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Giraudi PJ, Pascut D, Banfi C, Ghilardi S, Tiribelli C, Bondesan A, Caroli D, Minocci A, Sartorio A. Serum proteome signatures associated with liver steatosis in adolescents with obesity. J Endocrinol Invest 2024:10.1007/s40618-024-02419-x. [PMID: 39017916 DOI: 10.1007/s40618-024-02419-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Accepted: 06/19/2024] [Indexed: 07/18/2024]
Abstract
PURPOSE Childhood obesity, a pressing global health issue, significantly increases the risk of metabolic complications, including metabolic dysfunction associated with steatotic liver disease (MASLD). Accurate non-invasive tests for early detection and screening of steatosis are crucial. In this study, we explored the serum proteome, identifying proteins as potential biomarkers for inclusion in non-invasive steatosis diagnosis tests. METHODS Fifty-nine obese adolescents underwent ultrasonography to assess steatosis. Serum samples were collected and analyzed by targeted proteomics with the Proximity Extension Assay technology. Clinical and biochemical parameters were evaluated, and correlations among them, the individuated markers, and steatosis were performed. Receiver operating characteristic (ROC) curves were used to determine the steatosis diagnostic performance of the identified candidates, the fatty liver index (FLI), and their combination in a logistic regression model. RESULTS Significant differences were observed between subjects with and without steatosis in various clinical and biochemical parameters. Gender-related differences in the serum proteome were also noted. Five circulating proteins, including Cathepsin O (CTSO), Cadherin 2 (CDH2), and Prolyl endopeptidase (FAP), were identified as biomarkers for steatosis. CDH2, CTSO, Leukocyte Immunoglobulin Like Receptor A5 (LILRA5), BMI, waist circumference, HOMA-IR, and FLI, among others, significantly correlated with the steatosis degree. CDH2, FAP, and LDL combined in a logit model achieved a diagnostic performance with an AUC of 0.91 (95% CI 0.75-0.97, 100% sensitivity, 84% specificity). CONCLUSIONS CDH2 and FAP combined with other clinical parameters, represent useful tools for accurate diagnosis of fatty liver, emphasizing the importance of integrating novel markers into diagnostic algorithms for MASLD.
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Affiliation(s)
- P J Giraudi
- Metabolic Liver Disease Unit, Fondazione Italiana Fegato-ONLUS, Trieste, Italy.
| | - D Pascut
- Liver Cancer Unit, Fondazione Italiana Fegato-ONLUS, Trieste, Italy
| | - C Banfi
- Unit of Functional Proteomics, Metabolomics, and Network Analysis, Centro Cardiologico Monzino, IRCCS, Milan, Italy
| | - S Ghilardi
- Unit of Functional Proteomics, Metabolomics, and Network Analysis, Centro Cardiologico Monzino, IRCCS, Milan, Italy
| | - C Tiribelli
- Metabolic Liver Disease Unit, Fondazione Italiana Fegato-ONLUS, Trieste, Italy
- Liver Cancer Unit, Fondazione Italiana Fegato-ONLUS, Trieste, Italy
| | - A Bondesan
- Istituto Auxologico Italiano IRCCS, Experimental Laboratory for Auxo-endocrinological Research, Piancavallo-Verbania, Italy
| | - D Caroli
- Istituto Auxologico Italiano IRCCS, Experimental Laboratory for Auxo-endocrinological Research, Piancavallo-Verbania, Italy
| | - A Minocci
- Division of Metabolic Diseases, Istituto Auxologico Italiano IRCCS, Piancavallo-Verbania, Italy
| | - A Sartorio
- Istituto Auxologico Italiano IRCCS, Experimental Laboratory for Auxo-endocrinological Research, Piancavallo-Verbania, Italy
- Istituto Auxologico Italiano IRCCS, Experimental Laboratory for Auxo-endocrinological Research, Milan, Italy
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Zeng X, Lafferty TK, Sehrawat A, Chen Y, Ferreira PCL, Bellaver B, Povala G, Kamboh MI, Klunk WE, Cohen AD, Lopez OL, Ikonomovic MD, Pascoal TA, Ganguli M, Villemagne VL, Snitz BE, Karikari TK. Multi-analyte proteomic analysis identifies blood-based neuroinflammation, cerebrovascular and synaptic biomarkers in preclinical Alzheimer's disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.15.24308975. [PMID: 38947065 PMCID: PMC11213097 DOI: 10.1101/2024.06.15.24308975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Background Blood-based biomarkers are gaining grounds for Alzheimer's disease (AD) detection. However, two key obstacles need to be addressed: the lack of methods for multi-analyte assessments and the need for markers of neuroinflammation, vascular, and synaptic dysfunction. Here, we evaluated a novel multi-analyte biomarker platform, NULISAseq CNS disease panel, a multiplex NUcleic acid-linked Immuno-Sandwich Assay (NULISA) targeting ~120 analytes, including classical AD biomarkers and key proteins defining various disease hallmarks. Methods The NULISAseq panel was applied to 176 plasma samples from the MYHAT-NI cohort of cognitively normal participants from an economically underserved region in Western Pennsylvania. Classical AD biomarkers, including p-tau181 p-tau217, p-tau231, GFAP, NEFL, Aβ40, and Aβ42, were also measured using Single Molecule Array (Simoa). Amyloid pathology, tau pathology, and neurodegeneration were evaluated with [11C] PiB PET, [18F]AV-1451 PET, and MRI, respectively. Linear mixed models were used to examine cross-sectional and Wilcoxon rank sum tests for longitudinal associations between NULISA biomarkers and AD pathologies. Spearman correlations were used to compare NULISA and Simoa. Results NULISA concurrently measured 116 plasma biomarkers with good technical performance, and good correlation with Simoa measures. Cross-sectionally, p-tau217 was the top hit to identify Aβ pathology, with age, sex, and APOE genotype-adjusted AUC of 0.930 (95%CI: 0.878-0.983). Fourteen markers were significantly decreased in Aβ-PET+ participants, including TIMP3, which regulates brain Aβ production, the neurotrophic factor BDNF, the energy metabolism marker MDH1, and several cytokines. Longitudinally, FGF2, IL4, and IL9 exhibited Aβ PET-dependent yearly increases in Aβ-PET+ participants. Markers with tau PET-dependent longitudinal changes included the microglial activation marker CHIT1, the reactive astrogliosis marker CHI3L1, the synaptic protein NPTX1, and the cerebrovascular markers PGF, PDGFRB, and VEFGA; all previously linked to AD but only reliably measured in cerebrospinal fluid. SQSTM1, the autophagosome cargo protein, exhibited a significant association with neurodegeneration status after adjusting age, sex, and APOE ε4 genotype. Conclusions Together, our results demonstrate the feasibility and potential of immunoassay-based multiplexing to provide a comprehensive view of AD-associated proteomic changes. Further validation of the identified inflammation, synaptic, and vascular markers will be important for establishing disease state markers in asymptomatic AD.
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Affiliation(s)
- Xuemei Zeng
- Department of Psychiatry, School of Medicine, University of Pittsburgh, 3811 O’Hara Street, Pittsburgh, PA 15213, USA
| | - Tara K. Lafferty
- Department of Psychiatry, School of Medicine, University of Pittsburgh, 3811 O’Hara Street, Pittsburgh, PA 15213, USA
| | - Anuradha Sehrawat
- Department of Psychiatry, School of Medicine, University of Pittsburgh, 3811 O’Hara Street, Pittsburgh, PA 15213, USA
| | - Yijun Chen
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Pamela C. L. Ferreira
- Department of Psychiatry, School of Medicine, University of Pittsburgh, 3811 O’Hara Street, Pittsburgh, PA 15213, USA
| | - Bruna Bellaver
- Department of Psychiatry, School of Medicine, University of Pittsburgh, 3811 O’Hara Street, Pittsburgh, PA 15213, USA
| | - Guilherme Povala
- Department of Psychiatry, School of Medicine, University of Pittsburgh, 3811 O’Hara Street, Pittsburgh, PA 15213, USA
| | - M. Ilyas Kamboh
- Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - William E. Klunk
- Department of Psychiatry, School of Medicine, University of Pittsburgh, 3811 O’Hara Street, Pittsburgh, PA 15213, USA
| | - Ann D. Cohen
- Department of Psychiatry, School of Medicine, University of Pittsburgh, 3811 O’Hara Street, Pittsburgh, PA 15213, USA
| | - Oscar L. Lopez
- Department of Neurology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Milos D. Ikonomovic
- Department of Psychiatry, School of Medicine, University of Pittsburgh, 3811 O’Hara Street, Pittsburgh, PA 15213, USA
- Department of Neurology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
- Geriatric Research Education and Clinical Center, VA Pittsburgh HS, Pittsburgh, PA, USA
| | - Tharick A. Pascoal
- Department of Psychiatry, School of Medicine, University of Pittsburgh, 3811 O’Hara Street, Pittsburgh, PA 15213, USA
| | - Mary Ganguli
- Department of Psychiatry, School of Medicine, University of Pittsburgh, 3811 O’Hara Street, Pittsburgh, PA 15213, USA
- Department of Neurology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
- Department of Epidemiology, School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Victor L. Villemagne
- Department of Psychiatry, School of Medicine, University of Pittsburgh, 3811 O’Hara Street, Pittsburgh, PA 15213, USA
| | - Beth E. Snitz
- Department of Neurology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Thomas K. Karikari
- Department of Psychiatry, School of Medicine, University of Pittsburgh, 3811 O’Hara Street, Pittsburgh, PA 15213, USA
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Ibanez L, Liu M, Beric A, Timsina J, Kholfeld P, Bergmann K, Lowery J, Sykora N, Sanchez-Montejo B, Brock W, Budde JP, Bateman RJ, Barthelemy N, Schindler SE, Holtzman DM, Benzinger TLS, Xiong C, Tarawneh R, Moulder K, Morris JC, Sung YJ, Cruchaga C. Benchmarking of a multi-biomarker low-volume panel for Alzheimer's Disease and related dementia research. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.13.24308895. [PMID: 38947090 PMCID: PMC11213109 DOI: 10.1101/2024.06.13.24308895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Alzheimer's Disease (AD) biomarker measurement is key to aid in the diagnosis and prognosis of the disease. In the research setting, participant recruitment and retention and optimization of sample use, is one of the main challenges that observational studies face. Thus, obtaining accurate established biomarker measurements for stratification and maximizing use of the precious samples is key. Accurate technologies are currently available for established biomarkers, mainly immunoassays and immunoprecipitation liquid chromatography-mass spectrometry (IP-MS), and some of them are already being used in clinical settings. Although some immunoassays- and IP-MS based platforms provide multiplexing for several different coding proteins there is not a current platform that can measure all the stablished and emerging biomarkers in one run. The NUcleic acid Linked Immuno-Sandwich Assay (NULISA™) is a mid-throughput platform with antibody-based measurements with a sequencing output that requires 15μL of sample volume to measure more than 100 analytes, including those typically assayed for AD. Here we benchmarked and compared the AD-relevant biomarkers including in the NULISA against validated assays, in both CSF and plasma. Overall, we have found that CSF measures of Aß42/40, NfL, GFAP, and p-tau217 are highly correlated and have similar predictive performance when measured by immunoassay, mass-spectrometry or NULISA. In plasma, p-tau217 shows a performance similar to that reported with other technologies when predicting amyloidosis. Other established and exploratory biomarkers (total tau, p-tau181, NRGN, YKL40, sTREM2, VILIP1 among other) show a wide range of correlation values depending on the fluid and the platform. Our results indicate that the multiplexed immunoassay platform produces reliable results for established biomarkers in CSF that are useful in research settings, with the advantage of measuring additional novel biomarkers using minimal sample volume.
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Affiliation(s)
- Laura Ibanez
- Department of Psychiatry, Washington University School of Medicine
- Department of Neurology, Washington University School of Medicine
- NeuroGenomics and Informatics Center, Washington University School of Medicine
| | - Menghan Liu
- Department of Psychiatry, Washington University School of Medicine
- NeuroGenomics and Informatics Center, Washington University School of Medicine
| | - Aleksandra Beric
- Department of Psychiatry, Washington University School of Medicine
- NeuroGenomics and Informatics Center, Washington University School of Medicine
| | - Jigyasha Timsina
- Department of Psychiatry, Washington University School of Medicine
- NeuroGenomics and Informatics Center, Washington University School of Medicine
| | - Pat Kholfeld
- Department of Psychiatry, Washington University School of Medicine
- NeuroGenomics and Informatics Center, Washington University School of Medicine
| | - Kristy Bergmann
- Department of Psychiatry, Washington University School of Medicine
- NeuroGenomics and Informatics Center, Washington University School of Medicine
| | - Joey Lowery
- Department of Psychiatry, Washington University School of Medicine
- NeuroGenomics and Informatics Center, Washington University School of Medicine
| | - Nick Sykora
- Department of Psychiatry, Washington University School of Medicine
- NeuroGenomics and Informatics Center, Washington University School of Medicine
| | - Brenda Sanchez-Montejo
- Department of Psychiatry, Washington University School of Medicine
- NeuroGenomics and Informatics Center, Washington University School of Medicine
| | - Will Brock
- Department of Psychiatry, Washington University School of Medicine
- NeuroGenomics and Informatics Center, Washington University School of Medicine
| | - John P. Budde
- Department of Psychiatry, Washington University School of Medicine
- NeuroGenomics and Informatics Center, Washington University School of Medicine
| | - Randall J. Bateman
- Department of Neurology, Washington University School of Medicine
- Hope Center for Neurologic Diseases, Washington University School of Medicine
- The Tracy Family SILQ Center, Washington University School of Medicine
- Knight Alzheimer Disease Research Center, Washington University School of Medicine
| | - Nicolas Barthelemy
- Department of Neurology, Washington University School of Medicine
- The Tracy Family SILQ Center, Washington University School of Medicine
| | - Suzanne E. Schindler
- Department of Neurology, Washington University School of Medicine
- Hope Center for Neurologic Diseases, Washington University School of Medicine
- Knight Alzheimer Disease Research Center, Washington University School of Medicine
| | - David M Holtzman
- Department of Neurology, Washington University School of Medicine
- Hope Center for Neurologic Diseases, Washington University School of Medicine
- Knight Alzheimer Disease Research Center, Washington University School of Medicine
| | - Tammie L. S. Benzinger
- Hope Center for Neurologic Diseases, Washington University School of Medicine
- Knight Alzheimer Disease Research Center, Washington University School of Medicine
- Department of Radiology, Washington University School of Medicine
| | - Chengjie Xiong
- Division of Biostatistics, Washington University School of Medicine
| | - Rawan Tarawneh
- Department of Neurology, University of New Mexico School of Medicine
| | - Krista Moulder
- Department of Neurology, Washington University School of Medicine
- Knight Alzheimer Disease Research Center, Washington University School of Medicine
| | - John C. Morris
- Department of Neurology, Washington University School of Medicine
- Knight Alzheimer Disease Research Center, Washington University School of Medicine
| | - Yun Ju Sung
- Department of Psychiatry, Washington University School of Medicine
- NeuroGenomics and Informatics Center, Washington University School of Medicine
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University School of Medicine
- Department of Neurology, Washington University School of Medicine
- NeuroGenomics and Informatics Center, Washington University School of Medicine
- Hope Center for Neurologic Diseases, Washington University School of Medicine
- Knight Alzheimer Disease Research Center, Washington University School of Medicine
- Department of Genetics, Washington University School of Medicine
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4
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Molecular pathology markers of FTD and ALS in blood extracellular vesicles. Nat Med 2024; 30:1545-1546. [PMID: 38890532 DOI: 10.1038/s41591-024-02985-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/20/2024]
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5
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Sun BB, Suhre K, Gibson BW. Promises and Challenges of populational Proteomics in Health and Disease. Mol Cell Proteomics 2024; 23:100786. [PMID: 38761890 PMCID: PMC11193116 DOI: 10.1016/j.mcpro.2024.100786] [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: 02/06/2024] [Revised: 05/13/2024] [Accepted: 05/15/2024] [Indexed: 05/20/2024] Open
Abstract
Advances in proteomic assay technologies have significantly increased coverage and throughput, enabling recent increases in the number of large-scale population-based proteomic studies of human plasma and serum. Improvements in multiplexed protein assays have facilitated the quantification of thousands of proteins over a large dynamic range, a key requirement for detecting the lowest-ranging, and potentially the most disease-relevant, blood-circulating proteins. In this perspective, we examine how populational proteomic datasets in conjunction with other concurrent omic measures can be leveraged to better understand the genomic and non-genomic correlates of the soluble proteome, constructing biomarker panels for disease prediction, among others. Mass spectrometry workflows are discussed as they are becoming increasingly competitive with affinity-based array platforms in terms of speed, cost, and proteome coverage due to advances in both instrumentation and workflows. Despite much success, there remain considerable challenges such as orthogonal validation and absolute quantification. We also highlight emergent challenges associated with study design, analytical considerations, and data integration as population-scale studies are run in batches and may involve longitudinal samples collated over many years. Lastly, we take a look at the future of what the nascent next-generation proteomic technologies might provide to the analysis of large sets of blood samples, as well as the difficulties in designing large-scale studies that will likely require participation from multiple and complex funding sources and where data sharing, study designs, and financing must be solved.
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Affiliation(s)
- Benjamin B Sun
- Human Genetics, Informatics and Predictive Sciences, Bristol-Myers Squibb, Cambridge, Massachusetts, USA.
| | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar; Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York, USA
| | - Bradford W Gibson
- Pharmaceutical Chemistry, University of California, San Francisco, California, USA
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6
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Zeng X, Chen Y, Sehrawat A, Lee J, Lafferty TK, Kofler J, Berman SB, Sweet RA, Tudorascu DL, Klunk WE, Ikonomovic MD, Pfister A, Zetterberg H, Snitz BE, Cohen AD, Villemagne VL, Pascoal TA, Kamboh ML, Lopez OI, Blennow K, Karikari TK. Alzheimer blood biomarkers: practical guidelines for study design, sample collection, processing, biobanking, measurement and result reporting. Mol Neurodegener 2024; 19:40. [PMID: 38750570 PMCID: PMC11095038 DOI: 10.1186/s13024-024-00711-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 02/13/2024] [Indexed: 05/19/2024] Open
Abstract
Alzheimer's disease (AD), the most common form of dementia, remains challenging to understand and treat despite decades of research and clinical investigation. This might be partly due to a lack of widely available and cost-effective modalities for diagnosis and prognosis. Recently, the blood-based AD biomarker field has seen significant progress driven by technological advances, mainly improved analytical sensitivity and precision of the assays and measurement platforms. Several blood-based biomarkers have shown high potential for accurately detecting AD pathophysiology. As a result, there has been considerable interest in applying these biomarkers for diagnosis and prognosis, as surrogate metrics to investigate the impact of various covariates on AD pathophysiology and to accelerate AD therapeutic trials and monitor treatment effects. However, the lack of standardization of how blood samples and collected, processed, stored analyzed and reported can affect the reproducibility of these biomarker measurements, potentially hindering progress toward their widespread use in clinical and research settings. To help address these issues, we provide fundamental guidelines developed according to recent research findings on the impact of sample handling on blood biomarker measurements. These guidelines cover important considerations including study design, blood collection, blood processing, biobanking, biomarker measurement, and result reporting. Furthermore, the proposed guidelines include best practices for appropriate blood handling procedures for genetic and ribonucleic acid analyses. While we focus on the key blood-based AD biomarkers for the AT(N) criteria (e.g., amyloid-beta [Aβ]40, Aβ42, Aβ42/40 ratio, total-tau, phosphorylated-tau, neurofilament light chain, brain-derived tau and glial fibrillary acidic protein), we anticipate that these guidelines will generally be applicable to other types of blood biomarkers. We also anticipate that these guidelines will assist investigators in planning and executing biomarker research, enabling harmonization of sample handling to improve comparability across studies.
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Affiliation(s)
- Xuemei Zeng
- Department of Psychiatry, School of Medicine, University of Pittsburgh, 3811 O'Hara Street, Pittsburgh, PA, 15213, USA
| | - Yijun Chen
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Anuradha Sehrawat
- Department of Psychiatry, School of Medicine, University of Pittsburgh, 3811 O'Hara Street, Pittsburgh, PA, 15213, USA
| | - Jihui Lee
- Department of Psychiatry, School of Medicine, University of Pittsburgh, 3811 O'Hara Street, Pittsburgh, PA, 15213, USA
| | - Tara K Lafferty
- Department of Psychiatry, School of Medicine, University of Pittsburgh, 3811 O'Hara Street, Pittsburgh, PA, 15213, USA
| | - Julia Kofler
- Department of Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Sarah B Berman
- Department of Neurology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Robert A Sweet
- Department of Psychiatry, School of Medicine, University of Pittsburgh, 3811 O'Hara Street, Pittsburgh, PA, 15213, USA
- Department of Neurology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Dana L Tudorascu
- Department of Psychiatry, School of Medicine, University of Pittsburgh, 3811 O'Hara Street, Pittsburgh, PA, 15213, USA
| | - William E Klunk
- Department of Psychiatry, School of Medicine, University of Pittsburgh, 3811 O'Hara Street, Pittsburgh, PA, 15213, USA
| | - Milos D Ikonomovic
- Department of Psychiatry, School of Medicine, University of Pittsburgh, 3811 O'Hara Street, Pittsburgh, PA, 15213, USA
- Department of Neurology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
- Geriatric Research Education and Clinical Center, VA Pittsburgh HS, Pittsburgh, PA, USA
| | - Anna Pfister
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Beth E Snitz
- Department of Neurology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Anne D Cohen
- Department of Psychiatry, School of Medicine, University of Pittsburgh, 3811 O'Hara Street, Pittsburgh, PA, 15213, USA
| | - Victor L Villemagne
- Department of Psychiatry, School of Medicine, University of Pittsburgh, 3811 O'Hara Street, Pittsburgh, PA, 15213, USA
| | - Tharick A Pascoal
- Department of Psychiatry, School of Medicine, University of Pittsburgh, 3811 O'Hara Street, Pittsburgh, PA, 15213, USA
- Department of Neurology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - M Llyas Kamboh
- Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Oscar I Lopez
- Department of Neurology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Thomas K Karikari
- Department of Psychiatry, School of Medicine, University of Pittsburgh, 3811 O'Hara Street, Pittsburgh, PA, 15213, USA.
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden.
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7
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Abe K, Beer JC, Nguyen T, Ariyapala IS, Holmes TH, Feng W, Zhang B, Kuo D, Luo Y, Ma XJ, Maecker HT. Cross-Platform Comparison of Highly Sensitive Immunoassays for Inflammatory Markers in a COVID-19 Cohort. JOURNAL OF IMMUNOLOGY (BALTIMORE, MD. : 1950) 2024; 212:1244-1253. [PMID: 38334457 PMCID: PMC10948291 DOI: 10.4049/jimmunol.2300729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 01/19/2024] [Indexed: 02/10/2024]
Abstract
A variety of commercial platforms are available for the simultaneous detection of multiple cytokines and associated proteins, often employing Ab pairs to capture and detect target proteins. In this study, we comprehensively evaluated the performance of three distinct platforms: the fluorescent bead-based Luminex assay, the proximity extension-based Olink assay, and a novel proximity ligation assay platform known as Alamar NULISAseq. These assessments were conducted on human serum samples from the National Institutes of Health IMPACC study, with a focus on three essential performance metrics: detectability, correlation, and differential expression. Our results reveal several key findings. First, the Alamar platform demonstrated the highest overall detectability, followed by Olink and then Luminex. Second, the correlation of protein measurements between the Alamar and Olink platforms tended to be stronger than the correlation of either of these platforms with Luminex. Third, we observed that detectability differences across the platforms often translated to differences in differential expression findings, although high detectability did not guarantee the ability to identify meaningful biological differences. Our study provides valuable insights into the comparative performance of these assays, enhancing our understanding of their strengths and limitations when assessing complex biological samples, as exemplified by the sera from this COVID-19 cohort.
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Affiliation(s)
- Koji Abe
- Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, CA 94305
| | | | - Tran Nguyen
- Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, CA 94305
| | | | - Tyson H. Holmes
- Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, CA 94305
| | - Wei Feng
- Alamar Biosciences, Inc., Fremont, CA 94538
| | | | - Dwight Kuo
- Alamar Biosciences, Inc., Fremont, CA 94538
| | - Yuling Luo
- Alamar Biosciences, Inc., Fremont, CA 94538
| | | | - Holden T. Maecker
- Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, CA 94305
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8
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Wang L, Nykänen NP, Western D, Gorijala P, Timsina J, Li F, Wang Z, Ali M, Yang C, Liu M, Brock W, Marquié M, Boada M, Alvarez I, Aguilar M, Pastor P, Ruiz A, Puerta R, Orellana A, Rutledge J, Oh H, Greicius MD, Le Guen Y, Perrin RJ, Wyss-Coray T, Jefferson A, Hohman TJ, Graff-Radford N, Mori H, Goate A, Levin J, Sung YJ, Cruchaga C. Proteo-genomics of soluble TREM2 in cerebrospinal fluid provides novel insights and identifies novel modulators for Alzheimer's disease. Mol Neurodegener 2024; 19:1. [PMID: 38172904 PMCID: PMC10763080 DOI: 10.1186/s13024-023-00687-4] [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: 06/08/2023] [Accepted: 11/28/2023] [Indexed: 01/05/2024] Open
Abstract
Triggering receptor expressed on myeloid cells 2 (TREM2) plays a critical role in microglial activation, survival, and apoptosis, as well as in Alzheimer's disease (AD) pathogenesis. We previously reported the MS4A locus as a key modulator for soluble TREM2 (sTREM2) in cerebrospinal fluid (CSF). To identify additional novel genetic modifiers of sTREM2, we performed the largest genome-wide association study (GWAS) and identified four loci for CSF sTREM2 in 3,350 individuals of European ancestry. Through multi-ethnic fine mapping, we identified two independent missense variants (p.M178V in MS4A4A and p.A112T in MS4A6A) that drive the association in MS4A locus and showed an epistatic effect for sTREM2 levels and AD risk. The novel TREM2 locus on chr 6 contains two rare missense variants (rs75932628 p.R47H, P=7.16×10-19; rs142232675 p.D87N, P=2.71×10-10) associated with sTREM2 and AD risk. The third novel locus in the TGFBR2 and RBMS3 gene region (rs73823326, P=3.86×10-9) included a regulatory variant with a microglia-specific chromatin loop for the promoter of TGFBR2. Using cell-based assays we demonstrate that overexpression and knock-down of TGFBR2, but not RBMS3, leads to significant changes of sTREM2. The last novel locus is located on the APOE region (rs11666329, P=2.52×10-8), but we demonstrated that this signal was independent of APOE genotype. This signal colocalized with cis-eQTL of NECTIN2 in the brain cortex and cis-pQTL of NECTIN2 in CSF. Overexpression of NECTIN2 led to an increase of sTREM2 supporting the genetic findings. To our knowledge, this is the largest study to date aimed at identifying genetic modifiers of CSF sTREM2. This study provided novel insights into the MS4A and TREM2 loci, two well-known AD risk genes, and identified TGFBR2 and NECTIN2 as additional modulators involved in TREM2 biology.
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Affiliation(s)
- Lihua Wang
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Niko-Petteri Nykänen
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Daniel Western
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Priyanka Gorijala
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Jigyasha Timsina
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Fuhai Li
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA
| | - Zhaohua Wang
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Muhammad Ali
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Chengran Yang
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Menghan Liu
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - William Brock
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Marta Marquié
- Networking Research Center on Neurodegenerative Disease (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
- Research Center and Memory Clinic, ACE Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Mercè Boada
- Networking Research Center on Neurodegenerative Disease (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
- Research Center and Memory Clinic, ACE Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Ignacio Alvarez
- Memory Disorders Unit, Department of Neurology, University Hospital Mutua Terrassa, Terrassa, Spain
| | - Miquel Aguilar
- Memory Disorders Unit, Department of Neurology, University Hospital Mutua Terrassa, Terrassa, Spain
| | - Pau Pastor
- Unit of Neurodegenerative diseases, Department of Neurology, University Hospital Germans Trias i Pujol and The Germans Trias i Pujol Research Institute (IGTP) Badalona, Barcelona, Spain
| | - Agustín Ruiz
- Networking Research Center on Neurodegenerative Disease (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
- Research Center and Memory Clinic, ACE Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Raquel Puerta
- Networking Research Center on Neurodegenerative Disease (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
- Research Center and Memory Clinic, ACE Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Adelina Orellana
- Networking Research Center on Neurodegenerative Disease (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
- Research Center and Memory Clinic, ACE Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Jarod Rutledge
- Wu-Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Hamilton Oh
- Wu-Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | | | - Yann Le Guen
- Wu-Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Richard J Perrin
- Department of Pathology & Immunology, Washington University School of Medicine, St. Louis, MO, USA
| | - Tony Wyss-Coray
- Wu-Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Angela Jefferson
- Vanderbilt Memory & Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Timothy J Hohman
- Vanderbilt Memory & Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | | | - Alison Goate
- Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Johannes Levin
- Department of Neurology, University Hospital of Munich, Ludwig-Maximilians-Universität (LMU) Munich, Munich, Germany
| | - Yun Ju Sung
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
- Division of Biostatistics, Washington University School of Medicine, BJC Institute of Health, 425 S. Euclid Ave, Box 8134, St. Louis, MO, 63110, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA.
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA.
- Hope Center for Neurologic Diseases, Washington University, St. Louis, MO, USA.
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