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Gadgil MD, Cheng J, Herrington DM, Kandula NR, Kanaya AM. Adipose tissue-derived metabolite risk scores and risk for type 2 diabetes in South Asians. Int J Obes (Lond) 2024; 48:668-673. [PMID: 38245659 PMCID: PMC11058083 DOI: 10.1038/s41366-023-01457-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 12/13/2023] [Accepted: 12/21/2023] [Indexed: 01/22/2024]
Abstract
BACKGROUND South Asians are at higher risk for type 2 diabetes (T2D) than many other race/ethnic groups. Ectopic adiposity, specifically hepatic steatosis and visceral fat may partially explain this. Our objective was to derive metabolite risk scores for ectopic adiposity and assess associations with incident T2D in South Asians. METHODS We examined 550 participants in the Mediators of Atherosclerosis in South Asians Living in America (MASALA) cohort study aged 40-84 years without known cardiovascular disease or T2D and with metabolomic data. Computed tomography scans at baseline assessed hepatic attenuation and visceral fat area, and fasting serum specimens at baseline and after 5 years assessed T2D. LC-MS-based untargeted metabolomic analysis was performed followed by targeted integration and reporting of known signals. Elastic net regularized linear regression analyses was used to derive risk scores for hepatic steatosis and visceral fat using weighted coefficients. Logistic regression models associated metabolite risk score and incident T2D, adjusting for age, gender, study site, BMI, physical activity, diet quality, energy intake and use of cholesterol-lowering medication. RESULTS Average age of participants was 55 years, 36% women with an average body mass index (BMI) of 25 kg/m2 and 6% prevalence of hepatic steatosis, with 47 cases of incident T2D at 5 years. There were 445 metabolites of known identity. Of these, 313 metabolites were included in the MET-Visc score and 267 in the MET-Liver score. In most fully adjusted models, MET-Liver (OR 2.04 [95% CI 1.38, 3.03]) and MET-Visc (OR 2.80 [1.75, 4.46]) were associated with higher odds of T2D. These associations remained significant after adjustment for measured adiposity. CONCLUSIONS Metabolite risk scores for intrahepatic fat and visceral fat were strongly related to incident T2D independent of measured adiposity. Use of these biomarkers to target risk stratification may help capture pre-clinical metabolic abnormalities.
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Affiliation(s)
- Meghana D Gadgil
- Division of General Internal Medicine, Department of Medicine, University of California, San Francisco School of Medicine, 1545 Divisadero Street, Suite 320, San Francisco, CA, 94143, USA.
| | - Jing Cheng
- Department of Preventive and Restorative Dentistry, University of California, San Francisco School of Dentistry, 707 Parnassus Ave, #1026, San Francisco, CA, 94143, USA
| | - David M Herrington
- Section on Cardiovascular Medicine, Department of Internal Medicine, Wake Forest School of Medicine; Medical Center Boulevard, Winston-Salem, NC, 27157, USA
| | - Namratha R Kandula
- Division of General Internal Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, 750 N. Lakeshore Dr. 6h Floor, Chicago, IL, 60611, USA
| | - Alka M Kanaya
- Division of General Internal Medicine, Department of Medicine, University of California, San Francisco School of Medicine, 1545 Divisadero Street, Suite 320, San Francisco, CA, 94143, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco School of Medicine, 550 16th Street, Second Floor, San Francisco, CA, 94158, USA
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2
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Viant MR, Amstalden E, Athersuch T, Bouhifd M, Camuzeaux S, Crizer DM, Driemert P, Ebbels T, Ekman D, Flick B, Giri V, Gómez-Romero M, Haake V, Herold M, Kende A, Lai F, Leonards PEG, Lim PP, Lloyd GR, Mosley J, Namini C, Rice JR, Romano S, Sands C, Smith MJ, Sobanski T, Southam AD, Swindale L, van Ravenzwaay B, Walk T, Weber RJM, Zickgraf FM, Kamp H. Demonstrating the reliability of in vivo metabolomics based chemical grouping: towards best practice. Arch Toxicol 2024; 98:1111-1123. [PMID: 38368582 PMCID: PMC10944399 DOI: 10.1007/s00204-024-03680-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 01/15/2024] [Indexed: 02/19/2024]
Abstract
While grouping/read-across is widely used to fill data gaps, chemical registration dossiers are often rejected due to weak category justifications based on structural similarity only. Metabolomics provides a route to robust chemical categories via evidence of shared molecular effects across source and target substances. To gain international acceptance, this approach must demonstrate high reliability, and best-practice guidance is required. The MetAbolomics ring Trial for CHemical groupING (MATCHING), comprising six industrial, government and academic ring-trial partners, evaluated inter-laboratory reproducibility and worked towards best-practice. An independent team selected eight substances (WY-14643, 4-chloro-3-nitroaniline, 17α-methyl-testosterone, trenbolone, aniline, dichlorprop-p, 2-chloroaniline, fenofibrate); ring-trial partners were blinded to their identities and modes-of-action. Plasma samples were derived from 28-day rat tests (two doses per substance), aliquoted, and distributed to partners. Each partner applied their preferred liquid chromatography-mass spectrometry (LC-MS) metabolomics workflows to acquire, process, quality assess, statistically analyze and report their grouping results to the European Chemicals Agency, to ensure the blinding conditions of the ring trial. Five of six partners, whose metabolomics datasets passed quality control, correctly identified the grouping of eight test substances into three categories, for both male and female rats. Strikingly, this was achieved even though a range of metabolomics approaches were used. Through assessing intrastudy quality-control samples, the sixth partner observed high technical variation and was unable to group the substances. By comparing workflows, we conclude that some heterogeneity in metabolomics methods is not detrimental to consistent grouping, and that assessing data quality prior to grouping is essential. We recommend development of international guidance for quality-control acceptance criteria. This study demonstrates the reliability of metabolomics for chemical grouping and works towards best-practice.
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Affiliation(s)
- Mark R Viant
- Phenome Centre Birmingham, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
| | - E Amstalden
- Amsterdam Institute for Life and Environment (A-LIFE), Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081 HV, Amsterdam, The Netherlands
| | - T Athersuch
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, Hammersmith Hospital, Du Cane Road, London, W12 0NN, UK
| | - M Bouhifd
- European Chemicals Agency, Telakkakatu 6, FI-00121, Helsinki, Finland
| | - S Camuzeaux
- Department of Metabolism, Digestion and Reproduction, National Phenome Centre, Imperial College London, London, W12 0NN, UK
| | - D M Crizer
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, NC, 27709, USA
| | - P Driemert
- BASF Metabolome Solutions GmbH, Tegeler Weg 33, 10589, Berlin, Germany
| | - T Ebbels
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, Hammersmith Hospital, Du Cane Road, London, W12 0NN, UK
| | - D Ekman
- Center for Environmental Measurement and Modeling, Environmental Protection Agency, Athens, GA, 30605, USA
| | - B Flick
- BASF SE, Carl-Bosch-Str 38, 67056, Ludwigshafen, Germany
- NUVISAN ICB GmbH, Toxicology, 13353, Berlin, Germany
| | - V Giri
- BASF SE, Carl-Bosch-Str 38, 67056, Ludwigshafen, Germany
| | - M Gómez-Romero
- Department of Metabolism, Digestion and Reproduction, National Phenome Centre, Imperial College London, London, W12 0NN, UK
| | - V Haake
- BASF Metabolome Solutions GmbH, Tegeler Weg 33, 10589, Berlin, Germany
| | - M Herold
- BASF Metabolome Solutions GmbH, Tegeler Weg 33, 10589, Berlin, Germany
| | - A Kende
- Syngenta, Jealott's Hill International Research Centre, Bracknell, RG42 6EY, UK
| | - F Lai
- Syngenta, Jealott's Hill International Research Centre, Bracknell, RG42 6EY, UK
| | - P E G Leonards
- Amsterdam Institute for Life and Environment (A-LIFE), Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081 HV, Amsterdam, The Netherlands
| | - P P Lim
- Syngenta, Jealott's Hill International Research Centre, Bracknell, RG42 6EY, UK
| | - G R Lloyd
- Phenome Centre Birmingham, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - J Mosley
- Center for Environmental Measurement and Modeling, Environmental Protection Agency, Athens, GA, 30605, USA
| | - C Namini
- Center for Environmental Measurement and Modeling, Environmental Protection Agency, Athens, GA, 30605, USA
| | - J R Rice
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, NC, 27709, USA
| | - S Romano
- Center for Environmental Measurement and Modeling, Environmental Protection Agency, Athens, GA, 30605, USA
| | - C Sands
- Department of Metabolism, Digestion and Reproduction, National Phenome Centre, Imperial College London, London, W12 0NN, UK
| | - M J Smith
- Phenome Centre Birmingham, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - T Sobanski
- European Chemicals Agency, Telakkakatu 6, FI-00121, Helsinki, Finland
| | - A D Southam
- Phenome Centre Birmingham, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - L Swindale
- Syngenta, Jealott's Hill International Research Centre, Bracknell, RG42 6EY, UK
| | - B van Ravenzwaay
- BASF SE, Carl-Bosch-Str 38, 67056, Ludwigshafen, Germany
- Environmental Sciences Consulting, 67122, Altrip, Germany
| | - T Walk
- BASF Metabolome Solutions GmbH, Tegeler Weg 33, 10589, Berlin, Germany
| | - R J M Weber
- Phenome Centre Birmingham, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - F M Zickgraf
- BASF SE, Carl-Bosch-Str 38, 67056, Ludwigshafen, Germany
| | - H Kamp
- BASF Metabolome Solutions GmbH, Tegeler Weg 33, 10589, Berlin, Germany
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Huang HX, Inglese P, Tang J, Yagoubi R, Correia GDS, Horneffer-van der Sluis VM, Camuzeaux S, Wu V, Kopanitsa MV, Willumsen N, Jackson JS, Barron AM, Saito T, Saido TC, Gentlemen S, Takats Z, Matthews PM. Mass spectrometry imaging highlights dynamic patterns of lipid co-expression with Aβ plaques in mouse and human brains. J Neurochem 2024. [PMID: 38372586 DOI: 10.1111/jnc.16042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 11/13/2023] [Accepted: 12/06/2023] [Indexed: 02/20/2024]
Abstract
Lipids play crucial roles in the susceptibility and brain cellular responses to Alzheimer's disease (AD) and are increasingly considered potential soluble biomarkers in cerebrospinal fluid (CSF) and plasma. To delineate the pathological correlations of distinct lipid species, we conducted a comprehensive characterization of both spatially localized and global differences in brain lipid composition in AppNL-G-F mice with spatial and bulk mass spectrometry lipidomic profiling, using human amyloid-expressing (h-Aβ) and WT mouse brains controls. We observed age-dependent increases in lysophospholipids, bis(monoacylglycerol) phosphates, and phosphatidylglycerols around Aβ plaques in AppNL-G-F mice. Immunohistology-based co-localization identified associations between focal pro-inflammatory lipids, glial activation, and autophagic flux disruption. Likewise, in human donors with varying Braak stages, similar studies of cortical sections revealed co-expression of lysophospholipids and ceramides around Aβ plaques in AD (Braak stage V/VI) but not in earlier Braak stage controls. Our findings in mice provide evidence of temporally and spatially heterogeneous differences in lipid composition as local and global Aβ-related pathologies evolve. Observing similar lipidomic changes associated with pathological Aβ plaques in human AD tissue provides a foundation for understanding differences in CSF lipids with reported clinical stage or disease severity.
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Affiliation(s)
- Helen Xuexia Huang
- Section of Bioanalytical Chemistry, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- UK Dementia Research Institute at Imperial College London, Imperial College London, London, UK
| | - Paolo Inglese
- Section of Bioanalytical Chemistry, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Jiabin Tang
- Department of Brain Sciences, Imperial College London, London, UK
| | - Riad Yagoubi
- Section of Bioanalytical Chemistry, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- UK Dementia Research Institute at Imperial College London, Imperial College London, London, UK
| | - Gonçalo D S Correia
- Section of Bioanalytical Chemistry, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | | | - Stephane Camuzeaux
- National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Vincen Wu
- Section of Bioanalytical Chemistry, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Maksym V Kopanitsa
- UK Dementia Research Institute at Imperial College London, Imperial College London, London, UK
| | - Nanet Willumsen
- UK Dementia Research Institute at Imperial College London, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Johanna S Jackson
- UK Dementia Research Institute at Imperial College London, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Anna M Barron
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Takashi Saito
- Laboratory for Proteolytic Neuroscience, RIKEN Center for Brain Science, Saitama, Japan
- Department of Neurocognitive Science, Institute of Brain Science, Nagoya City University, Graduate School of Medical Sciences, Nagoya, Aichi, Japan
| | - Takaomi C Saido
- Laboratory for Proteolytic Neuroscience, RIKEN Center for Brain Science, Saitama, Japan
- Department of Neurocognitive Science, Institute of Brain Science, Nagoya City University, Graduate School of Medical Sciences, Nagoya, Aichi, Japan
| | - Steve Gentlemen
- Department of Brain Sciences, Imperial College London, London, UK
| | - Zoltan Takats
- Section of Bioanalytical Chemistry, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Paul M Matthews
- UK Dementia Research Institute at Imperial College London, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
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4
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Alexander JL, Wyatt NJ, Camuzeaux S, Chekmeneva E, Jimenez B, Sands CJ, Fuller H, Takis P, Ahmad T, Doyle JA, Hart A, Irving PM, Kennedy NA, Lees CW, Lindsay JO, McIntyre RE, Parkes M, Prescott NJ, Raine T, Satsangi J, Speight RA, Jostins-Dean L, Powell N, Marchesi JR, Stewart CJ, Lamb CA. Considerations for peripheral blood transport and storage during large-scale multicentre metabolome research. Gut 2024; 73:379-383. [PMID: 36754608 PMCID: PMC10850673 DOI: 10.1136/gutjnl-2022-329297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Accepted: 01/13/2023] [Indexed: 02/10/2023]
Affiliation(s)
- James L Alexander
- Department of Gastroenterology, Imperial College Healthcare NHS Trust, London, UK
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Nicola J Wyatt
- Department of Gastroenterology, Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, UK
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Stephane Camuzeaux
- National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Elena Chekmeneva
- National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Beatriz Jimenez
- National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Caroline J Sands
- National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Hannah Fuller
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Panteleimon Takis
- National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Tariq Ahmad
- Exeter Inflammatory Bowel Disease and Pharmacogenetics Research Group, University of Exeter, Exeter, Devon, UK
- Department of Gastroenterology, Royal Devon University Healthcare NHS Foundation Trust, Exeter, UK
| | - Jennifer A Doyle
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Ailsa Hart
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Department of Gastroenterology, St Mark's Hospital and Academic Institute, London, UK
| | - Peter M Irving
- Department of Gastroenterology, Guy's and St Thomas' Hospitals NHS Trust, London, UK
- School of Immunology & Microbial Sciences, King's College London, London, UK
| | - Nicholas A Kennedy
- Exeter Inflammatory Bowel Disease and Pharmacogenetics Research Group, University of Exeter, Exeter, Devon, UK
- Department of Gastroenterology, Royal Devon University Healthcare NHS Foundation Trust, Exeter, UK
| | - Charlie W Lees
- Edinburgh IBD Unit, Western General Hospital, Edinburgh, UK
- Institute of Genetics & Molecular Medicine, The University of Edinburgh, Edinburgh, UK
| | - James O Lindsay
- Centre for Immunobiology, Blizard Institute, Barts and The London School of Medicine, Queen Mary University of London, London, UK
- Department of Gastroenterology, Barts Health NHS Trust, London, UK
| | - Rebecca E McIntyre
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Miles Parkes
- Department of Gastroenterology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, Cambridgeshire, UK
| | - Natalie J Prescott
- Division of Genetics and Molecular Medicine, King's College London, London, UK
| | - Tim Raine
- Department of Gastroenterology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, Cambridgeshire, UK
| | - Jack Satsangi
- Nuffield Department of Medicine, University of Oxford, Oxford, Oxfordshire, UK
| | - Richard Alexander Speight
- Department of Gastroenterology, Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, UK
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Luke Jostins-Dean
- Kennedy Institute of Rheumatology, Oxford University, Oxford, Oxfordshire, UK
| | - Nick Powell
- Department of Gastroenterology, Imperial College Healthcare NHS Trust, London, UK
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Julian R Marchesi
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Christopher J Stewart
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Christopher A Lamb
- Department of Gastroenterology, Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, UK
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
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5
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Jackson PP, Wijeyesekera A, Williams CM, Theis S, van Harsselaar J, Rastall RA. Inulin-type fructans and 2'fucosyllactose alter both microbial composition and appear to alleviate stress-induced mood state in a working population compared to placebo (maltodextrin): the EFFICAD Trial, a randomized, controlled trial. Am J Clin Nutr 2023; 118:938-955. [PMID: 37657523 PMCID: PMC10636234 DOI: 10.1016/j.ajcnut.2023.08.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 08/16/2023] [Accepted: 08/18/2023] [Indexed: 09/03/2023] Open
Abstract
BACKGROUND There is increasing interest in the bidirectional relationship existing between the gut and brain and the effects of both oligofructose and 2'fucosyllactose to alter microbial composition and mood state. Yet, much remains unknown about the ability of oligofructose and 2'fucosyllactose to improve mood state via targeted manipulation of the gut microbiota. OBJECTIVES We aimed to compare the effects of oligofructose and 2'fucosyllactose alone and in combination against maltodextrin (comparator) on microbial composition and mood state in a working population. METHODS We conducted a 5-wk, 4-arm, parallel, double-blind, randomized, placebo-controlled trial in 92 healthy adults with mild-to-moderate levels of anxiety and depression. Subjects were randomized to oligofructose 8 g/d (plus 2 g/d maltodextrin); maltodextrin 10 g/d; oligofructose 8 g/d plus 2'fucosyllactose (2 g/d) or 2'fucosyllactose 2 g/d (plus 8 g/d maltodextrin). Changes in microbial load (fluorescence in situ hybridization-flow cytometry) and composition (16S ribosomal RNA sequencing) were the primary outcomes. Secondary outcomes included gastrointestinal sensations, bowel habits, and mood state parameters. RESULTS There were significant increases in several bacterial taxa including Bifidobacterium, Bacteroides, Roseburia, and Faecalibacterium prausnitzii in both the oligofructose and oligofructose/2'fucosyllactose interventions (all P ≤ 0.05). Changes in bacterial taxa were highly heterogenous upon 2'fuscoyllactose supplementation. Significant improvements in Beck Depression Inventory, State Trait Anxiety Inventory Y1 and Y2, and Positive and Negative Affect Schedule scores and cortisol awakening response were detected across oligofructose, 2'fucosyllactose, and oligofructose/2'fucosyllactose combination interventions (all P ≤ 0.05). Both sole oligofructose and oligofructose/2'fuscosyllactose combination interventions outperformed both sole 2'fucosyllactose and maltodextrin in improvements in several mood state parameters (all P ≤ 0.05). CONCLUSION The results of this study indicate that oligofructose and combination of oligofructose/2'fucosyllactose can beneficially alter microbial composition along with improving mood state parameters. Future work is needed to understand key microbial differences separating individual responses to 2'fucosyllactose supplementation. This trial was registered at clinicaltrials.gov as NCT05212545.
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Affiliation(s)
- Peter Pj Jackson
- Department of Food and Nutritional Sciences, University of Reading, Reading, United Kingdom
| | - Anisha Wijeyesekera
- Department of Food and Nutritional Sciences, University of Reading, Reading, United Kingdom
| | - Claire M Williams
- University of Reading, School of Psychology and Clinical Language Science, Reading, United Kingdom
| | | | | | - Robert A Rastall
- Department of Food and Nutritional Sciences, University of Reading, Reading, United Kingdom.
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6
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Routy B, Lenehan JG, Miller WH, Jamal R, Messaoudene M, Daisley BA, Hes C, Al KF, Martinez-Gili L, Punčochář M, Ernst S, Logan D, Belanger K, Esfahani K, Richard C, Ninkov M, Piccinno G, Armanini F, Pinto F, Krishnamoorthy M, Figueredo R, Thebault P, Takis P, Magrill J, Ramsay L, Derosa L, Marchesi JR, Parvathy SN, Elkrief A, Watson IR, Lapointe R, Segata N, Haeryfar SMM, Mullish BH, Silverman MS, Burton JP, Maleki Vareki S. Fecal microbiota transplantation plus anti-PD-1 immunotherapy in advanced melanoma: a phase I trial. Nat Med 2023; 29:2121-2132. [PMID: 37414899 DOI: 10.1038/s41591-023-02453-x] [Citation(s) in RCA: 45] [Impact Index Per Article: 45.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 06/08/2023] [Indexed: 07/08/2023]
Abstract
Fecal microbiota transplantation (FMT) represents a potential strategy to overcome resistance to immune checkpoint inhibitors in patients with refractory melanoma; however, the role of FMT in first-line treatment settings has not been evaluated. We conducted a multicenter phase I trial combining healthy donor FMT with the PD-1 inhibitors nivolumab or pembrolizumab in 20 previously untreated patients with advanced melanoma. The primary end point was safety. No grade 3 adverse events were reported from FMT alone. Five patients (25%) experienced grade 3 immune-related adverse events from combination therapy. Key secondary end points were objective response rate, changes in gut microbiome composition and systemic immune and metabolomics analyses. The objective response rate was 65% (13 of 20), including four (20%) complete responses. Longitudinal microbiome profiling revealed that all patients engrafted strains from their respective donors; however, the acquired similarity between donor and patient microbiomes only increased over time in responders. Responders experienced an enrichment of immunogenic and a loss of deleterious bacteria following FMT. Avatar mouse models confirmed the role of healthy donor feces in increasing anti-PD-1 efficacy. Our results show that FMT from healthy donors is safe in the first-line setting and warrants further investigation in combination with immune checkpoint inhibitors. ClinicalTrials.gov identifier NCT03772899 .
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Affiliation(s)
- Bertrand Routy
- Research Center of the Centre Hospitalier de l'Université de Montréal, Montréal (CRCHUM), Montreal, Quebec, Canada
- Hematology-Oncology Division, Department of Medicine, Centre Hospitalier de l'Université de Montréal (CHUM), Montreal, Quebec, Canada
| | - John G Lenehan
- Department of Oncology, Western University, London, Ontario, Canada
- Lawson Health Research Institute, London, Ontario, Canada
| | - Wilson H Miller
- Lady Davis Institute of the Jewish General Hospital, Segal Cancer Centre, Montreal, Quebec, Canada
- Departments of Oncology and Medicine, McGill University, Montreal, Quebec, Canada
| | - Rahima Jamal
- Research Center of the Centre Hospitalier de l'Université de Montréal, Montréal (CRCHUM), Montreal, Quebec, Canada
- Hematology-Oncology Division, Department of Medicine, Centre Hospitalier de l'Université de Montréal (CHUM), Montreal, Quebec, Canada
| | - Meriem Messaoudene
- Research Center of the Centre Hospitalier de l'Université de Montréal, Montréal (CRCHUM), Montreal, Quebec, Canada
| | - Brendan A Daisley
- Department of Microbiology & Immunology, Western University, London, Ontario, Canada
- Canadian Centre for Human Microbiome and Probiotics Research, London, Ontario, Canada
| | - Cecilia Hes
- Research Center of the Centre Hospitalier de l'Université de Montréal, Montréal (CRCHUM), Montreal, Quebec, Canada
- Peter Brojde Lung Cancer Center, Jewish General Hospital, Montreal, Quebec, Canada
| | - Kait F Al
- Lawson Health Research Institute, London, Ontario, Canada
- Department of Microbiology & Immunology, Western University, London, Ontario, Canada
- Canadian Centre for Human Microbiome and Probiotics Research, London, Ontario, Canada
| | - Laura Martinez-Gili
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, St Mary's Hospital Campus, Imperial College London, London, UK
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
| | - Michal Punčochář
- Department of Computational, Cellular and Integrative Biology, University of Trento, Trento, Italy
| | - Scott Ernst
- Department of Oncology, Western University, London, Ontario, Canada
| | - Diane Logan
- Department of Oncology, Western University, London, Ontario, Canada
| | - Karl Belanger
- Research Center of the Centre Hospitalier de l'Université de Montréal, Montréal (CRCHUM), Montreal, Quebec, Canada
- Hematology-Oncology Division, Department of Medicine, Centre Hospitalier de l'Université de Montréal (CHUM), Montreal, Quebec, Canada
| | - Khashayar Esfahani
- Lady Davis Institute of the Jewish General Hospital, Segal Cancer Centre, Montreal, Quebec, Canada
- Departments of Oncology and Medicine, McGill University, Montreal, Quebec, Canada
| | - Corentin Richard
- Research Center of the Centre Hospitalier de l'Université de Montréal, Montréal (CRCHUM), Montreal, Quebec, Canada
| | - Marina Ninkov
- Department of Microbiology & Immunology, Western University, London, Ontario, Canada
| | - Gianmarco Piccinno
- Department of Computational, Cellular and Integrative Biology, University of Trento, Trento, Italy
| | - Federica Armanini
- Department of Computational, Cellular and Integrative Biology, University of Trento, Trento, Italy
| | - Federica Pinto
- Department of Computational, Cellular and Integrative Biology, University of Trento, Trento, Italy
| | - Mithunah Krishnamoorthy
- Department of Pathology and Laboratory Medicine, Western University, London, Ontario, Canada
| | - Rene Figueredo
- Department of Oncology, Western University, London, Ontario, Canada
| | - Pamela Thebault
- Research Center of the Centre Hospitalier de l'Université de Montréal, Montréal (CRCHUM), Montreal, Quebec, Canada
| | - Panteleimon Takis
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, National Phenome Centre, Imperial College London, London, UK
- Section of Bioanalytical Chemistry, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Jamie Magrill
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montréal, Quebec, Canada
| | - LeeAnn Ramsay
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montréal, Quebec, Canada
| | - Lisa Derosa
- Gustave Roussy Cancer Campus, Villejuif, France
- Cancer Medicine Department, Gustave Roussy, Villejuif, France
- Institut National de la Santé Et et de la Recherche Médicale (INSERM) U1015, ClinicObiome, Equipe Labellisée-28 Ligue Nationale contre le Cancer, Villejuif, France
- Université Paris-Saclay, Ile-de-France, France
| | - Julian R Marchesi
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, St Mary's Hospital Campus, Imperial College London, London, UK
| | - Seema Nair Parvathy
- Department of Medicine, Division of Infectious Diseases, Western University, London, Ontario, Canada
- Division of Infectious Diseases, St Joseph's Health Care, London, Ontario, Canada
| | - Arielle Elkrief
- Research Center of the Centre Hospitalier de l'Université de Montréal, Montréal (CRCHUM), Montreal, Quebec, Canada
| | - Ian R Watson
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montréal, Quebec, Canada
- Department of Biochemistry, McGill University, Montréal, Quebec, Canada
| | - Rejean Lapointe
- Research Center of the Centre Hospitalier de l'Université de Montréal, Montréal (CRCHUM), Montreal, Quebec, Canada
- Département de Médecine, Faculté de Médecine, Université de Montréal, Montréal, Quebec, Canada
| | - Nicola Segata
- Department of Computational, Cellular and Integrative Biology, University of Trento, Trento, Italy
| | - S M Mansour Haeryfar
- Lawson Health Research Institute, London, Ontario, Canada
- Department of Microbiology & Immunology, Western University, London, Ontario, Canada
- Department of Medicine, Division of Clinical Immunology and Allergy, Western University, London, Ontario, Canada
- Department of Surgery, Division of General Surgery, Western University, London, Ontario, Canada
| | - Benjamin H Mullish
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, St Mary's Hospital Campus, Imperial College London, London, UK
- Departments of Gastroenterology and Hepatology, St Mary's Hospital, Imperial College Healthcare NHS Trust, London, UK
| | - Michael S Silverman
- Lawson Health Research Institute, London, Ontario, Canada
- Department of Microbiology & Immunology, Western University, London, Ontario, Canada
- Department of Medicine, Division of Infectious Diseases, Western University, London, Ontario, Canada
- Division of Infectious Diseases, St Joseph's Health Care, London, Ontario, Canada
| | - Jeremy P Burton
- Lawson Health Research Institute, London, Ontario, Canada
- Department of Microbiology & Immunology, Western University, London, Ontario, Canada
- Canadian Centre for Human Microbiome and Probiotics Research, London, Ontario, Canada
| | - Saman Maleki Vareki
- Department of Oncology, Western University, London, Ontario, Canada.
- Lawson Health Research Institute, London, Ontario, Canada.
- Department of Pathology and Laboratory Medicine, Western University, London, Ontario, Canada.
- Department of Medical Biophysics, Western University, London, Ontario, Canada.
- Ontario Institute of Cancer Research, Toronto, Ontario, Canada.
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7
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Alexander JL, Posma JM, Scott A, Poynter L, Mason SE, Doria ML, Herendi L, Roberts L, McDonald JAK, Cameron S, Hughes DJ, Liska V, Susova S, Soucek P, der Sluis VHV, Gomez-Romero M, Lewis MR, Hoyles L, Woolston A, Cunningham D, Darzi A, Gerlinger M, Goldin R, Takats Z, Marchesi JR, Teare J, Kinross J. Pathobionts in the tumour microbiota predict survival following resection for colorectal cancer. Microbiome 2023; 11:100. [PMID: 37158960 PMCID: PMC10165813 DOI: 10.1186/s40168-023-01518-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 03/15/2023] [Indexed: 05/10/2023]
Abstract
BACKGROUND AND AIMS The gut microbiota is implicated in the pathogenesis of colorectal cancer (CRC). We aimed to map the CRC mucosal microbiota and metabolome and define the influence of the tumoral microbiota on oncological outcomes. METHODS A multicentre, prospective observational study was conducted of CRC patients undergoing primary surgical resection in the UK (n = 74) and Czech Republic (n = 61). Analysis was performed using metataxonomics, ultra-performance liquid chromatography-mass spectrometry (UPLC-MS), targeted bacterial qPCR and tumour exome sequencing. Hierarchical clustering accounting for clinical and oncological covariates was performed to identify clusters of bacteria and metabolites linked to CRC. Cox proportional hazards regression was used to ascertain clusters associated with disease-free survival over median follow-up of 50 months. RESULTS Thirteen mucosal microbiota clusters were identified, of which five were significantly different between tumour and paired normal mucosa. Cluster 7, containing the pathobionts Fusobacterium nucleatum and Granulicatella adiacens, was strongly associated with CRC (PFDR = 0.0002). Additionally, tumoral dominance of cluster 7 independently predicted favourable disease-free survival (adjusted p = 0.031). Cluster 1, containing Faecalibacterium prausnitzii and Ruminococcus gnavus, was negatively associated with cancer (PFDR = 0.0009), and abundance was independently predictive of worse disease-free survival (adjusted p = 0.0009). UPLC-MS analysis revealed two major metabolic (Met) clusters. Met 1, composed of medium chain (MCFA), long-chain (LCFA) and very long-chain (VLCFA) fatty acid species, ceramides and lysophospholipids, was negatively associated with CRC (PFDR = 2.61 × 10-11); Met 2, composed of phosphatidylcholine species, nucleosides and amino acids, was strongly associated with CRC (PFDR = 1.30 × 10-12), but metabolite clusters were not associated with disease-free survival (p = 0.358). An association was identified between Met 1 and DNA mismatch-repair deficiency (p = 0.005). FBXW7 mutations were only found in cancers predominant in microbiota cluster 7. CONCLUSIONS Networks of pathobionts in the tumour mucosal niche are associated with tumour mutation and metabolic subtypes and predict favourable outcome following CRC resection. Video Abstract.
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Affiliation(s)
- James L Alexander
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Imperial College London, 10th Floor, QEQM Building, St. Mary's Hospital, Praed Street, London, W2 1NY, UK
- Department of Gastroenterology, Imperial College Healthcare NHS Trust, London, UK
| | - Joram M Posma
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Alasdair Scott
- Department of Surgery & Cancer, Imperial College London, London, UK
| | - Liam Poynter
- Department of Surgery & Cancer, Imperial College London, London, UK
| | - Sam E Mason
- Department of Surgery & Cancer, Imperial College London, London, UK
| | - M Luisa Doria
- Department of Surgery & Cancer, Imperial College London, London, UK
| | - Lili Herendi
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, National Phenome Centre, Imperial College London, London, UK
| | - Lauren Roberts
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Imperial College London, 10th Floor, QEQM Building, St. Mary's Hospital, Praed Street, London, W2 1NY, UK
| | - Julie A K McDonald
- Department of Life Sciences, MRC Centre for Molecular Bacteriology and Infection, Imperial College London, London, UK
| | - Simon Cameron
- Institute of Global Food Security, School of Biosciences, Queen's University Belfast, Belfast, UK
| | - David J Hughes
- Cancer Biology and Therapeutics Group, School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Vaclav Liska
- Department of Surgery, Faculty Hospital and Faculty of Medicine in Pilsen, Charles University in Prague, Pilsen, Czech Republic
| | - Simona Susova
- Faculty of Medicine in Pilsen, Biomedical Centre, Charles University in Prague, Pilsen, Czech Republic
| | - Pavel Soucek
- Faculty of Medicine in Pilsen, Biomedical Centre, Charles University in Prague, Pilsen, Czech Republic
| | - Verena Horneffer-van der Sluis
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, National Phenome Centre, Imperial College London, London, UK
| | - Maria Gomez-Romero
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, National Phenome Centre, Imperial College London, London, UK
| | - Matthew R Lewis
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, National Phenome Centre, Imperial College London, London, UK
| | - Lesley Hoyles
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Imperial College London, 10th Floor, QEQM Building, St. Mary's Hospital, Praed Street, London, W2 1NY, UK
- Department of Biosciences, Nottingham Trent University, Nottingham, NG11 8NS, UK
| | - Andrew Woolston
- Translational Oncogenomics Laboratory, The Institute of Cancer Research, 237 Fulham Road, London, SW3 6JB, UK
| | - David Cunningham
- GI Cancer Unit, Department of Medical Oncology, Royal Marsden NHS Foundation Trust, London, UK
| | - Ara Darzi
- Department of Surgery & Cancer, Imperial College London, London, UK
| | - Marco Gerlinger
- Translational Oncogenomics Laboratory, The Institute of Cancer Research, 237 Fulham Road, London, SW3 6JB, UK
- GI Cancer Unit, Department of Medical Oncology, Royal Marsden NHS Foundation Trust, London, UK
| | - Robert Goldin
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Imperial College London, 10th Floor, QEQM Building, St. Mary's Hospital, Praed Street, London, W2 1NY, UK
| | - Zoltan Takats
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, National Phenome Centre, Imperial College London, London, UK
| | - Julian R Marchesi
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Imperial College London, 10th Floor, QEQM Building, St. Mary's Hospital, Praed Street, London, W2 1NY, UK.
| | - Julian Teare
- Department of Surgery & Cancer, Imperial College London, London, UK
| | - James Kinross
- Department of Surgery & Cancer, Imperial College London, London, UK
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8
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Lu H, George J, Eslam M, Villanueva A, Bolondi L, Reeves HL, McCain M, Chambers E, Ward C, Sartika D, Sands C, Maslen L, Lewis MR, Ramaswami R, Sharma R. Discriminatory Changes in Circulating Metabolites as a Predictor of Hepatocellular Cancer in Patients with Metabolic (Dysfunction) Associated Fatty Liver Disease. Liver Cancer 2023; 12:19-31. [PMID: 36872928 PMCID: PMC9982340 DOI: 10.1159/000525911] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 06/25/2022] [Indexed: 02/19/2023] Open
Abstract
Introduction The burden of metabolic (dysfunction) associated fatty liver disease (MAFLD) is rising mirrored by an increase in hepatocellular cancer (HCC). MAFLD and its sequelae are characterized by perturbations in lipid handling, inflammation, and mitochondrial damage. The profile of circulating lipid and small molecule metabolites with the development of HCC is poorly characterized in MAFLD and could be used in future studies as a biomarker for HCC. Methods We assessed the profile of 273 lipid and small molecule metabolites by ultra-performance liquid chromatography coupled to high-resolution mass spectrometry in serum from patients with MAFLD (n = 113) and MAFLD-associated HCC (n = 144) from six different centers. Regression models were used to identify a predictive model of HCC. Results Twenty lipid species and one metabolite, reflecting changes in mitochondrial function and sphingolipid metabolism, were associated with the presence of cancer on a background of MAFLD with high accuracy (AUC 0.789, 95% CI: 0.721-0.858), which was enhanced with the addition of cirrhosis to the model (AUC 0.855, 95% CI: 0.793-0.917). In particular, the presence of these metabolites was associated with cirrhosis in the MAFLD subgroup (p < 0.001). When considering the HCC cohort alone, the metabolic signature was an independent predictor of overall survival (HR 1.42, 95% CI: 1.09-1.83, p < 0.01). Conclusion These exploratory findings reveal a metabolic signature in serum which is capable of accurately detecting the presence of HCC on a background of MAFLD. This unique serum signature will be taken forward for further investigation of diagnostic performance as biomarker of early stage HCC in patients with MAFLD in the future.
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Affiliation(s)
- Haonan Lu
- Division of Surgery and Cancer, Imperial College London, Hammersmith Hospital, London, UK
| | - Jacob George
- Storr Liver Centre, The Westmead Institute for Medical Research, Westmead Hospital and University of Sydney, Westmead, New South Wales, Australia
| | - Mohammed Eslam
- Storr Liver Centre, The Westmead Institute for Medical Research, Westmead Hospital and University of Sydney, Westmead, New South Wales, Australia
| | | | - Luigi Bolondi
- Division of Internal Medicine, University of Bologna, Bologna, Italy
| | - Helen L. Reeves
- Newcastle University Translational Clinical Research Institute, Newcastle University Centre for Cancer, Newcastle-upon-Tyne Hospitals NHS Foundation Trust, Newcastle-upon-Tyne, UK
| | - Misti McCain
- Newcastle University Translational Clinical Research Institute, Newcastle University Centre for Cancer, Newcastle-upon-Tyne Hospitals NHS Foundation Trust, Newcastle-upon-Tyne, UK
| | - Edward Chambers
- Department of Medicine, Imperial College London, Hammersmith Hospital, London, UK
| | - Caroline Ward
- Division of Surgery and Cancer, Imperial College London, Hammersmith Hospital, London, UK
| | - Dewi Sartika
- Division of Surgery and Cancer, Imperial College London, Hammersmith Hospital, London, UK
| | - Caroline Sands
- National Phenome Centre, Imperial College London, London, UK
- Section of Bioanalytical Chemistry, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Lynn Maslen
- National Phenome Centre, Imperial College London, London, UK
- Section of Bioanalytical Chemistry, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Matthew R. Lewis
- National Phenome Centre, Imperial College London, London, UK
- Section of Bioanalytical Chemistry, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Ramya Ramaswami
- Division of Surgery and Cancer, Imperial College London, Hammersmith Hospital, London, UK
| | - Rohini Sharma
- Division of Surgery and Cancer, Imperial College London, Hammersmith Hospital, London, UK
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9
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Alexander JL, Mullish BH, Danckert NP, Liu Z, Olbei ML, Saifuddin A, Torkizadeh M, Ibraheim H, Blanco JM, Roberts LA, Bewshea CM, Nice R, Lin S, Prabhudev H, Sands C, Horneffer-van der Sluis V, Lewis M, Sebastian S, Lees CW, Teare JP, Hart A, Goodhand JR, Kennedy NA, Korcsmaros T, Marchesi JR, Ahmad T, Powell N. The gut microbiota and metabolome are associated with diminished COVID-19 vaccine-induced antibody responses in immunosuppressed inflammatory bowel disease patients. EBioMedicine 2023; 88:104430. [PMID: 36634565 PMCID: PMC9831064 DOI: 10.1016/j.ebiom.2022.104430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 10/07/2022] [Accepted: 12/16/2022] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Patients with inflammatory bowel disease (IBD) treated with anti-TNF therapy exhibit attenuated humoral immune responses to vaccination against SARS-CoV-2. The gut microbiota and its functional metabolic output, which are perturbed in IBD, play an important role in shaping host immune responses. We explored whether the gut microbiota and metabolome could explain variation in anti-SARS-CoV-2 vaccination responses in immunosuppressed IBD patients. METHODS Faecal and serum samples were prospectively collected from infliximab-treated patients with IBD in the CLARITY-IBD study undergoing vaccination against SARS-CoV-2. Antibody responses were measured following two doses of either ChAdOx1 nCoV-19 or BNT162b2 vaccine. Patients were classified as having responses above or below the geometric mean of the wider CLARITY-IBD cohort. 16S rRNA gene amplicon sequencing, nuclear magnetic resonance (NMR) spectroscopy and bile acid profiling with ultra-high-performance liquid chromatography mass spectrometry (UHPLC-MS) were performed on faecal samples. Univariate, multivariable and correlation analyses were performed to determine gut microbial and metabolomic predictors of response to vaccination. FINDINGS Forty-three infliximab-treated patients with IBD were recruited (30 Crohn's disease, 12 ulcerative colitis, 1 IBD-unclassified; 26 with concomitant thiopurine therapy). Eight patients had evidence of prior SARS-CoV-2 infection. Seventeen patients (39.5%) had a serological response below the geometric mean. Gut microbiota diversity was lower in below average responders (p = 0.037). Bilophila abundance was associated with better serological response, while Streptococcus was associated with poorer response. The faecal metabolome was distinct between above and below average responders (OPLS-DA R2X 0.25, R2Y 0.26, Q2 0.15; CV-ANOVA p = 0.038). Trimethylamine, isobutyrate and omega-muricholic acid were associated with better response, while succinate, phenylalanine, taurolithocholate and taurodeoxycholate were associated with poorer response. INTERPRETATION Our data suggest that there is an association between the gut microbiota and variable serological response to vaccination against SARS-CoV-2 in immunocompromised patients. Microbial metabolites including trimethylamine may be important in mitigating anti-TNF-induced attenuation of the immune response. FUNDING JLA is the recipient of an NIHR Academic Clinical Lectureship (CL-2019-21-502), funded by Imperial College London and The Joyce and Norman Freed Charitable Trust. BHM is the recipient of an NIHR Academic Clinical Lectureship (CL-2019-21-002). The Division of Digestive Diseases at Imperial College London receives financial and infrastructure support from the NIHR Imperial Biomedical Research Centre (BRC) based at Imperial College Healthcare NHS Trust and Imperial College London. Metabolomics studies were performed at the MRC-NIHR National Phenome Centre at Imperial College London; this work was supported by the Medical Research Council (MRC), the National Institute of Health Research (NIHR) (grant number MC_PC_12025) and infrastructure support was provided by the NIHR Imperial Biomedical Research Centre (BRC). The NIHR Exeter Clinical Research Facility is a partnership between the University of Exeter Medical School College of Medicine and Health, and Royal Devon and Exeter NHS Foundation Trust. This project is supported by the National Institute for Health Research (NIHR) Exeter Clinical Research Facility. The views expressed are those of the authors and not necessarily those of the NIHR or the UK Department of Health and Social Care.
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Affiliation(s)
- James L Alexander
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom; Department of Gastroenterology and Hepatology, Imperial College Healthcare NHS Trust, London, United Kingdom.
| | - Benjamin H Mullish
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom; Department of Gastroenterology and Hepatology, Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Nathan P Danckert
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Zhigang Liu
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Marton L Olbei
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Aamir Saifuddin
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom; St Mark's Hospital and Academic Institute, Harrow, London, United Kingdom
| | - Melissa Torkizadeh
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom; King's College London, London, United Kingdom
| | - Hajir Ibraheim
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom; Department of Gastroenterology and Hepatology, Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Jesús Miguéns Blanco
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Lauren A Roberts
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
| | | | - Rachel Nice
- University of Exeter, Exeter, Devon, United Kingdom; Department of Gastroenterology, Royal Devon and Exeter NHS Foundation Trust, Exeter, Devon, United Kingdom
| | - Simeng Lin
- University of Exeter, Exeter, Devon, United Kingdom; Department of Gastroenterology, Royal Devon and Exeter NHS Foundation Trust, Exeter, Devon, United Kingdom
| | - Hemanth Prabhudev
- Department of Gastroenterology and Hepatology, Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Caroline Sands
- National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Verena Horneffer-van der Sluis
- National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Matthew Lewis
- National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Shaji Sebastian
- Hull University Teaching Hospitals NHS Trust, Gastroenterology, Hull, United Kingdom; University of Hull, Hull York Medical School, Hull, United Kingdom
| | - Charlie W Lees
- Western General Hospital, Edinburgh, United Kingdom; The University of Edinburgh Centre for Genomic and Experimental Medicine, Edinburgh, United Kingdom
| | - Julian P Teare
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Ailsa Hart
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom; St Mark's Hospital and Academic Institute, Harrow, London, United Kingdom
| | - James R Goodhand
- University of Exeter, Exeter, Devon, United Kingdom; Department of Gastroenterology, Royal Devon and Exeter NHS Foundation Trust, Exeter, Devon, United Kingdom
| | - Nicholas A Kennedy
- University of Exeter, Exeter, Devon, United Kingdom; Department of Gastroenterology, Royal Devon and Exeter NHS Foundation Trust, Exeter, Devon, United Kingdom
| | - Tamas Korcsmaros
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom; Earlham Institute, Norwich, United Kingdom; Quadram Institute Bioscience, Norwich, United Kingdom
| | - Julian R Marchesi
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Tariq Ahmad
- University of Exeter, Exeter, Devon, United Kingdom; Department of Gastroenterology, Royal Devon and Exeter NHS Foundation Trust, Exeter, Devon, United Kingdom
| | - Nick Powell
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom; Department of Gastroenterology and Hepatology, Imperial College Healthcare NHS Trust, London, United Kingdom.
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10
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Mullish BH, Martinez-Gili L, Chekmeneva E, Correia GDS, Lewis MR, Horneffer-Van Der Sluis V, Roberts LA, McDonald JAK, Pechlivanis A, Walters JRF, McClure EL, Marchesi JR, Allegretti JR. Assessing the clinical value of faecal bile acid profiling to predict recurrence in primary Clostridioides difficile infection. Aliment Pharmacol Ther 2022; 56:1556-1569. [PMID: 36250604 DOI: 10.1111/apt.17247] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Revised: 08/30/2022] [Accepted: 09/26/2022] [Indexed: 01/30/2023]
Abstract
BACKGROUND Factors influencing recurrence risk in primary Clostridioides difficile infection (CDI) are poorly understood, and tools predicting recurrence are lacking. Perturbations in bile acids (BAs) contribute to CDI pathogenesis and may be relevant to primary disease prognosis. AIMS To define stool BA dynamics in patients with primary CDI and to explore signatures predicting recurrence METHODS: Weekly stool samples were collected from patients with primary CDI from the last day of anti-CDI therapy until recurrence or, otherwise, through 8 weeks post-completion. Ultra-high performance liquid chromatography-mass spectrometry was used to profile BAs. Stool bile salt hydrolase (BSH) activity was measured to determine primary BA bacterial deconjugation capacity. Multivariate and univariate models were used to define differential BA trajectories in patients with recurrence versus those without, and to assess faecal BAs as predictive markers for recurrence. RESULTS Twenty (36%) of 56 patients (median age: 57, 64% male) had recurrence; 80% of recurrences occurred within the first 9 days post-antibiotic treatment. Principal component analysis of stool BA profiles demonstrated clustering by recurrence status and post-treatment timepoint. Longitudinal faecal BA trajectories showed recovery of secondary BAs and their derivatives only in patients without recurrence. BSH activity increased over time only among non-relapsing patients (β = 0.056; likelihood ratio test p = 0.018). A joint longitudinal-survival model identified five stool BAs with area under the receiver operating characteristic curve >0.73 for predicting recurrence within 9 days post-CDI treatment. CONCLUSIONS Gut BA metabolism dynamics differ in primary CDI patients between those developing recurrence and those who do not. Individual BAs show promise as potential novel biomarkers to predict CDI recurrence.
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Affiliation(s)
- Benjamin H Mullish
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, St Mary's Hospital Campus, Imperial College London, London, UK.,Departments of Gastroenterology and Hepatology, St Mary's Hospital, Imperial College Healthcare NHS Trust, London, UK
| | - Laura Martinez-Gili
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, St Mary's Hospital Campus, Imperial College London, London, UK.,Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
| | - Elena Chekmeneva
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, National Phenome Centre, IRDB Building, Hammersmith House Campus, Imperial College London, London, UK.,Section of Bioanalytical Chemistry, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Gonçalo D S Correia
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, National Phenome Centre, IRDB Building, Hammersmith House Campus, Imperial College London, London, UK.,Section of Bioanalytical Chemistry, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Matthew R Lewis
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, National Phenome Centre, IRDB Building, Hammersmith House Campus, Imperial College London, London, UK.,Section of Bioanalytical Chemistry, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Verena Horneffer-Van Der Sluis
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, National Phenome Centre, IRDB Building, Hammersmith House Campus, Imperial College London, London, UK.,Section of Bioanalytical Chemistry, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK.,Department for Diagnostics, Institute for Clinical Chemistry and Laboratory Medicine, University Hospital Hamburg-Eppendorf, Hamburg, Germany
| | - Lauren A Roberts
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, St Mary's Hospital Campus, Imperial College London, London, UK
| | - Julie A K McDonald
- MRC Centre for Molecular Bacteriology and Infection, Imperial College London, London, UK
| | - Alexandros Pechlivanis
- Section of Bioanalytical Chemistry, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK.,Laboratory of Analytical Chemistry, Department of Chemistry, Aristotle University of Thessaloniki, Thessaloniki, Greece.,Biomic_Auth, Bioanalysis and Omics Laboratory, Center for Interdisciplinary Research and Innovation (CIRI-AUTH), Thessaloniki, Greece
| | - Julian R F Walters
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, St Mary's Hospital Campus, Imperial College London, London, UK.,Departments of Gastroenterology and Hepatology, St Mary's Hospital, Imperial College Healthcare NHS Trust, London, UK
| | - Emma L McClure
- Division of Gastroenterology, Hepatology, and Endoscopy, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Julian R Marchesi
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, St Mary's Hospital Campus, Imperial College London, London, UK
| | - Jessica R Allegretti
- Division of Gastroenterology, Hepatology, and Endoscopy, Brigham and Women's Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
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Gadgil MD, Kanaya AM, Sands C, Chekmeneva E, Lewis MR, Kandula NR, Herrington DM. Diet Patterns Are Associated with Circulating Metabolites and Lipid Profiles of South Asians in the United States. J Nutr 2022; 152:2358-2366. [PMID: 36774102 PMCID: PMC10157813 DOI: 10.1093/jn/nxac191] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 08/03/2022] [Accepted: 08/18/2022] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND South Asians are at higher risk for cardiometabolic disease than many other racial/ethnic minority groups. Diet patterns in US South Asians have unique components associated with cardiometabolic disease. OBJECTIVES We aimed to characterize the metabolites associated with 3 representative diet patterns. METHODS We included 722 participants in the Mediators of Atherosclerosis in South Asians Living in America (MASALA) cohort study aged 40-84 y without known cardiovascular disease. Fasting serum specimens and diet and demographic questionnaires were collected at baseline and diet patterns previously generated through principal components analysis. LC-MS-based untargeted metabolomic and lipidomic analysis was conducted with targeted integration of known metabolite and lipid signals. Linear regression models of diet pattern factor score and log-transformed metabolites adjusted for age, sex, caloric intake, and BMI and adjusted for multiple comparisons were performed, followed by elastic net linear regression of significant metabolites. RESULTS There were 443 metabolites of known identity extracted from the profiling data. The "animal protein" diet pattern was associated with 61 metabolites and lipids, including glycerophospholipids phosphatidylethanolamine PE(O-16:1/20:4) and/or PE(P-16:0/20:4) (β: 0.13; 95% CI: 0.11, 0.14) and N-acyl phosphatidylethanolamines (NAPEs) NAPE(O-18:1/20:4/18:0) and/or NAPE(P-18:0/20:4/18:0) (β: 0.13; 95% CI: 0.11, 0.14), lysophosphatidylinositol (LPI) (22:6/0:0) (β: 0.14; 95% CI: 0.12, 0.17), and fatty acid (FA) (22:6) (β: 0.15; 95% CI: 0.13, 0.17). The "fried snacks, sweets, high-fat dairy" pattern was associated with 12 lipids, including PC(16:0/22:6) (β: -0.08; 95% CI: -0.09, -0.06) and FA (22:6) (β: 0.14; 95% CI: -0.17, -0.10). The "fruits, vegetables, nuts, and legumes" pattern was associated with 5 metabolites including proline betaine (β: 0.17; 95% CI: 0.09, 0.25) (P < 0.0002). CONCLUSIONS Three predominant dietary patterns in US South Asians are associated with circulating metabolites differentiated by lipids including glycerophospholipids and PUFAs and the amino acid proline betaine.
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Affiliation(s)
- Meghana D Gadgil
- Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.
| | - Alka M Kanaya
- Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Caroline Sands
- National Phenome Centre, Imperial College London, London, United Kingdom
| | - Elena Chekmeneva
- National Phenome Centre, Imperial College London, London, United Kingdom
| | - Matthew R Lewis
- National Phenome Centre, Imperial College London, London, United Kingdom
| | - Namratha R Kandula
- Division of General Internal Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - David M Herrington
- Section on Cardiovascular Medicine, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
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12
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Dehghan A, Pinto RC, Karaman I, Huang J, Durainayagam BR, Ghanbari M, Nazeer A, Zhong Q, Liggi S, Whiley L, Mustafa R, Kivipelto M, Solomon A, Ngandu T, Kanekiyo T, Aikawa T, Radulescu CI, Barnes SJ, Graça G, Chekmeneva E, Camuzeaux S, Lewis MR, Kaluarachchi MR, Ikram MA, Holmes E, Tzoulaki I, Matthews PM, Griffin JL, Elliott P. Metabolome-wide association study on ABCA7 indicates a role of ceramide metabolism in Alzheimer's disease. Proc Natl Acad Sci U S A 2022; 119:e2206083119. [PMID: 36269859 DOI: 10.1073/pnas.2206083119] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Genome-wide association studies (GWASs) have identified genetic loci associated with the risk of Alzheimer's disease (AD), but the molecular mechanisms by which they confer risk are largely unknown. We conducted a metabolome-wide association study (MWAS) of AD-associated loci from GWASs using untargeted metabolic profiling (metabolomics) by ultraperformance liquid chromatography-mass spectrometry (UPLC-MS). We identified an association of lactosylceramides (LacCer) with AD-related single-nucleotide polymorphisms (SNPs) in ABCA7 (P = 5.0 × 10-5 to 1.3 × 10-44). We showed that plasma LacCer concentrations are associated with cognitive performance and genetically modified levels of LacCer are associated with AD risk. We then showed that concentrations of sphingomyelins, ceramides, and hexosylceramides were altered in brain tissue from Abca7 knockout mice, compared with wild type (WT) (P = 0.049-1.4 × 10-5), but not in a mouse model of amyloidosis. Furthermore, activation of microglia increases intracellular concentrations of hexosylceramides in part through induction in the expression of sphingosine kinase, an enzyme with a high control coefficient for sphingolipid and ceramide synthesis. Our work suggests that the risk for AD arising from functional variations in ABCA7 is mediated at least in part through ceramides. Modulation of their metabolism or downstream signaling may offer new therapeutic opportunities for AD.
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13
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Harshfield EL, Sands CJ, Tuladhar AM, de Leeuw FE, Lewis MR, Markus HS. Metabolomic profiling in small vessel disease identifies multiple associations with disease severity. Brain 2022; 145:2461-2471. [PMID: 35254405 PMCID: PMC9337813 DOI: 10.1093/brain/awac041] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 12/20/2021] [Accepted: 01/11/2022] [Indexed: 11/17/2022] Open
Abstract
Cerebral small vessel disease is a major cause of vascular cognitive impairment and dementia. There are few treatments, largely reflecting limited understanding of the underlying pathophysiology. Metabolomics can be used to identify novel risk factors to better understand pathogenesis and to predict disease progression and severity. We analysed data from 624 patients with symptomatic cerebral small vessel disease from two prospective cohort studies. Serum samples were collected at baseline and patients underwent MRI scans and cognitive testing at regular intervals with up to 14 years of follow-up. Using ultra-performance liquid chromatography-mass spectrometry and nuclear magnetic resonance spectroscopy, we obtained metabolic and lipidomic profiles from 369 annotated metabolites and 54 764 unannotated features and examined their association with respect to disease severity, assessed using MRI small vessel disease markers, cognition and future risk of all-cause dementia. Our analysis identified 28 metabolites that were significantly associated with small vessel disease imaging markers and cognition. Decreased levels of multiple glycerophospholipids and sphingolipids were associated with increased small vessel disease load as evidenced by higher white matter hyperintensity volume, lower mean diffusivity normalized peak height, greater brain atrophy and impaired cognition. Higher levels of creatine, FA(18:2(OH)) and SM(d18:2/24:1) were associated with increased lacune count, higher white matter hyperintensity volume and impaired cognition. Lower baseline levels of carnitines and creatinine were associated with higher annualized change in peak width of skeletonized mean diffusivity, and 25 metabolites, including lipoprotein subclasses, amino acids and xenobiotics, were associated with future dementia incidence. Our results show multiple distinct metabolic signatures that are associated with imaging markers of small vessel disease, cognition and conversion to dementia. Further research should assess causality and the use of metabolomic screening to improve the ability to predict future disease severity and dementia risk in small vessel disease. The metabolomic profiles may also provide novel insights into disease pathogenesis and help identify novel treatment approaches.
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Affiliation(s)
- Eric L Harshfield
- Correspondence to: Dr Eric L. Harshfield Stroke Research Group Department of Clinical Neurosciences University of Cambridge R3, Box 83, Cambridge Biomedical Campus Cambridge CB2 0QQ, UK E-mail:
| | - Caroline J Sands
- National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, UK
| | - Anil M Tuladhar
- Department of Neurology, Donders Center for Medical Neuroscience, Radboud University Nijmegen Medical Center, 6500 HB Nijmegen, The Netherlands
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14
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Gadgil MD, Sarkar M, Sands C, Lewis MR, Herrington DM, Kanaya AM. Associations of NAFLD with circulating ceramides and impaired glycemia. Diabetes Res Clin Pract 2022; 186:109829. [PMID: 35292328 PMCID: PMC9082931 DOI: 10.1016/j.diabres.2022.109829] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 03/07/2022] [Accepted: 03/09/2022] [Indexed: 11/03/2022]
Abstract
AIM Determine the association of circulating ceramides with NAFLD and glycemic impairment. METHODS Sample: 669 participants in the Mediators of Atherosclerosis in South Asians Living in America (MASALA) cohort aged 40-84 years without cardiovascular disease, cirrhosis, or significant alcohol intake. CLINICAL MEASURES Computed tomography scans at baseline for hepatic attenuation. Fasting serum specimens at baseline and after 5 years. Lipidomics: LC-MS-based analysis of 19 known ceramide signals. STATISTICAL ANALYSIS Linear and logistic regression models of log-transformed ceramides, hepatic attenuation and glucose adjusted for age, sex, calories, study site, BMI, exercise, diet quality, alcohol, saturated fat, lipid-lowering medications and fasting glucose. RESULTS Average age was 55 years, 44% were women, mean BMI was 25.9 kg/m2, and 8% had NAFLD. In adjusted models, Cer(d16:1/20:0) and Cer(d18:1/18:0) were associated with lower mean hepatic attenuation (increased liver fat) (β -4.29; 95% CI [-5.98, -2.59]) and (β -3.40; 95% CI [-5.11, -1.70]), and LacCer(d18:1/16:0) with higher attenuation (β 4.44; 95% CI [2.15, 6.73]). All three ceramides partially mediated the relationship between hepatic attenuation and fasting glucose by 16%, 11% and 5%, respectively, after 5-years. CONCLUSIONS Three circulating ceramides were strongly associated with NAFLD and fasting glucose after 5 years, and partially mediated this association.
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Affiliation(s)
- Meghana D Gadgil
- Division of General Internal Medicine, Department of Medicine, University of California, 1545 Divisadero Street, Suite 320, San Francisco, CA 94143-0320, United States.
| | - Monika Sarkar
- Division of Gastroenterology, Department of Medicine, University of California, 513 Parnassus Avenue, MSB, San Francisco, CA 94117, United States
| | - Caroline Sands
- National Phenome Centre, Imperial College London, IRDB Building 5th Floor, Hammersmith Hospital Campus, London W12 0NN, United Kingdom
| | - Matthew R Lewis
- National Phenome Centre, Imperial College London, IRDB Building 5th Floor, Hammersmith Hospital Campus, London W12 0NN, United Kingdom
| | - David M Herrington
- Section on Cardiovascular Medicine, Department of Internal Medicine, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157, United States
| | - Alka M Kanaya
- Division of General Internal Medicine, Department of Medicine, University of California, 1545 Divisadero Street, Suite 320, San Francisco, CA 94143-0320, United States
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15
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Mehta R, Chekmeneva E, Jackson H, Sands C, Mills E, Arancon D, Li HK, Arkell P, Rawson TM, Hammond R, Amran M, Haber A, Cooke GS, Noursadeghi M, Kaforou M, Lewis MR, Takats Z, Sriskandan S. Antiviral metabolite 3'-deoxy-3',4'-didehydro-cytidine is detectable in serum and identifies acute viral infections including COVID-19. Med 2022; 3:204-215.e6. [PMID: 35128501 PMCID: PMC8801973 DOI: 10.1016/j.medj.2022.01.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 11/14/2021] [Accepted: 01/21/2022] [Indexed: 11/05/2022]
Abstract
BACKGROUND There is a critical need for rapid viral infection diagnostics to enable prompt case identification in pandemic settings and support targeted antimicrobial prescribing. METHODS Using untargeted high-resolution liquid chromatography coupled with mass spectrometry, we compared the admission serum metabolome of emergency department patients with viral infections (including COVID-19), bacterial infections, inflammatory conditions, and healthy controls. Sera from an independent cohort of emergency department patients admitted with viral or bacterial infections underwent profiling to validate findings. Associations between whole-blood gene expression and the identified metabolite of interest were examined. FINDINGS 3'-Deoxy-3',4'-didehydro-cytidine (ddhC), a free base of the only known human antiviral small molecule ddhC-triphosphate (ddhCTP), was detected for the first time in serum. When comparing 60 viral with 101 non-viral cases in the discovery cohort, ddhC was the most significantly differentially abundant metabolite, generating an area under the receiver operating characteristic curve (AUC) of 0.954 (95% CI: 0.923-0.986). In the validation cohort, ddhC was again the most significantly differentially abundant metabolite when comparing 40 viral with 40 bacterial cases, generating an AUC of 0.81 (95% CI 0.708-0.915). Transcripts of viperin and CMPK2, enzymes responsible for ddhCTP synthesis, were among the five genes most highly correlated with ddhC abundance. CONCLUSIONS The antiviral precursor molecule ddhC is detectable in serum and an accurate marker for acute viral infection. Interferon-inducible genes viperin and CMPK2 are implicated in ddhC production in vivo. These findings highlight a future diagnostic role for ddhC in viral diagnosis, pandemic preparedness, and acute infection management. FUNDING NIHR Imperial BRC; UKRI.
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Affiliation(s)
- Ravi Mehta
- Department of Infectious Disease, Imperial College London, London W12 0NN, UK
| | - Elena Chekmeneva
- National Phenome Centre, Imperial College London, London SW7 2AZ, UK
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, UK
| | - Heather Jackson
- Department of Infectious Disease, Imperial College London, London W12 0NN, UK
| | - Caroline Sands
- National Phenome Centre, Imperial College London, London SW7 2AZ, UK
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, UK
| | - Ewurabena Mills
- Department of Infectious Disease, Imperial College London, London W12 0NN, UK
| | | | - Ho Kwong Li
- Department of Infectious Disease, Imperial College London, London W12 0NN, UK
- MRC Centre for Molecular Bacteriology & Infection, Imperial College London, London SW7 2AZ, UK
| | - Paul Arkell
- Department of Infectious Disease, Imperial College London, London W12 0NN, UK
- Imperial College Healthcare NHS Trust, London W12 0HS, UK
| | - Timothy M. Rawson
- Department of Infectious Disease, Imperial College London, London W12 0NN, UK
- Imperial College Healthcare NHS Trust, London W12 0HS, UK
- Division of Infection & Immunity, University College London, London WC1 E6BT, UK
| | - Robert Hammond
- Imperial College Healthcare NHS Trust, London W12 0HS, UK
| | - Maisarah Amran
- Imperial College Healthcare NHS Trust, London W12 0HS, UK
| | - Anna Haber
- Imperial College Healthcare NHS Trust, London W12 0HS, UK
| | - Graham S. Cooke
- Department of Infectious Disease, Imperial College London, London W12 0NN, UK
| | - Mahdad Noursadeghi
- Division of Infection & Immunity, University College London, London WC1 E6BT, UK
| | - Myrsini Kaforou
- Department of Infectious Disease, Imperial College London, London W12 0NN, UK
| | - Matthew R. Lewis
- National Phenome Centre, Imperial College London, London SW7 2AZ, UK
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, UK
| | - Zoltan Takats
- National Phenome Centre, Imperial College London, London SW7 2AZ, UK
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, UK
| | - Shiranee Sriskandan
- Department of Infectious Disease, Imperial College London, London W12 0NN, UK
- MRC Centre for Molecular Bacteriology & Infection, Imperial College London, London SW7 2AZ, UK
- NIHR Health Protection Research Unit in Healthcare-associated Infection & Antimicrobial Resistance, Imperial College London, London W12 0NN, UK
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16
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Abstract
This Perspective outlines recent progress and future directions for using machine learning (ML), a data-driven method, to address critical questions in the design, synthesis, processing, and characterization of biomacromolecules. The achievement of these tasks requires the navigation of vast and complex chemical and biological spaces, difficult to accomplish with reasonable speed. Using modern algorithms and supercomputers, quantum physics methods are able to examine systems containing a few hundred interacting species and determine the probability of finding them in a particular region of phase space, thereby anticipating their properties. Likewise, modern approaches in chemistry and biomolecular simulation, supported by high performance computing, have culminated in producing data sets of escalating size and intrinsically high complexity. Hence, using ML to extract relevant information from these fields is of paramount importance to advance our understanding of chemical and biomolecular systems. At the heart of ML approaches lie statistical algorithms, which by evaluating a portion of a given data set, identify, learn, and manipulate the underlying rules that govern the whole data set. The assembly of a quality model to represent the data followed by the predictions and elimination of error sources are the key steps in ML. In addition to a growing infrastructure of ML tools to address complex problems, an increasing number of aspects related to our understanding of the fundamental properties of biomacromolecules are exposed to ML. These fields, including those residing at the interface of polymer science and biology (i.e., structure determination, de novo design, folding, and dynamics), strive to adopt and take advantage of the transformative power offered by approaches in the ML domain, which clearly has the potential of accelerating research in the field of biomacromolecules.
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Affiliation(s)
- Eleonora Gianti
- Institute for Computational Molecular Science (ICMS), Temple University, Philadelphia, Pennsylvania 19122, United States.,Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - Simona Percec
- Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
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17
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Pruski P, Correia GDS, Lewis HV, Capuccini K, Inglese P, Chan D, Brown RG, Kindinger L, Lee YS, Smith A, Marchesi J, McDonald JAK, Cameron S, Alexander-Hardiman K, David AL, Stock SJ, Norman JE, Terzidou V, Teoh TG, Sykes L, Bennett PR, Takats Z, MacIntyre DA. Direct on-swab metabolic profiling of vaginal microbiome host interactions during pregnancy and preterm birth. Nat Commun 2021; 12:5967. [PMID: 34645809 PMCID: PMC8514602 DOI: 10.1038/s41467-021-26215-w] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Accepted: 09/23/2021] [Indexed: 11/13/2022] Open
Abstract
The pregnancy vaginal microbiome contributes to risk of preterm birth, the primary cause of death in children under 5 years of age. Here we describe direct on-swab metabolic profiling by Desorption Electrospray Ionization Mass Spectrometry (DESI-MS) for sample preparation-free characterisation of the cervicovaginal metabolome in two independent pregnancy cohorts (VMET, n = 160; 455 swabs; VMET II, n = 205; 573 swabs). By integrating metataxonomics and immune profiling data from matched samples, we show that specific metabolome signatures can be used to robustly predict simultaneously both the composition of the vaginal microbiome and host inflammatory status. In these patients, vaginal microbiota instability and innate immune activation, as predicted using DESI-MS, associated with preterm birth, including in women receiving cervical cerclage for preterm birth prevention. These findings highlight direct on-swab metabolic profiling by DESI-MS as an innovative approach for preterm birth risk stratification through rapid assessment of vaginal microbiota-host dynamics.
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Affiliation(s)
- Pamela Pruski
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine Imperial College London, London, UK
| | - Gonçalo D S Correia
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine Imperial College London, London, UK
- National Phenome Centre, Imperial College London, London, UK
| | - Holly V Lewis
- March of Dimes Prematurity Research Centre at Imperial College London, London, UK
- Imperial College Parturition Research Group, Institute of Reproductive and Developmental Biology, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Queen Charlotte's & Chelsea Hospital, Imperial College London, London, UK
| | - Katia Capuccini
- March of Dimes Prematurity Research Centre at Imperial College London, London, UK
- Imperial College Parturition Research Group, Institute of Reproductive and Developmental Biology, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Paolo Inglese
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine Imperial College London, London, UK
- National Phenome Centre, Imperial College London, London, UK
| | - Denise Chan
- March of Dimes Prematurity Research Centre at Imperial College London, London, UK
- Imperial College Parturition Research Group, Institute of Reproductive and Developmental Biology, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Queen Charlotte's & Chelsea Hospital, Imperial College London, London, UK
| | - Richard G Brown
- Imperial College Parturition Research Group, Institute of Reproductive and Developmental Biology, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Queen Charlotte's & Chelsea Hospital, Imperial College London, London, UK
| | - Lindsay Kindinger
- Imperial College Parturition Research Group, Institute of Reproductive and Developmental Biology, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Elizabeth Garrett Anderson Institute for Women's Health, University College London, London, UK
| | - Yun S Lee
- March of Dimes Prematurity Research Centre at Imperial College London, London, UK
- Imperial College Parturition Research Group, Institute of Reproductive and Developmental Biology, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Ann Smith
- Faculty of Health and Applied Sciences, University West of England, Bristol, UK
| | - Julian Marchesi
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine Imperial College London, London, UK
- March of Dimes Prematurity Research Centre at Imperial College London, London, UK
| | - Julie A K McDonald
- MRC Centre for Molecular Bacteriology and Infection, Imperial College London, London, UK
| | - Simon Cameron
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine Imperial College London, London, UK
- School of Biological Sciences, Institute for Global Food Security, Queen's University Belfast, Belfast, UK
| | - Kate Alexander-Hardiman
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine Imperial College London, London, UK
| | - Anna L David
- Elizabeth Garrett Anderson Institute for Women's Health, University College London, London, UK
| | - Sarah J Stock
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, UK
| | - Jane E Norman
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, UK
- Faculty of Health Sciences, University of Bristol, Bristol, UK
| | - Vasso Terzidou
- March of Dimes Prematurity Research Centre at Imperial College London, London, UK
- Imperial College Parturition Research Group, Institute of Reproductive and Developmental Biology, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Chelsea & Westminster Hospital, NHS Trust, London, UK
| | - T G Teoh
- St Mary's Hospital, Imperial College Healthcare NHS Trust, London, UK
| | - Lynne Sykes
- March of Dimes Prematurity Research Centre at Imperial College London, London, UK
- Imperial College Parturition Research Group, Institute of Reproductive and Developmental Biology, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Queen Charlotte's & Chelsea Hospital, Imperial College London, London, UK
| | - Phillip R Bennett
- March of Dimes Prematurity Research Centre at Imperial College London, London, UK
- Imperial College Parturition Research Group, Institute of Reproductive and Developmental Biology, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Queen Charlotte's & Chelsea Hospital, Imperial College London, London, UK
- Tommy's National Centre for Miscarriage Research, Imperial College London, London, UK
| | - Zoltan Takats
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine Imperial College London, London, UK.
- National Phenome Centre, Imperial College London, London, UK.
- March of Dimes Prematurity Research Centre at Imperial College London, London, UK.
| | - David A MacIntyre
- March of Dimes Prematurity Research Centre at Imperial College London, London, UK.
- Imperial College Parturition Research Group, Institute of Reproductive and Developmental Biology, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK.
- Tommy's National Centre for Miscarriage Research, Imperial College London, London, UK.
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18
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Padthaisong S, Phetcharaburanin J, Klanrit P, Li JV, Namwat N, Khuntikeo N, Titapun A, Jarearnrat A, Wangwiwatsin A, Mahalapbutr P, Loilome W. Integration of global metabolomics and lipidomics approaches reveals the molecular mechanisms and the potential biomarkers for postoperative recurrence in early-stage cholangiocarcinoma. Cancer Metab 2021; 9:30. [PMID: 34348794 PMCID: PMC8335966 DOI: 10.1186/s40170-021-00266-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 07/21/2021] [Indexed: 02/08/2023] Open
Abstract
Background Cholangiocarcioma (CCA) treatment is challenging because most of the patients are diagnosed when the disease is advanced, and cancer recurrence is the main problem after treatment, leading to low survival rates. Therefore, our understanding of the mechanism underlying CCA recurrence is essential in order to prevent CCA recurrence and improve patient outcomes. Methods We performed 1H-NMR and UPLC-MS-based metabolomics on the CCA serum. The differential metabolites were further analyzed using pathway analysis and potential biomarker identification. Results At an early stage, the metabolites involved in energy metabolisms, such as pyruvate metabolism, and the TCA cycle, are downregulated, while most lipids, including TGs, PCs, PEs, and PAs, are upregulated in recurrence patients. This metabolic feature has been described in cancer stem-like cell (CSC) metabolism. In addition, the CSC markers CD44v6 and CD44v8-10 are associated with CD36 (a protein involved in lipid uptake) as well as with recurrence-free survival. We also found that citrate, sarcosine, succinate, creatine, creatinine and pyruvate, and TGs have good predictive values for CCA recurrence. Conclusion Our study demonstrates the possible molecular mechanisms underlying CCA recurrence, and these may associate with the existence of CSCs. The metabolic change involved in the recurrence pathway might be used to determine biomarkers for predicting CCA recurrence. Supplementary Information The online version contains supplementary material available at 10.1186/s40170-021-00266-5.
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Affiliation(s)
- Sureerat Padthaisong
- Department of Biochemistry, Faculty of Medicine, Khon Kaen University, 123 Mittraparp Road, Muang District, Khon Kaen, 40002, Thailand.,Cholangiocarcinoma Screening and Care Program (CASCAP), Khon Kaen University, Khon Kaen, 40002, Thailand.,Cholangiocarcinoma Research Institute, Faculty of Medicine, Khon Kaen University, Khon Kaen, 40002, Thailand
| | - Jutarop Phetcharaburanin
- Department of Biochemistry, Faculty of Medicine, Khon Kaen University, 123 Mittraparp Road, Muang District, Khon Kaen, 40002, Thailand.,Cholangiocarcinoma Screening and Care Program (CASCAP), Khon Kaen University, Khon Kaen, 40002, Thailand.,Cholangiocarcinoma Research Institute, Faculty of Medicine, Khon Kaen University, Khon Kaen, 40002, Thailand
| | - Poramate Klanrit
- Department of Biochemistry, Faculty of Medicine, Khon Kaen University, 123 Mittraparp Road, Muang District, Khon Kaen, 40002, Thailand.,Cholangiocarcinoma Screening and Care Program (CASCAP), Khon Kaen University, Khon Kaen, 40002, Thailand.,Cholangiocarcinoma Research Institute, Faculty of Medicine, Khon Kaen University, Khon Kaen, 40002, Thailand
| | - Jia V Li
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, SW7 2AZ, UK
| | - Nisana Namwat
- Department of Biochemistry, Faculty of Medicine, Khon Kaen University, 123 Mittraparp Road, Muang District, Khon Kaen, 40002, Thailand.,Cholangiocarcinoma Screening and Care Program (CASCAP), Khon Kaen University, Khon Kaen, 40002, Thailand.,Cholangiocarcinoma Research Institute, Faculty of Medicine, Khon Kaen University, Khon Kaen, 40002, Thailand
| | - Narong Khuntikeo
- Cholangiocarcinoma Screening and Care Program (CASCAP), Khon Kaen University, Khon Kaen, 40002, Thailand.,Cholangiocarcinoma Research Institute, Faculty of Medicine, Khon Kaen University, Khon Kaen, 40002, Thailand.,Department of Surgery, Faculty of Medicine, Khon Kaen University, Khon Kaen, 40002, Thailand
| | - Attapol Titapun
- Cholangiocarcinoma Screening and Care Program (CASCAP), Khon Kaen University, Khon Kaen, 40002, Thailand.,Cholangiocarcinoma Research Institute, Faculty of Medicine, Khon Kaen University, Khon Kaen, 40002, Thailand.,Department of Surgery, Faculty of Medicine, Khon Kaen University, Khon Kaen, 40002, Thailand
| | - Apiwat Jarearnrat
- Cholangiocarcinoma Screening and Care Program (CASCAP), Khon Kaen University, Khon Kaen, 40002, Thailand.,Cholangiocarcinoma Research Institute, Faculty of Medicine, Khon Kaen University, Khon Kaen, 40002, Thailand.,Department of Surgery, Faculty of Medicine, Khon Kaen University, Khon Kaen, 40002, Thailand
| | - Arporn Wangwiwatsin
- Department of Biochemistry, Faculty of Medicine, Khon Kaen University, 123 Mittraparp Road, Muang District, Khon Kaen, 40002, Thailand.,Cholangiocarcinoma Screening and Care Program (CASCAP), Khon Kaen University, Khon Kaen, 40002, Thailand.,Cholangiocarcinoma Research Institute, Faculty of Medicine, Khon Kaen University, Khon Kaen, 40002, Thailand
| | - Panupong Mahalapbutr
- Department of Biochemistry, Faculty of Medicine, Khon Kaen University, 123 Mittraparp Road, Muang District, Khon Kaen, 40002, Thailand.,Cholangiocarcinoma Screening and Care Program (CASCAP), Khon Kaen University, Khon Kaen, 40002, Thailand.,Cholangiocarcinoma Research Institute, Faculty of Medicine, Khon Kaen University, Khon Kaen, 40002, Thailand
| | - Watcharin Loilome
- Department of Biochemistry, Faculty of Medicine, Khon Kaen University, 123 Mittraparp Road, Muang District, Khon Kaen, 40002, Thailand. .,Cholangiocarcinoma Screening and Care Program (CASCAP), Khon Kaen University, Khon Kaen, 40002, Thailand. .,Cholangiocarcinoma Research Institute, Faculty of Medicine, Khon Kaen University, Khon Kaen, 40002, Thailand.
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19
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Blaise BJ, Correia GDS, Haggart GA, Surowiec I, Sands C, Lewis MR, Pearce JTM, Trygg J, Nicholson JK, Holmes E, Ebbels TMD. Statistical analysis in metabolic phenotyping. Nat Protoc 2021. [PMID: 34321638 DOI: 10.1038/s41596-021-00579-1] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 05/27/2021] [Indexed: 01/09/2023]
Abstract
Metabolic phenotyping is an important tool in translational biomedical research. The advanced analytical technologies commonly used for phenotyping, including mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy, generate complex data requiring tailored statistical analysis methods. Detailed protocols have been published for data acquisition by liquid NMR, solid-state NMR, ultra-performance liquid chromatography (LC-)MS and gas chromatography (GC-)MS on biofluids or tissues and their preprocessing. Here we propose an efficient protocol (guidelines and software) for statistical analysis of metabolic data generated by these methods. Code for all steps is provided, and no prior coding skill is necessary. We offer efficient solutions for the different steps required within the complete phenotyping data analytics workflow: scaling, normalization, outlier detection, multivariate analysis to explore and model study-related effects, selection of candidate biomarkers, validation, multiple testing correction and performance evaluation of statistical models. We also provide a statistical power calculation algorithm and safeguards to ensure robust and meaningful experimental designs that deliver reliable results. We exemplify the protocol with a two-group classification study and data from an epidemiological cohort; however, the protocol can be easily modified to cover a wider range of experimental designs or incorporate different modeling approaches. This protocol describes a minimal set of analyses needed to rigorously investigate typical datasets encountered in metabolic phenotyping.
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20
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Maciejewski M, Sands C, Nair N, Ling S, Verstappen S, Hyrich K, Barton A, Ziemek D, Lewis MR, Plant D. Prediction of response of methotrexate in patients with rheumatoid arthritis using serum lipidomics. Sci Rep 2021; 11:7266. [PMID: 33790392 DOI: 10.1038/s41598-021-86729-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 03/15/2021] [Indexed: 01/31/2023] Open
Abstract
Methotrexate (MTX) is a common first-line treatment for new-onset rheumatoid arthritis (RA). However, MTX is ineffective for 30–40% of patients and there is no way to know which patients might benefit. Here, we built statistical models based on serum lipid levels measured at two time-points (pre-treatment and following 4 weeks on-drug) to investigate if MTX response (by 6 months) could be predicted. Patients about to commence MTX treatment for the first time were selected from the Rheumatoid Arthritis Medication Study (RAMS). Patients were categorised as good or non-responders following 6 months on-drug using EULAR response criteria. Serum lipids were measured using ultra‐performance liquid chromatography–mass spectrometry and supervised machine learning methods (including regularized regression, support vector machine and random forest) were used to predict EULAR response. Models including lipid levels were compared to models including clinical covariates alone. The best performing classifier including lipid levels (assessed at 4 weeks) was constructed using regularized regression (ROC AUC 0.61 ± 0.02). However, the clinical covariate based model outperformed the classifier including lipid levels when either pre- or on-treatment time-points were investigated (ROC AUC 0.68 ± 0.02). Pre- or early-treatment serum lipid profiles are unlikely to inform classification of MTX response by 6 months with performance adequate for use in RA clinical management.
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21
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Gadgil MD, Kanaya AM, Sands C, Lewis MR, Kandula NR, Herrington DM. Circulating metabolites and lipids are associated with glycaemic measures in South Asians. Diabet Med 2021; 38:e14494. [PMID: 33617033 PMCID: PMC8115216 DOI: 10.1111/dme.14494] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 11/30/2020] [Accepted: 12/03/2020] [Indexed: 12/30/2022]
Abstract
BACKGROUND South Asians are at higher risk for diabetes (DM) than many other racial/ethnic groups. Circulating metabolites are measurable products of metabolic processes that may explain the aetiology of elevated risk. We characterized metabolites associated with prevalent DM and glycaemic measures in South Asians. METHODS We included 717 participants from the Mediators of Atherosclerosis in South Asians Living in America (MASALA) study, aged 40-84 years. We used baseline fasting serum for metabolomics and demographic, behavioural, glycaemic data from baseline and at 5 years. We performed LC-MS untargeted metabolomic and lipidomic analysis with targeted integration of known signals. Individual linear and ordinal logistic regression models were adjusted for age, sex, BMI, diet, exercise, alcohol, smoking and family history of DM followed by elastic net regression to identify metabolites most associated with the outcome. RESULTS There were 258 metabolites with detectable signal in >98% of samples. Thirty-four metabolites were associated with prevalent DM in an elastic net model. Predominant metabolites associated with DM were sphingomyelins, proline (OR 15.86; 95% CI 4.72, 53.31) and betaine (OR 0.03; 0.004, 0.14). Baseline tri- and di-acylglycerols [DG (18:0/16:0) (18.36; 11.79, 24.92)] were positively associated with fasting glucose and long-chain acylcarnitines [CAR 26:1 (-0.40; -0.54, -0.27)] were inversely associated with prevalent DM and HbA1c at follow-up. DISCUSSION A metabolomic signature in South Asians may help determine the unique aetiology of diabetes in this high-risk ethnic group. Future work will externally validate our findings and determine the effects of modifiable risk factors for DM.
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Affiliation(s)
- Meghana D. Gadgil
- Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, CA; 1545 Divisadero Street, Suite 320, San Francisco, CA 94143-0320
| | - Alka M. Kanaya
- Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, CA; 1545 Divisadero Street, Suite 320, San Francisco, CA 94143-0320
| | - Caroline Sands
- National Phenome Centre, Imperial College London, IRDB Building 5th Floor, Hammersmith Hospital Campus, London, W12 0NN
| | - Matthew R. Lewis
- National Phenome Centre, Imperial College London, IRDB Building 5th Floor, Hammersmith Hospital Campus, London, W12 0NN
| | - Namratha R. Kandula
- Division of General Internal Medicine and Geriatrics, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL; Rubloff Building 10th Floor 750 N Lake Shore Chicago IL 60611
| | - David M. Herrington
- Section on Cardiovascular Medicine, Department of Internal Medicine, Wake Forest School of Medicine; Medical Center Boulevard, Winston-Salem, NC 27157
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22
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Sands CJ, Gómez-Romero M, Correia G, Chekmeneva E, Camuzeaux S, Izzi-Engbeaya C, Dhillo WS, Takats Z, Lewis MR. Representing the Metabolome with High Fidelity: Range and Response as Quality Control Factors in LC-MS-Based Global Profiling. Anal Chem 2021; 93:1924-1933. [PMID: 33448796 DOI: 10.1021/acs.analchem.0c03848] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Liquid chromatography-mass spectrometry (LC-MS) is a powerful and widely used technique for measuring the abundance of chemical species in living systems. Its sensitivity, analytical specificity, and direct applicability to biofluids and tissue extracts impart great promise for the discovery and mechanistic characterization of biomarker panels for disease detection, health monitoring, patient stratification, and treatment personalization. Global metabolic profiling applications yield complex data sets consisting of multiple feature measurements for each chemical species observed. While this multiplicity can be useful in deriving enhanced analytical specificity and chemical identities from LC-MS data, data set inflation and quantitative imprecision among related features is problematic for statistical analyses and interpretation. This Perspective provides a critical evaluation of global profiling data fidelity with respect to measurement linearity and the quantitative response variation observed among components of the spectra. These elements of data quality are widely overlooked in untargeted metabolomics yet essential for the generation of data that accurately reflect the metabolome. Advanced feature filtering informed by linear range estimation and analyte response factor assessment is advocated as an attainable means of controlling LC-MS data quality in global profiling studies and exemplified herein at both the feature and data set level.
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Affiliation(s)
- Caroline J Sands
- National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, Hammersmith Campus, London W12 0NN, United Kingdom.,Section of Bioanalytical Chemistry, Department of Metabolism, Digestion and Reproduction, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom
| | - María Gómez-Romero
- National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, Hammersmith Campus, London W12 0NN, United Kingdom.,Section of Bioanalytical Chemistry, Department of Metabolism, Digestion and Reproduction, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom
| | - Gonçalo Correia
- National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, Hammersmith Campus, London W12 0NN, United Kingdom.,Section of Bioanalytical Chemistry, Department of Metabolism, Digestion and Reproduction, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom
| | - Elena Chekmeneva
- National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, Hammersmith Campus, London W12 0NN, United Kingdom.,Section of Bioanalytical Chemistry, Department of Metabolism, Digestion and Reproduction, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom
| | - Stephane Camuzeaux
- National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, Hammersmith Campus, London W12 0NN, United Kingdom.,Section of Bioanalytical Chemistry, Department of Metabolism, Digestion and Reproduction, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom
| | - Chioma Izzi-Engbeaya
- Section of Endocrinology and Investigative Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, Hammersmith Campus, London W12 0HS, United Kingdom
| | - Waljit S Dhillo
- Section of Endocrinology and Investigative Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, Hammersmith Campus, London W12 0HS, United Kingdom
| | - Zoltan Takats
- National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, Hammersmith Campus, London W12 0NN, United Kingdom.,Section of Bioanalytical Chemistry, Department of Metabolism, Digestion and Reproduction, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom
| | - Matthew R Lewis
- National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, Hammersmith Campus, London W12 0NN, United Kingdom.,Section of Bioanalytical Chemistry, Department of Metabolism, Digestion and Reproduction, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom
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23
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Kurbatova N, Garg M, Whiley L, Chekmeneva E, Jiménez B, Gómez-Romero M, Pearce J, Kimhofer T, D'Hondt E, Soininen H, Kłoszewska I, Mecocci P, Tsolaki M, Vellas B, Aarsland D, Nevado-Holgado A, Liu B, Snowden S, Proitsi P, Ashton NJ, Hye A, Legido-Quigley C, Lewis MR, Nicholson JK, Holmes E, Brazma A, Lovestone S. Urinary metabolic phenotyping for Alzheimer's disease. Sci Rep 2020; 10:21745. [PMID: 33303834 DOI: 10.1038/s41598-020-78031-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 11/18/2020] [Indexed: 01/28/2023] Open
Abstract
Finding early disease markers using non-invasive and widely available methods is essential to develop a successful therapy for Alzheimer’s Disease. Few studies to date have examined urine, the most readily available biofluid. Here we report the largest study to date using comprehensive metabolic phenotyping platforms (NMR spectroscopy and UHPLC-MS) to probe the urinary metabolome in-depth in people with Alzheimer’s Disease and Mild Cognitive Impairment. Feature reduction was performed using metabolomic Quantitative Trait Loci, resulting in the list of metabolites associated with the genetic variants. This approach helps accuracy in identification of disease states and provides a route to a plausible mechanistic link to pathological processes. Using these mQTLs we built a Random Forests model, which not only correctly discriminates between people with Alzheimer’s Disease and age-matched controls, but also between individuals with Mild Cognitive Impairment who were later diagnosed with Alzheimer’s Disease and those who were not. Further annotation of top-ranking metabolic features nominated by the trained model revealed the involvement of cholesterol-derived metabolites and small-molecules that were linked to Alzheimer’s pathology in previous studies.
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24
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Riquelme G, Zabalegui N, Marchi P, Jones CM, Monge ME. A Python-Based Pipeline for Preprocessing LC-MS Data for Untargeted Metabolomics Workflows. Metabolites 2020; 10:E416. [PMID: 33081373 DOI: 10.3390/metabo10100416] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Revised: 10/09/2020] [Accepted: 10/14/2020] [Indexed: 12/12/2022] Open
Abstract
Preprocessing data in a reproducible and robust way is one of the current challenges in untargeted metabolomics workflows. Data curation in liquid chromatography–mass spectrometry (LC–MS) involves the removal of biologically non-relevant features (retention time, m/z pairs) to retain only high-quality data for subsequent analysis and interpretation. The present work introduces TidyMS, a package for the Python programming language for preprocessing LC–MS data for quality control (QC) procedures in untargeted metabolomics workflows. It is a versatile strategy that can be customized or fit for purpose according to the specific metabolomics application. It allows performing quality control procedures to ensure accuracy and reliability in LC–MS measurements, and it allows preprocessing metabolomics data to obtain cleaned matrices for subsequent statistical analysis. The capabilities of the package are shown with pipelines for an LC–MS system suitability check, system conditioning, signal drift evaluation, and data curation. These applications were implemented to preprocess data corresponding to a new suite of candidate plasma reference materials developed by the National Institute of Standards and Technology (NIST; hypertriglyceridemic, diabetic, and African-American plasma pools) to be used in untargeted metabolomics studies in addition to NIST SRM 1950 Metabolites in Frozen Human Plasma. The package offers a rapid and reproducible workflow that can be used in an automated or semi-automated fashion, and it is an open and free tool available to all users.
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25
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Takis PG, Jiménez B, Sands CJ, Chekmeneva E, Lewis MR. SMolESY: an efficient and quantitative alternative to on-instrument macromolecular 1H-NMR signal suppression. Chem Sci 2020; 11:6000-6011. [PMID: 34094091 PMCID: PMC8159292 DOI: 10.1039/d0sc01421d] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 05/26/2020] [Indexed: 12/23/2022] Open
Abstract
One-dimensional (1D) proton-nuclear magnetic resonance (1H-NMR) spectroscopy is an established technique for measuring small molecules in a wide variety of complex biological sample types. It is demonstrably reproducible, easily automatable and consequently ideal for routine and large-scale application. However, samples containing proteins, lipids, polysaccharides and other macromolecules produce broad signals which overlap and convolute those from small molecules. NMR experiment types designed to suppress macromolecular signals during acquisition may be additionally performed, however these approaches add to the overall sample analysis time and cost, especially for large cohort studies, and fail to produce reliably quantitative data. Here, we propose an alternative way of computationally eliminating macromolecular signals, employing the mathematical differentiation of standard 1H-NMR spectra, producing small molecule-enhanced spectra with preserved quantitative capability and increased resolution. Our approach, presented in its simplest form, was implemented in a cheminformatic toolbox and successfully applied to more than 3000 samples of various biological matrices rich or potentially rich with macromolecules, offering an efficient alternative to on-instrument experimentation, facilitating NMR use in routine and large-scale applications.
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Affiliation(s)
- Panteleimon G Takis
- Section of Bioanalytical Chemistry, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, South Kensington Campus London SW7 2AZ UK
- National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, Hammersmith Campus IRDB Building London W12 0NN UK
| | - Beatriz Jiménez
- Section of Bioanalytical Chemistry, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, South Kensington Campus London SW7 2AZ UK
- National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, Hammersmith Campus IRDB Building London W12 0NN UK
| | - Caroline J Sands
- Section of Bioanalytical Chemistry, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, South Kensington Campus London SW7 2AZ UK
- National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, Hammersmith Campus IRDB Building London W12 0NN UK
| | - Elena Chekmeneva
- Section of Bioanalytical Chemistry, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, South Kensington Campus London SW7 2AZ UK
- National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, Hammersmith Campus IRDB Building London W12 0NN UK
| | - Matthew R Lewis
- Section of Bioanalytical Chemistry, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, South Kensington Campus London SW7 2AZ UK
- National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, Hammersmith Campus IRDB Building London W12 0NN UK
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