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Tang A, Peng B. Diversifying the Flavor of Black Rice Wines through Three Different Regional Xiaoqus in China and Unraveling Their Core Functional Microorganisms. Foods 2023; 12:3576. [PMID: 37835229 PMCID: PMC10572163 DOI: 10.3390/foods12193576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 09/20/2023] [Accepted: 09/25/2023] [Indexed: 10/15/2023] Open
Abstract
The flavor of black rice wine (BRW) can be diversified by the Xiaoqus, from different regions; however, the functional microbiota that contributes to its flavor remains unclear. Accordingly, this study selected three regional Xiaoqus from Sichuan Dazhu (Q1), Jiangxi Yingtan (Q2), and Hubei Fangxian (Q3) as starters to investigate flavor compounds and microbial communities during BRW brewing. Results indicated that altogether 61 flavor substances were identified, 16 of which were common characteristic flavor compounds (odor activity value > 0.1). Each BRW possessed unique characteristic flavor compounds. O2PLS and Spearman's correlation analysis determined that characteristic flavor compounds of BRW were mainly produced by Saccharomyces cerevisiae, non-Saccharomyces yeasts, and lactic acid bacteria, with the common core functional strains being Wickerhamomyces and Pediococcus, and with their unique core functional strain likely causing a unique characteristic flavor. This study could promote the high-quality development of the black rice wine industry.
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Affiliation(s)
- Aoxing Tang
- College of Food Science and Technology, Huazhong Agricultural University, Wuhan 430070, China;
- Hubei Key Laboratory of Fruit & Vegetable Processing & Quality Control, Huazhong Agricultural University, Wuhan 430070, China
| | - Bangzhu Peng
- College of Food Science and Technology, Huazhong Agricultural University, Wuhan 430070, China;
- Hubei Key Laboratory of Fruit & Vegetable Processing & Quality Control, Huazhong Agricultural University, Wuhan 430070, China
- Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan 430070, China
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
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2
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Gao J, Xiao Y. Metabolomics and its applications in assisted reproductive technology. IET Nanobiotechnol 2023. [PMID: 37248807 PMCID: PMC10374554 DOI: 10.1049/nbt2.12141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 03/19/2023] [Accepted: 04/01/2023] [Indexed: 05/31/2023] Open
Abstract
Metabolomics, an emerging omics technology developed in the post-gene age, is an important part of systems biology. It interprets the pathophysiological state of the subject by quantitatively describing the dynamic changes of metabolites through analytical methods, mainly mass spectrometry (MS) and nuclear magnetic resonance (NMR). Assisted reproductive technology (ART) is a method used to manipulate sperm, oocytes, and embryos to achieve conception. Recently, several studies have reported that metabolomics methods can be used to measure metabolites in ART samples; these metabolites can be used to evaluate the quality of gametes and embryos. This article reviews the progress of research on metabolomics and the application of this technology in the field of ART, thus providing a reference for research and development directions in the future.
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Affiliation(s)
- Jingying Gao
- Wuxi No.2 People's Hospital, Jiangnan University Medical Center, Wuxi, Jiangsu, China
| | - Yan Xiao
- Wuxi No.2 People's Hospital, Jiangnan University Medical Center, Wuxi, Jiangsu, China
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3
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Nisa MU, Farooq S, Ali S, Eachkoti R, Rehman MU, Hafiz S. Proteomics: A modern tool for identifying therapeutic targets in different types of carcinomas. Proteomics 2023. [DOI: 10.1016/b978-0-323-95072-5.00013-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/01/2023]
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4
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Guan Q, Huang T, Peng F, Huang J, Liu Z, Peng Z, Xie M, Xiong T. The microbial succession and their correlation with the dynamics of flavor compounds involved in the natural fermentation of suansun, a traditional Chinese fermented bamboo shoots. Food Res Int 2022; 157:111216. [DOI: 10.1016/j.foodres.2022.111216] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Revised: 04/01/2022] [Accepted: 04/02/2022] [Indexed: 01/20/2023]
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5
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Santos V, Freitas C, Fernandes MGO, Sousa C, Reboredo C, Cruz-Martins N, Mosquera J, Hespanhol V, Campelo R. Liquid biopsy: the value of different bodily fluids. Biomark Med 2022; 16:127-145. [DOI: 10.2217/bmm-2021-0370] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Liquid biopsies have gained an increasing interest in the last years among medical and scientific communities. Indeed, the value of liquid effusions, while less invasive and more accurate techniques, has been markedly highlighted. Peripheral blood comprises the most often analyzed sample, but recent evidences have pointed out the huge importance of other bodily fluids, including pleural and peritoneal fluids, urine, saliva and cerebrospinal fluid in the detection and monitoring of different tumor types. In face to these advances, this review aims to provide an overview of the value of tumor-associated mutations, detectable in different effusions, and how they can be used in clinical practice, namely in prognosis assessment and early disease and minimal disease recurrence detection, and in predicting the treatment response or acquired-resistance development.
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Affiliation(s)
- Vanessa Santos
- Department of Pulmonology, Centro Hospitalar Universitário de São João, Alameda Prof. Hernâni Monteiro, Porto, 4200319, Portugal
| | - Cláudia Freitas
- Department of Pulmonology, Centro Hospitalar Universitário de São João, Alameda Prof. Hernâni Monteiro, Porto, 4200319, Portugal
- Faculty of Medicine, University of Porto, Alameda Prof. Hernâni Monteiro, Porto, 4200319, Portugal
| | - Maria GO Fernandes
- Department of Pulmonology, Centro Hospitalar Universitário de São João, Alameda Prof. Hernâni Monteiro, Porto, 4200319, Portugal
- Faculty of Medicine, University of Porto, Alameda Prof. Hernâni Monteiro, Porto, 4200319, Portugal
- Institute for Research & Innovation in Health (I3S), University of Porto, Rua Alfredo Allen, Porto, 4200135, Portugal
- Institute of Molecular Pathology & Immunology of the University of Porto (IPATIMUP), Porto, 4200135, Portugal
| | - Catarina Sousa
- Department of Pulmonology, Centro Hospitalar Universitário de São João, Alameda Prof. Hernâni Monteiro, Porto, 4200319, Portugal
| | - Cristina Reboredo
- Department of Lung Cancer & Thoracic Tumours, Complejo Hospitalario Universitario de A Coruña, As Xubias, 84, 15006, A Coruña, La Coruña, Spain
| | - Natália Cruz-Martins
- Faculty of Medicine, University of Porto, Alameda Prof. Hernâni Monteiro, Porto, 4200319, Portugal
- Institute for Research & Innovation in Health (I3S), University of Porto, Rua Alfredo Allen, Porto, 4200135, Portugal
| | - Joaquín Mosquera
- Department of Lung Cancer & Thoracic Tumours, Complejo Hospitalario Universitario de A Coruña, As Xubias, 84, 15006, A Coruña, La Coruña, Spain
| | - Venceslau Hespanhol
- Department of Pulmonology, Centro Hospitalar Universitário de São João, Alameda Prof. Hernâni Monteiro, Porto, 4200319, Portugal
- Faculty of Medicine, University of Porto, Alameda Prof. Hernâni Monteiro, Porto, 4200319, Portugal
- Institute for Research & Innovation in Health (I3S), University of Porto, Rua Alfredo Allen, Porto, 4200135, Portugal
- Institute of Molecular Pathology & Immunology of the University of Porto (IPATIMUP), Porto, 4200135, Portugal
| | - Rosário Campelo
- Department of Lung Cancer & Thoracic Tumours, Complejo Hospitalario Universitario de A Coruña, As Xubias, 84, 15006, A Coruña, La Coruña, Spain
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6
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Jendoubi T. Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer. Metabolites 2021; 11:184. [PMID: 33801081 PMCID: PMC8003953 DOI: 10.3390/metabo11030184] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Revised: 03/17/2021] [Accepted: 03/18/2021] [Indexed: 12/14/2022] Open
Abstract
Metabolomics deals with multiple and complex chemical reactions within living organisms and how these are influenced by external or internal perturbations. It lies at the heart of omics profiling technologies not only as the underlying biochemical layer that reflects information expressed by the genome, the transcriptome and the proteome, but also as the closest layer to the phenome. The combination of metabolomics data with the information available from genomics, transcriptomics, and proteomics offers unprecedented possibilities to enhance current understanding of biological functions, elucidate their underlying mechanisms and uncover hidden associations between omics variables. As a result, a vast array of computational tools have been developed to assist with integrative analysis of metabolomics data with different omics. Here, we review and propose five criteria-hypothesis, data types, strategies, study design and study focus- to classify statistical multi-omics data integration approaches into state-of-the-art classes under which all existing statistical methods fall. The purpose of this review is to look at various aspects that lead the choice of the statistical integrative analysis pipeline in terms of the different classes. We will draw particular attention to metabolomics and genomics data to assist those new to this field in the choice of the integrative analysis pipeline.
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Affiliation(s)
- Takoua Jendoubi
- Department of Statistical Science, University College London, London WC1E 6BT, UK
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7
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Alkhalil A, Ball RL, Garg G, Day A, Carney BC, Kumar R, Hammamieh R, Moffatt LT, Shupp JW. Cutaneous Thermal Injury Modulates Blood and Skin Metabolomes Differently in a Murine Model. J Burn Care Res 2020; 42:727-742. [PMID: 33301570 PMCID: PMC8335952 DOI: 10.1093/jbcr/iraa209] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
As the field of metabolomics develops further, investigations of how the metabolome is affected following thermal injury may be helpful to inform diagnostics and guide treatments. In this study, changes to the metabolome were tested and validated in a murine burn injury model. After a 30% total body surface scald injury or sham procedure sera and skin biopsies were collected at 1, 2, 6, or 24 hr. Burn-specific changes in the metabolome were detected compared to sham animals. The sera metabolome exhibited a more rapid response to burn injury than that of the skin and it peaked more proximal to injury (6 vs 24 hr). Progression of metabolic response in the skin was less synchronous and showed a higher overlap of the significantly modified metabolites (SMMs) among tested time-points. Top affected pathways identified by SMMs of skin included inositol phosphate metabolism, ascorbate and alderate metabolism, caffeine metabolism, and the pentose phosphate pathway. Future research is warranted in human and larger animal models to further elucidate the role of metabolomic perturbations and the pathophysiology following burn injury.
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Affiliation(s)
- Abdulnaser Alkhalil
- Firefighters' Burn and Surgical Research Laboratory, MedStar Health Research Institute, Washington, District of Columbia
| | - Robert L Ball
- Firefighters' Burn and Surgical Research Laboratory, MedStar Health Research Institute, Washington, District of Columbia.,The Burn Center, MedStar Washington Hospital Center, Washington, District of Columbia
| | - Gaurav Garg
- Firefighters' Burn and Surgical Research Laboratory, MedStar Health Research Institute, Washington, District of Columbia.,The Burn Center, MedStar Washington Hospital Center, Washington, District of Columbia
| | - Anna Day
- The Oak Ridge Institute for Science and Education, Fort Detrick, Maryland
| | - Bonnie C Carney
- Firefighters' Burn and Surgical Research Laboratory, MedStar Health Research Institute, Washington, District of Columbia.,Department of Biochemistry and Molecular Biology, Georgetown University School of Medicine, Washington, District of Columbia
| | - Raina Kumar
- Advanced Biomedical Computational Science, Frederick National Lab for Cancer Research, Maryland.,Integrative Systems Biology, US Army Center for Environmental Health, Center for Environmental Health, Fort Detrick, Maryland
| | - Rasha Hammamieh
- Integrative Systems Biology, US Army Center for Environmental Health, Center for Environmental Health, Fort Detrick, Maryland
| | - Lauren T Moffatt
- Firefighters' Burn and Surgical Research Laboratory, MedStar Health Research Institute, Washington, District of Columbia.,Department of Biochemistry and Molecular Biology, Georgetown University School of Medicine, Washington, District of Columbia
| | - Jeffrey W Shupp
- Firefighters' Burn and Surgical Research Laboratory, MedStar Health Research Institute, Washington, District of Columbia.,The Burn Center, MedStar Washington Hospital Center, Washington, District of Columbia.,Department of Surgery, Georgetown University School of Medicine, Washington, District of Columbia
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8
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Surowiec I, Skotare T, Sjögren R, Gouveia-Figueira S, Orikiiriza J, Bergström S, Normark J, Trygg J. Joint and unique multiblock analysis of biological data - multiomics malaria study. Faraday Discuss 2020; 218:268-283. [PMID: 31120463 DOI: 10.1039/c8fd00243f] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Modern profiling technologies enable us to obtain large amounts of data which can be used later for a comprehensive understanding of the studied system. Proper evaluation of such data is challenging, and cannot be carried out by bare analysis of separate data sets. Integrated approaches are necessary, because only data integration allows us to find correlation trends common for all studied data sets and reveal hidden structures not known a priori. This improves the understanding and interpretation of complex systems. Joint and Unique MultiBlock Analysis (JUMBA) is an analysis method based on the OnPLS-algorithm that decomposes a set of matrices into joint parts containing variations shared with other connected matrices and variations that are unique for each single matrix. Mapping unique variations is important from a data integration perspective, since it certainly cannot be expected that all variation co-varies. In this work we used JUMBA for the integrated analysis of lipidomic, metabolomic and oxylipins data sets obtained from profiling of plasma samples from children infected with P. falciparum malaria. P. falciparum is one of the primary contributors to childhood mortality and obstetric complications in the developing world, which makes the development of new diagnostic and prognostic tools, as well as a better understanding of the disease, of utmost importance. In the presented work, JUMBA made it possible to detect already known trends related to the disease progression, but also to discover new structures in the data connected to food intake and personal differences in metabolism. By separating the variation in each data set into joint and unique, JUMBA reduced the complexity of the analysis and facilitated the detection of samples and variables corresponding to specific structures across multiple data sets, and by doing this enabled fast interpretation of the studied system. All of this makes JUMBA a perfect choice for multiblock analysis of systems biology data.
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Affiliation(s)
- Izabella Surowiec
- Computational Life Science Cluster (CLiC), Department of Chemistry, Umeå University, Linnaeus väg 10, 901 87 Umeå, Sweden.
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9
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Zhang L, Huang J, Lin Q, Ma Y, Xia R, Zhu Y, Abudubari S. Serum Proteomic Profiling Analysis of Rats Chronically Exposed to Arsenic. Med Sci Monit 2019; 25:9923-9932. [PMID: 31874112 PMCID: PMC6941779 DOI: 10.12659/msm.918696] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
Background Arsenic (As) is an environmental contaminant, and As pollution in water and soil is a public health issue worldwide. As exposure is associated with the incidence of many disorders, such as arteriosclerosis, diabetes, neurodegenerative diseases, and renal dysfunction. However, the mechanism of As toxicity remains unclear. Material/Methods We investigated the changes in serum protein profiles of rats chronically exposed to As. Twenty healthy rats were randomly divided into 4 groups, and sodium arsenite of varying final concentrations (0, 2, 10, and 50 mg/L, respectively) was add into the drinking water for each group. The administration lasted for 3 months. Two proteomic strategies, isobaric tags for relative and absolute quantitation (iTRAQ), and 2-dimensional gel electrophoresis (2-DE), were employed to screen the differential serum proteins between control and arsenite exposure groups. Results We identified a total of 27 differentially-expressed proteins, among which 9 proteins were significantly upregulated and 18 were downregulated by As exposure. Many of the differentially-expressed proteins were related to fat digestion and absorption, including 5 apolipoproteins, which indicated lipid metabolism may be the most affected by As exposure. Conclusions This study revealed the influence of As on lipid metabolism, suggesting an increased potential risk of relevant diseases in subjects chronically exposed to As.
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Affiliation(s)
- Ling Zhang
- Division of Endemic Disease Prevention, Xinjiang Uighur Autonomous Region Center for Disease Control and Prevention, Urumqi, Xinjiang, China (mainland)
| | - Jia Huang
- Division of Endemic Disease Prevention, Xinjiang Uighur Autonomous Region Center for Disease Control and Prevention, Urumqi, Xinjiang, China (mainland).,School of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China (mainland)
| | - Qin Lin
- Division of Endemic Disease Prevention, Xinjiang Uighur Autonomous Region Center for Disease Control and Prevention, Urumqi, Xinjiang, China (mainland)
| | - Yan Ma
- School of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China (mainland)
| | - Rongxiang Xia
- Division of Endemic Disease Prevention, Xinjiang Uighur Autonomous Region Center for Disease Control and Prevention, Urumqi, Xinjiang, China (mainland)
| | - Yuming Zhu
- Division of Endemic Disease Prevention, Xinjiang Uighur Autonomous Region Center for Disease Control and Prevention, Urumqi, Xinjiang, China (mainland)
| | - Saimaitikari Abudubari
- School of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China (mainland)
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10
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Signal pattern plot: a simple tool for time-dependent metabolomics studies by 1H NMR spectroscopy. Anal Bioanal Chem 2019; 411:6857-6866. [DOI: 10.1007/s00216-019-02055-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Revised: 06/21/2019] [Accepted: 07/29/2019] [Indexed: 10/26/2022]
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Pinu FR, Beale DJ, Paten AM, Kouremenos K, Swarup S, Schirra HJ, Wishart D. Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community. Metabolites 2019; 9:E76. [PMID: 31003499 PMCID: PMC6523452 DOI: 10.3390/metabo9040076] [Citation(s) in RCA: 314] [Impact Index Per Article: 62.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Revised: 04/15/2019] [Accepted: 04/16/2019] [Indexed: 02/07/2023] Open
Abstract
The use of multiple omics techniques (i.e., genomics, transcriptomics, proteomics, and metabolomics) is becoming increasingly popular in all facets of life science. Omics techniques provide a more holistic molecular perspective of studied biological systems compared to traditional approaches. However, due to their inherent data differences, integrating multiple omics platforms remains an ongoing challenge for many researchers. As metabolites represent the downstream products of multiple interactions between genes, transcripts, and proteins, metabolomics, the tools and approaches routinely used in this field could assist with the integration of these complex multi-omics data sets. The question is, how? Here we provide some answers (in terms of methods, software tools and databases) along with a variety of recommendations and a list of continuing challenges as identified during a peer session on multi-omics integration that was held at the recent 'Australian and New Zealand Metabolomics Conference' (ANZMET 2018) in Auckland, New Zealand (Sept. 2018). We envisage that this document will serve as a guide to metabolomics researchers and other members of the community wishing to perform multi-omics studies. We also believe that these ideas may allow the full promise of integrated multi-omics research and, ultimately, of systems biology to be realized.
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Affiliation(s)
- Farhana R Pinu
- The New Zealand Institute for Plant and Food Research Limited, Private Bag 92169, Auckland 1142, New Zealand.
| | - David J Beale
- Land and Water, Commonwealth Scientific and Industrial Research Organization (CSIRO), Ecosciences Precinct, Dutton Park, Dutton Park, QLD 4102, Australia.
| | - Amy M Paten
- Land and Water, Commonwealth Scientific and Industrial Research Organization (CSIRO), Research and Innovation Park, Acton, ACT 2601, Australia.
| | - Konstantinos Kouremenos
- Trajan Scientific and Medical, Ringwood, VIC 3134, Australia.
- Bio21 Institute, The University of Melbourne, Parkville, VIC 3010, Australia.
| | - Sanjay Swarup
- Department of Biological Sciences, National University of Singapore, Singapore 117411, Singapore.
| | - Horst J Schirra
- Centre for Advanced Imaging, The University of Queensland, St Lucia, QLD 4072, Australia.
| | - David Wishart
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E8, Canada.
- Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada.
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Xiao M, Xiong T, Peng Z, Liu C, Huang T, Yu H, Xie M. Correlation between microbiota and flavours in fermentation of Chinese Sichuan Paocai. Food Res Int 2018; 114:123-132. [PMID: 30361008 DOI: 10.1016/j.foodres.2018.06.051] [Citation(s) in RCA: 140] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Revised: 06/19/2018] [Accepted: 06/21/2018] [Indexed: 02/01/2023]
Abstract
Sichuan Paocai (SCP), a traditional fermentationed food, possesses unique flavours driven by various microbiota in fermentation ecosystems. However, the association between these microbiota and flavours is poorly understood. Here, we measured the structural dynamics of the microbial diversity and flavour compounds during SCP fermentation (0-7 days) using high-throughput sequencing and flavour analyses. Firstly, a total of 90 bacteria and 135 fungi were identified. The results showed that fermentation process was initiated by environmental microorganisms, such as Micrococcaceae, followed by a heterolactic fermentation phase dominated by Leuconostocaceae, and finally succeeded by homolactic fermentation led by Lactobacillaceae. Secondly, 6 organic acids, 12 amino acids and 86 volatile flavours were detected. The fermentation process was divided into 4 stages based on flavours: Day 0; Days 1-3; Days 4-6; and Day 7. Analysis of the correlation between microbiota and flavours indicated that bacteria contributed more to flavours formation than did fungi, and Lactobacillus, Leuconostoc, Achromobacter and Pediococcus were closely correlated with flavours. This study provides a systematic method for understanding the characteristics of fermented foods and useful guidelines for screening of strains that produce desirable flavours and functional substances.
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Affiliation(s)
- Muyan Xiao
- State Key Laboratory of Food Science & Technology, No. 235 Nanjing East Road, Nanchang, Jiangxi 330047, PR China; College of Food Science, Nanchang University, Nanchang, Jiangxi 330047, PR China
| | - Tao Xiong
- State Key Laboratory of Food Science & Technology, No. 235 Nanjing East Road, Nanchang, Jiangxi 330047, PR China; College of Food Science, Nanchang University, Nanchang, Jiangxi 330047, PR China.
| | - Zhen Peng
- State Key Laboratory of Food Science & Technology, No. 235 Nanjing East Road, Nanchang, Jiangxi 330047, PR China; College of Food Science, Nanchang University, Nanchang, Jiangxi 330047, PR China
| | - Changgen Liu
- State Key Laboratory of Food Science & Technology, No. 235 Nanjing East Road, Nanchang, Jiangxi 330047, PR China; College of Food Science, Nanchang University, Nanchang, Jiangxi 330047, PR China
| | - Tao Huang
- State Key Laboratory of Food Science & Technology, No. 235 Nanjing East Road, Nanchang, Jiangxi 330047, PR China; College of Food Science, Nanchang University, Nanchang, Jiangxi 330047, PR China
| | - Hong Yu
- State Key Laboratory of Food Science & Technology, No. 235 Nanjing East Road, Nanchang, Jiangxi 330047, PR China; College of Food Science, Nanchang University, Nanchang, Jiangxi 330047, PR China
| | - Mingyong Xie
- State Key Laboratory of Food Science & Technology, No. 235 Nanjing East Road, Nanchang, Jiangxi 330047, PR China; College of Food Science, Nanchang University, Nanchang, Jiangxi 330047, PR China
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Zheng X, Liu F, Shi X, Wang B, Li K, Li B, Zhuge B. Dynamic correlations between microbiota succession and flavor development involved in the ripening of Kazak artisanal cheese. Food Res Int 2018; 105:733-742. [DOI: 10.1016/j.foodres.2017.12.007] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2017] [Revised: 12/01/2017] [Accepted: 12/02/2017] [Indexed: 01/08/2023]
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Hua Y, Yao W, Ji P, Wei Y. Integrated metabonomic-proteomic studies on blood enrichment effects of Angelica sinensis on a blood deficiency mice model. PHARMACEUTICAL BIOLOGY 2017; 55:853-863. [PMID: 28140733 PMCID: PMC6130503 DOI: 10.1080/13880209.2017.1281969] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2016] [Revised: 10/19/2016] [Accepted: 01/10/2017] [Indexed: 05/18/2023]
Abstract
CONTEXT Angelica sinensis (Oliv.) Diels (Umbelliferae) (AS) is a well-known Traditional Chinese Medicine (TCM) that enriches and regulates the blood. OBJECTIVE An integrated metabonomic and proteomic method was developed and applied to study the blood enrichment effects and mechanisms of AS on blood deficiency (BD) mouse model. MATERIALS AND METHODS Forty mice were randomly divided into the control, BD, High-dose of AS (ASH), Middle-dose of AS (ASM), and Low-dose of AS (ASL) groups. BD model mice were established by injecting N-acetylphenylhydrazine (APH) and cyclophosphamide (CTX) (ip). The aqueous extract of AS was administered at three dose of 20, 10, or 5 g/kg b. wt. orally for 7 consecutive days before/after APH and CTX administration. Gas chromatography-mass spectrometry (GC-MS) combined with pattern recognition method and 2D gel electrophoresis (2-DE) proteomics were performed in this study to discover the underlying hematopoietic regulation mechanisms of AS on BD mouse model. RESULTS Unlike in the control group, the HSP90 and arginase levels increased significantly (p < 0.05) in the BD group, but the levels of carbonic anhydrase, GAPDH, catalase, fibrinogen, GSTP, carboxylesterase and hem binding protein in the BD group decreased significantly (p < 0.05). Unlike the levels in the BD group, the levels of these biomarkers were regulated to a normal state near the control group in the ASM group. Unlike in the control group, l-alanine, arachidonic acid, l-valine, octadecanoic acid, glycine, hexadecanoic acid, l-threonine, butanoic acid, malic acid, l-proline and propanoic acid levels increased significantly (p < 0.05) in the BD group, the levels of d-fructose in the BD group decreased significantly (p < 0.05). The relative concentrations of 12 endogenous metabolites were also significantly affected by the ASL, ASM, and ASH treatments. Notably, most of the altered BD-related metabolites were restored to normal state after ASM administration. CONCLUSION AS can promote hematopoietic activities, inhibit production of reactive oxygen species, regulate energy metabolism, increase antiapoptosis, and potentially contribute to the blood enrichment effects of AS against APH- and CTX-induced BD mice.
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Affiliation(s)
- Yongli Hua
- College of Veterinary Medicine, Gansu Agricultural University, Lanzhou, Gansu Province, People’s Republic of China
| | - Wangling Yao
- College of Veterinary Medicine, Gansu Agricultural University, Lanzhou, Gansu Province, People’s Republic of China
| | - Peng Ji
- College of Veterinary Medicine, Gansu Agricultural University, Lanzhou, Gansu Province, People’s Republic of China
| | - Yanming Wei
- College of Veterinary Medicine, Gansu Agricultural University, Lanzhou, Gansu Province, People’s Republic of China
- CONTACT Yanming WeiCollege of Veterinary Medicine, Gansu Agricultural University, Lanzhou, Gansu Province730070, People’s Republic of China
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15
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Wan C, Zhan Y, Xue R, Wu Y, Li X, Pei F. Gd-DTPA-induced dynamic metabonomic changes in rat biofluids. Magn Reson Imaging 2017; 44:15-25. [PMID: 28095303 DOI: 10.1016/j.mri.2017.01.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2016] [Revised: 01/09/2017] [Accepted: 01/09/2017] [Indexed: 01/22/2023]
Abstract
OBJECTIVES The purposes of this study were (1) to detect the dynamic metabonomic changes induced by gadopentetate dimeglumine (Gd-DTPA) and (2) to investigate the potential metabolic disturbances associated with the pathogenesis of nephrogenic systemic fibrosis (NSF) at the early stage. METHODS A nuclear magnetic resonance (NMR)-based metabolomics approach was used to investigate the urinary and serum metabolic changes induced by a single tail vein injection of Gd-DTPA (dosed at 2 and 5mmol/kg body weight) in rats. Urine and serum samples were collected on days 1, 2 and 7 after dosing. RESULTS Metabolic responses of rats to Gd-DTPA administration were systematic involving changes in lipid metabolism, glucose metabolism, TCA cycle, amino acid metabolism and gut microbiota functions. Urinary and serum metabonomic recovery could be observed in both the 2 and 5mmol/kg body weight group, but the metabolic effects of high-dosed (5mmol/kg body weight) Gd-DTPA lasted longer. It is worth noting that hyperlipidemia was observed after Gd-DTPA injection, and nicotinate might play a role in the subsequent self-recovery of lipid metabolism. The disturbance of tyrosine, glutamate and gut microbiota metabolism might associate with the progression of NSF. CONCLUSION These findings offered essential information about the metabolic changes induced by Gd-DTPA, and could be potentially important for investigating the pathogenesis of NSF at the early stage. Moreover, the recovery of rats administrated with Gd-DTPA may have implications in the treatment of early stage NSF.
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Affiliation(s)
- Chuanling Wan
- Changchun Institute of Applied Chemistry Chinese Academy of Sciences, No. 5625, Renmin Street, Changchun 130022, China; University of Chinese Academy of Sciences, No. 19, Yuquan Road 19, Beijing 100049, China
| | - Youyang Zhan
- Changchun Institute of Applied Chemistry Chinese Academy of Sciences, No. 5625, Renmin Street, Changchun 130022, China; University of Chinese Academy of Sciences, No. 19, Yuquan Road 19, Beijing 100049, China
| | - Rong Xue
- Changchun Institute of Applied Chemistry Chinese Academy of Sciences, No. 5625, Renmin Street, Changchun 130022, China
| | - Yijie Wu
- Changchun Institute of Applied Chemistry Chinese Academy of Sciences, No. 5625, Renmin Street, Changchun 130022, China
| | - Xiaojing Li
- Changchun Institute of Applied Chemistry Chinese Academy of Sciences, No. 5625, Renmin Street, Changchun 130022, China.
| | - Fengkui Pei
- Changchun Institute of Applied Chemistry Chinese Academy of Sciences, No. 5625, Renmin Street, Changchun 130022, China
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16
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Huang Z, Ma L, Huang C, Li Q, Nice EC. Proteomic profiling of human plasma for cancer biomarker discovery. Proteomics 2016; 17. [PMID: 27550791 DOI: 10.1002/pmic.201600240] [Citation(s) in RCA: 69] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2016] [Revised: 08/03/2016] [Accepted: 08/18/2016] [Indexed: 02/05/2023]
Affiliation(s)
- Zhao Huang
- Key Laboratory of Tropical Diseases and Translational Medicine of Ministry of Education & Department of Neurology; The Affiliated Hospital of Hainan Medical College; Haikou P. R. China
- Criminal police detachment of Guang'an City Public Security Bureau; P. R. China
| | - Linguang Ma
- Criminal police detachment of Guang'an City Public Security Bureau; P. R. China
| | - Canhua Huang
- State Key Laboratory for Biotherapy and Cancer Center; West China Hospital; Sichuan University, and Collaborative Innovation Center of Biotherapy; Chengdu P. R. China
| | - Qifu Li
- Key Laboratory of Tropical Diseases and Translational Medicine of Ministry of Education & Department of Neurology; The Affiliated Hospital of Hainan Medical College; Haikou P. R. China
| | - Edouard C. Nice
- Department of Biochemistry and Molecular Biology; Monash University; Clayton Australia
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17
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Exploring flavour-producing core microbiota in multispecies solid-state fermentation of traditional Chinese vinegar. Sci Rep 2016; 6:26818. [PMID: 27241188 PMCID: PMC4886211 DOI: 10.1038/srep26818] [Citation(s) in RCA: 118] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2015] [Accepted: 03/18/2016] [Indexed: 11/09/2022] Open
Abstract
Multispecies solid-state fermentation (MSSF), a natural fermentation process driven by reproducible microbiota, is an important technique to produce traditional fermented foods. Flavours, skeleton of fermented foods, was mostly produced by microbiota in food ecosystem. However, the association between microbiota and flavours and flavour-producing core microbiota are still poorly understood. Here, acetic acid fermentation (AAF) of Zhenjiang aromatic vinegar was taken as a typical case of MSSF. The structural and functional dynamics of microbiota during AAF process was determined by metagenomics and favour analyses. The dominant bacteria and fungi were identified as Acetobacter, Lactobacillus, Aspergillus, and Alternaria, respectively. Total 88 flavours including 2 sugars, 9 organic acids, 18 amino acids, and 59 volatile flavours were detected during AAF process. O2PLS-based correlation analysis between microbiota succession and flavours dynamics showed bacteria made more contribution to flavour formation than fungi. Seven genera including Acetobacter, Lactobacillus, Enhydrobacter, Lactococcus, Gluconacetobacer, Bacillus and Staphylococcus were determined as functional core microbiota for production of flavours in Zhenjiang aromatic vinegar, based on their dominance and functionality in microbial community. This study provides a perspective for bridging the gap between the phenotype and genotype of ecological system, and advances our understanding of MSSF mechanisms in Zhenjiang aromatic vinegar.
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18
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Su Q, Guan T, He Y, Lv H. Siderophore Biosynthesis Governs the Virulence of Uropathogenic Escherichia coli by Coordinately Modulating the Differential Metabolism. J Proteome Res 2016; 15:1323-32. [DOI: 10.1021/acs.jproteome.6b00061] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Qiao Su
- The Laboratory
for Functional
Omics and Innovative Chinese Medicine, Innovative Drug Research Center, Chongqing University, Chongqing 401331, P.R. China
| | - Tianbing Guan
- The Laboratory
for Functional
Omics and Innovative Chinese Medicine, Innovative Drug Research Center, Chongqing University, Chongqing 401331, P.R. China
| | - Yan He
- The Laboratory
for Functional
Omics and Innovative Chinese Medicine, Innovative Drug Research Center, Chongqing University, Chongqing 401331, P.R. China
| | - Haitao Lv
- The Laboratory
for Functional
Omics and Innovative Chinese Medicine, Innovative Drug Research Center, Chongqing University, Chongqing 401331, P.R. China
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19
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Kuligowski J, Pérez-Guaita D, Sánchez-Illana Á, León-González Z, de la Guardia M, Vento M, Lock EF, Quintás G. Analysis of multi-source metabolomic data using joint and individual variation explained (JIVE). Analyst 2016; 140:4521-9. [PMID: 25988771 DOI: 10.1039/c5an00706b] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Metabolic profiling is increasingly being used for understanding biological processes but there is no single analytical technique that provides a complete quantitative or qualitative profiling of the metabolome. Data fusion (i.e. joint analysis of data from multiple sources) has the potential to circumvent this issue facilitating knowledge discovery and reliable biomarker identification. Another field of application of data fusion is the simultaneous analysis of metabolomic changes through several biofluids or tissues. However, metabolomics typically deals with large datasets, with hundreds to thousands of variables and the identification of shared and individual factors or structures across multiple sources is challenging due to the high variable to sample ratios and differences in intensity and noise range. In this work we apply a recent method, Joint and Individual Variation Explained (JIVE), for the integrated unsupervised analysis of metabolomic profiles from multiple data sources. This method separates the shared patterns among data sources (i.e. the joint structure) from the individual structure of each data source that is unrelated to the joint structure. Two examples are described to show the applicability of JIVE for the simultaneous analysis of multi-source data using: (i) plasma samples subjected to different analytical techniques, sample treatment and measurement conditions; and (ii) plasma and urine samples subjected to liquid chromatography-mass spectrometry measured using two ionization conditions.
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Affiliation(s)
- Julia Kuligowski
- Neonatal Research Centre, Health Research Institute La Fe, Valencia, Spain
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20
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Nevedomskaya E, Perryman R, Solanki S, Syed N, Mayboroda OA, Keun HC. A Systems Oncology Approach Identifies NT5E as a Key Metabolic Regulator in Tumor Cells and Modulator of Platinum Sensitivity. J Proteome Res 2015; 15:280-90. [PMID: 26629888 DOI: 10.1021/acs.jproteome.5b00793] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Altered metabolism in tumor cells is required for rapid proliferation but also can influence other phenotypes that affect clinical outcomes such as metastasis and sensitivity to chemotherapy. Here, a genome-wide association study (GWAS)-guided integration of NCI-60 transcriptome and metabolome data identified ecto-5'-nucleotidase (NT5E or CD73) as a major determinant of metabolic phenotypes in cancer cells. NT5E expression and associated metabolome variations were also correlated with sensitivity to several chemotherapeutics including platinum-based treatment. NT5E mRNA levels were observed to be elevated in cells upon in vitro and in vivo acquisition of platinum resistance in ovarian cancer cells, and specific targeting of NT5E increased tumor cell sensitivity to platinum. We observed that tumor NT5E levels were prognostic for outcomes in ovarian cancer and were elevated after treatment with platinum, supporting the translational relevance of our findings. In this work, we integrated and analyzed a plethora of public data, demonstating the merit of such a systems oncology approach for the discovery of novel players in cancer biology and therapy. We experimentally validated the main findings of the NT5E gene being involved in both intrinsic and acquired resistance to platinum-based drugs. We propose that the efficacy of conventional chemotherapy could be improved by NT5E inhibition and that NT5E expression may be a useful prognostic and predictive clinical biomarker.
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Affiliation(s)
- Ekaterina Nevedomskaya
- Center for Proteomics and Metabolomics, Leiden University Medical Center (LUMC) , L4-Q, PO Box 9600, 2300RC Leiden, The Netherlands
| | - Richard Perryman
- Division of Cancer, Department of Surgery and Cancer, Imperial College London, Hammersmith Hospital , London W12 0NN, United Kingdom.,Division of Brain Sciences, Department of Medicine, Imperial College London , London W12 0NN, United Kingdom
| | - Shyam Solanki
- Division of Cancer, Department of Surgery and Cancer, Imperial College London, Hammersmith Hospital , London W12 0NN, United Kingdom
| | - Nelofer Syed
- Division of Brain Sciences, Department of Medicine, Imperial College London , London W12 0NN, United Kingdom
| | - Oleg A Mayboroda
- Center for Proteomics and Metabolomics, Leiden University Medical Center (LUMC) , L4-Q, PO Box 9600, 2300RC Leiden, The Netherlands
| | - Hector C Keun
- Division of Cancer, Department of Surgery and Cancer, Imperial College London, Hammersmith Hospital , London W12 0NN, United Kingdom
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21
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Ren S, Shao Y, Zhao X, Hong CS, Wang F, Lu X, Li J, Ye G, Yan M, Zhuang Z, Xu C, Xu G, Sun Y. Integration of Metabolomics and Transcriptomics Reveals Major Metabolic Pathways and Potential Biomarker Involved in Prostate Cancer. Mol Cell Proteomics 2015; 15:154-63. [PMID: 26545398 DOI: 10.1074/mcp.m115.052381] [Citation(s) in RCA: 126] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2015] [Indexed: 12/16/2022] Open
Abstract
Prostate cancer is a highly prevalent tumor affecting millions of men worldwide, but poor understanding of its pathogenesis has limited effective clinical management of patients. In addition to transcriptional profiling or transcriptomics, metabolomics is being increasingly utilized to discover key molecular changes underlying tumorigenesis. In this study, we integrated transcriptomics and metabolomics to analyze 25 paired human prostate cancer tissues and adjacent noncancerous tissues, followed by further validation of our findings in an additional cohort of 51 prostate cancer patients and 16 benign prostatic hyperplasia patients. We found several altered pathways aberrantly expressed at both metabolic and transcriptional levels, including cysteine and methionine metabolism, nicotinamide adenine dinucleotide metabolism, and hexosamine biosynthesis. Additionally, the metabolite sphingosine demonstrated high specificity and sensitivity for distinguishing prostate cancer from benign prostatic hyperplasia, particularly for patients with low prostate specific antigen level (0-10 ng/ml). We also found impaired sphingosine-1-phosphate receptor 2 signaling, downstream of sphingosine, representing a loss of tumor suppressor gene and a potential key oncogenic pathway for therapeutic targeting. By integrating metabolomics and transcriptomics, we have provided both a broad picture of the molecular perturbations underlying prostate cancer and a preliminary study of a novel metabolic signature, which may help to discriminate prostate cancer from normal tissue and benign prostatic hyperplasia.
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Affiliation(s)
- Shancheng Ren
- From the ‡Department of Urology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai, China
| | - Yaping Shao
- §Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 116023 Dalian, China
| | - Xinjie Zhao
- §Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 116023 Dalian, China
| | - Christopher S Hong
- ¶Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD
| | - Fubo Wang
- From the ‡Department of Urology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai, China
| | - Xin Lu
- §Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 116023 Dalian, China
| | - Jia Li
- §Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 116023 Dalian, China
| | - Guozhu Ye
- §Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 116023 Dalian, China
| | - Min Yan
- §Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 116023 Dalian, China
| | - Zhengping Zhuang
- ¶Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD
| | - Chuanliang Xu
- From the ‡Department of Urology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai, China
| | - Guowang Xu
- §Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 116023 Dalian, China,
| | - Yinghao Sun
- From the ‡Department of Urology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai, China,
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22
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Systematic integration of molecular profiles identifies miR-22 as a regulator of lipid and folate metabolism in breast cancer cells. Oncogene 2015; 35:2766-76. [PMID: 26477310 DOI: 10.1038/onc.2015.333] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2014] [Revised: 05/31/2015] [Accepted: 07/06/2015] [Indexed: 12/12/2022]
Abstract
Dysregulated microRNA (miRNA) mediate malignant phenotypes, including metabolic reprogramming. By performing an integrative analysis of miRNA and metabolome data for the NCI-60 cell line panel, we identified an miRNA cluster strongly associated with both c-Myc expression and global metabolic variation. Within this cluster the cancer-associated and cardioprotective miR-22 was shown to repress fatty acid synthesis and elongation in tumour cells by targeting ATP citrate lyase and fatty acid elongase 6, as well as impairing mitochondrial one-carbon metabolism by suppression of methylene tetrahydrofolate dehydrogenase/cyclohydrolase. Across several data sets, expression of these target genes were associated with poorer outcomes in breast cancer patients. Importantly, a beneficial effect of miR-22 on clinical outcomes in breast cancer was shown to depend on the expression levels of the identified target genes, demonstrating the relevance of miRNA/mRNA interactions to disease progression in vivo. Our systematic analysis establishes miR-22 as a novel regulator of tumour cell metabolism, a function that could contribute to the role of this miRNA in cellular differentiation and cancer development. Moreover, we provide a paradigmatic example of effect modification in outcome analysis as a consequence of miRNA-directed gene targeting, a phenomenon that could be exploited to improve patient prognosis and treatment.
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23
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Cavill R, Jennen D, Kleinjans J, Briedé JJ. Transcriptomic and metabolomic data integration. Brief Bioinform 2015; 17:891-901. [PMID: 26467821 DOI: 10.1093/bib/bbv090] [Citation(s) in RCA: 147] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2015] [Indexed: 01/12/2023] Open
Abstract
Many studies now produce parallel data sets from different omics technologies; however, the task of interpreting the acquired data in an integrated fashion is not trivial. This review covers those methods that have been used over the past decade to statistically integrate and interpret metabolomics and transcriptomic data sets. It defines four categories of approaches, correlation-based integration, concatenation-based integration, multivariate-based integration and pathway-based integration, into which all existing statistical methods fit. It also explores the choices in study design for generating samples for analysis by these omics technologies and the impact that these technical decisions have on the subsequent data analysis options.
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24
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Effects of surgical and dietary weight loss therapy for obesity on gut microbiota composition and nutrient absorption. BIOMED RESEARCH INTERNATIONAL 2015; 2015:806248. [PMID: 25710027 PMCID: PMC4330959 DOI: 10.1155/2015/806248] [Citation(s) in RCA: 205] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/27/2014] [Accepted: 10/10/2014] [Indexed: 12/19/2022]
Abstract
Evidence suggests a correlation between the gut microbiota composition and weight loss caused by caloric restriction. Laparoscopic sleeve gastrectomy (LSG), a surgical intervention for obesity, is classified as predominantly restrictive procedure. In this study we investigated functional weight loss mechanisms with regard to gut microbial changes and energy harvest induced by LSG and a very low calorie diet in ten obese subjects (n = 5 per group) demonstrating identical weight loss during a follow-up period of six months. For gut microbiome analysis next generation sequencing was performed and faeces were analyzed for targeted metabolomics. The energy-reabsorbing potential of the gut microbiota decreased following LSG, indicated by the Bacteroidetes/Firmicutes ratio, but increased during diet. Changes in butyrate-producing bacterial species were responsible for the Firmicutes changes in both groups. No alteration of faecal butyrate was observed, but the microbial capacity for butyrate fermentation decreased following LSG and increased following dietetic intervention. LSG resulted in enhanced faecal excretion of nonesterified fatty acids and bile acids. LSG, but not dietetic restriction, improved the obesity-associated gut microbiota composition towards a lean microbiome phenotype. Moreover, LSG increased malabsorption due to loss in energy-rich faecal substrates and impairment of bile acid circulation. This trial is registered with ClinicalTrials.gov NCT01344525.
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25
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Li Y, Liao Q, Lin M, Zhong D, Wei L, Han B, Miao H, Yao M, Xie Z. An integrated metabonomics and microbiology analysis of host-microbiota metabolic interactions in rats with Coptis chinensis-induced diarrhea. RSC Adv 2015. [DOI: 10.1039/c5ra16338b] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
Coptis-induced diarrhea has been found to associate with the perturbed gut microbiota which in return impaired host-gut microbial co-metabolism in SD rats.
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Affiliation(s)
- Yemeng Li
- School of Pharmaceutical Sciences
- Sun Yat-sen University
- Guangzhou
- P. R. China
| | - Qiongfeng Liao
- School of Chinese Materia Medica
- Guangzhou University of Chinese Medicine
- Guangzhou
- P. R. China
| | - Manna Lin
- School of Pharmaceutical Sciences
- Sun Yat-sen University
- Guangzhou
- P. R. China
- School of Chinese Materia Medica
| | - Danmin Zhong
- School of Pharmaceutical Sciences
- Sun Yat-sen University
- Guangzhou
- P. R. China
| | - Lin Wei
- School of Pharmaceutical Sciences
- Sun Yat-sen University
- Guangzhou
- P. R. China
| | - Bo Han
- School of Pharmacy
- Shihezi University
- Shihezi
- P. R. China
| | - Hui Miao
- School of Pharmaceutical Sciences
- Sun Yat-sen University
- Guangzhou
- P. R. China
| | - Meicun Yao
- School of Pharmaceutical Sciences
- Sun Yat-sen University
- Guangzhou
- P. R. China
| | - Zhiyong Xie
- School of Pharmaceutical Sciences
- Sun Yat-sen University
- Guangzhou
- P. R. China
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26
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Emwas AH, Luchinat C, Turano P, Tenori L, Roy R, Salek RM, Ryan D, Merzaban JS, Kaddurah-Daouk R, Zeri AC, Nagana Gowda GA, Raftery D, Wang Y, Brennan L, Wishart DS. Standardizing the experimental conditions for using urine in NMR-based metabolomic studies with a particular focus on diagnostic studies: a review. Metabolomics 2015; 11:872-894. [PMID: 26109927 PMCID: PMC4475544 DOI: 10.1007/s11306-014-0746-7] [Citation(s) in RCA: 150] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/29/2014] [Accepted: 10/27/2014] [Indexed: 02/08/2023]
Abstract
The metabolic composition of human biofluids can provide important diagnostic and prognostic information. Among the biofluids most commonly analyzed in metabolomic studies, urine appears to be particularly useful. It is abundant, readily available, easily stored and can be collected by simple, noninvasive techniques. Moreover, given its chemical complexity, urine is particularly rich in potential disease biomarkers. This makes it an ideal biofluid for detecting or monitoring disease processes. Among the metabolomic tools available for urine analysis, NMR spectroscopy has proven to be particularly well-suited, because the technique is highly reproducible and requires minimal sample handling. As it permits the identification and quantification of a wide range of compounds, independent of their chemical properties, NMR spectroscopy has been frequently used to detect or discover disease fingerprints and biomarkers in urine. Although protocols for NMR data acquisition and processing have been standardized, no consensus on protocols for urine sample selection, collection, storage and preparation in NMR-based metabolomic studies have been developed. This lack of consensus may be leading to spurious biomarkers being reported and may account for a general lack of reproducibility between laboratories. Here, we review a large number of published studies on NMR-based urine metabolic profiling with the aim of identifying key variables that may affect the results of metabolomics studies. From this survey, we identify a number of issues that require either standardization or careful accounting in experimental design and provide some recommendations for urine collection, sample preparation and data acquisition.
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Affiliation(s)
- Abdul-Hamid Emwas
- Imaging and Characterization Core Lab, King Abdullah University of Science and Technology, KSA, Thuwal, Saudi Arabia
| | - Claudio Luchinat
- Centro Risonanze Magnetiche – CERM, University of Florence, Florence, Italy
| | - Paola Turano
- Centro Risonanze Magnetiche – CERM, University of Florence, Florence, Italy
| | | | - Raja Roy
- Centre of Biomedical Research, Formerly known as Centre of Biomedical Magnetic Resonance, Sanjay Gandhi Post-Graduate Institute of Medical Sciences Campus, Lucknow, India
| | - Reza M. Salek
- Department of Biochemistry & Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Cambridge, CB10 1SD UK
| | - Danielle Ryan
- School of Agricultural and Wine Sciences, Charles Sturt University, Wagga Wagga, Australia
| | - Jasmeen S. Merzaban
- Biological and Environmental Sciences and Engineering, King Abdullah University of Science and Technology, KSA, Thuwal, Saudi Arabia
| | - Rima Kaddurah-Daouk
- Pharmacometabolomics Center, School of Medicine, Duke University, Durham, USA
| | - Ana Carolina Zeri
- Brazilian Biosciences National Laboratory, LNBio, Campinas, SP Brazil
| | - G. A. Nagana Gowda
- Department of Anethesiology and Pain Medicine, Northwest Metabolomics Research Center, University of Washington, 850 Republican St., Seattle, WA 98109 USA
| | - Daniel Raftery
- Department of Anethesiology and Pain Medicine, Northwest Metabolomics Research Center, University of Washington, 850 Republican St., Seattle, WA 98109 USA
| | - Yulan Wang
- Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Beijing, China
| | - Lorraine Brennan
- Institute of Food and Health and Conway Institute, School of Agriculture & Food Science, Dublin 4, Ireland
| | - David S. Wishart
- Department of Computing Science, University of Alberta, Edmonton, Alberta Canada
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27
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Chadeau-Hyam M, Campanella G, Jombart T, Bottolo L, Portengen L, Vineis P, Liquet B, Vermeulen RCH. Deciphering the complex: methodological overview of statistical models to derive OMICS-based biomarkers. ENVIRONMENTAL AND MOLECULAR MUTAGENESIS 2013; 54:542-557. [PMID: 23918146 DOI: 10.1002/em.21797] [Citation(s) in RCA: 86] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2013] [Revised: 05/21/2013] [Accepted: 05/28/2013] [Indexed: 05/28/2023]
Abstract
Recent technological advances in molecular biology have given rise to numerous large-scale datasets whose analysis imposes serious methodological challenges mainly relating to the size and complex structure of the data. Considerable experience in analyzing such data has been gained over the past decade, mainly in genetics, from the Genome-Wide Association Study era, and more recently in transcriptomics and metabolomics. Building upon the corresponding literature, we provide here a nontechnical overview of well-established methods used to analyze OMICS data within three main types of regression-based approaches: univariate models including multiple testing correction strategies, dimension reduction techniques, and variable selection models. Our methodological description focuses on methods for which ready-to-use implementations are available. We describe the main underlying assumptions, the main features, and advantages and limitations of each of the models. This descriptive summary constitutes a useful tool for driving methodological choices while analyzing OMICS data, especially in environmental epidemiology, where the emergence of the exposome concept clearly calls for unified methods to analyze marginally and jointly complex exposure and OMICS datasets.
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Affiliation(s)
- Marc Chadeau-Hyam
- Department of Epidemiology and Biostatistics, MRC-HPA Centre for Environment and Health, School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, United Kingdom.
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28
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Beger RD. A review of applications of metabolomics in cancer. Metabolites 2013; 3:552-74. [PMID: 24958139 PMCID: PMC3901293 DOI: 10.3390/metabo3030552] [Citation(s) in RCA: 168] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2013] [Revised: 05/17/2013] [Accepted: 06/24/2013] [Indexed: 12/17/2022] Open
Abstract
Cancer is a devastating disease that alters the metabolism of a cell and the surrounding milieu. Metabolomics is a growing and powerful technology capable of detecting hundreds to thousands of metabolites in tissues and biofluids. The recent advances in metabolomics technologies have enabled a deeper investigation into the metabolism of cancer and a better understanding of how cancer cells use glycolysis, known as the “Warburg effect,” advantageously to produce the amino acids, nucleotides and lipids necessary for tumor proliferation and vascularization. Currently, metabolomics research is being used to discover diagnostic cancer biomarkers in the clinic, to better understand its complex heterogeneous nature, to discover pathways involved in cancer that could be used for new targets and to monitor metabolic biomarkers during therapeutic intervention. These metabolomics approaches may also provide clues to personalized cancer treatments by providing useful information to the clinician about the cancer patient’s response to medical interventions.
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Affiliation(s)
- Richard D Beger
- National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA.
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29
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Loftus NJ, Lai L, Wilkinson RW, Odedra R, Wilson ID, Barnes AJ. Global metabolite profiling of human colorectal cancer xenografts in mice using HPLC-MS/MS. J Proteome Res 2013; 12:2980-6. [PMID: 23631600 DOI: 10.1021/pr400260h] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Reversed-phase gradient LC-MS was used to perform untargeted metabonomic analysis on extracts of human colorectal cancer (CRC) cell lines (COLO 205, HT-29, HCT 116 and SW620) subcutaneously implanted into age-matched athymic nude male mice to study small molecule metabolic profiles and examine possible correlations with human cancer biopsies. Following high mass accuracy data analysis using MS and MS/MS, metabolites were identified by searching against major metabolite databases including METLIN, MASSBANK, The Human Metabolome Database, PubChem, Biospider, LipidMaps and KEGG. HT-29 and COLO 205 tumor xenografts showed a distribution of metabolites that differed from SW620 and HCT 116 xenografts (predominantly on the basis of relative differences in the amounts of amino acids and lipids detected). This finding is consistent with NMR-based analysis of human colorectal tissue, where the metabolite profiles of HT-29 tumors exhibit the greatest similarity to human rectal cancer tissue with respect to changes in the relative amounts of lipids and choline-containing compounds. As the metabolic signatures of cancer cells result from oncogene-directed metabolic reprogramming, the HT-29 xenografts in mice may prove to be a useful model to further study the tumor microenvironment and cancer biology.
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Affiliation(s)
- Neil J Loftus
- Mass Spectrometry Business Unit, Shimadzu, Manchester, United Kingdom.
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Spermine and citrate as metabolic biomarkers for assessing prostate cancer aggressiveness. PLoS One 2013; 8:e62375. [PMID: 23626811 PMCID: PMC3633894 DOI: 10.1371/journal.pone.0062375] [Citation(s) in RCA: 125] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2012] [Accepted: 03/20/2013] [Indexed: 11/19/2022] Open
Abstract
Separating indolent from aggressive prostate cancer is an important clinical challenge for identifying patients eligible for active surveillance, thereby reducing the risk of overtreatment. The purpose of this study was to assess prostate cancer aggressiveness by metabolic profiling of prostatectomy tissue and to identify specific metabolites as biomarkers for aggressiveness. Prostate tissue samples (n = 158, 48 patients) with a high cancer content (mean: 61.8%) were obtained using a new harvesting method, and metabolic profiles of samples representing different Gleason scores (GS) were acquired by high resolution magic angle spinning magnetic resonance spectroscopy (HR-MAS). Multivariate analysis (PLS, PLS-DA) and absolute quantification (LCModel) were used to examine the ability to predict cancer aggressiveness by comparing low grade (GS = 6, n = 30) and high grade (GS≥7, n = 81) cancer with normal adjacent tissue (n = 47). High grade cancer tissue was distinguished from low grade cancer tissue by decreased concentrations of spermine (p = 0.0044) and citrate (p = 7.73·10−4), and an increase in the clinically applied (total choline+creatine+polyamines)/citrate (CCP/C) ratio (p = 2.17·10−4). The metabolic profiles were significantly correlated to the GS obtained from each tissue sample (r = 0.71), and cancer tissue could be distinguished from normal tissue with sensitivity 86.9% and specificity 85.2%. Overall, our findings show that metabolic profiling can separate aggressive from indolent prostate cancer. This holds promise for the benefit of applying in vivo magnetic resonance spectroscopy (MRS) within clinical MR imaging investigations, and HR-MAS analysis of transrectal ultrasound-guided biopsies has a potential as an additional diagnostic tool.
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GU HW, QI YP, XU N, DING JH, AN YB, CHEN HW. Nuclear Magnetic Resonance Spectroscopy and Mass Spectrometry-Based Metabolomics for Cancer Diagnosis. CHINESE JOURNAL OF ANALYTICAL CHEMISTRY 2012. [DOI: 10.1016/s1872-2040(11)60594-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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Zhang J, Wei S, Liu L, Nagana Gowda G, Bonney P, Stewart J, Knapp DW, Raftery D. NMR-based metabolomics study of canine bladder cancer. Biochim Biophys Acta Mol Basis Dis 2012; 1822:1807-14. [DOI: 10.1016/j.bbadis.2012.08.001] [Citation(s) in RCA: 51] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2012] [Revised: 07/14/2012] [Accepted: 08/02/2012] [Indexed: 12/12/2022]
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Bousette N, Gramolini AO, Kislinger T. Proteomics-based investigations of animal models of disease. Proteomics Clin Appl 2012; 2:638-53. [PMID: 21136864 DOI: 10.1002/prca.200780043] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Cells contain a large yet, constant genome, which contains all the coding information necessary to sustain cellular physiology. However, proteins are the end products of genes, and hence dictate the phenotype of cells and tissues. Therefore, proteomics can provide key information for the elucidation of physiological and pathophysiological mechanisms by identifying the protein profile from cells and tissues. The relatively novel techniques used for the study of proteomics thus have the potential to improve diagnostic, prognostic, as well as therapeutic avenues. In this review, we first discuss the benefits of animal models over the use of human samples for the proteomic analysis of human disease. Next, we aim to demonstrate the potential of proteomics in the elucidation of disease mechanisms that may not be possible by other conventional technologies. Following this, we describe the use of proteomics for the analysis of PTM and protein interactions in animal models and their relevance to the study of human disease. Finally, we discuss the development of clinical biomarkers for the early diagnosis of disease via proteomic analysis of animal models. We also discuss the development of standard proteomes and relate how this data will benefit future proteomic research. A comprehensive review of all animal models used in conjunction with proteomics is beyond the scope of this manuscript. Therefore, we aimed to cover a large breadth of topics, which together, demonstrate the potential of proteomics as a powerful tool in biomedical research.
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Affiliation(s)
- Nicolas Bousette
- Department of Physiology, University of Toronto, Toronto, Ontario, Canada; Heart and Stroke/Richard Lewar Centre of Cardiovascular Excellence, Toronto, Ontario, Canada
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Abstract
Testosterone is the major circulating androgen in men but exhibits an age-related decline in the ageing male. Late-onset hypogonadism or androgen deficiency syndrome (ADS) is a 'syndromic' disorder including both a persistent low testosterone serum concentration and major clinical symptoms, including erectile dysfunction, low libido, decreased muscle mass and strength, increased body fat, decreased vitality or depressed mood. Given its unspecific symptoms, treatment goals and monitoring parameters, this review will outline the various uncertainties concerning the diagnosis, therapy and monitoring of ADS to date. Literature was identified primarily through searches for specific investigators in the PubMed database. No date or language limits were applied in the literature search for the present review. The current state of research, showing that metabolomics is starting to have an impact not only on disease diagnosis and prognosis but also on drug treatment efficacy and safety monitoring, will be presented, and the application of metabolomics to improve the clinical management of ADS will be discussed. Finally, the scientific opportunities presented by metabolomics and other -omics as novel and promising tools for biomarker discovery and individualised testosterone replacement therapy in men will be explored.
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Affiliation(s)
- Robin Haring
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Ferdinand-Sauerbruch-Strasse, D-17475 Greifswald, Germany.
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Wang C, Jiang K, Kang X, Gao D, Sun C, Li Y, Sun L, Zhang S, Liu X, Wu W, Yang P, Guo K, Liu Y. Tumor-derived secretory clusterin induces epithelial-mesenchymal transition and facilitates hepatocellular carcinoma metastasis. Int J Biochem Cell Biol 2012; 44:2308-20. [PMID: 23010347 DOI: 10.1016/j.biocel.2012.09.012] [Citation(s) in RCA: 64] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2012] [Revised: 09/11/2012] [Accepted: 09/14/2012] [Indexed: 01/08/2023]
Abstract
Hepatocellular carcinoma (HCC) is the third most common cause of cancer mortality. Metastasis is the major concern that causes death in HCC. The goal of this study was to identify tumor-derived proteins in serum during HCC metastasis using an orthotopic xenograft tumor model and explore the role of key protein in HCC metastasis. Serum samples collected from HCCLM3-R metastatic HCC tumor model at specific stages of metastasis (1 wk, 3 wks and 6 wks) were subjected to iTRAQ labeling followed by 2DLC-ESI-MS/MS analysis. Twenty tumor-derived proteins were identified through human specific peptides. Secretory clusterin (sCLU), which was significantly upregulated during cancer progression and metastasis, was chosen for further study. The expression of sCLU was significantly higher in metastatic HCC cell lines and samples from metastatic HCC patients. ShRNA-mediated down-regulation of sCLU resulted in a reduced migratory capacity in HCC cell lines, as well as a reduction in pulmonary metastasis in vivo. Overexpression of sCLU in HepG2 cell line showed increased cell migratory ability. Further study found that sCLU contributed to HCC migration and epithelial-mesenchymal transition (EMT) in vitro, and metastasis in vivo. In addition, sCLU also plays an important role in the regulation of TGF-β1-smad3 signaling. These findings suggest that sCLU may promote HCC metastasis via the induction of EMT process and may be a candidate target for HCC therapy.
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Affiliation(s)
- Cun Wang
- Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai 200032, China
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Regional magnetic resonance imaging measures for multivariate analysis in Alzheimer's disease and mild cognitive impairment. Brain Topogr 2012; 26:9-23. [PMID: 22890700 PMCID: PMC3536978 DOI: 10.1007/s10548-012-0246-x] [Citation(s) in RCA: 140] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2012] [Accepted: 07/21/2012] [Indexed: 01/18/2023]
Abstract
Automated structural magnetic resonance imaging (MRI) processing pipelines are gaining popularity for Alzheimer’s disease (AD) research. They generate regional volumes, cortical thickness measures and other measures, which can be used as input for multivariate analysis. It is not clear which combination of measures and normalization approach are most useful for AD classification and to predict mild cognitive impairment (MCI) conversion. The current study includes MRI scans from 699 subjects [AD, MCI and controls (CTL)] from the Alzheimer’s disease Neuroimaging Initiative (ADNI). The Freesurfer pipeline was used to generate regional volume, cortical thickness, gray matter volume, surface area, mean curvature, gaussian curvature, folding index and curvature index measures. 259 variables were used for orthogonal partial least square to latent structures (OPLS) multivariate analysis. Normalisation approaches were explored and the optimal combination of measures determined. Results indicate that cortical thickness measures should not be normalized, while volumes should probably be normalized by intracranial volume (ICV). Combining regional cortical thickness measures (not normalized) with cortical and subcortical volumes (normalized with ICV) using OPLS gave a prediction accuracy of 91.5 % when distinguishing AD versus CTL. This model prospectively predicted future decline from MCI to AD with 75.9 % of converters correctly classified. Normalization strategy did not have a significant effect on the accuracies of multivariate models containing multiple MRI measures for this large dataset. The appropriate choice of input for multivariate analysis in AD and MCI is of great importance. The results support the use of un-normalised cortical thickness measures and volumes normalised by ICV.
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Kirwan GM, Johansson E, Kleemann R, Verheij ER, Wheelock ÅM, Goto S, Trygg J, Wheelock CE. Building multivariate systems biology models. Anal Chem 2012; 84:7064-71. [PMID: 22856472 DOI: 10.1021/ac301269r] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Systems biology methods using large-scale "omics" data sets face unique challenges: integrating and analyzing near limitless data space, while recognizing and removing systematic variation or noise. Herein we propose a complementary multivariate analysis workflow to both integrate "omics" data from disparate sources and analyze the results for specific and unique sample correlations. This workflow combines principal component analysis (PCA), orthogonal projections to latent structures discriminate analysis (OPLS-DA), orthogonal 2 projections to latent structures (O2PLS), and shared and unique structures (SUS) plots. The workflow is demonstrated using data from a study in which ApoE3Leiden mice were fed an atherogenic diet consisting of increasing cholesterol levels followed by therapeutic intervention (fenofibrate, rosuvastatin, and LXR activator T-0901317). The levels of structural lipids (lipidomics) and free fatty acids in liver were quantified via liquid chromatography-mass spectrometry (LC-MS). The complementary workflow identified diglycerides as key hepatic metabolites affected by dietary cholesterol and drug intervention. Modeling of the three therapeutics for mice fed a high-cholesterol diet further highlighted diglycerides as metabolites of interest in atherogenesis, suggesting a role in eliciting chronic liver inflammation. In particular, O2PLS-based SUS2 plots showed that treatment with T-0901317 or rosuvastatin returned the diglyceride profile in high-cholesterol-fed mice to that of control animals.
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Affiliation(s)
- Gemma M Kirwan
- Bioinformatics Centre, Institute for Chemical Research, Kyoto University, Kyoto, Japan.
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Westman E, Muehlboeck JS, Simmons A. Combining MRI and CSF measures for classification of Alzheimer's disease and prediction of mild cognitive impairment conversion. Neuroimage 2012; 62:229-38. [PMID: 22580170 DOI: 10.1016/j.neuroimage.2012.04.056] [Citation(s) in RCA: 204] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2011] [Revised: 04/25/2012] [Accepted: 04/28/2012] [Indexed: 01/24/2023] Open
Abstract
The suggested revision of the NINCDS-ADRDA criterion for the diagnosis of Alzheimer's disease (AD) includes at least one abnormal biomarker among magnetic resonance imaging (MRI), positron emission tomography (PET) and cerebrospinal fluid (CSF). We aimed to investigate if the combination of baseline MRI and CSF could enhance the classification of AD compared to using either alone and predict mild cognitive impairment (MCI) conversion at multiple future time points. 369 subjects from the Alzheimer's disease Neuroimaging Initiative (ADNI) were included in the study (AD=96, MCI=162 and CTL=111). Freesurfer was used to generate regional subcortical volumes and cortical thickness measures. A total of 60 variables were used for orthogonal partial least squares to latent structures (OPLS) multivariate analysis (57 MRI measures and 3 CSF measures: Aβ(42), t-tau and p-tau). Combining MRI and CSF gave the best results for distinguishing AD vs. CTL. We found an accuracy of 91.8% for the combined model at baseline compared to 81.6% for CSF measures and 87.0% for MRI measures alone. The combined model also gave the best accuracy when distinguishing between MCI vs. CTL (77.6%) at baseline. MCI subjects who converted to AD by 12 and 18month follow-up were accurately predicted at baseline using an AD vs. CTL model (82.9% and 86.4% respectively), with lower prediction accuracies for those MCI subjects converting by 24 and 36month follow up (75.4% and 68.0% respectively). The overall prediction accuracies for converters and non-converters ranged from 58.6% to 66.4% at different time points. Combining MRI and CSF measures in a multivariate model at baseline gave better accuracy for discriminating between AD and CTL, between MCI and CTL and for predicting future conversion from MCI to AD, than using either MRI or CSF separately.
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Affiliation(s)
- Eric Westman
- Department of Neuroimaging, Institute of Psychiatry, King's College London, London, UK.
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Eriksson L, Rosén J, Johansson E, Trygg J. Orthogonal PLS (OPLS) Modeling for Improved Analysis and Interpretation in Drug Design. Mol Inform 2012; 31:414-9. [PMID: 27477460 DOI: 10.1002/minf.201200158] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2011] [Accepted: 02/10/2012] [Indexed: 11/08/2022]
Abstract
Partial least squares (PLS) regression is a flexible data analytical approach, which can be made even more versatile and useful by various modifications. In this article we describe the extension into orthogonal PLS modeling, in terms of two new methods, called OPLS and O2PLS, with similar prediction capacity but improved model interpretation.
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Affiliation(s)
| | - Josefin Rosén
- MKS Umetrics AB, Stortorget 21, SE-211 34 Malmö, Sweden
| | | | - Johan Trygg
- Computational Life Science Cluster (CLiC), Department of Chemistry, Umeå University, SE-901 87 Umeå, Sweden
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Duarte IF, Gil AM. Metabolic signatures of cancer unveiled by NMR spectroscopy of human biofluids. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2012; 62:51-74. [PMID: 22364616 DOI: 10.1016/j.pnmrs.2011.11.002] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2011] [Accepted: 11/23/2011] [Indexed: 05/31/2023]
Affiliation(s)
- Iola F Duarte
- CICECO, Department of Chemistry, University of Aveiro, Campus de Santiago, 3810-193 Aveiro, Portugal.
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Haring R, Wallaschofski H. Diving through the "-omics": the case for deep phenotyping and systems epidemiology. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2012; 16:231-4. [PMID: 22320900 DOI: 10.1089/omi.2011.0108] [Citation(s) in RCA: 44] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Enabled by diverse high-throughput technologies, the rapidly evolving field of "-omics sciences" offers the potential to study health and disease in breadth and depth at the human population level. We have recently linked genomics and metabolomics to present the first genome-wide association study of metabolic traits in human urine providing new insights into the functional background of chronic kidney disease. We propose systems epidemiology as a novel approach to study the complexities of human pathophysiology by integrating various population-level omic-metrics and to identify new trans-omic biomarkers.
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Affiliation(s)
- Robin Haring
- Preventive Medicine & Epidemiology Section, Boston University School of Medicine, Boston, Massachusetts, USA.
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Gianazza E, Vegeto E, Eberini I, Sensi C, Miller I. Neglected markers: Altered serum proteome in murine models of disease. Proteomics 2012; 12:691-707. [DOI: 10.1002/pmic.201100320] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2011] [Accepted: 08/28/2011] [Indexed: 11/09/2022]
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Rezaei-Tavirani M, Fathi F, Darvizeh F, Zali MR, Rostami Nejad M, Rostami K, Tafazzoli M, Arefi oskouie A, Mortazavi-Tabatabaei SA. Advantage of Applying OSC to (1)H NMR-Based Metabonomic Data of Celiac Disease. Int J Endocrinol Metab 2012; 10:548-52. [PMID: 23843818 PMCID: PMC3693626 DOI: 10.5812/ijem.3058] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2011] [Revised: 02/17/2012] [Accepted: 04/17/2012] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Celiac disease (CD) is a disorder associated with body reaction to gluten. After the gluten intake, an immune reaction against the protein occurs and damages villi of small intestine in celiac patients gradually. OBJECTIVES The OSC, a filtering method for minimization of inter- and intra-spectrometer variations that influence on data acquisition, was applied to biofluid NMR data of CD patients. PATIENTS AND METHODS In this study, metabolites of total 56 serum samples from 12 CD patients, 15 CD patients taking gluten-free diet (GFD), and 29 healthy cases were analyzed using nuclear magnetic resonance (NMR) and associated theoretical analysis. Employing ProMetab (version ProMetab_v3_3) software, data obtained from NMR spectra were reduced and orthogonal signal correction (OSC) effect on celiac disease metabonomics before and after the separation by principle component analysis (PCA) was investigated. RESULTS The three groups were separated by OSC and findings were analyzed by partial least squares discriminant analysis (PLS-DA) method. Root mean square error of calibration (RMSEc) and correlation coefficient of calibration (Rc) for PLS-DA referred to an efficient group separation filtered by OSC. CONCLUSIONS The applied leave-one-out cross-validation to PLS-DA method performed along with OSC confirmed validation of data analysis. Finally four metabolites are introduced as CD biomarkers.
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Affiliation(s)
- Mostafa Rezaei-Tavirani
- Proteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, IR Iran
| | - Fariba Fathi
- Department of Chemistry, Sharif University of Technology, Tehran, IR Iran
| | - Fatemeh Darvizeh
- Department of Medicine, Debrecen Medical School, Debrecen, Hungary
| | - Mohamad Reza Zali
- Research Center for Gastroenterology and Liver Disease, Shahid Beheshti University of Medical Sciences, Tehran, IR Iran
| | | | - Kamran Rostami
- Department of Basic Science Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, IR Iran
| | - Mohsen Tafazzoli
- Department of Chemistry, Sharif University of Technology, Tehran, IR Iran
- Corresponding authors: Mohsen Tafazzoli, Department of Chemistry, Sharif University of Technology, P.O. Box 11155-9516, Tehran, IR Iran. Tel.: +98-2166165305, Fax: +98-2166012983, E-mail: ; Afsaneh Arefi oskouie, Department of Basic Science Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, P.O. Box: 19395-4618, Tehran, IR Iran. Tel.: +98-2122718505, Fax: +98-2166012983, E-mail:
| | - Afsaneh Arefi oskouie
- Department of Basic Science Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, IR Iran
- Corresponding authors: Mohsen Tafazzoli, Department of Chemistry, Sharif University of Technology, P.O. Box 11155-9516, Tehran, IR Iran. Tel.: +98-2166165305, Fax: +98-2166012983, E-mail: ; Afsaneh Arefi oskouie, Department of Basic Science Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, P.O. Box: 19395-4618, Tehran, IR Iran. Tel.: +98-2122718505, Fax: +98-2166012983, E-mail:
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McKenzie JS, Donarski JA, Wilson JC, Charlton AJ. Analysis of complex mixtures using high-resolution nuclear magnetic resonance spectroscopy and chemometrics. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2011; 59:336-59. [PMID: 22027342 DOI: 10.1016/j.pnmrs.2011.04.003] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2011] [Accepted: 04/27/2011] [Indexed: 05/16/2023]
Affiliation(s)
- James S McKenzie
- The Food and Environment Research Agency, Sand Hutton, York YO41 1LZ, United Kingdom
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Wang C, Guo K, Gao D, Kang X, Jiang K, Li Y, Sun L, Zhang S, Sun C, Liu X, Wu W, Yang P, Liu Y. Identification of transaldolase as a novel serum biomarker for hepatocellular carcinoma metastasis using xenografted mouse model and clinic samples. Cancer Lett 2011; 313:154-66. [PMID: 22023829 DOI: 10.1016/j.canlet.2011.08.031] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2011] [Revised: 08/29/2011] [Accepted: 08/31/2011] [Indexed: 01/19/2023]
Abstract
Hepatocellular carcinoma (HCC) is one of serious disorders with the highest morbidities and mortalities worldwide. Metastasis is the major concern that causes death in HCC. The goal of this study was to screen and identify potential serum proteins indicating HCC metastasis. Serum samples collected from control and HCCLM3-R metastatic HCC tumor model at specific stages of metastasis (1 wk, 3 wks and 6 wks) were subjected to iTRAQ labeling followed by 2DLC-ESI-MS/MS analysis. A total of 554 proteins were identified and 80 proteins were differential expressed at least between one adjacent time points. Among them, expression level of transaldolase (TALDO) was validated in mouse and human serum. The level of TALDO protein was found to be higher in metastatic mice serum compared to that of non-metastatic mice. Human specific TALDO was then identified in mouse serum through human specific peptides. Immunohistochemical and western blot analysis showed that the expression of TALDO in human HCC tissues and HCC cell lines was associated with its metastatic behavior. Subsequent screening of TALDO expression in 72 clinical serum samples (comprising 36 non-metastatic HCC and 36 metastatic HCC samples) revealed higher TALDO level in the serum of metastatic HCC patients. A receiver operating characteristic (ROC) curve estimated a maximal sensitivity of 77.8% and 86.1% specificity for TALDO in detection of HCC metastasis. The present results demonstrated that the nude mouse xenograft model is an efficient system for performing metastasis-related biomarker discovery. TALDO may be useful biomarkers for the detection of HCC metastasis.
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Affiliation(s)
- Cun Wang
- Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai 200032, China
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Westman E, Cavallin L, Muehlboeck JS, Zhang Y, Mecocci P, Vellas B, Tsolaki M, Kłoszewska I, Soininen H, Spenger C, Lovestone S, Simmons A, Wahlund LO. Sensitivity and specificity of medial temporal lobe visual ratings and multivariate regional MRI classification in Alzheimer's disease. PLoS One 2011; 6:e22506. [PMID: 21811624 PMCID: PMC3141068 DOI: 10.1371/journal.pone.0022506] [Citation(s) in RCA: 79] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2011] [Accepted: 06/22/2011] [Indexed: 11/18/2022] Open
Abstract
Background Visual assessment rating scales for medial temporal lobe (MTL) atrophy have been used by neuroradiologists in clinical practice to aid the diagnosis of Alzheimer's disease (AD). Recently multivariate classification methods for magnetic resonance imaging (MRI) data have been suggested as alternative tools. If computerized methods are to be implemented in clinical practice they need to be as good as, or better than experienced neuroradiologists and carefully validated. The aims of this study were: (1) To compare the ability of MTL atrophy visual assessment rating scales, a multivariate MRI classification method and manually measured hippocampal volumes to distinguish between subjects with AD and healthy elderly controls (CTL). (2) To assess how well the three techniques perform when predicting future conversion from mild cognitive impairment (MCI) to AD. Methods High resolution sagittal 3D T1w MP-RAGE datasets were acquired from 75 AD patients, 101 subjects with MCI and 81 CTL from the multi-centre AddNeuroMed study. An automated analysis method was used to generate regional volume and regional cortical thickness measures, providing 57 variables for multivariate analysis (orthogonal partial least squares to latent structures using seven-fold cross-validation). Manual hippocampal measurements were also determined for each subject. Visual rating assessment of MTL atrophy was performed by an experienced neuroradiologist according to the approach of Scheltens et al. Results We found prediction accuracies for distinguishing between AD and CTL of 83% for multivariate classification, 81% for the visual rating assessments and 89% for manual measurements of total hippocampal volume. The three different techniques showed similar accuracy in predicting conversion from MCI to AD at one year follow-up. Conclusion Visual rating assessment of the MTL gave similar prediction accuracy to multivariate classification and manual hippocampal volumes. This suggests a potential future role for computerized methods as a complement to clinical assessment of AD.
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Affiliation(s)
- Eric Westman
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.
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Westman E, Simmons A, Muehlboeck JS, Mecocci P, Vellas B, Tsolaki M, Kłoszewska I, Soininen H, Weiner MW, Lovestone S, Spenger C, Wahlund LO. AddNeuroMed and ADNI: similar patterns of Alzheimer's atrophy and automated MRI classification accuracy in Europe and North America. Neuroimage 2011; 58:818-28. [PMID: 21763442 DOI: 10.1016/j.neuroimage.2011.06.065] [Citation(s) in RCA: 90] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2011] [Revised: 06/21/2011] [Accepted: 06/23/2011] [Indexed: 11/29/2022] Open
Abstract
The European Union AddNeuroMed program and the US-based Alzheimer Disease Neuroimaging Initiative (ADNI) are two large multi-center initiatives designed to collect and validate biomarker data for Alzheimer's disease (AD). Both initiatives use the same MRI data acquisition scheme. The current study aims to compare and combine magnetic resonance imaging (MRI) data from the two study cohorts using an automated image analysis pipeline and a multivariate data analysis approach. We hypothesized that the two cohorts would show similar patterns of atrophy, despite demographic differences and could therefore be combined. MRI scans were analyzed from a total of 1074 subjects (AD=295, MCI=444 and controls=335) using Freesurfer, an automated segmentation scheme which generates regional volume and regional cortical thickness measures which were subsequently used for multivariate analysis (orthogonal partial least squares to latent structures (OPLS)). OPLS models were created for the individual cohorts and for the combined cohort to discriminate between AD patients and controls. The ADNI cohort was used as a replication dataset to validate the model created for the AddNeuroMed cohort and vice versa. The combined cohort model was used to predict conversion to AD at baseline of MCI subjects at 1 year follow-up. The AddNeuroMed, the ADNI and the combined cohort showed similar patterns of atrophy and the predictive power was similar (between 80 and 90%). The combined model also showed potential in predicting conversion from MCI to AD, resulting in 71% of the MCI converters (MCI-c) from both cohorts classified as AD-like and 60% of the stable MCI subjects (MCI-s) classified as control-like. This demonstrates that the methods used are robust and that large data sets can be combined if MRI imaging protocols are carefully aligned.
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Affiliation(s)
- Eric Westman
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.
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48
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Following dynamic biological processes through NMR-based metabonomics: A new tool in nanomedicine? J Control Release 2011; 153:34-9. [DOI: 10.1016/j.jconrel.2011.03.008] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2011] [Accepted: 03/08/2011] [Indexed: 01/09/2023]
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49
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Metabolic differences among melanoma and two prostate cancer cell lines by electrospray ion mobility mass spectrometry. ACTA ACUST UNITED AC 2011. [DOI: 10.1007/s12127-011-0066-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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50
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DeFeo EM, Wu CL, McDougal WS, Cheng LL. A decade in prostate cancer: from NMR to metabolomics. Nat Rev Urol 2011; 8:301-11. [PMID: 21587223 DOI: 10.1038/nrurol.2011.53] [Citation(s) in RCA: 49] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Over the past 30 years, continuous progress in the application of nuclear magnetic resonance (NMR) spectroscopy and magnetic resonance spectroscopic imaging (MRSI) to the detection, diagnosis and characterization of human prostate cancer has turned what began as scientific curiosity into a useful clinical option. In vivo MRSI technology has been integrated into the daily care of prostate cancer patients, and innovations in ex vivo methods have helped to establish NMR-based prostate cancer metabolomics. Metabolomic and multimodality imaging could be the future of the prostate cancer clinic--particularly given the rationale that more accurate interrogation of a disease as complex as human prostate cancer is most likely to be achieved through paradigms involving multiple, instead of single and isolated, parameters. The research and clinical results achieved through in vivo MRSI and ex vivo NMR investigations during the first 11 years of the 21st century illustrate areas where these technologies can be best translated into clinical practice.
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Affiliation(s)
- Elita M DeFeo
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA 02114, USA
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