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Feng T, Jie M, Deng K, Yang J, Jiang H. Targeted plasma proteomic analysis uncovers a high-performance biomarker panel for early diagnosis of gastric cancer. Clin Chim Acta 2024; 558:119675. [PMID: 38631604 DOI: 10.1016/j.cca.2024.119675] [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: 12/28/2023] [Revised: 03/30/2024] [Accepted: 04/14/2024] [Indexed: 04/19/2024]
Abstract
BACKGROUND Gastric cancer (GC) is characterized by high morbidity, high mortality and low early diagnosis rate. Early diagnosis plays a crucial role in radically treating GC. The aim of this study was to identify plasma biomarkers for GC and early GC diagnosis. METHODS We quantified 369 protein levels with plasma samples from discovery cohort (n = 88) and validation cohort (n = 50) via high-throughput proximity extension assay (PEA) utilizing the Olink-Explore-384-Cardiometabolic panel. The multi-protein signatures were derived from LASSO and Ridge regression models. RESULTS In the discovery cohort, 13 proteins (GDF15, ITIH3, BOC, DPP7, EGFR, AMY2A, CCDC80, CD163, GPNMB, LTBP2, CTSZ, CCL18 and NECTIN2) were identified to distinguish GC (Stage I-IV) and early GC (HGIN-I) groups from control group with AUC of 0.994 and AUC of 0.998, severally. The validation cohort yielded AUC of 0.930 and AUC of 0.818 for GC and early GC, respectively. CONCLUSIONS This study identified a multi-protein signature with the potential to benefit clinical GC diagnosis, especially for Asian and early GC patients, which may contribute to the development of a less-invasive, convenient, and efficient early screening tool, promoting early diagnosis and treatment of GC and ultimately improving patient survival.
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Affiliation(s)
- Tong Feng
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China
| | - Minwen Jie
- Laboratory for Aging and Cancer Research, Frontiers Science Center Disease-related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Kai Deng
- Department of Gastroenterology & Hepatology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Jinlin Yang
- Department of Gastroenterology & Hepatology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China.
| | - Hao Jiang
- Laboratory for Aging and Cancer Research, Frontiers Science Center Disease-related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China.
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2
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Dai L, Tan Q, Li L, Lou N, Zheng C, Yang J, Huang L, Wang S, Luo R, Fan G, Xie T, Yao J, Zhang Z, Tang L, Shi Y, Han X. High-Throughput Antigen Microarray Identifies Longitudinal Prognostic Autoantibody for Chemoimmunotherapy in Advanced Non-Small Cell Lung Cancer. Mol Cell Proteomics 2024; 23:100749. [PMID: 38513890 PMCID: PMC11070596 DOI: 10.1016/j.mcpro.2024.100749] [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: 08/11/2023] [Revised: 02/03/2024] [Accepted: 03/18/2024] [Indexed: 03/23/2024] Open
Abstract
Chemoimmunotherapy has evolved as a standard treatment for advanced non-small cell lung cancer (aNSCLC). However, inevitable drug resistance has limited its efficacy, highlighting the urgent need for biomarkers of chemoimmunotherapy. A three-phase strategy to discover, verify, and validate longitudinal predictive autoantibodies (AAbs) for aNSCLC before and after chemoimmunotherapy was employed. A total of 528 plasma samples from 267 aNSCLC patients before and after anti-PD1 immunotherapy were collected, plus 30 independent formalin-fixed paraffin-embedded samples. Candidate AAbs were firstly selected using a HuProt high-density microarray containing 21,000 proteins in the discovery phase, followed by validation using an aNSCLC-focused microarray. Longitudinal predictive AAbs were chosen for ELISA based on responders versus non-responders comparison and progression-free survival (PFS) survival analysis. Prognostic markers were also validated using immunohistochemistry and publicly available immunotherapy datasets. We identified and validated a panel of two AAbs (MAX and DHX29) as pre-treatment biomarkers and another panel of two AAbs (MAX and TAPBP) as on-treatment predictive markers in aNSCLC patients undergoing chemoimmunotherapy. All three AAbs exhibited a positive correlation with early responses and PFS (p < 0.05). The kinetics of MAX AAb showed an increasing trend in responders (p < 0.05) and a tendency to initially increase and then decrease in non-responders (p < 0.05). Importantly, MAX protein and mRNA levels effectively discriminated PFS (p < 0.05) in aNSCLC patients treated with immunotherapy. Our results present a longitudinal analysis of changes in prognostic AAbs in aNSCLC patients undergoing chemoimmunotherapy.
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Affiliation(s)
- Liyuan Dai
- Department of Clinical Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China
| | - Qiaoyun Tan
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China
| | - Lin Li
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Ning Lou
- Department of Clinical Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China
| | - Cuiling Zheng
- Department of Clinical Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China
| | - Jianliang Yang
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China
| | - Liling Huang
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China
| | - Shasha Wang
- Department of Clinical Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China
| | - Rongrong Luo
- Department of Clinical Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China
| | - Guangyu Fan
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China
| | - Tongji Xie
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China
| | - Jiarui Yao
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China
| | - Zhishang Zhang
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China
| | - Le Tang
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China
| | - Yuankai Shi
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China.
| | - Xiaohong Han
- Clinical Pharmacology Research Center, Peking Union Medical College Hospital, State Key Laboratory of Complex Severe and Rare Diseases, NMPA Key Laboratory for Clinical Research and Evaluation of Drug, Beijing Key Laboratory of Clinical PK & PD Investigation for Innovative Drugs, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
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3
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Kraus VB, Sun S, Reed A, Soderblom EJ, Moseley MA, Zhou K, Jain V, Arden N, Li YJ. An osteoarthritis pathophysiological continuum revealed by molecular biomarkers. SCIENCE ADVANCES 2024; 10:eadj6814. [PMID: 38669329 PMCID: PMC11051665 DOI: 10.1126/sciadv.adj6814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 03/22/2024] [Indexed: 04/28/2024]
Abstract
We aimed to identify serum biomarkers that predict knee osteoarthritis (OA) before the appearance of radiographic abnormalities in a cohort of 200 women. As few as six serum peptides, corresponding to six proteins, reached AUC 77% probability to distinguish those who developed OA from age-matched individuals who did not develop OA up to 8 years later. Prediction based on these blood biomarkers was superior to traditional prediction based on age and BMI (AUC 51%) or knee pain (AUC 57%). These results identify a prolonged molecular derangement of joint tissue before the onset of radiographic OA abnormalities consistent with an unresolved acute phase response. Among all 24 protein biomarkers predicting incident knee OA, the majority (58%) also predicted knee OA progression, revealing the existence of a pathophysiological "OA continuum" based on considerable similarity in the molecular pathophysiology of the progression to incident OA and the progression of established OA.
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Affiliation(s)
- Virginia Byers Kraus
- Duke Molecular Physiology Institute, Duke University, Durham, NC, USA
- Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | - Shuming Sun
- Duke Molecular Physiology Institute, Duke University, Durham, NC, USA
| | - Alexander Reed
- Duke Molecular Physiology Institute, Duke University, Durham, NC, USA
| | - Erik J. Soderblom
- Duke Proteomics and Metabolomics Core Facility, Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
| | - M. Arthur Moseley
- Duke Proteomics and Metabolomics Core Facility, Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
| | - Kaile Zhou
- Duke Molecular Physiology Institute, Duke University, Durham, NC, USA
| | - Vaibhav Jain
- Duke Molecular Physiology Institute, Duke University, Durham, NC, USA
| | - Nigel Arden
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), University of Oxford, UK
| | - Yi-Ju Li
- Duke Molecular Physiology Institute, Duke University, Durham, NC, USA
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
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4
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Quiros-Roldan E, Sottini A, Natali PG, Imberti L. The Impact of Immune System Aging on Infectious Diseases. Microorganisms 2024; 12:775. [PMID: 38674719 PMCID: PMC11051847 DOI: 10.3390/microorganisms12040775] [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: 03/01/2024] [Revised: 03/22/2024] [Accepted: 04/09/2024] [Indexed: 04/28/2024] Open
Abstract
Immune system aging is becoming a field of increasing public health interest because of prolonged life expectancy, which is not paralleled by an increase in health expectancy. As age progresses, innate and adaptive immune systems undergo changes, which are defined, respectively, as inflammaging and immune senescence. A wealth of available data demonstrates that these two conditions are closely linked, leading to a greater vulnerability of elderly subjects to viral, bacterial, and opportunistic infections as well as lower post-vaccination protection. To face this novel scenario, an in-depth assessment of the immune players involved in this changing epidemiology is demanded regarding the individual and concerted involvement of immune cells and mediators within endogenous and exogenous factors and co-morbidities. This review provides an overall updated description of the changes affecting the aging immune system, which may be of help in understanding the underlying mechanisms associated with the main age-associated infectious diseases.
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Affiliation(s)
- Eugenia Quiros-Roldan
- Department of Infectious and Tropical Diseases, ASST- Spedali Civili and DSCS- University of Brescia, 25123 Brescia, Italy;
| | - Alessandra Sottini
- Clinical Chemistry Laboratory, Services Department, ASST Spedali Civili of Brescia, 25123 Brescia, Italy;
| | - Pier Giorgio Natali
- Mediterranean Task Force for Cancer Control (MTCC), Via Pizzo Bernina, 14, 00141 Rome, Italy;
| | - Luisa Imberti
- Section of Microbiology, University of Brescia, P. le Spedali Civili, 1, 25123 Brescia, Italy
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5
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Hariharan R, Hood L, Price ND. A data-driven approach to improve wellness and reduce recurrence in cancer survivors. Front Oncol 2024; 14:1397008. [PMID: 38665952 PMCID: PMC11044254 DOI: 10.3389/fonc.2024.1397008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Accepted: 03/26/2024] [Indexed: 04/28/2024] Open
Abstract
For many cancer survivors, toxic side effects of treatment, lingering effects of the aftermath of disease and cancer recurrence adversely affect quality of life (QoL) and reduce healthspan. Data-driven approaches for quantifying and improving wellness in healthy individuals hold great promise for improving the lives of cancer survivors. The data-driven strategy will also guide personalized nutrition and exercise recommendations that may help prevent cancer recurrence and secondary malignancies in survivors.
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Affiliation(s)
- Ramkumar Hariharan
- College of Engineering, Northeastern University, Seattle, WA, United States
- Institute for Experiential Artificial Intelligence, Northeastern University, Boston, MA, United States
| | - Leroy Hood
- Institute for Systems Biology, Seattle, WA, United States
- Buck Institute for Research on Aging, Novato, CA, United States
- Phenome Health, Seattle, WA, United States
| | - Nathan D. Price
- Institute for Systems Biology, Seattle, WA, United States
- Thorne HealthTech, New York, NY, United States
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6
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Zhou X, Shen X, Johnson JS, Spakowicz DJ, Agnello M, Zhou W, Avina M, Honkala A, Chleilat F, Chen SJ, Cha K, Leopold S, Zhu C, Chen L, Lyu L, Hornburg D, Wu S, Zhang X, Jiang C, Jiang L, Jiang L, Jian R, Brooks AW, Wang M, Contrepois K, Gao P, Rose SMSF, Tran TDB, Nguyen H, Celli A, Hong BY, Bautista EJ, Dorsett Y, Kavathas PB, Zhou Y, Sodergren E, Weinstock GM, Snyder MP. Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease. Cell Host Microbe 2024; 32:506-526.e9. [PMID: 38479397 PMCID: PMC11022754 DOI: 10.1016/j.chom.2024.02.012] [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: 12/05/2023] [Revised: 01/23/2024] [Accepted: 02/20/2024] [Indexed: 03/26/2024]
Abstract
To understand the dynamic interplay between the human microbiome and host during health and disease, we analyzed the microbial composition, temporal dynamics, and associations with host multi-omics, immune, and clinical markers of microbiomes from four body sites in 86 participants over 6 years. We found that microbiome stability and individuality are body-site specific and heavily influenced by the host. The stool and oral microbiome are more stable than the skin and nasal microbiomes, possibly due to their interaction with the host and environment. We identify individual-specific and commonly shared bacterial taxa, with individualized taxa showing greater stability. Interestingly, microbiome dynamics correlate across body sites, suggesting systemic dynamics influenced by host-microbial-environment interactions. Notably, insulin-resistant individuals show altered microbial stability and associations among microbiome, molecular markers, and clinical features, suggesting their disrupted interaction in metabolic disease. Our study offers comprehensive views of multi-site microbial dynamics and their relationship with host health and disease.
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Affiliation(s)
- Xin Zhou
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Center for Genomics and Personalized Medicine, Stanford, CA 94305, USA; Stanford Diabetes Research Center, Stanford, CA 94305, USA; The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Xiaotao Shen
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Center for Genomics and Personalized Medicine, Stanford, CA 94305, USA
| | - Jethro S Johnson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Oxford Centre for Microbiome Studies, Kennedy Institute of Rheumatology, University of Oxford, Roosevelt Drive, Headington, Oxford OX3 7FY, UK
| | - Daniel J Spakowicz
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Division of Medical Oncology, Ohio State University Wexner Medical Center, James Cancer Hospital and Solove Research Institute, Columbus, OH 43210, USA
| | | | - Wenyu Zhou
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Center for Genomics and Personalized Medicine, Stanford, CA 94305, USA
| | - Monica Avina
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Alexander Honkala
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Healthcare Innovation Labs, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
| | - Faye Chleilat
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Shirley Jingyi Chen
- Stanford Healthcare Innovation Labs, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Kexin Cha
- Stanford Healthcare Innovation Labs, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Shana Leopold
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Department of Immunobiology, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Chenchen Zhu
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Lei Chen
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Shanghai Institute of Immunology, Shanghai Jiao Tong University, Shanghai 200240, PRC
| | - Lin Lyu
- Shanghai Institute of Immunology, Shanghai Jiao Tong University, Shanghai 200240, PRC
| | - Daniel Hornburg
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Si Wu
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Xinyue Zhang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Chao Jiang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Life Sciences Institute, Zhejiang University, Hangzhou, Zhejiang 310058, PRC
| | - Liuyiqi Jiang
- Life Sciences Institute, Zhejiang University, Hangzhou, Zhejiang 310058, PRC
| | - Lihua Jiang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Ruiqi Jian
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Andrew W Brooks
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Meng Wang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Kévin Contrepois
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Peng Gao
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | | | | | - Hoan Nguyen
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Alessandra Celli
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Bo-Young Hong
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Woody L Hunt School of Dental Medicine, Texas Tech University Health Science Center, El Paso, TX 79905, USA
| | - Eddy J Bautista
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Corporación Colombiana de Investigación Agropecuaria (Agrosavia), Headquarters-Mosquera, Cundinamarca 250047, Colombia
| | - Yair Dorsett
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Department of Medicine, University of Connecticut Health Center, Farmington, CT 06032, USA
| | - Paula B Kavathas
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT 06520, USA; Department of Laboratory Medicine, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Yanjiao Zhou
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Department of Medicine, University of Connecticut Health Center, Farmington, CT 06032, USA
| | - Erica Sodergren
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | | | - Michael P Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Center for Genomics and Personalized Medicine, Stanford, CA 94305, USA; Stanford Diabetes Research Center, Stanford, CA 94305, USA; Stanford Healthcare Innovation Labs, Stanford University School of Medicine, Stanford, CA 94305, USA.
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7
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Zhou X, Shen X, Johnson JS, Spakowicz DJ, Agnello M, Zhou W, Avina M, Honkala A, Chleilat F, Chen SJ, Cha K, Leopold S, Zhu C, Chen L, Lyu L, Hornburg D, Wu S, Zhang X, Jiang C, Jiang L, Jiang L, Jian R, Brooks AW, Wang M, Contrepois K, Gao P, Schüssler-Fiorenza Rose SM, Binh Tran TD, Nguyen H, Celli A, Hong BY, Bautista EJ, Dorsett Y, Kavathas P, Zhou Y, Sodergren E, Weinstock GM, Snyder MP. Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.01.577565. [PMID: 38352363 PMCID: PMC10862915 DOI: 10.1101/2024.02.01.577565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/26/2024]
Abstract
To understand dynamic interplay between the human microbiome and host during health and disease, we analyzed the microbial composition, temporal dynamics, and associations with host multi-omics, immune and clinical markers of microbiomes from four body sites in 86 participants over six years. We found that microbiome stability and individuality are body-site-specific and heavily influenced by the host. The stool and oral microbiome were more stable than the skin and nasal microbiomes, possibly due to their interaction with the host and environment. Also, we identified individual-specific and commonly shared bacterial taxa, with individualized taxa showing greater stability. Interestingly, microbiome dynamics correlated across body sites, suggesting systemic coordination influenced by host-microbial-environment interactions. Notably, insulin-resistant individuals showed altered microbial stability and associations between microbiome, molecular markers, and clinical features, suggesting their disrupted interaction in metabolic disease. Our study offers comprehensive views of multi-site microbial dynamics and their relationship with host health and disease. Study Highlights The stability of the human microbiome varies among individuals and body sites.Highly individualized microbial genera are more stable over time.At each of the four body sites, systematic interactions between the environment, the host and bacteria can be detected.Individuals with insulin resistance have lower microbiome stability, a more diversified skin microbiome, and significantly altered host-microbiome interactions.
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8
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Buckner JH. Translational immunology: Applying fundamental discoveries to human health and autoimmune diseases. Eur J Immunol 2023; 53:e2250197. [PMID: 37101346 PMCID: PMC10600327 DOI: 10.1002/eji.202250197] [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: 01/04/2023] [Revised: 03/10/2023] [Accepted: 04/25/2023] [Indexed: 04/28/2023]
Abstract
Studying the human immune system is challenging. These challenges stem from the complexity of the immune system itself, the heterogeneity of the immune system between individuals, and the many factors that lead to this heterogeneity including the influence of genetics, environment, and immune experience. Studies of the human immune system in the context of disease are increased in complexity as multiple combinations and variations in immune pathways can lead to a single disease. Thus, although individuals with a disease may share clinical features, the underlying disease mechanisms and resulting pathophysiology can be diverse among individuals with the same disease diagnosis. This has consequences for the treatment of diseases, as no single therapy will work for everyone, therapeutic efficacy varies among patients, and targeting a single immune pathway is rarely 100% effective. This review discusses how to address these challenges by identifying and managing the sources of variation, improving access to high-quality, well-curated biological samples by building cohorts, applying new technologies such as single-cell omics and imaging technologies to interrogate samples, and bringing to bear computational expertise in conjunction with immunologists and clinicians to interpret those results. The review has a focus on autoimmune diseases, including rheumatoid arthritis, MS, systemic lupus erythematosus, and type 1 diabetes, but its recommendations are also applicable to studies of other immune-mediated diseases.
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Affiliation(s)
- Jane H Buckner
- Center for Translational Immunology, Benaroya Research Institute, Virginia Mason Hospital, Seattle, WA, USA
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9
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Mohr AE, Ahern MM, Sears DD, Bruening M, Whisner CM. Gut microbiome diversity, variability, and latent community types compared with shifts in body weight during the freshman year of college in dormitory-housed adolescents. Gut Microbes 2023; 15:2250482. [PMID: 37642346 PMCID: PMC10467528 DOI: 10.1080/19490976.2023.2250482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 06/26/2023] [Accepted: 08/17/2023] [Indexed: 08/31/2023] Open
Abstract
Significant human gut microbiome changes during adolescence suggest that microbial community evolution occurs throughout important developmental periods including the transition to college, a typical life phase of weight gain. In this observational longitudinal study of 139 college freshmen living in on-campus dormitories, we tracked changes in the gut microbiome via 16S amplicon sequencing and body weight across a single academic year. Participants were grouped by weight change categories of gain (WG), loss (WL), and maintenance (WM). Upon assessment of the community structure, unweighted and weighted UniFrac metrics revealed significant shifts with substantial variation explained by individual effects within weight change categories. Genera that positively contributed to these associations with weight change included Bacteroides, Blautia, and Bifidobacterium in WG participants and Prevotella and Faecalibacterium in WL and WM participants. Moreover, the Prevotella/Bacteroides ratio was significantly different by weight change category, with WL participants displaying an increased ratio. Importantly, these genera did not display co-dominance nor ease of transition between Prevotella- and Bacteroides-dominated states. We further assessed the overall taxonomic variation, noting the increased stability of the WL compared to the WG microbiome. Finally, we found 30 latent community structures within the microbiome with significant associations with waist circumference, sleep, and dietary factors, with alcohol consumption chief among them. Our findings highlight the high level of individual variation and the importance of initial gut microbiome community structure in college students during a period of major lifestyle changes. Further work is needed to confirm these findings and explore mechanistic relationships between gut microbes and weight change in free-living individuals.
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Affiliation(s)
- Alex E. Mohr
- College of Health Solutions, Arizona State University, Phoenix, AZ, USA
- Center for Health Through Microbiomes, Biodesign Institute, Arizona State University, Tempe, AZ, USA
| | - Mary M. Ahern
- College of Health Solutions, Arizona State University, Phoenix, AZ, USA
| | - Dorothy D. Sears
- College of Health Solutions, Arizona State University, Phoenix, AZ, USA
| | - Meg Bruening
- College of Health Solutions, Arizona State University, Phoenix, AZ, USA
- Department of Nutritional Sciences, College of Health and Human Development, Pennsylvania State University, University Park, PA, USA
| | - Corrie M. Whisner
- College of Health Solutions, Arizona State University, Phoenix, AZ, USA
- Center for Health Through Microbiomes, Biodesign Institute, Arizona State University, Tempe, AZ, USA
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10
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Creskey M, Li L, Ning Z, Fekete EE, Mayne J, Walker K, Ampaw A, Ben R, Zhang X, Figeys D. An economic and robust TMT labeling approach for high throughput proteomic and metaproteomic analysis. Proteomics 2023; 23:e2200116. [PMID: 36528842 DOI: 10.1002/pmic.202200116] [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: 09/03/2022] [Revised: 12/13/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022]
Abstract
Multiplexed quantitative proteomics using tandem mass tag (TMT) is increasingly used in -omic study of complex samples. While TMT-based proteomics has the advantages of the higher quantitative accuracy, fewer missing values, and reduced instrument analysis time, it is limited by the additional reagent cost. In addition, current TMT labeling workflows involve repeated small volume pipetting of reagents in volatile solvents, which may increase the sample-to-sample variations and is not readily suitable for high throughput applications. In this study, we demonstrated that the TMT labeling procedures could be streamlined by using pre-aliquoted dry TMT reagents in a 96 well plate or 12-tube strip. As little as 50 μg dry TMT per channel was used to label 6-12 μg peptides, yielding high TMT labeling efficiency (∼99%) in both microbiome and mammalian cell line samples. We applied this workflow to analyze 97 samples in a study to evaluate whether ice recrystallization inhibitors improve the cultivability and activity of frozen microbiota. The results demonstrated tight sample clustering corresponding to groups and consistent microbiome responses to prebiotic treatments. This study supports the use of TMT reagents that are pre-aliquoted, dried, and stored for robust quantitative proteomics and metaproteomics in high throughput applications.
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Affiliation(s)
- Marybeth Creskey
- Regulatory Research Division, Centre for Oncology, Radiopharmaceuticals and Research, Biologic and Radiopharmaceutical Drugs Directorate, Health Products and Food Branch, Health Canada, Ottawa, Canada
| | - Leyuan Li
- School of Pharmaceutical Sciences, Faculty of Medicine, University of Ottawa, Ottawa, Canada
- Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, Canada
| | - Zhibin Ning
- School of Pharmaceutical Sciences, Faculty of Medicine, University of Ottawa, Ottawa, Canada
- Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, Canada
| | - Emily Ef Fekete
- Regulatory Research Division, Centre for Oncology, Radiopharmaceuticals and Research, Biologic and Radiopharmaceutical Drugs Directorate, Health Products and Food Branch, Health Canada, Ottawa, Canada
| | - Janice Mayne
- School of Pharmaceutical Sciences, Faculty of Medicine, University of Ottawa, Ottawa, Canada
- Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, Canada
| | - Krystal Walker
- School of Pharmaceutical Sciences, Faculty of Medicine, University of Ottawa, Ottawa, Canada
- Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, Canada
| | - Anna Ampaw
- Department of Chemistry, Faculty of Science, University of Ottawa, Ottawa, Canada
| | - Robert Ben
- Department of Chemistry, Faculty of Science, University of Ottawa, Ottawa, Canada
| | - Xu Zhang
- Regulatory Research Division, Centre for Oncology, Radiopharmaceuticals and Research, Biologic and Radiopharmaceutical Drugs Directorate, Health Products and Food Branch, Health Canada, Ottawa, Canada
- School of Pharmaceutical Sciences, Faculty of Medicine, University of Ottawa, Ottawa, Canada
| | - Daniel Figeys
- School of Pharmaceutical Sciences, Faculty of Medicine, University of Ottawa, Ottawa, Canada
- Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, Canada
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11
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Liechti T, Van Gassen S, Beddall M, Ballard R, Iftikhar Y, Du R, Venkataraman T, Novak D, Mangino M, Perfetto S, Larman HB, Spector T, Saeys Y, Roederer M. A robust pipeline for high-content, high-throughput immunophenotyping reveals age- and genetics-dependent changes in blood leukocytes. CELL REPORTS METHODS 2023; 3:100619. [PMID: 37883924 PMCID: PMC10626267 DOI: 10.1016/j.crmeth.2023.100619] [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: 09/20/2022] [Revised: 05/29/2023] [Accepted: 09/26/2023] [Indexed: 10/28/2023]
Abstract
High-dimensional flow cytometry is the gold standard to study the human immune system in large cohorts. However, large sample sizes increase inter-experimental variation because of technical and experimental inaccuracies introduced by batch variability. Our high-throughput sample processing pipeline in combination with 28-color flow cytometry focuses on increased throughput (192 samples/experiment) and high reproducibility. We implemented quality control checkpoints to reduce technical and experimental variation. Finally, we integrated FlowSOM clustering to facilitate automated data analysis and demonstrate the reproducibility of our pipeline in a study with 3,357 samples. We reveal age-associated immune dynamics in 2,300 individuals, signified by decreasing T and B cell subsets with age. In addition, by combining genetic analyses, our approach revealed unique immune signatures associated with a single nucleotide polymorphism (SNP) that abrogates CD45 isoform splicing. In summary, we provide a versatile and reliable high-throughput, flow cytometry-based pipeline for immune discovery and exploration in large cohorts.
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Affiliation(s)
- Thomas Liechti
- ImmunoTechnology Section, Vaccine Research Center, NIAID, NIH, Bethesda, MD, USA.
| | - Sofie Van Gassen
- Data Mining and Modeling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium; Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Margaret Beddall
- ImmunoTechnology Section, Vaccine Research Center, NIAID, NIH, Bethesda, MD, USA
| | - Reid Ballard
- ImmunoTechnology Section, Vaccine Research Center, NIAID, NIH, Bethesda, MD, USA
| | - Yaser Iftikhar
- ImmunoTechnology Section, Vaccine Research Center, NIAID, NIH, Bethesda, MD, USA
| | - Renguang Du
- ImmunoTechnology Section, Vaccine Research Center, NIAID, NIH, Bethesda, MD, USA
| | - Thiagarajan Venkataraman
- Institute for Cell Engineering, Division of Immunology, Department of Pathology, Johns Hopkins University, Baltimore, MD, USA
| | - David Novak
- Data Mining and Modeling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium; Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Massimo Mangino
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK; National Heart and Lung Institute, Cardiovascular Science Division, Imperial College London, London, UK
| | - Stephen Perfetto
- ImmunoTechnology Section, Vaccine Research Center, NIAID, NIH, Bethesda, MD, USA
| | - H Benjamin Larman
- Institute for Cell Engineering, Division of Immunology, Department of Pathology, Johns Hopkins University, Baltimore, MD, USA
| | - Tim Spector
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Yvan Saeys
- Data Mining and Modeling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium; Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Mario Roederer
- ImmunoTechnology Section, Vaccine Research Center, NIAID, NIH, Bethesda, MD, USA.
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12
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Zheng Y, Liu Y, Yang J, Dong L, Zhang R, Tian S, Yu Y, Ren L, Hou W, Zhu F, Mai Y, Han J, Zhang L, Jiang H, Lin L, Lou J, Li R, Lin J, Liu H, Kong Z, Wang D, Dai F, Bao D, Cao Z, Chen Q, Chen Q, Chen X, Gao Y, Jiang H, Li B, Li B, Li J, Liu R, Qing T, Shang E, Shang J, Sun S, Wang H, Wang X, Zhang N, Zhang P, Zhang R, Zhu S, Scherer A, Wang J, Wang J, Huo Y, Liu G, Cao C, Shao L, Xu J, Hong H, Xiao W, Liang X, Lu D, Jin L, Tong W, Ding C, Li J, Fang X, Shi L. Multi-omics data integration using ratio-based quantitative profiling with Quartet reference materials. Nat Biotechnol 2023:10.1038/s41587-023-01934-1. [PMID: 37679543 DOI: 10.1038/s41587-023-01934-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 07/31/2023] [Indexed: 09/09/2023]
Abstract
Characterization and integration of the genome, epigenome, transcriptome, proteome and metabolome of different datasets is difficult owing to a lack of ground truth. Here we develop and characterize suites of publicly available multi-omics reference materials of matched DNA, RNA, protein and metabolites derived from immortalized cell lines from a family quartet of parents and monozygotic twin daughters. These references provide built-in truth defined by relationships among the family members and the information flow from DNA to RNA to protein. We demonstrate how using a ratio-based profiling approach that scales the absolute feature values of a study sample relative to those of a concurrently measured common reference sample produces reproducible and comparable data suitable for integration across batches, labs, platforms and omics types. Our study identifies reference-free 'absolute' feature quantification as the root cause of irreproducibility in multi-omics measurement and data integration and establishes the advantages of ratio-based multi-omics profiling with common reference materials.
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Affiliation(s)
- Yuanting Zheng
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China.
| | - Yaqing Liu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jingcheng Yang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
- Greater Bay Area Institute of Precision Medicine, Guangzhou, China
| | | | - Rui Zhang
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital, Beijing, China
| | - Sha Tian
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Ying Yu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Luyao Ren
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Wanwan Hou
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Feng Zhu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yuanbang Mai
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | | | | | | | - Ling Lin
- Zhangjiang Center for Translational Medicine, Shanghai Biotecan Medical Diagnostics Co. Ltd., Shanghai, China
| | - Jingwei Lou
- Zhangjiang Center for Translational Medicine, Shanghai Biotecan Medical Diagnostics Co. Ltd., Shanghai, China
| | - Ruiqiang Li
- Novogene Bioinformatics Institute, Beijing, China
| | - Jingchao Lin
- Metabo-Profile Biotechnology (Shanghai) Co. Ltd., Shanghai, China
| | | | | | - Depeng Wang
- Nextomics Biosciences Institute, Wuhan, China
| | | | - Ding Bao
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Zehui Cao
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Qiaochu Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Qingwang Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Xingdong Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yuechen Gao
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - He Jiang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Bin Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Bingying Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jingjing Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
- Nextomics Biosciences Institute, Wuhan, China
| | - Ruimei Liu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Tao Qing
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Erfei Shang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jun Shang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Shanyue Sun
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Haiyan Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Xiaolin Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Naixin Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Peipei Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Ruolan Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Sibo Zhu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Andreas Scherer
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- EATRIS ERIC-European Infrastructure for Translational Medicine, Amsterdam, the Netherlands
| | - Jiucun Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jing Wang
- National Institute of Metrology, Beijing, China
| | - Yinbo Huo
- Key Laboratory of Bioanalysis and Metrology for State Market Regulation, Shanghai Institute of Measurement and Testing Technology, Shanghai, China
| | - Gang Liu
- Key Laboratory of Bioanalysis and Metrology for State Market Regulation, Shanghai Institute of Measurement and Testing Technology, Shanghai, China
| | - Chengming Cao
- Key Laboratory of Bioanalysis and Metrology for State Market Regulation, Shanghai Institute of Measurement and Testing Technology, Shanghai, China
| | - Li Shao
- Key Laboratory of Bioanalysis and Metrology for State Market Regulation, Shanghai Institute of Measurement and Testing Technology, Shanghai, China
| | - Joshua Xu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Wenming Xiao
- Office of Oncologic Diseases, Office of New Drugs, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Xiaozhen Liang
- Shanghai Institute of Immunity and Infection, Chinese Academy of Sciences, Shanghai, China
| | - Daru Lu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Li Jin
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Weida Tong
- Key Laboratory of Bioanalysis and Metrology for State Market Regulation, Shanghai Institute of Measurement and Testing Technology, Shanghai, China
| | - Chen Ding
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China.
| | - Jinming Li
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital, Beijing, China.
| | - Xiang Fang
- National Institute of Metrology, Beijing, China.
| | - Leming Shi
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China.
- International Human Phenome Institutes (Shanghai), Shanghai, China.
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13
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Álvez MB, Edfors F, von Feilitzen K, Zwahlen M, Mardinoglu A, Edqvist PH, Sjöblom T, Lundin E, Rameika N, Enblad G, Lindman H, Höglund M, Hesselager G, Stålberg K, Enblad M, Simonson OE, Häggman M, Axelsson T, Åberg M, Nordlund J, Zhong W, Karlsson M, Gyllensten U, Ponten F, Fagerberg L, Uhlén M. Next generation pan-cancer blood proteome profiling using proximity extension assay. Nat Commun 2023; 14:4308. [PMID: 37463882 DOI: 10.1038/s41467-023-39765-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 06/27/2023] [Indexed: 07/20/2023] Open
Abstract
A comprehensive characterization of blood proteome profiles in cancer patients can contribute to a better understanding of the disease etiology, resulting in earlier diagnosis, risk stratification and better monitoring of the different cancer subtypes. Here, we describe the use of next generation protein profiling to explore the proteome signature in blood across patients representing many of the major cancer types. Plasma profiles of 1463 proteins from more than 1400 cancer patients are measured in minute amounts of blood collected at the time of diagnosis and before treatment. An open access Disease Blood Atlas resource allows the exploration of the individual protein profiles in blood collected from the individual cancer patients. We also present studies in which classification models based on machine learning have been used for the identification of a set of proteins associated with each of the analyzed cancers. The implication for cancer precision medicine of next generation plasma profiling is discussed.
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Affiliation(s)
- María Bueno Álvez
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Fredrik Edfors
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Kalle von Feilitzen
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Martin Zwahlen
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Adil Mardinoglu
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, UK
| | - Per-Henrik Edqvist
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Tobias Sjöblom
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Emma Lundin
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Natallia Rameika
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Gunilla Enblad
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Henrik Lindman
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Martin Höglund
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Göran Hesselager
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Karin Stålberg
- Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
| | - Malin Enblad
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Oscar E Simonson
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Michael Häggman
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Tomas Axelsson
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Mikael Åberg
- Department of Medical Sciences, Clinical Chemistry and SciLifeLab Affinity Proteomics, Uppsala University, Uppsala, Sweden
| | - Jessica Nordlund
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Wen Zhong
- Science for Life Laboratory, Department of Biomedical and Clinical Sciences (BKV), Linköping University, Linköping, Sweden
| | - Max Karlsson
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Ulf Gyllensten
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Fredrik Ponten
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Linn Fagerberg
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Mathias Uhlén
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden.
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden.
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14
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Nimer RM, Abdel Rahman AM. Recent advances in proteomic-based diagnostics of cystic fibrosis. Expert Rev Proteomics 2023; 20:151-169. [PMID: 37766616 DOI: 10.1080/14789450.2023.2258282] [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: 01/03/2023] [Accepted: 07/06/2023] [Indexed: 09/29/2023]
Abstract
INTRODUCTION Cystic fibrosis (CF) is a genetic disease characterized by thick and sticky mucus accumulation, which may harm numerous internal organs. Various variables such as gene modifiers, environmental factors, age of diagnosis, and CF transmembrane conductance regulator (CFTR) gene mutations influence phenotypic disease diversity. Biomarkers that are based on genomic information may not accurately represent the underlying mechanism of the disease as well as its lethal complications. Therefore, recent advancements in mass spectrometry (MS)-based proteomics may provide deep insights into CF mechanisms and cellular functions by examining alterations in the protein expression patterns from various samples of individuals with CF. AREAS COVERED We present current developments in MS-based proteomics, its application, and findings in CF. In addition, the future roles of proteomics in finding diagnostic and prognostic novel biomarkers. EXPERT OPINION Despite significant advances in MS-based proteomics, extensive research in a large cohort for identifying and validating diagnostic, prognostic, predictive, and therapeutic biomarkers for CF disease is highly needed.
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Affiliation(s)
- Refat M Nimer
- Department of Medical Laboratory Sciences, Jordan University of Science and Technology, Irbid, Jordan
| | - Anas M Abdel Rahman
- Metabolomics Section, Department of Clinical Genomics, Center for Genome Medicine, King Faisal Specialist Hospital and Research Centre (KFSHRC), Riyadh, Saudi Arabia
- Department of Biochemistry and Molecular Medicine, College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
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15
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Lokhov PG, Balashova EE, Trifonova OP, Maslov DL, Plotnikova OA, Sharafetdinov KK, Nikityuk DB, Tutelyan VA, Ponomarenko EA, Archakov AI. Clinical Blood Metabogram: Application to Overweight and Obese Patients. Metabolites 2023; 13:798. [PMID: 37512504 PMCID: PMC10386708 DOI: 10.3390/metabo13070798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 06/23/2023] [Accepted: 06/24/2023] [Indexed: 07/30/2023] Open
Abstract
Recently, the concept of a mass spectrometric blood metabogram was introduced, which allows the analysis of the blood metabolome in terms of the time, cost, and reproducibility of clinical laboratory tests. It was demonstrated that the components of the metabogram are related groups of the blood metabolites associated with humoral regulation; the metabolism of lipids, carbohydrates, and amines; lipid intake into the organism; and liver function, thereby providing clinically relevant information. The purpose of this work was to evaluate the relevance of using the metabogram in a disease. To do this, the metabogram was used to analyze patients with various degrees of metabolic alterations associated with obesity. The study involved 20 healthy individuals, 20 overweight individuals, and 60 individuals with class 1, 2, or 3 obesity. The results showed that the metabogram revealed obesity-associated metabolic alterations, including changes in the blood levels of steroids, amino acids, fatty acids, and phospholipids, which are consistent with the available scientific data to date. Therefore, the metabogram allows testing of metabolically unhealthy overweight or obese patients, providing both a general overview of their metabolic alterations and detailing their individual characteristics. It was concluded that the metabogram is an accurate and clinically applicable test for assessing an individual's metabolic status in disease.
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Affiliation(s)
- Petr G Lokhov
- Institute of Biomedical Chemistry, 10 Building 8, Pogodinskaya Street, 119121 Moscow, Russia
| | - Elena E Balashova
- Institute of Biomedical Chemistry, 10 Building 8, Pogodinskaya Street, 119121 Moscow, Russia
| | - Oxana P Trifonova
- Institute of Biomedical Chemistry, 10 Building 8, Pogodinskaya Street, 119121 Moscow, Russia
| | - Dmitry L Maslov
- Institute of Biomedical Chemistry, 10 Building 8, Pogodinskaya Street, 119121 Moscow, Russia
| | - Oksana A Plotnikova
- Federal Research Centre of Nutrition, Biotechnology and Food Safety, Russian Academy of Sciences, Ustinsky Passage 2/14, 109240 Moscow, Russia
| | - Khaider K Sharafetdinov
- Federal Research Centre of Nutrition, Biotechnology and Food Safety, Russian Academy of Sciences, Ustinsky Passage 2/14, 109240 Moscow, Russia
| | - Dmitry B Nikityuk
- Federal Research Centre of Nutrition, Biotechnology and Food Safety, Russian Academy of Sciences, Ustinsky Passage 2/14, 109240 Moscow, Russia
| | - Victor A Tutelyan
- Federal Research Centre of Nutrition, Biotechnology and Food Safety, Russian Academy of Sciences, Ustinsky Passage 2/14, 109240 Moscow, Russia
| | - Elena A Ponomarenko
- Institute of Biomedical Chemistry, 10 Building 8, Pogodinskaya Street, 119121 Moscow, Russia
| | - Alexander I Archakov
- Institute of Biomedical Chemistry, 10 Building 8, Pogodinskaya Street, 119121 Moscow, Russia
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16
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A Proof of Principle Proteomic Study Detects Dystrophin in Human Plasma: Implications in DMD Diagnosis and Clinical Monitoring. Int J Mol Sci 2023; 24:ijms24065215. [PMID: 36982290 PMCID: PMC10049465 DOI: 10.3390/ijms24065215] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 03/03/2023] [Accepted: 03/06/2023] [Indexed: 03/12/2023] Open
Abstract
Duchenne muscular dystrophy (DMD) is a rare neuromuscular disease caused by pathogenic variations in the DMD gene. There is a need for robust DMD biomarkers for diagnostic screening and to aid therapy monitoring. Creatine kinase, to date, is the only routinely used blood biomarker for DMD, although it lacks specificity and does not correlate with disease severity. To fill this critical gap, we present here novel data about dystrophin protein fragments detected in human plasma by a suspension bead immunoassay using two validated anti-dystrophin-specific antibodies. Using both antibodies, a reduction of the dystrophin signal is detected in a small cohort of plasma samples from DMD patients when compared to healthy controls, female carriers, and other neuromuscular diseases. We also demonstrate the detection of dystrophin protein by an antibody-independent method using targeted liquid chromatography mass spectrometry. This last assay detects three different dystrophin peptides in all healthy individuals analysed and supports our finding that dystrophin protein is detectable in plasma. The results of our proof-of-concept study encourage further studies in larger sample cohorts to investigate the value of dystrophin protein as a low invasive blood biomarker for diagnostic screening and clinical monitoring of DMD.
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17
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Tebani A, Barbey F, Dormond O, Ducatez F, Marret S, Nowak A, Bekri S. Deep next-generation proteomics and network analysis reveal systemic and tissue-specific patterns in Fabry disease. Transl Res 2023:S1931-5244(23)00038-5. [PMID: 36863609 DOI: 10.1016/j.trsl.2023.02.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 01/18/2023] [Accepted: 02/21/2023] [Indexed: 03/04/2023]
Abstract
Fabry disease (FD) is an X-linked lysosomal rare disease due to a deficiency of α-galactosidase A activity. The accumulation of glycosphingolipids mainly affects the kidney, heart, and central nervous system, considerably reducing life expectancy. Although the accumulation of undegraded substrate is considered the primary cause of FD, it is established that secondary dysfunctions at the cellular, tissue, and organ levels ultimately give rise to the clinical phenotype. To parse this biological complexity, a large-scale deep plasma targeted proteomic profiling has been performed. We analyzed the plasma protein profiles of FD deeply phenotyped patients (n = 55) compared to controls (n = 30) using next-generation plasma proteomics including 1463 proteins. Systems biology and machine learning approaches have been used. The analysis enabled the identification of proteomic profiles that unambiguously separated FD patients from controls (615 differentially expressed proteins, 476 upregulated, and 139 downregulated) and 365 proteins are newly reported. We observed functional remodeling of several processes, such as cytokine-mediated pathways, extracellular matrix, and vacuolar/lysosomal proteome. Using network strategies, we probed patient-specific tissue metabolic remodeling and described a robust predictive consensus protein signature including 17 proteins CD200, SPINT1, CD34, FGFR2, GRN, ERBB4, AXL, ADAM15, PTPRM, IL13RA1, NBL1, NOTCH1, VASN, ROR1, AMBP, CCN3, and HAVCR2. Our findings highlight the pro-inflammatory cytokines' involvement in FD pathogenesis along with extracellular matrix remodeling. The study shows a tissue-wide metabolic remodeling connection to plasma proteomics in FD. These results will facilitate further studies to understand the molecular mechanisms in FD to pave the way for better diagnostics and therapeutics.
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Affiliation(s)
- Abdellah Tebani
- Normandie Univ, UNIROUEN, INSERM U1245, CHU Rouen, Department of Metabolic Biochemistry, Rouen, France
| | - Frédéric Barbey
- University of Lausanne and University Hospital of Lausanne, Department of Immunology, Switzerland
| | - Olivier Dormond
- Lausanne University Hospital and University of Lausanne, Department of Visceral Surgery, Lausanne, Switzerland
| | - Franklin Ducatez
- Normandie Univ, UNIROUEN, INSERM U1245, CHU Rouen, Department of Metabolic Biochemistry, Rouen, France; Normandie Univ, UNIROUEN, INSERM U1245, CHU Rouen, Department of Neonatal Pediatrics, Intensive Care, and Neuropediatrics, Rouen, France
| | - Stéphane Marret
- Normandie Univ, UNIROUEN, INSERM U1245, CHU Rouen, Department of Neonatal Pediatrics, Intensive Care, and Neuropediatrics, Rouen, France
| | - Albina Nowak
- University Hospital and University of Zurich, Department of Endocrinology and Clinical Nutrition, Zurich, Switzerland
| | - Soumeya Bekri
- Normandie Univ, UNIROUEN, INSERM U1245, CHU Rouen, Department of Metabolic Biochemistry, Rouen, France.
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18
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Hober A, Rekanovic M, Forsström B, Hansson S, Kotol D, Percy AJ, Uhlén M, Oscarsson J, Edfors F, Miliotis T. Targeted proteomics using stable isotope labeled protein fragments enables precise and robust determination of total apolipoprotein(a) in human plasma. PLoS One 2023; 18:e0281772. [PMID: 36791076 PMCID: PMC9931122 DOI: 10.1371/journal.pone.0281772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 01/31/2023] [Indexed: 02/16/2023] Open
Abstract
Lipoprotein(a), also known as Lp(a), is an LDL-like particle composed of apolipoprotein(a) (apo(a)) bound covalently to apolipoprotein B100. Plasma concentrations of Lp(a) are highly heritable and vary widely between individuals. Elevated plasma concentration of Lp(a) is considered as an independent, causal risk factor of cardiovascular disease (CVD). Targeted mass spectrometry (LC-SRM/MS) combined with stable isotope-labeled recombinant proteins provides robust and precise quantification of proteins in the blood, making LC-SRM/MS assays appealing for monitoring plasma proteins for clinical implications. This study presents a novel quantitative approach, based on proteotypic peptides, to determine the absolute concentration of apo(a) from two microliters of plasma and qualified according to guideline requirements for targeted proteomics assays. After optimization, assay parameters such as linearity, lower limits of quantification (LLOQ), intra-assay variability (CV: 4.7%) and inter-assay repeatability (CV: 7.8%) were determined and the LC-SRM/MS results were benchmarked against a commercially available immunoassay. In summary, the measurements of an apo(a) single copy specific peptide and a kringle 4 specific peptide allow for the determination of molar concentration and relative size of apo(a) in individuals.
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Affiliation(s)
- Andreas Hober
- Science for Life Laboratory, Solna, Sweden
- Division of Systems Biology, Department of Protein Science, School of Chemistry, Biotechnology and Health, The Royal Institute of Technology (KTH), Stockholm, Sweden
| | - Mirela Rekanovic
- Translational Science and Experimental Medicine, Cardiovascular, Renal and Metabolism, IMED Biotech Unit, AstraZeneca, Gothenburg, Sweden
| | - Björn Forsström
- Science for Life Laboratory, Solna, Sweden
- Division of Systems Biology, Department of Protein Science, School of Chemistry, Biotechnology and Health, The Royal Institute of Technology (KTH), Stockholm, Sweden
| | - Sara Hansson
- Translational Science and Experimental Medicine, Cardiovascular, Renal and Metabolism, IMED Biotech Unit, AstraZeneca, Gothenburg, Sweden
| | - David Kotol
- Science for Life Laboratory, Solna, Sweden
- Division of Systems Biology, Department of Protein Science, School of Chemistry, Biotechnology and Health, The Royal Institute of Technology (KTH), Stockholm, Sweden
| | - Andrew J. Percy
- Department of Applications Development, Cambridge Isotope Laboratories, Inc., Tewksbury, Massachusetts, United States of America
| | - Mathias Uhlén
- Science for Life Laboratory, Solna, Sweden
- Division of Systems Biology, Department of Protein Science, School of Chemistry, Biotechnology and Health, The Royal Institute of Technology (KTH), Stockholm, Sweden
| | - Jan Oscarsson
- Late-stage Development, Cardiovascular, Renal and Metabolism, Biopharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Fredrik Edfors
- Science for Life Laboratory, Solna, Sweden
- Division of Systems Biology, Department of Protein Science, School of Chemistry, Biotechnology and Health, The Royal Institute of Technology (KTH), Stockholm, Sweden
| | - Tasso Miliotis
- Translational Science and Experimental Medicine, Cardiovascular, Renal and Metabolism, IMED Biotech Unit, AstraZeneca, Gothenburg, Sweden
- * E-mail:
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19
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Digre A, Lindskog C. The human protein atlas-Integrated omics for single cell mapping of the human proteome. Protein Sci 2023; 32:e4562. [PMID: 36604173 PMCID: PMC9850435 DOI: 10.1002/pro.4562] [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: 09/07/2022] [Revised: 11/30/2022] [Accepted: 01/03/2023] [Indexed: 01/07/2023]
Abstract
Studying the spatial distribution of proteins provides the basis for understanding the biology, molecular repertoire, and architecture of every human cell. The Human Protein Atlas (HPA) has grown into one of the world's largest biological databases, and in the most recent version, a major update of the structure of the database was performed. The data has now been organized into 10 different comprehensive sections, each summarizing different aspects of the human proteome and the protein-coding genes. In particular, large datasets with information on the single cell type level have been integrated, refining the tissue and cell type specificity and detailing the expression in cell states with an increased resolution. The multi-modal data constitute an important resource for both basic and translational science, and hold promise for integration with novel emerging technologies at the protein and RNA level.
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Affiliation(s)
- Andreas Digre
- Department of Immunology, Genetics and PathologyUppsala UniversityUppsalaSweden
| | - Cecilia Lindskog
- Department of Immunology, Genetics and PathologyUppsala UniversityUppsalaSweden
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20
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Kharb S, Joshi A. Multi-omics and machine learning for the prevention and management of female reproductive health. Front Endocrinol (Lausanne) 2023; 14:1081667. [PMID: 36909346 PMCID: PMC9996332 DOI: 10.3389/fendo.2023.1081667] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 02/06/2023] [Indexed: 02/25/2023] Open
Abstract
Females typically carry most of the burden of reproduction in mammals. In humans, this burden is exacerbated further, as the evolutionary advantage of a large and complex human brain came at a great cost of women's reproductive health. Pregnancy thus became a highly demanding phase in a woman's life cycle both physically and emotionally and therefore needs monitoring to assure an optimal outcome. Moreover, an increasing societal trend towards reproductive complications partly due to the increasing maternal age and global obesity pandemic demands closer monitoring of female reproductive health. This review first provides an overview of female reproductive biology and further explores utilization of large-scale data analysis and -omics techniques (genomics, transcriptomics, proteomics, and metabolomics) towards diagnosis, prognosis, and management of female reproductive disorders. In addition, we explore machine learning approaches for predictive models towards prevention and management. Furthermore, mobile apps and wearable devices provide a promise of continuous monitoring of health. These complementary technologies can be combined towards monitoring female (fertility-related) health and detection of any early complications to provide intervention solutions. In summary, technological advances (e.g., omics and wearables) have shown a promise towards diagnosis, prognosis, and management of female reproductive disorders. Systematic integration of these technologies is needed urgently in female reproductive healthcare to be further implemented in the national healthcare systems for societal benefit.
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Affiliation(s)
- Simmi Kharb
- Department of Biochemistry, Postgraduate Institute of Medical Sciences, Rohtak, Haryana, India
- *Correspondence: Simmi Kharb, ; Anagha Joshi,
| | - Anagha Joshi
- Computational Biology Unit (CBU), Department of Clinical Science, University of Bergen, Bergen, Norway
- *Correspondence: Simmi Kharb, ; Anagha Joshi,
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21
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Trifonova OP, Maslov DL, Balashova EE, Lokhov PG. Current State and Future Perspectives on Personalized Metabolomics. Metabolites 2023; 13:metabo13010067. [PMID: 36676992 PMCID: PMC9863827 DOI: 10.3390/metabo13010067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 12/27/2022] [Accepted: 12/29/2022] [Indexed: 01/03/2023] Open
Abstract
Metabolomics is one of the most promising 'omics' sciences for the implementation in medicine by developing new diagnostic tests and optimizing drug therapy. Since in metabolomics, the end products of the biochemical processes in an organism are studied, which are under the influence of both genetic and environmental factors, the metabolomics analysis can detect any changes associated with both lifestyle and pathological processes. Almost every case-controlled metabolomics study shows a high diagnostic accuracy. Taking into account that metabolomics processes are already described for most nosologies, there are prerequisites that a high-speed and comprehensive metabolite analysis will replace, in near future, the narrow range of chemical analyses used today, by the medical community. However, despite the promising perspectives of personalized metabolomics, there are currently no FDA-approved metabolomics tests. The well-known problem of complexity of personalized metabolomics data analysis and their interpretation for the end-users, in addition to a traditional need for analytical methods to address the quality control, standardization, and data treatment are reported in the review. Possible ways to solve the problems and change the situation with the introduction of metabolomics tests into clinical practice, are also discussed.
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22
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Lagging C, Klasson S, Pedersen A, Nilsson S, Jood K, Stanne TM, Jern C. Investigation of 91 proteins implicated in neurobiological processes identifies multiple candidate plasma biomarkers of stroke outcome. Sci Rep 2022; 12:20080. [PMID: 36418382 PMCID: PMC9684578 DOI: 10.1038/s41598-022-23288-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Accepted: 10/28/2022] [Indexed: 11/24/2022] Open
Abstract
The inter-individual variation in stroke outcomes is large and protein studies could point to potential underlying biological mechanisms. We measured plasma levels of 91 neurobiological proteins in 209 cases included in the Sahlgrenska Academy Study on Ischemic Stroke using a Proximity Extension Assay, and blood was sampled in the acute phase and at 3-month and 7-year follow-ups. Levels were also determined once in 209 controls. Acute stroke severity and neurological outcome were evaluated by the National Institutes of Health Stroke Scale. In linear regression models corrected for age, sex, and sampling day, acute phase levels of 37 proteins were associated with acute stroke severity, and 47 with 3-month and/or 7-year outcome at false discovery rate < 0.05. Three-month levels of 8 proteins were associated with 7-year outcome, of which the associations for BCAN and Nr-CAM were independent also of acute stroke severity. Most proteins followed a trajectory with lower levels in the acute phase compared to the 3-month follow-up and the control sampling point. Conclusively, we identified multiple candidate plasma biomarkers of stroke severity and neurological outcome meriting further investigation. This study adds novel information, as most of the reported proteins have not been previously investigated in a stroke cohort.
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Affiliation(s)
- Cecilia Lagging
- grid.8761.80000 0000 9919 9582Department of Laboratory Medicine, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Box 440, 405 30 Gothenburg, Sweden ,grid.1649.a000000009445082XDepartment of Clinical Genetics and Genomics, Sahlgrenska University Hospital, Region Västra Götaland, Gothenburg, Sweden
| | - Sofia Klasson
- grid.8761.80000 0000 9919 9582Department of Laboratory Medicine, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Box 440, 405 30 Gothenburg, Sweden
| | - Annie Pedersen
- grid.8761.80000 0000 9919 9582Department of Laboratory Medicine, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Box 440, 405 30 Gothenburg, Sweden ,grid.1649.a000000009445082XDepartment of Clinical Genetics and Genomics, Sahlgrenska University Hospital, Region Västra Götaland, Gothenburg, Sweden
| | - Staffan Nilsson
- grid.8761.80000 0000 9919 9582Department of Laboratory Medicine, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Box 440, 405 30 Gothenburg, Sweden ,grid.5371.00000 0001 0775 6028Division of Applied Mathematics and Statistics, Department of Mathematical Sciences, Chalmers University of Technology, Gothenburg, Sweden
| | - Katarina Jood
- grid.8761.80000 0000 9919 9582Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden ,grid.1649.a000000009445082XDepartment of Neurology, Sahlgrenska University Hospital, Region Västra Götaland, Gothenburg, Sweden
| | - Tara M. Stanne
- grid.8761.80000 0000 9919 9582Department of Laboratory Medicine, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Box 440, 405 30 Gothenburg, Sweden
| | - Christina Jern
- grid.8761.80000 0000 9919 9582Department of Laboratory Medicine, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Box 440, 405 30 Gothenburg, Sweden ,grid.1649.a000000009445082XDepartment of Clinical Genetics and Genomics, Sahlgrenska University Hospital, Region Västra Götaland, Gothenburg, Sweden
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23
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Zhou J, Zhong L. Applications of liquid chromatography-mass spectrometry based metabolomics in predictive and personalized medicine. Front Mol Biosci 2022; 9:1049016. [PMID: 36406271 PMCID: PMC9669074 DOI: 10.3389/fmolb.2022.1049016] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 10/24/2022] [Indexed: 11/05/2022] Open
Abstract
Metabolomics is a fast-developing technique used in biomedical researches focusing on pathological mechanism illustration or novel biomarker development for diseases. The ability of simultaneously quantifying thousands of metabolites in samples makes metabolomics a promising technique in predictive or personalized medicine-oriented researches and applications. Liquid chromatography-mass spectrometry is the most widely employed analytical strategy for metabolomics. In this current mini-review, we provide a brief update on the recent developments and novel applications of LC-MS based metabolomics in the predictive and personalized medicine sector, such as early diagnosis, molecular phenotyping or prognostic evaluation. COVID-19 related metabolomic studies are also summarized. We also discuss the prospects of metabolomics in precision medicine-oriented researches, as well as critical issues that need to be addressed when employing metabolomic strategy in clinical applications.
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Affiliation(s)
- Juntuo Zhou
- Beijing Boyuan Precision Medicine Co., Ltd., Beijing, China
- *Correspondence: Juntuo Zhou, ; Lijun Zhong,
| | - Lijun Zhong
- Center of Medical and Health Analysis, Peking University Health Science Center, Beijing, China
- *Correspondence: Juntuo Zhou, ; Lijun Zhong,
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24
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He B, Huang Z, Huang C, Nice EC. Clinical applications of plasma proteomics and peptidomics: Towards precision medicine. Proteomics Clin Appl 2022; 16:e2100097. [PMID: 35490333 DOI: 10.1002/prca.202100097] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 04/16/2022] [Accepted: 04/28/2022] [Indexed: 02/05/2023]
Abstract
In the context of precision medicine, disease treatment requires individualized strategies based on the underlying molecular characteristics to overcome therapeutic challenges posed by heterogeneity. For this purpose, it is essential to develop new biomarkers to diagnose, stratify, or possibly prevent diseases. Plasma is an available source of biomarkers that greatly reflects the physiological and pathological conditions of the body. An increasing number of studies are focusing on proteins and peptides, including many involving the Human Proteome Project (HPP) of the Human Proteome Organization (HUPO), and proteomics and peptidomics techniques are emerging as critical tools for developing novel precision medicine preventative measures. Excitingly, the emerging plasma proteomics and peptidomics toolbox exhibits a huge potential for studying pathogenesis of diseases (e.g., COVID-19 and cancer), identifying valuable biomarkers and improving clinical management. However, the enormous complexity and wide dynamic range of plasma proteins makes plasma proteome profiling challenging. Herein, we summarize the recent advances in plasma proteomics and peptidomics with a focus on their emerging roles in COVID-19 and cancer research, aiming to emphasize the significance of plasma proteomics and peptidomics in clinical applications and precision medicine.
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Affiliation(s)
- Bo He
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, and West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University and Collaborative Innovation Center for Biotherapy, Chengdu, P. R. China
| | - Zhao Huang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, and West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University and Collaborative Innovation Center for Biotherapy, Chengdu, P. R. China
| | - Canhua Huang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, and West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University and Collaborative Innovation Center for Biotherapy, Chengdu, P. R. China.,Department of Pharmacology, and Provincial Key Laboratory of Pathophysiology in Ningbo University School of Medicine, Ningbo, Zhejiang, China
| | - Edouard C Nice
- Department of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria, Australia
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25
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Liu Y, Yang Q, Du Z, Liu J, Zhang Y, Zhang W, Qin W. Synthesis of Surface-Functionalized Molybdenum Disulfide Nanomaterials for Efficient Adsorption and Deep Profiling of the Human Plasma Proteome by Data-Independent Acquisition. Anal Chem 2022; 94:14956-14964. [PMID: 36264706 DOI: 10.1021/acs.analchem.2c02736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Blood is one of the most important clinical samples for protein biomarker discovery, as it provides rich physiological and pathological information and is easy to obtain with low invasiveness. However, the discovery of protein biomarkers in the blood by mass spectrometry (MS)-based proteomic strategies has been shown to be highly challenging due to the particularly large concentration range of proteins and the strong interference by the high-abundant proteins in the blood. Therefore, developing sensitive methods for low-abundant biomarker protein identification is a key issue that has received great attention. Here, we report the synthesis and characterization of surface-functionalized magnetic molybdenum disulfide (MoS2) for the large-scale adsorption of low-abundant plasma proteins and deep profiling by MS. MoS2 nanomaterials resulted in the coverage of more than 3400 proteins (including a single-peptide hit) in a single LC-MS analysis without peptide prefractionation using pooled plasma samples, which were five times more than those obtained by the direct analysis of the plasma proteome. A detection limit in the low ng L-1 range was obtained, which is rare compared with previous reports.
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Affiliation(s)
- Yuanyuan Liu
- State Key Laboratory of Proteomics, National Center for Protein Sciences Beijing, Beijing Proteome Research Center, Beijing Institute of Lifeomics, Beijing 102206, P.R. China
| | - Qianying Yang
- State Key Laboratory of Proteomics, National Center for Protein Sciences Beijing, Beijing Proteome Research Center, Beijing Institute of Lifeomics, Beijing 102206, P.R. China.,School of Basic Medical Science, Anhui Medical University, Hefei 230032, China
| | - Zhuokun Du
- State Key Laboratory of Proteomics, National Center for Protein Sciences Beijing, Beijing Proteome Research Center, Beijing Institute of Lifeomics, Beijing 102206, P.R. China.,School of Basic Medical Science, Anhui Medical University, Hefei 230032, China
| | - Jiayu Liu
- Department of Laboratory Medicine, the First Medical Centre, Chinese PLA General Hospital, Beijing 100853, P. R. China
| | - Yangjun Zhang
- State Key Laboratory of Proteomics, National Center for Protein Sciences Beijing, Beijing Proteome Research Center, Beijing Institute of Lifeomics, Beijing 102206, P.R. China.,School of Basic Medical Science, Anhui Medical University, Hefei 230032, China
| | - Wanjun Zhang
- State Key Laboratory of Proteomics, National Center for Protein Sciences Beijing, Beijing Proteome Research Center, Beijing Institute of Lifeomics, Beijing 102206, P.R. China.,School of Basic Medical Science, Anhui Medical University, Hefei 230032, China
| | - Weijie Qin
- State Key Laboratory of Proteomics, National Center for Protein Sciences Beijing, Beijing Proteome Research Center, Beijing Institute of Lifeomics, Beijing 102206, P.R. China.,School of Basic Medical Science, Anhui Medical University, Hefei 230032, China
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26
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Abstract
We are host to an assembly of microorganisms that vary in structure and function along the length of the gut and from the lumen to the mucosa. This ecosystem is collectively known as the gut microbiota and significant efforts have been spent during the past 2 decades to catalog and functionally describe the normal gut microbiota and how it varies during a wide spectrum of disease states. The gut microbiota is altered in several cardiometabolic diseases and recent work has established microbial signatures that may advance disease. However, most research has focused on identifying associations between the gut microbiota and human diseases states and to investigate causality and potential mechanisms using cells and animals. Since the gut microbiota functions on the intersection between diet and host metabolism, and can contribute to inflammation, several microbially produced metabolites and molecules may modulate cardiometabolic diseases. Here we discuss how the gut bacterial composition is altered in, and can contribute to, cardiometabolic disease, as well as how the gut bacteria can be targeted to treat and prevent metabolic diseases.
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Affiliation(s)
- Louise E Olofsson
- Wallenberg Laboratory, Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Sweden
| | - Fredrik Bäckhed
- Wallenberg Laboratory, Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Sweden.,Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health Sciences, University of Copenhagen, Denmark.,Region Västra Götaland, Sahlgrenska University Hospital, Department of Clinical Physiology, Gothenburg, Sweden
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27
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Leukotriene A4 Hydrolase and Hepatocyte Growth Factor Are Risk Factors of Sudden Cardiac Death Due to First-Ever Myocardial Infarction. Int J Mol Sci 2022; 23:ijms231810251. [PMID: 36142157 PMCID: PMC9499415 DOI: 10.3390/ijms231810251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 09/03/2022] [Indexed: 11/16/2022] Open
Abstract
Patients at a high risk for sudden cardiac death (SCD) without previous history of cardiovascular disease remain a challenge to identify. Atherosclerosis and prothrombotic states involve inflammation and non-cardiac tissue damage that may play active roles in SCD development. Therefore, we hypothesized that circulating proteins implicated in inflammation and tissue damage are linked to the future risk of SCD. We conducted a prospective nested case–control study of SCD cases with verified myocardial infarction (N = 224) and matched controls without myocardial infarction (N = 224), aged 60 ± 10 years time and median time to event was 8 years. Protein concentrations (N = 122) were measured using a proximity extension immunoassay. The analyses revealed 14 proteins significantly associated with an increased risk of SCD, from which two remained significant after adjusting for smoking status, systolic blood pressure, BMI, cholesterol, and glucose levels. We identified leukotriene A4 hydrolase (LTA4H, odds ratio 1.80, corrected confidence interval (CIcorr) 1.02–3.17) and hepatocyte growth factor (HGF; odds ratio 1.81, CIcorr 1.06–3.11) as independent risk markers of SCD. Elevated LTA4H may reflect increased systemic and pulmonary neutrophilic inflammatory processes that can contribute to atherosclerotic plaque instability. Increased HGF levels are linked to obesity-related metabolic disturbances that are more prevalent in SCD cases than the controls.
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28
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Tebani A, Bekri S. [The promise of omics in the precision medicine era]. Rev Med Interne 2022; 43:649-660. [PMID: 36041909 DOI: 10.1016/j.revmed.2022.07.009] [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: 06/10/2022] [Accepted: 07/12/2022] [Indexed: 10/15/2022]
Abstract
The rise of omics technologies that simultaneously measure thousands of molecules in a complex biological sample represents the core of systems biology. These technologies have profoundly impacted biomarkers and therapeutic targets discovery in the precision medicine era. Systems biology aims to perform a systematic probing of complex interactions in biological systems. Powered by high-throughput omics technologies and high-performance computing, systems biology provides relevant, resolving, and multi-scale overviews from cells to populations. Precision medicine takes advantage of these conceptual and technological developments and is based on two main pillars: the generation of multimodal data and their subsequent modeling. High-throughput omics technologies enable the comprehensive and holistic extraction of biological information, while computational capabilities enable multidimensional modeling and, as a result, offer an intuitive and intelligible visualization. Despite their promise, translating these technologies into clinically actionable tools has been slow. In this contribution, we present the most recent multi-omics data generation and analysis strategies and their clinical deployment in the post-genomic era. Furthermore, medical application challenges of omics-based biomarkers are discussed.
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Affiliation(s)
- A Tebani
- UNIROUEN, Inserm U1245, Department of Metabolic Biochemistry, Normandie University, CHU Rouen, 76000 Rouen, France.
| | - S Bekri
- UNIROUEN, Inserm U1245, Department of Metabolic Biochemistry, Normandie University, CHU Rouen, 76000 Rouen, France
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29
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Temporal response characterization across individual multiomics profiles of prediabetic and diabetic subjects. Sci Rep 2022; 12:12098. [PMID: 35840765 PMCID: PMC9284494 DOI: 10.1038/s41598-022-16326-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 07/08/2022] [Indexed: 11/08/2022] Open
Abstract
Longitudinal deep multiomics profiling, which combines biomolecular, physiological, environmental and clinical measures data, shows great promise for precision health. However, integrating and understanding the complexity of such data remains a big challenge. Here we utilize an individual-focused bottom-up approach aimed at first assessing single individuals’ multiomics time series, and using the individual-level responses to assess multi-individual grouping based directly on similarity of their longitudinal deep multiomics profiles. We used this individual-focused approach to analyze profiles from a study profiling longitudinal responses in type 2 diabetes mellitus. After generating periodograms for individual subject omics signals, we constructed within-person omics networks and analyzed personal-level immune changes. The results identified both individual-level responses to immune perturbation, and the clusters of individuals that have similar behaviors in immune response and which were associated to measures of their diabetic status.
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30
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Proffitt C, Bidkhori G, Lee S, Tebani A, Mardinoglu A, Uhlen M, Moyes DL, Shoaie S. Genome-scale metabolic modelling of the human gut microbiome reveals changes of the glyoxylate and dicarboxylate metabolism in metabolic disorders. iScience 2022; 25:104513. [PMID: 35754734 PMCID: PMC9213702 DOI: 10.1016/j.isci.2022.104513] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 03/14/2022] [Accepted: 05/27/2022] [Indexed: 11/20/2022] Open
Abstract
The human gut microbiome has been associated with metabolic disorders including obesity, type 2 diabetes, and atherosclerosis. Understanding the contribution of microbiome metabolic changes is important for elucidating the role of gut bacteria in regulating metabolism. We used available metagenomics data from these metabolic disorders, together with genome-scale metabolic modeling of key bacteria in the individual and community-level to investigate the mechanistic role of the gut microbiome in metabolic diseases. Modeling predicted increased levels of glutamate consumption along with the production of ammonia, arginine, and proline in gut bacteria common across the disorders. Abundance profiles and network-dependent analysis identified the enrichment of tartrate dehydrogenase in the disorders. Moreover, independent plasma metabolite levels showed associations between metabolites including proline and tyrosine and an increased tartrate metabolism in healthy obese individuals. We, therefore, propose that an increased tartrate metabolism could be a significant mediator of the microbiome metabolic changes in metabolic disorders. Metagenomic analysis highlights key common bacterial species across metabolic diseases Metabolic models showed higher levels of acetate produced by disease enriched bacteria Reaction analysis revealed increases in the glyoxylate and dicarboxylate pathway Metabolomics and modeling analysis showed the potential role of tartrate metabolism
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Affiliation(s)
- Ceri Proffitt
- Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, SE1 9RT, UK
| | - Gholamreza Bidkhori
- Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, SE1 9RT, UK
| | - Sunjae Lee
- Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, SE1 9RT, UK
| | - Abdellah Tebani
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| | - Adil Mardinoglu
- Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, SE1 9RT, UK
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| | - Mathias Uhlen
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| | - David L. Moyes
- Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, SE1 9RT, UK
| | - Saeed Shoaie
- Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, SE1 9RT, UK
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
- Corresponding author
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31
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Rusanov VB, Pastushkova LK, Larina IM, Orlov OI. Possibilities of Proteomics Profiling in Predicting Dysfunction of the Cardiovascular System. Front Physiol 2022; 13:897694. [PMID: 35547587 PMCID: PMC9081713 DOI: 10.3389/fphys.2022.897694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 04/06/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- V B Rusanov
- State Scientific Center of the Russian Federation Institute of Biomedical Problems of Russian Academy of Sciences, Moscow, Russia
| | - L Kh Pastushkova
- State Scientific Center of the Russian Federation Institute of Biomedical Problems of Russian Academy of Sciences, Moscow, Russia
| | - I M Larina
- State Scientific Center of the Russian Federation Institute of Biomedical Problems of Russian Academy of Sciences, Moscow, Russia
| | - O I Orlov
- State Scientific Center of the Russian Federation Institute of Biomedical Problems of Russian Academy of Sciences, Moscow, Russia
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Olsson LM, Boulund F, Nilsson S, Khan MT, Gummesson A, Fagerberg L, Engstrand L, Perkins R, Uhlén M, Bergström G, Tremaroli V, Bäckhed F. Dynamics of the normal gut microbiota: A longitudinal one-year population study in Sweden. Cell Host Microbe 2022; 30:726-739.e3. [PMID: 35349787 DOI: 10.1016/j.chom.2022.03.002] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 01/17/2022] [Accepted: 03/03/2022] [Indexed: 02/07/2023]
Abstract
Temporal dynamics of the gut microbiota potentially limit the identification of microbial features associated with health status. Here, we used whole-genome metagenomic and 16S rRNA gene sequencing to characterize the intra- and inter-individual variations of gut microbiota composition and functional potential of a disease-free Swedish population (n = 75) over one year. We found that 23% of the total compositional variance was explained by intra-individual variation. The degree of intra-individual compositional variability was negatively associated with the abundance of Faecalibacterium prausnitzii (a butyrate producer) and two Bifidobacterium species. By contrast, the abundance of facultative anaerobes and aerotolerant bacteria such as Escherichia coli and Lactobacillus acidophilus varied extensively, independent of compositional stability. The contribution of intra-individual variance to the total variance was greater for functional pathways than for microbial species. Thus, reliable quantification of microbial features requires repeated samples to address the issue of intra-individual variations of the gut microbiota.
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Affiliation(s)
- Lisa M Olsson
- The Wallenberg Laboratory, Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Fredrik Boulund
- Department of Microbiology, Tumour and Cell Biology, Karolinska Institutet, Stockholm, Sweden
| | - Staffan Nilsson
- Department of Mathematical Sciences, Chalmers University of Technology, Gothenburg, Sweden; Department of Laboratory Medicine, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden
| | - Muhammad Tanweer Khan
- The Wallenberg Laboratory, Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Anders Gummesson
- The Wallenberg Laboratory, Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Department of Clinical Genetics and Genomics, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Linn Fagerberg
- Department of Proteomics, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Lars Engstrand
- Department of Microbiology, Tumour and Cell Biology, Karolinska Institutet, Stockholm, Sweden; Clinical Genomics Facility, Science for Life Laboratory, Solna, Sweden
| | - Rosie Perkins
- The Wallenberg Laboratory, Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Mathias Uhlén
- Department of Proteomics, KTH-Royal Institute of Technology, Stockholm, Sweden; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Hørsholm, Denmark
| | - Göran Bergström
- The Wallenberg Laboratory, Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Department of Clinical Physiology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Valentina Tremaroli
- The Wallenberg Laboratory, Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
| | - Fredrik Bäckhed
- The Wallenberg Laboratory, Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Department of Clinical Physiology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden; Novo Nordisk Foundation Center for Basic Metabolic Research, Section for Metabolic Receptology and Enteroendocrinology, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark.
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Edfors F, Iglesias MJ, Butler LM, Odeberg J. Proteomics in thrombosis research. Res Pract Thromb Haemost 2022; 6:e12706. [PMID: 35494505 PMCID: PMC9039028 DOI: 10.1002/rth2.12706] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 03/23/2022] [Accepted: 03/24/2022] [Indexed: 11/24/2022] Open
Abstract
A State of the Art lecture titled “Proteomics in Thrombosis Research” was presented at the ISTH Congress in 2021. In clinical practice, there is a need for improved plasma biomarker‐based tools for diagnosis and risk prediction of venous thromboembolism (VTE). Analysis of blood, to identify plasma proteins with potential utility for such tools, could enable an individualized approach to treatment and prevention. Technological advances to study the plasma proteome on a large scale allows broad screening for the identification of novel plasma biomarkers, both by targeted and nontargeted proteomics methods. However, assay limitations need to be considered when interpreting results, with orthogonal validation required before conclusions are drawn. Here, we review and provide perspectives on the application of affinity‐ and mass spectrometry‐based methods for the identification and analysis of plasma protein biomarkers, with potential application in the field of VTE. We also provide a future perspective on discovery strategies and emerging technologies for targeted proteomics in thrombosis research. Finally, we summarize relevant new data on this topic, presented during the 2021 ISTH Congress.
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Affiliation(s)
- Fredrik Edfors
- Science for Life Laboratory Department of Protein Science CBH KTH Royal Institute of Technology Stockholm Sweden
- Karolinska University Laboratory Karolinska University Hospital Stockholm Sweden
| | - Maria Jesus Iglesias
- Science for Life Laboratory Department of Protein Science CBH KTH Royal Institute of Technology Stockholm Sweden
| | - Lynn M. Butler
- Science for Life Laboratory Department of Protein Science CBH KTH Royal Institute of Technology Stockholm Sweden
- Clinical Chemistry and Blood Coagulation Research Department of Molecular Medicine and Surgery Karolinska Institute Stockholm Sweden
- Clinical Chemistry Karolinska University Laboratory Karolinska University Hospital Stockholm Sweden
- Department of Clinical Medicine The Arctic University of Norway Tromsø Norway
| | - Jacob Odeberg
- Science for Life Laboratory Department of Protein Science CBH KTH Royal Institute of Technology Stockholm Sweden
- Department of Clinical Medicine The Arctic University of Norway Tromsø Norway
- Division of Internal Medicine University Hospital of North Norway Tromsø Norway
- Coagulation Unit Department of Hematology Karolinska University Hospital Stockholm Sweden
- Department of Medicine Solna Karolinska Institute Stockholm Sweden
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Omenn GS, Magis AT, Price ND, Hood L. Personal Dense Dynamic Data Clouds Connect Systems Biomedicine to Scientific Wellness. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2486:315-334. [PMID: 35437729 DOI: 10.1007/978-1-0716-2265-0_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The dramatic convergence of molecular biology, genomics, proteomics, metabolomics, bioinformatics, and artificial intelligence has provided a substrate for deep understanding of the biological basis of health and disease. Systems biology is a holistic, dynamic, integrative, cross-disciplinary approach to biological complexity that embraces experimentation, technology, computation, and clinical translation. Systems Medicine integrates genome analyses and longitudinal deep phenotyping with biological pathways and networks to understand mechanisms of disease, identify relevant blood biomarkers, define druggable molecular targets, and enhance the maintenance or restoration of wellness. Two programs initiated our understanding of data-driven population-based wellness. The Pioneer 100 Study of Scientific Wellness and the much larger Arivale commercial program that followed had two spectacular results: demonstrating the feasibility and utility of collecting longitudinal multiomic data, and then generating dense, dynamic data clouds for each individual to utilize actionable metrics for promoting health and preventing disease when combined with personalized coaching. Future developments in these domains will enable better population health and personal, preventive, predictive, participatory (P4) health care.
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Affiliation(s)
- Gilbert S Omenn
- Departments of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, and School of Public Health, University of Michigan, Ann Arbor, MI, USA. .,Institute for Systems Biology, Seattle, WA, USA.
| | | | - Nathan D Price
- Institute for Systems Biology, Seattle, WA, USA.,Onegevity, New York, New York, USA
| | - Leroy Hood
- Institute for Systems Biology, Seattle, WA, USA.,Providence Saint Joseph Healthcare System, Seattle, USA
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35
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Temporal reproducibility of IgG and IgM autoantibodies in serum from healthy women. Sci Rep 2022; 12:6192. [PMID: 35418192 PMCID: PMC9008031 DOI: 10.1038/s41598-022-10174-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Accepted: 03/30/2022] [Indexed: 11/09/2022] Open
Abstract
Autoantibodies are present in healthy individuals and altered in chronic diseases. We used repeated samples collected from participants in the NYU Women's Health Study to assess autoantibody reproducibility and repertoire stability over a one-year period using the HuProt array. We included two samples collected one year apart from each of 46 healthy women (92 samples). We also included eight blinded replicate samples to assess laboratory reproducibility. A total of 21,211 IgG and IgM autoantibodies were interrogated. Of those, 86% of IgG (n = 18,303) and 34% of IgM (n = 7,242) autoantibodies showed adequate lab reproducibility (coefficient of variation [CV] < 20%). Intraclass correlation coefficients (ICCs) were estimated to assess temporal reproducibility. A high proportion of both IgG and IgM autoantibodies with CV < 20% (76% and 98%, respectively) showed excellent temporal reproducibility (ICC > 0.8). Temporal reproducibility was lower after using quantile normalization suggesting that batch variability was not an important source of error, and that normalization removed some informative biological information. To our knowledge this study is the largest in terms of sample size and autoantibody numbers to assess autoantibody reproducibility in healthy women. The results suggest that for many autoantibodies a single measurement may be used to rank individuals in studies of autoantibodies as etiologic markers of disease.
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Pattanaik B, Hammarlund M, Mjörnstedt F, Ulleryd MA, Zhong W, Uhlén M, Gummesson A, Bergström G, Johansson ME. Polymorphisms in alpha 7 nicotinic acetylcholine receptor gene, CHRNA7, and its partially duplicated gene, CHRFAM7A, associate with increased inflammatory response in human peripheral mononuclear cells. FASEB J 2022; 36:e22271. [PMID: 35344211 DOI: 10.1096/fj.202101898r] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 02/14/2022] [Accepted: 03/11/2022] [Indexed: 01/16/2023]
Abstract
The vagus nerve can, via the alpha 7 nicotinic acetylcholine receptor (α7nAChR), regulate inflammation. The gene coding for the α7nAChR, CHRNA7, can be partially duplicated, that is, CHRFAM7A, which is reported to impair the anti-inflammatory effect mediated via the α7nAChR. Several single nucleotide polymorphisms (SNPs) have been described in both CHRNA7 and CHRFAM7A, however, the functional role of these SNPs for immune responses remains to be investigated. In the current study, we set out to investigate whether genetic variants of CHRNA7 and CHRFAM7A can influence immune responses. By investigating data available from the Swedish SciLifeLab SCAPIS Wellness Profiling (S3WP) study, in combination with droplet digital PCR and freshly isolated PBMCs from the S3WP participants, challenged with lipopolysaccharide (LPS), we show that CHRNA7 and CHRFAM7A are expressed in human PBMCs, with approximately four times higher expression of CHRFAM7A compared with CHRNA7. One SNP in CHRFAM7A, rs34007223, is positively associated with hsCRP in healthy individuals. Furthermore, gene ontology (GO)-terms analysis of plasma proteins associated with gene expression of CHRNA7 and CHRFAM7A demonstrated an involvement for these genes in immune responses. This was further supported by in vitro data showing that several SNPs in both CHRNA7 and CHRFAM7A are significantly associated with cytokine response. In conclusion, genetic variants of CHRNA7 and CHRFAM7A alters cytokine responses. Furthermore, given that CHRFAM7A SNP rs34007223 is associated with inflammatory marker hsCRP in healthy individuals suggests that CHRFAM7A may have a more pronounced role in regulating inflammatory processes in humans than previously been recognized.
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Affiliation(s)
- Bagmi Pattanaik
- Department of Physiology, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Maria Hammarlund
- Department of Physiology, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Filip Mjörnstedt
- Department of Physiology, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Marcus A Ulleryd
- Department of Physiology, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Wen Zhong
- Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Mathias Uhlén
- Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Anders Gummesson
- Department of Molecular and Clinical Medicine, Wallenberg Laboratory for Cardiovascular and Metabolic Research, University of Gothenburg, Gothenburg, Sweden
| | - Göran Bergström
- Department of Molecular and Clinical Medicine, Wallenberg Laboratory for Cardiovascular and Metabolic Research, University of Gothenburg, Gothenburg, Sweden
| | - Maria E Johansson
- Department of Physiology, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
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Ferguson LB, Roberts AJ, Mayfield RD, Messing RO. Blood and brain gene expression signatures of chronic intermittent ethanol consumption in mice. PLoS Comput Biol 2022; 18:e1009800. [PMID: 35176017 PMCID: PMC8853518 DOI: 10.1371/journal.pcbi.1009800] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 01/03/2022] [Indexed: 02/03/2023] Open
Abstract
Alcohol Use Disorder (AUD) is a chronic, relapsing syndrome diagnosed by a heterogeneous set of behavioral signs and symptoms. There are no laboratory tests that provide direct objective evidence for diagnosis. Microarray and RNA-Seq technologies enable genome-wide transcriptome profiling at low costs and provide an opportunity to identify biomarkers to facilitate diagnosis, prognosis, and treatment of patients. However, access to brain tissue in living patients is not possible. Blood contains cellular and extracellular RNAs that provide disease-relevant information for some brain diseases. We hypothesized that blood gene expression profiles can be used to diagnose AUD. We profiled brain (prefrontal cortex, amygdala, and hypothalamus) and blood gene expression levels in C57BL/6J mice using RNA-seq one week after chronic intermittent ethanol (CIE) exposure, a mouse model of alcohol dependence. We found a high degree of preservation (rho range: [0.50, 0.67]) between blood and brain transcript levels. There was small overlap between blood and brain DEGs, and considerable overlap of gene networks perturbed after CIE related to cell-cell signaling (e.g., GABA and glutamate receptor signaling), immune responses (e.g., antigen presentation), and protein processing / mitochondrial functioning (e.g., ubiquitination, oxidative phosphorylation). Blood gene expression data were used to train classifiers (logistic regression, random forest, and partial least squares discriminant analysis), which were highly accurate at predicting alcohol dependence status (maximum AUC: 90.1%). These results suggest that gene expression profiles from peripheral blood samples contain a biological signature of alcohol dependence that can discriminate between CIE and Air subjects.
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Affiliation(s)
- Laura B. Ferguson
- Waggoner Center for Alcohol and Addiction Research, University of Texas at Austin, Austin, Texas, United States of America
- Department of Neurology, Dell Medical School, University of Texas at Austin, Austin, Texas, United States of America
- Department of Neuroscience, University of Texas at Austin, Austin, Texas, United States of America
| | - Amanda J. Roberts
- Animal Models Core Facility, The Scripps Research Institute, San Diego, California, United States of America
| | - R. Dayne Mayfield
- Waggoner Center for Alcohol and Addiction Research, University of Texas at Austin, Austin, Texas, United States of America
- Department of Neuroscience, University of Texas at Austin, Austin, Texas, United States of America
| | - Robert O. Messing
- Waggoner Center for Alcohol and Addiction Research, University of Texas at Austin, Austin, Texas, United States of America
- Department of Neurology, Dell Medical School, University of Texas at Austin, Austin, Texas, United States of America
- Department of Neuroscience, University of Texas at Austin, Austin, Texas, United States of America
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Xu X, Han Z, Ruan Y, Liu M, Cao G, Li C, Li F. HPV16-LINC00393 Integration Alters Local 3D Genome Architecture in Cervical Cancer Cells. Front Cell Infect Microbiol 2021; 11:785169. [PMID: 34950609 PMCID: PMC8691139 DOI: 10.3389/fcimb.2021.785169] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 11/04/2021] [Indexed: 11/30/2022] Open
Abstract
High-risk human papillomavirus (hrHPV) infection and integration were considered as essential onset factors for the development of cervical cancer. However, the mechanism on how hrHPV integration influences the host genome structure remains not fully understood. In this study, we performed in situ high-throughput chromosome conformation capture (Hi-C) sequencing, chromatin immunoprecipitation and sequencing (ChIP-seq), and RNA-sequencing (RNA-seq) in two cervical cells, 1) NHEK normal human epidermal keratinocyte; and 2) HPV16-integrated SiHa tumorigenic cervical cancer cells. Our results reveal that the HPV-LINC00393 integrated chromosome 13 exhibited significant genomic variation and differential gene expression, which was verified by calibrated CTCF and H3K27ac ChIP-Seq chromatin restructuring. Importantly, HPV16 integration led to differential responses in topologically associated domain (TAD) boundaries, with a decrease in the tumor suppressor KLF12 expression downstream of LINC00393. Overall, this study provides significant insight into the understanding of HPV16 integration induced 3D structural changes and their contributions on tumorigenesis, which supplements the theory basis for the cervical carcinogenic mechanism of HPV16 integration.
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Affiliation(s)
- Xinxin Xu
- Department of Obstetrics and Gynecology, East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Zhiqiang Han
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yetian Ruan
- Department of Obstetrics and Gynecology, East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Min Liu
- Department of Obstetrics and Gynecology, East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Guangxu Cao
- Department of Obstetrics and Gynecology, East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Chao Li
- Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Fang Li
- Department of Obstetrics and Gynecology, East Hospital, Tongji University School of Medicine, Shanghai, China
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Gander J, Carrard J, Gallart-Ayala H, Borreggine R, Teav T, Infanger D, Colledge F, Streese L, Wagner J, Klenk C, Nève G, Knaier R, Hanssen H, Schmidt-Trucksäss A, Ivanisevic J. Metabolic Impairment in Coronary Artery Disease: Elevated Serum Acylcarnitines Under the Spotlights. Front Cardiovasc Med 2021; 8:792350. [PMID: 34977199 PMCID: PMC8716394 DOI: 10.3389/fcvm.2021.792350] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Accepted: 11/09/2021] [Indexed: 12/26/2022] Open
Abstract
Coronary artery disease (CAD) remains the leading cause of death worldwide. Expanding patients' metabolic phenotyping beyond clinical chemistry investigations could lead to earlier recognition of disease onset and better prevention strategies. Additionally, metabolic phenotyping, at the molecular species level, contributes to unravel the roles of metabolites in disease development. In this cross-sectional study, we investigated clinically healthy individuals (n = 116, 65% male, 70.8 ± 8.7 years) and patients with CAD (n = 54, 91% male, 67.0 ± 11.5 years) of the COmPLETE study. We applied a high-coverage quantitative liquid chromatography-mass spectrometry approach to acquire a comprehensive profile of serum acylcarnitines, free carnitine and branched-chain amino acids (BCAAs), as markers of mitochondrial health and energy homeostasis. Multivariable linear regression analyses, adjusted for confounders, were conducted to assess associations between metabolites and CAD phenotype. In total, 20 short-, medium- and long-chain acylcarnitine species, along with L-carnitine, valine and isoleucine were found to be significantly (adjusted p ≤ 0.05) and positively associated with CAD. For 17 acylcarnitine species, associations became stronger as the number of affected coronary arteries increased. This implies that circulating acylcarnitine levels reflect CAD severity and might play a role in future patients' stratification strategies. Altogether, CAD is characterized by elevated serum acylcarnitine and BCAA levels, which indicates mitochondrial imbalance between fatty acid and glucose oxidation.
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Affiliation(s)
- Joséphine Gander
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland
| | - Justin Carrard
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland
| | - Hector Gallart-Ayala
- Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Rébecca Borreggine
- Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Tony Teav
- Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Denis Infanger
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland
| | - Flora Colledge
- Division of Sports Science, Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland
| | - Lukas Streese
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland
| | - Jonathan Wagner
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland
| | - Christopher Klenk
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland
| | - Gilles Nève
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland
| | - Raphael Knaier
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland
| | - Henner Hanssen
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland
| | - Arno Schmidt-Trucksäss
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland
- Arno Schmidt-Trucksäss
| | - Julijana Ivanisevic
- Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
- *Correspondence: Julijana Ivanisevic
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40
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Multiomics and digital monitoring during lifestyle changes reveal independent dimensions of human biology and health. Cell Syst 2021; 13:241-255.e7. [PMID: 34856119 DOI: 10.1016/j.cels.2021.11.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 09/15/2021] [Accepted: 11/09/2021] [Indexed: 01/04/2023]
Abstract
We explored opportunities for personalized and predictive health care by collecting serial clinical measurements, health surveys, genomics, proteomics, autoantibodies, metabolomics, and gut microbiome data from 96 individuals who participated in a data-driven health coaching program over a 16-month period with continuous digital monitoring of activity and sleep. We generated a resource of >20,000 biological samples from this study and a compendium of >53 million primary data points for 558,032 distinct features. Multiomics factor analysis revealed distinct and independent molecular factors linked to obesity, diabetes, liver function, cardiovascular disease, inflammation, immunity, exercise, diet, and hormonal effects. For example, ethinyl estradiol, a common oral contraceptive, produced characteristic molecular and physiological effects, including increased levels of inflammation and impact on thyroid, cortisol levels, and pulse, that were distinct from other sources of variability observed in our study. In total, this work illustrates the value of combining deep molecular and digital monitoring of human health. A record of this paper's transparent peer review process is included in the supplemental information.
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Next generation plasma proteome profiling of COVID-19 patients with mild to moderate symptoms. EBioMedicine 2021; 74:103723. [PMID: 34844191 PMCID: PMC8626206 DOI: 10.1016/j.ebiom.2021.103723] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 11/04/2021] [Accepted: 11/16/2021] [Indexed: 12/15/2022] Open
Abstract
Background COVID-19 has caused millions of deaths globally, yet the cellular mechanisms underlying the various effects of the disease remain poorly understood. Recently, a new analytical platform for comprehensive analysis of plasma protein profiles using proximity extension assays combined with next generation sequencing has been developed, which allows for multiple proteins to be analyzed simultaneously without sacrifice on accuracy or sensitivity. Methods We analyzed the plasma protein profiles of COVID-19 patients (n = 50) with mild and moderate symptoms by comparing the protein levels in newly diagnosed patients with the protein levels in the same individuals after 14 days. Findings The study has identified more than 200 proteins that are significantly elevated during infection and many of these are related to cytokine response and other immune-related functions. In addition, several other proteins are shown to be elevated, including SCARB2, a host cell receptor protein involved in virus entry. A comparison with the plasma protein response in patients with severe symptoms shows a highly similar pattern, but with some interesting differences. Interpretation The study presented here demonstrates the usefulness of “next generation plasma protein profiling” to identify molecular signatures of importance for disease progression and to allow monitoring of disease during recovery from the infection. The results will facilitate further studies to understand the molecular mechanism of the immune-related response of the SARS-CoV-2 virus. Funding This work was financially supported by Knut and Alice Wallenberg Foundation.
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Tan SH, Koh HWL, Chua JY, Burla B, Ong CC, Teo LSL, Yang X, Benke PI, Choi H, Torta F, Richards AM, Wenk MR, Chan MY. Variability of the Plasma Lipidome and Subclinical Coronary Atherosclerosis. Arterioscler Thromb Vasc Biol 2021; 42:100-112. [PMID: 34809445 PMCID: PMC8691371 DOI: 10.1161/atvbaha.121.316847] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Supplemental Digital Content is available in the text. Objective: While the risk of acute coronary events has been associated with biological variability of circulating cholesterol, the association with variability of other atherogenic lipids remains less understood. We evaluated the longitudinal variability of 284 lipids and investigated their association with asymptomatic coronary atherosclerosis. Approach and Results: Circulating lipids were extracted from fasting blood samples of 83 community-sampled symptom-free participants (age 41–75 years), collected longitudinally over 6 months. Three types of coronary plaque volume (calcified, lipid-rich, and fibrotic) were quantified using computed tomography coronary angiogram. We first deconvoluted between-subject (CVg) and within-subject (CVw) lipid variabilities. We then tested whether the mean lipid abundance was different across groups categorized by Framingham risk score and plaques phenotypes (lipid-rich, fibrotic, and calcified). Finally, we investigated whether visit-to-visit variability of each lipid was associated with plaque burden. Most lipids (72.5%) exhibited higher CVg than CVw. Among the lipids (n=145) with 1.2-fold higher CVg than CVw, 26 species including glycerides and ceramides were significantly associated with Framingham risk score and the 3 plaque phenotypes (false discovery rate <0.05). In an exploratory analysis of person-specific visit-to-visit variability without multiple testing correction, high variability of 3 lysophospholipids (lysophosphatidylethanolamines 16:0, 18:0, and lysophosphatidylcholine O-18:1) was associated with lipid-rich and fibrotic (noncalcified) plaque volume while high variability of diacylglycerol 18:1_20:0, triacylglycerols 52:2, 52:3, and 52:4, ceramide d18:0/20:0, dihexosylceramide d18:1/16:0, and sphingomyelin 36:3 was associated with calcified plaque volume. Conclusions: High person-specific longitudinal variation of specific nonsterol lipids is associated with the burden of subclinical coronary atherosclerosis. Larger studies are needed to confirm these exploratory findings.
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Affiliation(s)
- Sock Hwee Tan
- Cardiovascular Research Institute (S.H.T., H.W.L.K., X.Y., H.C., A.M.R., M.Y.C.), Singapore Lipidomics Incubator (SLING), National University of Singapore.,National University Heart Center, Singapore (S.H.T., J.Y.C., A.M.R., M.Y.C.)
| | - Hiromi W L Koh
- Cardiovascular Research Institute (S.H.T., H.W.L.K., X.Y., H.C., A.M.R., M.Y.C.), Singapore Lipidomics Incubator (SLING), National University of Singapore
| | - Jing Yi Chua
- National University Heart Center, Singapore (S.H.T., J.Y.C., A.M.R., M.Y.C.)
| | - Bo Burla
- Life Sciences Institute (B.B., P.I.B., F.T., M.R.W.), National University of Singapore
| | - Ching Ching Ong
- Department of Diagnostic Imaging, National University Hospital, Singapore (C.C.O., L.S.L.T.)
| | - Li San Lynette Teo
- Department of Diagnostic Imaging, National University Hospital, Singapore (C.C.O., L.S.L.T.)
| | - Xiaoxun Yang
- Cardiovascular Research Institute (S.H.T., H.W.L.K., X.Y., H.C., A.M.R., M.Y.C.), Singapore Lipidomics Incubator (SLING), National University of Singapore
| | - Peter I Benke
- Life Sciences Institute (B.B., P.I.B., F.T., M.R.W.), National University of Singapore
| | - Hyungwon Choi
- Cardiovascular Research Institute (S.H.T., H.W.L.K., X.Y., H.C., A.M.R., M.Y.C.), Singapore Lipidomics Incubator (SLING), National University of Singapore
| | - Federico Torta
- Department of Biochemistry and Precision Medicine Translational Research Programme (F.T., M.R.W.), Singapore Lipidomics Incubator (SLING), National University of Singapore.,Life Sciences Institute (B.B., P.I.B., F.T., M.R.W.), National University of Singapore
| | - A Mark Richards
- Cardiovascular Research Institute (S.H.T., H.W.L.K., X.Y., H.C., A.M.R., M.Y.C.), Singapore Lipidomics Incubator (SLING), National University of Singapore.,National University Heart Center, Singapore (S.H.T., J.Y.C., A.M.R., M.Y.C.).,Christchurch Heart Institute, University of Otago Christchurch, Christchurch Hospital (A.M.R.)
| | - Markus R Wenk
- Department of Biochemistry and Precision Medicine Translational Research Programme (F.T., M.R.W.), Singapore Lipidomics Incubator (SLING), National University of Singapore.,Life Sciences Institute (B.B., P.I.B., F.T., M.R.W.), National University of Singapore
| | - Mark Y Chan
- Cardiovascular Research Institute (S.H.T., H.W.L.K., X.Y., H.C., A.M.R., M.Y.C.), Singapore Lipidomics Incubator (SLING), National University of Singapore.,National University Heart Center, Singapore (S.H.T., J.Y.C., A.M.R., M.Y.C.)
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Deutsch EW, Omenn GS, Sun Z, Maes M, Pernemalm M, Palaniappan KK, Letunica N, Vandenbrouck Y, Brun V, Tao SC, Yu X, Geyer PE, Ignjatovic V, Moritz RL, Schwenk JM. Advances and Utility of the Human Plasma Proteome. J Proteome Res 2021; 20:5241-5263. [PMID: 34672606 PMCID: PMC9469506 DOI: 10.1021/acs.jproteome.1c00657] [Citation(s) in RCA: 65] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
The study of proteins circulating in blood offers tremendous opportunities to diagnose, stratify, or possibly prevent diseases. With recent technological advances and the urgent need to understand the effects of COVID-19, the proteomic analysis of blood-derived serum and plasma has become even more important for studying human biology and pathophysiology. Here we provide views and perspectives about technological developments and possible clinical applications that use mass-spectrometry(MS)- or affinity-based methods. We discuss examples where plasma proteomics contributed valuable insights into SARS-CoV-2 infections, aging, and hemostasis and the opportunities offered by combining proteomics with genetic data. As a contribution to the Human Proteome Organization (HUPO) Human Plasma Proteome Project (HPPP), we present the Human Plasma PeptideAtlas build 2021-07 that comprises 4395 canonical and 1482 additional nonredundant human proteins detected in 240 MS-based experiments. In addition, we report the new Human Extracellular Vesicle PeptideAtlas 2021-06, which comprises five studies and 2757 canonical proteins detected in extracellular vesicles circulating in blood, of which 74% (2047) are in common with the plasma PeptideAtlas. Our overview summarizes the recent advances, impactful applications, and ongoing challenges for translating plasma proteomics into utility for precision medicine.
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Affiliation(s)
- Eric W Deutsch
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Gilbert S Omenn
- Institute for Systems Biology, Seattle, Washington 98109, United States.,Departments of Computational Medicine & Bioinformatics, Internal Medicine, and Human Genetics and School of Public Health, University of Michigan, Ann Arbor, Michigan 48109-2218, United States
| | - Zhi Sun
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Michal Maes
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Maria Pernemalm
- Department of Oncology and Pathology/Science for Life Laboratory, Karolinska Institutet, 171 65 Stockholm, Sweden
| | | | - Natasha Letunica
- Murdoch Children's Research Institute, 50 Flemington Road, Parkville 3052, Victoria, Australia
| | - Yves Vandenbrouck
- Université Grenoble Alpes, CEA, Inserm U1292, Grenoble 38000, France
| | - Virginie Brun
- Université Grenoble Alpes, CEA, Inserm U1292, Grenoble 38000, France
| | - Sheng-Ce Tao
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, B207 SCSB Building, 800 Dongchuan Road, Shanghai 200240, China
| | - Xiaobo Yu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences-Beijing (PHOENIX Center), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Philipp E Geyer
- OmicEra Diagnostics GmbH, Behringstr. 6, 82152 Planegg, Germany
| | - Vera Ignjatovic
- Murdoch Children's Research Institute, 50 Flemington Road, Parkville 3052, Victoria, Australia.,Department of Paediatrics, The University of Melbourne, 50 Flemington Road, Parkville 3052, Victoria, Australia
| | - Robert L Moritz
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Jochen M Schwenk
- Affinity Proteomics, Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Tomtebodavägen 23, SE-171 65 Solna, Sweden
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45
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Bondt A, Hoek M, Tamara S, de Graaf B, Peng W, Schulte D, van Rijswijck DMH, den Boer MA, Greisch JF, Varkila MRJ, Snijder J, Cremer OL, Bonten MJM, Heck AJR. Human plasma IgG1 repertoires are simple, unique, and dynamic. Cell Syst 2021; 12:1131-1143.e5. [PMID: 34613904 PMCID: PMC8691384 DOI: 10.1016/j.cels.2021.08.008] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 06/29/2021] [Accepted: 08/19/2021] [Indexed: 01/30/2023]
Abstract
Although humans can produce billions of IgG1 variants through recombination and hypermutation, the diversity of IgG1 clones circulating in human blood plasma has largely eluded direct characterization. Here, we combined several mass-spectrometry-based approaches to reveal that the circulating IgG1 repertoire in human plasma is dominated by a limited number of clones in healthy donors and septic patients. We observe that each individual donor exhibits a unique serological IgG1 repertoire, which remains stable over time but can adapt rapidly to changes in physiology. We introduce an integrative protein- and peptide-centric approach to obtain and validate a full sequence of an individual plasma IgG1 clone de novo. This IgG1 clone emerged at the onset of a septic episode and exhibited a high mutation rate (13%) compared with the closest matching germline DNA sequence, highlighting the importance of de novo sequencing at the protein level. A record of this paper’s transparent peer review process is included in the supplemental information. Novel LC-MS-based methods enable personalized IgG1 profiling in plasma Each donor exhibits a simple but unique serological IgG1 repertoire This repertoire adapts to changes in physiology, e.g., sepsis Individual plasma IgG1 clones can be identified by combining top-down and bottom-up proteomics
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Affiliation(s)
- Albert Bondt
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Padualaan 8, Utrecht 3584 CH, the Netherlands; Netherlands Proteomics Center, Padualaan 8, Utrecht 3584 CH, the Netherlands
| | - Max Hoek
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Padualaan 8, Utrecht 3584 CH, the Netherlands; Netherlands Proteomics Center, Padualaan 8, Utrecht 3584 CH, the Netherlands
| | - Sem Tamara
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Padualaan 8, Utrecht 3584 CH, the Netherlands; Netherlands Proteomics Center, Padualaan 8, Utrecht 3584 CH, the Netherlands
| | - Bastiaan de Graaf
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Padualaan 8, Utrecht 3584 CH, the Netherlands; Netherlands Proteomics Center, Padualaan 8, Utrecht 3584 CH, the Netherlands
| | - Weiwei Peng
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Padualaan 8, Utrecht 3584 CH, the Netherlands; Netherlands Proteomics Center, Padualaan 8, Utrecht 3584 CH, the Netherlands
| | - Douwe Schulte
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Padualaan 8, Utrecht 3584 CH, the Netherlands; Netherlands Proteomics Center, Padualaan 8, Utrecht 3584 CH, the Netherlands
| | - Danique M H van Rijswijck
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Padualaan 8, Utrecht 3584 CH, the Netherlands; Netherlands Proteomics Center, Padualaan 8, Utrecht 3584 CH, the Netherlands
| | - Maurits A den Boer
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Padualaan 8, Utrecht 3584 CH, the Netherlands; Netherlands Proteomics Center, Padualaan 8, Utrecht 3584 CH, the Netherlands
| | - Jean-François Greisch
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Padualaan 8, Utrecht 3584 CH, the Netherlands; Netherlands Proteomics Center, Padualaan 8, Utrecht 3584 CH, the Netherlands
| | - Meri R J Varkila
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands; Department of Intensive Care Medicine, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Joost Snijder
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Padualaan 8, Utrecht 3584 CH, the Netherlands; Netherlands Proteomics Center, Padualaan 8, Utrecht 3584 CH, the Netherlands
| | - Olaf L Cremer
- Department of Intensive Care Medicine, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Marc J M Bonten
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands; Department of Medical Microbiology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Albert J R Heck
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Padualaan 8, Utrecht 3584 CH, the Netherlands; Netherlands Proteomics Center, Padualaan 8, Utrecht 3584 CH, the Netherlands.
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Baldanzi G, Hammar U, Fall T, Lindberg E, Lind L, Elmståhl S, Theorell-Haglöw J. Evening chronotype is associated with elevated biomarkers of cardiometabolic risk in the EpiHealth cohort: a cross-sectional study. Sleep 2021; 45:6364133. [PMID: 34480568 PMCID: PMC8842133 DOI: 10.1093/sleep/zsab226] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 09/01/2021] [Indexed: 11/12/2022] Open
Abstract
STUDY OBJECTIVES Individuals with evening chronotype have a higher risk of cardiovascular and metabolic disorders, although the underlying mechanisms are not well understood. In a population-based cohort, we aimed to investigate the association between chronotype and 242 circulating proteins from three panels of established or candidate biomarkers of cardiometabolic processes. METHODS In 2,471 participants (49.7% men, mean age 61.2±8.4 SD years) from the EpiHealth cohort, circulating proteins were analyzed with a multiplex proximity extension technique. Participants self-reported their chronotype on a five-level scale from extreme morning to extreme evening chronotype. With the intermediate chronotype set as the reference, each protein was added as the dependent variable in a series of linear regression models adjusted for confounders. Next, the chronotype coefficients were jointly tested and the resulting p-values adjusted for multiple testing using false discovery rate (5%). For the associations identified, we then analyzed the marginal effect of each chronotype category. RESULTS We identified 17 proteins associated with chronotype. Evening chronotype was positively associated with proteins previously linked to insulin resistance and cardiovascular risk, namely retinoic acid receptor protein 2, fatty acid-binding protein adipocyte, tissue-type plasminogen activator, and plasminogen activator inhibitor 1 (PAI-1). Additionally, PAI-1 was inversely associated with the extreme morning chronotype. CONCLUSIONS In this population-based study, proteins previously related with cardiometabolic risk were elevated in the evening chronotypes. These results may guide future research in the relation between chronotype and cardiometabolic disorders.
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Affiliation(s)
- Gabriel Baldanzi
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala
| | - Ulf Hammar
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala
| | - Tove Fall
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala
| | - Eva Lindberg
- Department of Medical Sciences, Respiratory, Allergy and Sleep Research, Uppsala University, Sweden
| | - Lars Lind
- Department of Medical Sciences, Cardiovascular Epidemiology, Uppsala University, Sweden
| | - Sölve Elmståhl
- Department of Clinical Sciences in Malmö, Division of Geriatric Medicine, Lund University, Sweden; CRC, Skåne University Hospital, Malmö, Sweden
| | - Jenny Theorell-Haglöw
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala.,Department of Medical Sciences, Respiratory, Allergy and Sleep Research, Uppsala University, Sweden
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Li F, Song J, Zhang Y, Wang S, Wang J, Lin L, Yang C, Li P, Huang H. LINT-Web: A Web-Based Lipidomic Data Mining Tool Using Intra-Omic Integrative Correlation Strategy. SMALL METHODS 2021; 5:e2100206. [PMID: 34928054 DOI: 10.1002/smtd.202100206] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Revised: 07/14/2021] [Indexed: 06/14/2023]
Abstract
Lipidomics is a younger member of the "omics" family. It aims to profile lipidome alterations occurring in biological systems. Similar to the other "omics", lipidomic data is highly dimensional and contains a massive amount of information awaiting deciphering and data mining. Currently, the available bioinformatic tools targeting lipidomic data processing and lipid pathway analysis are limited. A few tools designed for lipidomic analysis perform only basic statistical analyses, and lipid pathway analyses rely heavily on public databases (KEGG, Reactome, and HMDB). Due to the inadequate understanding of lipid signaling and metabolism, the use of public databases for lipid pathway analysis can be biased and misleading. Instead of using public databases to interpret lipidomic ontology, the authors introduce an intra-omic integrative correlation strategy for lipidomic data mining. Such an intra-omic strategy allows researchers to unscramble and predict lipid biological functions from correlated genomic ontological results using statistical approaches. To simplify and improve the lipidomic data processing experience, they designed an interactive web-based tool: LINT-web (http://www.lintwebomics.info/) to perform the intra-omic analysis strategy, and validated the functions of LINT-web using two biological systems. Users without sophisticated statistical experience can easily process lipidomic datasets and predict the potential lipid biological functions using LINT-web.
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Affiliation(s)
- Fengsheng Li
- Shanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism and Integrative Biology, Fudan University, Shanghai, 200438, China
| | - Jia Song
- Institute of Molecular Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Yingkun Zhang
- The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, State Key Laboratory for Physical Chemistry of Solid Surfaces, Key Laboratory for Chemical Biology of Fujian Province, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Shuaikang Wang
- Shanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism and Integrative Biology, Fudan University, Shanghai, 200438, China
| | - Jinhui Wang
- Shanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism and Integrative Biology, Fudan University, Shanghai, 200438, China
| | - Li Lin
- The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, State Key Laboratory for Physical Chemistry of Solid Surfaces, Key Laboratory for Chemical Biology of Fujian Province, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Chaoyong Yang
- Institute of Molecular Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
- The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, State Key Laboratory for Physical Chemistry of Solid Surfaces, Key Laboratory for Chemical Biology of Fujian Province, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Peng Li
- Shanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism and Integrative Biology, Fudan University, Shanghai, 200438, China
- Shanghai Qi Zhi Institute, Shanghai, 200030, China
| | - He Huang
- Shanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism and Integrative Biology, Fudan University, Shanghai, 200438, China
- Shanghai Qi Zhi Institute, Shanghai, 200030, China
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Flores-Guerrero JL, Grzegorczyk MA, Connelly MA, Garcia E, Navis G, Dullaart RPF, Bakker SJL. Mahalanobis distance, a novel statistical proxy of homeostasis loss is longitudinally associated with risk of type 2 diabetes. EBioMedicine 2021; 71:103550. [PMID: 34425309 PMCID: PMC8379628 DOI: 10.1016/j.ebiom.2021.103550] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 08/07/2021] [Accepted: 08/08/2021] [Indexed: 01/10/2023] Open
Abstract
Background The potential role of individual plasma biomarkers in the pathogenesis of type 2 diabetes (T2D) has been broadly studied, but the impact of biomarkers interaction remains underexplored. Recently, the Mahalanobis distance (MD) of plasma biomarkers has been proposed as a proxy of physiological dysregulation. Here we aimed to investigate whether the MD calculated from circulating biomarkers is prospectively associated with development of T2D. Methods We calculated the MD of the Principal Components (PCs) integrating the information of 32 circulating biomarkers (comprising inflammation, glycemic, lipid, microbiome and one-carbon metabolism) measured in 6247 participants of the PREVEND study without T2D at baseline. Cox proportional-hazards regression analyses were performed to study the association of MD with T2D development. Findings After a median follow-up of 7·3 years, 312 subjects developed T2D. The overall MD (mean (SD)) was higher in subjects who developed T2D compared to those who did not: 35·65 (26·67) and 30.75 (27·57), respectively (P = 0·002). The highest hazard ratio (HR) was obtained using the MD calculated from the first 31 PCs (per 1 log-unit increment) (1·72 (95% CI 1·42,2·07), P < 0·001). Such associations remained after the adjustment for age, sex, plasma glucose, parental history of T2D, lipids, blood pressure medication, and BMI (HRadj 1·37 (95% CI 1·11,1·70), P = 0·004). Interpretation Our results are in line with the premise that MD represents an estimate of homeostasis loss. This study suggests that MD is able to provide information about physiological dysregulation also in the pathogenesis of T2D. Funding The Dutch Kidney Foundation (Grant E.033).
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Affiliation(s)
- Jose L Flores-Guerrero
- Department of Internal Medicine, Division of Nephrology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, Groningen 9713 GZ, the Netherlands.
| | - Marco A Grzegorczyk
- Johann Bernoulli Institute, University of Groningen, Groningen, the Netherlands
| | - Margery A Connelly
- Laboratory Corporation of America Holdings (Labcorp), Morrisville, North Carolina, USA
| | - Erwin Garcia
- Laboratory Corporation of America Holdings (Labcorp), Morrisville, North Carolina, USA
| | - Gerjan Navis
- Department of Internal Medicine, Division of Nephrology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, Groningen 9713 GZ, the Netherlands
| | - Robin P F Dullaart
- Department of Internal Medicine, Division of Endocrinology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Stephan J L Bakker
- Department of Internal Medicine, Division of Nephrology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, Groningen 9713 GZ, the Netherlands
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Chen C, Sheng Y. Prognostic Impact of MITD1 and Associates With Immune Infiltration in Kidney Renal Clear Cell Carcinoma. Technol Cancer Res Treat 2021; 20:15330338211036233. [PMID: 34346239 PMCID: PMC8351032 DOI: 10.1177/15330338211036233] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Kidney renal clear cell carcinoma (KIRC) is one of the most malignant diseases with poor survival rate over the world. The tumor microenvironment (TME) is highly related to the oncogenesis, development, and prognosis of KIRC. Thus, making the identification of KIRC biomarkers and immune infiltrates critically important. Microtubule Interacting and Trafficking Domain containing 1(MITD1) was reported to participate in cytokinesis of cell division. In the present study, multiple bioinformatics tools and databases were applied to investigate the expression level and clinical value of MITD1 in KIRC. We found that the expression of MITD1 was significantly increased in KIRC tissues. Further, the KIRC patients with high MITD1 levels showed a worse overall survival (OS) rate and disease free survival (DFS) rate. Otherwise, we found a significant correlation MITD1 expression and the abundance of CD8+ T cells. Functional enrichment analyses revealed that immune response and cytokine-cytokine receptor are very critical signaling pathways which associated with MITD1 in KIRC. In conclusion, our findings indicated that MITD1 may be a potential biomarker and associated with immune infiltration in KIRC.
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Affiliation(s)
- Chujie Chen
- Department of Urology, Seventh Affiliated Hospital, Sun Yat-sen University, Guangming District, Shenzhen, People's Republic of China
| | - Yiyu Sheng
- Department of Urology, Seventh Affiliated Hospital, Sun Yat-sen University, Guangming District, Shenzhen, People's Republic of China
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50
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Cao X, Sandberg A, Araújo JE, Cvetkovski F, Berglund E, Eriksson LE, Pernemalm M. Evaluation of Spin Columns for Human Plasma Depletion to Facilitate MS-Based Proteomics Analysis of Plasma. J Proteome Res 2021; 20:4610-4620. [PMID: 34320313 PMCID: PMC8419864 DOI: 10.1021/acs.jproteome.1c00378] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
High abundant protein depletion is a common strategy applied to increase analytical depth in global plasma proteomics experiment setups. The standard strategies for depletion of the highest abundant proteins currently rely on multiple-use HPLC columns or multiple-use spin columns. Here we evaluate the performance of single-use spin columns for plasma depletion and show that the single-use spin reduces handling time by allowing parallelization and is easily adapted to a nonspecialized lab environment without reducing the high plasma proteome coverage and reproducibility. In addition, we evaluate the effect of viral heat inactivation on the plasma proteome, an additional step in the plasma preparation workflow that allows the sample preparation of SARS-Cov2-infected samples to be performed in a BSL3 laboratory, and report the advantage of performing the heat inactivation postdepletion. We further show the possibility of expanding the use of the depletion column cross-species to macaque plasma samples. In conclusion, we report that single-use spin columns for high abundant protein depletion meet the requirements for reproducibly in in-depth plasma proteomics and can be applied on a common animal model while also reducing the sample handling time.
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Affiliation(s)
- Xiaofang Cao
- Cancer Proteomics Mass Spectrometry, Scilifelab, Department of Oncology and Pathology, Karolinska Institutet, SE-141 86 Stockholm, Sweden
| | - AnnSofi Sandberg
- Cancer Proteomics Mass Spectrometry, Scilifelab, Department of Oncology and Pathology, Karolinska Institutet, SE-141 86 Stockholm, Sweden
| | - José Eduardo Araújo
- Cancer Proteomics Mass Spectrometry, Scilifelab, Department of Oncology and Pathology, Karolinska Institutet, SE-141 86 Stockholm, Sweden
| | - Filip Cvetkovski
- Research and Development, ITB-Med AB, SE-113 66 Stockholm, Sweden
| | - Erik Berglund
- Section of Endocrine and Sarcoma Surgery, Department of Molecular Medicine and Surgery; Department of Clinical Science, Intervention and Technology (CLINTEC), Division of Transplantation, Surgery, Karolinska Institute, SE-141 86 Stockholm, Sweden
| | - Lars E Eriksson
- Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, SE-171 77 Stockholm, Sweden.,Medical Unit Infectious Diseases, Karolinska University Hospital, SE-141 86 Huddinge, Sweden.,School of Health Sciences, City University of London, London EC1 V 0HB, United Kingdom
| | - Maria Pernemalm
- Cancer Proteomics Mass Spectrometry, Scilifelab, Department of Oncology and Pathology, Karolinska Institutet, SE-141 86 Stockholm, Sweden
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