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Chou L, Zhang S, Luo W, Zhu W, Guo J, Tu K, Tan H, Wang C, Wei S, Yu H, Zhang X, Shi W. Identification of Key Toxic Substances Considering Metabolic Activation: A Combination of Transcriptome and Nontarget Analysis. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:14831-14842. [PMID: 39120612 DOI: 10.1021/acs.est.4c03683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/10/2024]
Abstract
There have been numerous studies using effect-directed analysis (EDA) to identify key toxic substances present in source and drinking water, but none of these studies have considered the effects of metabolic activation. This study developed a comprehensive method including a pretreatment process based on an in vitro metabolic activation system, a comprehensive biological effect evaluation based on concentration-dependent transcriptome (CDT), and a chemical feature identification based on nontarget chemical analysis (NTA), to evaluate the changes in the toxic effects and differences in the chemical composition after metabolism. Models for matching metabolites and precursors as well as data-driven identification methods were further constructed to identify toxic metabolites and key toxic precursor substances in drinking water samples from the Yangtze River. After metabolism, the metabolic samples showed a general trend of reduced toxicity in terms of overall biological potency (mean: 3.2-fold). However, metabolic activation led to an increase in some types of toxic effects, including pathways such as excision repair, mismatch repair, protein processing in endoplasmic reticulum, nucleotide excision repair, and DNA replication. Meanwhile, metabolic samples showed a decrease (17.8%) in the number of peaks and average peak area after metabolism, while overall polarity, hydrophilicity, and average molecular weight increased slightly (10.3%). Based on the models for matching of metabolites and precursors and the data-driven identification methods, 32 chemicals were efficiently identified as key toxic substances as main contributors to explain the different transcriptome biological effects such as cellular component, development, and DNA damage related, including 15 industrial compounds, 7 PPCPs, 6 pesticides, and 4 natural products. This study avoids the process of structure elucidation of toxic metabolites and can trace them directly to the precursors based on MS spectra, providing a new idea for the identification of key toxic pollutants of metabolites.
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Affiliation(s)
- Liben Chou
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Shaoqing Zhang
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Wenrui Luo
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Wenxuan Zhu
- Department of Mathematics, Statistics, and Computer Science, Macalester College, Saint Paul, Minnesota 55105, United States
| | - Jing Guo
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Keng Tu
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Haoyue Tan
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Chang Wang
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, Institute of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Si Wei
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
- Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, China
| | - Hongxia Yu
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
- Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, China
| | - Xiaowei Zhang
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
- Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, China
| | - Wei Shi
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
- Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, China
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2
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Zhong C, Hu C, Xu C, Zhang Z, Hu G. Metabolomics reveals changes in soil metabolic profiles during vegetation succession in karst area. Front Microbiol 2024; 15:1337672. [PMID: 38989027 PMCID: PMC11233535 DOI: 10.3389/fmicb.2024.1337672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 06/13/2024] [Indexed: 07/12/2024] Open
Abstract
Soil metabolites are critical in regulating the dynamics of ecosystem structure and function, particularly in fragile karst ecosystems. Clarification of response of soil metabolism to vegetation succession in karst areas will contribute to the overall understanding and management of karst soils. Here, we investigated the metabolite characteristics of karst soils with different vegetation stages (grassland, brushwood, secondary forest and primary forest) based on untargeted metabolomics. We confirmed that the abundance and composition of soil metabolites altered with vegetation succession. Of the 403 metabolites we found, 157 had significantly varied expression levels across vegetation soils, including mainly lipids and lipid-like molecules, phenylpropanoids and polyketides, organic acids and derivatives. Certain soil metabolites, such as maltotetraose and bifurcose, were sensitive to vegetation succession, increasing significantly from grassland to brushwood and then decreasing dramatically in secondary and primary forests, making them possible indicators of karst vegetation succession. In addition, soil metabolic pathways, such as galactose metabolism and biosynthesis of unsaturated fatty acids, also changed with vegetation succession. This study characterized the soil metabolic profile in different vegetation stages during karst secondary succession, which would provide new insights for the management of karst soils.
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Affiliation(s)
| | | | | | - Zhonghua Zhang
- Key Laboratory of Wildlife Evolution and Conservation in Mountain Ecosystem of Guangxi, College of Environmental and Life Sciences, Nanning Normal University, Nanning, China
| | - Gang Hu
- Key Laboratory of Wildlife Evolution and Conservation in Mountain Ecosystem of Guangxi, College of Environmental and Life Sciences, Nanning Normal University, Nanning, China
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3
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Zhang C, Xu Z, Xu Y, Ma M, Xu S, Gebre BA, Corke H, Sui Z. Absolute Quantitative Lipidomics Reveals Different Granule-Associated Surface Lipid Roles in the Digestibility and Pasting of Waxy, Normal, and High-Amylose Rice Starches. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024; 72:12842-12858. [PMID: 38767652 DOI: 10.1021/acs.jafc.4c00079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Granule-associated surface lipids (GASLs) and internal lipids showed different lipid-amylose relationships, contents, and distributions, suggesting their differing biological origins and functions, among waxy, normal, and high-amylose rice starch. The GASL content mainly depended on the pore size, while internal lipids regulated starch biosynthesis, as indicated by correlations of internal lipids with the chain length distribution of amylopectin and amylose content. Of the 1346 lipids detected, 628, 562, and 408 differentially expressed lipids were observed between normal-waxy, high-amylose-waxy, and normal-high-amylose starch, respectively. After the removal of GASLs, the higher lysophospholipid content induced greater decreases in the peak and breakdown viscosity and swelling power, while the highest digestibility increase was found with the highest triacylglycerol content. Thus, different GASL compositions led to different digestibility, swelling, and pasting outcomes. This study sheds new light on the mechanism of the role of GASLs in the structure and properties of starch, as well as in potential modifications and amyloplast membrane development.
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Affiliation(s)
- Chuangchuang Zhang
- Department of Food Science & Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Zekun Xu
- Department of Food Science & Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yuting Xu
- Department of Food Science & Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Mengting Ma
- Department of Food Science & Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Song Xu
- College of Food Science and Engineering, Shandong Agricultural University, Taian, Shandong 271018, China
| | - Bilatu Agza Gebre
- Department of Food Science & Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Harold Corke
- Biotechnology and Food Engineering Program, Guangdong Technion-Israel Institute of Technology, Shantou 515063, China
- Faculty of Biotechnology and Food Engineering, Technion─Israel Institute of Technology, Haifa 3200003, Israel
| | - Zhongquan Sui
- Department of Food Science & Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
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4
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Downing T, Angelopoulos N. A primer on correlation-based dimension reduction methods for multi-omics analysis. J R Soc Interface 2023; 20:20230344. [PMID: 37817584 PMCID: PMC10565429 DOI: 10.1098/rsif.2023.0344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 09/19/2023] [Indexed: 10/12/2023] Open
Abstract
The continuing advances of omic technologies mean that it is now more tangible to measure the numerous features collectively reflecting the molecular properties of a sample. When multiple omic methods are used, statistical and computational approaches can exploit these large, connected profiles. Multi-omics is the integration of different omic data sources from the same biological sample. In this review, we focus on correlation-based dimension reduction approaches for single omic datasets, followed by methods for pairs of omics datasets, before detailing further techniques for three or more omic datasets. We also briefly detail network methods when three or more omic datasets are available and which complement correlation-oriented tools. To aid readers new to this area, these are all linked to relevant R packages that can implement these procedures. Finally, we discuss scenarios of experimental design and present road maps that simplify the selection of appropriate analysis methods. This review will help researchers navigate emerging methods for multi-omics and integrating diverse omic datasets appropriately. This raises the opportunity of implementing population multi-omics with large sample sizes as omics technologies and our understanding improve.
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Affiliation(s)
- Tim Downing
- Pirbright Institute, Pirbright, Surrey, UK
- Department of Biotechnology, Dublin City University, Dublin, Ireland
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5
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Correia GD, Marchesi JR, MacIntyre DA. Moving beyond DNA: towards functional analysis of the vaginal microbiome by non-sequencing-based methods. Curr Opin Microbiol 2023; 73:102292. [PMID: 36931094 DOI: 10.1016/j.mib.2023.102292] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 02/10/2023] [Accepted: 02/14/2023] [Indexed: 03/17/2023]
Abstract
Over the last two decades, sequencing-based methods have revolutionised our understanding of niche-specific microbial complexity. In the lower female reproductive tract, these approaches have enabled identification of bacterial compositional structures associated with health and disease. Application of metagenomics and metatranscriptomics strategies have provided insight into the putative function of these communities but it is increasingly clear that direct measures of microbial and host cell function are required to understand the contribution of microbe-host interactions to pathophysiology. Here we explore and discuss current methods and approaches, many of which rely upon mass-spectrometry, being used to capture functional insight into the vaginal mucosal interface. In addition to improving mechanistic understanding, these methods offer innovative solutions for the development of diagnostic and therapeutic strategies designed to improve women's health.
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Affiliation(s)
- Gonçalo Ds Correia
- Institute of Reproductive and Developmental Biology, Department of Metabolism, Digestion and Reproduction, Imperial College London, London W12 0NN, UK; March of Dimes Prematurity Research Centre at Imperial College London, London, UK
| | - Julian R Marchesi
- March of Dimes Prematurity Research Centre at Imperial College London, London, UK; Centre for Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Imperial College London, Imperial College London, London W2 1NY, UK
| | - David A MacIntyre
- Institute of Reproductive and Developmental Biology, Department of Metabolism, Digestion and Reproduction, Imperial College London, London W12 0NN, UK; March of Dimes Prematurity Research Centre at Imperial College London, London, UK.
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6
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Wehrli P, Ge J, Michno W, Koutarapu S, Dreos A, Jha D, Zetterberg H, Blennow K, Hanrieder J. Correlative Chemical Imaging and Spatial Chemometrics Delineate Alzheimer Plaque Heterogeneity at High Spatial Resolution. JACS AU 2023; 3:762-774. [PMID: 37006756 PMCID: PMC10052239 DOI: 10.1021/jacsau.2c00492] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 02/21/2023] [Accepted: 02/22/2023] [Indexed: 06/19/2023]
Abstract
We present a novel, correlative chemical imaging strategy based on multimodal matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI), hyperspectral microscopy, and spatial chemometrics. Our workflow overcomes challenges associated with correlative MSI data acquisition and alignment by implementing 1 + 1-evolutionary image registration for precise geometric alignment of multimodal imaging data and their integration in a common, truly multimodal imaging data matrix with maintained MSI resolution (10 μm). This enabled multivariate statistical modeling of multimodal imaging data using a novel multiblock orthogonal component analysis approach to identify covariations of biochemical signatures between and within imaging modalities at MSI pixel resolution. We demonstrate the method's potential through its application toward delineating chemical traits of Alzheimer's disease (AD) pathology. Here, trimodal MALDI MSI of transgenic AD mouse brain delineates beta-amyloid (Aβ) plaque-associated co-localization of lipids and Aβ peptides. Finally, we establish an improved image fusion approach for correlative MSI and functional fluorescence microscopy. This allowed for high spatial resolution (300 nm) prediction of correlative, multimodal MSI signatures toward distinct amyloid structures within single plaque features critically implicated in Aβ pathogenicity.
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Affiliation(s)
- Patrick
M. Wehrli
- Department
of Psychiatry and Neurochemistry, Institute
of Neuroscience and Physiology, Sahlgrenska Academy, University of
Gothenburg, Mölndal 431 80, Sweden
| | - Junyue Ge
- Clinical
Neurochemistry Laboratory, Sahlgrenska University
Hospital Mölndal, Mölndal 431 80, Sweden
| | - Wojciech Michno
- Department
of Psychiatry and Neurochemistry, Institute
of Neuroscience and Physiology, Sahlgrenska Academy, University of
Gothenburg, Mölndal 431 80, Sweden
| | - Srinivas Koutarapu
- Department
of Psychiatry and Neurochemistry, Institute
of Neuroscience and Physiology, Sahlgrenska Academy, University of
Gothenburg, Mölndal 431 80, Sweden
| | - Ambra Dreos
- Department
of Psychiatry and Neurochemistry, Institute
of Neuroscience and Physiology, Sahlgrenska Academy, University of
Gothenburg, Mölndal 431 80, Sweden
| | - Durga Jha
- Department
of Psychiatry and Neurochemistry, Institute
of Neuroscience and Physiology, Sahlgrenska Academy, University of
Gothenburg, Mölndal 431 80, Sweden
| | - Henrik Zetterberg
- Department
of Psychiatry and Neurochemistry, Institute
of Neuroscience and Physiology, Sahlgrenska Academy, University of
Gothenburg, Mölndal 431 80, Sweden
- Clinical
Neurochemistry Laboratory, Sahlgrenska University
Hospital Mölndal, Mölndal 431 80, Sweden
- Department
of Neurodegenerative Disease, Queen Square Institute of Neurology, University College London, London WC1N 3BG, U.K.
- U.
K. Dementia Research Institute at University College London, London WC1N 3BG, U.K.
- Hong
Kong Center for Neurodegenerative Diseases, Sha Tin, N.T. 1512-1518, Hong Kong, China
| | - Kaj Blennow
- Department
of Psychiatry and Neurochemistry, Institute
of Neuroscience and Physiology, Sahlgrenska Academy, University of
Gothenburg, Mölndal 431 80, Sweden
- Clinical
Neurochemistry Laboratory, Sahlgrenska University
Hospital Mölndal, Mölndal 431 80, Sweden
| | - Jörg Hanrieder
- Department
of Psychiatry and Neurochemistry, Institute
of Neuroscience and Physiology, Sahlgrenska Academy, University of
Gothenburg, Mölndal 431 80, Sweden
- Clinical
Neurochemistry Laboratory, Sahlgrenska University
Hospital Mölndal, Mölndal 431 80, Sweden
- Department
of Neurodegenerative Disease, Queen Square Institute of Neurology, University College London, London WC1N 3BG, U.K.
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7
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Millar-Wilson A, Ward Ó, Duffy E, Hardiman G. Multiscale modeling in the framework of biological systems and its potential for spaceflight biology studies. iScience 2022; 25:105421. [DOI: 10.1016/j.isci.2022.105421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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8
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The Role of Systems Biology in Deciphering Asthma Heterogeneity. LIFE (BASEL, SWITZERLAND) 2022; 12:life12101562. [PMID: 36294997 PMCID: PMC9605413 DOI: 10.3390/life12101562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 09/28/2022] [Accepted: 10/04/2022] [Indexed: 11/17/2022]
Abstract
Asthma is one of the most common and lifelong and chronic inflammatory diseases characterized by inflammation, bronchial hyperresponsiveness, and airway obstruction episodes. It is a heterogeneous disease of varying and overlapping phenotypes with many confounding factors playing a role in disease susceptibility and management. Such multifactorial disorders will benefit from using systems biology as a strategy to elucidate molecular insights from complex, quantitative, massive clinical, and biological data that will help to understand the underlying disease mechanism, early detection, and treatment planning. Systems biology is an approach that uses the comprehensive understanding of living systems through bioinformatics, mathematical, and computational techniques to model diverse high-throughput molecular, cellular, and the physiologic profiling of healthy and diseased populations to define biological processes. The use of systems biology has helped understand and enrich our knowledge of asthma heterogeneity and molecular basis; however, such methods have their limitations. The translational benefits of these studies are few, and it is recommended to reanalyze the different studies and omics in conjugation with one another which may help understand the reasons for this variation and help overcome the limitations of understanding the heterogeneity in asthma pathology. In this review, we aim to show the different factors that play a role in asthma heterogeneity and how systems biology may aid in understanding and deciphering the molecular basis of asthma.
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9
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Adua E, Afrifa-Yamoah E, Peprah-Yamoah E, Anto EO, Acheampong E, Awuah-Mensah KA, Wang W. Multi-block data integration analysis for identifying and validating targeted N-glycans as biomarkers for type II diabetes mellitus. Sci Rep 2022; 12:10974. [PMID: 35768493 PMCID: PMC9243128 DOI: 10.1038/s41598-022-15172-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 04/28/2022] [Indexed: 11/08/2022] Open
Abstract
Plasma N-glycan profiles have been shown to be defective in type II diabetes Mellitus (T2DM) and holds a promise to discovering biomarkers. The study comprised 232 T2DM patients and 219 healthy individuals. N-glycans were analysed by high-performance liquid chromatography. The multivariate integrative framework, DIABLO was employed for the statistical analysis. N-glycan groups (GPs 34, 32, 26, 31, 36 and 30) were significantly expressed in T2DM in component 1 and GPs 38 and 20 were related to T2DM in component 2. Four clusters were observed based on the correlation of the expressive signatures of the 39 N-glycans across T2DM and controls. Cluster A, B, C and D had 16, 16, 4 and 3 N-glycans respectively, of which 11, 8, 1 and 1 were found to express differently between controls and T2DM in a univariate analysis [Formula: see text]. Multi-block analysis revealed that trigalactosylated (G3), triantennary (TRIA), high branching (HB) and trisialylated (S3) expressed significantly highly in T2DM than healthy controls. A bipartite relevance network revealed that HB, monogalactosylated (G1) and G3 were central in the network and observed more connections, highlighting their importance in discriminating between T2DM and healthy controls. Investigation of these N-glycans can enhance the understanding of T2DM.
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Affiliation(s)
- Eric Adua
- Rural Clinical School, Medicine and Health, University of New South Wales, Sydney, NSW, Australia.
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia.
| | | | | | - Enoch Odame Anto
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
- Department of Medical Diagnostics, Faculty of Allied Health Science, Kwame Nkrumah University of Science and Technology, 9800, Kumasi, Ashanti Region, Ghana
| | - Emmanuel Acheampong
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
| | | | - Wei Wang
- Centre for Precision Health, Edith Cowan University, Joondalup, Australia
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Melandri G, Monteverde E, Riewe D, AbdElgawad H, McCouch SR, Bouwmeester H. Can biochemical traits bridge the gap between genomics and plant performance? A study in rice under drought. PLANT PHYSIOLOGY 2022; 189:1139-1152. [PMID: 35166848 PMCID: PMC9157150 DOI: 10.1093/plphys/kiac053] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 01/17/2022] [Indexed: 05/13/2023]
Abstract
The possibility of introducing metabolic/biochemical phenotyping to complement genomics-based predictions in breeding pipelines has been considered for years. Here we examine to what extent and under what environmental conditions metabolic/biochemical traits can effectively contribute to understanding and predicting plant performance. In this study, multivariable statistical models based on flag leaf central metabolism and oxidative stress status were used to predict grain yield (GY) performance for 271 indica rice (Oryza sativa) accessions grown in the field under well-watered and reproductive stage drought conditions. The resulting models displayed significantly higher predictability than multivariable models based on genomic data for the prediction of GY under drought (Q2 = 0.54-0.56 versus 0.35) and for stress-induced GY loss (Q2 = 0.59-0.64 versus 0.03-0.06). Models based on the combined datasets showed predictabilities similar to metabolic/biochemical-based models alone. In contrast to genetic markers, models with enzyme activities and metabolite values also quantitatively integrated the effect of physiological differences such as plant height on GY. The models highlighted antioxidant enzymes of the ascorbate-glutathione cycle and a lipid oxidation stress marker as important predictors of rice GY stability under drought at the reproductive stage, and these stress-related variables were more predictive than leaf central metabolites. These findings provide evidence that metabolic/biochemical traits can integrate dynamic cellular and physiological responses to the environment and can help bridge the gap between the genome and the phenome of crops as predictors of GY performance under drought.
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Affiliation(s)
- Giovanni Melandri
- Laboratory of Plant Physiology, Wageningen University and Research, Wageningen, the Netherlands
- School of Integrative Plant Sciences, Plant Breeding and Genetics Section, Cornell University, Ithaca, New York, USA
| | - Eliana Monteverde
- School of Integrative Plant Sciences, Plant Breeding and Genetics Section, Cornell University, Ithaca, New York, USA
- Departamento de Biología Vegetal, Facultad de Agronomía, Laboratorio de Evolución y Domesticación de las Plantas, Universidad de La República, Montevideo, Uruguay
| | - David Riewe
- Julius Kühn-Institute (JKI), Federal Research Centre for Cultivated Plants, Institute for Ecological Chemistry, Plant Analysis and Stored Product Protection, Berlin, Germany
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Seeland, Germany
| | - Hamada AbdElgawad
- Laboratory for Integrated Molecular Plant Physiology Research, University of Antwerp, Antwerp, Belgium
- Department of Botany, Faculty of Science, Beni-Suef University, Beni Suef, Egypt
| | - Susan R McCouch
- School of Integrative Plant Sciences, Plant Breeding and Genetics Section, Cornell University, Ithaca, New York, USA
| | - Harro Bouwmeester
- Laboratory of Plant Physiology, Wageningen University and Research, Wageningen, the Netherlands
- Plant Hormone Biology group, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, the Netherlands
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11
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Zhao Z, Cai Z, Chen A, Cai M, Yang K. Application of metabolomics in osteoporosis research. Front Endocrinol (Lausanne) 2022; 13:993253. [PMID: 36452325 PMCID: PMC9702081 DOI: 10.3389/fendo.2022.993253] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 10/28/2022] [Indexed: 11/15/2022] Open
Abstract
Osteoporosis (OP) is a systemic disease characterized by bone metabolism imbalance and bone microstructure destruction, which causes serious social and economic burden. At present, the diagnosis and treatment of OP mainly rely on imaging combined with drugs. However, the existing pathogenic mechanisms, diagnosis and treatment strategies for OP are not clear and effective enough, and the disease progression that cannot reflect OP further restricts its effective treatment. The application of metabolomics has facilitated the study of OP, further exploring the mechanism and behavior of bone cells, prevention, and treatment of the disease from various metabolic perspectives, finally realizing the possibility of a holistic approach. In this review, we focus on the application of metabolomics in OP research, especially the newer systematic application of metabolomics and treatment with herbal medicine and their extracts. In addition, the prospects of clinical transformation in related fields are also discussed. The aim of this study is to highlight the use of metabolomics in OP research, especially in exploring the pathogenesis of OP and the therapeutic mechanisms of natural herbal medicine, for the benefit of interdisciplinary researchers including clinicians, biologists, and materials engineers.
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Affiliation(s)
- Zhenyu Zhao
- Department of Orthopaedics, Shanghai Tenth People’s Hospital, School of Medicine, Tongji University, Shanghai, China
- Shanghai Key Laboratory for Prevention and Treatment of Bone and Joint Diseases, Shanghai Institute of Traumatology and Orthopaedics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhengwei Cai
- Shanghai Key Laboratory for Prevention and Treatment of Bone and Joint Diseases, Shanghai Institute of Traumatology and Orthopaedics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Aopan Chen
- Department of Orthopaedics, Shanghai Tenth People’s Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Ming Cai
- Department of Orthopaedics, Shanghai Tenth People’s Hospital, School of Medicine, Tongji University, Shanghai, China
- *Correspondence: Ming Cai, ; Kai Yang,
| | - Kai Yang
- Shanghai Key Laboratory for Prevention and Treatment of Bone and Joint Diseases, Shanghai Institute of Traumatology and Orthopaedics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- *Correspondence: Ming Cai, ; Kai Yang,
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12
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Ezzamouri B, Shoaie S, Ledesma-Amaro R. Synergies of Systems Biology and Synthetic Biology in Human Microbiome Studies. Front Microbiol 2021; 12:681982. [PMID: 34531833 PMCID: PMC8438329 DOI: 10.3389/fmicb.2021.681982] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 05/31/2021] [Indexed: 12/26/2022] Open
Abstract
A number of studies have shown that the microbial communities of the human body are integral for the maintenance of human health. Advances in next-generation sequencing have enabled rapid and large-scale quantification of the composition of microbial communities in health and disease. Microorganisms mediate diverse host responses including metabolic pathways and immune responses. Using a system biology approach to further understand the underlying alterations of the microbiota in physiological and pathological states can help reveal potential novel therapeutic and diagnostic interventions within the field of synthetic biology. Tools such as biosensors, memory arrays, and engineered bacteria can rewire the microbiome environment. In this article, we review the computational tools used to study microbiome communities and the current limitations of these methods. We evaluate how genome-scale metabolic models (GEMs) can advance our understanding of the microbe-microbe and microbe-host interactions. Moreover, we present how synergies between these system biology approaches and synthetic biology can be harnessed in human microbiome studies to improve future therapeutics and diagnostics and highlight important knowledge gaps for future research in these rapidly evolving fields.
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Affiliation(s)
- Bouchra Ezzamouri
- Unit for Population-Based Dermatology Research, St John’s Institute of Dermatology, Guy’s and St Thomas’ NHS Foundation Trust and King’s College London, London, United Kindom
- Faculty of Dentistry, Centre for Host-Microbiome Interactions, Oral and Craniofacial Sciences, King’s College London, London, United Kingdom
- Department of Bioengineering and Imperial College Centre for Synthetic Biology, Imperial College London, London, United Kingdom
| | - Saeed Shoaie
- Faculty of Dentistry, Centre for Host-Microbiome Interactions, Oral and Craniofacial Sciences, King’s College London, London, United Kingdom
- Science for Life Laboratory, KTH—Royal Institute of Technology, Stockholm, Sweden
| | - Rodrigo Ledesma-Amaro
- Department of Bioengineering and Imperial College Centre for Synthetic Biology, Imperial College London, London, United Kingdom
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13
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Liu P, Yang Q, Yu N, Cao Y, Wang X, Wang Z, Qiu WY, Ma C. Phenylalanine Metabolism is Dysregulated in Human Hippocampus with Alzheimer's Disease Related Pathological Changes. J Alzheimers Dis 2021; 83:609-622. [PMID: 34334403 DOI: 10.3233/jad-210461] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
BACKGROUND Alzheimer's disease (AD) is one of the most challenging diseases causing an increasing burden worldwide. Although the neuropathologic diagnosis of AD has been established for many years, the metabolic changes in neuropathologic diagnosed AD samples have not been fully investigated. OBJECTIVE To elucidate the potential metabolism dysregulation in the postmortem human brain samples assessed by AD related pathological examination. METHODS We performed untargeted and targeted metabolomics in 44 postmortem human brain tissues. The metabolic differences in the hippocampus between AD group and control (NC) group were compared. RESULTS The results show that a pervasive metabolic dysregulation including phenylalanine metabolism, valine, leucine, and isoleucine biosynthesis, biotin metabolism, and purine metabolism are associated with AD pathology. Targeted metabolomics reveal that phenylalanine, phenylpyruvic acid, and N-acetyl-L-phenylalanine are upregulated in AD samples. In addition, the enzyme IL-4I1 catalyzing transformation from phenylalanine to phenylpyruvic acid is also upregulated in AD samples. CONCLUSION There is a pervasive metabolic dysregulation in hippocampus with AD-related pathological changes. Our study suggests that the dysregulation of phenylalanine metabolism in hippocampus may be an important pathogenesis for AD pathology formation.
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Affiliation(s)
- Pan Liu
- Institute of Basic Medical Sciences, Neuroscience Center, National Human Brain Bank for Development and Function, Chinese Academy of Medical Sciences; Department of Human Anatomy, Histology and Embryology, School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Qian Yang
- Institute of Basic Medical Sciences, Neuroscience Center, National Human Brain Bank for Development and Function, Chinese Academy of Medical Sciences; Department of Human Anatomy, Histology and Embryology, School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Ning Yu
- Institute of Basic Medical Sciences, Neuroscience Center, National Human Brain Bank for Development and Function, Chinese Academy of Medical Sciences; Department of Human Anatomy, Histology and Embryology, School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Yan Cao
- Institute of Basic Medical Sciences, Neuroscience Center, National Human Brain Bank for Development and Function, Chinese Academy of Medical Sciences; Department of Human Anatomy, Histology and Embryology, School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Xue Wang
- Institute of Basic Medical Sciences, Neuroscience Center, National Human Brain Bank for Development and Function, Chinese Academy of Medical Sciences; Department of Human Anatomy, Histology and Embryology, School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Zhao Wang
- Institute of Basic Medical Sciences, Neuroscience Center, National Human Brain Bank for Development and Function, Chinese Academy of Medical Sciences; Department of Human Anatomy, Histology and Embryology, School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Wen-Ying Qiu
- Institute of Basic Medical Sciences, Neuroscience Center, National Human Brain Bank for Development and Function, Chinese Academy of Medical Sciences; Department of Human Anatomy, Histology and Embryology, School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Chao Ma
- Institute of Basic Medical Sciences, Neuroscience Center, National Human Brain Bank for Development and Function, Chinese Academy of Medical Sciences; Department of Human Anatomy, Histology and Embryology, School of Basic Medicine, Peking Union Medical College, Beijing, China.,Joint Laboratory of Anesthesia and Pain, Peking Union Medical College, Beijing, China
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14
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Galindo-Prieto B, Geladi P, Trygg J. Multiblock variable influence on orthogonal projections (MB-VIOP) for enhanced interpretation of total, global, local and unique variations in OnPLS models. BMC Bioinformatics 2021; 22:176. [PMID: 33812384 PMCID: PMC8019512 DOI: 10.1186/s12859-021-04015-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Accepted: 02/10/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND For multivariate data analysis involving only two input matrices (e.g., X and Y), the previously published methods for variable influence on projection (e.g., VIPOPLS or VIPO2PLS) are widely used for variable selection purposes, including (i) variable importance assessment, (ii) dimensionality reduction of big data and (iii) interpretation enhancement of PLS, OPLS and O2PLS models. For multiblock analysis, the OnPLS models find relationships among multiple data matrices (more than two blocks) by calculating latent variables; however, a method for improving the interpretation of these latent variables (model components) by assessing the importance of the input variables was not available up to now. RESULTS A method for variable selection in multiblock analysis, called multiblock variable influence on orthogonal projections (MB-VIOP) is explained in this paper. MB-VIOP is a model based variable selection method that uses the data matrices, the scores and the normalized loadings of an OnPLS model in order to sort the input variables of more than two data matrices according to their importance for both simplification and interpretation of the total multiblock model, and also of the unique, local and global model components separately. MB-VIOP has been tested using three datasets: a synthetic four-block dataset, a real three-block omics dataset related to plant sciences, and a real six-block dataset related to the food industry. CONCLUSIONS We provide evidence for the usefulness and reliability of MB-VIOP by means of three examples (one synthetic and two real-world cases). MB-VIOP assesses in a trustable and efficient way the importance of both isolated and ranges of variables in any type of data. MB-VIOP connects the input variables of different data matrices according to their relevance for the interpretation of each latent variable, yielding enhanced interpretability for each OnPLS model component. Besides, MB-VIOP can deal with strong overlapping of types of variation, as well as with many data blocks with very different dimensionality. The ability of MB-VIOP for generating dimensionality reduced models with high interpretability makes this method ideal for big data mining, multi-omics data integration and any study that requires exploration and interpretation of large streams of data.
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Affiliation(s)
- Beatriz Galindo-Prieto
- Department of Chemistry, Computational Life Science Cluster (CLiC), Umeå University, Umeå, Sweden.
- Industrial Doctoral School (IDS), Umeå, Sweden.
- Department of Engineering Cybernetics (ITK), Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
- Helen and Robert Appel Alzheimer's Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine (WCM), Cornell University, New York, NY, USA.
| | - Paul Geladi
- Forest Biomaterials and Technology, Swedish University of Agricultural Sciences (SLU), Umeå, Sweden
| | - Johan Trygg
- Department of Chemistry, Computational Life Science Cluster (CLiC), Umeå University, Umeå, Sweden.
- Sartorius Corporate Research, Umeå, Sweden.
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15
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Wörheide MA, Krumsiek J, Kastenmüller G, Arnold M. Multi-omics integration in biomedical research - A metabolomics-centric review. Anal Chim Acta 2021; 1141:144-162. [PMID: 33248648 PMCID: PMC7701361 DOI: 10.1016/j.aca.2020.10.038] [Citation(s) in RCA: 101] [Impact Index Per Article: 33.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Revised: 10/09/2020] [Accepted: 10/19/2020] [Indexed: 02/07/2023]
Abstract
Recent advances in high-throughput technologies have enabled the profiling of multiple layers of a biological system, including DNA sequence data (genomics), RNA expression levels (transcriptomics), and metabolite levels (metabolomics). This has led to the generation of vast amounts of biological data that can be integrated in so-called multi-omics studies to examine the complex molecular underpinnings of health and disease. Integrative analysis of such datasets is not straightforward and is particularly complicated by the high dimensionality and heterogeneity of the data and by the lack of universal analysis protocols. Previous reviews have discussed various strategies to address the challenges of data integration, elaborating on specific aspects, such as network inference or feature selection techniques. Thereby, the main focus has been on the integration of two omics layers in their relation to a phenotype of interest. In this review we provide an overview over a typical multi-omics workflow, focusing on integration methods that have the potential to combine metabolomics data with two or more omics. We discuss multiple integration concepts including data-driven, knowledge-based, simultaneous and step-wise approaches. We highlight the application of these methods in recent multi-omics studies, including large-scale integration efforts aiming at a global depiction of the complex relationships within and between different biological layers without focusing on a particular phenotype.
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Affiliation(s)
- Maria A Wörheide
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Jan Krumsiek
- Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Matthias Arnold
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA.
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16
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Ma Y, Zhu Q, Liang J, Li Y, Li M, Zhang Y, Wang X, Zeng Y, Jiao Y. A CRISPR knockout negative screen reveals synergy between CDKs inhibitor and metformin in the treatment of human cancer in vitro and in vivo. Signal Transduct Target Ther 2020; 5:152. [PMID: 32811807 PMCID: PMC7434905 DOI: 10.1038/s41392-020-0203-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 04/13/2020] [Accepted: 04/26/2020] [Indexed: 02/06/2023] Open
Abstract
Laboratory research and pharmacoepidemiology provide support for metformin as a potential antitumor agent. However, the lack of a clear understanding of the indications of metformin limits its efficacy. Here, we performed a genome-wide CRISPR knockout negative screen to identify potential targets that might synergize with metformin. Next-generation sequencing of pooled genomic DNAs isolated from surviving cells after 18 days of metformin treatment (T18) compared to those of the untreated cells at day 0 (T0) yielded candidate genes. Knockdown of a group of cyclin-dependent kinases (CDKs), including CDK1, CDK4, and CDK6, confirmed the results of the screen. Combination treatment of the CDKs inhibitor abemaciclib with metformin profoundly inhibited tumor viability in vitro and in vivo. Although cell cycle parameters were not further altered under the combination treatment, investigation of the metabolome revealed significant changes in cell metabolism, especially with regard to fatty acid oxidation, the tricarboxylic acid cycle and aspartate metabolism. Such changes appeared to be mediated through inhibition of the mTOR pathway. Collectively, our study suggests that the combination of CDKs inhibitor with metformin could be recognized as a potential therapy in future clinical applications.
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Affiliation(s)
- Yarui Ma
- State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China
| | - Qing Zhu
- State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China
| | - Junbo Liang
- State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, 10005, Beijing, China
| | - Yifei Li
- State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China
| | - Mo Li
- State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China
| | - Ying Zhang
- State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China
| | - Xiaobing Wang
- State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China. .,Key Laboratory of Gene Editing Screening and R&D of Digestive System Tumor Drugs, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China.
| | - Yixin Zeng
- State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China. .,State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
| | - Yuchen Jiao
- State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China. .,Key Laboratory of Gene Editing Screening and R&D of Digestive System Tumor Drugs, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China. .,Department of Clinical Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China.
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17
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Jamil IN, Remali J, Azizan KA, Nor Muhammad NA, Arita M, Goh HH, Aizat WM. Systematic Multi-Omics Integration (MOI) Approach in Plant Systems Biology. FRONTIERS IN PLANT SCIENCE 2020; 11:944. [PMID: 32754171 PMCID: PMC7371031 DOI: 10.3389/fpls.2020.00944] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Accepted: 06/10/2020] [Indexed: 05/03/2023]
Abstract
Across all facets of biology, the rapid progress in high-throughput data generation has enabled us to perform multi-omics systems biology research. Transcriptomics, proteomics, and metabolomics data can answer targeted biological questions regarding the expression of transcripts, proteins, and metabolites, independently, but a systematic multi-omics integration (MOI) can comprehensively assimilate, annotate, and model these large data sets. Previous MOI studies and reviews have detailed its usage and practicality on various organisms including human, animals, microbes, and plants. Plants are especially challenging due to large poorly annotated genomes, multi-organelles, and diverse secondary metabolites. Hence, constructive and methodological guidelines on how to perform MOI for plants are needed, particularly for researchers newly embarking on this topic. In this review, we thoroughly classify multi-omics studies on plants and verify workflows to ensure successful omics integration with accurate data representation. We also propose three levels of MOI, namely element-based (level 1), pathway-based (level 2), and mathematical-based integration (level 3). These MOI levels are described in relation to recent publications and tools, to highlight their practicality and function. The drawbacks and limitations of these MOI are also discussed for future improvement toward more amenable strategies in plant systems biology.
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Affiliation(s)
- Ili Nadhirah Jamil
- Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi, Malaysia
| | - Juwairiah Remali
- Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi, Malaysia
| | - Kamalrul Azlan Azizan
- Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi, Malaysia
| | - Nor Azlan Nor Muhammad
- Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi, Malaysia
| | - Masanori Arita
- Bioinformation & DDBJ Center, National Institute of Genetics (NIG), Mishima, Japan
- Metabolome Informatics Team, RIKEN Center for Sustainable Resource Science, Yokohama, Japan
| | - Hoe-Han Goh
- Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi, Malaysia
| | - Wan Mohd Aizat
- Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi, Malaysia
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18
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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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Mendez KM, Broadhurst DI, Reinke SN. Migrating from partial least squares discriminant analysis to artificial neural networks: a comparison of functionally equivalent visualisation and feature contribution tools using jupyter notebooks. Metabolomics 2020; 16:17. [PMID: 31965332 PMCID: PMC6974504 DOI: 10.1007/s11306-020-1640-0] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2019] [Accepted: 01/13/2020] [Indexed: 01/25/2023]
Abstract
INTRODUCTION Metabolomics data is commonly modelled multivariately using partial least squares discriminant analysis (PLS-DA). Its success is primarily due to ease of interpretation, through projection to latent structures, and transparent assessment of feature importance using regression coefficients and Variable Importance in Projection scores. In recent years several non-linear machine learning (ML) methods have grown in popularity but with limited uptake essentially due to convoluted optimisation and interpretation. Artificial neural networks (ANNs) are a non-linear projection-based ML method that share a structural equivalence with PLS, and as such should be amenable to equivalent optimisation and interpretation methods. OBJECTIVES We hypothesise that standardised optimisation, visualisation, evaluation and statistical inference techniques commonly used by metabolomics researchers for PLS-DA can be migrated to a non-linear, single hidden layer, ANN. METHODS We compared a standardised optimisation, visualisation, evaluation and statistical inference techniques workflow for PLS with the proposed ANN workflow. Both workflows were implemented in the Python programming language. All code and results have been made publicly available as Jupyter notebooks on GitHub. RESULTS The migration of the PLS workflow to a non-linear, single hidden layer, ANN was successful. There was a similarity in significant metabolites determined using PLS model coefficients and ANN Connection Weight Approach. CONCLUSION We have shown that it is possible to migrate the standardised PLS-DA workflow to simple non-linear ANNs. This result opens the door for more widespread use and to the investigation of transparent interpretation of more complex ANN architectures.
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Affiliation(s)
- Kevin M Mendez
- Centre for Integrative Metabolomics & Computational Biology, School of Science, Edith Cowan University, Joondalup, 6027, Australia
| | - David I Broadhurst
- Centre for Integrative Metabolomics & Computational Biology, School of Science, Edith Cowan University, Joondalup, 6027, Australia.
| | - Stacey N Reinke
- Centre for Integrative Metabolomics & Computational Biology, School of Science, Edith Cowan University, Joondalup, 6027, Australia.
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20
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Mendez KM, Broadhurst DI, Reinke SN. The application of artificial neural networks in metabolomics: a historical perspective. Metabolomics 2019; 15:142. [PMID: 31628551 DOI: 10.1007/s11306-019-1608-0] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Accepted: 10/11/2019] [Indexed: 02/08/2023]
Abstract
BACKGROUND Metabolomics data, with its complex covariance structure, is typically modelled by projection-based machine learning (ML) methods such as partial least squares (PLS) regression, which project data into a latent structure. Biological data are often non-linear, so it is reasonable to hypothesize that metabolomics data may also have a non-linear latent structure, which in turn would be best modelled using non-linear equations. A non-linear ML method with a similar projection equation structure to PLS is artificial neural networks (ANNs). While ANNs were first applied to metabolic profiling data in the 1990s, the lack of community acceptance combined with limitations in computational capacity and the lack of volume of data for robust non-linear model optimisation inhibited their widespread use. Due to recent advances in computational power, modelling improvements, community acceptance, and the more demanding needs for data science, ANNs have made a recent resurgence in interest across research communities, including a small yet growing usage in metabolomics. As metabolomics experiments become more complex and start to be integrated with other omics data, there is potential for ANNs to become a viable alternative to linear projection methods. AIM OF REVIEW We aim to first describe ANNs and their structural equivalence to linear projection-based methods, including PLS regression. We then review the historical, current, and future uses of ANNs in the field of metabolomics. KEY SCIENTIFIC CONCEPT OF REVIEW Is metabolomics ready for the return of artificial neural networks?
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Affiliation(s)
- Kevin M Mendez
- Centre for Integrative Metabolomics & Computational Biology, School of Science, Edith Cowan University, Joondalup, 6027, Australia
| | - David I Broadhurst
- Centre for Integrative Metabolomics & Computational Biology, School of Science, Edith Cowan University, Joondalup, 6027, Australia.
| | - Stacey N Reinke
- Centre for Integrative Metabolomics & Computational Biology, School of Science, Edith Cowan University, Joondalup, 6027, Australia.
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Mendez KM, Pritchard L, Reinke SN, Broadhurst DI. Toward collaborative open data science in metabolomics using Jupyter Notebooks and cloud computing. Metabolomics 2019; 15:125. [PMID: 31522294 PMCID: PMC6745024 DOI: 10.1007/s11306-019-1588-0] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Accepted: 09/07/2019] [Indexed: 12/20/2022]
Abstract
BACKGROUND A lack of transparency and reporting standards in the scientific community has led to increasing and widespread concerns relating to reproduction and integrity of results. As an omics science, which generates vast amounts of data and relies heavily on data science for deriving biological meaning, metabolomics is highly vulnerable to irreproducibility. The metabolomics community has made substantial efforts to align with FAIR data standards by promoting open data formats, data repositories, online spectral libraries, and metabolite databases. Open data analysis platforms also exist; however, they tend to be inflexible and rely on the user to adequately report their methods and results. To enable FAIR data science in metabolomics, methods and results need to be transparently disseminated in a manner that is rapid, reusable, and fully integrated with the published work. To ensure broad use within the community such a framework also needs to be inclusive and intuitive for both computational novices and experts alike. AIM OF REVIEW To encourage metabolomics researchers from all backgrounds to take control of their own data science, mould it to their personal requirements, and enthusiastically share resources through open science. KEY SCIENTIFIC CONCEPTS OF REVIEW This tutorial introduces the concept of interactive web-based computational laboratory notebooks. The reader is guided through a set of experiential tutorials specifically targeted at metabolomics researchers, based around the Jupyter Notebook web application, GitHub data repository, and Binder cloud computing platform.
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Affiliation(s)
- Kevin M Mendez
- Centre for Metabolomics & Computational Biology, School of Science, Edith Cowan University, Joondalup, 6027, Australia
| | - Leighton Pritchard
- Strathclyde Institute of Pharmacy & Biomedical Sciences, University of Strathclyde, Cathedral Street, Glasgow, G1 1XQ, Scotland, UK
| | - Stacey N Reinke
- Centre for Metabolomics & Computational Biology, School of Science, Edith Cowan University, Joondalup, 6027, Australia.
| | - David I Broadhurst
- Centre for Metabolomics & Computational Biology, School of Science, Edith Cowan University, Joondalup, 6027, Australia.
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22
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Chu SH, Huang M, Kelly RS, Benedetti E, Siddiqui JK, Zeleznik OA, Pereira A, Herrington D, Wheelock CE, Krumsiek J, McGeachie M, Moore SC, Kraft P, Mathé E, Lasky-Su J. Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective. Metabolites 2019; 9:E117. [PMID: 31216675 PMCID: PMC6630728 DOI: 10.3390/metabo9060117] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Revised: 06/12/2019] [Accepted: 06/14/2019] [Indexed: 12/30/2022] Open
Abstract
It is not controversial that study design considerations and challenges must be addressed when investigating the linkage between single omic measurements and human phenotypes. It follows that such considerations are just as critical, if not more so, in the context of multi-omic studies. In this review, we discuss (1) epidemiologic principles of study design, including selection of biospecimen source(s) and the implications of the timing of sample collection, in the context of a multi-omic investigation, and (2) the strengths and limitations of various techniques of data integration across multi-omic data types that may arise in population-based studies utilizing metabolomic data.
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Affiliation(s)
- Su H Chu
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
| | - Mengna Huang
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
| | - Rachel S Kelly
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
| | - Elisa Benedetti
- Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10021, USA.
| | - Jalal K Siddiqui
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA.
| | - Oana A Zeleznik
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
| | - Alexandre Pereira
- Department of Genetics and Molecular Medicine, University of Sao Paulo Medical School, Sao Paulo 01246-903, Brazil.
| | - David Herrington
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA.
| | - Craig E Wheelock
- Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institute, 171 77 Stockholm, Sweden.
| | - Jan Krumsiek
- Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10021, USA.
| | - Michael McGeachie
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
| | - Steven C Moore
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20850, USA.
| | - Peter Kraft
- Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA.
| | - Ewy Mathé
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA.
| | - Jessica Lasky-Su
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
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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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Newton RU, Christophersen CT, Fairman CM, Hart NH, Taaffe DR, Broadhurst D, Devine A, Chee R, Tang CI, Spry N, Galvão DA. Does exercise impact gut microbiota composition in men receiving androgen deprivation therapy for prostate cancer? A single-blinded, two-armed, randomised controlled trial. BMJ Open 2019; 9:e024872. [PMID: 30987986 PMCID: PMC6500366 DOI: 10.1136/bmjopen-2018-024872] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
INTRODUCTION A potential link exists between prostate cancer (PCa) disease and treatment and increased inflammatory levels from gut dysbiosis. This study aims to examine if exercise favourably alters gut microbiota in men receiving androgen deprivation therapy (ADT) for PCa. Specifically, this study will explore whether: (1) exercise improves the composition of gut microbiota and increases the abundance of bacteria associated with health promotion and (2) whether gut health correlates with favourable inflammatory status, bowel function, continence and nausea among patients participating in the exercise intervention. METHODS AND ANALYSIS A single-blinded, two-armed, randomised controlled trial will explore the influence of a 3-month exercise programme (3 days/week) for men with high-risk localised PCa receiving ADT. Sixty patients will be randomly assigned to either exercise intervention or usual care. The primary endpoint (gut health and function assessed via feacal samples) and secondary endpoints (self-reported quality of life via standardised questionnaires, blood biomarkers, body composition and physical fitness) will be measured at baseline and following the intervention. A variety of statistical methods will be used to understand the covariance between microbial diversity and metabolomics profile across time and intervention. An intention-to-treat approach will be utilised for the analyses with multiple imputations followed by a secondary sensitivity analysis to ensure data robustness using a complete cases approach. ETHICS AND DISSEMINATION Ethics approval was obtained from the Human Research Ethics Committee of Edith Cowan University (ID: 19827 NEWTON). Findings will be reported in peer-reviewed publications and scientific conferences in addition to working with national support groups to translate findings for the broader community. If exercise is shown to result in favourable changes in gut microbial diversity, composition and metabolic profile, and reduce gastrointestinal complications in PCa patients receiving ADT, this study will form the basis of a future phase III trial. TRIAL REGISTRATION NUMBER ANZCTR12618000280202.
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Affiliation(s)
- Robert U Newton
- Exercise Medicine Research Institute, Edith Cowan University, Perth, Western Australia, Australia
- School of Human Movement and Nutrition Sciences, University of Queensland, Brisbane, Queensland, Australia
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia
| | - Claus T Christophersen
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia
- School of Molecular and Life Science, Curtin University - Perth City Campus, Perth, Western Australia, Australia
| | - Ciaran M Fairman
- Exercise Medicine Research Institute, Edith Cowan University, Perth, Western Australia, Australia
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia
| | - Nicolas H Hart
- Exercise Medicine Research Institute, Edith Cowan University, Perth, Western Australia, Australia
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia
- Institute for Health Research, University of Notre Dame Australia, Perth, Western Australia, Australia
| | - Dennis R Taaffe
- Exercise Medicine Research Institute, Edith Cowan University, Perth, Western Australia, Australia
- School of Human Movement and Nutrition Sciences, University of Queensland, Brisbane, Queensland, Australia
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia
| | - David Broadhurst
- School of Science, Edith Cowan University, Perth, Western Australia, Australia
- Centre for Integrative Metabolomics and Computational Biology, Edith Cowan University, Perth, Western Australia, Australia
| | - Amanda Devine
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia
- Centre for Integrative Metabolomics and Computational Biology, Edith Cowan University, Perth, Western Australia, Australia
| | - Raphael Chee
- Exercise Medicine Research Institute, Edith Cowan University, Perth, Western Australia, Australia
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia
- Department of Radiation Oncology, Genesis Cancer Care, Perth, Western Australia, Australia
| | - Colin I Tang
- Department of Radiation Oncology, Sir Charles Gairdner Hospital, Perth, Western Australia, Australia
| | - Nigel Spry
- Exercise Medicine Research Institute, Edith Cowan University, Perth, Western Australia, Australia
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia
- Department of Radiation Oncology, Genesis Cancer Care, Perth, Western Australia, Australia
| | - Daniel A Galvão
- Exercise Medicine Research Institute, Edith Cowan University, Perth, Western Australia, Australia
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia
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Skotare T, Nilsson D, Xiong S, Geladi P, Trygg J. Joint and Unique Multiblock Analysis for Integration and Calibration Transfer of NIR Instruments. Anal Chem 2019; 91:3516-3524. [DOI: 10.1021/acs.analchem.8b05188] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Tomas Skotare
- Computational Life Science Cluster (CLiC), Department of Chemistry, Umeå University, 901 81 Umeå, Sweden
| | - David Nilsson
- Computational Life Science Cluster (CLiC), Department of Chemistry, Umeå University, 901 81 Umeå, Sweden
| | - Shaojun Xiong
- Department of Forest Biomaterials and Technology, Swedish University of Agricultural Sciences, 901 83 Umeå, Sweden
| | - Paul Geladi
- Department of Forest Biomaterials and Technology, Swedish University of Agricultural Sciences, 901 83 Umeå, Sweden
| | - Johan Trygg
- Computational Life Science Cluster (CLiC), Department of Chemistry, Umeå University, 901 81 Umeå, Sweden
- Corporate Research, Sartorius AG, 37079 Göttingen, Germany
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