Abstract
Purpose
Dengue is a mosquito vector-borne disease caused by the dengue virus, which affects 125 million people globally. The disease causes considerable morbidity. The disease, based on symptoms, is classified into three characteristic phases, which can further lead to complications in the second phase. Molecular signatures that are associated with the three phases have not been well characterized. We performed an integrated clinical and metabolomic analysis of our patient cohort and compared it with omics data from the literature to identify signatures unique to the different phases.
Methods
The dengue patients are recruited by clinicians after standard-of-care diagnostic tests and evaluation of symptoms. Blood from the patients was collected. NS1 antigen, IgM, IgG antibodies, and cytokines in serum were analyzed using ELISA. Targeted metabolomics was performed using LC–MS triple quad. The results were compared with analyzed transcriptomic data from the GEO database and metabolomic data sets from the literature.
Results
The dengue patients displayed characteristic features of the disease, including elevated NS1 levels. TNF-α was found to be elevated in all three phases compared to healthy controls. The metabolic pathways were found to be deregulated compared to healthy controls only in phases I and II of dengue patients. The pathways represent viral replication and host response mediated pathways. The major pathways include nucleotide metabolism of various amino acids and fatty acids, biotin, etc.
Conclusion
The results show elevated TNF-α and metabolites that are characteristic of viral infection and host response. IL10 and IFN-γ were not significant, consistent with the absence of any complications.
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References
Alcon, S., Talarmin, A., Debruyne, M., Falconar, A., Deubel, V., & Flamand, M. (2002). Enzyme-linked immunosorbent assay specific to dengue virus type 1 nonstructural protein NS1 reveals circulation of the antigen in the blood during the acute phase of disease in patients experiencing primary or secondary infections. Journal of Clinical Microbiology, 40, 376–381. https://doi.org/10.1128/JCM.40.02.376-381.2002
Balasubramanian, S., Ramachandran, B., & Amperayani, S. (2012). Dengue viral infection in children: A perspective. Archives of Disease in Childhood, 97, 907–912. https://doi.org/10.1136/archdischild-2012-301710
Barbosa-Lima, G., Hottz, E. D., de Assis, E. F., Liechocki, S., Souza, T. M. L., Zimmerman, G. A., Bozza, F. A., & Bozza, P. T. (2020). Dengue virus-activated platelets modulate monocyte immunometabolic response through lipid droplet biogenesis and cytokine signaling. Journal of Leukocyte Biology, 108, 1293–1306. https://doi.org/10.1002/JLB.4MA0620-658R
Bhagavatham, S. K. S., Khanchandani, P., Kannan, V., Potikuri, D., Sridharan, D., Pulukool, S. K., Naik, A. A., Dandamudi, R. B., Divi, S. M., Pargaonkar, A., Ray, R., Santha, S. S. R., Seshagiri, P. B., Narasimhan, K., Gumdal, N., & Sivaramakrishnan, V. (2021). Adenosine deaminase modulates metabolic remodeling and orchestrates joint destruction in rheumatoid arthritis. Science and Reports, 11, 15129. https://doi.org/10.1038/s41598-021-94607-5
Bozza, F. A., Cruz, O. G., Zagne, S. M. O., Azeredo, E. L., Nogueira, R. M. R., Assis, E. F., Bozza, P. T., & Kubelka, C. F. (2008). Multiplex cytokine profile from dengue patients: MIP-1beta and IFN-gamma as predictive factors for severity. BMC Infectious Diseases, 8, 86. https://doi.org/10.1186/1471-2334-8-86
Butler, M., Chotiwan, N., Brewster, C. D., DiLisio, J. E., Ackart, D. F., Podell, B. K., Basaraba, R. J., Perera, R., Quackenbush, S. L., & Rovnak, J. (2020). Cyclin-dependent kinases 8 and 19 regulate host cell metabolism during dengue virus serotype 2 infection. Viruses, 12, 1–21. https://doi.org/10.3390/v12060654
Byers, N. M., Fleshman, A. C., Perera, R., & Molins, C. R. (2019). Metabolomic insights into human arboviral infections: Dengue Chikungunya, and Zika viruses. Viruses, 11, 1–30. https://doi.org/10.3390/v11030225
Caradonna, K. L., Engel, J. C., Jacobi, D., Lee, C. H., & Burleigh, B. A. (2013). Host metabolism regulates intracellular growth of Trypanosoma cruzi. Cell Host & Microbe, 13, 108–117. https://doi.org/10.1016/j.chom.2012.11.011
Castro-Jorge, L. A., Machado, P. R. L., Fávero, C. A., Borges, M. C., Passos, L. M. R., de Oliveira, R. M., & Fonseca, B. A. L. (2010). Clinical evaluation of the NS1 antigen-capture ELISA for early diagnosis of dengue virus infection in Brazil. Journal of Medical Virology, 82, 1400–1405. https://doi.org/10.1002/jmv.21814
Chaloemwong, J., Tantiworawit, A., Rattanathammethee, T., Hantrakool, S., Chai-Adisaksopha, C., Rattarittamrong, E., & Norasetthada, L. (2018). Useful clinical features and hematological parameters for the diagnosis of dengue infection in patients with acute febrile illness: A retrospective study. BMC Hematology, 18, 1–10. https://doi.org/10.1186/s12878-018-0116-1
Chan, M., & Johansson, M. A. (2012). The incubation periods of dengue viruses. PLoS ONE, 7, 1–7. https://doi.org/10.1371/journal.pone.0050972
Chatel-chaix, L. (2016). The remodeling of the cytoplasm by dengue virus. Journal of Bacteriology and Mycology, 3, 1–6.
Chaturvedi, U. C., Agarwal, R., Elbishbishi, E. A., & Mustafa, A. S. (2000). Cytokine cascade in dengue hemorrhagic fever implications for pathogenesis. FEMS Immunology and Medical Microbiology, 28, 183–188.
Cui, L., Hou, J., Fang, J., Lee, Y. H., Costa, V. V., Wong, L. H., Chen, Q., Ooi, E. E., Tannenbaum, S. R., Chen, J., & Ong, C. N. (2017). Serum metabolomics investigation of humanized mouse model of dengue virus infection. Journal of Virology, 91, 1–10. https://doi.org/10.1128/JVI.00386-17
Cui, L., Lee, Y. H., Kumar, Y., Xu, F., Lu, K., Ooi, E. E., Tannenbaum, S. R., & Ong, C. N. (2013). Serum metabolome and lipidome changes in adult patients with primary dengue infection. PLoS Neglected Tropical Diseases. https://doi.org/10.1371/journal.pntd.0002373
Cui, L., Lee, Y. H., Thein, T. L., Fang, J., Pang, J., Ooi, E. E., Leo, Y. S., Ong, C. N., & Tannenbaum, S. R. (2016). Serum metabolomics reveals serotonin as a predictor of severe dengue in the early phase of dengue fever. PLoS Neglected Tropical Diseases, 10, 1–19. https://doi.org/10.1371/journal.pntd.0004607
Cui, L., Pang, J., Lee, Y. H., Ooi, E. E., Ong, C. N., Leo, S., & Tannenbaum, S. R. (2018). Serum metabolome changes in adult patients with severe dengue in the critical and recovery phases of dengue infection. PLOS Neglected Tropical Diseases, 12, 1–15.
Dussart, P., Labeau, B., Lagathu, G., Louis, P., Nunes, M. R. T., Rodrigues, S. G., Storck-Herrmann, C., Cesaire, R., Morvan, J., Flamand, M., & Baril, L. (2006). Evaluation of an enzyme immunoassay for detection of dengue virus NS1 antigen in human serum. Clinical and Vaccine Immunology, 13, 1185–1189. https://doi.org/10.1128/CVI.00229-06
Fink, J., Gu, F., Ling, L., Tolfvenstam, T., Olfat, F., Chin, K. C., Aw, P., George, J., Kuznetsov, V. A., Schreiber, M., Vasudevan, S. G., & Hibberd, M. L. (2007). Host gene expression profiling of dengue virus infection in cell lines and patients. PLoS Neglected Tropical Diseases. https://doi.org/10.1371/journal.pntd.0000086
John, D. V., Lin, Y. S., & Perng, G. C. (2015). Biomarkers of severe dengue disease—a review. Journal of Biomedical Science, 22, 83. https://doi.org/10.1186/s12929-015-0191-6
Kittigul, L., Temprom, W., Sujirarat, D., & Kittigul, C. (2000). Determination of tumor necrosis factor-alpha levels in dengue virus infected patients by sensitive biotin-streptavidin enzyme-linked immunosorbent assay. Journal of Virological Methods, 90, 51–57. https://doi.org/10.1016/S0166-0934(00)00215-9
Kulkarni, A. D., Rudolph, F. B., & Van Buren, C. T. (1993). The role of dietary sources of nucleotides immune function: A review. The Journal of Nutrition., 124, 1442.
Limonta, D., Falcón, V., Torres, G., Capó, V., Menéndez, I., Rosario, D., Castellanos, Y., Alvarez, M., Rodríguez-Roche, R., de la Rosa, M. C., Pavón, A., López, L., González, K., Guillén, G., Diaz, J., & Guzmán, M. G. (2012). Dengue virus identification by transmission electron microscopy and molecular methods in fatal dengue hemorrhagic fever. Infection, 40, 689–694. https://doi.org/10.1007/s15010-012-0260-7
Mangada, M. M., Endy, T. P., Nisalak, A., Chunsuttiwat, S., Vaughn, D. W., Libraty, D. H., Green, S., Ennis, F. A., & Rothman, A. L. (2002). Dengue-specific T cell responses in peripheral blood mononuclear cells obtained prior to secondary dengue virus infections in Thai schoolchildren. Journal of Infectious Diseases, 185, 1697–1703. https://doi.org/10.1086/340822
Martina, B. E. E., Koraka, P., & Osterhaus, A. D. M. E. (2009). Dengue virus pathogenesis: An integrated view. Clinical Microbiology Reviews, 22, 564–581. https://doi.org/10.1128/CMR.00035-09
Matsushita, K., Morrell, C. N., Cambien, B., Yang, S., Bao, C., Hara, M. R., Quick, R. A., Cao, W., O’Rourke, B., Lowenstein, J. M., Pevsner, J., Wagner, D. D., & Lowenstein, C. J. (2003). Nitric oxide regulates exocytosis by S-nitrosylation of N- ethylmaleimide-sensitive factor. Cell, 115, 139–150. https://doi.org/10.1016/s0092-8674(03)00803-1
Messina, J. P., Brady, O. J., Golding, N., Kraemer, M. U. G., Wint, G. R. W., Ray, S. E., Pigott, D. M., Shearer, F. M., Johnson, K., Earl, L., Marczak, L. B., Shirude, S., Davis Weaver, N., Gilbert, M., Velayudhan, R., Jones, P., Jaenisch, T., Scott, T. W., Reiner, R. C., & Hay, S. I. (2019). The current and future global distribution and population at risk of dengue. Nature Microbiology. https://doi.org/10.1038/s41564-019-0476-8
Naik, A. A., Narayanan, A., Khanchandani, P., Sridharan, D., Sukumar, P., Bhagavatam, S. K. S., Seshagiri, P. B., & Sivaramakrishnan, V. (2020). Systems analysis of avascular necrosis of femoral head using integrative data analysis and literature mining delineates pathways associated with disease. Science and Reports, 10, 1–20. https://doi.org/10.1038/s41598-020-75197-0
Nanaware, N., Banerjee, A., Bagchi, S. M., Bagchi, P., & Mukherjee, A. (2021). Dengue virus infection: A tale of viral exploitations and host responses. Viruses. https://doi.org/10.3390/v13101967
Narayanan, A., Khanchandani, P., Borkar, R. M., Ambati, C. R., Roy, A., Han, X., Bhoskar, R. N., Ragampeta, S., Gannon, F., Mysorekar, V., Karanam, B., Sai Muthukumar, V., & Sivaramakrishnan, V. (2017). Avascular necrosis of femoral head: a metabolomic, biophysical, biochemical, electron microscopic and histopathological characterization. Scientific Reports, 7, 10721. https://doi.org/10.1038/s41598-017-10817-w
Nascimento, E. J. M., Huleatt, J. W., Cordeiro, M. T., Castanha, P. M. S., George, J. K., Grebe, E., Welte, A., Brown, M., Burke, D. S., & Marques, E. T. A. (2018). Development of antibody biomarkers of long term and recent dengue virus infections. Journal of Virological Methods, 257, 62–68. https://doi.org/10.1016/j.jviromet.2018.04.009
O’Connor, J. C., André, C., Wang, Y., Lawson, M. A., Szegedi, S. S., Lestage, J., Castanon, N., Kelley, K. W., & Dantzer, R. (2009). Interferon-gamma and tumor necrosis factor-alpha mediate the upregulation of indoleamine 2,3-dioxygenase and the induction of depressive-like behavior in mice in response to bacillus Calmette-Guerin. The Journal of Neuroscience, 29, 4200–4209. https://doi.org/10.1523/JNEUROSCI.5032-08.2009
Odenkirk, M. T., Reif, D. M., & Baker, E. S. (2021). Multiomic big data analysis challenges: Increasing confidence in the interpretation of artificial intelligence assessments. Analytical Chemistry, 93, 7763–7773. https://doi.org/10.1021/acs.analchem.0c04850
Oliveros, J.C. (2007). Venny. An interactive tool for comparing lists with Venn’s diagrams. https://bioinfogp.cnb.csic.es/tools/venny/index.html
Pang, Z., Chong, J., Li, S., & Xia, J. (2020). MetaboAnalystR 3.0: Toward an optimized workflow for global metabolomics. Metabolites. https://doi.org/10.3390/metabo10050186
Perera, R., Riley, C., Isaac, G., Hopf-Jannasch, A. S., Moore, R. J., Weitz, K. W., Pasa-Tolic, L., Metz, T. O., Adamec, J., & Kuhn, R. J. (2012). Dengue virus infection perturbs lipid homeostasis in infected mosquito cells. PLoS Pathogens. https://doi.org/10.1371/journal.ppat.1002584
Pradhan, S. S., Thota, S. M., Rajaratnam, S., Bhagavatham, S. K. S., Pulukool, S. K., Rathnakumar, S., Phalguna, K. S., Dandamudi, R. B., Pargaonkar, A., Joseph, P., Joshy, E. V., & Sivaramakrishnan, V. (2022). Integrated multi-omics analysis of Huntington disease identifies pathways that modulate protein aggregation. Disease Models & Mechanisms. https://doi.org/10.1242/dmm.049492
Priyadarshini, D., Gadia, R. R., Tripathy, A., Gurukumar, K. R., Bhagat, A., Patwardhan, S., Mokashi, N., Vaidya, D., Shah, P. S., & Cecilia, D. (2010). Clinical findings and pro-inflammatory cytokines in dengue patients in Western India: A facility-based study. PLoS ONE. https://doi.org/10.1371/journal.pone.0008709
Pulukool, S. K., Krishna, S., Bhagavatham, S., & Kannan, V. (2021). Elevated dimethylarginine, ATP, cytokines, metabolic remodeling involving tryptophan metabolism and potential microglial inflammation characterize primary open angle glaucoma. Scientific Reports. https://doi.org/10.1038/s41598-021-89137-z
Pulukool, S. K., Srimadh Bhagavatham, S. K., Kannan, V., Parim, B., Challa, S., Karnatam, V., Ahmad Mir, I., Sukumar, P., Venkateshan, V., Sharma, A., & Sivaramakrishnan, V. (2022). Elevated ATP, cytokines and potential microglial inflammation distinguish exfoliation glaucoma from exfoliation syndrome. Cytokine, 151, 155807. https://doi.org/10.1016/j.cyto.2022.155807
Ralapanawa, U., Alawattegama, A. T. M., Gunrathne, M., Tennakoon, S., & Kularatne, S. A. M. (2018). Value of peripheral blood count for dengue severity prediction. BMC Research Notes. https://doi.org/10.1186/s13104-018-3505-4
Rathakrishnan, A., Wang, S. M., Hu, Y., Khan, A. M., Ponnampalavanar, S., Chai, L., Lum, S., Manikam, R., & Sekaran, S. D. (2012). Cytokine expression profile of dengue patients at different phases of illness. PLoS ONE, 7, 1–10. https://doi.org/10.1371/journal.pone.0052215
Rodríguez Meléndez, R. (2000). Importance of biotin metabolism. Revista De Investigación Clínica., 52, 194–199.
Sai Swaroop, R., Akhil, P. S., Sai Sanwid, P., Bandana, P., Raksha, R. K., Meghana, M., Bibha, C., & Sivaramakrishnan, V. (2022). Integrated multi-omic data analysis and validation with yeast model show oxidative phosphorylation modulates protein aggregation in amyotrophic lateral sclerosis. Journal of Biomolecular Structure and Dynamics. https://doi.org/10.1080/07391102.2022.2090441
Sa-Ngasang, A., Anantapreecha, S., Nuegoonpipat, A. A., Chanama, S., Wibulwattanakuij, S., Pattanakul, K., Sawanpanyalert, P., & Kurane, I. (2006). Specific IgM and IgG responses in primary and secondary dengue virus infections determined by enzyme-linked immunosorbent assay. Epidemiology and Infection, 134, 820. https://doi.org/10.1017/S0950268805005753
Shrinet, J., Shastri, J. S., Gaind, R., Bhavesh, N. S., & Sunil, S. (2016). Serum metabolomics analysis of patients with chikungunya and dengue mono/co-infections reveals distinct metabolite signatures in the three disease conditions. Science and Reports, 6, 36833. https://doi.org/10.1038/srep36833
Shu, P. Y., Chen, L. K., Chang, S. F., Yueh, Y. Y., Chow, L., Chien, L. J., Chin, C., Lin, T. H., & Huang, J. H. (2000). Dengue NS1-specific antibody responses: Isotype distribution and serotyping in patients with dengue fever and dengue hemorrhagec fever. Journal of Medical Virology, 62, 224–232. https://doi.org/10.1002/1096-9071(200010)62:2%3c224::AID-JMV14%3e3.0.CO;2-C
Shu, P. Y., & Huang, J. H. (2004). Current advances in dengue diagnosis. Clinical and Diagnostic Laboratory Immunology, 11, 642–650. https://doi.org/10.1128/CDLI.11.4.642-650.2004
Singla, M., Kar, M., Sethi, T., Kabra, S. K., Lodha, R., Chandele, A., & Medigeshi, G. R. (2016). Immune response to dengue virus infection in pediatric patients in New Delhi, India—Association of Viremia, inflammatory mediators and monocytes with disease severity. PLoS Neglected Tropical Diseases, 10, 1–25. https://doi.org/10.1371/journal.pntd.0004497
Smyth, G. K. (2005). Limma: Linear models for microarray data. Bioinformatics and computational biology solutions using R and bioconductor (pp. 397–420). Springer.
Srimadh Bhagavatham, S. K., Pulukool, S. K., Pradhan, S. S., Saiswaroop, R., Ashok Naik, A., Datta Darshan, V. M., & Sivaramakrishnan, V. (2022). Systems biology approach delineates critical pathways associated with disease progression in rheumatoid arthritis. Journal of Biomolecular Structure and Dynamics. https://doi.org/10.1080/07391102.2022.2115555
Sun, P., & Kochel, T. J. (2013). The battle between infection and host immune responses of dengue virus and its implication in dengue disease pathogenesis. The Scientific World Journal. https://doi.org/10.1155/2013/843469
Tolfvenstam, T., Lindblom, A., Schreiber, M. J., Ling, L., Chow, A., Ooi, E. E., & Hibberd, M. L. (2011). Characterization of early host responses in adults with dengue disease. BMC Infectious Diseases, 11, 1471–2334. https://doi.org/10.1186/1471-2334-11-209
Voge, N. V., Perera, R., Mahapatra, S., Gresh, L., Balmaseda, A., Loroño-Pino, M. A., Hopf-Jannasch, A. S., Belisle, J. T., Harris, E., Blair, C. D., & Beaty, B. J. (2016). Metabolomics-based discovery of small molecule biomarkers in serum associated with dengue virus infections and disease outcomes. PLoS Neglected Tropical Diseases, 10, 1–27. https://doi.org/10.1371/journal.pntd.0004449
World Health Organization. (1997). Dengue hemorrhagic fever: diagnosis, treatment and control. American Journal of Tropical Medicine and Hygiene, 36, 670–670. https://doi.org/10.4269/ajtmh.1987.36.670
World Health Organization. (2009). Dengue: guidelines for diagnosis, treatment. Prevention and Control, 34, 329–330. https://doi.org/10.1055/s-0029-1186356
Wu, Y., Li, K., Zeng, M., Qiao, B., & Zhou, B. (2022). Serum metabolomics analysis of the anti-inflammatory effects of gallic acid on rats with acute inflammation. Frontiers in Pharmacology, 13, 830439. https://doi.org/10.3389/fphar.2022.830439
Xia, J., & Wishart, D. S. (2011). Web-based inference of biological patterns, functions and pathways from metabolomic data using metaboanalyst. Nature Protocols, 6, 743–760. https://doi.org/10.1038/nprot.2011.319
Xu, H., Di, B., Pan, Y. X., Qiu, L. W., Di Wang, Y., Hao, W., He, L. J., Yuen, K. Y., & Che, X. Y. (2006). Serotype 1-specific monoclonal antibody-based antigen capture immunoassay for detection of circulating nonstructural protein NS1: Implications for early diagnosis and serotyping of dengue virus infections. Journal of Clinical Microbiology, 44, 2872–2878. https://doi.org/10.1128/JCM.00777-06
Zhang, H., Zhou, Y. P., Peng, H. J., Zhang, X. H., Zhou, F. Y., Liu, Z. H., & Chen, X. G. (2014). Predictive symptoms and signs of severe dengue disease for patients with dengue fever: A meta-analysis. BioMed Research International. https://doi.org/10.1155/2014/359308
Zhang, S., Carriere, J., Lin, X., Xie, N., & Feng, P. (2018). Interplay between cellular metabolism and cytokine responses during viral infection. Viruses. https://doi.org/10.3390/v10100521
Zhou, G., Soufan, O., Ewald, J., Hancock, R. E. W., Basu, N., & Xia, J. (2019). NetworkAnalyst 3.0: A visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Research, 47, W234–W241. https://doi.org/10.1093/nar/gkz240
Acknowledgements
We acknowledge Sri Sathya Sai Institute of Higher Learning (SSSIHL)- Central Research Instruments Facility (CRIF) for extending the usage of the required instrumentation facility. We thank the staff and doctors of Sri Sathya Sai Institute of Higher Medical Sciences (SSSIHMS), Whitefield, and Sri Sathya Sai General Hospital, Puttaparthi, for lending their support for the collection of blood samples from patients. We thank Kothari Distributors, Bengaluru, for providing the ELISA kits. We also thank the patient volunteers who willingly agreed to be a part of this study. We thank the Defence Research and Development Organization- Life Science Research Board (DRDO-LSRB) [O/o DG/81/48222/LSRB-337/BTB/2018], Tata Education and Development Trust [TEDT/MUM/HEA/SSSIHL/2017-2018/0069-RM-db], Prasanthi Trust, Inc., USA [22-06-2018], and the Department of Science and Technology- Technology Development Program (DST-TDP) [IDP/MED/19/2016] for providing the financial support. We also acknowledge the grant support from Department of Science and Technology- The Science and Engineering Research Board- Extra Mural Research [DST-SERB-EMR: EMR/2017/005381], Department of Science and Technology- Fund for Improvement of Science and Technology Infrastructure in Higher Educational Institutions (DST-FIST) [SR/FST/LSI-616/2014], and the University Grants Commission- Special Assistance Program (UGC-SAP III) [F.3-19 /2018/DRS-III(SAP-II)] for infrastructure funding.
Funding
Financial support was provided by Defence Research and Development Organization- LSRB [O/o DG/81/48222/LSRB-337/BTB/2018], Tata Education and Development Trust [TEDT/MUM/HEA/SSSIHL/2017–2018/0069-RM-db], Prasanthi Trust, Inc., USA [22-06-2018], DST-Technology Development Program [IDP/MED/19/2016]. We also acknowledge the grant support from Department of Department of Science and Technology-The Science and Engineering Research Board–Extra Mural Research [DST-SERB-EMR: EMR/2017/005381], Department of Science and Technology- Fund for improvement of Science and Technology Infrastructure in Higher Educational Institutions (DST-FIST) [SR/FST/LSI-616/2014], University Grants Commission-Special Assistance Program (UGC-SAP III) [F.3–19 /2018/DRS-III(SAP-II)] for infrastructure funding.
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SR carried out all the experiments, and analysis and assisted in manuscript writing and figures generation. SR and SSP helped in patient serum metabolomics sample processing and method development analysis used for the study. NSVK, RG, NB, GKM, and SK helped in collecting dengue patient and appropriate control serum samples for the study and provided clinical inputs. SKJ helped in standardizing and processing the ELISA protocols used for the study. AP helped in some of the methodology related to mass spectrometry and also with comments on the manuscript. VS and SSR conceptualized the entire idea, interpreted the results, and played a major role in the preparation of the manuscript.
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Rathnakumar, S., Kambhampati, N.S.V., Saiswaroop, R. et al. Integrated clinical and metabolomic analysis of dengue infection shows molecular signatures associated with host-pathogen interaction in different phases of the disease. Metabolomics 19, 47 (2023). https://doi.org/10.1007/s11306-023-02011-z
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DOI: https://doi.org/10.1007/s11306-023-02011-z