1
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Baxter RC. Endocrine and cellular physiology and pathology of the insulin-like growth factor acid-labile subunit. Nat Rev Endocrinol 2024; 20:414-425. [PMID: 38514815 DOI: 10.1038/s41574-024-00970-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/26/2024] [Indexed: 03/23/2024]
Abstract
The acid-labile subunit (ALS) of the insulin-like growth factor (IGF) binding protein (IGFBP) complex, encoded in humans by IGFALS, has a vital role in regulating the endocrine transport and bioavailability of IGF-1 and IGF-2. Accordingly, ALS has a considerable influence on postnatal growth and metabolism. ALS is a leucine-rich glycoprotein that forms high-affinity ternary complexes with IGFBP-3 or IGFBP-5 when they are occupied by either IGF-1 or IGF-2. These complexes constitute a stable reservoir of circulating IGFs, blocking the potentially hypoglycaemic activity of unbound IGFs. ALS is primarily synthesized by hepatocytes and its expression is lower in non-hepatic tissues. ALS synthesis is strongly induced by growth hormone and suppressed by IL-1β, thus potentially serving as a marker of growth hormone secretion and/or activity and of inflammation. IGFALS mutations in humans and Igfals deletion in mice cause modest growth retardation and pubertal delay, accompanied by decreased osteogenesis and enhanced adipogenesis. In hepatocellular carcinoma, IGFALS is described as a tumour suppressor; however, its contribution to other cancers is not well delineated. This Review addresses the endocrine physiology and pathology of ALS, discusses the latest cell and proteomic studies that suggest emerging cellular roles for ALS and outlines its involvement in other disease states.
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Affiliation(s)
- Robert C Baxter
- University of Sydney, Kolling Institute, Royal North Shore Hospital, St Leonards, New South Wales, Australia.
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2
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Viode A, Smolen KK, van Zalm P, Stevenson D, Jha M, Parker K, Levy O, Steen JA, Steen H. Longitudinal plasma proteomic analysis of 1117 hospitalized patients with COVID-19 identifies features associated with severity and outcomes. SCIENCE ADVANCES 2024; 10:eadl5762. [PMID: 38787940 PMCID: PMC11122669 DOI: 10.1126/sciadv.adl5762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 04/18/2024] [Indexed: 05/26/2024]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is characterized by highly heterogeneous manifestations ranging from asymptomatic cases to death for still incompletely understood reasons. As part of the IMmunoPhenotyping Assessment in a COVID-19 Cohort study, we mapped the plasma proteomes of 1117 hospitalized patients with COVID-19 from 15 hospitals across the United States. Up to six samples were collected within ~28 days of hospitalization resulting in one of the largest COVID-19 plasma proteomics cohorts with 2934 samples. Using perchloric acid to deplete the most abundant plasma proteins allowed for detecting 2910 proteins. Our findings show that increased levels of neutrophil extracellular trap and heart damage markers are associated with fatal outcomes. Our analysis also identified prognostic biomarkers for worsening severity and death. Our comprehensive longitudinal plasma proteomics study, involving 1117 participants and 2934 samples, allowed for testing the generalizability of the findings of many previous COVID-19 plasma proteomics studies using much smaller cohorts.
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Affiliation(s)
- Arthur Viode
- Department of Pathology, Boston Children’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Kinga K. Smolen
- Harvard Medical School, Boston, MA, USA
- Precision Vaccines Program, Boston Children’s Hospital, Boston, MA, USA
| | - Patrick van Zalm
- Department of Pathology, Boston Children’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Department of Neuropsychology and Psychopharmacology, EURON, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - David Stevenson
- Department of Pathology, Boston Children’s Hospital, Boston, MA, USA
| | - Meenakshi Jha
- Department of Pathology, Boston Children’s Hospital, Boston, MA, USA
| | - Kenneth Parker
- Department of Pathology, Boston Children’s Hospital, Boston, MA, USA
| | - IMPACC Network‡
- Department of Pathology, Boston Children’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Precision Vaccines Program, Boston Children’s Hospital, Boston, MA, USA
- Department of Neuropsychology and Psychopharmacology, EURON, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
- Broad Institute of MIT & Harvard, Cambridge, MA, USA
- F. M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA, USA
- Neurobiology Program, Boston Children's Hospital, Boston, MA, USA
| | - Ofer Levy
- Harvard Medical School, Boston, MA, USA
- Precision Vaccines Program, Boston Children’s Hospital, Boston, MA, USA
- Broad Institute of MIT & Harvard, Cambridge, MA, USA
| | - Judith A. Steen
- Harvard Medical School, Boston, MA, USA
- F. M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA, USA
- Neurobiology Program, Boston Children's Hospital, Boston, MA, USA
| | - Hanno Steen
- Department of Pathology, Boston Children’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Precision Vaccines Program, Boston Children’s Hospital, Boston, MA, USA
- Neurobiology Program, Boston Children's Hospital, Boston, MA, USA
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3
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Gygi JP, Maguire C, Patel RK, Shinde P, Konstorum A, Shannon CP, Xu L, Hoch A, Jayavelu ND, Haddad EK, Reed EF, Kraft M, McComsey GA, Metcalf JP, Ozonoff A, Esserman D, Cairns CB, Rouphael N, Bosinger SE, Kim-Schulze S, Krammer F, Rosen LB, van Bakel H, Wilson M, Eckalbar WL, Maecker HT, Langelier CR, Steen H, Altman MC, Montgomery RR, Levy O, Melamed E, Pulendran B, Diray-Arce J, Smolen KK, Fragiadakis GK, Becker PM, Sekaly RP, Ehrlich LI, Fourati S, Peters B, Kleinstein SH, Guan L. Integrated longitudinal multiomics study identifies immune programs associated with acute COVID-19 severity and mortality. J Clin Invest 2024; 134:e176640. [PMID: 38690733 PMCID: PMC11060740 DOI: 10.1172/jci176640] [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: 10/26/2023] [Accepted: 03/12/2024] [Indexed: 05/03/2024] Open
Abstract
BACKGROUNDPatients hospitalized for COVID-19 exhibit diverse clinical outcomes, with outcomes for some individuals diverging over time even though their initial disease severity appears similar to that of other patients. A systematic evaluation of molecular and cellular profiles over the full disease course can link immune programs and their coordination with progression heterogeneity.METHODSWe performed deep immunophenotyping and conducted longitudinal multiomics modeling, integrating 10 assays for 1,152 Immunophenotyping Assessment in a COVID-19 Cohort (IMPACC) study participants and identifying several immune cascades that were significant drivers of differential clinical outcomes.RESULTSIncreasing disease severity was driven by a temporal pattern that began with the early upregulation of immunosuppressive metabolites and then elevated levels of inflammatory cytokines, signatures of coagulation, formation of neutrophil extracellular traps, and T cell functional dysregulation. A second immune cascade, predictive of 28-day mortality among critically ill patients, was characterized by reduced total plasma Igs and B cells and dysregulated IFN responsiveness. We demonstrated that the balance disruption between IFN-stimulated genes and IFN inhibitors is a crucial biomarker of COVID-19 mortality, potentially contributing to failure of viral clearance in patients with fatal illness.CONCLUSIONOur longitudinal multiomics profiling study revealed temporal coordination across diverse omics that potentially explain the disease progression, providing insights that can inform the targeted development of therapies for patients hospitalized with COVID-19, especially those who are critically ill.TRIAL REGISTRATIONClinicalTrials.gov NCT04378777.FUNDINGNIH (5R01AI135803-03, 5U19AI118608-04, 5U19AI128910-04, 4U19AI090023-11, 4U19AI118610-06, R01AI145835-01A1S1, 5U19AI062629-17, 5U19AI057229-17, 5U19AI125357-05, 5U19AI128913-03, 3U19AI077439-13, 5U54AI142766-03, 5R01AI104870-07, 3U19AI089992-09, 3U19AI128913-03, and 5T32DA018926-18); NIAID, NIH (3U19AI1289130, U19AI128913-04S1, and R01AI122220); and National Science Foundation (DMS2310836).
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Affiliation(s)
| | - Cole Maguire
- The University of Texas at Austin, Austin, Texas, USA
| | | | - Pramod Shinde
- La Jolla Institute for Immunology, La Jolla, California, USA
| | | | - Casey P. Shannon
- Centre for Heart Lung Innovation, University of British Columbia, Vancouver, Canada
- Prevention of Organ Failure (PROOF) Centre of Excellence, Providence Research, Vancouver, British Columbia, Canada
| | - Leqi Xu
- Yale School of Public Health, New Haven, Connecticut, USA
| | - Annmarie Hoch
- Clinical and Data Coordinating Center (CDCC) and
- Precision Vaccines Program, Boston Children’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | | | - Elias K. Haddad
- Drexel University, Tower Health Hospital, Philadelphia, Pennsylvania, USA
| | - IMPACC Network
- The Immunophenotyping Assessment in a COVID-19 Cohort (IMPACC) Network is detailed in Supplemental Acknowledgments
| | - Elaine F. Reed
- David Geffen School of Medicine at the UCLA, Los Angeles, California, USA
| | - Monica Kraft
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Grace A. McComsey
- Case Western Reserve University and University Hospitals of Cleveland, Cleveland, Ohio, USA
| | - Jordan P. Metcalf
- Oklahoma University Health Sciences Center, Oklahoma City, Oklahoma, USA
| | - Al Ozonoff
- Clinical and Data Coordinating Center (CDCC) and
- Precision Vaccines Program, Boston Children’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Department of Pediatrics, Boston Children’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | | | - Charles B. Cairns
- Drexel University, Tower Health Hospital, Philadelphia, Pennsylvania, USA
| | | | | | | | - Florian Krammer
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Ignaz Semmelweis Institute, Interuniversity Institute for Infection Research, Medical University of Vienna, Vienna, Austria
| | - Lindsey B. Rosen
- National Institute of Allergy and Infectious Diseases (NIAID), NIH, Bethesda, Maryland, USA
| | - Harm van Bakel
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | | | | | | | - Hanno Steen
- Precision Vaccines Program, Boston Children’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Department of Pathology, Boston Children’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | | | | | - Ofer Levy
- Precision Vaccines Program, Boston Children’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Department of Pediatrics, Boston Children’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | | | - Bali Pulendran
- Stanford University School of Medicine, Palo Alto, California, USA
| | - Joann Diray-Arce
- Clinical and Data Coordinating Center (CDCC) and
- Precision Vaccines Program, Boston Children’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Department of Pediatrics, Boston Children’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Kinga K. Smolen
- Precision Vaccines Program, Boston Children’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Department of Pediatrics, Boston Children’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | | | - Patrice M. Becker
- National Institute of Allergy and Infectious Diseases (NIAID), NIH, Bethesda, Maryland, USA
| | - Rafick P. Sekaly
- Case Western Reserve University and University Hospitals of Cleveland, Cleveland, Ohio, USA
| | | | - Slim Fourati
- Case Western Reserve University and University Hospitals of Cleveland, Cleveland, Ohio, USA
| | - Bjoern Peters
- La Jolla Institute for Immunology, La Jolla, California, USA
- Department of Medicine, UCSD, La Jolla, California, USA
| | | | - Leying Guan
- Yale School of Public Health, New Haven, Connecticut, USA
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4
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Anandakrishnan N, Yi Z, Sun Z, Liu T, Haydak J, Eddy S, Jayaraman P, DeFronzo S, Saha A, Sun Q, Yang D, Mendoza A, Mosoyan G, Wen HH, Schaub JA, Fu J, Kehrer T, Menon R, Otto EA, Godfrey B, Suarez-Farinas M, Leffters S, Twumasi A, Meliambro K, Charney AW, García-Sastre A, Campbell KN, Gusella GL, He JC, Miorin L, Nadkarni GN, Wisnivesky J, Li H, Kretzler M, Coca SG, Chan L, Zhang W, Azeloglu EU. Integrated multiomics implicates dysregulation of ECM and cell adhesion pathways as drivers of severe COVID-associated kidney injury. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.18.24304401. [PMID: 38562892 PMCID: PMC10984064 DOI: 10.1101/2024.03.18.24304401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
COVID-19 has been a significant public health concern for the last four years; however, little is known about the mechanisms that lead to severe COVID-associated kidney injury. In this multicenter study, we combined quantitative deep urinary proteomics and machine learning to predict severe acute outcomes in hospitalized COVID-19 patients. Using a 10-fold cross-validated random forest algorithm, we identified a set of urinary proteins that demonstrated predictive power for both discovery and validation set with 87% and 79% accuracy, respectively. These predictive urinary biomarkers were recapitulated in non-COVID acute kidney injury revealing overlapping injury mechanisms. We further combined orthogonal multiomics datasets to understand the mechanisms that drive severe COVID-associated kidney injury. Functional overlap and network analysis of urinary proteomics, plasma proteomics and urine sediment single-cell RNA sequencing showed that extracellular matrix and autophagy-associated pathways were uniquely impacted in severe COVID-19. Differentially abundant proteins associated with these pathways exhibited high expression in cells in the juxtamedullary nephron, endothelial cells, and podocytes, indicating that these kidney cell types could be potential targets. Further, single-cell transcriptomic analysis of kidney organoids infected with SARS-CoV-2 revealed dysregulation of extracellular matrix organization in multiple nephron segments, recapitulating the clinically observed fibrotic response across multiomics datasets. Ligand-receptor interaction analysis of the podocyte and tubule organoid clusters showed significant reduction and loss of interaction between integrins and basement membrane receptors in the infected kidney organoids. Collectively, these data suggest that extracellular matrix degradation and adhesion-associated mechanisms could be a main driver of COVID-associated kidney injury and severe outcomes.
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5
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de Fátima Cobre A, Alves AC, Gotine ARM, Domingues KZA, Lazo REL, Ferreira LM, Tonin FS, Pontarolo R. Novel COVID-19 biomarkers identified through multi-omics data analysis: N-acetyl-4-O-acetylneuraminic acid, N-acetyl-L-alanine, N-acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate. Intern Emerg Med 2024:10.1007/s11739-024-03547-1. [PMID: 38416303 DOI: 10.1007/s11739-024-03547-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 01/16/2024] [Indexed: 02/29/2024]
Abstract
This study aims to apply machine learning models to identify new biomarkers associated with the early diagnosis and prognosis of SARS-CoV-2 infection.Plasma and serum samples from COVID-19 patients (mild, moderate, and severe), patients with other pneumonia (but with negative COVID-19 RT-PCR), and healthy volunteers (control) from hospitals in four different countries (China, Spain, France, and Italy) were analyzed by GC-MS, LC-MS, and NMR. Machine learning models (PCA and PLS-DA) were developed to predict the diagnosis and prognosis of COVID-19 and identify biomarkers associated with these outcomes.A total of 1410 patient samples were analyzed. The PLS-DA model presented a diagnostic and prognostic accuracy of around 95% of all analyzed data. A total of 23 biomarkers (e.g., spermidine, taurine, L-aspartic, L-glutamic, L-phenylalanine and xanthine, ornithine, and ribothimidine) have been identified as being associated with the diagnosis and prognosis of COVID-19. Additionally, we also identified for the first time five new biomarkers (N-Acetyl-4-O-acetylneuraminic acid, N-Acetyl-L-Alanine, N-Acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate) that are also associated with the severity and diagnosis of COVID-19. These five new biomarkers were elevated in severe COVID-19 patients compared to patients with mild disease or healthy volunteers.The PLS-DA model was able to predict the diagnosis and prognosis of COVID-19 around 95%. Additionally, our investigation pinpointed five novel potential biomarkers linked to the diagnosis and prognosis of COVID-19: N-Acetyl-4-O-acetylneuraminic acid, N-Acetyl-L-Alanine, N-Acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate. These biomarkers exhibited heightened levels in severe COVID-19 patients compared to those with mild COVID-19 or healthy volunteers.
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Affiliation(s)
| | - Alexessander Couto Alves
- School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | | | | | | | - Luana Mota Ferreira
- Department of Pharmacy, Universidade Federal do Paraná, Campus III, Av. Pref. Lothário Meissner, 632, Jardim Botânico, Curitiba, PR, 80210-170, Brazil
| | - Fernanda Stumpf Tonin
- H&TRC - Health & Technology Research Centre, ESTeSL, Escola Superior de Tecnologia da Saúde, Instituto Politécnico de Lisboa, Lisbon, Portugal
| | - Roberto Pontarolo
- Department of Pharmacy, Universidade Federal do Paraná, Campus III, Av. Pref. Lothário Meissner, 632, Jardim Botânico, Curitiba, PR, 80210-170, Brazil.
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6
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Mohammed Y, Tran K, Carlsten C, Ryerson C, Wong A, Lee T, Cheng MP, Vinh DC, Lee TC, Winston BW, Sweet D, Boyd JH, Walley KR, Haljan G, McGeer A, Lamontagne F, Fowler R, Maslove D, Singer J, Patrick DM, Marshall JC, Murthy S, Jain F, Borchers CH, Goodlett DR, Levin A, Russell JA. Proteomic Evolution from Acute to Post-COVID-19 Conditions. J Proteome Res 2024; 23:52-70. [PMID: 38048423 PMCID: PMC10775146 DOI: 10.1021/acs.jproteome.3c00324] [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: 05/30/2023] [Revised: 10/30/2023] [Accepted: 11/13/2023] [Indexed: 12/06/2023]
Abstract
Many COVID-19 survivors have post-COVID-19 conditions, and females are at a higher risk. We sought to determine (1) how protein levels change from acute to post-COVID-19 conditions, (2) whether females have a plasma protein signature different from that of males, and (3) which biological pathways are associated with COVID-19 when compared to restrictive lung disease. We measured protein levels in 74 patients on the day of admission and at 3 and 6 months after diagnosis. We determined protein concentrations by multiple reaction monitoring (MRM) using a panel of 269 heavy-labeled peptides. The predicted forced vital capacity (FVC) and diffusing capacity of the lungs for carbon monoxide (DLCO) were measured by routine pulmonary function testing. Proteins associated with six key lipid-related pathways increased from admission to 3 and 6 months; conversely, proteins related to innate immune responses and vasoconstriction-related proteins decreased. Multiple biological functions were regulated differentially between females and males. Concentrations of eight proteins were associated with FVC, %, and they together had c-statistics of 0.751 (CI:0.732-0.779); similarly, concentrations of five proteins had c-statistics of 0.707 (CI:0.676-0.737) for DLCO, %. Lipid biology may drive evolution from acute to post-COVID-19 conditions, while activation of innate immunity and vascular regulation pathways decreased over that period. (ProteomeXchange identifiers: PXD041762, PXD029437).
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Affiliation(s)
- Yassene Mohammed
- Center
for Proteomics and Metabolomics, Leiden
University Medical Center, Leiden 2333 ZA, The Netherlands
- UVic-Genome
BC Proteomics Centre, University of Victoria, Victoria V8Z 5N3, BC Canada
- Gerald
Bronfman Department of Oncology, McGill
University, Montreal, QC H3A 0G4, Canada
| | - Karen Tran
- Division
of General Internal Medicine, Vancouver
General Hospital and University of British Columbia, 2775 Laurel St, Vancouver, BC V5Z 1M9, Canada
| | - Chris Carlsten
- Division
of Respiratory Medicine, Vancouver General Hospital, University of British Columbia, Vancouver, BC V5Z 1M9, Canada
| | - Christopher Ryerson
- Division
of Respiratory Medicine, Vancouver General Hospital, University of British Columbia, Vancouver, BC V5Z 1M9, Canada
| | - Alyson Wong
- Division
of Respiratory Medicine, Vancouver General Hospital, University of British Columbia, Vancouver, BC V5Z 1M9, Canada
| | - Terry Lee
- Centre for
Health Evaluation and Outcome Science (CHEOS), St. Paul’s Hospital, University of British Columbia, 1081 Burrard Street, Vancouver, BC V6Z 1Y6, Canada
| | - Matthew P. Cheng
- Division
of Infectious Diseases, Department of Medicine, McGill University Health Centre, Montreal, PQ H4A 3J1, Canada
| | - Donald C. Vinh
- Division
of Infectious Diseases, Department of Medicine, McGill University Health Centre, Montreal, PQ H4A 3J1, Canada
| | - Todd C. Lee
- Division
of Infectious Diseases, Department of Medicine, McGill University Health Centre, Montreal, PQ H4A 3J1, Canada
| | - Brent W. Winston
- Departments
of Critical Care Medicine, Medicine and Biochemistry and Molecular
Biology, Foothills Medical Centre and University
of Calgary, 1403 29 Street
NW, Calgary, Alberta T2N 4N1, Canada
| | - David Sweet
- Division
of Critical Care Medicine, Vancouver General
Hospital, 2775 Laurel St, Vancouver, BC V5Z 1M9, Canada
| | - John H. Boyd
- Centre
for Heart Lung Innovation, St. Paul’s Hospital, University of British Columbia, 1081 Burrard Street, Vancouver, BC V6Z 1Y6, Canada
- Division of Critical Care Medicine, St.
Paul’s Hospital, University of British
Columbia, 1081 Burrard Street, Vancouver, BC V6Z 1Y6, Canada
| | - Keith R. Walley
- Centre
for Heart Lung Innovation, St. Paul’s Hospital, University of British Columbia, 1081 Burrard Street, Vancouver, BC V6Z 1Y6, Canada
- Division of Critical Care Medicine, St.
Paul’s Hospital, University of British
Columbia, 1081 Burrard Street, Vancouver, BC V6Z 1Y6, Canada
| | - Greg Haljan
- Department of Medicine, Surrey Memorial
Hospital, 13750 96th
Avenue, Surrey, BC V3V 1Z2, Canada
| | - Allison McGeer
- Mt. Sinai Hospital and University of Toronto, 600 University Avenue, Toronto, ON M5G 1X5, Canada
| | | | - Robert Fowler
- Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, Toronto, ON M4N 3M5, Canada
| | - David Maslove
- Department
of Critical Care, Kingston General Hospital
and Queen’s University, 76 Stuart Street, Kingston, ON K7L 2V7, Canada
| | - Joel Singer
- Centre for
Health Evaluation and Outcome Science (CHEOS), St. Paul’s Hospital, University of British Columbia, 1081 Burrard Street, Vancouver, BC V6Z 1Y6, Canada
| | - David M. Patrick
- British Columbia Centre for Disease Control
(BCCDC) and University
of British Columbia, 655 West 12th Avenue, Vancouver, BC V5Z 4R4, Canada
| | - John C. Marshall
- Department of Surgery, St. Michael’s
Hospital, 30 Bond Street, Toronto, ON M5B
1W8, Canada
| | - Srinivas Murthy
- BC Children’s Hospital and University of British Columbia, 4500 Oak Street, Vancouver, BC V6H 3N1, Canada
| | - Fagun Jain
- Black Tusk Research Group, Vancouver, BC V6Z 2C7, Canada
| | - Christoph H. Borchers
- Segal Cancer Proteomics, Centre, Lady Davis
Institute
for Medical Research, McGill University, Montreal, QC H3T 1E2, Canada
- Gerald Bronfman Department of Oncology, Jewish General Hospital, Montreal, QC H3T 1E2, Canada
- Division of Experimental Medicine, McGill
University, Montreal, QC H3T 1E2, Canada
- Department of Pathology, McGill
University, Montreal, QC H3T 1E2, Canada
| | - David R. Goodlett
- UVic-Genome
BC Proteomics Centre, University of Victoria, Victoria V8Z 5N3, BC Canada
| | - Adeera Levin
- Division of Nephrology, St.
Paul’s Hospital, 1081 Burrard Street, Vancouver, BC V6Z 1Y6, Canada
| | - James A. Russell
- Centre
for Heart Lung Innovation, St. Paul’s Hospital, University of British Columbia, 1081 Burrard Street, Vancouver, BC V6Z 1Y6, Canada
- Division of Critical Care Medicine, St.
Paul’s Hospital, University of British
Columbia, 1081 Burrard Street, Vancouver, BC V6Z 1Y6, Canada
| | - ARBs CORONA I Consortium
- Center
for Proteomics and Metabolomics, Leiden
University Medical Center, Leiden 2333 ZA, The Netherlands
- UVic-Genome
BC Proteomics Centre, University of Victoria, Victoria V8Z 5N3, BC Canada
- Gerald
Bronfman Department of Oncology, McGill
University, Montreal, QC H3A 0G4, Canada
- Division
of General Internal Medicine, Vancouver
General Hospital and University of British Columbia, 2775 Laurel St, Vancouver, BC V5Z 1M9, Canada
- Division
of Respiratory Medicine, Vancouver General Hospital, University of British Columbia, Vancouver, BC V5Z 1M9, Canada
- Centre for
Health Evaluation and Outcome Science (CHEOS), St. Paul’s Hospital, University of British Columbia, 1081 Burrard Street, Vancouver, BC V6Z 1Y6, Canada
- Division
of Infectious Diseases, Department of Medicine, McGill University Health Centre, Montreal, PQ H4A 3J1, Canada
- Departments
of Critical Care Medicine, Medicine and Biochemistry and Molecular
Biology, Foothills Medical Centre and University
of Calgary, 1403 29 Street
NW, Calgary, Alberta T2N 4N1, Canada
- Division
of Critical Care Medicine, Vancouver General
Hospital, 2775 Laurel St, Vancouver, BC V5Z 1M9, Canada
- Centre
for Heart Lung Innovation, St. Paul’s Hospital, University of British Columbia, 1081 Burrard Street, Vancouver, BC V6Z 1Y6, Canada
- Division of Critical Care Medicine, St.
Paul’s Hospital, University of British
Columbia, 1081 Burrard Street, Vancouver, BC V6Z 1Y6, Canada
- Department of Medicine, Surrey Memorial
Hospital, 13750 96th
Avenue, Surrey, BC V3V 1Z2, Canada
- Mt. Sinai Hospital and University of Toronto, 600 University Avenue, Toronto, ON M5G 1X5, Canada
- University of Sherbrooke, Sherbrooke, PQ J1K 2R1, Canada
- Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, Toronto, ON M4N 3M5, Canada
- Department
of Critical Care, Kingston General Hospital
and Queen’s University, 76 Stuart Street, Kingston, ON K7L 2V7, Canada
- British Columbia Centre for Disease Control
(BCCDC) and University
of British Columbia, 655 West 12th Avenue, Vancouver, BC V5Z 4R4, Canada
- Department of Surgery, St. Michael’s
Hospital, 30 Bond Street, Toronto, ON M5B
1W8, Canada
- BC Children’s Hospital and University of British Columbia, 4500 Oak Street, Vancouver, BC V6H 3N1, Canada
- Black Tusk Research Group, Vancouver, BC V6Z 2C7, Canada
- Segal Cancer Proteomics, Centre, Lady Davis
Institute
for Medical Research, McGill University, Montreal, QC H3T 1E2, Canada
- Gerald Bronfman Department of Oncology, Jewish General Hospital, Montreal, QC H3T 1E2, Canada
- Division of Experimental Medicine, McGill
University, Montreal, QC H3T 1E2, Canada
- Department of Pathology, McGill
University, Montreal, QC H3T 1E2, Canada
- Division of Nephrology, St.
Paul’s Hospital, 1081 Burrard Street, Vancouver, BC V6Z 1Y6, Canada
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7
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Souchet B, Michaïl A, Billoir B, Braudeau J. Biological Diagnosis of Alzheimer's Disease Based on Amyloid Status: An Illustration of Confirmation Bias in Medical Research? Int J Mol Sci 2023; 24:17544. [PMID: 38139372 PMCID: PMC10744068 DOI: 10.3390/ijms242417544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 12/08/2023] [Accepted: 12/14/2023] [Indexed: 12/24/2023] Open
Abstract
Alzheimer's disease (AD) was first characterized by Dr. Alois Alzheimer in 1906 by studying a demented patient and discovering cerebral amyloid plaques and neurofibrillary tangles. Subsequent research highlighted the roles of Aβ peptides and tau proteins, which are the primary constituents of these lesions, which led to the amyloid cascade hypothesis. Technological advances, such as PET scans using Florbetapir, have made it possible to visualize amyloid plaques in living patients, thus improving AD's risk assessment. The National Institute on Aging and the Alzheimer's Association introduced biological diagnostic criteria in 2011, which underlined the amyloid deposits diagnostic value. However, potential confirmation bias may have led researchers to over-rely on amyloid markers independent of AD's symptoms, despite evidence of their limited specificity. This review provides a critical examination of the current research paradigm in AD, including, in particular, the predominant focus on amyloid and tau species in diagnostics. We discuss the potential multifaceted consequences of this approach and propose strategies to mitigate its overemphasis in the development of new biomarkers. Furthermore, our study presents comprehensive guidelines aimed at enhancing the creation of biomarkers for accurately predicting AD dementia onset. These innovations are crucial for refining patient selection processes in clinical trial enrollment and for the optimization of therapeutic strategies. Overcoming confirmation bias is essential to advance the diagnosis and treatment of AD and to move towards precision medicine by incorporating a more nuanced understanding of amyloid biomarkers.
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Affiliation(s)
| | | | | | - Jérôme Braudeau
- AgenT SAS, 4 Rue Pierre Fontaine, 91000 Evry-Courcouronnes, France; (B.S.); (A.M.); (B.B.)
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8
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Gygi JP, Maguire C, Patel RK, Shinde P, Konstorum A, Shannon CP, Xu L, Hoch A, Jayavelu ND, Network I, Haddad EK, Reed EF, Kraft M, McComsey GA, Metcalf J, Ozonoff A, Esserman D, Cairns CB, Rouphael N, Bosinger SE, Kim-Schulze S, Krammer F, Rosen LB, van Bakel H, Wilson M, Eckalbar W, Maecker H, Langelier CR, Steen H, Altman MC, Montgomery RR, Levy O, Melamed E, Pulendran B, Diray-Arce J, Smolen KK, Fragiadakis GK, Becker PM, Augustine AD, Sekaly RP, Ehrlich LIR, Fourati S, Peters B, Kleinstein SH, Guan L. Integrated longitudinal multi-omics study identifies immune programs associated with COVID-19 severity and mortality in 1152 hospitalized participants. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.03.565292. [PMID: 37986828 PMCID: PMC10659275 DOI: 10.1101/2023.11.03.565292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Hospitalized COVID-19 patients exhibit diverse clinical outcomes, with some individuals diverging over time even though their initial disease severity appears similar. A systematic evaluation of molecular and cellular profiles over the full disease course can link immune programs and their coordination with progression heterogeneity. In this study, we carried out deep immunophenotyping and conducted longitudinal multi-omics modeling integrating ten distinct assays on a total of 1,152 IMPACC participants and identified several immune cascades that were significant drivers of differential clinical outcomes. Increasing disease severity was driven by a temporal pattern that began with the early upregulation of immunosuppressive metabolites and then elevated levels of inflammatory cytokines, signatures of coagulation, NETosis, and T-cell functional dysregulation. A second immune cascade, predictive of 28-day mortality among critically ill patients, was characterized by reduced total plasma immunoglobulins and B cells, as well as dysregulated IFN responsiveness. We demonstrated that the balance disruption between IFN-stimulated genes and IFN inhibitors is a crucial biomarker of COVID-19 mortality, potentially contributing to the failure of viral clearance in patients with fatal illness. Our longitudinal multi-omics profiling study revealed novel temporal coordination across diverse omics that potentially explain disease progression, providing insights that inform the targeted development of therapies for hospitalized COVID-19 patients, especially those critically ill.
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9
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Pileggi CA, Parmar G, Elkhatib H, Stewart CM, Alecu I, Côté M, Bennett SA, Sandhu JK, Cuperlovic-Culf M, Harper ME. The SARS-CoV-2 spike glycoprotein interacts with MAO-B and impairs mitochondrial energetics. CURRENT RESEARCH IN NEUROBIOLOGY 2023; 5:100112. [PMID: 38020812 PMCID: PMC10663135 DOI: 10.1016/j.crneur.2023.100112] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 08/21/2023] [Accepted: 09/25/2023] [Indexed: 12/01/2023] Open
Abstract
SARS-CoV-2 infection is associated with both acute and post-acute neurological symptoms. Emerging evidence suggests that SARS-CoV-2 can alter mitochondrial metabolism, suggesting that changes in brain metabolism may contribute to the development of acute and post-acute neurological complications. Monoamine oxidase B (MAO-B) is a flavoenzyme located on the outer mitochondrial membrane that catalyzes the oxidative deamination of monoamine neurotransmitters. Computational analyses have revealed high similarity between the SARS-CoV-2 spike glycoprotein receptor binding domain on the ACE2 receptor and MAO-B, leading to the hypothesis that SARS-CoV-2 spike glycoprotein may alter neurotransmitter metabolism by interacting with MAO-B. Our results empirically establish that the SARS-CoV-2 spike glycoprotein interacts with MAO-B, leading to increased MAO-B activity in SH-SY5Y neuron-like cells. Common to neurodegenerative disease pathophysiological mechanisms, we also demonstrate that the spike glycoprotein impairs mitochondrial bioenergetics, induces oxidative stress, and perturbs the degradation of depolarized aberrant mitochondria through mitophagy. Our findings also demonstrate that SH-SY5Y neuron-like cells expressing the SARS-CoV-2 spike protein were more susceptible to MPTP-induced necrosis, likely necroptosis. Together, these results reveal novel mechanisms that may contribute to SARS-CoV-2-induced neurodegeneration.
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Affiliation(s)
- Chantal A. Pileggi
- Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, K1H 8M5, Canada
- Ottawa Institute of Systems Biology, University of Ottawa, ON, K1H 8M5, Canada
- National Research Council of Canada, Digital Technologies Research Centre, 1200 Montreal Road, Ottawa, ON, K1A 0R6, Canada
| | - Gaganvir Parmar
- Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, K1H 8M5, Canada
- Ottawa Institute of Systems Biology, University of Ottawa, ON, K1H 8M5, Canada
| | - Hussein Elkhatib
- Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, K1H 8M5, Canada
- Ottawa Institute of Systems Biology, University of Ottawa, ON, K1H 8M5, Canada
| | - Corina M. Stewart
- Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, K1H 8M5, Canada
- Ottawa Institute of Systems Biology, University of Ottawa, ON, K1H 8M5, Canada
- Current Address: Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Irina Alecu
- Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, K1H 8M5, Canada
- Ottawa Institute of Systems Biology, University of Ottawa, ON, K1H 8M5, Canada
- Neural Regeneration Laboratory, Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, K1H 8M5, Canada
| | - Marceline Côté
- Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, K1H 8M5, Canada
- Ottawa Institute of Systems Biology, University of Ottawa, ON, K1H 8M5, Canada
- Centre for Infection, Immunity and Inflammation, University of Ottawa, ON, K1H 8M5, Canada
| | - Steffany A.L. Bennett
- Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, K1H 8M5, Canada
- Ottawa Institute of Systems Biology, University of Ottawa, ON, K1H 8M5, Canada
- Neural Regeneration Laboratory, Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, K1H 8M5, Canada
| | - Jagdeep K. Sandhu
- Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, K1H 8M5, Canada
- Ottawa Institute of Systems Biology, University of Ottawa, ON, K1H 8M5, Canada
- Centre for Infection, Immunity and Inflammation, University of Ottawa, ON, K1H 8M5, Canada
- Human Health Therapeutics Research Centre, National Research Council Canada, 1200 Montreal Road, Ottawa, ON, K1A 0R6, Canada
| | - Miroslava Cuperlovic-Culf
- Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, K1H 8M5, Canada
- Ottawa Institute of Systems Biology, University of Ottawa, ON, K1H 8M5, Canada
- National Research Council of Canada, Digital Technologies Research Centre, 1200 Montreal Road, Ottawa, ON, K1A 0R6, Canada
| | - Mary-Ellen Harper
- Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, K1H 8M5, Canada
- Ottawa Institute of Systems Biology, University of Ottawa, ON, K1H 8M5, Canada
- Centre for Infection, Immunity and Inflammation, University of Ottawa, ON, K1H 8M5, Canada
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Hu M, Zhu J, Peng G, Lu W, Wang H, Xie Z. IMOVNN: incomplete multi-omics data integration variational neural networks for gut microbiome disease prediction and biomarker identification. Brief Bioinform 2023; 24:bbad394. [PMID: 37930027 DOI: 10.1093/bib/bbad394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 09/03/2023] [Accepted: 10/14/2023] [Indexed: 11/07/2023] Open
Abstract
The gut microbiome has been regarded as one of the fundamental determinants regulating human health, and multi-omics data profiling has been increasingly utilized to bolster the deep understanding of this complex system. However, stemming from cost or other constraints, the integration of multi-omics often suffers from incomplete views, which poses a great challenge for the comprehensive analysis. In this work, a novel deep model named Incomplete Multi-Omics Variational Neural Networks (IMOVNN) is proposed for incomplete data integration, disease prediction application and biomarker identification. Benefiting from the information bottleneck and the marginal-to-joint distribution integration mechanism, the IMOVNN can learn the marginal latent representation of each individual omics and the joint latent representation for better disease prediction. Moreover, owing to the feature-selective layer predicated upon the concrete distribution, the model is interpretable and can identify the most relevant features. Experiments on inflammatory bowel disease multi-omics datasets demonstrate that our method outperforms several state-of-the-art methods for disease prediction. In addition, IMOVNN has identified significant biomarkers from multi-omics data sources.
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Affiliation(s)
- Mingyi Hu
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China
| | - Jinlin Zhu
- School of Food Science and Technology, Jiangnan University, Wuxi, China
| | | | - Wenwei Lu
- School of Food Science and Technology, Jiangnan University, Wuxi, China
| | - Hongchao Wang
- School of Food Science and Technology, Jiangnan University, Wuxi, China
| | - Zhenping Xie
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China
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11
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Lodge S, Lawler NG, Gray N, Masuda R, Nitschke P, Whiley L, Bong SH, Yeap BB, Dwivedi G, Spraul M, Schaefer H, Gil-Redondo R, Embade N, Millet O, Holmes E, Wist J, Nicholson JK. Integrative Plasma Metabolic and Lipidomic Modelling of SARS-CoV-2 Infection in Relation to Clinical Severity and Early Mortality Prediction. Int J Mol Sci 2023; 24:11614. [PMID: 37511373 PMCID: PMC10380980 DOI: 10.3390/ijms241411614] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 07/10/2023] [Accepted: 07/15/2023] [Indexed: 07/30/2023] Open
Abstract
An integrative multi-modal metabolic phenotyping model was developed to assess the systemic plasma sequelae of SARS-CoV-2 (rRT-PCR positive) induced COVID-19 disease in patients with different respiratory severity levels. Plasma samples from 306 unvaccinated COVID-19 patients were collected in 2020 and classified into four levels of severity ranging from mild symptoms to severe ventilated cases. These samples were investigated using a combination of quantitative Nuclear Magnetic Resonance (NMR) spectroscopy and Mass Spectrometry (MS) platforms to give broad lipoprotein, lipidomic and amino acid, tryptophan-kynurenine pathway, and biogenic amine pathway coverage. All platforms revealed highly significant differences in metabolite patterns between patients and controls (n = 89) that had been collected prior to the COVID-19 pandemic. The total number of significant metabolites increased with severity with 344 out of the 1034 quantitative variables being common to all severity classes. Metabolic signatures showed a continuum of changes across the respiratory severity levels with the most significant and extensive changes being in the most severely affected patients. Even mildly affected respiratory patients showed multiple highly significant abnormal biochemical signatures reflecting serious metabolic deficiencies of the type observed in Post-acute COVID-19 syndrome patients. The most severe respiratory patients had a high mortality (56.1%) and we found that we could predict mortality in this patient sub-group with high accuracy in some cases up to 61 days prior to death, based on a separate metabolic model, which highlighted a different set of metabolites to those defining the basic disease. Specifically, hexosylceramides (HCER 16:0, HCER 20:0, HCER 24:1, HCER 26:0, HCER 26:1) were markedly elevated in the non-surviving patient group (Cliff's delta 0.91-0.95) and two phosphoethanolamines (PE.O 18:0/18:1, Cliff's delta = -0.98 and PE.P 16:0/18:1, Cliff's delta = -0.93) were markedly lower in the non-survivors. These results indicate that patient morbidity to mortality trajectories is determined relatively soon after infection, opening the opportunity to select more intensive therapeutic interventions to these "high risk" patients in the early disease stages.
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Affiliation(s)
- Samantha Lodge
- Australian National Phenome Center, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, WA 6150, Australia; (S.L.); (N.G.L.); (N.G.); (R.M.); (P.N.); (L.W.); (S.-H.B.); (E.H.)
- Center for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, WA 6150, Australia
| | - Nathan G. Lawler
- Australian National Phenome Center, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, WA 6150, Australia; (S.L.); (N.G.L.); (N.G.); (R.M.); (P.N.); (L.W.); (S.-H.B.); (E.H.)
- Center for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, WA 6150, Australia
| | - Nicola Gray
- Australian National Phenome Center, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, WA 6150, Australia; (S.L.); (N.G.L.); (N.G.); (R.M.); (P.N.); (L.W.); (S.-H.B.); (E.H.)
- Center for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, WA 6150, Australia
| | - Reika Masuda
- Australian National Phenome Center, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, WA 6150, Australia; (S.L.); (N.G.L.); (N.G.); (R.M.); (P.N.); (L.W.); (S.-H.B.); (E.H.)
| | - Philipp Nitschke
- Australian National Phenome Center, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, WA 6150, Australia; (S.L.); (N.G.L.); (N.G.); (R.M.); (P.N.); (L.W.); (S.-H.B.); (E.H.)
| | - Luke Whiley
- Australian National Phenome Center, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, WA 6150, Australia; (S.L.); (N.G.L.); (N.G.); (R.M.); (P.N.); (L.W.); (S.-H.B.); (E.H.)
- Center for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, WA 6150, Australia
| | - Sze-How Bong
- Australian National Phenome Center, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, WA 6150, Australia; (S.L.); (N.G.L.); (N.G.); (R.M.); (P.N.); (L.W.); (S.-H.B.); (E.H.)
| | - Bu B. Yeap
- Medical School, University of Western Australia, Perth, WA 6150, Australia; (B.B.Y.); (G.D.)
- Department of Endocrinology and Diabetes, Fiona Stanley Hospital, Perth, WA 6150, Australia
| | - Girish Dwivedi
- Medical School, University of Western Australia, Perth, WA 6150, Australia; (B.B.Y.); (G.D.)
- Department of Cardiology, Fiona Stanley Hospital, Perth, WA 6150, Australia
| | | | | | - Rubén Gil-Redondo
- Precision Medicine and Metabolism Laboratory, CIC bioGUNE, Parque Tecnológico de Bizkaia, Bld. 800, 48160 Derio, Spain; (R.G.-R.); (N.E.); (O.M.)
| | - Nieves Embade
- Precision Medicine and Metabolism Laboratory, CIC bioGUNE, Parque Tecnológico de Bizkaia, Bld. 800, 48160 Derio, Spain; (R.G.-R.); (N.E.); (O.M.)
| | - Oscar Millet
- Precision Medicine and Metabolism Laboratory, CIC bioGUNE, Parque Tecnológico de Bizkaia, Bld. 800, 48160 Derio, Spain; (R.G.-R.); (N.E.); (O.M.)
| | - Elaine Holmes
- Australian National Phenome Center, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, WA 6150, Australia; (S.L.); (N.G.L.); (N.G.); (R.M.); (P.N.); (L.W.); (S.-H.B.); (E.H.)
- Center for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, WA 6150, Australia
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, Sir Alexander Fleming Building, South Kensington, London SW7 2AZ, UK
| | - Julien Wist
- Australian National Phenome Center, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, WA 6150, Australia; (S.L.); (N.G.L.); (N.G.); (R.M.); (P.N.); (L.W.); (S.-H.B.); (E.H.)
- Center for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, WA 6150, Australia
- Chemistry Department, Universidad del Valle, Cali 76001, Colombia
| | - Jeremy K. Nicholson
- Australian National Phenome Center, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, WA 6150, Australia; (S.L.); (N.G.L.); (N.G.); (R.M.); (P.N.); (L.W.); (S.-H.B.); (E.H.)
- Center for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, WA 6150, Australia
- Institute of Global Health Innovation, Faculty of Medicine, Imperial College London, Faculty Building, South Kensington Campus, London SW7 2NA, UK
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Markuskova L, Javorova Rihova Z, Fazekas T, Martinkovicova A, Havrisko M, Dingova D, Solavova M, Rabarova D, Hrabovska A. Serum butyrylcholinesterase as a marker of COVID-19 mortality: Results of the monocentric prospective observational study. Chem Biol Interact 2023; 381:110557. [PMID: 37209860 DOI: 10.1016/j.cbi.2023.110557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 05/15/2023] [Accepted: 05/18/2023] [Indexed: 05/22/2023]
Abstract
The COVID-19 pandemic represents an excessive burden on health care systems worldwide and the number of patients who require special care in the clinical setting is often hard to predict. Consequently, there is an unmet need for a reliable biomarker that could predict clinical outcomes of high-risk patients. Lower serum butyrylcholinesterase (BChE) activity was recently linked with poor outcomes of COVID-19 patients. In line with this, our monocentric observational study on hospitalized COVID-19 patients focused on changes in serum BChE activity in relation to disease progression. Blood samples from 148 adult patients of both sexes were collected during their hospital stay at the Clinics of Infectiology and Clinics of Anesthesiology and Intensive Care, Trnava University Hospital in alignment with routine blood tests. Sera were analyzed using modified Ellman's method. Patient data with information about the health status, comorbidities and other blood parameters were collected in pseudonymized form. Our results show a lower serum BChE activity together with progressive decline of BChE activity in non-survivors, while higher stable values were present in discharged or transferred patients requiring further care. Lower BChE activity was associated with higher age and lower BMI. Moreover, we observed a negative correlation of serum BChE activity with the routinely used inflammatory markers, C-reactive protein and interleukin-6. Serum BChE activity mirrored clinical outcomes of COVID-19 patients and thus serves as a novel prognostic marker in high-risk patients.
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Affiliation(s)
- Lucia Markuskova
- Department of Pharmacology and Toxicology, Faculty of Pharmacy, Comenius University Bratislava, Odbojarov 10, 832 32, Bratislava, Slovakia
| | - Zuzana Javorova Rihova
- Department of Pharmacology, Faculty of Medicine, Slovak Medical University Bratislava, Limbova 12, 832 03, Bratislava, Slovakia; Department of Clinical Pharmacology, Trnava University Hospital, A. Zarnova 11, 917 75, Trnava, Slovakia
| | - Tomas Fazekas
- Department of Physical Chemistry of Drugs, Faculty of Pharmacy, Comenius University Bratislava, 832 32, Bratislava, Slovakia
| | - Anna Martinkovicova
- Department of Pharmacology and Toxicology, Faculty of Pharmacy, Comenius University Bratislava, Odbojarov 10, 832 32, Bratislava, Slovakia
| | - Martina Havrisko
- Department of Clinical Pharmacology, Trnava University Hospital, A. Zarnova 11, 917 75, Trnava, Slovakia; Department of Laboratory Medicine Methods in Healthcare, Faculty of Healthcare and Social Work, University of Trnava in Trnava, 917 75, Trnava, Slovakia
| | - Dominika Dingova
- Department of Pharmacology and Toxicology, Faculty of Pharmacy, Comenius University Bratislava, Odbojarov 10, 832 32, Bratislava, Slovakia
| | - Maria Solavova
- Clinic of Infectiology, Trnava University Hospital, A. Zarnova 11, 917 75, Trnava, Slovakia
| | - Daria Rabarova
- Clinic of Anesthesiology and Intensive Care, Trnava University Hospital, A. Zarnova 11, 917 75, Trnava, Slovakia
| | - Anna Hrabovska
- Department of Pharmacology and Toxicology, Faculty of Pharmacy, Comenius University Bratislava, Odbojarov 10, 832 32, Bratislava, Slovakia.
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13
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Gajula SNR, Khairnar AS, Jock P, Kumari N, Pratima K, Munjal V, Kalan P, Sonti R. LC-MS/MS: A sensitive and selective analytical technique to detect COVID-19 protein biomarkers in the early disease stage. Expert Rev Proteomics 2023; 20:5-18. [PMID: 36919634 DOI: 10.1080/14789450.2023.2191845] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023]
Abstract
INTRODUCTION The COVID-19 outbreak has put enormous pressure on the scientific community to detect infection rapidly, identify the status of disease severity, and provide an immediate vaccine/drug for the treatment. Relying on immunoassay and a real-time reverse transcription polymerase chain reaction (rRT-PCR) led to many false-negative and false-positive reports. Therefore, detecting biomarkers is an alternative and reliable approach for determining the infection, its severity, and disease progression. Recent advances in liquid chromatography and mass spectrometry (LC-MS/MS) enable the protein biomarkers even at low concentrations, thus facilitating clinicians to monitor the treatment in hospitals. AREAS COVERED This review highlights the role of LC-MS/MS in identifying protein biomarkers and discusses the clinically significant protein biomarkers such as Serum amyloid A, Interleukin-6, C-Reactive Protein, Lactate dehydrogenase, D-dimer, cardiac troponin, ferritin, Alanine transaminase, Aspartate transaminase, gelsolin and galectin-3-binding protein in COVID-19, and their analysis by LC-MS/MS in the early stage. EXPERT OPINION Clinical doctors monitor significant biomarkers to understand, stratify, and treat patients according to disease severity. Knowledge of clinically significant COVID-19 protein biomarkers is critical not only for COVID-19 caused by the coronavirus but also to prepare us for future pandemics of other diseases in detecting by LC-MS/MS at the early stages.
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Affiliation(s)
- Siva Nageswara Rao Gajula
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, Balanagar, India
| | - Ankita Sahebrao Khairnar
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, Balanagar, India
| | - Pallavi Jock
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, Balanagar, India
| | - Nikita Kumari
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, Balanagar, India
| | - Kendre Pratima
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, Balanagar, India
| | - Vijay Munjal
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, Balanagar, India
| | - Pavan Kalan
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, Balanagar, India
| | - Rajesh Sonti
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, Balanagar, India
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14
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Sahin AT, Yurtseven A, Dadmand S, Ozcan G, Akarlar BA, Kucuk NEO, Senturk A, Ergonul O, Can F, Tuncbag N, Ozlu N. Plasma proteomics identify potential severity biomarkers from COVID-19 associated network. Proteomics Clin Appl 2023; 17:e2200070. [PMID: 36217943 PMCID: PMC9874836 DOI: 10.1002/prca.202200070] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 09/27/2022] [Accepted: 10/07/2022] [Indexed: 11/05/2022]
Abstract
PURPOSE Coronavirus disease 2019 (COVID-19) continues to threaten public health globally. Severe acute respiratory coronavirus type 2 (SARS-CoV-2) infection-dependent alterations in the host cell signaling network may unveil potential target proteins and pathways for therapeutic strategies. In this study, we aim to define early severity biomarkers and monitor altered pathways in the course of SARS-CoV-2 infection. EXPERIMENTAL DESIGN We systematically analyzed plasma proteomes of COVID-19 patients from Turkey by using mass spectrometry. Different severity grades (moderate, severe, and critical) and periods of disease (early, inflammatory, and recovery) are monitored. Significant alterations in protein expressions are used to reconstruct the COVID-19 associated network that was further extended to connect viral and host proteins. RESULTS Across all COVID-19 patients, 111 differentially expressed proteins were found, of which 28 proteins were unique to our study mainly enriching in immunoglobulin production. By monitoring different severity grades and periods of disease, CLEC3B, MST1, and ITIH2 were identified as potential early predictors of COVID-19 severity. Most importantly, we extended the COVID-19 associated network with viral proteins and showed the connectedness of viral proteins with human proteins. The most connected viral protein ORF8, which has a role in immune evasion, targets many host proteins tightly connected to the deregulated human plasma proteins. CONCLUSIONS AND CLINICAL RELEVANCE Plasma proteomes from critical patients are intrinsically clustered in a distinct group than severe and moderate patients. Importantly, we did not recover any grouping based on the infection period, suggesting their distinct proteome even in the recovery phase. The new potential early severity markers can be further studied for their value in the clinics to monitor COVID-19 prognosis. Beyond the list of plasma proteins, our disease-associated network unravels altered pathways, and the possible therapeutic targets in SARS-CoV-2 infection by connecting human and viral proteins. Follow-up studies on the disease associated network that we propose here will be useful to determine molecular details of viral perturbation and to address how the infection affects human physiology.
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Affiliation(s)
- Ayse Tugce Sahin
- Department of Molecular Biology and Genetics, Koc University, Istanbul, Turkey.,Graduate School of Science and Engineering, Koc University, Istanbul, Turkey
| | - Ali Yurtseven
- Department of Molecular Biology and Genetics, Koc University, Istanbul, Turkey.,Graduate School of Science and Engineering, Koc University, Istanbul, Turkey
| | - Sina Dadmand
- Graduate School of Science and Engineering, Koc University, Istanbul, Turkey.,Department of Chemical and Biological Engineering, Koc University, Istanbul, Turkey
| | - Gulin Ozcan
- Koc University Research Center for Translational Medicine (KUTTAM), Istanbul, Turkey.,Graduate School of Health Sciences, Koc University, Istanbul, Turkey
| | - Busra A Akarlar
- Department of Molecular Biology and Genetics, Koc University, Istanbul, Turkey.,Department of Chemical and Biological Engineering, Koc University, Istanbul, Turkey
| | - Nazli Ezgi Ozkan Kucuk
- Department of Molecular Biology and Genetics, Koc University, Istanbul, Turkey.,Department of Chemical and Biological Engineering, Koc University, Istanbul, Turkey
| | - Aydanur Senturk
- Department of Molecular Biology and Genetics, Koc University, Istanbul, Turkey
| | - Onder Ergonul
- Graduate School of Health Sciences, Koc University, Istanbul, Turkey.,Koc University Is Bank Research Center for Infectious Diseases (KUISCID), Istanbul, Turkey
| | - Fusun Can
- Graduate School of Health Sciences, Koc University, Istanbul, Turkey.,Department of Infectious Diseases, School of Medicine, Koc University, Istanbul, Turkey
| | - Nurcan Tuncbag
- Department of Chemical and Biological Engineering, Koc University, Istanbul, Turkey.,Department of Medical Microbiology, School of Medicine, Koc University, Istanbul, Turkey.,Department of Medical Biology, School of Medicine, Koc University, Istanbul, Turkey
| | - Nurhan Ozlu
- Department of Molecular Biology and Genetics, Koc University, Istanbul, Turkey.,Department of Chemical and Biological Engineering, Koc University, Istanbul, Turkey.,Department of Medical Biology, School of Medicine, Koc University, Istanbul, Turkey
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15
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Avila-Ponce de León U, Vazquez-Jimenez A, Cervera A, Resendis-González G, Neri-Rosario D, Resendis-Antonio O. Machine Learning and COVID-19: Lessons from SARS-CoV-2. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1412:311-335. [PMID: 37378775 DOI: 10.1007/978-3-031-28012-2_17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/29/2023]
Abstract
Currently, methods in machine learning have opened a significant number of applications to construct classifiers with capacities to recognize, identify, and interpret patterns hidden in massive amounts of data. This technology has been used to solve a variety of social and health issues against coronavirus disease 2019 (COVID-19). In this chapter, we present some supervised and unsupervised machine learning techniques that have contributed in three aspects to supplying information to health authorities and diminishing the deadly effects of the current worldwide outbreak on the population. First is the identification and construction of powerful classifiers capable of predicting severe, moderate, or asymptomatic responses in COVID-19 patients starting from clinical or high-throughput technologies. Second is the identification of groups of patients with similar physiological responses to improve the triage classification and inform treatments. The final aspect is the combination of machine learning methods and schemes from systems biology to link associative studies with mechanistic frameworks. This chapter aims to discuss some practical applications in the use of machine learning techniques to handle data coming from social behavior and high-throughput technologies, associated with COVID-19 evolution.
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Affiliation(s)
- Ugo Avila-Ponce de León
- Programa de Doctorado en Ciencias Biológicas, Universidad Nacional Autónoma de México, Ciudad de México, Mexico
- Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica (INMEGEN), Ciudad de México, Mexico
| | - Aarón Vazquez-Jimenez
- Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica (INMEGEN), Ciudad de México, Mexico
| | - Alejandra Cervera
- Instituto Nacional de Medicina Genómica (INMEGEN), Ciudad de México, Mexico
| | - Galilea Resendis-González
- Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica (INMEGEN), Ciudad de México, Mexico
| | - Daniel Neri-Rosario
- Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica (INMEGEN), Ciudad de México, Mexico
| | - Osbaldo Resendis-Antonio
- Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica (INMEGEN), Ciudad de México, Mexico.
- Coordinación de la Investigación Científica - Red de Apoyo a la Investigación - Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México (UNAM), Ciudad de México, Mexico.
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16
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Omics Data and Data Representations for Deep Learning-Based Predictive Modeling. Int J Mol Sci 2022; 23:ijms232012272. [PMID: 36293133 PMCID: PMC9603455 DOI: 10.3390/ijms232012272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 10/03/2022] [Accepted: 10/12/2022] [Indexed: 11/25/2022] Open
Abstract
Medical discoveries mainly depend on the capability to process and analyze biological datasets, which inundate the scientific community and are still expanding as the cost of next-generation sequencing technologies is decreasing. Deep learning (DL) is a viable method to exploit this massive data stream since it has advanced quickly with there being successive innovations. However, an obstacle to scientific progress emerges: the difficulty of applying DL to biology, and this because both fields are evolving at a breakneck pace, thus making it hard for an individual to occupy the front lines of both of them. This paper aims to bridge the gap and help computer scientists bring their valuable expertise into the life sciences. This work provides an overview of the most common types of biological data and data representations that are used to train DL models, with additional information on the models themselves and the various tasks that are being tackled. This is the essential information a DL expert with no background in biology needs in order to participate in DL-based research projects in biomedicine, biotechnology, and drug discovery. Alternatively, this study could be also useful to researchers in biology to understand and utilize the power of DL to gain better insights into and extract important information from the omics data.
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