Urinary metabolomic profling of a cohort of Colombian patients with systemic lupus erythematosus
Systemic lupus erythematosus (SLE) is an autoimmune and multisystem disease with a high public health impact. Lupus nephritis (LN), commonly known as renal involvement in SLE, is associated with a poorer prognosis and increased rates of morbidity and mortality in patients with SLE. Identifying new u...
- Autores:
-
Rojo‑Sánchez, Alejandra
Carmona‑Martes, Ada
Díaz‑Olmos, Yirys
Santamaría‑Torres, Mary
P. Cala, Mónica P.
Orozco‑Acosta, Erick
Aroca‑Martínez, Gustavo
Pacheco‑Londoño, Leonardo
Navarro‑Quiroz, Elkin
Pacheco‑Lugo, Lisandro A.
- Tipo de recurso:
- Fecha de publicación:
- 2024
- Institución:
- Universidad Simón Bolívar
- Repositorio:
- Repositorio Digital USB
- Idioma:
- eng
- OAI Identifier:
- oai:bonga.unisimon.edu.co:20.500.12442/14561
- Acceso en línea:
- https://hdl.handle.net/20.500.12442/14561
https://doi.org/10.1038/s41598-024-60217-0
https://www.nature.com/articles/s41598-024-60217-0
- Palabra clave:
- Biochemistry
Bioinformatics
Biomarkers
Computational biology and bioinformatics
Kidney
Mass spectrometry
Metabolomics
Proteomic analysis
- Rights
- openAccess
- License
- http://purl.org/coar/access_right/c_abf2
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Urinary metabolomic profling of a cohort of Colombian patients with systemic lupus erythematosus |
| title |
Urinary metabolomic profling of a cohort of Colombian patients with systemic lupus erythematosus |
| spellingShingle |
Urinary metabolomic profling of a cohort of Colombian patients with systemic lupus erythematosus Biochemistry Bioinformatics Biomarkers Computational biology and bioinformatics Kidney Mass spectrometry Metabolomics Proteomic analysis |
| title_short |
Urinary metabolomic profling of a cohort of Colombian patients with systemic lupus erythematosus |
| title_full |
Urinary metabolomic profling of a cohort of Colombian patients with systemic lupus erythematosus |
| title_fullStr |
Urinary metabolomic profling of a cohort of Colombian patients with systemic lupus erythematosus |
| title_full_unstemmed |
Urinary metabolomic profling of a cohort of Colombian patients with systemic lupus erythematosus |
| title_sort |
Urinary metabolomic profling of a cohort of Colombian patients with systemic lupus erythematosus |
| dc.creator.fl_str_mv |
Rojo‑Sánchez, Alejandra Carmona‑Martes, Ada Díaz‑Olmos, Yirys Santamaría‑Torres, Mary P. Cala, Mónica P. Orozco‑Acosta, Erick Aroca‑Martínez, Gustavo Pacheco‑Londoño, Leonardo Navarro‑Quiroz, Elkin Pacheco‑Lugo, Lisandro A. |
| dc.contributor.author.none.fl_str_mv |
Rojo‑Sánchez, Alejandra Carmona‑Martes, Ada Díaz‑Olmos, Yirys Santamaría‑Torres, Mary P. Cala, Mónica P. Orozco‑Acosta, Erick Aroca‑Martínez, Gustavo Pacheco‑Londoño, Leonardo Navarro‑Quiroz, Elkin Pacheco‑Lugo, Lisandro A. |
| dc.subject.eng.fl_str_mv |
Biochemistry Bioinformatics Biomarkers Computational biology and bioinformatics Kidney Mass spectrometry Metabolomics Proteomic analysis |
| topic |
Biochemistry Bioinformatics Biomarkers Computational biology and bioinformatics Kidney Mass spectrometry Metabolomics Proteomic analysis |
| description |
Systemic lupus erythematosus (SLE) is an autoimmune and multisystem disease with a high public health impact. Lupus nephritis (LN), commonly known as renal involvement in SLE, is associated with a poorer prognosis and increased rates of morbidity and mortality in patients with SLE. Identifying new urinary biomarkers that can be used for LN prognosis or diagnosis is essential and is part of current active research. In this study, we applied an untargeted metabolomics approach involving liquid and gas chromatography coupled with mass spectrometry to urine samples collected from 17 individuals with SLE and no kidney damage, 23 individuals with LN, and 10 clinically healthy controls (HCs) to identify differential metabolic profiles for SLE and LN. The data analysis revealed a differentially abundant metabolite expression profile for each study group, and those metabolites may act as potential differential biomarkers of SLE and LN. The differential metabolic pathways found between the LN and SLE patients with no kidney involvement included primary bile acid biosynthesis, branched-chain amino acid synthesis and degradation, pantothenate and coenzyme A biosynthesis, lysine degradation, and tryptophan metabolism. Receiver operating characteristic curve analysis revealed that monopalmitin, glycolic acid, and glutamic acid allowed for the differentiation of individuals with SLE and no kidney involvement and individuals with LN considering high confidence levels. While the results offer promise, it is important to recognize the significant influence of medications and other external factors on metabolomics studies. This impact has the potential to obscure differences in metabolic profiles, presenting a considerable challenge in the identification of disease biomarkers. Therefore, experimental validation should be conducted with a larger sample size to explore the diagnostic potential of the metabolites found as well as to examine how treatment and disease activity influence the identified chemical compounds. This will be crucial for refining the accuracy and effectiveness of using urine metabolomics for diagnosing and monitoring lupus and lupus nephritis. |
| publishDate |
2024 |
| dc.date.accessioned.none.fl_str_mv |
2024-04-29T21:57:05Z |
| dc.date.available.none.fl_str_mv |
2024-04-29T21:57:05Z |
| dc.date.issued.none.fl_str_mv |
2024 |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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info:eu-repo/semantics/article |
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Artículo científico |
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20452322 (en línea) |
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https://hdl.handle.net/20.500.12442/14561 |
| dc.identifier.doi.none.fl_str_mv |
https://doi.org/10.1038/s41598-024-60217-0 |
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https://www.nature.com/articles/s41598-024-60217-0 |
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20452322 (en línea) |
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https://hdl.handle.net/20.500.12442/14561 https://doi.org/10.1038/s41598-024-60217-0 https://www.nature.com/articles/s41598-024-60217-0 |
| dc.language.iso.eng.fl_str_mv |
eng |
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eng |
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http://purl.org/coar/access_right/c_abf2 |
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info:eu-repo/semantics/openAccess |
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openAccess |
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pdf |
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Springer nature |
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Scientific reports |
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Vol. 14 No. 9555, (2024) |
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Universidad Simón Bolívar |
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Rojo‑Sánchez, Alejandraeb8769ec-445a-4322-9a4b-d69807ea43bf-1Carmona‑Martes, Ada508bd740-e7dc-4721-a02f-c0973ad7c7aa-1Díaz‑Olmos, Yirys72deca26-a1db-4fe8-b217-52301ba1a0e7-1Santamaría‑Torres, Marye35fa852-da5a-4704-9fe4-2a5d66c68561P. Cala, Mónica P.d7ee42a3-3b34-4ce8-8127-f80a52ab394e-1Orozco‑Acosta, Ericka118e646-b09b-4da9-8a2b-768049908544-1Aroca‑Martínez, Gustavo2dc29dcb-55df-45d5-b5dd-dd282b9ece40-1Pacheco‑Londoño, Leonardoea6f75e6-385b-440a-9279-58df0ae6941e-1Navarro‑Quiroz, Elkin75d3b74f-8128-4236-b752-e60ea6088060-1Pacheco‑Lugo, Lisandro A.133cdcaf-4307-4ce6-9c75-a55f009447a0-12024-04-29T21:57:05Z2024-04-29T21:57:05Z202420452322 (en línea)https://hdl.handle.net/20.500.12442/14561https://doi.org/10.1038/s41598-024-60217-0https://www.nature.com/articles/s41598-024-60217-0Systemic lupus erythematosus (SLE) is an autoimmune and multisystem disease with a high public health impact. Lupus nephritis (LN), commonly known as renal involvement in SLE, is associated with a poorer prognosis and increased rates of morbidity and mortality in patients with SLE. Identifying new urinary biomarkers that can be used for LN prognosis or diagnosis is essential and is part of current active research. In this study, we applied an untargeted metabolomics approach involving liquid and gas chromatography coupled with mass spectrometry to urine samples collected from 17 individuals with SLE and no kidney damage, 23 individuals with LN, and 10 clinically healthy controls (HCs) to identify differential metabolic profiles for SLE and LN. The data analysis revealed a differentially abundant metabolite expression profile for each study group, and those metabolites may act as potential differential biomarkers of SLE and LN. The differential metabolic pathways found between the LN and SLE patients with no kidney involvement included primary bile acid biosynthesis, branched-chain amino acid synthesis and degradation, pantothenate and coenzyme A biosynthesis, lysine degradation, and tryptophan metabolism. Receiver operating characteristic curve analysis revealed that monopalmitin, glycolic acid, and glutamic acid allowed for the differentiation of individuals with SLE and no kidney involvement and individuals with LN considering high confidence levels. While the results offer promise, it is important to recognize the significant influence of medications and other external factors on metabolomics studies. This impact has the potential to obscure differences in metabolic profiles, presenting a considerable challenge in the identification of disease biomarkers. Therefore, experimental validation should be conducted with a larger sample size to explore the diagnostic potential of the metabolites found as well as to examine how treatment and disease activity influence the identified chemical compounds. This will be crucial for refining the accuracy and effectiveness of using urine metabolomics for diagnosing and monitoring lupus and lupus nephritis.pdfengSpringer natureScientific reportsVol. 14 No. 9555, (2024)BiochemistryBioinformaticsBiomarkersComputational biology and bioinformaticsKidneyMass spectrometryMetabolomicsProteomic analysisUrinary metabolomic profling of a cohort of Colombian patients with systemic lupus erythematosusinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/articleArtículo científicohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1Tsokos, G. C. Systemic lupus erythematosus. N. Engl. J. Med. 365, 2110–2121 (2011).Mok, C. C. Metabolic syndrome and systemic lupus erythematosus: The connection. Expert Rev. Clin. Immunol. 15, 765–775 (2019).Maria, N. I. & Davidson, A. Protecting the kidney in systemic lupus erythematosus: from diagnosis to therapy. Nat. Rev. Rheumatol. 16, 255–267 (2020).Aragón, C. C. et al. Urinary biomarkers in lupus nephritis. J. Transl. Autoimmun. 3, 100042 (2020)Clish, C. B. Metabolomics: An emerging but powerful tool for precision medicine. Mol. Case Stud. 1, a000588 (2015).Zhang, Y. et al. Metabolic profiling reveals new serum signatures to discriminate lupus nephritis from systemic lupus erythematosus. Front. Immunol. 13, 967371 (2022).Zhang, A., Sun, H., Yan, G., Wang, P. & Wang, X. Metabolomics for biomarker discovery: Moving to the clinic. BioMed Res. Int. 2015, 1–6 (2015).Kalantari, S., Chashmniam, S., Nafar, M., Zakeri, Z. & Parvin, M. Metabolomics approach reveals urine biomarkers and pathways associated with the pathogenesis of lupus nephritis. Iran. J. Basic Med. Sci. 22, (2019).Aringer, M. et al. 2019 European League Against Rheumatism/American College of Rheumatology classification criteria for systemic lupus erythematosus. Ann. Rheum. Dis. 78, 1151–1159 (2019).Rey-Stolle, F. et al. Low and high resolution gas chromatography-mass spectrometry for untargeted metabolomics: A tutorial. Anal. Chim. Acta 1210, 339043 (2022).Kind, T. et al. FiehnLib: Mass spectral and retention index libraries for metabolomics based on quadrupole and time-of-flight gas chromatography/mass spectrometry. Anal. Chem. 81, 10038–10048 (2009).Blaženović, I., Kind, T., Ji, J. & Fiehn, O. Software tools and approaches for compound identification of LC-MS/MS data in metabolomics. Metabolites 8(2), 31. https://doi.org/10.3390/metabo8020031 (2018).Karaman, I. Preprocessing and Pretreatment of Metabolomics Data for Statistical Analysis. in Metabolomics: From Fundamentals to Clinical Applications (ed. Sussulini, A.) vol. 965 145–161 (Springer International Publishing, Cham, 2017).Warrack, B. M. et al. Normalization strategies for metabonomic analysis of urine samples. J. Chromatogr. B 877, 547–552 (2009).Ouyang, X., Dai, Y., Wen, J. & Wang, L. 1 H NMR-based metabolomic study of metabolic profiling for systemic lupus erythematosus. Lupus 20, 1411–1420 (2011).Shin, T. H. et al. Analysis of the free fatty acid metabolome in the plasma of patients with systemic lupus erythematosus and fever. Metabolomics 14, 14 (2018).Ruan, X. Z., Varghese, Z. & Moorhead, J. F. An update on the lipid nephrotoxicity hypothesis. Nat. Rev. Nephrol. 5, 713–721 (2009).Frostegård, J. SLE, atherosclerosis and cardiovascular disease. J. Intern. Med. 257, 485–495 (2005).Sun, W. et al. Lipid metabolism: Immune regulation and therapeutic prospectives in systemic lupus erythematosus. Front. Immunol. 13, (2022).Ferrara, D., Montecucco, F., Dallegri, F. & Carbone, F. Impact of different ectopic fat depots on cardiovascular and metabolic diseases. J. Cell. Physiol. 234, 21630–21641 (2019).Roubicek, T. et al. Increased production of proinflammatory cytokines in adipose tissue of patients with end-stage renal disease. Nutrition 25, 762–768 (2009).Frostegård, J. et al. Lipid peroxidation is enhanced in patients with systemic lupus erythematosus and is associated with arterial and renal disease manifestations. Arthritis Rheum. 52, 192–200 (2005).Hu, C. et al. Lipidomics revealed aberrant metabolism of lipids including FAHFAs in renal tissue in the progression of lupus nephritis in a murine model. Metabolites 11, 142 (2021).Yoshida, N. et al. ICER is requisite for Th17 differentiation. Nat. Commun. 7, 12993 (2016).Kono, M., Yoshida, N., Maeda, K. & Tsokos, G. C. Transcriptional factor ICER promotes glutaminolysis and the generation of Th17 cells. Proc. Natl. Acad. Sci. U. S. A. 115, 2478–2483 (2018).Kono, M. et al. Glutaminase 1 inhibition reduces glycolysis and ameliorates lupus-like disease in MRL/lpr mice and experimental autoimmune encephalomyelitis. Arthritis Rheumatol. Hoboken NJ 71, 1869–1878 (2019).Kono, M., Yoshida, N. & Tsokos, G. C. Amino acid metabolism in lupus. Front. Immunol. 12, 623844 (2021).Xu, T. et al. Metabolic control of TH17 and induced Treg cell balance by an epigenetic mechanism. Nature 548, 228–233 (2017).Chu, L., Zhang, K., Zhang, Y., Jin, X. & Jiang, H. Mechanism underlying an elevated serum bile acid level in chronic renal failure patients. Int. Urol. Nephrol. 47, 345–351 (2015).Erlinger, S. Bile acids in cholestasis: Bad for the liver, not so good for the kidney. Clin. Res. Hepatol. Gastroenterol. 38, 392–394 (2014).He, J. et al. Microbiome and metabolome analyses reveal the disruption of lipid metabolism in systemic lupus erythematosus. Front. Immunol. 11, 1703 (2020).Sarkissian, T., Beyene, J., Feldman, B., McCrindle, B. & Silverman, E. D. Longitudinal examination of lipid profiles in pediatric systemic lupus erythematosus. Arthritis Rheum. 56, 631–638 (2007).Godlewska, U., Bulanda, E. & Wypych, T. P. Bile acids in immunity: Bidirectional mediators between the host and the microbiota. Front. Immunol. 13, 949033 (2022).Lian, F. et al. Activation of farnesoid X receptor attenuates liver injury in systemic lupus erythematosus. Rheumatol. Int. 32, 1705–1710 (2012).Zhang, L. et al. Gut Microbiome and Metabolites in Systemic Lupus Erythematosus: Link, Mechanisms and Intervention. Front. Immunol. 12, (2021).Hang, S. et al. Bile acid metabolites control TH17 and Treg cell differentiation. Nature 576, 143–148 (2019).Kato, H. & Perl, A. Mechanistic target of rapamycin complex 1 expands Th17 and IL-4+ CD4-CD8- double-negative T cells and contracts regulatory T cells in systemic lupus erythematosus. J. Immunol. Baltim. Md 1950(192), 4134–4144 (2014).Shi, H. et al. Amino acids license kinase mTORC1 activity and Treg cell function via small G proteins Rag and Rheb. Immunity 51, 1012-1027.e7 (2019).Ananieva, E. A., Patel, C. H., Drake, C. H., Powell, J. D. & Hutson, S. M. Cytosolic branched chain aminotransferase (BCATc) regulates mTORC1 signaling and glycolytic metabolism in CD4+ T Cells. J. Biol. Chem. 289, 18793–18804 (2014).Papathanassiu, A. E. et al. BCAT1 controls metabolic reprogramming in activated human macrophages and is associated with inflammatory diseases. Nat. Commun. 8, 16040 (2017).Zhang, T. & Mohan, C. Caution in studying and interpreting the lupus metabolome. Arthritis Res. Ther. 22, 172 (2020).Wu, T. et al. Metabolic disturbances associated with systemic lupus erythematosus. PloS One 7, e37210 (2012).Barone, F. P. et al. Metabolomics and biomarkers for lupus nephritis—a systematic review. Surg. Exp. Pathol. 6, 11 (2023).Alexander, J. J., Zwingmann, C., Jacob, A. & Quigg, R. Alteration in kidney glucose and amino acids are implicated in renal pathology in MRL/lpr mice. Biochim. Biophys. Acta BBA - Mol. Basis Dis. 1772, 1143–1149 (2007).Brown, J. et al. Microbiota-mediated skewing of tryptophan catabolism modulates CD4+ T cells in lupus-prone mice. iScience 25, 104241 (2022).Perl, A. et al. Comprehensive metabolome analyses reveal N-acetylcysteine-responsive accumulation of kynurenine in systemic lupus erythematosus: implications for activation of the mechanistic target of rapamycin. Metabolomics 11, 1157–1174 (2015).Oaks, Z., Winans, T., Huang, N., Banki, K. & Perl, A. Activation of the mechanistic target of rapamycin in SLE: Explosion of evidence in the last five years. Curr. Rheumatol. Rep. 18, 73 (2016).Tzeng, H.-T. & Chyuan, I.-T. Immunometabolism in systemic lupus erythematosus: Relevant pathogenetic mechanisms and potential clinical applications. J. Formos. Med. Assoc. 120, 1667–1675 (2021).Anekthanakul, K. et al. Predicting lupus membranous nephritis using reduced picolinic acid to tryptophan ratio as a urinary biomarker. iScience 24, 103355 (2021).Pawlak, K., Kowalewska, A., Mysliwiec, M. & Pawlak, D. 3-hydroxyanthranilic acid is independently associated with monocyte chemoattractant protein-1 (CCL2) and macrophage inflammatory protein-1β (CCL4) in patients with chronic kidney disease. Clin. Biochem. 43, 1101–1106 (2010).Guleria, A. et al. NMR based serum metabolomics reveals a distinctive signature in patients with Lupus Nephritis. Sci. Rep. 6, 35309 (2016).Wen, Y. & Parikh, C. R. Current concepts and advances in biomarkers of acute kidney injury. Crit. Rev. Clin. Lab. Sci. 58, 354–368 (2021).Ganguly, S. et al. Nuclear magnetic resonance–based targeted profiling of urinary acetate and citrate following cyclophosphamide therapy in patients with lupus nephritis. Lupus 29, 782–786 (2020)Malkawi, A. K. et al. Metabolomics based profiling of dexamethasone side effects in rats. Front. Pharmacol. 9, 46 (2018).Bordag, N. et al. Glucocorticoid (dexamethasone)-induced metabolome changes in healthy males suggest prediction of response and side effects. Sci. Rep. 5, 15954 (2015).Di Dalmazi, G. et al. Cortisol-related metabolic alterations assessed by mass spectrometry assay in patients with Cushing’s syndrome. Eur. J. Endocrinol. 177, 227–237 (2017).Babary, H. et al. Favorable effects of hydroxychloroquine on serum low density lipid in patients with systemic lupus erythematosus: A systematic review and meta-analysis. Int. J. Rheum. Dis. 21, 84–92 (2018).Durcan, L. et al. Longitudinal evaluation of lipoprotein variables in systemic lupus erythematosus reveals adverse changes with disease activity and prednisone and more favorable profiles with hydroxychloroquine therapy. J. Rheumatol. 43, 745–750 (2016).Pereira, M. J. et al. The immunosuppressive agents rapamycin, cyclosporin A and tacrolimus increase lipolysis, inhibit lipid storage and alter expression of genes involved in lipid metabolism in human adipose tissue. Mol. Cell. Endocrinol. 365, 260–269 (2013).Fernandez Nieto, M. & Jayne, D. R. Con: The use of calcineurin inhibitors in the treatment of lupus nephritis: Table 1. Nephrol. Dial. 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