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Wei F, Kouro T, Nakamura Y, Ueda H, Iiizumi S, Hasegawa K, Asahina Y, Kishida T, Morinaga S, Himuro H, Horaguchi S, Tsuji K, Mano Y, Nakamura N, Kawamura T, Sasada T. Enhancing Mass spectrometry-based tumor immunopeptide identification: machine learning filter leveraging HLA binding affinity, aliphatic index and retention time deviation. Comput Struct Biotechnol J 2024; 23:859-869. [PMID: 38356658 PMCID: PMC10864759 DOI: 10.1016/j.csbj.2024.01.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 01/31/2024] [Accepted: 01/31/2024] [Indexed: 02/16/2024] Open
Abstract
Accurately identifying neoantigens is crucial for developing effective cancer vaccines and improving tumor immunotherapy. Mass spectrometry-based immunopeptidomics has emerged as a promising approach to identifying human leukocyte antigen (HLA) peptides presented on the surface of cancer cells, but false-positive identifications remain a significant challenge. In this study, liquid chromatography-tandem mass spectrometry-based proteomics and next-generation sequencing were utilized to identify HLA-presenting neoantigenic peptides resulting from non-synonymous single nucleotide variations in tumor tissues from 18 patients with renal cell carcinoma or pancreatic cancer. Machine learning was utilized to evaluate Mascot identifications through the prediction of MS/MS spectral consistency, and four descriptors for each candidate sequence: the max Mascot ion score, predicted HLA binding affinity, aliphatic index and retention time deviation, were selected as important features in filtering out identifications with inadequate fragmentation consistency. This suggests that incorporating rescoring filters based on peptide physicochemical characteristics could enhance the identification rate of MS-based immunopeptidomics compared to the traditional Mascot approach predominantly used for proteomics, indicating the potential for optimizing neoantigen identification pipelines as well as clinical applications.
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Affiliation(s)
- Feifei Wei
- Division of Cancer Immunotherapy, Kanagawa Cancer Center Research Institute, Yokohama, Japan
- Cancer Vaccine and Immunotherapy Center, Kanagawa Cancer Center, Yokohama, Japan
| | - Taku Kouro
- Division of Cancer Immunotherapy, Kanagawa Cancer Center Research Institute, Yokohama, Japan
- Cancer Vaccine and Immunotherapy Center, Kanagawa Cancer Center, Yokohama, Japan
| | - Yuko Nakamura
- Isotope Science Center, The University of Tokyo, Tokyo, Japan
| | - Hiroki Ueda
- Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan
| | - Susumu Iiizumi
- Division of Cancer Immunotherapy, Kanagawa Cancer Center Research Institute, Yokohama, Japan
- Research & Early Development Division, BrightPath Biotherapeutics Co., Ltd., Kawasaki, Japan
| | - Kyoko Hasegawa
- Division of Cancer Immunotherapy, Kanagawa Cancer Center Research Institute, Yokohama, Japan
- Research & Early Development Division, BrightPath Biotherapeutics Co., Ltd., Kawasaki, Japan
| | - Yuki Asahina
- Division of Cancer Immunotherapy, Kanagawa Cancer Center Research Institute, Yokohama, Japan
| | - Takeshi Kishida
- Department of Urology, Kanagawa Cancer Center, Yokohama, Japan
| | - Soichiro Morinaga
- Department of Hepato-Biliary and Pancreatic Surgery, Kanagawa Cancer Center, Yokohama, Japan
| | - Hidetomo Himuro
- Division of Cancer Immunotherapy, Kanagawa Cancer Center Research Institute, Yokohama, Japan
- Cancer Vaccine and Immunotherapy Center, Kanagawa Cancer Center, Yokohama, Japan
| | - Shun Horaguchi
- Division of Cancer Immunotherapy, Kanagawa Cancer Center Research Institute, Yokohama, Japan
- Cancer Vaccine and Immunotherapy Center, Kanagawa Cancer Center, Yokohama, Japan
- Department of Pediatric Surgery, Nihon University School of Medicine, Tokyo, Japan
| | - Kayoko Tsuji
- Division of Cancer Immunotherapy, Kanagawa Cancer Center Research Institute, Yokohama, Japan
- Cancer Vaccine and Immunotherapy Center, Kanagawa Cancer Center, Yokohama, Japan
| | - Yasunobu Mano
- Division of Cancer Immunotherapy, Kanagawa Cancer Center Research Institute, Yokohama, Japan
- Cancer Vaccine and Immunotherapy Center, Kanagawa Cancer Center, Yokohama, Japan
| | - Norihiro Nakamura
- Research & Early Development Division, BrightPath Biotherapeutics Co., Ltd., Kawasaki, Japan
| | | | - Tetsuro Sasada
- Division of Cancer Immunotherapy, Kanagawa Cancer Center Research Institute, Yokohama, Japan
- Cancer Vaccine and Immunotherapy Center, Kanagawa Cancer Center, Yokohama, Japan
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Zhou Z, Zhang R, Zhou A, Lv J, Chen S, Zou H, Zhang G, Lin T, Wang Z, Zhang Y, Weng S, Han X, Liu Z. Proteomics appending a complementary dimension to precision oncotherapy. Comput Struct Biotechnol J 2024; 23:1725-1739. [PMID: 38689716 PMCID: PMC11058087 DOI: 10.1016/j.csbj.2024.04.044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 04/11/2024] [Accepted: 04/17/2024] [Indexed: 05/02/2024] Open
Abstract
Recent advances in high-throughput proteomic profiling technologies have facilitated the precise quantification of numerous proteins across multiple specimens concurrently. Researchers have the opportunity to comprehensively analyze the molecular signatures in plentiful medical specimens or disease pattern cell lines. Along with advances in data analysis and integration, proteomics data could be efficiently consolidated and employed to recognize precise elementary molecular mechanisms and decode individual biomarkers, guiding the precision treatment of tumors. Herein, we review a broad array of proteomics technologies and the progress and methods for the integration of proteomics data and further discuss how to better merge proteomics in precision medicine and clinical settings.
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Affiliation(s)
- Zhaokai Zhou
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, Henan 450052, China
| | - Ruiqi Zhang
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Aoyang Zhou
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Jinxiang Lv
- Department of Gastroenterology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Shuang Chen
- Center of Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Haijiao Zou
- Center of Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Ge Zhang
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Ting Lin
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Zhan Wang
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, Henan 450052, China
| | - Yuyuan Zhang
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Siyuan Weng
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Xinwei Han
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
- Interventional Institute of Zhengzhou University, Zhengzhou, Henan 450052, China
- Interventional Treatment and Clinical Research Center of Henan Province, Zhengzhou, Henan 450052, China
| | - Zaoqu Liu
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
- Interventional Institute of Zhengzhou University, Zhengzhou, Henan 450052, China
- Interventional Treatment and Clinical Research Center of Henan Province, Zhengzhou, Henan 450052, China
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
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3
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Li Y, Dong T, Wan S, Xiong R, Jin S, Dai Y, Guan C. Application of multi-omics techniques to androgenetic alopecia: Current status and perspectives. Comput Struct Biotechnol J 2024; 23:2623-2636. [PMID: 39021583 PMCID: PMC11253216 DOI: 10.1016/j.csbj.2024.06.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2024] [Revised: 06/17/2024] [Accepted: 06/18/2024] [Indexed: 07/20/2024] Open
Abstract
The rapid advancement of sequencing technologies has enabled the generation of vast datasets, allowing for the in-depth analysis of sequencing data. This analysis has facilitated the validation of novel pathogenesis hypotheses for understanding and treating diseases through ex vivo and in vivo experiments. Androgenetic alopecia (AGA), a common hair loss disorder, has been a key focus of investigators attempting to uncover its underlying mechanisms. Abnormal changes in mRNA, proteins, and metabolites have been identified in individuals with AGA, and future developments in sequencing technologies may reveal new biomarkers for AGA. By integrating multiple omics analysis datasets such as genomics, transcriptomics, proteomics, and metabolomics-along with clinical phenotype data-we can achieve a comprehensive understanding of the molecular underpinnings of AGA. This review summarizes the data-mining studies conducted on various omics analysis datasets as related to AGA that have been adopted to interpret the biological data obtained from different omics layers. We herein discuss the challenges of integrative omics analyses, and suggest that collaborative multi-omics studies can enhance the understanding of the complete pathomechanism(s) of AGA by focusing on the interaction networks comprising DNA, RNA, proteins, and metabolites.
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Affiliation(s)
- Yujie Li
- Hangzhou Third Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou 310009, China
| | - Tingru Dong
- Hangzhou Third Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou 310009, China
| | - Sheng Wan
- Hangzhou Third Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou 310009, China
- Department of Dermatology, Hangzhou Third People's Hospital, Hangzhou 310009, China
| | - Renxue Xiong
- Hangzhou Third Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou 310009, China
- Department of Dermatology, Hangzhou Third People's Hospital, Hangzhou 310009, China
| | - Shiyu Jin
- Hangzhou Third Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou 310009, China
| | - Yeqin Dai
- Hangzhou Third Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou 310009, China
- Department of Dermatology, Hangzhou Third People's Hospital, Hangzhou 310009, China
| | - Cuiping Guan
- Hangzhou Third Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou 310009, China
- Department of Dermatology, Hangzhou Third People's Hospital, Hangzhou 310009, China
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Li G, Stampas A, Komatsu Y, Gao X, Huard J, Pan S. Proteomics in orthopedic research: Recent studies and their translational implications. J Orthop Res 2024; 42:1631-1640. [PMID: 38897819 DOI: 10.1002/jor.25917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 05/10/2024] [Accepted: 06/01/2024] [Indexed: 06/21/2024]
Abstract
Proteomics is a growing field that offers insights into various aspects of disease processes and therapy responses. Within the field of orthopedics, there are a variety of diseases that have a poor prognosis due to a lack of targeted curative therapy or disease modifying therapy. Other diseases have been difficult to manage in part due to lack of clinical biomarkers that offer meaningful insight into disease progression or severity. As an emerging technology, proteomics has been increasingly applied in studying bone biology and an assortment of orthopedics related diseases, such as osteoarthritis, osteosarcoma and bone tumors, osteoporosis, traumatic bone injury, spinal cord injury, hip and knee arthroplasty, and fragile healing. These efforts range from mechanistic studies for elucidating novel insights in tissue activity and metabolism to identification of candidate biomarkers for diagnosis, prognosis, and targeted treatment. The knowledge gained from these proteomic and functional studies has provided unique perspectives in studying orthopedic diseases. In this review, we seek to report on the current state of the proteomic study in the field of orthopedics, overview the advances in clinically applicable discoveries, and discuss the opportunities that may guide us for future research.
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Affiliation(s)
- George Li
- School of Medicine, Texas A&M University, Bryan, Texas, USA
| | - Argyrios Stampas
- Department of Physical Medicine and Rehabilitation, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, USA
- Department of Physical Medicine and Rehabilitation, TIRR Memorial Hermann Hospital, Houston, Texas, USA
| | - Yoshihiro Komatsu
- Department of Pediatrics, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, USA
- Graduate Program in Genetics & Epigenetics, The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas, USA
| | - Xueqin Gao
- Linda and Mitch Hart Center for Regenerative and Personalized Medicine, Steadman Philippon Research Institute, Vail, Colorado, USA
| | - Johnny Huard
- Linda and Mitch Hart Center for Regenerative and Personalized Medicine, Steadman Philippon Research Institute, Vail, Colorado, USA
| | - Sheng Pan
- Graduate Program in Genetics & Epigenetics, The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas, USA
- The Brown Foundation Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, Houston, Texas, USA
- Department of Integrative Biology and Pharmacology, University of Texas Health Science Center at Houston, Houston, Texas, USA
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Prudhomme N, Pastora R, Thomson S, Zheng E, Sproule A, Krieger JR, Murphy JP, Overy DP, Cossar D, McLean MD, Geddes‐McAlister J. Bacterial growth-mediated systems remodelling of Nicotiana benthamiana defines unique signatures of target protein production in molecular pharming. PLANT BIOTECHNOLOGY JOURNAL 2024; 22:2248-2266. [PMID: 38516995 PMCID: PMC11258984 DOI: 10.1111/pbi.14342] [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: 07/01/2023] [Revised: 03/01/2024] [Accepted: 03/08/2024] [Indexed: 03/23/2024]
Abstract
The need for therapeutics to treat a plethora of medical conditions and diseases is on the rise and the demand for alternative approaches to mammalian-based production systems is increasing. Plant-based strategies provide a safe and effective alternative to produce biological drugs but have yet to enter mainstream manufacturing at a competitive level. Limitations associated with batch consistency and target protein production levels are present; however, strategies to overcome these challenges are underway. In this study, we apply state-of-the-art mass spectrometry-based proteomics to define proteome remodelling of the plant following agroinfiltration with bacteria grown under shake flask or bioreactor conditions. We observed distinct signatures of bacterial protein production corresponding to the different growth conditions that directly influence the plant defence responses and target protein production on a temporal axis. Our integration of proteomic profiling with small molecule detection and quantification reveals the fluctuation of secondary metabolite production over time to provide new insight into the complexities of dual system modulation in molecular pharming. Our findings suggest that bioreactor bacterial growth may promote evasion of early plant defence responses towards Agrobacterium tumefaciens (updated nomenclature to Rhizobium radiobacter). Furthermore, we uncover and explore specific targets for genetic manipulation to suppress host defences and increase recombinant protein production in molecular pharming.
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Affiliation(s)
- Nicholas Prudhomme
- Department of Molecular and Cellular BiologyUniversity of GuelphGuelphONCanada
| | | | - Sarah Thomson
- Department of Molecular and Cellular BiologyUniversity of GuelphGuelphONCanada
| | - Edison Zheng
- Department of Molecular and Cellular BiologyUniversity of GuelphGuelphONCanada
| | - Amanda Sproule
- Ottawa Research and Development CentreAgriculture and Agri‐Food CanadaOttawaONCanada
| | | | - J. Patrick Murphy
- Department of BiologyUniversity of Prince Edward IslandCharlottetownPECanada
| | - David P. Overy
- Ottawa Research and Development CentreAgriculture and Agri‐Food CanadaOttawaONCanada
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Hadpech S, Thongboonkerd V. Proteomic investigations of dengue virus infection: key discoveries over the last 10 years. Expert Rev Proteomics 2024:1-15. [PMID: 39049185 DOI: 10.1080/14789450.2024.2383580] [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: 05/19/2023] [Accepted: 07/12/2024] [Indexed: 07/27/2024]
Abstract
INTRODUCTION Dengue virus (DENV) infection remains one of the most significant infectious diseases in humans. Several efforts have been made to address its molecular mechanisms. Over the last 10 years, proteomics has been widely applied to investigate various aspects of DENV infection. AREAS COVERED In this review, we briefly introduce common proteomics approaches using various mass spectrometric modalities followed by summarizing all the discoveries obtained from proteomic investigations of DENV infection over the last 10 years. These include the data on DENV-vector interactions and host responses to address the DENV biology and disease mechanisms. Moreover, applications of proteomics to disease prevention, diagnosis, vaccine design, development of anti-DENV agents and other new treatment strategies are discussed. EXPERT OPINION Despite efforts on disease prevention, DENV infection is still a significant global healthcare burden that affects the general population. As summarized herein, proteomic technologies with high-throughput capabilities have provided more in-depth details of protein dynamics during DENV infection. More extensive applications of proteomics and other powerful research tools would provide a promise to better cope and prevent this mosquito-borne infectious disease.
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Affiliation(s)
- Sudarat Hadpech
- Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
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Lapcik P, Synkova K, Janacova L, Bouchalova P, Potesil D, Nenutil R, Bouchal P. A hybrid DDA/DIA-PASEF based assay library for a deep proteotyping of triple-negative breast cancer. Sci Data 2024; 11:794. [PMID: 39025866 PMCID: PMC11258311 DOI: 10.1038/s41597-024-03632-2] [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: 02/14/2024] [Accepted: 07/10/2024] [Indexed: 07/20/2024] Open
Abstract
Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer, and deeper proteome coverage is needed for its molecular characterization. We present comprehensive library of targeted mass spectrometry assays specific for TNBC and demonstrate its applicability. Proteins were extracted from 105 TNBC tissues and digested. Aliquots were pooled, fractionated using hydrophilic chromatography and analyzed by LC-MS/MS in data-dependent acquisition (DDA) parallel accumulation-serial fragmentation (PASEF) mode on timsTOF Pro LC-MS system. 16 individual lysates were analyzed in data-independent acquisition (DIA)-PASEF mode. Hybrid library was generated in Spectronaut software and covers 244,464 precursors, 168,006 peptides and 11,564 protein groups (FDR = 1%). Application of our library for pilot quantitative analysis of 16 tissues increased identification numbers in Spectronaut 18.5 and DIA-NN 1.8.1 software compared to library-free setting, with Spectronaut achieving the best results represented by 190,310 precursors, 140,566 peptides, and 10,463 protein groups. In conclusion, we introduce assay library that offers the deepest coverage of TNBC proteome to date. The TNBC library is available via PRIDE repository (PXD047793).
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Grants
- NU22-08-00230 Ministerstvo Zdravotnictví Ceské Republiky (Ministry of Health of the Czech Republic)
- NU22-08-00230 Ministerstvo Zdravotnictví Ceské Republiky (Ministry of Health of the Czech Republic)
- NU22-08-00230 Ministerstvo Zdravotnictví Ceské Republiky (Ministry of Health of the Czech Republic)
- NU22-08-00230 Ministerstvo Zdravotnictví Ceské Republiky (Ministry of Health of the Czech Republic)
- LX22NPO5102 Ministerstvo Školství, Mládeže a Tělovýchovy (Ministry of Education, Youth and Sports)
- LX22NPO5102 Ministerstvo Školství, Mládeže a Tělovýchovy (Ministry of Education, Youth and Sports)
- LX22NPO5102 Ministerstvo Školství, Mládeže a Tělovýchovy (Ministry of Education, Youth and Sports)
- LX22NPO5102 Ministerstvo Školství, Mládeže a Tělovýchovy (Ministry of Education, Youth and Sports)
- CZ.02.1.01/0.0/0.0/18_046/0015974 Ministerstvo Školství, Mládeže a Tělovýchovy (Ministry of Education, Youth and Sports)
- LM2023033 Ministerstvo Školství, Mládeže a Tělovýchovy (Ministry of Education, Youth and Sports)
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Affiliation(s)
- Petr Lapcik
- Department of Biochemistry, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Klara Synkova
- Department of Biochemistry, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Lucia Janacova
- Department of Biochemistry, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Pavla Bouchalova
- Department of Biochemistry, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - David Potesil
- Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Rudolf Nenutil
- Department of Oncological Pathology, Masaryk Memorial Cancer Institute, Brno, Czech Republic
| | - Pavel Bouchal
- Department of Biochemistry, Faculty of Science, Masaryk University, Brno, Czech Republic.
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Wang N, Gao Y, Wang Y, Dai Y, Tang Y, Huang J, Sun L, Qian G, Ma J, Li X, Liu Y, Yang D, Huang X, Wang W, Li W, Zhuo W, Lv H, Liu Z. Plasma proteomic profiling reveals that SERPINE1 is a potential biomarker associated with coronary artery lesions in Kawasaki disease. Int Immunopharmacol 2024; 139:112698. [PMID: 39029232 DOI: 10.1016/j.intimp.2024.112698] [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: 01/15/2024] [Revised: 06/28/2024] [Accepted: 07/13/2024] [Indexed: 07/21/2024]
Abstract
BACKGROUND Kawasaki disease (KD) is the most common cause of acquired heart disease in childhood. Coronary artery lesions (CALs) are serious complications of KD that can result in stenosis and thrombosis, but the specific underlying pathogenic mechanisms have not been elucidated. Therefore, exploring biomarkers to help predict early CALs is urgently needed for clinical treatment. METHODS Patients were recruited from three independent cohorts. In the discovery cohort, Data-Independent Acquisition Mass Spectrometry (DIA-MS) was performed to screen plasma proteins from healthy controls (HCs), KD patients prior to intravenous immunoglobulin (IVIG) treatment, and KD patients post-IVIG treatment. KD patients were further divided into KD patients without CALs (nCAL) and with CALs (CALs) groups. Bioinformatic analysis was carried out for the differentially expressed proteins (DEPs) and hub proteins. Candidate proteins were quantified by enzyme-linked immunosorbent assay (ELISA) in the validation cohort 1 and 2. Furthermore, candida albicans cell wall extract (CAWS)-induced KD vasculitis mice and cell models were established to investigate the expression of biomarkers identified in the aforementioned clinical cohort. RESULTS According to the quantitative proteomics analysis, SERPINE1 was significantly increased in KD patients with CALs. Receiver operating characteristic curves (ROC) revealed that plasma SERPINE1 exhibited greater ability in predicting CALs (AUC = 0.824, P < 0.0001). After IVIG treatment, the concentrations of SERPINE1 in the nCALs group significantly decreased. However, the concentration of SERPINE1 remained persistently elevated in the CALs group. Moreover, the expression of SERPINE1 was significantly upregulated in the heart tissue of KD mice, KD plasma, or tumor necrosis factor-α (TNF-α)-stimulated human coronary artery endothelial cells (HCAECs). CONCLUSIONS Overall, our results suggest that the plasma concentration of SERPINE1 might serve as a new potential predictive biomarker for CALs in KD patients.
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Affiliation(s)
- Nana Wang
- Department of Cardiology, Children's Hospital of Soochow University, Suzhou, JiangSu province, China
| | - Yang Gao
- Department of Cardiology, Children's Hospital of Soochow University, Suzhou, JiangSu province, China; Department of Pediatrics, The First People's Hospital of Lianyungang, Xuzhou Medical University Affiliated Hospital of Lianyungang (Lianyungang Clinical College of Nanjing Medical University), Lianyungang, JiangSu province, China
| | - Yan Wang
- Department of Cardiology, Children's Hospital of Soochow University, Suzhou, JiangSu province, China; Department of Cardiology, Children's Hospital Affiliated to Xuzhou Medical University, Xuzhou, JiangSu province, China
| | - Yuan Dai
- Department of Cardiology, Children's Hospital of Soochow University, Suzhou, JiangSu province, China
| | - Yunjia Tang
- Department of Cardiology, Children's Hospital of Soochow University, Suzhou, JiangSu province, China
| | - Jie Huang
- Department of Cardiology, Children's Hospital of Soochow University, Suzhou, JiangSu province, China
| | - Ling Sun
- Department of Cardiology, Children's Hospital of Soochow University, Suzhou, JiangSu province, China
| | - Guanghui Qian
- Institute of Pediatric Research, Children's Hospital of Soochow University, Suzhou, JiangSu province, China
| | - Jin Ma
- Department of Cardiology, Children's Hospital of Soochow University, Suzhou, JiangSu province, China
| | - Xuan Li
- Department of Cardiology, Children's Hospital of Soochow University, Suzhou, JiangSu province, China
| | - Ying Liu
- Institute of Pediatric Research, Children's Hospital of Soochow University, Suzhou, JiangSu province, China
| | - Daoping Yang
- Department of Cardiology, Children's Hospital of Soochow University, Suzhou, JiangSu province, China
| | - Xin Huang
- Department of Cardiology, Children's Hospital of Soochow University, Suzhou, JiangSu province, China
| | - Wang Wang
- Institute of Pediatric Research, Children's Hospital of Soochow University, Suzhou, JiangSu province, China
| | - Wenjie Li
- Department of Cardiology, Children's Hospital of Soochow University, Suzhou, JiangSu province, China
| | - Wenyu Zhuo
- Department of Cardiology, Children's Hospital of Soochow University, Suzhou, JiangSu province, China
| | - Haitao Lv
- Department of Cardiology, Children's Hospital of Soochow University, Suzhou, JiangSu province, China.
| | - Zhiheng Liu
- Department of Cardiology, Children's Hospital of Soochow University, Suzhou, JiangSu province, China.
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He Y, Li Y, Zhao L, Ying G, Lu G, Zhang L, Zhang Z. An Optimized Miniaturized Filter-Aided Sample Preparation Method for Sensitive Cross-Linking Mass Spectrometry Analysis of Microscale Samples. Anal Chem 2024. [PMID: 39007547 DOI: 10.1021/acs.analchem.4c01600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Cross-linking mass spectrometry (XL-MS) is a powerful tool for elucidating protein structures and protein-protein interactions (PPIs) at the global scale. However, sensitive XL-MS analysis of mass-limited samples remains challenging, due to serious sample loss during sample preparation of the low-abundance cross-linked peptides. Herein, an optimized miniaturized filter-aided sample preparation (O-MICROFASP) method was presented for sensitive XL-MS analysis of microscale samples. By systematically investigating and optimizing crucial experimental factors, this approach dramatically improves the XL identification of low and submicrogram samples. Compared with the conventional FASP method, more than 7.4 times cross-linked peptides were identified from single-shot analysis of 1 μg DSS cross-linked HeLa cell lysates (440 vs 59). The number of cross-linked peptides identified from 0.5 μg HeLa cell lysates was increased by 58% when further reducing the surface area of the filter to 0.058 mm2 in the microreactor. To deepen the identification coverage of XL-proteome, five different types of cross-linkers were used and each μg of cross-linked HeLa cell lysates was processed by O-MICROFASP integrated with tip-based strong cation exchange (SCX) fractionation. Up to 2741 unique cross-linked peptides were identified from the 5 μg HeLa cell lysates, representing 2579 unique K-K linkages on 1092 proteins. About 96% of intraprotein cross-links were within the maximal distance restraints of 26 Å, and 75% of the identified PPIs reported by the STRING database were with high confidence (scores ≥0.9), confirming the high validity of the identified cross-links for protein structural mapping and PPI analysis. This study demonstrates that O-MICROFASP is a universal and efficient method for proteome-wide XL-MS analysis of microscale samples with high sensitivity and reliability.
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Affiliation(s)
- Yu He
- Institute of Drug Discovery Technology, Ningbo University, Ningbo, Zhejiang 315211, China
| | - Yang Li
- Institute of Drug Discovery Technology, Ningbo University, Ningbo, Zhejiang 315211, China
| | - Lili Zhao
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Science, Dalian, Liaoning 116023, China
| | - Guojin Ying
- Institute of Drug Discovery Technology, Ningbo University, Ningbo, Zhejiang 315211, China
| | - Gang Lu
- Institute of Drug Discovery Technology, Ningbo University, Ningbo, Zhejiang 315211, China
| | - Lihua Zhang
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Science, Dalian, Liaoning 116023, China
| | - Zhenbin Zhang
- Institute of Drug Discovery Technology, Ningbo University, Ningbo, Zhejiang 315211, China
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10
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Ctortecka C, Clark NM, Boyle BW, Seth A, Mani DR, Udeshi ND, Carr SA. Automated single-cell proteomics providing sufficient proteome depth to study complex biology beyond cell type classifications. Nat Commun 2024; 15:5707. [PMID: 38977691 PMCID: PMC11231172 DOI: 10.1038/s41467-024-49651-w] [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: 01/24/2024] [Accepted: 06/14/2024] [Indexed: 07/10/2024] Open
Abstract
The recent technological and computational advances in mass spectrometry-based single-cell proteomics have pushed the boundaries of sensitivity and throughput. However, reproducible quantification of thousands of proteins within a single cell remains challenging. To address some of those limitations, we present a dedicated sample preparation chip, the proteoCHIP EVO 96 that directly interfaces with the Evosep One. This, in combination with the Bruker timsTOF demonstrates double the identifications without manual sample handling and the newest generation timsTOF Ultra identifies up to 4000 with an average of 3500 protein groups per single HEK-293T without a carrier or match-between runs. Our workflow spans 4 orders of magnitude, identifies over 50 E3 ubiquitin-protein ligases, and profiles key regulatory proteins upon small molecule stimulation. This study demonstrates that the proteoCHIP EVO 96-based sample preparation with the timsTOF Ultra provides sufficient proteome depth to study complex biology beyond cell-type classifications.
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Affiliation(s)
| | | | - Brian W Boyle
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - D R Mani
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Steven A Carr
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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11
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Yuan FF, Wang P, Han XJ, Qin TT, Lu X, Bai HJ. Efficient and rapid digestion of proteins with a dual-enzyme microreactor featuring 3-D pores formed by dopamine/polyethyleneimine/acrylamide-coated KIT-6 molecular sieve. Sci Rep 2024; 14:15667. [PMID: 38977741 PMCID: PMC11231357 DOI: 10.1038/s41598-024-65045-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Accepted: 06/17/2024] [Indexed: 07/10/2024] Open
Abstract
The microreactor with two types of immobilized enzymes, exhibiting excellent orthogonal performance, represents an effective approach to counteract the reduced digestion efficiency resulting from the absence of a single enzyme cleavage site, thereby impacting protein identification. In this study, we developed a hydrophilic dual-enzyme microreactor characterized by rapid mass transfer and superior enzymatic activity. Initially, we selected KIT-6 molecular sieve as the carrier for the dual-IMER due to its three-dimensional network pore structure. Modification involved co-deposition of polyethyleneimine (PEI) and acrylamide (AM) as amine donors, along with dopamine to enhance material hydrophilicity. Remaining amino and double bond functional groups facilitated stepwise immobilization of trypsin and Glu-C. Digestion times for bovine serum albumin (BSA) and bovine hemoglobin (BHb) on the dual-IMER were significantly reduced compared to solution-based digestion (1 min vs. 36 h), resulting in improved sequence coverage (91.30% vs. 82.7% for BSA; 90.24% vs. 89.20% for BHb). Additionally, the dual-IMER demonstrated excellent durability, retaining 96.08% relative activity after 29 reuse cycles. Enhanced protein digestion efficiency can be attributed to several factors: (1) KIT-6's large specific surface area, enabling higher enzyme loading capacity; (2) Its three-dimensional network pore structure, facilitating faster mass transfer and substance diffusion; (3) Orthogonality of trypsin and Glu-C enzyme cleavage sites; (4) The spatial effect introduced by the chain structure of PEI and glutaraldehyde's spacing arm, reducing spatial hindrance and enhancing enzyme-substrate interactions; (5) Mild and stable enzyme immobilization. The KIT-6-based dual-IMER offers a promising technical tool for protein digestion, while the PDA/PEI/AM-KIT-6 platform holds potential for immobilizing other proteins or active substances.
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Affiliation(s)
- Fang-Fang Yuan
- Tianjin Institute for Drug Control, Tianjin, 300070, China
| | - Pei Wang
- Tianjin Institute for Drug Control, Tianjin, 300070, China
| | - Xiao-Jie Han
- Tianjin Institute for Drug Control, Tianjin, 300070, China
| | - Ting-Ting Qin
- Tianjin Institute for Drug Control, Tianjin, 300070, China
| | - Xin Lu
- Tianjin Institute for Drug Control, Tianjin, 300070, China
| | - Hai-Jiao Bai
- Tianjin Institute for Drug Control, Tianjin, 300070, China.
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12
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Wang R, Li Y, Ji J, Kong L, Huang Y, Liu ZQ, Lu LL. The emerging role of herbal medicines in cancer by interfering with post-translational modification. Antioxid Redox Signal 2024. [PMID: 38970420 DOI: 10.1089/ars.2023.0418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/08/2024]
Abstract
SIGNIFICANCE Herbal medicines demonstrate clinical promise for cancer treatment. Protein post translational modifications (PTMs) regulate tumorigenesis and cancer progression. While PTMs contributing to cancer are well-studied, the precise mechanisms and defined targets of herbal medicines on PTM-associated carcinogenesis remain unclear. Hence, comprehensively understanding how PTMs regulate cancer hallmarks is crucial to elucidate the pharmacological mechanisms of herbal medicines for cancer treatment. RECENT ADVANCES Advanced development in highly sensitive mass spectrometry (MS)-based techniques has helped utilize PTM-focused studies on cancers. Accumulating evidence has been achieved in laboratory to ascertain the biological mechanism of herbal medicines in cancer therapy. Implication of the strong association between cancer and PTM makes new perspective to comprehend the intricate dialogues between herbal medicines and cellular contexts. CRITICAL ISSUES Complex components of herbal medicines limit the benefits of herbal-based cancer therapies. In this review, we address that PTMs add a layer of proteomic complexity to the cancer through altering the protein structure, expression, function, and localization. Elaborating PTM implicated in cell signaling, apoptosis and transcriptional regulation function, and the possible cellular signaling, have provided important information about the mechanism of many herbal therapies. Continued optimization of proteomic strategies for PTM analysis in herbal medicines are also discussed. FUTURE DIRECTIONS Rigorous evaluations of herbal medicines and the chemoproteomic strategies are necessary to explore the aberrant regulation of PTM dynamics contributed to the cancer development and herbal associated pharmacological issues. These efforts will eventually help develop more herbal drugs as modern therapeutic agents.
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Affiliation(s)
- Rui Wang
- Shenzhen Bay Laboratory, Pingshan Translational Medicine Center, Shenzhen, China;
| | - Yu Li
- Guangzhou University of Chinese Medicine, Joint Laboratory for Translational Cancer Research of Chinese Medicine of the Ministry of Education of the People's Republic of China, International Institute for Translational Chinese Medicine, Guangzhou, Guangdong, China;
| | - Jiahui Ji
- Guangzhou University of Chinese Medicine, Joint Laboratory for Translational Cancer Research of Chinese Medicine of the Ministry of Education of the People's Republic of China, International Institute for Translational Chinese Medicine, Guangzhou, Guangdong, China;
| | - Lingwei Kong
- Shenzhen Bay Laboratory, Pingshan Translational Medicine Center, Shenzhen, China;
| | - Yukai Huang
- Guangzhou University of Chinese Medicine, Joint Laboratory for Translational Cancer Research of Chinese Medicine of the Ministry of Education of the People's Republic of China, International Institute for Translational Chinese Medicine, Guangzhou, Guangdong, China;
| | - Zhong-Qiu Liu
- Guangzhou University of Chinese Medicine, Joint Laboratory for Translational Cancer Research of Chinese Medicine of the Ministry of Education of the People's Republic of China, International Institute for Translational Chinese Medicine, Guangzhou, Guangdong, China;
| | - Lin-Lin Lu
- Guangzhou University of Chinese Medicine, International Institute for Translational Chinese Medicine, Guangzhou University of Chinese Medicine University City Campus, No. 232 Waihuan East Road, University Town, Panyu Distr, Guangzhou, Guangdong, China, 510006;
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13
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Zhang N, Wu J, Zheng Q. Chemical proteomics approaches for protein post-translational modification studies. BIOCHIMICA ET BIOPHYSICA ACTA. PROTEINS AND PROTEOMICS 2024; 1872:141017. [PMID: 38641087 DOI: 10.1016/j.bbapap.2024.141017] [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: 03/05/2024] [Revised: 04/05/2024] [Accepted: 04/14/2024] [Indexed: 04/21/2024]
Abstract
The diversity and dynamics of proteins play essential roles in maintaining the basic constructions and functions of cells. The abundance of functional proteins is regulated by the transcription and translation processes, while the alternative splicing enables the same gene to generate distinct protein isoforms of different lengths. Beyond the transcriptional and translational regulations, post-translational modifications (PTMs) are able to further expand the diversity and functional scope of proteins. PTMs have been shown to make significant changes in the surface charges, structures, activation states, and interactome of proteins. Due to the functional complexity, highly dynamic nature, and low presence percentage, the study of protein PTMs remains challenging. Here we summarize and discuss the major chemical biology tools and chemical proteomics approaches to enrich and investigate the protein PTM of interest.
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Affiliation(s)
- Nan Zhang
- Department of Radiation Oncology, College of Medicine, The Ohio State University, Columbus, OH 43210, United States; Center for Cancer Metabolism, James Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, United States
| | - Jinghua Wu
- Department of Radiation Oncology, College of Medicine, The Ohio State University, Columbus, OH 43210, United States; Center for Cancer Metabolism, James Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, United States
| | - Qingfei Zheng
- Department of Radiation Oncology, College of Medicine, The Ohio State University, Columbus, OH 43210, United States; Center for Cancer Metabolism, James Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, United States; Department of Biological Chemistry and Pharmacology, College of Medicine, The Ohio State University, Columbus, OH 43210, United States.
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14
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Doll S, Schweizer L, Bollwein C, Steiger K, Pfarr N, Walker M, Wörtler K, Knebel C, von Eisenhart-Rothe R, Hartmann W, Weichert W, Mann M, Kuhn PH, Specht K. Proteomic Characterization of Undifferentiated Small Round Cell Sarcomas With EWSR1 and CIC::DUX4 Translocations Reveals Diverging Tumor Biology and Distinct Diagnostic Markers. Mod Pathol 2024; 37:100511. [PMID: 38705279 DOI: 10.1016/j.modpat.2024.100511] [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: 10/16/2023] [Revised: 04/11/2024] [Accepted: 04/26/2024] [Indexed: 05/07/2024]
Abstract
Undifferentiated small round cell sarcomas (USRS) of bone and soft tissue are a group of tumors with heterogenic genomic alterations sharing similar morphology. In the present study, we performed a comparative large-scale proteomic analysis of USRS (n = 42) with diverse genomic translocations including classic Ewing sarcomas with EWSR1::FLI1 fusions (n = 24) or EWSR1::ERG fusions (n = 4), sarcomas with an EWSR1 rearrangement (n = 2), CIC::DUX4 fusion (n = 8), as well as tumors classified as USRS with no genetic data available (n = 4). Proteins extracted from formalin-fixed, paraffin-embedded pretherapeutic biopsies were analyzed qualitatively and quantitatively using shotgun mass spectrometry (MS). More than 8000 protein groups could be quantified using data-independent acquisition. Unsupervised hierarchical cluster analysis based on proteomic data allowed stratification of the 42 cases into distinct groups reflecting the different molecular genotypes. Protein signatures that significantly correlated with the respective genomic translocations were identified and used to generate a heatmap of all 42 sarcomas with assignment of cases with unknown molecular genetic data to either the EWSR1- or CIC-rearranged groups. MS-based prediction of sarcoma subtypes was molecularly confirmed in 2 cases where next-generation sequencing was technically feasible. MS also detected proteins routinely used in the immunohistochemical approach for the differential diagnosis of USRS. BCL11B highly expressed in Ewing sarcomas, and BACH2 as well as ETS-1 highly expressed in CIC::DUX4-associated sarcomas, were among proteins identified by the present proteomic study, and were chosen for immunohistochemical confirmation of MS data in our study cohort. Differential expressions of these 3 markers in the 2 genetic groups were further validated in an independent cohort of n = 34 USRS. Finally, our proteomic results point toward diverging signaling pathways in the different USRS subgroups.
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Affiliation(s)
- Sophia Doll
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Bavaria, Germany; OmicEra Diagnostics GmbH, Planegg, Bavaria, Germany
| | - Lisa Schweizer
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Bavaria, Germany
| | | | - Katja Steiger
- Institute of Pathology, Technical University of Munich, Munich, Germany
| | - Nicole Pfarr
- Institute of Pathology, Technical University of Munich, Munich, Germany
| | - Maria Walker
- Institute of Pathology, Technical University of Munich, Munich, Germany
| | - Klaus Wörtler
- Musculoskeletal Radiology Section, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Carolin Knebel
- Department of Orthopaedic Surgery, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | | | | | - Wilko Weichert
- Institute of Pathology, Technical University of Munich, Munich, Germany; German Cancer Consortium (DKTK), Partner-site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Matthias Mann
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Bavaria, Germany
| | - Peer-Hendrik Kuhn
- Institute of Pathology, Technical University of Munich, Munich, Germany
| | - Katja Specht
- Institute of Pathology, Technical University of Munich, Munich, Germany.
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15
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Fedorov II, Bubis JA, Kazakova EM, Lobas AA, Ivanov MV, Emekeeva DD, Tarasova IA, Nazarov AA, Gorshkov MV. On the utility of ultrafast MS1-only proteomics in drug target discovery studies based on thermal proteome profiling method. Anal Bioanal Chem 2024; 416:4083-4089. [PMID: 38744720 DOI: 10.1007/s00216-024-05330-9] [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: 04/01/2024] [Revised: 05/03/2024] [Accepted: 05/06/2024] [Indexed: 05/16/2024]
Abstract
Advances in high-throughput high-resolution mass spectrometry and the development of thermal proteome profiling approach (TPP) have made it possible to accelerate a drug target search. Since its introduction in 2014, TPP quickly became a method of choice in chemical proteomics for identifying drug-to-protein interactions on a proteome-wide scale and mapping the pathways of these interactions, thus further elucidating the unknown mechanisms of action of a drug under study. However, the current TPP implementations based on tandem mass spectrometry (MS/MS), associated with employing lengthy peptide separation protocols and expensive labeling techniques for sample multiplexing, limit the scaling of this approach for the ever growing variety of drug-to-proteomes. A variety of ultrafast proteomics methods have been developed in the last couple of years. Among them, DirectMS1 provides MS/MS-free quantitative proteome-wide analysis in 5-min time scale, thus opening the way for sample-hungry applications, such as TPP. In this work, we demonstrate the first implementation of the TPP approach using the ultrafast proteome-wide analysis based on DirectMS1. Using a drug topotecan, which is a known topoisomerase I (TOP1) inhibitor, the feasibility of the method for identifying drug targets at the whole proteome level was demonstrated for an ovarian cancer cell line.
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Affiliation(s)
- Ivan I Fedorov
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, Leninsky Pr. 38, Bld.2, 119334, Moscow, Russia
- Moscow Center for Advanced Studies, Kulakova Str. 20, 123592, Moscow, Russia
| | - Julia A Bubis
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, Leninsky Pr. 38, Bld.2, 119334, Moscow, Russia
| | - Elizaveta M Kazakova
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, Leninsky Pr. 38, Bld.2, 119334, Moscow, Russia
| | - Anna A Lobas
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, Leninsky Pr. 38, Bld.2, 119334, Moscow, Russia
| | - Mark V Ivanov
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, Leninsky Pr. 38, Bld.2, 119334, Moscow, Russia
| | - Daria D Emekeeva
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, Leninsky Pr. 38, Bld.2, 119334, Moscow, Russia
| | - Irina A Tarasova
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, Leninsky Pr. 38, Bld.2, 119334, Moscow, Russia
| | - Alexey A Nazarov
- Faculty of Chemistry, Lomonosov Moscow State University, Leninskie Gory 1/3, 119991, Moscow, Russia
- Faculty of Chemistry of the National Research University Higher School of Economics, Vavilova Str. 7, 101000, Moscow, Russia
| | - Mikhail V Gorshkov
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, Leninsky Pr. 38, Bld.2, 119334, Moscow, Russia.
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16
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Yang JC, Chen SP, Wang YF, Chang CH, Chang KH, Fuh JL, Chow LH, Han CL, Chen YJ, Wang SJ. Cerebrospinal Fluid Proteome Map Reveals Molecular Signatures of Reversible Cerebral Vasoconstriction Syndrome. Mol Cell Proteomics 2024; 23:100794. [PMID: 38839039 PMCID: PMC11263949 DOI: 10.1016/j.mcpro.2024.100794] [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: 10/06/2023] [Revised: 04/08/2024] [Accepted: 05/07/2024] [Indexed: 06/07/2024] Open
Abstract
Reversible cerebral vasoconstriction syndrome (RCVS) is a complex neurovascular disorder characterized by repetitive thunderclap headaches and reversible cerebral vasoconstriction. The pathophysiological mechanism of this mysterious syndrome remains underexplored and there is no clinically available molecular biomarker. To provide insight into the pathogenesis of RCVS, this study reported the first landscape of dysregulated proteome of cerebrospinal fluid (CSF) in patients with RCVS (n = 21) compared to the age- and sex-matched controls (n = 20) using data-independent acquisition mass spectrometry. Protein-protein interaction and functional enrichment analysis were employed to construct functional protein networks using the RCVS proteome. An RCVS-CSF proteome library resource of 1054 proteins was established, which illuminated large groups of upregulated proteins enriched in the brain and blood-brain barrier (BBB). Personalized RCVS-CSF proteomic profiles from 17 RCVS patients and 20 controls reveal proteomic changes involving the complement system, adhesion molecules, and extracellular matrix, which may contribute to the disruption of BBB and dysregulation of neurovascular units. Moreover, an additional validation cohort validated a panel of biomarker candidates and a two-protein signature predicted by machine learning model to discriminate RCVS patients from controls with an area under the curve of 0.997. This study reveals the first RCVS proteome and a potential pathogenetic mechanism of BBB and neurovascular unit dysfunction. It also nominates potential biomarker candidates that are mechanistically plausible for RCVS, which may offer potential diagnostic and therapeutic opportunities beyond the clinical manifestations.
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Affiliation(s)
- Jhih-Ci Yang
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan; Sustainable Chemical Science and Technology, Taiwan International Graduate Program, Academia Sinica, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Applied Chemistry, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Shih-Pin Chen
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan; Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan; Division of Translational Research, Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan; Institute of Clinical Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
| | - Yen-Feng Wang
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan; Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan; College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chan-Hua Chang
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan; Department of Chemistry, National Central University, Taoyuan, Taiwan
| | - Kun-Hao Chang
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan; Molecular Science and Technology Program, Taiwan International Graduate Program, Academia Sinica, Taipei, Taiwan; Department of Chemistry, Institute of Chemistry, Academia Sinica, Naitonal Tsing Hua University, Hsinchu, Taiwan
| | - Jong-Ling Fuh
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan; Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan; College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Lok-Hi Chow
- College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Anesthesiology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Chia-Li Han
- Master Program in Clinical Genomics and Proteomics, College of Pharmacy, Taipei Medical University, Taipei, Taiwan.
| | - Yu-Ju Chen
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan; Sustainable Chemical Science and Technology, Taiwan International Graduate Program, Academia Sinica, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Chemistry, National Taiwan University, Taipei, Taiwan.
| | - Shuu-Jiun Wang
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan; Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan; College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
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17
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Guo W, Liu Y, Han Y, Tang H, Fan X, Wang C, Chen PR. Amplifiable protein identification via residue-resolved barcoding and composition code counting. Natl Sci Rev 2024; 11:nwae183. [PMID: 39055168 PMCID: PMC11272068 DOI: 10.1093/nsr/nwae183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Revised: 05/06/2024] [Accepted: 05/08/2024] [Indexed: 07/27/2024] Open
Abstract
Ultrasensitive protein identification is of paramount importance in basic research and clinical diagnostics but remains extremely challenging. A key bottleneck in preventing single-molecule protein sequencing is that, unlike the revolutionary nucleic acid sequencing methods that rely on the polymerase chain reaction (PCR) to amplify DNA and RNA molecules, protein molecules cannot be directly amplified. Decoding the proteins via amplification of certain fingerprints rather than the intact protein sequence thus represents an appealing alternative choice to address this formidable challenge. Herein, we report a proof-of-concept method that relies on residue-resolved DNA barcoding and composition code counting for amplifiable protein fingerprinting (AmproCode). In AmproCode, selective types of residues on peptides or proteins are chemically labeled with a DNA barcode, which can be amplified and quantified via quantitative PCR. The operation generates a relative ratio as the residue-resolved 'composition code' for each target protein that can be utilized as the fingerprint to determine its identity from the proteome database. We developed a database searching algorithm and applied it to assess the coverage of the whole proteome and secretome via computational simulations, proving the theoretical feasibility of AmproCode. We then designed the residue-specific DNA barcoding and amplification workflow, and identified different synthetic model peptides found in the secretome at as low as the fmol/L level for demonstration. These results build the foundation for an unprecedented amplifiable protein fingerprinting method. We believe that, in the future, AmproCode could ultimately realize single-molecule amplifiable identification of trace complex samples without further purification, and it may open a new avenue in the development of next-generation protein sequencing techniques.
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Affiliation(s)
- Weiming Guo
- Synthetic and Functional Biomolecules Center, Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Yuan Liu
- Synthetic and Functional Biomolecules Center, Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Yu Han
- Synthetic and Functional Biomolecules Center, Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Huan Tang
- Synthetic and Functional Biomolecules Center, Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Xinyuan Fan
- Synthetic and Functional Biomolecules Center, Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Chu Wang
- Synthetic and Functional Biomolecules Center, Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Peng R Chen
- Synthetic and Functional Biomolecules Center, Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
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18
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Webel H, Niu L, Nielsen AB, Locard-Paulet M, Mann M, Jensen LJ, Rasmussen S. Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning. Nat Commun 2024; 15:5405. [PMID: 38926340 PMCID: PMC11208500 DOI: 10.1038/s41467-024-48711-5] [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: 02/01/2023] [Accepted: 05/13/2024] [Indexed: 06/28/2024] Open
Abstract
Imputation techniques provide means to replace missing measurements with a value and are used in almost all downstream analysis of mass spectrometry (MS) based proteomics data using label-free quantification (LFQ). Here we demonstrate how collaborative filtering, denoising autoencoders, and variational autoencoders can impute missing values in the context of LFQ at different levels. We applied our method, proteomics imputation modeling mass spectrometry (PIMMS), to an alcohol-related liver disease (ALD) cohort with blood plasma proteomics data available for 358 individuals. Removing 20 percent of the intensities we were able to recover 15 out of 17 significant abundant protein groups using PIMMS-VAE imputations. When analyzing the full dataset we identified 30 additional proteins (+13.2%) that were significantly differentially abundant across disease stages compared to no imputation and found that some of these were predictive of ALD progression in machine learning models. We, therefore, suggest the use of deep learning approaches for imputing missing values in MS-based proteomics on larger datasets and provide workflows for these.
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Affiliation(s)
- Henry Webel
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen N, Denmark
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen N, Denmark
| | - Lili Niu
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen N, Denmark
| | - Annelaura Bach Nielsen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen N, Denmark
| | - Marie Locard-Paulet
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen N, Denmark
- Institut de Pharmacologie et de Biologie Structurale (IPBS), Université de Toulouse, CNRS, Université Toulouse III - Paul Sabatier (UT3), Toulouse, France
| | - Matthias Mann
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen N, Denmark
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen N, Denmark
| | - Simon Rasmussen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen N, Denmark.
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen N, Denmark.
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
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19
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Chang R, Chang C, Cai Y, Liao R. An efficient, amine-specific iTRAQ labeling method improves the peptide and protein identification rates. J Proteomics 2024; 305:105244. [PMID: 38942233 DOI: 10.1016/j.jprot.2024.105244] [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: 05/16/2024] [Revised: 06/21/2024] [Accepted: 06/25/2024] [Indexed: 06/30/2024]
Abstract
Isotope tags for relative and absolute quantification (iTRAQ) are among the most widely used proteomics quantification techniques. These tags can be rapidly coupled to the primary amines of proteins/peptides through chemical reactions under mild conditions, making this technique universally applicable to any kind of sample. However, iTRAQ reagents also partially react with the hydroxyl groups of serine, threonine and tyrosine residues, particularly when these residues coexist with a histidine residue in the same peptide. This overlabeling of peptides causes systematic biases and significantly compromises protein/peptide identification rates. In this study, we report a novel iTRAQ labeling method that overcomes the detrimental overlabeling while providing high amine labeling efficiency. The impacts of reaction temperature, reactant concentrations, reaction time, buffer compositions, and pH on iTRAQ labeling performance were investigated in-depth. In a comparison experiment between our method and the standard labeling method provided by the iTRAQ manufacturer, our method reduced the number of overlabeled peptides by 55-fold while achieving comparable amine labeling efficiency. This improvement allowed our method to eliminates the systematic bias against histidyl- and hydroxyl-containing peptides, and more importantly, enabled the identification of 23.9% more peptides and 9.8% more proteins. SIGNIFICANCE: In addition to amines, the hydroxyl groups in serine, threonine, and tyrosine residues can also partially labeled by iTRAQ reagents, which leads to systematic biases and significantly compromises the analytical sensitivity. To address this issue, we developed a novel iTRAQ labeling method that overcomes the detrimental overlabeling while providing high labeling efficiency of amines. When benchmarking our method against the standard method provided by the reagent manufacturer, our method achieved comparable labeling efficiency but reduced the overlabeled species by 55-fold. This significant improvement eliminated the systematic biases, and more importantly, enabled the identification of 23.9% more peptides and 9.8% more proteins, demonstrating its superior performance and potential to enhance proteome quantification using iTRAQ labeling.
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Affiliation(s)
- Ruomeng Chang
- Shanghai Institute of Precision Medicine, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200125, China
| | - Chenchen Chang
- Shanghai Institute of Precision Medicine, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200125, China
| | - Yan Cai
- Shanghai Institute of Precision Medicine, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200125, China
| | - Rijing Liao
- Shanghai Institute of Precision Medicine, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200125, China.
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20
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Liu Y, Lu S, Yang J, Yang Y, Jiao L, Hu J, Li Y, Yang F, Pang Y, Zhao Y, Gao Y, Liu W, Shu P, Ge W, He Z, Peng X. Analysis of the aging-related biomarker in a nonhuman primate model using multilayer omics. BMC Genomics 2024; 25:639. [PMID: 38926642 PMCID: PMC11209966 DOI: 10.1186/s12864-024-10556-z] [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: 02/09/2024] [Accepted: 06/24/2024] [Indexed: 06/28/2024] Open
Abstract
BACKGROUND Aging is a prominent risk factor for diverse diseases; therefore, an in-depth understanding of its physiological mechanisms is required. Nonhuman primates, which share the closest genetic relationship with humans, serve as an ideal model for exploring the complex aging process. However, the potential of the nonhuman primate animal model in the screening of human aging markers is still not fully exploited. Multiomics analysis of nonhuman primate peripheral blood offers a promising approach to evaluate new therapies and biomarkers. This study explores aging-related biomarker through multilayer omics, including transcriptomics (mRNA, lncRNA, and circRNA) and proteomics (serum and serum-derived exosomes) in rhesus monkeys (Macaca mulatta). RESULTS Our findings reveal that, unlike mRNAs and circRNAs, highly expressed lncRNAs are abundant during the key aging period and are associated with cancer pathways. Comparative analysis highlighted exosomal proteins contain more types of proteins than serum proteins, indicating that serum-derived exosomes primarily regulate aging through metabolic pathways. Finally, eight candidate aging biomarkers were identified, which may serve as blood-based indicators for detecting age-related brain changes. CONCLUSIONS Our results provide a comprehensive understanding of nonhuman primate blood transcriptomes and proteomes, offering novel insights into the aging mechanisms for preventing or treating age-related diseases.
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Affiliation(s)
- Yunpeng Liu
- State Key Laboratory of Respiratory Health and Multimorbidity, National Center of Technology Innovation for Animal Model, National Human Diseases Animal Model Resource Center, NHC Key Laboratory of Comparative Medicine, Beijing Engineering Research Center for Experimental Animal Models of Human Critical Diseases, Institute of Laboratory Animal Sciences, CAMS & PUMC, Beijing, 100021, China
| | - Shuaiyao Lu
- Institute of Medical Biology, Chinese Academy of Medical Sciences, Peking Union Medical College, Kunming, 650031, China
| | - Jing Yang
- Institute of Medical Biology, Chinese Academy of Medical Sciences, Peking Union Medical College, Kunming, 650031, China
| | - Yun Yang
- Institute of Medical Biology, Chinese Academy of Medical Sciences, Peking Union Medical College, Kunming, 650031, China
| | - Li Jiao
- Institute of Medical Biology, Chinese Academy of Medical Sciences, Peking Union Medical College, Kunming, 650031, China
| | - Jingwen Hu
- Institute of Medical Biology, Chinese Academy of Medical Sciences, Peking Union Medical College, Kunming, 650031, China
| | - Yanyan Li
- Institute of Medical Biology, Chinese Academy of Medical Sciences, Peking Union Medical College, Kunming, 650031, China
| | - Fengmei Yang
- Institute of Medical Biology, Chinese Academy of Medical Sciences, Peking Union Medical College, Kunming, 650031, China
| | - Yunli Pang
- Institute of Medical Biology, Chinese Academy of Medical Sciences, Peking Union Medical College, Kunming, 650031, China
| | - Yuan Zhao
- Institute of Medical Biology, Chinese Academy of Medical Sciences, Peking Union Medical College, Kunming, 650031, China
| | - Yanpan Gao
- Department of Molecular Biology and Biochemistry, Institute of Basic Medical Sciences, Medical Primate Research Center, Neuroscience Center, CAMS & PUMC, Beijing, 100005, China
| | - Wei Liu
- Department of Molecular Biology and Biochemistry, Institute of Basic Medical Sciences, Medical Primate Research Center, Neuroscience Center, CAMS & PUMC, Beijing, 100005, China
| | - Pengcheng Shu
- Department of Molecular Biology and Biochemistry, Institute of Basic Medical Sciences, Medical Primate Research Center, Neuroscience Center, CAMS & PUMC, Beijing, 100005, China
| | - Wei Ge
- Department of Molecular Biology and Biochemistry, Institute of Basic Medical Sciences, Medical Primate Research Center, Neuroscience Center, CAMS & PUMC, Beijing, 100005, China
| | - Zhanlong He
- Institute of Medical Biology, Chinese Academy of Medical Sciences, Peking Union Medical College, Kunming, 650031, China.
| | - Xiaozhong Peng
- State Key Laboratory of Respiratory Health and Multimorbidity, National Center of Technology Innovation for Animal Model, National Human Diseases Animal Model Resource Center, NHC Key Laboratory of Comparative Medicine, Beijing Engineering Research Center for Experimental Animal Models of Human Critical Diseases, Institute of Laboratory Animal Sciences, CAMS & PUMC, Beijing, 100021, China.
- Institute of Medical Biology, Chinese Academy of Medical Sciences, Peking Union Medical College, Kunming, 650031, China.
- Department of Molecular Biology and Biochemistry, Institute of Basic Medical Sciences, Medical Primate Research Center, Neuroscience Center, CAMS & PUMC, Beijing, 100005, China.
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21
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De Silva S, Alli-Shaik A, Gunaratne J. Machine Learning-Enhanced Extraction of Biomarkers for High-Grade Serous Ovarian Cancer from Proteomics Data. Sci Data 2024; 11:685. [PMID: 38918474 PMCID: PMC11199488 DOI: 10.1038/s41597-024-03536-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 06/17/2024] [Indexed: 06/27/2024] Open
Abstract
Comprehensive biomedical proteomic datasets are accumulating exponentially, warranting robust analytics to deconvolute them for identifying novel biological insights. Here, we report a strategic machine learning (ML)-based feature extraction workflow that was applied to unveil high-performing protein markers for high-grade serous ovarian carcinoma (HGSOC) from publicly available ovarian cancer tissue and serum proteomics datasets. Diagnosis of HGSOC, an aggressive form of ovarian cancer, currently relies on diagnostic methods based on tissue biopsy and/or non-specific biomarkers such as the cancer antigen 125 (CA125) and human epididymis protein 4 (HE4). Our newly developed ML-based approach enabled the identification of new serum proteomic biomarkers for HGSOC. The performance verification of these marker combinations using two independent cohorts affirmed their outperformance against known biomarkers for ovarian cancer including clinically used serum markers with >97% AUC. Our analysis also added novel biological insights such as enriched cancer-related processes associated with HGSOC.
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Affiliation(s)
- Senuri De Silva
- Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), Singapore, 138673, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117594, Singapore
| | - Asfa Alli-Shaik
- Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), Singapore, 138673, Singapore
| | - Jayantha Gunaratne
- Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), Singapore, 138673, Singapore.
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117594, Singapore.
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22
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Peng M, Zhou Y, Zhang Y, Cong Y, Zhao M, Wang F, Ding T, Liu C, Ni C, Ding J, Sun W, Lyu X, Fan C, Li D, Guo X, Liu X, Li X. Small extracellular vesicle CA1 as a promising diagnostic biomarker for nasopharyngeal carcinoma. Int J Biol Macromol 2024; 275:133403. [PMID: 38917926 DOI: 10.1016/j.ijbiomac.2024.133403] [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: 01/11/2024] [Revised: 06/20/2024] [Accepted: 06/22/2024] [Indexed: 06/27/2024]
Abstract
Nasopharyngeal carcinoma (NPC), a malignant cancer originating from the epithelial cells of the nasopharynx, presents diagnostic challenges with current methods such as plasma Epstein-Barr virus (EBV) DNA testing showing limited efficacy. This study focused on identifying small extracellular vesicle (sEV) proteins as potential noninvasive biomarkers to enhance NPC diagnostic accuracy. We isolated sEVs from plasma and utilized 4D label-free proteomics to identify differentially expressed proteins (DEPs) among healthy controls (NC = 10), early-stage NPC (E-NPC = 10), and late-stage NPC (L-NPC = 10). Eighteen sEV proteins were identified as potential biomarkers. Subsequently, parallel reaction monitoring (PRM) proteomic analysis preliminarily confirmed sEV carbonic anhydrase 1 (CA1) as a highly promising biomarker for NPC, particularly in early-stage diagnosis (NC = 15; E-NPC = 10; L-NPC = 15). To facilitate this, we developed an automated, high-throughput and highly sensitive CA1 immune-chemiluminescence chip technology characterized by a broad linear detection range and robust controls. Further validation in an independent retrospective cohort (NC = 89; E-NPC = 39; L-NPC = 172) using this technology confirmed sEV CA1 as a reliable diagnostic biomarker for NPC (AUC = 0.9809) and E-NPC (AUC = 0.9893), independent of EBV-DNA testing. Notably, sEV CA1 exhibited superior diagnostic performance compared to EBV-DNA, with a significant incremental net reclassification improvement of 27.61 % for NPC and 72.11 % for E-NPC detection. Thus, this study identifies sEV CA1 as an innovative diagnostic biomarker for NPC and E-NPC independent of EBV-DNA. Additionally, it establishes an immune-chemiluminescence chip technology for the detection of sEV CA1 protein, paving the way for further validation and clinical application.
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Affiliation(s)
- Manli Peng
- Shenzhen Key Laboratory of Viral Oncology, The Clinical Innovation & Research Center (CIRC), Shenzhen Hospital, Southern Medical University, Shenzhen, Guangdong, China; The Third School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, China
| | - Yanqing Zhou
- Shenzhen Key Laboratory of Viral Oncology, The Clinical Innovation & Research Center (CIRC), Shenzhen Hospital, Southern Medical University, Shenzhen, Guangdong, China; The Third School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, China
| | - Yuanbin Zhang
- Shenzhen Key Laboratory of Viral Oncology, The Clinical Innovation & Research Center (CIRC), Shenzhen Hospital, Southern Medical University, Shenzhen, Guangdong, China; The Third School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, China
| | - Ying Cong
- Shenzhen Key Laboratory of Viral Oncology, The Clinical Innovation & Research Center (CIRC), Shenzhen Hospital, Southern Medical University, Shenzhen, Guangdong, China; The Third School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, China
| | - Min Zhao
- PANACRO (Hefei) Pharmaceutical Technology Co., Ltd., Hefei, China
| | - Fei Wang
- Shenzhen Key Laboratory of Viral Oncology, The Clinical Innovation & Research Center (CIRC), Shenzhen Hospital, Southern Medical University, Shenzhen, Guangdong, China
| | - Tengteng Ding
- Shenzhen Key Laboratory of Viral Oncology, The Clinical Innovation & Research Center (CIRC), Shenzhen Hospital, Southern Medical University, Shenzhen, Guangdong, China; The Third School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, China
| | - Changlin Liu
- Shenzhen Key Laboratory of Viral Oncology, The Clinical Innovation & Research Center (CIRC), Shenzhen Hospital, Southern Medical University, Shenzhen, Guangdong, China; The Third School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, China
| | - Chuping Ni
- Shenzhen Key Laboratory of Viral Oncology, The Clinical Innovation & Research Center (CIRC), Shenzhen Hospital, Southern Medical University, Shenzhen, Guangdong, China; The Third School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, China
| | - Junjie Ding
- Sanliant Biological Engineering Co., Ltd., Jiangsu, China
| | - Wenwen Sun
- Sanliant Biological Engineering Co., Ltd., Jiangsu, China
| | - Xiaoming Lyu
- Department of Laboratory Medicine, The Third Affiliated Hospital of Southern Medical University. Guangzhou, Guangdong, 510630, China
| | - Chao Fan
- Shenzhen Key Laboratory of Viral Oncology, The Clinical Innovation & Research Center (CIRC), Shenzhen Hospital, Southern Medical University, Shenzhen, Guangdong, China
| | - Dengke Li
- Guangdong Provincial Key Laboratory of Tumor Immunotherapy, Cancer Research Institute, School of Basic Medical Sciences, Southern Medical University, Guangzhou, PR China
| | - Xia Guo
- Shenzhen Key Laboratory of Viral Oncology, The Clinical Innovation & Research Center (CIRC), Shenzhen Hospital, Southern Medical University, Shenzhen, Guangdong, China; The Third School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, China.
| | - Xiong Liu
- Department of Otolaryngology, Nanfang Hospital, Southern Medical University, Guangdong, China.
| | - Xin Li
- Shenzhen Key Laboratory of Viral Oncology, The Clinical Innovation & Research Center (CIRC), Shenzhen Hospital, Southern Medical University, Shenzhen, Guangdong, China; The Third School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, China.
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23
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Oliinyk D, Will A, Schneidmadel FR, Böhme M, Rinke J, Hochhaus A, Ernst T, Hahn N, Geis C, Lubeck M, Raether O, Humphrey SJ, Meier F. µPhos: a scalable and sensitive platform for high-dimensional phosphoproteomics. Mol Syst Biol 2024:10.1038/s44320-024-00050-9. [PMID: 38907068 DOI: 10.1038/s44320-024-00050-9] [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: 08/25/2023] [Revised: 06/06/2024] [Accepted: 06/11/2024] [Indexed: 06/23/2024] Open
Abstract
Mass spectrometry has revolutionized cell signaling research by vastly simplifying the analysis of many thousands of phosphorylation sites in the human proteome. Defining the cellular response to perturbations is crucial for further illuminating the functionality of the phosphoproteome. Here we describe µPhos ('microPhos'), an accessible phosphoproteomics platform that permits phosphopeptide enrichment from 96-well cell culture and small tissue amounts in <8 h total processing time. By greatly minimizing transfer steps and liquid volumes, we demonstrate increased sensitivity, >90% selectivity, and excellent quantitative reproducibility. Employing highly sensitive trapped ion mobility mass spectrometry, we quantify ~17,000 Class I phosphosites in a human cancer cell line using 20 µg starting material, and confidently localize ~6200 phosphosites from 1 µg. This depth covers key signaling pathways, rendering sample-limited applications and perturbation experiments with hundreds of samples viable. We employ µPhos to study drug- and time-dependent response signatures in a leukemia cell line, and by quantifying 30,000 Class I phosphosites in the mouse brain we reveal distinct spatial kinase activities in subregions of the hippocampal formation.
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Affiliation(s)
- Denys Oliinyk
- Functional Proteomics, Jena University Hospital, 07747, Jena, Germany
- Comprehensive Cancer Center Central Germany, 07747, Jena, Germany
| | - Andreas Will
- Functional Proteomics, Jena University Hospital, 07747, Jena, Germany
- Comprehensive Cancer Center Central Germany, 07747, Jena, Germany
| | - Felix R Schneidmadel
- Functional Proteomics, Jena University Hospital, 07747, Jena, Germany
- Comprehensive Cancer Center Central Germany, 07747, Jena, Germany
| | - Maximilian Böhme
- Comprehensive Cancer Center Central Germany, 07747, Jena, Germany
- Klinik für Innere Medizin II, Jena University Hospital, 07747, Jena, Germany
| | - Jenny Rinke
- Comprehensive Cancer Center Central Germany, 07747, Jena, Germany
- Klinik für Innere Medizin II, Jena University Hospital, 07747, Jena, Germany
| | - Andreas Hochhaus
- Comprehensive Cancer Center Central Germany, 07747, Jena, Germany
- Klinik für Innere Medizin II, Jena University Hospital, 07747, Jena, Germany
| | - Thomas Ernst
- Comprehensive Cancer Center Central Germany, 07747, Jena, Germany
- Klinik für Innere Medizin II, Jena University Hospital, 07747, Jena, Germany
| | - Nina Hahn
- Section of Translational Neuroimmunology, Department of Neurology, Jena University Hospital, 07747, Jena, Germany
- Center for Sepsis Control and Care, Jena University Hospital, 07747, Jena, Germany
| | - Christian Geis
- Section of Translational Neuroimmunology, Department of Neurology, Jena University Hospital, 07747, Jena, Germany
- Center for Sepsis Control and Care, Jena University Hospital, 07747, Jena, Germany
| | - Markus Lubeck
- Bruker Daltonics GmbH & Co. KG, 28359, Bremen, Germany
| | | | - Sean J Humphrey
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, 3052, Victoria, Australia.
| | - Florian Meier
- Functional Proteomics, Jena University Hospital, 07747, Jena, Germany.
- Comprehensive Cancer Center Central Germany, 07747, Jena, Germany.
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24
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Fan Q, Wen S, Zhang Y, Feng X, Zheng W, Liang X, Lin Y, Zhao S, Xie K, Jiang H, Tang H, Zeng X, Guo Y, Wang F, Yang X. Assessment of circulating proteins in thyroid cancer: Proteome-wide Mendelian randomization and colocalization analysis. iScience 2024; 27:109961. [PMID: 38947504 PMCID: PMC11214373 DOI: 10.1016/j.isci.2024.109961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 03/29/2024] [Accepted: 05/09/2024] [Indexed: 07/02/2024] Open
Abstract
The causality between circulating proteins and thyroid cancer (TC) remains unclear. We employed five large-scale circulating proteomic genome-wide association studies (GWASs) with up to 100,000 participants and a TC meta-GWAS (nCase = 3,418, nControl = 292,703) to conduct proteome-wide Mendelian randomization (MR) and Bayesian colocalization analysis. Protein and gene expressions were validated in thyroid tissue. Through MR analysis, we identified 26 circulating proteins with a putative causal relationship with TCs, among which NANS protein passed multiple corrections (P BH = 3.28e-5, 0.05/1,525). These proteins were involved in amino acids and organic acid synthesis pathways. Colocalization analysis further identified six proteins associated with TCs (VCAM1, LGMN, NPTX1, PLEKHA7, TNFAIP3, and BMP1). Tissue validation confirmed BMP1, LGMN, and PLEKHA7's differential expression between normal and TC tissues. We found limited evidence for linking circulating proteins and the risk of TCs. Our study highlighted the contribution of proteins, particularly those involved in amino acid metabolism, to TCs.
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Affiliation(s)
- Qinghua Fan
- The School of Public Health, Guangxi Medical University, Nanning 530000, Guangxi, China
- Guangxi Key Laboratory on Precise Prevention and Treatment for Thyroid Tumor, The Second Affiliated Hospital, Guangxi University of Science and Technology, Liuzhou 545000, Guangxi, China
| | - Shifeng Wen
- The School of Public Health, Guangxi Medical University, Nanning 530000, Guangxi, China
- Guangxi Key Laboratory on Precise Prevention and Treatment for Thyroid Tumor, The Second Affiliated Hospital, Guangxi University of Science and Technology, Liuzhou 545000, Guangxi, China
| | - Yi Zhang
- The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, Henan, China
| | - Xiuming Feng
- The School of Public Health, Guangxi Medical University, Nanning 530000, Guangxi, China
- Guangxi Key Laboratory on Precise Prevention and Treatment for Thyroid Tumor, The Second Affiliated Hospital, Guangxi University of Science and Technology, Liuzhou 545000, Guangxi, China
| | - Wanting Zheng
- The School of Public Health, Guangxi Medical University, Nanning 530000, Guangxi, China
- Guangxi Key Laboratory on Precise Prevention and Treatment for Thyroid Tumor, The Second Affiliated Hospital, Guangxi University of Science and Technology, Liuzhou 545000, Guangxi, China
| | - Xiaolin Liang
- The School of Public Health, Guangxi Medical University, Nanning 530000, Guangxi, China
- Guangxi Key Laboratory on Precise Prevention and Treatment for Thyroid Tumor, The Second Affiliated Hospital, Guangxi University of Science and Technology, Liuzhou 545000, Guangxi, China
| | - Yutong Lin
- The School of Public Health, Guangxi Medical University, Nanning 530000, Guangxi, China
- Guangxi Key Laboratory on Precise Prevention and Treatment for Thyroid Tumor, The Second Affiliated Hospital, Guangxi University of Science and Technology, Liuzhou 545000, Guangxi, China
| | - Shimei Zhao
- The Second Affiliated Hospital of Guangxi University of Science and Technology, Liuzhou 545000, Guangxi, China
| | - Kaisheng Xie
- The Second Affiliated Hospital of Guangxi University of Science and Technology, Liuzhou 545000, Guangxi, China
| | - Hancheng Jiang
- Liuzhou Workers' Hospital, Liuzhou 545000, Guangxi, China
| | - Haifeng Tang
- The Second People’s Hospital of Yulin, Yulin 537000, Guangxi, China
| | - Xiangtai Zeng
- The First Affiliated Hospital of Gannan Medical University, Ganzhou 341000, Jiangxi, China
| | - You Guo
- The First Affiliated Hospital of Gannan Medical University, Ganzhou 341000, Jiangxi, China
| | - Fei Wang
- The School of Public Health, Guangxi Medical University, Nanning 530000, Guangxi, China
- Guangxi Key Laboratory on Precise Prevention and Treatment for Thyroid Tumor, The Second Affiliated Hospital, Guangxi University of Science and Technology, Liuzhou 545000, Guangxi, China
| | - Xiaobo Yang
- The School of Public Health, Guangxi Medical University, Nanning 530000, Guangxi, China
- Guangxi Key Laboratory on Precise Prevention and Treatment for Thyroid Tumor, The Second Affiliated Hospital, Guangxi University of Science and Technology, Liuzhou 545000, Guangxi, China
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25
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Fucito M, Spedicato M, Felletti S, Yu AC, Busin M, Pasti L, Franchina FA, Cavazzini A, De Luca C, Catani M. A Look into Ocular Diseases: The Pivotal Role of Omics Sciences in Ophthalmology Research. ACS MEASUREMENT SCIENCE AU 2024; 4:247-259. [PMID: 38910860 PMCID: PMC11191728 DOI: 10.1021/acsmeasuresciau.3c00067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 02/06/2024] [Accepted: 02/07/2024] [Indexed: 06/25/2024]
Abstract
Precision medicine is a new medical approach which considers both population characteristics and individual variability to provide customized healthcare. The transition from traditional reactive medicine to personalized medicine is based on a biomarker-driven process and a deep knowledge of biological mechanisms according to which the development of diseases occurs. In this context, the advancements in high-throughput omics technologies represent a unique opportunity to discover novel biomarkers and to provide an unbiased picture of the biological system. One of the medical fields in which omics science has started to be recently applied is that of ophthalmology. Ocular diseases are very common, and some of them could be highly disabling, thus leading to vision loss and blindness. The pathogenic mechanism of most ocular diseases may be dependent on various genetic and environmental factors, whose effect has not been yet completely understood. In this context, large-scale omics approaches are fundamental to have a comprehensive evaluation of the whole system and represent an essential tool for the development of novel therapies. This Review summarizes the recent advancements in omics science applied to ophthalmology in the last ten years, in particular by focusing on proteomics, metabolomics and lipidomics applications from an analytical perspective. The role of high-efficiency separation techniques coupled to (high-resolution) mass spectrometry ((HR)MS) is also discussed, as well as the impact of sampling, sample preparation and data analysis as integrating parts of the analytical workflow.
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Affiliation(s)
- Maurine Fucito
- Department
of Chemical, Pharmaceutical and Agricultural Sciences, University of Ferrara, via L. Borsari 46, 44121 Ferrara, Italy
| | - Matteo Spedicato
- Department
of Chemical, Pharmaceutical and Agricultural Sciences, University of Ferrara, via L. Borsari 46, 44121 Ferrara, Italy
| | - Simona Felletti
- Department
of Environmental and Prevention Sciences, University of Ferrara, via L. Borsari 46, Ferrara 44121, Italy
| | - Angeli Christy Yu
- Department
of Translational Medicine and for Romagna, University of Ferrara, via Aldo Moro 8, 44124 Ferrara, Italy
| | - Massimo Busin
- Department
of Translational Medicine and for Romagna, University of Ferrara, via Aldo Moro 8, 44124 Ferrara, Italy
| | - Luisa Pasti
- Department
of Environmental and Prevention Sciences, University of Ferrara, via L. Borsari 46, Ferrara 44121, Italy
| | - Flavio A. Franchina
- Department
of Chemical, Pharmaceutical and Agricultural Sciences, University of Ferrara, via L. Borsari 46, 44121 Ferrara, Italy
| | - Alberto Cavazzini
- Department
of Chemical, Pharmaceutical and Agricultural Sciences, University of Ferrara, via L. Borsari 46, 44121 Ferrara, Italy
- Council
for Agricultural Research and Economics, via della Navicella 2/4, Rome 00184, Italy
| | - Chiara De Luca
- Department
of Chemical, Pharmaceutical and Agricultural Sciences, University of Ferrara, via L. Borsari 46, 44121 Ferrara, Italy
| | - Martina Catani
- Department
of Chemical, Pharmaceutical and Agricultural Sciences, University of Ferrara, via L. Borsari 46, 44121 Ferrara, Italy
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26
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Roberts DS, Loo JA, Tsybin YO, Liu X, Wu S, Chamot-Rooke J, Agar JN, Paša-Tolić L, Smith LM, Ge Y. Top-down proteomics. NATURE REVIEWS. METHODS PRIMERS 2024; 4:38. [PMID: 39006170 PMCID: PMC11242913 DOI: 10.1038/s43586-024-00318-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/24/2024] [Indexed: 07/16/2024]
Abstract
Proteoforms, which arise from post-translational modifications, genetic polymorphisms and RNA splice variants, play a pivotal role as drivers in biology. Understanding proteoforms is essential to unravel the intricacies of biological systems and bridge the gap between genotypes and phenotypes. By analysing whole proteins without digestion, top-down proteomics (TDP) provides a holistic view of the proteome and can decipher protein function, uncover disease mechanisms and advance precision medicine. This Primer explores TDP, including the underlying principles, recent advances and an outlook on the future. The experimental section discusses instrumentation, sample preparation, intact protein separation, tandem mass spectrometry techniques and data collection. The results section looks at how to decipher raw data, visualize intact protein spectra and unravel data analysis. Additionally, proteoform identification, characterization and quantification are summarized, alongside approaches for statistical analysis. Various applications are described, including the human proteoform project and biomedical, biopharmaceutical and clinical sciences. These are complemented by discussions on measurement reproducibility, limitations and a forward-looking perspective that outlines areas where the field can advance, including potential future applications.
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Affiliation(s)
- David S Roberts
- Department of Chemistry, Stanford University, Stanford, CA, USA
- Sarafan ChEM-H, Stanford University, Stanford, CA, USA
| | - Joseph A Loo
- Department of Chemistry and Biochemistry, Department of Biological Chemistry, University of California - Los Angeles, Los Angeles, CA, USA
| | | | - Xiaowen Liu
- Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, USA
| | - Si Wu
- Department of Chemistry and Biochemistry, The University of Alabama, Tuscaloosa, AL, USA
| | | | - Jeffrey N Agar
- Departments of Chemistry and Chemical Biology and Pharmaceutical Sciences, Northeastern University, Boston, MA, USA
| | - Ljiljana Paša-Tolić
- Environmental and Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Lloyd M Smith
- Department of Chemistry, University of Wisconsin, Madison, WI, USA
| | - Ying Ge
- Department of Chemistry, University of Wisconsin, Madison, WI, USA
- Department of Cell and Regenerative Biology, Human Proteomics Program, University of Wisconsin - Madison, Madison, WI, USA
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Desmurget C, Perilleux A, Souquet J, Borth N, Douet J. Molecular biomarkers identification and applications in CHO bioprocessing. J Biotechnol 2024; 392:11-24. [PMID: 38852681 DOI: 10.1016/j.jbiotec.2024.06.005] [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: 12/18/2023] [Revised: 05/23/2024] [Accepted: 06/04/2024] [Indexed: 06/11/2024]
Abstract
Biomarkers are valuable tools in clinical research where they allow to predict susceptibility to diseases, or response to specific treatments. Likewise, biomarkers can be extremely useful in the biomanufacturing of therapeutic proteins. Indeed, constraints such as short timelines and the need to find hyper-productive cells could benefit from a data-driven approach during cell line and process development. Many companies still rely on large screening capacities to develop productive cell lines, but as they reach a limit of production, there is a need to go from empirical to rationale procedures. Similarly, during bioprocessing runs, substrate consumption and metabolism wastes are commonly monitored. None of them possess the ability to predict the culture behavior in the bioreactor. Big data driven approaches are being adapted to the study of industrial mammalian cell lines, enabled by the publication of Chinese hamster and CHO genome assemblies which allowed the use of next-generation sequencing with these cells, as well as continuous proteome and metabolome annotation. However, if these different -omics technologies contributed to the characterization of CHO cells, there is a significant effort remaining to apply this knowledge to biomanufacturing methods. The correlation of a complex phenotype such as high productivity or rapid growth to the presence or expression level of a specific biomarker could save time and effort in the screening of manufacturing cell lines or culture conditions. In this review we will first discuss the different biological molecules that can be identified and quantified in cells, their detection techniques, and associated challenges. We will then review how these markers are used during the different steps of cell line and bioprocess development, and the inherent limitations of this strategy.
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Affiliation(s)
- Caroline Desmurget
- Merck Biotech Development Center, Ares Trading SA (an affiliate of Merck KGaA, Darmstadt, Germany), Fenil-sur-Corsier, Switzerland
| | - Arnaud Perilleux
- Merck Biotech Development Center, Ares Trading SA (an affiliate of Merck KGaA, Darmstadt, Germany), Fenil-sur-Corsier, Switzerland
| | - Jonathan Souquet
- Merck Biotech Development Center, Ares Trading SA (an affiliate of Merck KGaA, Darmstadt, Germany), Fenil-sur-Corsier, Switzerland
| | - Nicole Borth
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria
| | - Julien Douet
- Merck Biotech Development Center, Ares Trading SA (an affiliate of Merck KGaA, Darmstadt, Germany), Fenil-sur-Corsier, Switzerland.
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Zhang C, Tang H, Li T, Wu H, Gu Y, Zhang J, Zhang Z, Zhao L, Li Y, Gu L, Zhang H. Integrating Physiological Features and Proteomic Analyses Provides New Insights in Blue/Red Light-Treated Moso Bamboo ( Phyllostachys edulis). JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024; 72:12859-12870. [PMID: 38780458 DOI: 10.1021/acs.jafc.4c00724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
Bamboo is one of the most important nontimber forestry products in the world. Light is not only the most critical source of energy for plant photosynthesis but also involved in regulating the biological processes of plants. However, there are few reports on how blue/red light affects Moso bamboo. This study investigated the growth status and physiological responses of Moso bamboo (Phyllostachys edulis) to blue/red light treatments. The growth status of the bamboo plants was evaluated, revealing that both blue- and red-light treatments promoted plant height and overall growth. Gas exchange parameters, chlorophyll fluorescence, and enzyme activity were measured to assess the photosystem response of Moso bamboo to light treatments. Additionally, the blue light treatment led to a higher chlorophyll content and enzyme activities compared to the red light treatment. A tandem mass tag quantitative proteomics approach identified significant changes in protein abundance under different light conditions with specific response proteins associated with distinct pathways, such as photosynthesis and starch metabolism. Overall, this study provides valuable insights into the physiological and proteomic responses of Moso bamboo to blue/red light treatments, highlighting their potential impact on growth and development.
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Affiliation(s)
- Chuanyu Zhang
- College of Forestry, Basic Forestry and Proteomics Research Center, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Haohao Tang
- College of Forestry, Basic Forestry and Proteomics Research Center, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Tuhe Li
- College of Forestry, Basic Forestry and Proteomics Research Center, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Hongwei Wu
- College of Forestry, Basic Forestry and Proteomics Research Center, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Yuying Gu
- School of Future Technology, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Jun Zhang
- College of Life Science, Basic Forestry and Proteomics Research Center, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Zeyu Zhang
- College of Forestry, Basic Forestry and Proteomics Research Center, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Liangzhen Zhao
- College of Forestry, Basic Forestry and Proteomics Research Center, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Yaxing Li
- College of Forestry, Basic Forestry and Proteomics Research Center, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Lianfeng Gu
- College of Forestry, Basic Forestry and Proteomics Research Center, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Hangxiao Zhang
- College of Forestry, Basic Forestry and Proteomics Research Center, Fujian Agriculture and Forestry University, Fuzhou 350002, China
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29
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Nygaard U, Nielsen AB, Dungu KHS, Drici L, Holm M, Ottenheijm ME, Nielsen AB, Glenthøj JP, Schmidt LS, Cortes D, Jørgensen IM, Mogensen TH, Schmiegelow K, Mann M, Vissing NH, Wewer Albrechtsen NJ. Proteomic profiling reveals diagnostic signatures and pathogenic insights in multisystem inflammatory syndrome in children. Commun Biol 2024; 7:688. [PMID: 38839859 PMCID: PMC11153518 DOI: 10.1038/s42003-024-06370-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Accepted: 05/22/2024] [Indexed: 06/07/2024] Open
Abstract
Multisystem inflammatory syndrome in children (MIS-C) is a severe disease that emerged during the COVID-19 pandemic. Although recognized as an immune-mediated condition, the pathogenesis remains unresolved. Furthermore, the absence of a diagnostic test can lead to delayed immunotherapy. Using state-of-the-art mass-spectrometry proteomics, assisted by artificial intelligence (AI), we aimed to identify a diagnostic signature for MIS-C and to gain insights into disease mechanisms. We identified a highly specific 4-protein diagnostic signature in children with MIS-C. Furthermore, we identified seven clusters that differed between MIS-C and controls, indicating an interplay between apolipoproteins, immune response proteins, coagulation factors, platelet function, and the complement cascade. These intricate protein patterns indicated MIS-C as an immunometabolic condition with global hypercoagulability. Our findings emphasize the potential of AI-assisted proteomics as a powerful and unbiased tool for assessing disease pathogenesis and suggesting avenues for future interventions and impact on pediatric disease trajectories through early diagnosis.
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Affiliation(s)
- Ulrikka Nygaard
- Department of Pediatrics and Adolescent Medicine, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
| | - Annelaura Bach Nielsen
- NNF Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Kia Hee Schultz Dungu
- Department of Pediatrics and Adolescent Medicine, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Lylia Drici
- NNF Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Mette Holm
- Department of Pediatrics and Adolescent Medicine, Aarhus University Hospital, Aarhus, Denmark
| | - Maud Eline Ottenheijm
- NNF Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Allan Bybeck Nielsen
- Department of Pediatrics and Adolescent Medicine, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Department of Pediatrics and Adolescent Medicine, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
| | - Jonathan Peter Glenthøj
- Department of Pediatrics and Adolescent Medicine, Copenhagen University Hospital North Zealand, Hillerød, Denmark
| | - Lisbeth Samsø Schmidt
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Pediatrics and Adolescent Medicine, Copenhagen University Hospital Herlev, Herlev, Denmark
| | - Dina Cortes
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Pediatrics and Adolescent Medicine, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
| | - Inger Merete Jørgensen
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Pediatrics and Adolescent Medicine, Copenhagen University Hospital North Zealand, Hillerød, Denmark
| | | | - Kjeld Schmiegelow
- Department of Pediatrics and Adolescent Medicine, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Matthias Mann
- NNF Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Nadja Hawwa Vissing
- Department of Pediatrics and Adolescent Medicine, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Nicolai J Wewer Albrechtsen
- NNF Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Biochemistry, Copenhagen University Hospital - Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark
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30
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Lewis JM, Jebeli L, Coulon PML, Lay CE, Scott NE. Glycoproteomic and proteomic analysis of Burkholderia cenocepacia reveals glycosylation events within FliF and MotB are dispensable for motility. Microbiol Spectr 2024; 12:e0034624. [PMID: 38709084 DOI: 10.1128/spectrum.00346-24] [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: 02/10/2024] [Accepted: 04/16/2024] [Indexed: 05/07/2024] Open
Abstract
Across the Burkholderia genus O-linked protein glycosylation is highly conserved. While the inhibition of glycosylation has been shown to be detrimental for virulence in Burkholderia cepacia complex species, such as Burkholderia cenocepacia, little is known about how specific glycosylation sites impact protein functionality. Within this study, we sought to improve our understanding of the breadth, dynamics, and requirement for glycosylation across the B. cenocepacia O-glycoproteome. Assessing the B. cenocepacia glycoproteome across different culture media using complementary glycoproteomic approaches, we increase the known glycoproteome to 141 glycoproteins. Leveraging this repertoire of glycoproteins, we quantitively assessed the glycoproteome of B. cenocepacia using Data-Independent Acquisition (DIA) revealing the B. cenocepacia glycoproteome is largely stable across conditions with most glycoproteins constitutively expressed. Examination of how the absence of glycosylation impacts the glycoproteome reveals that the protein abundance of only five glycoproteins (BCAL1086, BCAL2974, BCAL0525, BCAM0505, and BCAL0127) are altered by the loss of glycosylation. Assessing ΔfliF (ΔBCAL0525), ΔmotB (ΔBCAL0127), and ΔBCAM0505 strains, we demonstrate the loss of FliF, and to a lesser extent MotB, mirror the proteomic effects observed in the absence of glycosylation in ΔpglL. While both MotB and FliF are essential for motility, we find loss of glycosylation sites in MotB or FliF does not impact motility supporting these sites are dispensable for function. Combined this work broadens our understanding of the B. cenocepacia glycoproteome supporting that the loss of glycoproteins in the absence of glycosylation is not an indicator of the requirement for glycosylation for protein function. IMPORTANCE Burkholderia cenocepacia is an opportunistic pathogen of concern within the Cystic Fibrosis community. Despite a greater appreciation of the unique physiology of B. cenocepacia gained over the last 20 years a complete understanding of the proteome and especially the O-glycoproteome, is lacking. In this study, we utilize systems biology approaches to expand the known B. cenocepacia glycoproteome as well as track the dynamics of glycoproteins across growth phases, culturing media and in response to the loss of glycosylation. We show that the glycoproteome of B. cenocepacia is largely stable across conditions and that the loss of glycosylation only impacts five glycoproteins including the motility associated proteins FliF and MotB. Examination of MotB and FliF shows, while these proteins are essential for motility, glycosylation is dispensable. Combined this work supports that B. cenocepacia glycosylation can be dispensable for protein function and may influence protein properties beyond stability.
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Affiliation(s)
- Jessica M Lewis
- Department of Microbiology and Immunology, University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Leila Jebeli
- Department of Microbiology and Immunology, University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Pauline M L Coulon
- Department of Microbiology and Immunology, University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Catrina E Lay
- Department of Microbiology and Immunology, University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Nichollas E Scott
- Department of Microbiology and Immunology, University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
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Chamrád I, Simerský R, Lenobel R, Novák O. Exploring affinity chromatography in proteomics: A comprehensive review. Anal Chim Acta 2024; 1306:342513. [PMID: 38692783 DOI: 10.1016/j.aca.2024.342513] [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: 12/12/2023] [Revised: 03/19/2024] [Accepted: 03/20/2024] [Indexed: 05/03/2024]
Abstract
Over the past decades, the proteomics field has undergone rapid growth. Progress in mass spectrometry and bioinformatics, together with separation methods, has brought many innovative approaches to the study of the molecular biology of the cell. The potential of affinity chromatography was recognized immediately after its first application in proteomics, and since that time, it has become one of the cornerstones of many proteomic protocols. Indeed, this chromatographic technique exploiting the specific binding between two molecules has been employed for numerous purposes, from selective removal of interfering (over)abundant proteins or enrichment of scarce biomarkers in complex biological samples to mapping the post-translational modifications and protein interactions with other proteins, nucleic acids or biologically active small molecules. This review presents a comprehensive survey of this versatile analytical tool in current proteomics. To navigate the reader, the haphazard space of affinity separations is classified according to the experiment's aims and the separated molecule's nature. Different types of available ligands and experimental strategies are discussed in further detail for each of the mentioned procedures.
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Affiliation(s)
- Ivo Chamrád
- Laboratory of Growth Regulators, Faculty of Science, Palacký University and Institute of Experimental Botany of the Czech Academy of Sciences, Šlechtitelů 241/27, CZ-77900, Olomouc, Holice, Czech Republic.
| | - Radim Simerský
- Department of Chemical Biology, Faculty of Science, Palacký University, Šlechtitelů 241/27, CZ-77900, Olomouc, Holice, Czech Republic
| | - René Lenobel
- Laboratory of Growth Regulators, Faculty of Science, Palacký University and Institute of Experimental Botany of the Czech Academy of Sciences, Šlechtitelů 241/27, CZ-77900, Olomouc, Holice, Czech Republic
| | - Ondřej Novák
- Laboratory of Growth Regulators, Faculty of Science, Palacký University and Institute of Experimental Botany of the Czech Academy of Sciences, Šlechtitelů 241/27, CZ-77900, Olomouc, Holice, Czech Republic
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32
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Thiery J, Fahrner M. Integration of proteomics in the molecular tumor board. Proteomics 2024; 24:e2300002. [PMID: 38143279 DOI: 10.1002/pmic.202300002] [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: 07/24/2023] [Revised: 12/03/2023] [Accepted: 12/05/2023] [Indexed: 12/26/2023]
Abstract
Cancer remains one of the most complex and challenging diseases in mankind. To address the need for a personalized treatment approach for particularly complex tumor cases, molecular tumor boards (MTBs) have been initiated. MTBs are interdisciplinary teams that perform in-depth molecular diagnostics to cooperatively and interdisciplinarily advise on the best therapeutic strategy. Current molecular diagnostics are routinely performed on the transcriptomic and genomic levels, aiming to identify tumor-driving mutations. However, these approaches can only partially capture the actual phenotype and the molecular key players of tumor growth and progression. Thus, direct investigation of the expressed proteins and activated signaling pathways provide valuable complementary information on the tumor-driving molecular characteristics of the tissue. Technological advancements in mass spectrometry-based proteomics enable the robust, rapid, and sensitive detection of thousands of proteins in minimal sample amounts, paving the way for clinical proteomics and the probing of oncogenic signaling activity. Therefore, proteomics is currently being integrated into molecular diagnostics within MTBs and holds promising potential in aiding tumor classification and identifying personalized treatment strategies. This review introduces MTBs and describes current clinical proteomics, its potential in precision oncology, and highlights the benefits of multi-omic data integration.
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Affiliation(s)
- Johanna Thiery
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Matthias Fahrner
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK) and Cancer Research Center (DKFZ), Freiburg, Germany
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33
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Kalogeropoulos K, Moldt Haack A, Madzharova E, Di Lorenzo A, Hanna R, Schoof EM, Auf dem Keller U. CLIPPER 2.0: Peptide-Level Annotation and Data Analysis for Positional Proteomics. Mol Cell Proteomics 2024; 23:100781. [PMID: 38703894 PMCID: PMC11192779 DOI: 10.1016/j.mcpro.2024.100781] [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: 02/21/2024] [Revised: 04/11/2024] [Accepted: 05/01/2024] [Indexed: 05/06/2024] Open
Abstract
Positional proteomics methodologies have transformed protease research, and have brought mass spectrometry (MS)-based degradomics studies to the forefront of protease characterization and system-wide interrogation of protease signaling. Considerable advancements in both sensitivity and throughput of liquid chromatography (LC)-MS/MS instrumentation enable the generation of enormous positional proteomics datasets of natural and protein termini and neo-termini of cleaved protease substrates. However, concomitant progress has not been observed to the same extent in data analysis and post-processing steps, arguably constituting the largest bottleneck in positional proteomics workflows. Here, we present a computational tool, CLIPPER 2.0, that builds on prior algorithms developed for MS-based protein termini analysis, facilitating peptide-level annotation and data analysis. CLIPPER 2.0 can be used with several sample preparation workflows and proteomics search algorithms and enables fast and automated database information retrieval, statistical and network analysis, as well as visualization of terminomic datasets. We demonstrate the applicability of our tool by analyzing GluC and MMP9 cleavages in HeLa lysates. CLIPPER 2.0 is available at https://github.com/UadKLab/CLIPPER-2.0.
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Affiliation(s)
| | - Aleksander Moldt Haack
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kgs. Lyngby, Denmark.
| | - Elizabeta Madzharova
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Antea Di Lorenzo
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Rawad Hanna
- Faculty of Biology, Technion-Israel Institute of Technology, Technion City Haifa, Israel
| | - Erwin M Schoof
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Ulrich Auf dem Keller
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kgs. Lyngby, Denmark
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Sun T, Chen J, Xu Y, Li Y, Liu X, Li H, Fu R, Liu W, Xue F, Ju M, Dong H, Wang W, Chi Y, Yang R, Chen Y, Zhang L. Proteomics landscape and machine learning prediction of long-term response to splenectomy in primary immune thrombocytopenia. Br J Haematol 2024; 204:2418-2428. [PMID: 38513635 DOI: 10.1111/bjh.19420] [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: 03/11/2024] [Accepted: 03/13/2024] [Indexed: 03/23/2024]
Abstract
This study aimed to identify key proteomic analytes correlated with response to splenectomy in primary immune thrombocytopenia (ITP). Thirty-four patients were retrospectively collected in the training cohort and 26 were prospectively enrolled as validation cohort. Bone marrow biopsy samples of all participants were collected prior to the splenectomy. A total of 12 modules of proteins were identified by weighted gene co-expression network analysis (WGCNA) method in the developed cohort. The tan module positively correlated with megakaryocyte counts before splenectomy (r = 0.38, p = 0.027), and time to peak platelet level after splenectomy (r = 0.47, p = 0.005). The blue module significantly correlated with response to splenectomy (r = 0.37, p = 0.0031). KEGG pathways analysis found that the PI3K-Akt signalling pathway was predominantly enriched in the tan module, while ribosomal and spliceosome pathways were enriched in the blue module. Machine learning algorithm identified the optimal combination of biomarkers from the blue module in the training cohort, and importantly, cofilin-1 (CFL1) was independently confirmed in the validation cohort. The C-index of CFL1 was >0.7 in both cohorts. Our results highlight the use of bone marrow proteomics analysis for deriving key analytes that predict the response to splenectomy, warranting further exploration of plasma proteomics in this patient population.
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Affiliation(s)
- Ting Sun
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, CAMS Key Laboratory of Gene Therapy for Blood Diseases, Tianjin Key Laboratory of Gene Therapy for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
- Tianjin Institutes of Health Science, Tianjin, China
| | - Jia Chen
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, CAMS Key Laboratory of Gene Therapy for Blood Diseases, Tianjin Key Laboratory of Gene Therapy for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
- Tianjin Institutes of Health Science, Tianjin, China
| | - Yuan Xu
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, CAMS Key Laboratory of Gene Therapy for Blood Diseases, Tianjin Key Laboratory of Gene Therapy for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
- Tianjin Institutes of Health Science, Tianjin, China
| | - Yang Li
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, CAMS Key Laboratory of Gene Therapy for Blood Diseases, Tianjin Key Laboratory of Gene Therapy for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
- Tianjin Institutes of Health Science, Tianjin, China
| | - Xiaofan Liu
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, CAMS Key Laboratory of Gene Therapy for Blood Diseases, Tianjin Key Laboratory of Gene Therapy for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
- Tianjin Institutes of Health Science, Tianjin, China
| | - Huiyuan Li
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, CAMS Key Laboratory of Gene Therapy for Blood Diseases, Tianjin Key Laboratory of Gene Therapy for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
- Tianjin Institutes of Health Science, Tianjin, China
| | - Rongfeng Fu
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, CAMS Key Laboratory of Gene Therapy for Blood Diseases, Tianjin Key Laboratory of Gene Therapy for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
- Tianjin Institutes of Health Science, Tianjin, China
| | - Wei Liu
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, CAMS Key Laboratory of Gene Therapy for Blood Diseases, Tianjin Key Laboratory of Gene Therapy for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
- Tianjin Institutes of Health Science, Tianjin, China
| | - Feng Xue
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, CAMS Key Laboratory of Gene Therapy for Blood Diseases, Tianjin Key Laboratory of Gene Therapy for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
- Tianjin Institutes of Health Science, Tianjin, China
| | - Mankai Ju
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, CAMS Key Laboratory of Gene Therapy for Blood Diseases, Tianjin Key Laboratory of Gene Therapy for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
- Tianjin Institutes of Health Science, Tianjin, China
| | - Huan Dong
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, CAMS Key Laboratory of Gene Therapy for Blood Diseases, Tianjin Key Laboratory of Gene Therapy for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
- Tianjin Institutes of Health Science, Tianjin, China
| | - Wentian Wang
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, CAMS Key Laboratory of Gene Therapy for Blood Diseases, Tianjin Key Laboratory of Gene Therapy for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
- Tianjin Institutes of Health Science, Tianjin, China
| | - Ying Chi
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, CAMS Key Laboratory of Gene Therapy for Blood Diseases, Tianjin Key Laboratory of Gene Therapy for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
- Tianjin Institutes of Health Science, Tianjin, China
| | - Renchi Yang
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, CAMS Key Laboratory of Gene Therapy for Blood Diseases, Tianjin Key Laboratory of Gene Therapy for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
- Tianjin Institutes of Health Science, Tianjin, China
| | - Yunfei Chen
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, CAMS Key Laboratory of Gene Therapy for Blood Diseases, Tianjin Key Laboratory of Gene Therapy for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
- Tianjin Institutes of Health Science, Tianjin, China
| | - Lei Zhang
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, CAMS Key Laboratory of Gene Therapy for Blood Diseases, Tianjin Key Laboratory of Gene Therapy for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
- Tianjin Institutes of Health Science, Tianjin, China
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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35
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Lin Y, Huang H, Cao J, Zhang K, Chen R, Jiang J, Yi X, Feng S, Liu J, Zheng S, Ling Q. An integrated proteomics and metabolomics approach to assess graft quality and predict early allograft dysfunction after liver transplantation: a retrospective cohort study. Int J Surg 2024; 110:3480-3494. [PMID: 38502860 PMCID: PMC11175820 DOI: 10.1097/js9.0000000000001292] [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: 09/18/2023] [Accepted: 02/22/2024] [Indexed: 03/21/2024]
Abstract
BACKGROUND Early allograft dysfunction (EAD) is a common complication after liver transplantation (LT) and is associated with poor prognosis. Graft itself plays a major role in the development of EAD. We aimed to reveal the EAD-specific molecular profiles to assess graft quality and establish EAD predictive models. METHODS A total of 223 patients who underwent LT were enrolled and divided into training ( n =73) and validation ( n =150) sets. In the training set, proteomics was performed on graft biopsies, together with metabolomics on paired perfusates. Differential expression, enrichment analysis, and protein-protein interaction network were used to identify the key molecules and pathways involved. EAD predictive models were constructed using machine learning and verified in the validation set. RESULTS A total of 335 proteins were differentially expressed between the EAD and non-EAD groups. These proteins were significantly enriched in triglyceride and glycerophospholipid metabolism, neutrophil degranulation, and the MET-related signaling pathway. The top 12 graft proteins involved in the aforementioned processes were identified, including GPAT1, LPIN3, TGFB1, CD59, and SOS1. Moreover, downstream metabolic products, such as lactate dehydrogenase, interleukin-8, triglycerides, and the phosphatidylcholine/phosphorylethanolamine ratio in the paired perfusate displayed a close relationship with the graft proteins. To predict the occurrence of EAD, an integrated model using perfusate metabolic products and clinical parameters showed areas under the curve of 0.915 and 0.833 for the training and validation sets, respectively. It displayed superior predictive efficacy than that of currently existing models, including donor risk index and D-MELD scores. CONCLUSIONS We identified novel biomarkers in both grafts and perfusates that could be used to assess graft quality and provide new insights into the etiology of EAD. Herein, we also offer a valid tool for the early prediction of EAD.
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Affiliation(s)
- Yimou Lin
- Department of Surgery, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- NHC Key Laboratory of Combined Multi-organ Transplantation, Hangzhou, China
| | - Haitao Huang
- Department of Surgery, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Jiaying Cao
- Department of Surgery, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- NHC Key Laboratory of Combined Multi-organ Transplantation, Hangzhou, China
| | - Ke Zhang
- Department of Surgery, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- NHC Key Laboratory of Combined Multi-organ Transplantation, Hangzhou, China
| | - Ruihan Chen
- Department of Surgery, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- NHC Key Laboratory of Combined Multi-organ Transplantation, Hangzhou, China
| | - Jingyu Jiang
- Department of Surgery, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- NHC Key Laboratory of Combined Multi-organ Transplantation, Hangzhou, China
| | - Xuewen Yi
- Department of Surgery, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- NHC Key Laboratory of Combined Multi-organ Transplantation, Hangzhou, China
| | - Shi Feng
- Department of Pathology, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jimin Liu
- Department of Pathology and Molecular Medicine, Faculty of Health Sciences, McMaster University, Hamilton, Canada
| | - Shusen Zheng
- Department of Surgery, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- NHC Key Laboratory of Combined Multi-organ Transplantation, Hangzhou, China
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Medical Center for Infectious Diseases, Hangzhou, China
| | - Qi Ling
- Department of Surgery, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Surgery, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Medical Center for Infectious Diseases, Hangzhou, China
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Li K, Teo GC, Yang KL, Yu F, Nesvizhskii AI. diaTracer enables spectrum-centric analysis of diaPASEF proteomics data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.25.595875. [PMID: 38854051 PMCID: PMC11160675 DOI: 10.1101/2024.05.25.595875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Data-independent acquisition (DIA) has become a widely used strategy for peptide and protein quantification in mass spectrometry-based proteomics studies. The integration of ion mobility separation into DIA analysis, such as the diaPASEF technology available on Bruker's timsTOF platform, further improves the quantification accuracy and protein depth achievable using DIA. We introduce diaTracer, a new spectrum-centric computational tool optimized for diaPASEF data. diaTracer performs three-dimensional (m/z, retention time, ion mobility) peak tracing and feature detection to generate precursor-resolved "pseudo-MS/MS" spectra, facilitating direct ("spectral-library free") peptide identification and quantification from diaPASEF data. diaTracer is available as a stand-alone tool and is fully integrated into the widely used FragPipe computational platform. We demonstrate the performance of diaTracer and FragPipe using diaPASEF data from cerebrospinal fluid (CSF) and plasma samples, data from phosphoproteomics and HLA immunopeptidomics experiments, and low-input data from a spatial proteomics study. We also show that diaTracer enables unrestricted identification of post-translational modifications from diaPASEF data using open/mass offset searches.
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Affiliation(s)
- Kai Li
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Guo Ci Teo
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Kevin L. Yang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Fengchao Yu
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Alexey I. Nesvizhskii
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
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Dowling P, Gargan S, Zweyer M, Henry M, Meleady P, Swandulla D, Ohlendieck K. Proteomic reference map for sarcopenia research: mass spectrometric identification of key muscle proteins of organelles, cellular signaling, bioenergetic metabolism and molecular chaperoning. Eur J Transl Myol 2024; 34:12565. [PMID: 38787292 PMCID: PMC11264233 DOI: 10.4081/ejtm.2024.12565] [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: 04/12/2024] [Accepted: 04/12/2024] [Indexed: 05/25/2024] Open
Abstract
During the natural aging process, frailty is often associated with abnormal muscular performance. Although inter-individual differences exit, in most elderly the tissue mass and physiological functionality of voluntary muscles drastically decreases. In order to study age-related contractile decline, animal model research is of central importance in the field of biogerontology. Here we have analyzed wild type mouse muscle to establish a proteomic map of crude tissue extracts. Proteomics is an advanced and large-scale biochemical method that attempts to identify all accessible proteins in a given biological sample. It is a technology-driven approach that uses mass spectrometry for the characterization of individual protein species. Total protein extracts were used in this study in order to minimize the potential introduction of artefacts due to excess subcellular fractionation procedures. In this report, the proteomic survey of aged muscles has focused on organellar marker proteins, as well as proteins that are involved in cellular signaling, the regulation of ion homeostasis, bioenergetic metabolism and molecular chaperoning. Hence, this study has establish a proteomic reference map of a highly suitable model system for future aging research.
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Affiliation(s)
- Paul Dowling
- Department of Biology, Maynooth University, National University of Ireland, Maynooth, Co. Kildare, Ireland; Kathleen Lonsdale Institute for Human Health Research, Maynooth University, Maynooth, Co. Kildare.
| | - Stephen Gargan
- Department of Biology, Maynooth University, National University of Ireland, Maynooth, Co. Kildare, Ireland; Kathleen Lonsdale Institute for Human Health Research, Maynooth University, Maynooth, Co. Kildare.
| | - Margit Zweyer
- Department of Neonatology and Paediatric Intensive Care, Children's Hospital, University of Bonn, Bonn, Germany; German Center for Neurodegenerative Diseases, Bonn.
| | - Michael Henry
- National Institute for Cellular Biotechnology, Dublin City University, Dublin.
| | - Paula Meleady
- National Institute for Cellular Biotechnology, Dublin City University, Dublin.
| | - Dieter Swandulla
- Institute of Physiology, Medical Faculty, University of Bonn, Bonn.
| | - Kay Ohlendieck
- Department of Biology, Maynooth University, National University of Ireland, Maynooth, Co. Kildare, Ireland; Kathleen Lonsdale Institute for Human Health Research, Maynooth University, Maynooth, Co. Kildare.
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Peters-Clarke TM, Coon JJ, Riley NM. Instrumentation at the Leading Edge of Proteomics. Anal Chem 2024; 96:7976-8010. [PMID: 38738990 DOI: 10.1021/acs.analchem.3c04497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2024]
Affiliation(s)
- Trenton M Peters-Clarke
- Department of Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
- Department of Biomolecular Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
| | - Joshua J Coon
- Department of Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
- Department of Biomolecular Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
- Morgridge Institute for Research, Madison, Wisconsin 53715, United States
| | - Nicholas M Riley
- Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
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Ross AB, Gorhe D, Kim JK, Hodapp S, DeVine L, Chan KM, Chio IIC, Jovanovic M, Ayres Pereira M. Systematic analysis of proteome turnover in an organoid model of pancreatic cancer by dSILO. CELL REPORTS METHODS 2024; 4:100760. [PMID: 38677284 PMCID: PMC11133751 DOI: 10.1016/j.crmeth.2024.100760] [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: 09/13/2023] [Revised: 01/26/2024] [Accepted: 03/25/2024] [Indexed: 04/29/2024]
Abstract
The role of protein turnover in pancreatic ductal adenocarcinoma (PDA) metastasis has not been previously investigated. We introduce dynamic stable-isotope labeling of organoids (dSILO): a dynamic SILAC derivative that combines a pulse of isotopically labeled amino acids with isobaric tandem mass-tag (TMT) labeling to measure proteome-wide protein turnover rates in organoids. We applied it to a PDA model and discovered that metastatic organoids exhibit an accelerated global proteome turnover compared to primary tumor organoids. Globally, most turnover changes are not reflected at the level of protein abundance. Interestingly, the group of proteins that show the highest turnover increase in metastatic PDA compared to tumor is involved in mitochondrial respiration. This indicates that metastatic PDA may adopt alternative respiratory chain functionality that is controlled by the rate at which proteins are turned over. Collectively, our analysis of proteome turnover in PDA organoids offers insights into the mechanisms underlying PDA metastasis.
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Affiliation(s)
- Alison B Ross
- Department of Biological Sciences, Columbia University, New York City, NY 10027, USA
| | - Darvesh Gorhe
- Department of Biological Sciences, Columbia University, New York City, NY 10027, USA
| | - Jenny Kim Kim
- Department of Biological Sciences, Columbia University, New York City, NY 10027, USA
| | - Stefanie Hodapp
- Department of Biological Sciences, Columbia University, New York City, NY 10027, USA
| | - Lela DeVine
- Department of Biology, Barnard College, New York, NY 10027, USA; Institute for Cancer Genetics, Department of Genetics and Development, Columbia University Irving Medical Center, New York, NY 10032, USA; Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Karina M Chan
- Institute for Cancer Genetics, Department of Genetics and Development, Columbia University Irving Medical Center, New York, NY 10032, USA; Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Iok In Christine Chio
- Institute for Cancer Genetics, Department of Genetics and Development, Columbia University Irving Medical Center, New York, NY 10032, USA; Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA.
| | - Marko Jovanovic
- Department of Biological Sciences, Columbia University, New York City, NY 10027, USA.
| | - Marina Ayres Pereira
- Institute for Cancer Genetics, Department of Genetics and Development, Columbia University Irving Medical Center, New York, NY 10032, USA; Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA.
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Murfuni MS, Prestagiacomo LE, Giuliano A, Gabriele C, Signoretti S, Cuda G, Gaspari M. Evaluation of PAC and FASP Performance: DIA-Based Quantitative Proteomic Analysis. Int J Mol Sci 2024; 25:5141. [PMID: 38791181 PMCID: PMC11121386 DOI: 10.3390/ijms25105141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 04/30/2024] [Accepted: 05/03/2024] [Indexed: 05/26/2024] Open
Abstract
The aim of this study was to compare filter-aided sample preparation (FASP) and protein aggregation capture (PAC) starting from a three-species protein mix (Human, Soybean and Pisum sativum) and two different starting amounts (1 and 10 µg). Peptide mixtures were analyzed by data-independent acquisition (DIA) and raw files were processed by three commonly used software: Spectronaut, MaxDIA and DIA-NN. Overall, the highest number of proteins (mean value of 5491) were identified by PAC (10 µg), while the lowest number (4855) was identified by FASP (1 µg). The latter experiment displayed the worst performance in terms of both specificity (0.73) and precision (0.24). Other tested conditions showed better diagnostic accuracy, with specificity values of 0.95-0.99 and precision values between 0.61 and 0.86. In order to provide guidance on the data analysis pipeline, the accuracy diagnostic of three software was investigated: (i) the highest sensitivity was obtained with Spectronaut (median of 0.67) highlighting the ability of Spectronaut to quantify low-abundance proteins, (ii) the best precision value was obtained by MaxDIA (median of 0.84), but with a reduced number of identifications compared to Spectronaut and DIA-NN data, and (iii) the specificity values were similar (between 0.93 and 0.99). The data are available on ProteomeXchange with the identifier PXD044349.
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41
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Sun Z, Ning Z, Figeys D. The Landscape and Perspectives of the Human Gut Metaproteomics. Mol Cell Proteomics 2024; 23:100763. [PMID: 38608842 PMCID: PMC11098955 DOI: 10.1016/j.mcpro.2024.100763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 02/26/2024] [Accepted: 04/09/2024] [Indexed: 04/14/2024] Open
Abstract
The human gut microbiome is closely associated with human health and diseases. Metaproteomics has emerged as a valuable tool for studying the functionality of the gut microbiome by analyzing the entire proteins present in microbial communities. Recent advancements in liquid chromatography and tandem mass spectrometry (LC-MS/MS) techniques have expanded the detection range of metaproteomics. However, the overall coverage of the proteome in metaproteomics is still limited. While metagenomics studies have revealed substantial microbial diversity and functional potential of the human gut microbiome, few studies have summarized and studied the human gut microbiome landscape revealed with metaproteomics. In this article, we present the current landscape of human gut metaproteomics studies by re-analyzing the identification results from 15 published studies. We quantified the limited proteome coverage in metaproteomics and revealed a high proportion of annotation coverage of metaproteomics-identified proteins. We conducted a preliminary comparison between the metaproteomics view and the metagenomics view of the human gut microbiome, identifying key areas of consistency and divergence. Based on the current landscape of human gut metaproteomics, we discuss the feasibility of using metaproteomics to study functionally unknown proteins and propose a whole workflow peptide-centric analysis. Additionally, we suggest enhancing metaproteomics analysis by refining taxonomic classification and calculating confidence scores, as well as developing tools for analyzing the interaction between taxonomy and function.
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Affiliation(s)
- Zhongzhi Sun
- School of Pharmaceutical Sciences, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada; Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Zhibin Ning
- School of Pharmaceutical Sciences, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Daniel Figeys
- School of Pharmaceutical Sciences, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada; Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada.
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42
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Makey DM, Ruotolo BT. Liquid-phase separations coupled with ion mobility-mass spectrometry for next-generation biopharmaceutical analysis. Expert Rev Proteomics 2024; 21:259-270. [PMID: 38934922 DOI: 10.1080/14789450.2024.2373707] [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: 03/28/2024] [Accepted: 06/06/2024] [Indexed: 06/28/2024]
Abstract
INTRODUCTION The pharmaceutical industry continues to expand its search for innovative biotherapeutics. The comprehensive characterization of such therapeutics requires many analytical techniques to fully evaluate critical quality attributes, making analysis a bottleneck in discovery and development timelines. While thorough characterization is crucial for ensuring the safety and efficacy of biotherapeutics, there is a need to further streamline analytical characterization and expedite the overall timeline from discovery to market. AREAS COVERED This review focuses on recent developments in liquid-phase separations coupled with ion mobility-mass spectrometry (IM-MS) for the development and characterization of biotherapeutics. We cover uses of IM-MS to improve the characterization of monoclonal antibodies, antibody-drug conjugates, host cell proteins, glycans, and nucleic acids. This discussion is based on an extensive literature search using Web of Science, Google Scholar, and SciFinder. EXPERT OPINION IM-MS has the potential to enhance the depth and efficiency of biotherapeutic characterization by providing additional insights into conformational changes, post-translational modifications, and impurity profiles. The rapid timescale of IM-MS positions it well to enhance the information content of existing assays through its facile integration with standard liquid-phase separation techniques that are commonly used for biopharmaceutical analysis.
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Affiliation(s)
- Devin M Makey
- Department of Chemistry, University of Michigan, Ann Arbor, MI, USA
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Ahn B, Ahn HS, Shin J, Jun E, Koh EY, Ryu YM, Kim SY, Sung CO, Shim JH, Hong J, Kim K, Kang HJ. Characterization of lymphocyte-rich hepatocellular carcinoma and the prognostic role of tertiary lymphoid structures. Liver Int 2024; 44:1202-1218. [PMID: 38363048 DOI: 10.1111/liv.15865] [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: 09/13/2023] [Revised: 01/13/2024] [Accepted: 01/27/2024] [Indexed: 02/17/2024]
Abstract
BACKGROUND & AIMS Lymphocyte-rich hepatocellular carcinoma (LR-HCC) is largely unknown and a rare subtype of HCC with immune-rich stroma. Tertiary lymphoid structures (TLS), frequently observed in LR-HCC, are known to be prognostically significant in various malignancies; however, their significance in HCC remains unevaluated. METHODS Clinicopathologic data of 191 cases of surgically resected conventional HCC (C-HCC, n = 160) and LR-HCC (n = 31) were retrieved. Immunohistochemistry, multiplex immunofluorescence staining, RNA sequencing and proteomic analysis were conducted. Differences between the subtypes were statistically evaluated. RESULTS LR-HCC was significantly correlated to larger tumour size, higher Edmondson-Steiner grade, presence of TLS and higher CD3-, CD8- and FOXP3-positive T cell, high PD-1 and PD-L1 expression (p < .001 for all) compared to C-HCC. Patients with LR-HCC exhibited significantly better overall survival (OS) (p = .044) and recurrence-free survival (RFS) (p = .025) than C-HCC. LR-HCC demonstrated TLS signatures with significantly higher proteomic-based immune scores in 14 of 17 types of tumour-infiltrating immune cells. Furthermore, C-HCC with secondary follicles, the most mature form of TLS, exhibited significantly better OS (p = .031) and RFS (p = .033) than those without. Across the global proteome, LR-HCC was well-differentiated from C-HCC and a map of protein-protein interactions between tumour-infiltrating lymphocytes and HCC in tumour microenvironment was completed. CONCLUSION LR-HCC is clinicopathologically and molecularly distinct and shows better prognosis compared to C-HCC. Also, the presence of secondary follicle can be an important prognostic marker for better prognosis in both LR-HCC and C-HCC.
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Affiliation(s)
- Bokyung Ahn
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Hee-Sung Ahn
- Department of Convergence Medicine, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Republic of Korea
| | - Jinho Shin
- Division of Hepato-Biliary and Pancreatic Surgery, Department of Surgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Eunsung Jun
- Department of Medicine, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Eun-Young Koh
- Department of Convergence Medicine, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Republic of Korea
| | - Yeon-Mi Ryu
- Asan Institute for Life Sciences, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Sang-Yeob Kim
- Asan Institute for Life Sciences, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Chang Ohk Sung
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Ju Hyun Shim
- Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
- Asan Liver Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - JeongYeon Hong
- Asan Institute for Life Sciences, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
- Department of Digital Medicine, University of Ulsan College of Medicine, Seoul, Korea
| | - Kyunggon Kim
- Department of Convergence Medicine, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Republic of Korea
- Asan Institute for Life Sciences, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
- Department of Digital Medicine, University of Ulsan College of Medicine, Seoul, Korea
| | - Hyo Jeong Kang
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
- Asan Liver Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
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Mouysset B, Le Grand M, Camoin L, Pasquier E. Poly-pharmacology of existing drugs: How to crack the code? Cancer Lett 2024; 588:216800. [PMID: 38492768 DOI: 10.1016/j.canlet.2024.216800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 02/15/2024] [Accepted: 03/05/2024] [Indexed: 03/18/2024]
Abstract
Drug development in oncology is highly challenging, with less than 5% success rate in clinical trials. This alarming figure points out the need to study in more details the multiple biological effects of drugs in specific contexts. Indeed, the comprehensive assessment of drug poly-pharmacology can provide insights into their therapeutic and adverse effects, to optimize their utilization and maximize the success rate of clinical trials. Recent technological advances have made possible in-depth investigation of drug poly-pharmacology. This review first highlights high-throughput methodologies that have been used to unveil new mechanisms of action of existing drugs. Then, we discuss how emerging chemo-proteomics strategies allow effectively dissecting the poly-pharmacology of drugs in an unsupervised manner.
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Affiliation(s)
- Baptiste Mouysset
- Centre de Recherche en Cancérologie de Marseille Inserm U1068, CNRS UMR7258, Aix-Marseille University U105, Marseille, France.
| | - Marion Le Grand
- Centre de Recherche en Cancérologie de Marseille Inserm U1068, CNRS UMR7258, Aix-Marseille University U105, Marseille, France.
| | - Luc Camoin
- Centre de Recherche en Cancérologie de Marseille Inserm U1068, CNRS UMR7258, Aix-Marseille University U105, Marseille, France.
| | - Eddy Pasquier
- Centre de Recherche en Cancérologie de Marseille Inserm U1068, CNRS UMR7258, Aix-Marseille University U105, Marseille, France.
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Moretti-Horten DN, Peselj C, Taskin AA, Myketin L, Schulte U, Einsle O, Drepper F, Luzarowski M, Vögtle FN. Synchronized assembly of the oxidative phosphorylation system controls mitochondrial respiration in yeast. Dev Cell 2024; 59:1043-1057.e8. [PMID: 38508182 DOI: 10.1016/j.devcel.2024.02.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 01/19/2024] [Accepted: 02/28/2024] [Indexed: 03/22/2024]
Abstract
Control of protein stoichiometry is essential for cell function. Mitochondrial oxidative phosphorylation (OXPHOS) presents a complex stoichiometric challenge as the ratio of the electron transport chain (ETC) and ATP synthase must be tightly controlled, and assembly requires coordinated integration of proteins encoded in the nuclear and mitochondrial genome. How correct OXPHOS stoichiometry is achieved is unknown. We identify the Mitochondrial Regulatory hub for respiratory Assembly (MiRA) platform, which synchronizes ETC and ATP synthase biogenesis in yeast. Molecularly, this is achieved by a stop-and-go mechanism: the uncharacterized protein Mra1 stalls complex IV assembly. Two "Go" signals are required for assembly progression: binding of the complex IV assembly factor Rcf2 and Mra1 interaction with an Atp9-translating mitoribosome induce Mra1 degradation, allowing synchronized maturation of complex IV and the ATP synthase. Failure of the stop-and-go mechanism results in cell death. MiRA controls OXPHOS assembly, ensuring correct stoichiometry of protein machineries encoded by two different genomes.
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Affiliation(s)
- Daiana N Moretti-Horten
- Center for Molecular Biology of Heidelberg University (ZMBH), DKFZ-ZMBH Alliance, 69120 Heidelberg, Germany; Institute of Biochemistry and Molecular Biology, ZBMZ, Faculty of Medicine, University of Freiburg, 79104 Freiburg, Germany; Faculty of Biology, University of Freiburg, 79104 Freiburg, Germany
| | - Carlotta Peselj
- Center for Molecular Biology of Heidelberg University (ZMBH), DKFZ-ZMBH Alliance, 69120 Heidelberg, Germany
| | - Asli Aras Taskin
- Institute of Biochemistry and Molecular Biology, ZBMZ, Faculty of Medicine, University of Freiburg, 79104 Freiburg, Germany
| | - Lisa Myketin
- Institute of Biochemistry and Molecular Biology, ZBMZ, Faculty of Medicine, University of Freiburg, 79104 Freiburg, Germany
| | - Uwe Schulte
- Institute of Physiology, Faculty of Medicine, University of Freiburg, 79104 Freiburg, Germany; CIBSS-Centre for Integrative Biological Signalling Studies, University of Freiburg, 79104 Freiburg, Germany
| | - Oliver Einsle
- Institut für Biochemie, University of Freiburg, 79104 Freiburg, Germany; BIOSS Centre for Biological Signalling Studies, University of Freiburg, 79104 Freiburg, Germany
| | - Friedel Drepper
- CIBSS-Centre for Integrative Biological Signalling Studies, University of Freiburg, 79104 Freiburg, Germany; BIOSS Centre for Biological Signalling Studies, University of Freiburg, 79104 Freiburg, Germany; Biochemistry & Functional Proteomics, Institute of Biology II, Faculty of Biology, University of Freiburg, 79104 Freiburg, Germany
| | - Marcin Luzarowski
- Core Facility for Mass Spectrometry and Proteomics, Center for Molecular Biology of Heidelberg University (ZMBH), DKFZ-ZMBH Alliance, 69120 Heidelberg, Germany
| | - F-Nora Vögtle
- Center for Molecular Biology of Heidelberg University (ZMBH), DKFZ-ZMBH Alliance, 69120 Heidelberg, Germany; Institute of Biochemistry and Molecular Biology, ZBMZ, Faculty of Medicine, University of Freiburg, 79104 Freiburg, Germany; CIBSS-Centre for Integrative Biological Signalling Studies, University of Freiburg, 79104 Freiburg, Germany; Network Aging Research, Heidelberg University, 69120 Heidelberg, Germany.
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46
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Makey DM, Gadkari VV, Kennedy RT, Ruotolo BT. Cyclic Ion Mobility-Mass Spectrometry and Tandem Collision Induced Unfolding for Quantification of Elusive Protein Biomarkers. Anal Chem 2024; 96:6021-6029. [PMID: 38557001 PMCID: PMC11081454 DOI: 10.1021/acs.analchem.4c00477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Sensitive analytical techniques that are capable of detecting and quantifying disease-associated biomolecules are indispensable in our efforts to understand disease mechanisms and guide therapeutic intervention through early detection, accurate diagnosis, and effective monitoring of disease. Parkinson's Disease (PD), for example, is one of the most prominent neurodegenerative disorders in the world, but the diagnosis of PD has primarily been based on the observation of clinical symptoms. The protein α-synuclein (α-syn) has emerged as a promising biomarker candidate for PD, but a lack of analytical methods to measure complex disease-associated variants of α-syn has prevented its widespread use as a biomarker. Antibody-based methods such as immunoassays and mass spectrometry-based approaches have been used to measure a limited number of α-syn forms; however, these methods fail to differentiate variants of α-syn that display subtle differences in only the sequence and structure. In this work, we developed a cyclic ion mobility-mass spectrometry method that combines multiple stages of activation and timed ion selection to quantify α-syn variants using both mass- and structure-based measurements. This method can allow for the quantification of several α-syn variants present at physiological levels in biological fluid. Taken together, this approach can be used to galvanize future efforts aimed at understanding the underlying mechanisms of PD and serves as a starting point for the development of future protein-structure-based diagnostics and therapeutic interventions.
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Affiliation(s)
- Devin M. Makey
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Varun V. Gadkari
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Robert T. Kennedy
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
- Department of Pharmacology, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Brandon T. Ruotolo
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
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47
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De Silva S, Alli-Shaik A, Gunaratne J. FlexStat: combinatory differentially expressed protein extraction. BIOINFORMATICS ADVANCES 2024; 4:vbae056. [PMID: 38681522 PMCID: PMC11055397 DOI: 10.1093/bioadv/vbae056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 03/25/2024] [Accepted: 04/10/2024] [Indexed: 05/01/2024]
Abstract
Motivation Mass spectrometry-based system proteomics allows identification of dysregulated protein hubs and associated disease-related features. Obtaining differentially expressed proteins (DEPs) is the most important step of downstream bioinformatics analysis. However, the extraction of statistically significant DEPs from datasets with multiple experimental conditions or disease types through currently available tools remains a laborious task. More often such an analysis requires considerable bioinformatics expertise, making it inaccessible to researchers with limited computational analytics experience. Results To uncover the differences among the many conditions within the data in a user-friendly manner, here we introduce FlexStat, a web-based interface that extracts DEPs through combinatory analysis. This tool accepts a protein expression matrix as input and systematically generates DEP results for every conceivable combination of various experimental conditions or disease types. FlexStat includes a suite of robust statistical tools for data preprocessing, in addition to DEP extraction, and publication-ready visualization, which are built on established R scientific libraries in an automated manner. This analytics suite was validated in diverse public proteomic datasets to showcase its high performance of rapid and simultaneous pairwise comparisons of comprehensive datasets. Availability and implementation FlexStat is implemented in R and is freely available at https://jglab.shinyapps.io/flexstatv1-pipeline-only/. The source code is accessible at https://github.com/kts-desilva/FlexStat/tree/main.
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Affiliation(s)
- Senuri De Silva
- Translational Biomedical Proteomics Laboratory, Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), Singapore 138673, Singapore
- Department of Anatomy, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117594, Singapore
| | - Asfa Alli-Shaik
- Translational Biomedical Proteomics Laboratory, Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), Singapore 138673, Singapore
| | - Jayantha Gunaratne
- Translational Biomedical Proteomics Laboratory, Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), Singapore 138673, Singapore
- Department of Anatomy, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117594, Singapore
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48
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von Hardenberg S, Klefenz I, Steinemann D, Di Donato N, Baumann U, Auber B, Klemann C. Current genetic diagnostics in inborn errors of immunity. Front Pediatr 2024; 12:1279112. [PMID: 38659694 PMCID: PMC11039790 DOI: 10.3389/fped.2024.1279112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 03/28/2024] [Indexed: 04/26/2024] Open
Abstract
New technologies in genetic diagnostics have revolutionized the understanding and management of rare diseases. This review highlights the significant advances and latest developments in genetic diagnostics in inborn errors of immunity (IEI), which encompass a diverse group of disorders characterized by defects in the immune system, leading to increased susceptibility to infections, autoimmunity, autoinflammatory diseases, allergies, and malignancies. Various diagnostic approaches, including targeted gene sequencing panels, whole exome sequencing, whole genome sequencing, RNA sequencing, or proteomics, have enabled the identification of causative genetic variants of rare diseases. These technologies not only facilitated the accurate diagnosis of IEI but also provided valuable insights into the underlying molecular mechanisms. Emerging technologies, currently mainly used in research, such as optical genome mapping, single cell sequencing or the application of artificial intelligence will allow even more insights in the aetiology of hereditary immune defects in the near future. The integration of genetic diagnostics into clinical practice significantly impacts patient care. Genetic testing enables early diagnosis, facilitating timely interventions and personalized treatment strategies. Additionally, establishing a genetic diagnosis is necessary for genetic counselling and prognostic assessments. Identifying specific genetic variants associated with inborn errors of immunity also paved the way for the development of targeted therapies and novel therapeutic approaches. This review emphasizes the challenges related with genetic diagnosis of rare diseases and provides future directions, specifically focusing on IEI. Despite the tremendous progress achieved over the last years, several obstacles remain or have become even more important due to the increasing amount of genetic data produced for each patient. This includes, first and foremost, the interpretation of variants of unknown significance (VUS) in known IEI genes and of variants in genes of unknown significance (GUS). Although genetic diagnostics have significantly contributed to the understanding and management of IEI and other rare diseases, further research, exchange between experts from different clinical disciplines, data integration and the establishment of comprehensive guidelines are crucial to tackle the remaining challenges and maximize the potential of genetic diagnostics in the field of rare diseases, such as IEI.
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Affiliation(s)
| | - Isabel Klefenz
- Department of Human Genetics, Hannover Medical School, Hannover, Germany
| | - Doris Steinemann
- Department of Human Genetics, Hannover Medical School, Hannover, Germany
| | - Nataliya Di Donato
- Department of Human Genetics, Hannover Medical School, Hannover, Germany
| | - Ulrich Baumann
- Department of Pediatric Pneumology, Allergology and Neonatology, Hannover Medical School, Hannover, Germany
| | - Bernd Auber
- Department of Human Genetics, Hannover Medical School, Hannover, Germany
| | - Christian Klemann
- Department of Human Genetics, Hannover Medical School, Hannover, Germany
- Department of Pediatric Immunology, Rheumatology and Infectiology, Hospital for Children and Adolescents, University of Leipzig, Leipzig, Germany
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49
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Potiris A, Alyfanti E, Drakaki E, Mavrogianni D, Karampitsakos T, Machairoudias P, Topis S, Zikopoulos A, Skentou C, Panagopoulos P, Drakakis P, Stavros S. The Contribution of Proteomics in Understanding Endometrial Protein Expression in Women with Recurrent Implantation Failure. J Clin Med 2024; 13:2145. [PMID: 38610911 PMCID: PMC11012239 DOI: 10.3390/jcm13072145] [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: 03/13/2024] [Revised: 04/01/2024] [Accepted: 04/05/2024] [Indexed: 04/14/2024] Open
Abstract
Recurrent implantation failure (RIF) poses a significant challenge in assisted reproductive technology (ART) outcomes. The endometrium plays a crucial role in embryo implantation, and its protein expression profile is integral in determining receptivity. Proteomics has emerged as a valuable tool in unraveling the molecular intricacies underlying endometrial receptivity and RIF. The aim of the present review is to analyze the contribution of proteomics to the understanding of endometrial protein expression in women with RIF, based on the results of significant proteomic studies. Medline/Pubmed databases were searched using keywords pertaining to proteomics combined with terms related to RIF. 15 studies were included in the present review. Several proteins have been found to exbibit differential expression in endometrial biopsies and fluid samples between fertile women and women with RIF during the receptive endometrial phase. The profile of endometrial proteins varied significantly among the studies. Nevertheless, similar changes in the expression levels of annexin-6, progesterone receptor, MMP-2, and MMP-9 in the endometrium of women with RIF, were found in more than one study indicating that certain proteins could potentially be effective biomarkers of endometrial receptivity. Proteomics contributes significantly to the understanding of protein expression in the endometrium of women with RIF and the analysis of proteins in endometrial fluid are promising for improving the clinical management of RIF.
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Affiliation(s)
- Anastasios Potiris
- Third Department of Obstetrics and Gynecology, Medical School, University General Hospital “ATTIKON”, National and Kapodistrian University of Athens, 12462 Athens, Greece; (E.A.); (T.K.); (P.M.); (S.T.); (P.P.); (P.D.); (S.S.)
| | - Eleni Alyfanti
- Third Department of Obstetrics and Gynecology, Medical School, University General Hospital “ATTIKON”, National and Kapodistrian University of Athens, 12462 Athens, Greece; (E.A.); (T.K.); (P.M.); (S.T.); (P.P.); (P.D.); (S.S.)
| | - Eirini Drakaki
- First Department of Obstetrics and Gynecology, Medical School, Alexandra Hospital, National and Kapodistrian University of Athens, 11528 Athens, Greece; (E.D.); (D.M.)
| | - Despoina Mavrogianni
- First Department of Obstetrics and Gynecology, Medical School, Alexandra Hospital, National and Kapodistrian University of Athens, 11528 Athens, Greece; (E.D.); (D.M.)
| | - Theodoros Karampitsakos
- Third Department of Obstetrics and Gynecology, Medical School, University General Hospital “ATTIKON”, National and Kapodistrian University of Athens, 12462 Athens, Greece; (E.A.); (T.K.); (P.M.); (S.T.); (P.P.); (P.D.); (S.S.)
| | - Pavlos Machairoudias
- Third Department of Obstetrics and Gynecology, Medical School, University General Hospital “ATTIKON”, National and Kapodistrian University of Athens, 12462 Athens, Greece; (E.A.); (T.K.); (P.M.); (S.T.); (P.P.); (P.D.); (S.S.)
| | - Spyridon Topis
- Third Department of Obstetrics and Gynecology, Medical School, University General Hospital “ATTIKON”, National and Kapodistrian University of Athens, 12462 Athens, Greece; (E.A.); (T.K.); (P.M.); (S.T.); (P.P.); (P.D.); (S.S.)
| | - Athanasios Zikopoulos
- Department of Obstetrics and Gynecology, Royal Cornwall Hospital, Treliske, Truro TR1 3LQ, UK;
| | - Chara Skentou
- Department of Obstetrics and Gynecology, Medical School of the University of Ioannina, 45110 Ioannina, Greece;
| | - Periklis Panagopoulos
- Third Department of Obstetrics and Gynecology, Medical School, University General Hospital “ATTIKON”, National and Kapodistrian University of Athens, 12462 Athens, Greece; (E.A.); (T.K.); (P.M.); (S.T.); (P.P.); (P.D.); (S.S.)
| | - Peter Drakakis
- Third Department of Obstetrics and Gynecology, Medical School, University General Hospital “ATTIKON”, National and Kapodistrian University of Athens, 12462 Athens, Greece; (E.A.); (T.K.); (P.M.); (S.T.); (P.P.); (P.D.); (S.S.)
| | - Sofoklis Stavros
- Third Department of Obstetrics and Gynecology, Medical School, University General Hospital “ATTIKON”, National and Kapodistrian University of Athens, 12462 Athens, Greece; (E.A.); (T.K.); (P.M.); (S.T.); (P.P.); (P.D.); (S.S.)
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50
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Shrestha HK, Lee D, Wu Z, Wang Z, Fu Y, Wang X, Serrano GE, Beach TG, Peng J. Profiling Protein-Protein Interactions in the Human Brain by Refined Cofractionation Mass Spectrometry. J Proteome Res 2024; 23:1221-1231. [PMID: 38507900 PMCID: PMC11065482 DOI: 10.1021/acs.jproteome.3c00685] [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] [Indexed: 03/22/2024]
Abstract
Proteins usually execute their biological functions through interactions with other proteins and by forming macromolecular complexes, but global profiling of protein complexes directly from human tissue samples has been limited. In this study, we utilized cofractionation mass spectrometry (CF-MS) to map protein complexes within the postmortem human brain with experimental replicates. First, we used concatenated anion and cation Ion Exchange Chromatography (IEX) to separate native protein complexes in 192 fractions and then proceeded with Data-Independent Acquisition (DIA) mass spectrometry to analyze the proteins in each fraction, quantifying a total of 4,804 proteins with 3,260 overlapping in both replicates. We improved the DIA's quantitative accuracy by implementing a constant amount of bovine serum albumin (BSA) in each fraction as an internal standard. Next, advanced computational pipelines, which integrate both a database-based complex analysis and an unbiased protein-protein interaction (PPI) search, were applied to identify protein complexes and construct protein-protein interaction networks in the human brain. Our study led to the identification of 486 protein complexes and 10054 binary protein-protein interactions, which represents the first global profiling of human brain PPIs using CF-MS. Overall, this study offers a resource and tool for a wide range of human brain research, including the identification of disease-specific protein complexes in the future.
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Affiliation(s)
- Him K. Shrestha
- Departments of Structural Biology and Developmental Neurobiology
| | - DongGeun Lee
- Departments of Structural Biology and Developmental Neurobiology
| | - Zhiping Wu
- Departments of Structural Biology and Developmental Neurobiology
| | - Zhen Wang
- Departments of Structural Biology and Developmental Neurobiology
| | - Yingxue Fu
- Departments of Structural Biology and Developmental Neurobiology
- Center for Proteomics and Metabolomics, St. Jude Children’s Research Hospital, Memphis, Tennessee, 38105, USA
| | - Xusheng Wang
- Center for Proteomics and Metabolomics, St. Jude Children’s Research Hospital, Memphis, Tennessee, 38105, USA
| | | | - Thomas G. Beach
- Banner Sun Health Research Institute, Sun City, AZ 85351, USA
| | - Junmin Peng
- Departments of Structural Biology and Developmental Neurobiology
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