci 2023; 10:1186822. [PMID: 37168260 PMCID: PMC10165066 DOI: 10.3389/fmolb.2023.1186822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 04/14/2023] [Indexed: 05/13/2023] Open
Affiliation(s)
- Ornella Cominetti
- Nestlé Research Center, Lausanne, Switzerland
- *Correspondence: Ornella Cominetti,
| | | | - Sergio Oller-Moreno
- Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology, Barcelona, Spain
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Zhan X, Li N. Editorial: New molecular targets involved in lung adenocarcinoma. Front Endocrinol (Lausanne) 2023; 14:1138849. [PMID: 36755924 PMCID: PMC9900114 DOI: 10.3389/fendo.2023.1138849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 01/16/2023] [Indexed: 01/24/2023] Open
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Zhao LR, Zhang JB, Han W, Zhu L, Chen T, Guan FL. Application Prospect of Integrative Omics in Forensic Identification of Methamphetamine-Associated Psychosis. Fa Yi Xue Za Zhi 2022; 38:650-656. [PMID: 36727182 DOI: 10.12116/j.issn.1004-5619.2021.101201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
The clinical symptoms and signs of methamphetamine-associated psychosis (MAP) and schizophrenia are highly similar, but the situation is completely different when MAP and schizophrenia patients need to be assessed for criminal responsibility after they comitted a harmful behavior. Therefore, the distinction between the two psychoses is very important in forensic psychiatry. At present, the identification of these two psychoses is mainly dependent on the corresponding criteria such as the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) and the Chinese Classification of Mental Disorders Version 3 (CCMD-3). It's challenging to diagnose and distinguish between the two in practical cases due to their similar clinical symptoms and the lack of effective objective indexes. Different from the limitations of single omics, integrative omics intergrates data from multiple dimensions and has been extensively studied in the field of schizophrenia and has achieved some preliminary results. In view of the correlation between MAP and schizophrenia and the potential application value of integrative omics, this paper proposes an integrative omics strategy for MAP pathogenesis and forensic identification, aiming to improve the further understanding of the relationship between the two psychoses and the corresponding pathogenesis. It also provides references for the future exploration of integrative omics in forensic precise identification and effective monitoring and early warning methods.
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Affiliation(s)
- Long-Rui Zhao
- College of Medicine and Forensics, Xi'an Jiaotong University Health Science Center, Shaanxi 710061, China
| | - Jian-Bo Zhang
- College of Medicine and Forensics, Xi'an Jiaotong University Health Science Center, Shaanxi 710061, China
| | - Wei Han
- College of Medicine and Forensics, Xi'an Jiaotong University Health Science Center, Shaanxi 710061, China
| | - Li Zhu
- College of Medicine and Forensics, Xi'an Jiaotong University Health Science Center, Shaanxi 710061, China
| | - Teng Chen
- College of Medicine and Forensics, Xi'an Jiaotong University Health Science Center, Shaanxi 710061, China
| | - Fang-Lin Guan
- College of Medicine and Forensics, Xi'an Jiaotong University Health Science Center, Shaanxi 710061, China
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Haq IU, Ijaz S, Khan NA, Khan IA, Ali HM, Moya-Elizondo EA. Integrative Pathogenicity Assay and Operational Taxonomy-Based Detection of New Forma Specialis of Fusarium oxysporum Causing Datepalm Wilt. Plants (Basel) 2022; 11:2643. [PMID: 36235510 PMCID: PMC9571862 DOI: 10.3390/plants11192643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 09/28/2022] [Accepted: 10/04/2022] [Indexed: 06/16/2023]
Abstract
Pathogenicity-associated genes are highly host-specific and contribute to host-specific virulence. We tailored the traditional Koch's postulates with integrative omics by hypothesizing that the effector genes associated with host-pathogenicity are determinant markers for virulence, and developed Integrative Pathogenicity (IP) postulates for authenticated pathogenicity testing in plants. To set the criteria, we experimented on datepalm (Phoenix dactylifera) for the vascular wilt pathogen and confirmed the pathogen based on secreted in xylem genes (effectors genes) using genomic and transcriptomic approaches, and found it a reliable solution when pathogenicity is in question. The genic regions ITS, TEF1-α, and RPBII of Fusarium isolates were examined by phylogenetic analysis to unveil the validated operational taxonomy at the species level. The hierarchical tree generated through phylogenetic analysis declared the fungal pathogen as Fusarium oxysporum. Moreover, the Fusarium isolates were investigated at the subspecies level by probing the IGS, TEF1-α, and Pgx4 genic regions to detect the forma specialis of F. oxysporum that causes wilt in datepalm. The phylogram revealed a new forma specialis in F. oxysporum that causes vascular wilt in datepalm.
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Affiliation(s)
- Imran Ul Haq
- Department of Plant Pathology, University of Agriculture Faisalabad, Faisalabad 38040, Pakistan
| | - Siddra Ijaz
- Centre of Agricultural Biochemistry and Biotechnology, University of Agriculture Faisalabad, Faisalabad 38040, Pakistan
| | - Nabeeha Aslam Khan
- Department of Plant Pathology, University of Agriculture Faisalabad, Faisalabad 38040, Pakistan
| | - Iqrar Ahmad Khan
- Institute of Horticultural Sciences, University of Agriculture Faisalabad, Faisalabad 38040, Pakistan
| | - Hayssam M. Ali
- Botany and Microbiology Department, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
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Li RJ, Jie ZY, Feng Q, Fang RL, Li F, Gao Y, Xia HH, Zhong HZ, Tong B, Madsen L, Zhang JH, Liu CL, Xu ZG, Wang J, Yang HM, Xu X, Hou Y, Brix S, Kristiansen K, Yu XL, Jia HJ, He KL. Network of Interactions Between Gut Microbiome, Host Biomarkers, and Urine Metabolome in Carotid Atherosclerosis. Front Cell Infect Microbiol 2021; 11:708088. [PMID: 34692558 PMCID: PMC8529068 DOI: 10.3389/fcimb.2021.708088] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 09/21/2021] [Indexed: 01/06/2023] Open
Abstract
Comprehensive analyses of multi-omics data may provide insights into interactions between different biological layers concerning distinct clinical features. We integrated data on the gut microbiota, blood parameters and urine metabolites of treatment-naive individuals presenting a wide range of metabolic disease phenotypes to delineate clinically meaningful associations. Trans-omics correlation networks revealed that candidate gut microbial biomarkers and urine metabolite feature were covaried with distinct clinical phenotypes. Integration of the gut microbiome, the urine metabolome and the phenome revealed that variations in one of these three systems correlated with changes in the other two. In a specific note about clinical parameters of liver function, we identified Eubacteriumeligens, Faecalibacteriumprausnitzii and Ruminococcuslactaris to be associated with a healthy liver function, whereas Clostridium bolteae, Tyzzerellanexills, Ruminococcusgnavus, Blautiahansenii, and Atopobiumparvulum were associated with blood biomarkers for liver diseases. Variations in these microbiota features paralleled changes in specific urine metabolites. Network modeling yielded two core clusters including one large gut microbe-urine metabolite close-knit cluster and one triangular cluster composed of a gut microbe-blood-urine network, demonstrating close inter-system crosstalk especially between the gut microbiome and the urine metabolome. Distinct clinical phenotypes are manifested in both the gut microbiome and the urine metabolome, and inter-domain connectivity takes the form of high-dimensional networks. Such networks may further our understanding of complex biological systems, and may provide a basis for identifying biomarkers for diseases. Deciphering the complexity of human physiology and disease requires a holistic and trans-omics approach integrating multi-layer data sets, including the gut microbiome and profiles of biological fluids. By studying the gut microbiome on carotid atherosclerosis, we identified microbial features associated with clinical parameters, and we observed that groups of urine metabolites correlated with groups of clinical parameters. Combining the three data sets, we revealed correlations of entities across the three systems, suggesting that physiological changes are reflected in each of the omics. Our findings provided insights into the interactive network between the gut microbiome, blood clinical parameters and the urine metabolome concerning physiological variations, and showed the promise of trans-omics study for biomarker discovery.
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Affiliation(s)
- Rui-Jun Li
- Department of Geriatric Cardiology, the Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China.,Beijing Key Laboratory of Chronic Heart Failure Precision Medicine, Chinese PLA General Hospital, Beijing, China
| | - Zhu-Ye Jie
- Department of Research, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,China National GeneBank, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,Shenzhen Key Laboratory of Human Commensal Microorganisms and Health Research, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China
| | - Qiang Feng
- Department of Research, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,China National GeneBank, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,Shenzhen Engineering Laboratory of Detection and Intervention of Human Intestinal Microbiome, Shenzhen, China.,Department of Biology, Laboratory of Genomics and Molecular Biomedicine, University of Copenhagen, Copenhagen, Denmark.,Department of Human Microbiome, School of Stomatology, Shandong University, Shandong Provincial Key Laboratory of Oral Tissue Regeneration, Jinan, China
| | - Rui-Ling Fang
- Department of Research, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China
| | - Fei Li
- Department of Research, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,China National GeneBank, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China
| | - Yuan Gao
- Department of Research, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,China National GeneBank, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China
| | - Hui-Hua Xia
- Department of Research, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,China National GeneBank, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China
| | - Huan-Zi Zhong
- Department of Research, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,China National GeneBank, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,Shenzhen Key Laboratory of Human Commensal Microorganisms and Health Research, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,Department of Biology, Laboratory of Genomics and Molecular Biomedicine, University of Copenhagen, Copenhagen, Denmark
| | - Bin Tong
- Department of Research, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,China National GeneBank, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China
| | - Lise Madsen
- Department of Research, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,Department of Biology, Laboratory of Genomics and Molecular Biomedicine, University of Copenhagen, Copenhagen, Denmark.,Institute Marine Research (IMR), Bergen, Norway
| | - Jia-Hao Zhang
- Department of Research, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,China National GeneBank, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China
| | - Chun-Lei Liu
- Beijing Key Laboratory of Chronic Heart Failure Precision Medicine, Chinese PLA General Hospital, Beijing, China
| | - Zhen-Guo Xu
- Beijing Key Laboratory of Chronic Heart Failure Precision Medicine, Chinese PLA General Hospital, Beijing, China
| | - Jian Wang
- Department of Research, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,James D. Watson Institute of Genome Sciences, Hangzhou, China
| | - Huan-Ming Yang
- Department of Research, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,James D. Watson Institute of Genome Sciences, Hangzhou, China
| | - Xun Xu
- Department of Research, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,China National GeneBank, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China
| | - Yong Hou
- Department of Research, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,China National GeneBank, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China
| | - Susanne Brix
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Karsten Kristiansen
- Department of Research, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,China National GeneBank, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,Department of Biology, Laboratory of Genomics and Molecular Biomedicine, University of Copenhagen, Copenhagen, Denmark
| | - Xin-Lei Yu
- Department of Research, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,China National GeneBank, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China
| | - Hui-Jue Jia
- Department of Research, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,China National GeneBank, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,Shenzhen Key Laboratory of Human Commensal Microorganisms and Health Research, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,Macau University of Science and Technology, Macau, China
| | - Kun-Lun He
- Beijing Key Laboratory of Chronic Heart Failure Precision Medicine, Chinese PLA General Hospital, Beijing, China.,Analysis Center of Big Data, Medical Innovation Research Division, Chinese PLA General Hospital, Beijing, China
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13
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Abstract
Microbial specialized metabolites are key mediators in host-microbiome interactions. Most of the chemical space produced by the microbiome currently remains unexplored and uncharacterized. This situation calls for new and improved methods to exploit the growing publicly available genomic and metabolomic data sets and connect the outcomes to structural and functional knowledge inferred from transcriptomics and proteomics experiments. Here, we first describe currently available approaches that support the comprehensive mining of metabolomics and genomics data. Next, we provide our vision on how to move forward toward the automated linking of omics data of specialized metabolites to their structures, biosynthesis pathways, producers, and functions.
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14
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Rai M, Rai A, Mori T, Nakabayashi R, Yamamoto M, Nakamura M, Suzuki H, Saito K, Yamazaki M. Gene-Metabolite Network Analysis Revealed Tissue-Specific Accumulation of Therapeutic Metabolites in Mallotus japonicus. Int J Mol Sci 2021; 22:8835. [PMID: 34445541 DOI: 10.3390/ijms22168835] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 08/11/2021] [Accepted: 08/12/2021] [Indexed: 02/06/2023] Open
Abstract
Mallotus japonicus is a valuable traditional medicinal plant in East Asia for applications as a gastrointestinal drug. However, the molecular components involved in the biosynthesis of bioactive metabolites have not yet been explored, primarily due to a lack of omics resources. In this study, we established metabolome and transcriptome resources for M. japonicus to capture the diverse metabolite constituents and active transcripts involved in its biosynthesis and regulation. A combination of untargeted metabolite profiling with data-dependent metabolite fragmentation and metabolite annotation through manual curation and feature-based molecular networking established an overall metabospace of M. japonicus represented by 2129 metabolite features. M. japonicus de novo transcriptome assembly showed 96.9% transcriptome completeness, representing 226,250 active transcripts across seven tissues. We identified specialized metabolites biosynthesis in a tissue-specific manner, with a strong correlation between transcripts expression and metabolite accumulations in M. japonicus. The correlation- and network-based integration of metabolome and transcriptome datasets identified candidate genes involved in the biosynthesis of key specialized metabolites of M. japonicus. We further used phylogenetic analysis to identify 13 C-glycosyltransferases and 11 methyltransferases coding candidate genes involved in the biosynthesis of medicinally important bergenin. This study provides comprehensive, high-quality multi-omics resources to further investigate biological properties of specialized metabolites biosynthesis in M. japonicus.
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15
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Rai A, Rai M, Kamochi H, Mori T, Nakabayashi R, Nakamura M, Suzuki H, Saito K, Yamazaki M. Multiomics-based characterization of specialized metabolites biosynthesis in Cornus Officinalis. DNA Res 2021; 27:5840485. [PMID: 32426807 PMCID: PMC7320821 DOI: 10.1093/dnares/dsaa009] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Accepted: 05/12/2020] [Indexed: 12/14/2022] Open
Abstract
Cornus officinalis, an important traditional medicinal plant, is used as major constituents of tonics, analgesics, and diuretics. While several studies have focused on its characteristic bioactive compounds, little is known on their biosynthesis. In this study, we performed LC-QTOF-MS-based metabolome and RNA-seq-based transcriptome profiling for seven tissues of C. officinalis. Untargeted metabolome analysis assigned chemical identities to 1,215 metabolites and showed tissue-specific accumulation for specialized metabolites with medicinal properties. De novo transcriptome assembly established for C. officinalis showed 96% of transcriptome completeness. Co-expression analysis identified candidate genes involved in the biosynthesis of iridoids, triterpenoids, and gallotannins, the major group of bioactive metabolites identified in C. officinalis. Integrative omics analysis identified 45 cytochrome P450s genes correlated with iridoids accumulation in C. officinalis. Network-based integration of genes assigned to iridoids biosynthesis pathways with these candidate CYPs further identified seven promising CYPs associated with iridoids’ metabolism. This study provides a valuable resource for further investigation of specialized metabolites’ biosynthesis in C. officinalis.
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Affiliation(s)
- Amit Rai
- Graduate School of Pharmaceutical Sciences, Chiba University, Chiba 260-8675, Japan.,Plant Molecular Science Center, Chiba University, Chiba 260-8675, Japan
| | - Megha Rai
- Graduate School of Pharmaceutical Sciences, Chiba University, Chiba 260-8675, Japan
| | - Hidetaka Kamochi
- Graduate School of Pharmaceutical Sciences, Chiba University, Chiba 260-8675, Japan
| | - Tetsuya Mori
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan
| | - Ryo Nakabayashi
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan
| | - Michimi Nakamura
- Graduate School of Pharmaceutical Sciences, Chiba University, Chiba 260-8675, Japan
| | - Hideyuki Suzuki
- Department of Research and Development, Kazusa DNA Research Institute, Kisarazu, Chiba 292-0818, Japan
| | - Kazuki Saito
- Graduate School of Pharmaceutical Sciences, Chiba University, Chiba 260-8675, Japan.,Plant Molecular Science Center, Chiba University, Chiba 260-8675, Japan.,RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan
| | - Mami Yamazaki
- Graduate School of Pharmaceutical Sciences, Chiba University, Chiba 260-8675, Japan.,Plant Molecular Science Center, Chiba University, Chiba 260-8675, Japan
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16
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Oldham WM, Hemnes AR, Aldred MA, Barnard J, Brittain EL, Chan SY, Cheng F, Cho MH, Desai AA, Garcia JGN, Geraci MW, Ghiassian SD, Hall KT, Horn EM, Jain M, Kelly RS, Leopold JA, Lindstrom S, Modena BD, Nichols WC, Rhodes CJ, Sun W, Sweatt AJ, Vanderpool RR, Wilkins MR, Wilmot B, Zamanian RT, Fessel JP, Aggarwal NR, Loscalzo J, Xiao L. NHLBI-CMREF Workshop Report on Pulmonary Vascular Disease Classification: JACC State-of-the-Art Review. J Am Coll Cardiol 2021; 77:2040-2052. [PMID: 33888254 PMCID: PMC8065203 DOI: 10.1016/j.jacc.2021.02.056] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 02/16/2021] [Accepted: 02/22/2021] [Indexed: 12/12/2022]
Abstract
The National Heart, Lung, and Blood Institute and the Cardiovascular Medical Research and Education Fund held a workshop on the application of pulmonary vascular disease omics data to the understanding, prevention, and treatment of pulmonary vascular disease. Experts in pulmonary vascular disease, omics, and data analytics met to identify knowledge gaps and formulate ideas for future research priorities in pulmonary vascular disease in line with National Heart, Lung, and Blood Institute Strategic Vision goals. The group identified opportunities to develop analytic approaches to multiomic datasets, to identify molecular pathways in pulmonary vascular disease pathobiology, and to link novel phenotypes to meaningful clinical outcomes. The committee suggested support for interdisciplinary research teams to develop and validate analytic methods, a national effort to coordinate biosamples and data, a consortium of preclinical investigators to expedite target evaluation and drug development, longitudinal assessment of molecular biomarkers in clinical trials, and a task force to develop a master clinical trials protocol for pulmonary vascular disease.
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Affiliation(s)
- William M Oldham
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.
| | - Anna R Hemnes
- Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | | | - John Barnard
- Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Evan L Brittain
- Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Stephen Y Chan
- Center for Pulmonary Vascular Biology and Medicine, Pittsburgh Heart, Lung, Blood, and Vascular Medicine Institute, Division of Cardiology, Department of Medicine, University of Pittsburgh and University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA
| | - Feixiong Cheng
- Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Michael H Cho
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Ankit A Desai
- Indiana University School of Medicine, Indianapolis, Indiana, USA
| | | | - Mark W Geraci
- Department of Medicine, University of Pittsburgh and University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA
| | | | - Kathryn T Hall
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Evelyn M Horn
- Weill Cornell Medical Center, New York, New York, USA
| | - Mohit Jain
- University of California at San Diego, San Diego, California, USA
| | - Rachel S Kelly
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Jane A Leopold
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | | | | | - William C Nichols
- Cincinnati Children's Hospital Medical Center and the University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | | | - Wei Sun
- Center for Pulmonary Vascular Biology and Medicine, Pittsburgh Heart, Lung, Blood, and Vascular Medicine Institute, Division of Cardiology, Department of Medicine, University of Pittsburgh and University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA
| | - Andrew J Sweatt
- Stanford University School of Medicine, Stanford, California, USA
| | - Rebecca R Vanderpool
- Department of Medicine, University of Pittsburgh and University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA
| | | | - Beth Wilmot
- Division of Geriatrics and Clinical Gerontology, National Institute on Aging and the School of Medicine, Oregon Health and Science University, Portland, Oregon, USA
| | - Roham T Zamanian
- Stanford University School of Medicine, Stanford, California, USA
| | - Joshua P Fessel
- Division of Lung Diseases, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Neil R Aggarwal
- Division of Lung Diseases, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Joseph Loscalzo
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Lei Xiao
- Division of Lung Diseases, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA.
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17
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Sun R, Xu M, Li X, Gaynor S, Zhou H, Li Z, Bossé Y, Lam S, Tsao MS, Tardon A, Chen C, Doherty J, Goodman G, Bojesen SE, Landi MT, Johansson M, Field JK, Bickeböller H, Wichmann HE, Risch A, Rennert G, Arnold S, Wu X, Melander O, Brunnström H, Le Marchand L, Liu G, Andrew A, Duell E, Kiemeney LA, Shen H, Haugen A, Johansson M, Grankvist K, Caporaso N, Woll P, Teare MD, Scelo G, Hong YC, Yuan JM, Lazarus P, Schabath MB, Aldrich MC, Albanes D, Mak R, Barbie D, Brennan P, Hung RJ, Amos CI, Christiani DC, Lin X. Integration of multiomic annotation data to prioritize and characterize inflammation and immune-related risk variants in squamous cell lung cancer. Genet Epidemiol 2021; 45:99-114. [PMID: 32924180 PMCID: PMC7855632 DOI: 10.1002/gepi.22358] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 08/09/2020] [Accepted: 08/27/2020] [Indexed: 02/05/2023]
Abstract
Clinical trial results have recently demonstrated that inhibiting inflammation by targeting the interleukin-1β pathway can offer a significant reduction in lung cancer incidence and mortality, highlighting a pressing and unmet need to understand the benefits of inflammation-focused lung cancer therapies at the genetic level. While numerous genome-wide association studies (GWAS) have explored the genetic etiology of lung cancer, there remains a large gap between the type of information that may be gleaned from an association study and the depth of understanding necessary to explain and drive translational findings. Thus, in this study we jointly model and integrate extensive multiomics data sources, utilizing a total of 40 genome-wide functional annotations that augment previously published results from the International Lung Cancer Consortium (ILCCO) GWAS, to prioritize and characterize single nucleotide polymorphisms (SNPs) that increase risk of squamous cell lung cancer through the inflammatory and immune responses. Our work bridges the gap between correlative analysis and translational follow-up research, refining GWAS association measures in an interpretable and systematic manner. In particular, reanalysis of the ILCCO data highlights the impact of highly associated SNPs from nuclear factor-κB signaling pathway genes as well as major histocompatibility complex mediated variation in immune responses. One consequence of prioritizing likely functional SNPs is the pruning of variants that might be selected for follow-up work by over an order of magnitude, from potentially tens of thousands to hundreds. The strategies we introduce provide informative and interpretable approaches for incorporating extensive genome-wide annotation data in analysis of genetic association studies.
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Affiliation(s)
- Ryan Sun
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Miao Xu
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Xihao Li
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Sheila Gaynor
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Hufeng Zhou
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Zilin Li
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Yohan Bossé
- Institut Universitaire de Cardiologie et de Pneumologie de Quebec, Quebec, Canada
| | - Stephen Lam
- British Columbia Cancer Agency, University of British Columbia, Vancouver, Canada
| | - Ming-Sound Tsao
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - Adonina Tardon
- Faculty of Medicine, University of Oviedo and CIBERESP, Oviedo, Spain
| | - Chu Chen
- Department of Epidemiology, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Jennifer Doherty
- Department of Epidemiology, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
- Department of Epidemiology, Geisel School of Medicine, Hanover, New Hampshire, United States of America
| | - Gary Goodman
- Department of Medical Oncology, Swedish Medical Group, Seattle, Washington, United States of America
| | - Stig Egil Bojesen
- Copenhagen General Population Study, Herlev and Gentofte Hospital, Herlev, Denmark
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Herlev, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Maria Teresa Landi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Mattias Johansson
- Genetic Epidemiology Group, International Agency for Research on Cancer, Lyon, France
| | - John K. Field
- Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom
| | - Heike Bickeböller
- Department of Genetic Epidemiology, University Medical Center, Georg-August-University, Göttingen, Germany
| | - H-Erich Wichmann
- Institute of Medical Informatics, Biometry and Epidemiology, Ludwig Maximilians University, Munich, Germany
- Institute of Epidemiology, Helmholtz Center Munich, Neuherberg, Germany
- Institute of Medical Statistics and Epidemiology, Technical University Munich, Munich, Germany
| | - Angela Risch
- Cancer Cluster Salzburg, University of Salzburg, Salzburg, Austria
- Translational Lung Research Center Heidelberg, University Hospital Heidelberg, Heidelberg, Germany
- Translational Lung Research Center, German Center for Lung Research, Heidelberg, Germany
| | - Gadi Rennert
- Clalit National Cancer Control Center, Carmel Medical Center, Haifa, Israel
| | - Suzanne Arnold
- Markey Cancer Center, University of Kentucky, Lexington, Kentucky, United States of America
| | - Xifeng Wu
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Olle Melander
- Unit of Nutrition and Cancer, Catalan Institute of Oncology Barcelona, Spain
- Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Hans Brunnström
- Laboratory Medicine Region, Skäne University Hospital, Lund, Sweden
| | - Loic Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii, United States of America
| | - Geoffrey Liu
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Canada
| | - Angeline Andrew
- Department of Epidemiology, Geisel School of Medicine, Hanover, New Hampshire, United States of America
| | - Eric Duell
- Unit of Nutrition and Cancer, Catalan Institute of Oncology Barcelona, Spain
| | - Lambertus A. Kiemeney
- Faculty of Medical Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Hongbing Shen
- Department of Epidemiology and Biostatistics, Nanjing Medical University, Nanjing, China
| | - Aage Haugen
- National Institute of Occupational Health, Oslo, Norway
| | | | - Kjell Grankvist
- Department of Medical Biosciences, Umeä University, Umeä, Sweden
| | - Neil Caporaso
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Penella Woll
- Department of Oncology, University of Sheffield, Sheffield, United Kingdom
| | - M. Dawn Teare
- School of Health and Related Research, University of Sheffield, Sheffield, United Kingdom
| | - Ghislaine Scelo
- Genetic Epidemiology Group, International Agency for Research on Cancer, Lyon, France
| | - Yun-Chul Hong
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jian-Min Yuan
- University of Pittsburgh Medical Center Hillman Cancer Center, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America
| | - Philip Lazarus
- Department of Pharmaceutical Sciences, Washington State University, Spokane, Washington, United States of America
| | - Matthew B. Schabath
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, United States of America
| | - Melinda C. Aldrich
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Demetrios Albanes
- Russian N.N. Blokhin Cancer Research Centre, Russian Academy of Medical Sciences, Moscow, Russia
| | - Raymond Mak
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America
| | - David Barbie
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America
| | - Paul Brennan
- Genetic Epidemiology Group, International Agency for Research on Cancer, Lyon, France
| | - Rayjean J. Hung
- Prosserman Centre for Population Health Research, Sinai Health System, Toronto, Canada
| | - Christopher I. Amos
- Dan L. Duncan Comprehensive Cancer Center and Department of Medicine, Baylor College of Medicine, Houston, Texas, United States of America
| | - David C. Christiani
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Xihong Lin
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Department of Statistics, Harvard University, Cambridge, Massachusetts, United States of America
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18
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Li C, Li Y, Qin G, Chen Z, Qu M, Zhang B, Han X, Wang X, Qian PY, Lin Q. Regulatory Role of Retinoic Acid in Male Pregnancy of the Seahorse. ACTA ACUST UNITED AC 2020; 1:100052. [PMID: 34557717 PMCID: PMC8454549 DOI: 10.1016/j.xinn.2020.100052] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Accepted: 10/12/2020] [Indexed: 02/01/2023]
Abstract
Seahorses epitomize the exuberance of evolution. They have the unique characteristic of male pregnancy, which includes the carrying of many embryos in a brood pouch that incubates and nourishes the embryos, similar to the mammalian placenta. However, the regulatory networks underlying brood pouch formation and pregnancy remain largely unknown. In this study, comparative transcriptomic and metabolomic profiling on the lined seahorse Hippocampus erectus, with unformed, newly formed, and pregnant brood pouches identified a total of 141 and 2,533 differentially expressed genes together with 73 and 121 significantly differential metabolites related to brood pouch formation and pregnancy, respectively. Specifically, integrative omics analysis revealed that retinoic acid (RA) synthesis and signaling pathway played essential roles in the formation of the brood pouch and pregnancy. RA might function upstream of testosterone and progesterone, thereby directly influencing brood pouch formation by regulating the expression of fshr and cyp7a1. Our results also revealed that RA regulates antioxidant defenses, particularly during male pregnancy. Alternatively, pregnancy caused a consistent decrease in RA, canthaxanthin, astaxanthin, and glutathione synthetase, and an increase in susceptibility to oxidative stress, which may balance brood pouch development and reproduction in seahorses and pave the way to successful gestation.
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Affiliation(s)
- Chunyan Li
- CAS Key Laboratory of Tropical Marine Bio-Resources and Ecology, South China Sea Institute of Oceanology, Innovation of South China Sea Ecology and Environmental Engineering, Chinese Academy of Sciences, 510301 Guangzhou, China.,Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), 511458 Guangzhou, China
| | - Yongxin Li
- Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), 511458 Guangzhou, China.,Department of Chemistry, The University of Hong Kong, Pokfulam, Hong Kong, Hong Kong SAR, China
| | - Geng Qin
- CAS Key Laboratory of Tropical Marine Bio-Resources and Ecology, South China Sea Institute of Oceanology, Innovation of South China Sea Ecology and Environmental Engineering, Chinese Academy of Sciences, 510301 Guangzhou, China
| | - Zelin Chen
- CAS Key Laboratory of Tropical Marine Bio-Resources and Ecology, South China Sea Institute of Oceanology, Innovation of South China Sea Ecology and Environmental Engineering, Chinese Academy of Sciences, 510301 Guangzhou, China
| | - Meng Qu
- CAS Key Laboratory of Tropical Marine Bio-Resources and Ecology, South China Sea Institute of Oceanology, Innovation of South China Sea Ecology and Environmental Engineering, Chinese Academy of Sciences, 510301 Guangzhou, China
| | - Bo Zhang
- CAS Key Laboratory of Tropical Marine Bio-Resources and Ecology, South China Sea Institute of Oceanology, Innovation of South China Sea Ecology and Environmental Engineering, Chinese Academy of Sciences, 510301 Guangzhou, China
| | - Xue Han
- CAS Key Laboratory of Tropical Marine Bio-Resources and Ecology, South China Sea Institute of Oceanology, Innovation of South China Sea Ecology and Environmental Engineering, Chinese Academy of Sciences, 510301 Guangzhou, China
| | - Xin Wang
- CAS Key Laboratory of Tropical Marine Bio-Resources and Ecology, South China Sea Institute of Oceanology, Innovation of South China Sea Ecology and Environmental Engineering, Chinese Academy of Sciences, 510301 Guangzhou, China
| | - Pei-Yuan Qian
- Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), 511458 Guangzhou, China.,Department of Ocean Science and Hong Kong Branch of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Hong Kong University of Science and Technology, Kowloon, Hong Kong, China
| | - Qiang Lin
- CAS Key Laboratory of Tropical Marine Bio-Resources and Ecology, South China Sea Institute of Oceanology, Innovation of South China Sea Ecology and Environmental Engineering, Chinese Academy of Sciences, 510301 Guangzhou, China.,Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), 511458 Guangzhou, China
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19
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Shao C, Lu W, Wan N, Wu M, Bao Q, Tian Y, Lu G, Wang N, Hao H, Ye H. Integrative Omics Analysis Revealed that Metabolic Intervention Combined with Metronomic Chemotherapy Selectively Kills Cancer Cells. J Proteome Res 2019; 18:2643-2653. [PMID: 31094201 DOI: 10.1021/acs.jproteome.9b00138] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Metronomic chemotherapy, a relatively new dosing paradigm for anticancer therapy, is an alternative to traditional chemotherapy that uses maximal tolerated dose (MTD). Although these two dosing regimens both lead to tumor cell death, how cell metabolism is differentially affected during apoptosis remains elusive. Herein, we employed metabolomics to monitor the metabolic profiles of MCF-7 cells in response to the two dosing regimens that mimic MTD and MN treatments using a model chemotherapeutic drug, doxorubicin (Dox), and correlated the changes of metabolic genes examined by PCR array to integratively describe the reprogrammed metabolic patterns. We found glycolysis, amino acid, and nucleotide synthesis-associated metabolic pathways were activated in response to the MN treatment, whereas these pathways were inhibited in a pronounced way in response to the MTD treatment. Direct supplementation of key metabolites and pharmacological modulation of targeted metabolic enzymes can both regulate cell fates. Subsequently, we tested the combined use of MN dosing with targeted metabolic intervention using a normal cell line and found the combined treatment hardly affected its apoptotic rate. Our in vitro findings using MCF-7 and MCF-10A cells thus suggest the promising perspective of combining MN dosing of chemotherapeutic agents with metabolic modulation to selectively kill cancer cells rather than normal cells.
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Affiliation(s)
| | | | | | - Mengqiu Wu
- Department of Nephrology , Children's Hospital of Nanjing Medical University , Guangzhou Road No. 72 , Nanjing 210008 , China
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20
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Abstract
HIV infection has been associated with alterations in gut microbiota and related microbial metabolite production. However, the mechanisms of how these functional microbial metabolites may affect HIV immunopathogenesis and comorbidities, such as cardiovascular disease and other metabolic diseases, remain largely unknown. Here we review the current understanding of gut microbiota and related metabolites in the context of HIV infection. We focus on several bacteria-produced metabolites, including tryptophan catabolites, short-chain fatty acids and trimethylamine-N-oxide (TMAO), and discuss their implications in HIV infection and comorbidities. We also prospect future studies using integrative multiomics approaches to better understand host-microbiota-metabolites interactions in HIV infection, and facilitate integrative medicine utilizing the microbiota in HIV infection.
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Affiliation(s)
- Zheng Wang
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Qibin Qi
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA
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21
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Moritz CP, Mühlhaus T, Tenzer S, Schulenborg T, Friauf E. Poor transcript-protein correlation in the brain: negatively correlating gene products reveal neuronal polarity as a potential cause. J Neurochem 2019; 149:582-604. [PMID: 30664243 DOI: 10.1111/jnc.14664] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Revised: 12/15/2018] [Accepted: 01/02/2019] [Indexed: 01/02/2023]
Abstract
Transcription, translation, and turnover of transcripts and proteins are essential for cellular function. The contribution of those factors to protein levels is under debate, as transcript levels and cognate protein levels do not necessarily correlate due to regulation of translation and protein turnover. Here we propose neuronal polarity as a third factor that is particularly evident in the CNS, leading to considerable distances between somata and axon terminals. Consequently, transcript levels may negatively correlate with cognate protein levels in CNS regions, i.e., transcript and protein levels behave reciprocally. To test this hypothesis, we performed an integrative inter-omics study and analyzed three interconnected rat auditory brainstem regions (cochlear nuclear complex, CN; superior olivary complex, SOC; inferior colliculus, IC) and the rest of the brain as a reference. We obtained transcript and protein sets in these regions of interest (ROIs) by DNA microarrays and label-free mass spectrometry, and performed principal component and correlation analyses. We found 508 transcript|protein pairs and detected poor to moderate transcript|protein correlation in all ROIs, as evidenced by coefficients of determination from 0.34 to 0.54. We identified 57-80 negatively correlating gene products in the ROIs and intensively analyzed four of them for which the correlation was poorest. Three cognate proteins (Slc6a11, Syngr1, Tppp) were synaptic and hence candidates for a negative correlation because of protein transport into axon terminals. Thus, we systematically analyzed the negatively correlating gene products. Gene ontology analyses revealed overrepresented transport/synapse-related proteins, supporting our hypothesis. We present 30 synapse/transport-related proteins with poor transcript|protein correlation. In conclusion, our analyses support that protein transport in polar cells is a third factor that influences the protein level and, thereby, the transcript|protein correlation. OPEN SCIENCE BADGES: This article has received a badge for *Open Materials* and *Open Data* because it provided all relevant information to reproduce the study in the manuscript and because it made the data publicly available. The data can be accessed at https://osf.io/ha28n/. The complete Open Science Disclosure form for this article can be found at the end of the article. More information about the Open Practices badges can be found at https://cos.io/our-services/open-science-badges/.
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Affiliation(s)
- Christian P Moritz
- Animal Physiology Group, Department of Biology, University of Kaiserslautern, Kaiserslautern, Germany.,Synaptopathies and Autoantibodies, Institut NeuroMyoGène INSERM U1217/ CNRS, UMR 5310, Faculty of Medicine, University Jean Monnet, Saint-Étienne, France
| | - Timo Mühlhaus
- Computational Systems Biology, Department of Biology, University of Kaiserslautern, Kaiserslautern, Germany
| | - Stefan Tenzer
- Institute of Immunology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Thomas Schulenborg
- Animal Physiology Group, Department of Biology, University of Kaiserslautern, Kaiserslautern, Germany.,Division of Allergology, Paul-Ehrlich-Institut, Langen, Germany
| | - Eckhard Friauf
- Animal Physiology Group, Department of Biology, University of Kaiserslautern, Kaiserslautern, Germany
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22
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Maldonado EM, Taha F, Rahman J, Rahman S. Systems Biology Approaches Toward Understanding Primary Mitochondrial Diseases. Front Genet 2019; 10:19. [PMID: 30774647 PMCID: PMC6367241 DOI: 10.3389/fgene.2019.00019] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Accepted: 01/14/2019] [Indexed: 12/14/2022] Open
Abstract
Primary mitochondrial diseases form one of the most common and severe groups of genetic disease, with a birth prevalence of at least 1 in 5000. These disorders are multi-genic and multi-phenotypic (even within the same gene defect) and span the entire age range from prenatal to late adult onset. Mitochondrial disease typically affects one or multiple high-energy demanding organs, and is frequently fatal in early life. Unfortunately, to date there are no known curative therapies, mostly owing to the rarity and heterogeneity of individual mitochondrial diseases, leading to diagnostic odysseys and difficulties in clinical trial design. This review aims to discuss recent advances and challenges of systems approaches for the study of primary mitochondrial diseases. Although there has been an explosion in the generation of omics data, few studies have progressed toward the integration of multiple levels of omics. It is evident that the integration of different types of data to create a more complete representation of biology remains challenging, perhaps due to the scarcity of available integrative tools and the complexity inherent in their use. In addition, "bottom-up" systems approaches have been adopted for use in the iterative cycle of systems biology: from data generation to model prediction and validation. Primary mitochondrial diseases, owing to their complex nature, will most likely benefit from a multidisciplinary approach encompassing clinical, molecular and computational studies integrated together by systems biology to elucidate underlying pathomechanisms for better diagnostics and therapeutic discovery. Just as next generation sequencing has rapidly increased diagnostic rates from approximately 5% up to 60% over two decades, more recent advancing technologies are encouraging; the generation of multi-omics, the integration of multiple types of data, and the ability to predict perturbations will, ultimately, be translated into improved patient care.
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Affiliation(s)
- Elaina M. Maldonado
- Mitochondrial Research Group, UCL Great Ormond Street Institute of Child Health, London, United Kingdom
| | - Fatma Taha
- Mitochondrial Research Group, UCL Great Ormond Street Institute of Child Health, London, United Kingdom
| | - Joyeeta Rahman
- Mitochondrial Research Group, UCL Great Ormond Street Institute of Child Health, London, United Kingdom
| | - Shamima Rahman
- Mitochondrial Research Group, UCL Great Ormond Street Institute of Child Health, London, United Kingdom
- Metabolic Unit, Great Ormond Street Hospital for Children NHS Foundation Trust, London, United Kingdom
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23
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Kelly RS, Chawes BL, Blighe K, Virkud YV, Croteau-Chonka DC, McGeachie MJ, Clish CB, Bullock K, Celedón JC, Weiss ST, Lasky-Su JA. An Integrative Transcriptomic and Metabolomic Study of Lung Function in Children With Asthma. Chest 2018; 154:335-348. [PMID: 29908154 DOI: 10.1016/j.chest.2018.05.038] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2017] [Revised: 04/17/2018] [Accepted: 05/01/2018] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Single omic analyses have provided some insight into the basis of lung function in children with asthma, but the underlying biologic pathways are still poorly understood. METHODS Weighted gene coexpression network analysis (WGCNA) was used to identify modules of coregulated gene transcripts and metabolites in blood among 325 children with asthma from the Genetic Epidemiology of Asthma in Costa Rica study. The biology of modules associated with lung function as measured by FEV1, the FEV1/FVC ratio, bronchodilator response, and airway responsiveness to methacholine was explored. Significantly correlated gene-metabolite module pairs were then identified, and their constituent features were analyzed for biologic pathway enrichments. RESULTS WGCNA clustered 25,060 gene probes and 8,185 metabolite features into eight gene modules and eight metabolite modules, where four and six, respectively, were associated with lung function (P ≤ .05). The gene modules were enriched for immune, mitotic, and metabolic processes and asthma-associated microRNA targets. The metabolite modules were enriched for lipid and amino acid metabolism. Integration of correlated gene-metabolite modules expanded the single omic findings, linking the FEV1/FVC ratio with ORMDL3 and dysregulated lipid metabolism. This finding was replicated in an independent population. CONCLUSIONS The results of this hypothesis-generating study suggest a mechanistic basis for multiple asthma genes, including ORMDL3, and a role for lipid metabolism. They demonstrate that integrating multiple omic technologies may provide a more informative picture of asthmatic lung function biology than single omic analyses.
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Affiliation(s)
- Rachel S Kelly
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA.
| | - Bo L Chawes
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA; COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Kevin Blighe
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Yamini V Virkud
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA; Division of Pulmonary Medicine, Allergy and Immunology, Children's Hospital of Pittsburgh of the University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, PA
| | - Damien C Croteau-Chonka
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Michael J McGeachie
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | | | | | - Juan C Celedón
- Division of Allergy and Immunology, Department of Pediatrics, Massachusetts General Hospital, Boston, MA
| | - Scott T Weiss
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Jessica A Lasky-Su
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
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24
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Khoomrung S, Wanichthanarak K, Nookaew I, Thamsermsang O, Seubnooch P, Laohapand T, Akarasereenont P. Metabolomics and Integrative Omics for the Development of Thai Traditional Medicine. Front Pharmacol 2017; 8:474. [PMID: 28769804 PMCID: PMC5513896 DOI: 10.3389/fphar.2017.00474] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2017] [Accepted: 07/03/2017] [Indexed: 12/28/2022] Open
Abstract
In recent years, interest in studies of traditional medicine in Asian and African countries has gradually increased due to its potential to complement modern medicine. In this review, we provide an overview of Thai traditional medicine (TTM) current development, and ongoing research activities of TTM related to metabolomics. This review will also focus on three important elements of systems biology analysis of TTM including analytical techniques, statistical approaches and bioinformatics tools for handling and analyzing untargeted metabolomics data. The main objective of this data analysis is to gain a comprehensive understanding of the system wide effects that TTM has on individuals. Furthermore, potential applications of metabolomics and systems medicine in TTM will also be discussed.
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Affiliation(s)
- Sakda Khoomrung
- Center of Applied Thai Traditional Medicine, Faculty of Medicine Siriraj Hospital, Mahidol UniversityBangkok, Thailand.,Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol UniversityBangkok, Thailand.,Systems and Synthetic Biology, Department of Biology and Biological Engineering, Chalmers University of TechnologyGothenburg, Sweden
| | - Kwanjeera Wanichthanarak
- Center of Applied Thai Traditional Medicine, Faculty of Medicine Siriraj Hospital, Mahidol UniversityBangkok, Thailand.,Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol UniversityBangkok, Thailand
| | - Intawat Nookaew
- Center of Applied Thai Traditional Medicine, Faculty of Medicine Siriraj Hospital, Mahidol UniversityBangkok, Thailand.,Systems and Synthetic Biology, Department of Biology and Biological Engineering, Chalmers University of TechnologyGothenburg, Sweden.,Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical SciencesLittle Rock, AR, United States
| | - Onusa Thamsermsang
- Center of Applied Thai Traditional Medicine, Faculty of Medicine Siriraj Hospital, Mahidol UniversityBangkok, Thailand
| | - Patcharamon Seubnooch
- Department of Pharmacology, Faculty of Medicine Siriraj Hospital, Mahidol UniversityBangkok, Thailand
| | - Tawee Laohapand
- Center of Applied Thai Traditional Medicine, Faculty of Medicine Siriraj Hospital, Mahidol UniversityBangkok, Thailand
| | - Pravit Akarasereenont
- Center of Applied Thai Traditional Medicine, Faculty of Medicine Siriraj Hospital, Mahidol UniversityBangkok, Thailand.,Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol UniversityBangkok, Thailand.,Department of Pharmacology, Faculty of Medicine Siriraj Hospital, Mahidol UniversityBangkok, Thailand
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25
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Rai A, Saito K, Yamazaki M. Integrated omics analysis of specialized metabolism in medicinal plants. Plant J 2017; 90:764-787. [PMID: 28109168 DOI: 10.1111/tpj.13485] [Citation(s) in RCA: 121] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2016] [Revised: 01/10/2017] [Accepted: 01/11/2017] [Indexed: 05/19/2023]
Abstract
Medicinal plants are a rich source of highly diverse specialized metabolites with important pharmacological properties. Until recently, plant biologists were limited in their ability to explore the biosynthetic pathways of these metabolites, mainly due to the scarcity of plant genomics resources. However, recent advances in high-throughput large-scale analytical methods have enabled plant biologists to discover biosynthetic pathways for important plant-based medicinal metabolites. The reduced cost of generating omics datasets and the development of computational tools for their analysis and integration have led to the elucidation of biosynthetic pathways of several bioactive metabolites of plant origin. These discoveries have inspired synthetic biology approaches to develop microbial systems to produce bioactive metabolites originating from plants, an alternative sustainable source of medicinally important chemicals. Since the demand for medicinal compounds are increasing with the world's population, understanding the complete biosynthesis of specialized metabolites becomes important to identify or develop reliable sources in the future. Here, we review the contributions of major omics approaches and their integration to our understanding of the biosynthetic pathways of bioactive metabolites. We briefly discuss different approaches for integrating omics datasets to extract biologically relevant knowledge and the application of omics datasets in the construction and reconstruction of metabolic models.
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Affiliation(s)
- Amit Rai
- Graduate School of Pharmaceutical Sciences, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba, 260-8675, Japan
| | - Kazuki Saito
- Graduate School of Pharmaceutical Sciences, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba, 260-8675, Japan
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, 230-0045, Japan
| | - Mami Yamazaki
- Graduate School of Pharmaceutical Sciences, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba, 260-8675, Japan
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26
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Kelly RS, Dahlin A, McGeachie MJ, Qiu W, Sordillo J, Wan ES, Wu AC, Lasky-Su J. Asthma Metabolomics and the Potential for Integrative Omics in Research and the Clinic. Chest 2016; 151:262-277. [PMID: 27776981 DOI: 10.1016/j.chest.2016.10.008] [Citation(s) in RCA: 120] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2016] [Revised: 09/26/2016] [Accepted: 10/07/2016] [Indexed: 12/11/2022] Open
Abstract
Asthma is a complex disease well-suited to metabolomic profiling, both for the development of novel biomarkers and for the improved understanding of pathophysiology. In this review, we summarize the 21 existing metabolomic studies of asthma in humans, all of which reported significant findings and concluded that individual metabolites and metabolomic profiles measured in exhaled breath condensate, urine, plasma, and serum could identify people with asthma and asthma phenotypes with high discriminatory ability. There was considerable consistency across the studies in terms of the reported biomarkers, regardless of biospecimen, profiling technology, and population age. In particular, acetate, adenosine, alanine, hippurate, succinate, threonine, and trans-aconitate, and pathways relating to hypoxia response, oxidative stress, immunity, inflammation, lipid metabolism and the tricarboxylic acid cycle were all identified as significant in at least two studies. There were also a number of nonreplicated results; however, the literature is not yet sufficiently developed to determine whether these represent spurious findings or reflect the substantial heterogeneity and limited statistical power in the studies and their methods to date. This review highlights the need for additional asthma metabolomic studies to explore these issues, and, further, the need for standardized methods in the way these studies are conducted. We conclude by discussing the potential of translation of these metabolomic findings into clinically useful biomarkers and the crucial role that integrated omics is likely to play in this endeavor.
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Affiliation(s)
- Rachel S Kelly
- Channing Division of Network Medicine, Brigham Women's Hospital and Harvard Medical School, Boston, MA
| | - Amber Dahlin
- Channing Division of Network Medicine, Brigham Women's Hospital and Harvard Medical School, Boston, MA
| | - Michael J McGeachie
- Channing Division of Network Medicine, Brigham Women's Hospital and Harvard Medical School, Boston, MA
| | - Weiliang Qiu
- Channing Division of Network Medicine, Brigham Women's Hospital and Harvard Medical School, Boston, MA
| | - Joanne Sordillo
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care, Boston, MA
| | - Emily S Wan
- Channing Division of Network Medicine, Brigham Women's Hospital and Harvard Medical School, Boston, MA; VA Boston Healthcare System, Department of Veterans Affairs, Boston, MA
| | - Ann Chen Wu
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care, Boston, MA
| | - Jessica Lasky-Su
- Channing Division of Network Medicine, Brigham Women's Hospital and Harvard Medical School, Boston, MA.
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27
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Balliu B, Tsonaka R, Boehringer S, Houwing-Duistermaat J. A retrospective likelihood approach for efficient integration of multiple omics factors in case-control association studies. Genet Epidemiol 2015; 39:156-65. [PMID: 25620726 DOI: 10.1002/gepi.21884] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2014] [Revised: 10/08/2014] [Accepted: 12/02/2014] [Indexed: 11/09/2022]
Abstract
Integrative omics, the joint analysis of outcome and multiple types of omics data, such as genomics, epigenomics, and transcriptomics data, constitute a promising approach for powerful and biologically relevant association studies. These studies often employ a case-control design, and often include nonomics covariates, such as age and gender, that may modify the underlying omics risk factors. An open question is how to best integrate multiple omics and nonomics information to maximize statistical power in case-control studies that ascertain individuals based on the phenotype. Recent work on integrative omics have used prospective approaches, modeling case-control status conditional on omics, and nonomics risk factors. Compared to univariate approaches, jointly analyzing multiple risk factors with a prospective approach increases power in nonascertained cohorts. However, these prospective approaches often lose power in case-control studies. In this article, we propose a novel statistical method for integrating multiple omics and nonomics factors in case-control association studies. Our method is based on a retrospective likelihood function that models the joint distribution of omics and nonomics factors conditional on case-control status. The new method provides accurate control of Type I error rate and has increased efficiency over prospective approaches in both simulated and real data.
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Affiliation(s)
- Brunilda Balliu
- Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, The Netherlands
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28
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Vucic EA, Chari R, Thu KL, Wilson IM, Cotton AM, Kennett JY, Zhang M, Lonergan KM, Steiling K, Brown CJ, McWilliams A, Ohtani K, Lenburg ME, Sin DD, Spira A, MacAulay CE, Lam S, Lam WL. DNA methylation is globally disrupted and associated with expression changes in chronic obstructive pulmonary disease small airways. Am J Respir Cell Mol Biol 2014; 50:912-22. [PMID: 24298892 PMCID: PMC4068945 DOI: 10.1165/rcmb.2013-0304oc] [Citation(s) in RCA: 96] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2013] [Accepted: 12/03/2013] [Indexed: 01/06/2023] Open
Abstract
DNA methylation is an epigenetic modification that is highly disrupted in response to cigarette smoke and involved in a wide spectrum of malignant and nonmalignant diseases, but surprisingly not previously assessed in small airways of patients with chronic obstructive pulmonary disease (COPD). Small airways are the primary sites of airflow obstruction in COPD. We sought to determine whether DNA methylation patterns are disrupted in small airway epithelia of patients with COPD, and evaluate whether changes in gene expression are associated with these disruptions. Genome-wide methylation and gene expression analysis were performed on small airway epithelial DNA and RNA obtained from the same patient during bronchoscopy, using Illumina's Infinium HM27 and Affymetrix's Genechip Human Gene 1.0 ST arrays. To control for known effects of cigarette smoking on DNA methylation, methylation and gene expression profiles were compared between former smokers with and without COPD matched for age, pack-years, and years of smoking cessation. Our results indicate that aberrant DNA methylation is (1) a genome-wide phenomenon in small airways of patients with COPD, and (2) associated with altered expression of genes and pathways important to COPD, such as the NF-E2-related factor 2 oxidative response pathway. DNA methylation is likely an important mechanism contributing to modulation of genes important to COPD pathology. Because these methylation events may underlie disease-specific gene expression changes, their characterization is a critical first step toward the development of epigenetic markers and an opportunity for developing novel epigenetic therapeutic interventions for COPD.
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Affiliation(s)
- Emily A. Vucic
- Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
| | - Raj Chari
- Department of Genetics, Harvard Medical School, Boston, Massachusetts
| | - Kelsie L. Thu
- Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
| | - Ian M. Wilson
- Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
| | - Allison M. Cotton
- Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Jennifer Y. Kennett
- Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
| | - May Zhang
- Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
| | - Kim M. Lonergan
- Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
| | - Katrina Steiling
- Division of Computational Biomedicine, Department of Medicine, Boston University Medical Center, Boston, Massachusetts; and
| | - Carolyn J. Brown
- Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Annette McWilliams
- Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
| | - Keishi Ohtani
- Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
| | - Marc E. Lenburg
- Division of Computational Biomedicine, Department of Medicine, Boston University Medical Center, Boston, Massachusetts; and
| | - Don D. Sin
- University of British Columbia James Hogg Research Centre and the Institute of Heart and Lung Health, St. Paul’s Hospital, Vancouver, British Columbia, Canada
| | - Avrum Spira
- Division of Computational Biomedicine, Department of Medicine, Boston University Medical Center, Boston, Massachusetts; and
| | - Calum E. MacAulay
- Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
| | - Stephen Lam
- Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
| | - Wan L. Lam
- Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
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29
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Abstract
Diabetes is a common disease worldwide and can cause several complications, leading to systemic derangements and end-organ damage. Despite blood sugar control and adequate therapy with currently available drugs, diabetic complications remain a serious issue in clinical practice, indicating that our knowledge of diabetes and its complications is only at the tip of the iceberg. Better understanding of its pathogenesis and pathophysiology is crucial to achieve better therapeutic outcomes and to prevent its complications. This review provides an overview of proteomics and introduces proteomic technologies commonly used for diabetes research. Recent proteomic studies for the investigation of diabetes and its complications are summarized. Finally, the future perspectives for the field of proteomics in diabetes research are discussed.
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Affiliation(s)
- Visith Thongboonkerd
- a Medical Molecular Biology Unit, Office for Research and Development, Faculty of Medicine at Siriraj Hospital, Mahidol University, 12th Floor, Adulyadej Vikrom Building, Siriraj Hospital, 2 Prannok Road, Bangkoknoi, Bangkok, 10700, Thailand.
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