1
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Schweickart A, Chetnik K, Batra R, Kaddurah-Daouk R, Suhre K, Halama A, Krumsiek J. AutoFocus: a hierarchical framework to explore multi-omic disease associations spanning multiple scales of biomolecular interaction. Commun Biol 2024; 7:1094. [PMID: 39237774 PMCID: PMC11377741 DOI: 10.1038/s42003-024-06724-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 08/13/2024] [Indexed: 09/07/2024] Open
Abstract
Recent advances in high-throughput measurement technologies have enabled the analysis of molecular perturbations associated with disease phenotypes at the multi-omic level. Such perturbations can range in scale from fluctuations of individual molecules to entire biological pathways. Data-driven clustering algorithms have long been used to group interactions into interpretable functional modules; however, these modules are typically constrained to a fixed size or statistical cutoff. Furthermore, modules are often analyzed independently of their broader biological context. Consequently, such clustering approaches limit the ability to explore functional module associations with disease phenotypes across multiple scales. Here, we introduce AutoFocus, a data-driven method that hierarchically organizes biomolecules and tests for phenotype enrichment at every level within the hierarchy. As a result, the method allows disease-associated modules to emerge at any scale. We evaluated this approach using two datasets: First, we explored associations of biomolecules from the multi-omic QMDiab dataset (n = 388) with the well-characterized type 2 diabetes phenotype. Secondly, we utilized the ROS/MAP Alzheimer's disease dataset (n = 500), consisting of high-throughput measurements of brain tissue to explore modules associated with multiple Alzheimer's Disease-related phenotypes. Our method identifies modules that are multi-omic, span multiple pathways, and vary in size. We provide an interactive tool to explore this hierarchy at different levels and probe enriched modules, empowering users to examine the full hierarchy, delve into biomolecular drivers of disease phenotype within a module, and incorporate functional annotations.
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Affiliation(s)
- Annalise Schweickart
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Kelsey Chetnik
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Richa Batra
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Institute of Brain Sciences, Duke University, Durham, NC, USA
- Department of Medicine, Duke University, Durham, NC, USA
| | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- Bioinformatics Core, Weill Cornell Medical College-Qatar Education City, Doha, Qatar
| | - Anna Halama
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- Bioinformatics Core, Weill Cornell Medical College-Qatar Education City, Doha, Qatar
| | - Jan Krumsiek
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA.
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA.
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2
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Weinisch P, Raffler J, Römisch-Margl W, Arnold M, Mohney RP, Rist MJ, Prehn C, Skurk T, Hauner H, Daniel H, Suhre K, Kastenmüller G. The HuMet Repository: Watching human metabolism at work. Cell Rep 2024; 43:114416. [PMID: 39033506 DOI: 10.1016/j.celrep.2024.114416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 05/11/2024] [Accepted: 06/13/2024] [Indexed: 07/23/2024] Open
Abstract
Metabolism oscillates between catabolic and anabolic states depending on food intake, exercise, or stresses that change a multitude of metabolic pathways simultaneously. We present the HuMet Repository for exploring dynamic metabolic responses to oral glucose/lipid loads, mixed meals, 36-h fasting, exercise, and cold stress in healthy subjects. Metabolomics data from blood, urine, and breath of 15 young, healthy men at up to 56 time points are integrated and embedded within an interactive web application, enabling researchers with and without computational expertise to search, visualize, analyze, and contextualize the dynamic metabolite profiles of 2,656 metabolites acquired on multiple platforms. With examples, we demonstrate the utility of the resource for research into the dynamics of human metabolism, highlighting differences and similarities in systemic metabolic responses across challenges and the complementarity of metabolomics platforms. The repository, providing a reference for healthy metabolite changes to six standardized physiological challenges, is freely accessible through a web portal.
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Affiliation(s)
- Patrick Weinisch
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Johannes Raffler
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany; Digital Medicine, University Hospital of Augsburg, Augsburg, Germany
| | - Werner Römisch-Margl
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Matthias Arnold
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany; Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | | | - Manuela J Rist
- Department of Physiology and Biochemistry of Nutrition, Max Rubner-Institut, Karlsruhe, Germany
| | - Cornelia Prehn
- Metabolomics and Proteomics Core, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Thomas Skurk
- ZIEL Institute for Food and Health, Core Facility Human Studies, Technical University of Munich, Freising, Germany; Else Kröner Fresenius Center for Nutritional Medicine, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Hans Hauner
- Else Kröner Fresenius Center for Nutritional Medicine, School of Life Sciences, Technical University of Munich, Freising, Germany; Institute for Nutritional Medicine, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Hannelore Daniel
- School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Karsten Suhre
- Department of Biophysics and Physiology, Weill Cornell Medicine - Qatar, Doha, Qatar
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.
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3
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Schweickart A, Batra R, Neth BJ, Martino C, Shenhav L, Zhang AR, Shi P, Karu N, Huynh K, Meikle PJ, Schimmel L, Dilmore AH, Blennow K, Zetterberg H, Blach C, Dorrestein PC, Knight R, Craft S, Kaddurah-Daouk R, Krumsiek J. Serum and CSF metabolomics analysis shows Mediterranean Ketogenic Diet mitigates risk factors of Alzheimer's disease. NPJ METABOLIC HEALTH AND DISEASE 2024; 2:15. [PMID: 38962750 PMCID: PMC11216994 DOI: 10.1038/s44324-024-00016-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 05/16/2024] [Indexed: 07/05/2024]
Abstract
Alzheimer's disease (AD) is influenced by a variety of modifiable risk factors, including a person's dietary habits. While the ketogenic diet (KD) holds promise in reducing metabolic risks and potentially affecting AD progression, only a few studies have explored KD's metabolic impact, especially on blood and cerebrospinal fluid (CSF). Our study involved participants at risk for AD, either cognitively normal or with mild cognitive impairment. The participants consumed both a modified Mediterranean Ketogenic Diet (MMKD) and the American Heart Association diet (AHAD) for 6 weeks each, separated by a 6-week washout period. We employed nuclear magnetic resonance (NMR)-based metabolomics to profile serum and CSF and metagenomics profiling on fecal samples. While the AHAD induced no notable metabolic changes, MMKD led to significant alterations in both serum and CSF. These changes included improved modifiable risk factors, like increased HDL-C and reduced BMI, reversed serum metabolic disturbances linked to AD such as a microbiome-mediated increase in valine levels, and a reduction in systemic inflammation. Additionally, the MMKD was linked to increased amino acid levels in the CSF, a breakdown of branched-chain amino acids (BCAAs), and decreased valine levels. Importantly, we observed a strong correlation between metabolic changes in the CSF and serum, suggesting a systemic regulation of metabolism. Our findings highlight that MMKD can improve AD-related risk factors, reverse some metabolic disturbances associated with AD, and align metabolic changes across the blood-CSF barrier.
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Affiliation(s)
- Annalise Schweickart
- Tri-Institutional Program in Computational Biology & Medicine, Weill Cornell Medicine, New York, NY USA
- Department of Physiology and Biophysics, Weill Cornell Medicine, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, New York, NY USA
| | - Richa Batra
- Department of Physiology and Biophysics, Weill Cornell Medicine, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, New York, NY USA
| | - Bryan J. Neth
- Department of Neurology, Mayo Clinic, Rochester, MN USA
| | - Cameron Martino
- Department of Pediatrics, University of California San Diego, La Jolla, CA USA
| | - Liat Shenhav
- Department of Microbiology, New York University Grossman School of Medicine, New York, NY USA
| | - Anru R. Zhang
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC USA
| | - Pixu Shi
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC USA
| | - Naama Karu
- Tasmanian Independent Metabolomics and Analytical Chemistry Solutions (TIMACS), Hobart, TAS Australia
| | - Kevin Huynh
- Baker Heart and Diabetes Institute, 75 Commercial Road, Melbourne, VIC Australia
- Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Bundoora, VIC Australia
| | - Peter J. Meikle
- Baker Heart and Diabetes Institute, 75 Commercial Road, Melbourne, VIC Australia
- Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Bundoora, VIC Australia
| | - Leyla Schimmel
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC USA
| | | | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden
| | - Colette Blach
- Duke Molecular Physiology Institute, Duke University, Durham, NC USA
| | - Pieter C. Dorrestein
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA USA
| | - Rob Knight
- Departments of Pediatrics, Computer Science and Engineering, Bioengineering, University of California San Diego, La Jolla, CA USA
| | - Alzheimer’s Gut Microbiome Project Consortium
- Tri-Institutional Program in Computational Biology & Medicine, Weill Cornell Medicine, New York, NY USA
- Department of Physiology and Biophysics, Weill Cornell Medicine, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, New York, NY USA
- Department of Neurology, Mayo Clinic, Rochester, MN USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA USA
- Department of Microbiology, New York University Grossman School of Medicine, New York, NY USA
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC USA
- Tasmanian Independent Metabolomics and Analytical Chemistry Solutions (TIMACS), Hobart, TAS Australia
- Baker Heart and Diabetes Institute, 75 Commercial Road, Melbourne, VIC Australia
- Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Bundoora, VIC Australia
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC USA
- Department of Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden
- Duke Molecular Physiology Institute, Duke University, Durham, NC USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA USA
- Departments of Pediatrics, Computer Science and Engineering, Bioengineering, University of California San Diego, La Jolla, CA USA
- Department of Gerontology and Geriatric Medicine, Wake Forest University School of Medicine, Winston Salem, NC USA
- Duke Institute of Brain Sciences, Duke University, Durham, NC USA
- Department of Medicine, Duke University, Durham, NC USA
| | - Suzanne Craft
- Department of Gerontology and Geriatric Medicine, Wake Forest University School of Medicine, Winston Salem, NC USA
| | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC USA
- Duke Institute of Brain Sciences, Duke University, Durham, NC USA
- Department of Medicine, Duke University, Durham, NC USA
| | - Jan Krumsiek
- Department of Physiology and Biophysics, Weill Cornell Medicine, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, New York, NY USA
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Schweickart A, Batra R, Neth BJ, Martino C, Shenhav L, Zhang AR, Shi P, Karu N, Huynh K, Meikle PJ, Schimmel L, Dilmore AH, Blennow K, Zetterberg H, Blach C, Dorrestein PC, Knight R, Craft S, Kaddurah-Daouk R, Krumsiek J. A Modified Mediterranean Ketogenic Diet mitigates modifiable risk factors of Alzheimer's Disease: a serum and CSF-based metabolic analysis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.27.23298990. [PMID: 38076824 PMCID: PMC10705656 DOI: 10.1101/2023.11.27.23298990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Alzheimer's disease (AD) is influenced by a variety of modifiable risk factors, including a person's dietary habits. While the ketogenic diet (KD) holds promise in reducing metabolic risks and potentially affecting AD progression, only a few studies have explored KD's metabolic impact, especially on blood and cerebrospinal fluid (CSF). Our study involved participants at risk for AD, either cognitively normal or with mild cognitive impairment. The participants consumed both a modified Mediterranean-ketogenic diet (MMKD) and the American Heart Association diet (AHAD) for 6 weeks each, separated by a 6-week washout period. We employed nuclear magnetic resonance (NMR)-based metabolomics to profile serum and CSF and metagenomics profiling on fecal samples. While the AHAD induced no notable metabolic changes, MMKD led to significant alterations in both serum and CSF. These changes included improved modifiable risk factors, like increased HDL-C and reduced BMI, reversed serum metabolic disturbances linked to AD such as a microbiome-mediated increase in valine levels, and a reduction in systemic inflammation. Additionally, the MMKD was linked to increased amino acid levels in the CSF, a breakdown of branched-chain amino acids (BCAAs), and decreased valine levels. Importantly, we observed a strong correlation between metabolic changes in the CSF and serum, suggesting a systemic regulation of metabolism. Our findings highlight that MMKD can improve AD-related risk factors, reverse some metabolic disturbances associated with AD, and align metabolic changes across the blood-CSF barrier.
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Affiliation(s)
- Annalise Schweickart
- Tri-Institutional Program in Computational Biology & Medicine, Weill Cornell Medicine, New York, NY, USA
- Department of Physiology and Biophysics, Weill Cornell Medicine, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, New York, NY 10021, USA
| | - Richa Batra
- Department of Physiology and Biophysics, Weill Cornell Medicine, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, New York, NY 10021, USA
| | | | - Cameron Martino
- Department of Pediatrics, University of California San Diego, La Jolla, CA
| | - Liat Shenhav
- Department of Microbiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Anru R. Zhang
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
| | - Pixu Shi
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
| | - Naama Karu
- Tasmanian Independent Metabolomics and Analytical Chemistry Solutions (TIMACS), Hobart, 7008 Tasmania, Australia
| | - Kevin Huynh
- Baker Heart and Diabetes Institute, 75 Commercial Road, Melbourne, VIC, Australia
- Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Bundoora, VIC, Australia
| | - Peter J. Meikle
- Baker Heart and Diabetes Institute, 75 Commercial Road, Melbourne, VIC, Australia
- Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Bundoora, VIC, Australia
| | - Leyla Schimmel
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | | | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden
| | - Colette Blach
- Duke Molecular Physiology Institute, Duke University, Durham, NC, USA
| | - Pieter C Dorrestein
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA
| | - Rob Knight
- Departments of Pediatrics, Computer Science and Engineering, Bioengineering, University of California San Diego, La Jolla, CA
| | | | - Suzanne Craft
- Department of Gerontology and Geriatric Medicine, Wake Forest University School of Medicine, Winston Salem, NC, USA
| | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Institute of Brain Sciences, Duke University, Durham, NC, USA
- Department of Medicine, Duke University, Durham, NC, USA
| | - Jan Krumsiek
- Department of Physiology and Biophysics, Weill Cornell Medicine, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, New York, NY 10021, USA
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5
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Benedetti E, Chetnik K, Flynn T, Barbieri CE, Scherr DS, Loda M, Krumsiek J. Plasma metabolomics profiling of 580 patients from an Early Detection Research Network prostate cancer cohort. Sci Data 2023; 10:830. [PMID: 38007532 PMCID: PMC10676366 DOI: 10.1038/s41597-023-02750-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 11/14/2023] [Indexed: 11/27/2023] Open
Abstract
Prostate cancer is the second most common cancer in men and affects 1 in 9 men in the United States. Early screening for prostate cancer often involves monitoring levels of prostate-specific antigen (PSA) and performing digital rectal exams. However, a prostate biopsy is always required for definitive cancer diagnosis. The Early Detection Research Network (EDRN) is a consortium within the National Cancer Institute aimed at improving screening approaches and early detection of cancers. As part of this effort, the Weill Cornell EDRN Prostate Cancer has collected and biobanked specimens from men undergoing a prostate biopsy between 2008 and 2017. In this report, we describe blood metabolomics measurements for a subset of this population. The dataset includes detailed clinical and prospective records for 580 patients who underwent prostate biopsy, 287 of which were subsequentially diagnosed with prostate cancer, combined with profiling of 1,482 metabolites from plasma samples collected at the time of biopsy. We expect this dataset to provide a valuable resource for scientists investigating prostate cancer metabolism.
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Affiliation(s)
- Elisa Benedetti
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Kelsey Chetnik
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Thomas Flynn
- Department of Urology, Weill Cornell Medical College, New York-Presbyterian Hospital, New York, NY, USA
| | - Christopher E Barbieri
- Department of Urology, Weill Cornell Medical College, New York-Presbyterian Hospital, New York, NY, USA
| | - Douglas S Scherr
- Department of Urology, Weill Cornell Medical College, New York-Presbyterian Hospital, New York, NY, USA
| | - Massimo Loda
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Jan Krumsiek
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA.
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA.
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA.
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6
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Batra R, Krumsiek J, Wang X, Allen M, Blach C, Kastenmüller G, Arnold M, Ertekin-Taner N, Kaddurah-Daouk RF. Comparative brain metabolomics reveals shared and distinct metabolic alterations in Alzheimer's disease and progressive supranuclear palsy. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.07.25.23293055. [PMID: 37546878 PMCID: PMC10402214 DOI: 10.1101/2023.07.25.23293055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Metabolic dysregulation is a hallmark of neurodegenerative diseases, including Alzheimer's disease (AD) and progressive supranuclear palsy (PSP). While metabolic dysregulation is a common link between these two tauopathies, a comprehensive brain metabolic comparison of the diseases has not yet been performed. We analyzed 342 postmortem brain samples from the Mayo Clinic Brain Bank and examined 658 metabolites in the cerebellar cortex and the temporal cortex between the two tauopathies. Our findings indicate that both diseases display oxidative stress associated with lipid metabolism, mitochondrial dysfunction linked to lysine metabolism, and an indication of tau-induced polyamine stress response. However, specific to AD, we detected glutathione-related neuroinflammation, deregulations of enzymes tied to purines, and cognitive deficits associated with vitamin B. Taken together, our findings underscore vast alterations in the brain's metabolome, illuminating shared neurodegenerative pathways and disease-specific traits in AD and PSP.
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Affiliation(s)
- Richa Batra
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Jan Krumsiek
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Xue Wang
- Department of Quantitative Health Sciences, Mayo Clinic Florida, Jacksonville, FL, USA
| | - Mariet Allen
- Department of Neuroscience, Mayo Clinic Florida, Jacksonville, FL, USA
| | - Colette Blach
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Matthias Arnold
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Nilüfer Ertekin-Taner
- Department of Neuroscience, Mayo Clinic Florida, Jacksonville, FL, USA
- Department of Neurology, Mayo Clinic Florida, Jacksonville, FL, USA
| | - Rima F Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke Institute for Brain Sciences and Department of Medicine, Duke University, Durham, NC, USA
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7
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Batra R, Uni R, Akchurin OM, Alvarez-Mulett S, Gómez-Escobar LG, Patino E, Hoffman KL, Simmons W, Whalen W, Chetnik K, Buyukozkan M, Benedetti E, Suhre K, Schenck E, Cho SJ, Choi AMK, Schmidt F, Choi ME, Krumsiek J. Urine-based multi-omic comparative analysis of COVID-19 and bacterial sepsis-induced ARDS. Mol Med 2023; 29:13. [PMID: 36703108 PMCID: PMC9879238 DOI: 10.1186/s10020-023-00609-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 01/11/2023] [Indexed: 01/28/2023] Open
Abstract
BACKGROUND Acute respiratory distress syndrome (ARDS), a life-threatening condition during critical illness, is a common complication of COVID-19. It can originate from various disease etiologies, including severe infections, major injury, or inhalation of irritants. ARDS poses substantial clinical challenges due to a lack of etiology-specific therapies, multisystem involvement, and heterogeneous, poor patient outcomes. A molecular comparison of ARDS groups holds the potential to reveal common and distinct mechanisms underlying ARDS pathogenesis. METHODS We performed a comparative analysis of urine-based metabolomics and proteomics profiles from COVID-19 ARDS patients (n = 42) and bacterial sepsis-induced ARDS patients (n = 17). To this end, we used two different approaches, first we compared the molecular omics profiles between ARDS groups, and second, we correlated clinical manifestations within each group with the omics profiles. RESULTS The comparison of the two ARDS etiologies identified 150 metabolites and 70 proteins that were differentially abundant between the two groups. Based on these findings, we interrogated the interplay of cell adhesion/extracellular matrix molecules, inflammation, and mitochondrial dysfunction in ARDS pathogenesis through a multi-omic network approach. Moreover, we identified a proteomic signature associated with mortality in COVID-19 ARDS patients, which contained several proteins that had previously been implicated in clinical manifestations frequently linked with ARDS pathogenesis. CONCLUSION In summary, our results provide evidence for significant molecular differences in ARDS patients from different etiologies and a potential synergy of extracellular matrix molecules, inflammation, and mitochondrial dysfunction in ARDS pathogenesis. The proteomic mortality signature should be further investigated in future studies to develop prediction models for COVID-19 patient outcomes.
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Affiliation(s)
- Richa Batra
- grid.5386.8000000041936877XDepartment of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021 USA
| | - Rie Uni
- Division of Nephrology and Hypertension, Joan and Sanford I. Weill Department of Medicine, New York, NY USA
| | - Oleh M. Akchurin
- grid.5386.8000000041936877XDivision of Pediatric Nephrology, Department of Pediatrics, Weill Cornell Medicine, New York, NY USA ,grid.413734.60000 0000 8499 1112New York-Presbyterian Hospital, New York, NY USA
| | - Sergio Alvarez-Mulett
- grid.5386.8000000041936877XDivision of Pulmonary and Critical Care Medicine, Department of Medicine, Weill Cornell Medicine, New York, NY USA
| | - Luis G. Gómez-Escobar
- grid.5386.8000000041936877XDivision of Pulmonary and Critical Care Medicine, Department of Medicine, Weill Cornell Medicine, New York, NY USA
| | - Edwin Patino
- Division of Nephrology and Hypertension, Joan and Sanford I. Weill Department of Medicine, New York, NY USA
| | - Katherine L. Hoffman
- grid.5386.8000000041936877XDivision of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY USA
| | - Will Simmons
- grid.5386.8000000041936877XDivision of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY USA
| | - William Whalen
- grid.5386.8000000041936877XDivision of Pulmonary and Critical Care Medicine, Department of Medicine, Weill Cornell Medicine, New York, NY USA
| | - Kelsey Chetnik
- grid.5386.8000000041936877XDepartment of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021 USA
| | - Mustafa Buyukozkan
- grid.5386.8000000041936877XDepartment of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021 USA
| | - Elisa Benedetti
- grid.5386.8000000041936877XDepartment of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021 USA
| | - Karsten Suhre
- grid.418818.c0000 0001 0516 2170Bioinformatics Core, Weill Cornell Medicine –Qatar, Qatar Foundation, Doha, Qatar
| | - Edward Schenck
- grid.5386.8000000041936877XDivision of Pulmonary and Critical Care Medicine, Department of Medicine, Weill Cornell Medicine, New York, NY USA
| | - Soo Jung Cho
- grid.5386.8000000041936877XDivision of Pulmonary and Critical Care Medicine, Department of Medicine, Weill Cornell Medicine, New York, NY USA
| | - Augustine M. K. Choi
- grid.5386.8000000041936877XDivision of Pulmonary and Critical Care Medicine, Department of Medicine, Weill Cornell Medicine, New York, NY USA
| | - Frank Schmidt
- Proteomics Core, Weill Cornell Medicine -Qatar, Qatar Foundation, Doha, Qatar.
| | - Mary E. Choi
- Division of Nephrology and Hypertension, Joan and Sanford I. Weill Department of Medicine, New York, NY USA
| | - Jan Krumsiek
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, 10021, USA.
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8
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Buyukozkan M, Benedetti E, Krumsiek J. rox: A Statistical Model for Regression with Missing Values. Metabolites 2023; 13:metabo13010127. [PMID: 36677052 PMCID: PMC9861384 DOI: 10.3390/metabo13010127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 11/15/2022] [Accepted: 11/17/2022] [Indexed: 01/18/2023] Open
Abstract
High-dimensional omics datasets frequently contain missing data points, which typically occur due to concentrations below the limit of detection (LOD) of the profiling platform. The presence of such missing values significantly limits downstream statistical analysis and result interpretation. Two common techniques to deal with this issue include the removal of samples with missing values and imputation approaches that substitute the missing measurements with reasonable estimates. Both approaches, however, suffer from various shortcomings and pitfalls. In this paper, we present "rox", a novel statistical model for the analysis of omics data with missing values without the need for imputation. The model directly incorporates missing values as "low" concentrations into the calculation. We show the superiority of rox over common approaches on simulated data and on six metabolomics datasets. Fully leveraging the information contained in LOD-based missing values, rox provides a powerful tool for the statistical analysis of omics data.
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9
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Ferrara CR, Bai JDK, McNally EM, Putzel GG, Zhou XK, Wang H, Lang A, Nagle D, Denoya P, Krumsiek J, Dannenberg AJ, Montrose DC. Microbes Contribute to Chemopreventive Efficacy, Intestinal Tumorigenesis, and the Metabolome. Cancer Prev Res (Phila) 2022; 15:803-814. [PMID: 36049217 DOI: 10.1158/1940-6207.capr-22-0244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 07/21/2022] [Accepted: 08/30/2022] [Indexed: 01/31/2023]
Abstract
Bacteria are believed to play an important role in intestinal tumorigenesis and contribute to both gut luminal and circulating metabolites. Celecoxib, a selective cyclooxygenase-2 inhibitor, alters gut bacteria and metabolites in association with suppressing the development of intestinal polyps in mice. The current study sought to evaluate whether celecoxib exerts its chemopreventive effects, in part, through intestinal bacteria and metabolomic alterations. Using ApcMin/+ mice, we demonstrated that treatment with broad-spectrum antibiotics (ABx) reduced abundance of gut bacteria and attenuated the ability of celecoxib to suppress intestinal tumorigenesis. Use of ABx also impaired celecoxib's ability to shift microbial populations and gut luminal and circulating metabolites. Treatment with ABx alone markedly reduced tumor number and size in ApcMin/+ mice, in conjunction with profoundly altering the metabolite profiles of the intestinal lumen and blood. Many of the metabolite changes in the gut and circulation overlapped and included shifts in microbially derived metabolites. To complement these findings in mice, we evaluated the effects of ABx on circulating metabolites in patients with colon cancer. This showed that ABx treatment led to a shift in blood metabolites, including several that were of bacterial origin. Importantly, changes in metabolites in patients given ABx overlapped with alterations found in mice that also received ABx. Taken together, these findings suggest a potential role for bacterial metabolites in mediating both the chemopreventive effects of celecoxib and intestinal tumor growth. PREVENTION RELEVANCE This study demonstrates novel mechanisms by which chemopreventive agents exert their effects and gut microbiota impact intestinal tumor development. These findings have the potential to lead to improved cancer prevention strategies by modulating microbes and their metabolites.
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Affiliation(s)
- Carmen R Ferrara
- Department of Pathology, Renaissance School of Medicine, Stony Brook University, Stony Brook, New York
| | - Ji Dong K Bai
- Department of Pathology, Renaissance School of Medicine, Stony Brook University, Stony Brook, New York
| | - Erin M McNally
- Departments of Medicine, Weill Cornell Medical College, New York, New York
| | - Gregory G Putzel
- Departments of Medicine, Weill Cornell Medical College, New York, New York
| | - Xi Kathy Zhou
- Healthcare Policy and Research, Weill Cornell Medical College, New York, New York
| | - Hanhan Wang
- Healthcare Policy and Research, Weill Cornell Medical College, New York, New York
| | - Alan Lang
- Department of Pathology, Renaissance School of Medicine, Stony Brook University, Stony Brook, New York
| | - Deborah Nagle
- Department of Surgery, Renaissance School of Medicine, Stony Brook University, Stony Brook, New York
| | - Paula Denoya
- Department of Surgery, Renaissance School of Medicine, Stony Brook University, Stony Brook, New York
| | - Jan Krumsiek
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, New York.,Sandra and Edward Meyer Cancer Center, New York, New York.,Caryl and Israel Englander Institute for Precision Medicine, New York, New York
| | - Andrew J Dannenberg
- Department of Medicine (retired), Weill Cornell Medical College, New York, New York
| | - David C Montrose
- Department of Pathology, Renaissance School of Medicine, Stony Brook University, Stony Brook, New York.,Stony Brook Cancer Center, Stony Brook, New York
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10
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Ikhalaynen YA, Plyushchenko IV, Rodin IA. Hopomics: Humulus lupulus Brewing Cultivars Classification Based on LC-MS Profiling and Nested Feature Selection. Metabolites 2022; 12:metabo12100945. [PMID: 36295846 PMCID: PMC9609554 DOI: 10.3390/metabo12100945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 09/23/2022] [Accepted: 10/03/2022] [Indexed: 11/16/2022] Open
Abstract
Omics approaches in plant analysis find many different applications, from classification to new bioactive compounds discovery. Metabolomics seems to be one of the most informative ways of describing plants’ phenotypes, since commonly used methods such as liquid chromatography–mass spectrometry (LC-MS) and nuclear magnetic resonance spectroscopy (NMR) could provide a huge amount of information about samples. However, due to high efficiency, many disadvantages arise with the complexity of the experimental design. In the present work, we demonstrate an untargeted metabolomics pipeline with the example of a Humulus lupulus classification task. LC-MS profiling of brewing cultivars samples was carried out as a starting point. Hierarchical cluster analysis (HCA)-based classification in combination with nested feature selection was provided for sample discrimination and marker compounds discovery. Obtained metabolome-based classification showed an expected difference compared to genetic-based classification data. Nine compounds were found to have the biggest classification power during nested feature selection. Using database search and molecular network construction, five of them were identified as known hops bitter compounds.
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Affiliation(s)
| | | | - Igor Alexandrovich Rodin
- Chemistry Department, Lomonosov Moscow State University, 119991 Moscow, Russia
- Department of Epidemiology and Evidence-Based Medicine, Sechenov First Moscow State Medical University, 119435 Moscow, Russia
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11
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Han X, Liang L. metabolomicsR: a streamlined workflow to analyze metabolomic data in R. BIOINFORMATICS ADVANCES 2022; 2:vbac067. [PMID: 36177485 PMCID: PMC9512519 DOI: 10.1093/bioadv/vbac067] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 09/08/2022] [Accepted: 09/15/2022] [Indexed: 01/27/2023]
Abstract
Summary metabolomicsR is a streamlined, flexible and user-friendly R package to preprocess, analyze and visualize metabolomic data. metabolomicsR includes comprehensive functionalities for sample and metabolite quality control, outlier detection, missing value imputation, dimensional reduction, batch effect normalization, data integration, regression, metabolite annotation and visualization of data and results. In this application note, we demonstrate the step-by-step use of the main functions from this package. Availability and implementation The metabolomicsR package is available via CRAN and GitHub (https://github.com/XikunHan/metabolomicsR/). A step-by-step online tutorial is available at https://xikunhan.github.io/metabolomicsR/docs/articles/Introduction.html. Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
- Xikun Han
- To whom correspondence should be addressed. or
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12
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Batra R, Whalen W, Alvarez-Mulett S, Gomez-Escobar LG, Hoffman KL, Simmons W, Harrington J, Chetnik K, Buyukozkan M, Benedetti E, Choi ME, Suhre K, Schenck E, Choi AMK, Schmidt F, Cho SJ, Krumsiek J. Multi-omic comparative analysis of COVID-19 and bacterial sepsis-induced ARDS. PLoS Pathog 2022; 18:e1010819. [PMID: 36121875 PMCID: PMC9484674 DOI: 10.1371/journal.ppat.1010819] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 08/19/2022] [Indexed: 12/06/2022] Open
Abstract
BACKGROUND Acute respiratory distress syndrome (ARDS), a life-threatening condition characterized by hypoxemia and poor lung compliance, is associated with high mortality. ARDS induced by COVID-19 has similar clinical presentations and pathological manifestations as non-COVID-19 ARDS. However, COVID-19 ARDS is associated with a more protracted inflammatory respiratory failure compared to traditional ARDS. Therefore, a comprehensive molecular comparison of ARDS of different etiologies groups may pave the way for more specific clinical interventions. METHODS AND FINDINGS In this study, we compared COVID-19 ARDS (n = 43) and bacterial sepsis-induced (non-COVID-19) ARDS (n = 24) using multi-omic plasma profiles covering 663 metabolites, 1,051 lipids, and 266 proteins. To address both between- and within- ARDS group variabilities we followed two approaches. First, we identified 706 molecules differently abundant between the two ARDS etiologies, revealing more than 40 biological processes differently regulated between the two groups. From these processes, we assembled a cascade of therapeutically relevant pathways downstream of sphingosine metabolism. The analysis suggests a possible overactivation of arginine metabolism involved in long-term sequelae of ARDS and highlights the potential of JAK inhibitors to improve outcomes in bacterial sepsis-induced ARDS. The second part of our study involved the comparison of the two ARDS groups with respect to clinical manifestations. Using a data-driven multi-omic network, we identified signatures of acute kidney injury (AKI) and thrombocytosis within each ARDS group. The AKI-associated network implicated mitochondrial dysregulation which might lead to post-ARDS renal-sequalae. The thrombocytosis-associated network hinted at a synergy between prothrombotic processes, namely IL-17, MAPK, TNF signaling pathways, and cell adhesion molecules. Thus, we speculate that combination therapy targeting two or more of these processes may ameliorate thrombocytosis-mediated hypercoagulation. CONCLUSION We present a first comprehensive molecular characterization of differences between two ARDS etiologies-COVID-19 and bacterial sepsis. Further investigation into the identified pathways will lead to a better understanding of the pathophysiological processes, potentially enabling novel therapeutic interventions.
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Affiliation(s)
- Richa Batra
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York, United States of America
| | - William Whalen
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, New York, United States of America
| | - Sergio Alvarez-Mulett
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, New York, United States of America
| | - Luis G. Gomez-Escobar
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, New York, United States of America
| | - Katherine L. Hoffman
- Department of Population Health Sciences, Division of Biostatistics, Weill Cornell Medicine, New York, New York, United States of America
| | - Will Simmons
- Department of Population Health Sciences, Division of Biostatistics, Weill Cornell Medicine, New York, New York, United States of America
| | - John Harrington
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, New York, United States of America
| | - Kelsey Chetnik
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York, United States of America
| | - Mustafa Buyukozkan
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York, United States of America
| | - Elisa Benedetti
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York, United States of America
| | - Mary E. Choi
- Division of Nephrology and Hypertension, Joan and Sanford I. Weill Department of Medicine, New York, New York, United States of America
| | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine–Qatar, Qatar Foundation, Doha, Qatar
| | - Edward Schenck
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, New York, United States of America
| | - Augustine M. K. Choi
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, New York, United States of America
| | - Frank Schmidt
- Proteomics Core, Weill Cornell Medicine–Qatar, Qatar Foundation, Doha, Qatar
| | - Soo Jung Cho
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, New York, United States of America
| | - Jan Krumsiek
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York, United States of America
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13
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Batra R, Whalen W, Alvarez-Mulett S, Gómez-Escobar LG, Hoffman KL, Simmons W, Harrington J, Chetnik K, Buyukozkan M, Benedetti E, Choi ME, Suhre K, Schenck E, Choi AMK, Schmidt F, Cho SJ, Krumsiek J. Multi-omic comparative analysis of COVID-19 and bacterial sepsis-induced ARDS. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2022.05.16.22274587. [PMID: 35982655 PMCID: PMC9387161 DOI: 10.1101/2022.05.16.22274587] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Background Acute respiratory distress syndrome (ARDS), a life-threatening condition characterized by hypoxemia and poor lung compliance, is associated with high mortality. ARDS induced by COVID-19 has similar clinical presentations and pathological manifestations as non-COVID-19 ARDS. However, COVID-19 ARDS is associated with a more protracted inflammatory respiratory failure compared to traditional ARDS. Therefore, a comprehensive molecular comparison of ARDS of different etiologies groups may pave the way for more specific clinical interventions. Methods and Findings In this study, we compared COVID-19 ARDS (n=43) and bacterial sepsis-induced (non-COVID-19) ARDS (n=24) using multi-omic plasma profiles covering 663 metabolites, 1,051 lipids, and 266 proteins. To address both between- and within-ARDS group variabilities we followed two approaches. First, we identified 706 molecules differently abundant between the two ARDS etiologies, revealing more than 40 biological processes differently regulated between the two groups. From these processes, we assembled a cascade of therapeutically relevant pathways downstream of sphingosine metabolism. The analysis suggests a possible overactivation of arginine metabolism involved in long-term sequelae of ARDS and highlights the potential of JAK inhibitors to improve outcomes in bacterial sepsis-induced ARDS. The second part of our study involved the comparison of the two ARDS groups with respect to clinical manifestations. Using a data-driven multi-omic network, we identified signatures of acute kidney injury (AKI) and thrombocytosis within each ARDS group. The AKI-associated network implicated mitochondrial dysregulation which might lead to post-ARDS renal-sequalae. The thrombocytosis-associated network hinted at a synergy between prothrombotic processes, namely IL-17, MAPK, TNF signaling pathways, and cell adhesion molecules. Thus, we speculate that combination therapy targeting two or more of these processes may ameliorate thrombocytosis-mediated hypercoagulation. Conclusion We present a first comprehensive molecular characterization of differences between two ARDS etiologies - COVID-19 and bacterial sepsis. Further investigation into the identified pathways will lead to a better understanding of the pathophysiological processes, potentially enabling novel therapeutic interventions.
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Affiliation(s)
- Richa Batra
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - William Whalen
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Sergio Alvarez-Mulett
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Luis G Gómez-Escobar
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Katherine L Hoffman
- Department of Population Health Sciences, Division of Biostatistics, Weill Cornell Medicine, New York, NY, USA
| | - Will Simmons
- Department of Population Health Sciences, Division of Biostatistics, Weill Cornell Medicine, New York, NY, USA
| | - John Harrington
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Kelsey Chetnik
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Mustafa Buyukozkan
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Elisa Benedetti
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Mary E Choi
- Division of Nephrology and Hypertension, Joan and Sanford I. Weill Department of Medicine, New York, NY, USA
| | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine - Qatar, Qatar Foundation, Doha, Qatar
| | - Edward Schenck
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Augustine M K Choi
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Frank Schmidt
- Proteomics Core, Weill Cornell Medicine - Qatar, Qatar Foundation, Doha, Qatar
| | - Soo Jung Cho
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Jan Krumsiek
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA
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14
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Batra R, Uni R, Akchurin OM, Alvarez-Mulett S, Gómez-Escobar LG, Patino E, Hoffman KL, Simmons W, Chetnik K, Buyukozkan M, Benedetti E, Suhre K, Schenck E, Cho SJ, Choi AMK, Schmidt F, Choi ME, Krumsiek J. Urine-based multi-omic comparative analysis of COVID-19 and bacterial sepsis-induced ARDS. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2022.08.10.22277939. [PMID: 35982662 PMCID: PMC9387152 DOI: 10.1101/2022.08.10.22277939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Acute respiratory distress syndrome (ARDS), a life-threatening condition during critical illness, is a common complication of COVID-19. It can originate from various disease etiologies, including severe infections, major injury, or inhalation of irritants. ARDS poses substantial clinical challenges due to a lack of etiology-specific therapies, multisystem involvement, and heterogeneous, poor patient outcomes. A molecular comparison of ARDS groups holds the potential to reveal common and distinct mechanisms underlying ARDS pathogenesis. In this study, we performed a comparative analysis of urine-based metabolomics and proteomics profiles from COVID-19 ARDS patients (n = 42) and bacterial sepsis-induced ARDS patients (n = 17). The comparison of these ARDS etiologies identified 150 metabolites and 70 proteins that were differentially abundant between the two groups. Based on these findings, we interrogated the interplay of cell adhesion/extracellular matrix molecules, inflammation, and mitochondrial dysfunction in ARDS pathogenesis through a multi-omic network approach. Moreover, we identified a proteomic signature associated with mortality in COVID-19 ARDS patients, which contained several proteins that had previously been implicated in clinical manifestations frequently linked with ARDS pathogenesis. In summary, our results provide evidence for significant molecular differences in ARDS patients from different etiologies and a potential synergy of extracellular matrix molecules, inflammation, and mitochondrial dysfunction in ARDS pathogenesis. The proteomic mortality signature should be further investigated in future studies to develop prediction models for COVID-19 patient outcomes.
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Affiliation(s)
- Richa Batra
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Rie Uni
- Division of Nephrology and Hypertension, Joan and Sanford I. Weill Department of Medicine, New York, NY, USA
| | - Oleh M Akchurin
- Department of Pediatrics, Division of Pediatric Nephrology, Weill Cornell Medicine, New York, NY, USA
- New York-Presbyterian Hospital, New York, NY, USA
| | - Sergio Alvarez-Mulett
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Luis G Gómez-Escobar
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Edwin Patino
- Division of Nephrology and Hypertension, Joan and Sanford I. Weill Department of Medicine, New York, NY, USA
| | - Katherine L Hoffman
- Department of Population Health Sciences, Division of Biostatistics, Weill Cornell Medicine, New York, NY, USA
| | - Will Simmons
- Department of Population Health Sciences, Division of Biostatistics, Weill Cornell Medicine, New York, NY, USA
| | - Kelsey Chetnik
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Mustafa Buyukozkan
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Elisa Benedetti
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine - Qatar, Qatar Foundation, Doha, Qatar
| | - Edward Schenck
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Soo Jung Cho
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Augustine M K Choi
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Frank Schmidt
- Proteomics Core, Weill Cornell Medicine - Qatar, Qatar Foundation, Doha, Qatar
| | - Mary E Choi
- Division of Nephrology and Hypertension, Joan and Sanford I. Weill Department of Medicine, New York, NY, USA
| | - Jan Krumsiek
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA
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15
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Price DR, Benedetti E, Hoffman KL, Gomez-Escobar L, Alvarez-Mulett S, Capili A, Sarwath H, Parkhurst CN, Lafond E, Weidman K, Ravishankar A, Cheong JG, Batra R, Büyüközkan M, Chetnik K, Easthausen I, Schenck EJ, Racanelli AC, Outtz Reed H, Laurence J, Josefowicz SZ, Lief L, Choi ME, Schmidt F, Borczuk AC, Choi AMK, Krumsiek J, Rafii S. Angiopoietin 2 Is Associated with Vascular Necroptosis Induction in Coronavirus Disease 2019 Acute Respiratory Distress Syndrome. THE AMERICAN JOURNAL OF PATHOLOGY 2022; 192:1001-1015. [PMID: 35469796 PMCID: PMC9027298 DOI: 10.1016/j.ajpath.2022.04.002] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 03/10/2022] [Accepted: 04/04/2022] [Indexed: 12/12/2022]
Abstract
Vascular injury is a well-established, disease-modifying factor in acute respiratory distress syndrome (ARDS) pathogenesis. Recently, coronavirus disease 2019 (COVID-19)-induced injury to the vascular compartment has been linked to complement activation, microvascular thrombosis, and dysregulated immune responses. This study sought to assess whether aberrant vascular activation in this prothrombotic context was associated with the induction of necroptotic vascular cell death. To achieve this, proteomic analysis was performed on blood samples from COVID-19 subjects at distinct time points during ARDS pathogenesis (hospitalized at risk, N = 59; ARDS, N = 31; and recovery, N = 12). Assessment of circulating vascular markers in the at-risk cohort revealed a signature of low vascular protein abundance that tracked with low platelet levels and increased mortality. This signature was replicated in the ARDS cohort and correlated with increased plasma angiopoietin 2 levels. COVID-19 ARDS lung autopsy immunostaining confirmed a link between vascular injury (angiopoietin 2) and platelet-rich microthrombi (CD61) and induction of necrotic cell death [phosphorylated mixed lineage kinase domain-like (pMLKL)]. Among recovery subjects, the vascular signature identified patients with poor functional outcomes. Taken together, this vascular injury signature was associated with low platelet levels and increased mortality and can be used to identify ARDS patients most likely to benefit from vascular targeted therapies.
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Affiliation(s)
- David R Price
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, Weill Cornell Medicine, New York, New York; Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, New York, New York
| | - Elisa Benedetti
- Institute of Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York
| | - Katherine L Hoffman
- Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, New York, New York
| | - Luis Gomez-Escobar
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, Weill Cornell Medicine, New York, New York
| | - Sergio Alvarez-Mulett
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, Weill Cornell Medicine, New York, New York
| | - Allyson Capili
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, Weill Cornell Medicine, New York, New York
| | - Hina Sarwath
- Proteomics Core, Weill Cornell Medicine-Qatar, Qatar Foundation-Education City, Doha, Qatar
| | - Christopher N Parkhurst
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, Weill Cornell Medicine, New York, New York; Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, New York, New York
| | - Elyse Lafond
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, Weill Cornell Medicine, New York, New York; Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, New York, New York
| | - Karissa Weidman
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, Weill Cornell Medicine, New York, New York; Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, New York, New York
| | - Arjun Ravishankar
- Laboratory of Epigenetics and Immunity, Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, New York
| | - Jin Gyu Cheong
- Laboratory of Epigenetics and Immunity, Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, New York
| | - Richa Batra
- Institute of Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York
| | - Mustafa Büyüközkan
- Institute of Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York
| | - Kelsey Chetnik
- Institute of Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York
| | - Imaani Easthausen
- Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, New York, New York
| | - Edward J Schenck
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, Weill Cornell Medicine, New York, New York; Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, New York, New York
| | - Alexandra C Racanelli
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, Weill Cornell Medicine, New York, New York; Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, New York, New York
| | - Hasina Outtz Reed
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, Weill Cornell Medicine, New York, New York; Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, New York, New York
| | - Jeffrey Laurence
- Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, New York, New York; Division of Hematology and Medical Oncology, Department of Medicine, Weill Cornell Medicine, New York, New York
| | - Steven Z Josefowicz
- Laboratory of Epigenetics and Immunity, Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, New York
| | - Lindsay Lief
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, Weill Cornell Medicine, New York, New York; Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, New York, New York
| | - Mary E Choi
- Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, New York, New York; Division of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medicine, New York, New York
| | - Frank Schmidt
- Proteomics Core, Weill Cornell Medicine-Qatar, Qatar Foundation-Education City, Doha, Qatar
| | - Alain C Borczuk
- Department of Pathology and Laboratory Medicine, New York Presbyterian-Weill Cornell Medicine, New York, New York
| | - Augustine M K Choi
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, Weill Cornell Medicine, New York, New York; Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, New York, New York
| | - Jan Krumsiek
- Institute of Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York.
| | - Shahin Rafii
- Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, New York, New York; Ansary Stem Cell Institute, Division of Regenerative Medicine, Department of Medicine, Weill Cornell Medicine, New York, New York.
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16
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Hughes DA, Taylor K, McBride N, Lee MA, Mason D, Lawlor DA, Timpson NJ, Corbin LJ. metaboprep: an R package for preanalysis data description and processing. Bioinformatics 2022; 38:1980-1987. [PMID: 35134881 PMCID: PMC8963298 DOI: 10.1093/bioinformatics/btac059] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 12/10/2021] [Accepted: 01/28/2022] [Indexed: 02/04/2023] Open
Abstract
MOTIVATION Metabolomics is an increasingly common part of health research and there is need for preanalytical data processing. Researchers typically need to characterize the data and to exclude errors within the context of the intended analysis. Whilst some preprocessing steps are common, there is currently a lack of standardization and reporting transparency for these procedures. RESULTS Here, we introduce metaboprep, a standardized data processing workflow to extract and characterize high quality metabolomics datasets. The package extracts data from preformed worksheets, provides summary statistics and enables the user to select samples and metabolites for their analysis based on a set of quality metrics. A report summarizing quality metrics and the influence of available batch variables on the data are generated for the purpose of open disclosure. Where possible, we provide users flexibility in defining their own selection thresholds. AVAILABILITY AND IMPLEMENTATION metaboprep is an open-source R package available at https://github.com/MRCIEU/metaboprep. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- David A Hughes
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 1TH, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol BS8 1TH, UK
| | - Kurt Taylor
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 1TH, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol BS8 1TH, UK
| | - Nancy McBride
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 1TH, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol BS8 1TH, UK
- NIHR Bristol Biomedical Research Centre, University of Bristol, Bristol BS8 1TH, UK
| | - Matthew A Lee
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 1TH, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol BS8 1TH, UK
| | - Dan Mason
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford BD9 6RJ, UK
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 1TH, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol BS8 1TH, UK
- NIHR Bristol Biomedical Research Centre, University of Bristol, Bristol BS8 1TH, UK
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 1TH, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol BS8 1TH, UK
| | - Laura J Corbin
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 1TH, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol BS8 1TH, UK
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