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Lee GB, Mazli WNAB, Hao L. Multiomics Evaluation of Human iPSCs and iPSC-Derived Neurons. J Proteome Res 2024; 23:3149-3160. [PMID: 38415376 DOI: 10.1021/acs.jproteome.3c00790] [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] [Indexed: 02/29/2024]
Abstract
Human induced pluripotent stem cells (iPSCs) can be differentiated into neurons, providing living human neurons to model brain diseases. However, it is unclear how different types of molecules work together to regulate stem cell and neuron biology in healthy and disease states. In this study, we conducted integrated proteomics, lipidomics, and metabolomics analyses with confident identification, accurate quantification, and reproducible measurements to compare the molecular profiles of human iPSCs and iPSC-derived neurons. Proteins, lipids, and metabolites related to mitosis, DNA replication, pluripotency, glycosphingolipids, and energy metabolism were highly enriched in iPSCs, whereas synaptic proteins, neurotransmitters, polyunsaturated fatty acids, cardiolipins, and axon guidance pathways were highly enriched in neurons. Mutations in the GRN gene lead to the deficiency of the progranulin (PGRN) protein, which has been associated with various neurodegenerative diseases. Using this multiomics platform, we evaluated the impact of PGRN deficiency on iPSCs and neurons at the whole-cell level. Proteomics, lipidomics, and metabolomics analyses implicated PGRN's roles in neuroinflammation, purine metabolism, and neurite outgrowth, revealing commonly altered pathways related to neuron projection, synaptic dysfunction, and brain metabolism. Multiomics data sets also pointed toward the same hypothesis that neurons seem to be more susceptible to PGRN loss compared to iPSCs, consistent with the neurological symptoms and cognitive impairment from patients carrying inherited GRN mutations.
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Affiliation(s)
- Gwang Bin Lee
- Department of Chemistry, The George Washington University, 800 22nd St. NW, Washington, D.C. 20052, United States
| | - Wan Nur Atiqah Binti Mazli
- Department of Chemistry, The George Washington University, 800 22nd St. NW, Washington, D.C. 20052, United States
| | - Ling Hao
- Department of Chemistry, The George Washington University, 800 22nd St. NW, Washington, D.C. 20052, United States
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Lee H, Amatya B, Villar VAM, Asico LD, Jeong JK, Feranil J, Moore SC, Zheng X, Bishop M, Gomes JP, Polzin J, Smeriglio N, de Castro PASV, Armando I, Felder RA, Hao L, Jose PA. Renal autocrine neuropeptide FF (NPFF) signaling regulates blood pressure. Sci Rep 2024; 14:15407. [PMID: 38965251 PMCID: PMC11224344 DOI: 10.1038/s41598-024-64484-9] [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: 09/17/2022] [Accepted: 06/10/2024] [Indexed: 07/06/2024] Open
Abstract
The kidney and brain play critical roles in the regulation of blood pressure. Neuropeptide FF (NPFF), originally isolated from the bovine brain, has been suggested to contribute to the pathogenesis of hypertension. However, the roles of NPFF and its receptors, NPFF-R1 and NPFF-R2, in the regulation of blood pressure, via the kidney, are not known. In this study, we found that the transcripts and proteins of NPFF and its receptors, NPFF-R1 and NPFF-R2, were expressed in mouse and human renal proximal tubules (RPTs). In mouse RPT cells (RPTCs), NPFF, but not RF-amide-related peptide-2 (RFRP-2), decreased the forskolin-stimulated cAMP production in a concentration- and time-dependent manner. Furthermore, dopamine D1-like receptors colocalized and co-immunoprecipitated with NPFF-R1 and NPFF-R2 in human RPTCs. The increase in cAMP production in human RPTCs caused by fenoldopam, a D1-like receptor agonist, was attenuated by NPFF, indicating an antagonistic interaction between NPFF and D1-like receptors. The renal subcapsular infusion of NPFF in C57BL/6 mice decreased renal sodium excretion and increased blood pressure. The NPFF-mediated increase in blood pressure was prevented by RF-9, an antagonist of NPFF receptors. Taken together, our findings suggest that autocrine NPFF and its receptors in the kidney regulate blood pressure, but the mechanisms remain to be determined.
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Affiliation(s)
- Hewang Lee
- Division of Renal Diseases and Hypertension, Department of Medicine, The George Washington University School of Medicine and Health Sciences, 2300 Eye Street, NW, Washington, DC, 20052, USA.
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, 21201, USA.
| | - Bibhas Amatya
- Division of Renal Diseases and Hypertension, Department of Medicine, The George Washington University School of Medicine and Health Sciences, 2300 Eye Street, NW, Washington, DC, 20052, USA
| | - Van Anthony M Villar
- Division of Renal Diseases and Hypertension, Department of Medicine, The George Washington University School of Medicine and Health Sciences, 2300 Eye Street, NW, Washington, DC, 20052, USA
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Laureano D Asico
- Division of Renal Diseases and Hypertension, Department of Medicine, The George Washington University School of Medicine and Health Sciences, 2300 Eye Street, NW, Washington, DC, 20052, USA
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Jin Kwon Jeong
- Department of Pharmacology and Physiology, The George Washington University School of Medicine and Health Sciences, Washington, DC, 20052, USA
| | - Jun Feranil
- Division of Renal Diseases and Hypertension, Department of Medicine, The George Washington University School of Medicine and Health Sciences, 2300 Eye Street, NW, Washington, DC, 20052, USA
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Shaun C Moore
- Division of Renal Diseases and Hypertension, Department of Medicine, The George Washington University School of Medicine and Health Sciences, 2300 Eye Street, NW, Washington, DC, 20052, USA
| | - Xiaoxu Zheng
- Division of Renal Diseases and Hypertension, Department of Medicine, The George Washington University School of Medicine and Health Sciences, 2300 Eye Street, NW, Washington, DC, 20052, USA
| | - Michael Bishop
- Division of Renal Diseases and Hypertension, Department of Medicine, The George Washington University School of Medicine and Health Sciences, 2300 Eye Street, NW, Washington, DC, 20052, USA
| | - Jerald P Gomes
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Jacob Polzin
- Division of Renal Diseases and Hypertension, Department of Medicine, The George Washington University School of Medicine and Health Sciences, 2300 Eye Street, NW, Washington, DC, 20052, USA
| | - Noah Smeriglio
- Department of Chemistry, Columbian College of Arts and Sciences, The George Washington University, Washington, DC, 20052, USA
| | - Pedro A S Vaz de Castro
- Division of Renal Diseases and Hypertension, Department of Medicine, The George Washington University School of Medicine and Health Sciences, 2300 Eye Street, NW, Washington, DC, 20052, USA
| | - Ines Armando
- Division of Renal Diseases and Hypertension, Department of Medicine, The George Washington University School of Medicine and Health Sciences, 2300 Eye Street, NW, Washington, DC, 20052, USA
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Robin A Felder
- Department of Pathology, University of Virginia Health Sciences Center, Charlottesville, VA, 22908.5, USA
| | - Ling Hao
- Department of Chemistry, Columbian College of Arts and Sciences, The George Washington University, Washington, DC, 20052, USA
| | - Pedro A Jose
- Division of Renal Diseases and Hypertension, Department of Medicine, The George Washington University School of Medicine and Health Sciences, 2300 Eye Street, NW, Washington, DC, 20052, USA
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
- Department of Pharmacology and Physiology, The George Washington University School of Medicine and Health Sciences, Washington, DC, 20052, USA
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Wurth R, Turgeon C, Stander Z, Oglesbee D. An evaluation of untargeted metabolomics methods to characterize inborn errors of metabolism. Mol Genet Metab 2024; 141:108115. [PMID: 38181458 PMCID: PMC10843816 DOI: 10.1016/j.ymgme.2023.108115] [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: 10/10/2023] [Revised: 11/19/2023] [Accepted: 12/12/2023] [Indexed: 01/07/2024]
Abstract
Inborn errors of metabolism (IEMs) encompass a diverse group of disorders that can be difficult to classify due to heterogenous clinical, molecular, and biochemical manifestations. Untargeted metabolomics platforms have become a popular approach to analyze IEM patient samples because of their ability to detect many metabolites at once, accelerating discovery of novel biomarkers, and metabolic mechanisms of disease. However, there are concerns about the reproducibility of untargeted metabolomics research due to the absence of uniform reporting practices, data analyses, and experimental design guidelines. Therefore, we critically evaluated published untargeted metabolomic platforms used to characterize IEMs to summarize the strengths and areas for improvement of this technology as it progresses towards the clinical laboratory. A total of 96 distinct IEMs were collectively evaluated by the included studies. However, most of these IEMs were evaluated by a single untargeted metabolomic method, in a single study, with a limited cohort size (55/96, 57%). The goals of the included studies generally fell into two, often overlapping, categories: detecting known biomarkers from many biochemically distinct IEMs using a single platform, and detecting novel metabolites or metabolic pathways. There was notable diversity in the design of the untargeted metabolomic platforms. Importantly, the majority of studies reported adherence to quality metrics, including the use of quality control samples and internal standards in their experiments, as well as confirmation of at least some of their feature annotations with commercial reference standards. Future applications of untargeted metabolomics platforms to the study of IEMs should move beyond single-subject analyses, and evaluate reproducibility using a prospective, or validation cohort.
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Affiliation(s)
- Rachel Wurth
- Mayo Clinic Graduate School of Biomedical Sciences, Mayo Clinic, 200 1(st) St SW, Rochester, MN 55905, USA
| | - Coleman Turgeon
- Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 1st St SW, Rochester, MN 55905, USA
| | - Zinandré Stander
- Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 1st St SW, Rochester, MN 55905, USA
| | - Devin Oglesbee
- Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 1st St SW, Rochester, MN 55905, USA.
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Shastry A, Dunham-Snary K. Metabolomics and mitochondrial dysfunction in cardiometabolic disease. Life Sci 2023; 333:122137. [PMID: 37788764 DOI: 10.1016/j.lfs.2023.122137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 09/21/2023] [Accepted: 09/29/2023] [Indexed: 10/05/2023]
Abstract
Circulating metabolites are indicators of systemic metabolic dysfunction and can be detected through contemporary techniques in metabolomics. These metabolites are involved in numerous mitochondrial metabolic processes including glycolysis, fatty acid β-oxidation, and amino acid catabolism, and changes in the abundance of these metabolites is implicated in the pathogenesis of cardiometabolic diseases (CMDs). Epigenetic regulation and direct metabolite-protein interactions modulate metabolism, both within cells and in the circulation. Dysfunction of multiple mitochondrial components stemming from mitochondrial DNA mutations are implicated in disease pathogenesis. This review will summarize the current state of knowledge regarding: i) the interactions between metabolites found within the mitochondrial environment during CMDs, ii) various metabolites' effects on cellular and systemic function, iii) how harnessing the power of metabolomic analyses represents the next frontier of precision medicine, and iv) how these concepts integrate to expand the clinical potential for translational cardiometabolic medicine.
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Affiliation(s)
- Abhishek Shastry
- Department of Medicine, Queen's University, Kingston, ON, Canada
| | - Kimberly Dunham-Snary
- Department of Medicine, Queen's University, Kingston, ON, Canada; Department of Biomedical & Molecular Sciences, Queen's University, Kingston, ON, Canada.
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Ma C, Zhang Y, Dou X, Liu L, Zhang W, Ye J. Combining multiple acquisition modes and computational data annotation for structural characterization in traditional Chinese medicine: Miao Nationality medicine Qijiao Shengbai Capsule as a case study. RSC Adv 2022; 12:27781-27792. [PMID: 36320242 PMCID: PMC9520537 DOI: 10.1039/d2ra04720a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 09/21/2022] [Indexed: 11/26/2022] Open
Abstract
Qijiao Shengbai Capsule (QSC) is a reputable Miao Nationality medicine used for treating leukopenia, but its chemical composition has not yet been elucidated. We herein present a strategy, by integrating multiple data acquisition, computational data annotation and processing methods to visualize and identify the complicated constituents in QSC based on ultra-high-performance liquid chromatography coupled with traveling wave ion mobility quadrupole time-of-flight mass spectrometry (UPLC-TWIMS-QTOF-MS). The multiple data acquisition modes, including data-independent mass spectrometryEnergy (MSE), data-independent high-definition mass spectrometryEnergy (HDMSE), and fast data-dependent acquisition (fast-DDA), in both positive and negative ion modes, were conducted on a Waters-SYNAPT G2-Si mass spectrometer with an ESI source. An in-house library built by the UNIFI platform could efficiently process the peak annotation of known compounds, whilst different structural types were clustered in the molecular networks for the analogous classification and structural annotation of the unknown ones. Neutral loss, diagnostic ions, feature fragmentation behaviors, and community curation of mass spectrometry data of known compounds helped exploit those similar neighboring nodes of unknown compounds. Moreover, by combination of the predicted CCS values from CCS platform with the experimental CCS values from HDMSE, as well as diagnostic fragment ions, isomer compounds were annotated. By integrating reference compound comparison, a total of 202 constituents, including 94 flavonoids, 12 saponins, 30 phthalides, 38 organic acids, 3 amino acids, 7 alkaloids and 18 others, were unambiguously characterized or tentatively identified in QSC. Among them, 5 potential new compounds were detected and 12 pairs of isomers were comprehensively distinguished. Conclusively, the established multiple acquisition modes, computational data processing and analysis strategy proved to be useful for the in-depth structural identification of QSC.
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Affiliation(s)
- Chi Ma
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine Shanghai 201203 China +86 021 81871244
| | - Yuhao Zhang
- School of Traditional Chinese Pharmacy, China Pharmaceutical University Nanjing 211198 China
| | - Xiuxiu Dou
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine Shanghai 201203 China +86 021 81871244
| | - Li Liu
- Guizhou Hanfang Pharmaceutical Co., Ltd. Guizhou 550014 China
| | - Weidong Zhang
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine Shanghai 201203 China +86 021 81871244
- School of Pharmacy, Naval Medical University Shanghai 200433 China +86 021 81871248
- School of Traditional Chinese Pharmacy, China Pharmaceutical University Nanjing 211198 China
| | - Ji Ye
- School of Pharmacy, Naval Medical University Shanghai 200433 China +86 021 81871248
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Frankenfield AM, Ni J, Ahmed M, Hao L. Protein Contaminants Matter: Building Universal Protein Contaminant Libraries for DDA and DIA Proteomics. J Proteome Res 2022; 21:2104-2113. [PMID: 35793413 PMCID: PMC10040255 DOI: 10.1021/acs.jproteome.2c00145] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Mass spectrometry-based proteomics is constantly challenged by the presence of contaminant background signals. In particular, protein contaminants from reagents and sample handling are almost impossible to avoid. For data-dependent acquisition (DDA) proteomics, an exclusion list can be used to reduce the influence of protein contaminants. However, protein contamination has not been evaluated and is rarely addressed in data-independent acquisition (DIA). How protein contaminants influence proteomic data is also unclear. In this study, we established new protein contaminant FASTA and spectral libraries that are applicable to all proteomic workflows and evaluated the impact of protein contaminants on both DDA and DIA proteomics. We demonstrated that including our contaminant libraries can reduce false discoveries and increase protein identifications, without influencing the quantification accuracy in various proteomic software platforms. With the pressing need to standardize proteomic workflow in the research community, we highly recommend including our contaminant FASTA and spectral libraries in all bottom-up proteomic data analysis. Our contaminant libraries and a step-by-step tutorial to incorporate these libraries in various DDA and DIA data analysis platforms can be valuable resources for proteomic researchers, freely accessible at https://github.com/HaoGroup-ProtContLib.
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Affiliation(s)
- Ashley M Frankenfield
- Department of Chemistry, The George Washington University, Science and Engineering Hall 4000, 800, 22nd St., Northwest, Washington, DC 20052, United States
| | - Jiawei Ni
- Department of Chemistry, The George Washington University, Science and Engineering Hall 4000, 800, 22nd St., Northwest, Washington, DC 20052, United States
| | - Mustafa Ahmed
- Department of Chemistry, The George Washington University, Science and Engineering Hall 4000, 800, 22nd St., Northwest, Washington, DC 20052, United States
| | - Ling Hao
- Department of Chemistry, The George Washington University, Science and Engineering Hall 4000, 800, 22nd St., Northwest, Washington, DC 20052, United States
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