[en] Inflammation is a pathogenic driver of many diseases, including atherosclerosis and rheumatoid arthritis. Hyperinflammation can be seen as any inflammatory response that is deleterious to the host, regardless of cause. In medicine, hyperinflammation is defined as severe, deleterious, and fluctuating systemic or local inflammation with presence of a cytokine storm. It has been associated with rare autoinflammatory disorders. However, advances in omics technologies, including genomics, proteomics, and metabolomics, have revealed it to be more common, occurring in sepsis and severe coronavirus disease 2019. With a focus on proteomics, this review highlights the key role of omics in this shift. Through an exploration of research, we present how omics technologies have contributed to improved diagnostics, prognostics, and targeted therapeutics in the field of hyperinflammation. We also discuss the integration of advanced technologies, multiomics approaches, and artificial intelligence in analyzing complex datasets to develop targeted therapies, and we address their potential for revolutionizing the clinical aspects of hyperinflammation. We emphasize personalized medicine approaches for effective treatments and outline challenges, including the need for standardized methodologies, robust bioinformatics tools, and ethical considerations regarding data privacy. This review aims to provide a comprehensive overview of the molecular mechanisms underpinning hyperinflammation and underscores the potential of omics technologies in enabling successful clinical management.
Disciplines :
Life sciences: Multidisciplinary, general & others
Author, co-author :
Keskitalo, Salla; Institute of Biotechnology, Helsinki Institute of Life Science HiLIFE, University of Helsinki, Helsinki, Finland. Electronic address: salla.keskitalo@helsinki.fi
Seppänen, Mikko R J; Pediatric Research Center, New Children's Hospital, University of Helsinki and HUS Helsinki University Hospital, Helsinki, Finland, Translational Immunology Research Program, University of Helsinki, Helsinki, Finland, European Reference Network Rare Immunodeficiency Autoinflammatory and Autoimmune Diseases Network (ERN RITA) Core Center, Utrecht, The Netherlands
DEL SOL MESA, Antonio ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Computational Biology
Varjosalo, Markku; Institute of Biotechnology, Helsinki Institute of Life Science HiLIFE, University of Helsinki, Helsinki, Finland, Department of Biochemistry and Developmental Biology and Translational Cancer Medicine Program, Faculty of Medicine, University of Helsinki, Helsinki, Finland. Electronic address: markku.varjosalo@helsinki.fi
External co-authors :
yes
Language :
English
Title :
From rare to more common: The emerging role of omics in improving understanding and treatment of severe inflammatory and hyperinflammatory conditions.
Moos, A.B., Baecher-Steppan, L., Kerkvliet, N.I., Acute inflammatory response to sheep red blood cells in mice treated with 2,3,7,8-tetrachlorodibenzo-p-dioxin: the role of proinflammatory cytokines, IL-1 and TNF. Toxicol Appl Pharmacol 127 (1994), 331–335.
Stankey, C.T., Bourges, C., Haag, L.M., Turner-Stokes, T., Piedade, A.P., Palmer-Jones, C., et al. A disease-associated gene desert directs macrophage inflammation through ETS2. Nature 630 (2024), 447–456.
Silverstein, A.M., Autoimmunity versus horror autotoxicus: the struggle for recognition. Nat Immunol 2 (2001), 279–281.
McDermott, M.F., Aksentijevich, I., Galon, J., McDermott, E.M., Ogunkolade, B.W., Centola, M., et al. Germline mutations in the extracellular domains of the 55 kDa TNF receptor, TNFR1, define a family of dominantly inherited autoinflammatory syndromes. Cell 97 (1999), 133–144.
Göös, H., Fogarty, C.L., Sahu, B., Plagnol, V., Rajamäki, K., Nurmi, K., et al. Gain-of-function CEBPE mutation causes noncanonical autoinflammatory inflammasomopathy. J Allergy Clin Immunol 144 (2019), 1364–1376.
Fornes, O., Jia, A., Kuehn, H.S., Min, Q., Pannicke, U., Schleussner, N., et al. A multimorphic mutation in IRF4 causes human autosomal dominant combined immunodeficiency. Sci Immunol, 8, 2023, eade7953.
Visscher, P.M., Wray, N.R., Zhang, Q., Sklar, P., McCarthy, M.I., Brown, M.A., et al. 10 years of GWAS discovery: biology, function, and translation. Am J Hum Genet 101 (2017), 5–22.
Liu, C., Joehanes, R., Ma, J., Wang, Y., Sun, X., Keshawarz, A., et al. Whole genome DNA and RNA sequencing of whole blood elucidates the genetic architecture of gene expression underlying a wide range of diseases. Sci Rep, 12, 2022, 20167.
MacArthur, J., Bowler, E., Cerezo, M., Gil, L., Hall, P., Hastings, E., et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res 45 (2017), D896–D901.
Ammann, S., Lehmberg, K., Zur Stadt, U., Klemann, C., Bode, S.F.N., Speckmann, C., et al. Effective immunological guidance of genetic analyses including exome sequencing in patients evaluated for hemophagocytic lymphohistiocytosis. J Clin Immunol 37 (2017), 770–780.
Schulert, G.S., Zhang, K., Genetics of acquired cytokine storm syndromes: secondary HLH genetics. Adv Exp Med Biol 1448 (2024), 103–119.
Bousfiha, A., Moundir, A., Tangye, S.G., Picard, C., Jeddane, L., Al-Herz, W., et al. The 2022 update of IUIS phenotypical classification for human inborn errors of immunity. J Clin Immunol 42 (2022), 1508–1520.
Ancient missense mutations in a new member of the RoRet gene family are likely to cause familial Mediterranean fever. The International FMF Consortium. Cell 90 (1997), 797–807.
Ozen, S., Bilginer, Y., A clinical guide to autoinflammatory diseases: familial Mediterranean fever and next-of-kin. Nat Rev Rheumatol 10 (2014), 135–147.
Canna, S.W., Girard, C., Malle, L., de Jesus, A., Romberg, N., Kelsen, J., et al. Life-threatening NLRC4-associated hyperinflammation successfully treated with IL-18 inhibition. J Allergy Clin Immunol 139 (2017), 1698–1701.
Al Nabhani, Z., Dietrich, G., Hugot, J.P., Barreau, F., Nod2: the intestinal gate keeper. PLoS Pathog, 13, 2017, e1006177.
Negroni, A., Pierdomenico, M., Cucchiara, S., Stronati, L., NOD2 and inflammation: current insights. J Inflamm Res 11 (2018), 49–60.
Borzutzky, A., Fried, A., Chou, J., Bonilla, F.A., Kim, S., Dedeoglu, F., NOD2-associated diseases: bridging innate immunity and autoinflammation. Clin Immunol 134 (2010), 251–261.
Zheng, C., Huang, Y., Ye, Z., Wang, Y., Tang, Z., Lu, J., et al. Infantile onset intractable inflammatory bowel disease due to novel heterozygous mutations in TNFAIP3 (A20). Inflamm Bowel Dis 24 (2018), 2613–2620.
Zhou, Q., Wang, H., Schwartz, D.M., Stoffels, M., Park, Y.H., Zhang, Y., et al. Loss-of-function mutations in TNFAIP3 leading to A20 haploinsufficiency cause an early-onset autoinflammatory disease. Nat Genet 48 (2016), 67–73.
Lee, Y.H., Bae, S.C., Choi, S.J., Ji, J.D., Song, G.G., Associations between TNFAIP3 gene polymorphisms and rheumatoid arthritis: a meta-analysis. Inflamm Res 61 (2012), 635–641.
Woellner, C., Gertz, E.M., Schäffer, A.A., Lagos, M., Perro, M., Glocker, E.O., et al. Mutations in STAT3 and diagnostic guidelines for hyper-IgE syndrome. J Allergy Clin Immunol 125 (2010), 424–432.e8.
Chandesris, M.O., Melki, I., Natividad, A., Puel, A., Fieschi, C., Yun, L., et al. Autosomal dominant STAT3 deficiency and hyper-IgE syndrome: molecular, cellular, and clinical features from a French national survey. Medicine (Baltimore) 91 (2012), e1–e19.
Flanagan, S.E., Haapaniemi, E., Russell, M.A., Caswell, R., Allen, H.L., De Franco, E., et al. Activating germline mutations in STAT3 cause early-onset multi-organ autoimmune disease. Nat Genet 46 (2014), 812–814.
Leiding, J.W., Vogel, T.P., Santarlas, V.G.J., Mhaskar, R., Smith, M.R., Carisey, A., et al. Monogenic early-onset lymphoproliferation and autoimmunity: natural history of STAT3 gain-of-function syndrome. J Allergy Clin Immunol 151 (2023), 1081–1095.
Boehmer, D.F.R., Koehler, L.M., Magg, T., Metzger, P., Rohlfs, M., Ahlfeld, J., et al. A novel complete autosomal-recessive STAT1 LOF variant causes immunodeficiency with hemophagocytic lymphohistiocytosis-like hyperinflammation. J Allergy Clin Immunol Pract 8 (2020), 3102–3111.
Babić Leko, M., Nikolac Perković, M., Klepac, N., Štrac, D.Š., Borovečki, F., Pivac, N., et al. IL-1β, IL-6, IL-10, and TNFα single nucleotide polymorphisms in human influence the susceptibility to Alzheimer's disease pathology. J Alzheimers Dis 75 (2020), 1029–1047.
Slonková, V., Vašků, A., Vašků, V., Polymorphisms in some proinflammatory genes (TNFα and β, IL-1β, IL-6, ADAM17) in severe chronic venous disease. J Eur Acad Dermatol Venereol 37 (2023), 590–597.
Bitner, A., Kalinka, J., IL-1β, IL-6 promoter, TNF-α promoter and IL-1RA gene polymorphisms and the risk of preterm delivery due to preterm premature rupture of membranes in a population of Polish women. Arch Med Sci 6 (2010), 552–557.
Vuong, N.L., Cheung, K.W., Periaswamy, B., Vi, T.T., Duyen, H.T.L., Leong, Y.S., et al. Hyperinflammatory syndrome, natural killer cell function, and genetic polymorphisms in the pathogenesis of severe dengue. J Infect Dis 226 (2022), 1338–1347.
Vagrecha, A., Zhang, M., Acharya, S., Lozinsky, S., Singer, A., Levine, C., et al. Hemophagocytic lymphohistiocytosis gene variants in multisystem inflammatory syndrome in children. Biology (Basel), 11, 2022, 417.
Sidore, C., Orrù, V., Cocco, E., Steri, M., Inshaw, J.R., Pitzalis, M., et al. PRF1 mutation alters immune system activation, inflammation, and risk of autoimmunity. Mult Scler 27 (2021), 1332–1340.
Schulert, G.S., Canna, S.W., Convergent pathways of the hyperferritinemic syndromes. Int Immunol 30 (2018), 195–203.
Ravelli, A., Davì, S., Minoia, F., Martini, A., Cron, R.Q., Macrophage activation syndrome. Hematol Oncol Clin North Am 29 (2015), 927–941.
Zhang, M., Behrens, E.M., Atkinson, T.P., Shakoory, B., Grom, A.A., Cron, R.Q., Genetic defects in cytolysis in macrophage activation syndrome. Curr Rheumatol Rep, 16, 2014, 439.
Correia Marques, M., Rubin, D., Shuldiner, E.G., Datta, M., Schmitz, E., Gutierrez Cruz, G., et al. Enrichment of rare variants of hemophagocytic lymphohistiocytosis genes in systemic juvenile idiopathic arthritis. Arthritis Rheumatol 76 (2024), 1566–1572.
Matsubara, T., Ichiyama, T., Furukawa, S., Immunological profile of peripheral blood lymphocytes and monocytes/macrophages in Kawasaki disease. Clin Exp Immunol 141 (2005), 381–387.
García-Pavón, S., Yamazaki-Nakashimada, M.A., Báez, M., Borjas-Aguilar, K.L., Murata, C., Kawasaki disease complicated with macrophage activation syndrome: a systematic review. J Pediatr Hematol Oncol 39 (2017), 445–451.
Doria, A., Zen, M., Bettio, S., Gatto, M., Bassi, N., Nalotto, L., et al. Autoinflammation and autoimmunity: bridging the divide. Autoimmun Rev 12 (2012), 22–30.
Kötter, I., Krusche, M., VEXAS syndrome: an adult-onset autoinflammatory disorder with underlying somatic mutation. Curr Opin Rheumatol 37 (2025), 21–31.
Schulert, G.S., Grom, A.A., Pathogenesis of macrophage activation syndrome and potential for cytokine-directed therapies. Annu Rev Med 66 (2015), 145–159.
Mehta, P., McAuley, D.F., Brown, M., Sanchez, E., Tattersall, R.S., Manson, J.J., COVID-19: consider cytokine storm syndromes and immunosuppression. Lancet 395 (2020), 1033–1034.
Fajgenbaum, D.C., June, C.H., Cytokine storm. N Engl J Med 383 (2020), 2255–2273.
Bracaglia, C., Prencipe, G., De Benedetti, F., Macrophage activation syndrome: different mechanisms leading to a one clinical syndrome. Pediatr Rheumatol Online J, 15, 2017, 5.
Tyers, M., Mann, M., From genomics to proteomics. Nature 422 (2003), 193–197.
de Azambuja Rodrigues, P.M., Valente, R.H., Brunoro, G.V.F., Nakaya, H.T.I., Araújo-Pereira, M., Bozza, P.T., et al. Proteomics reveals disturbances in the immune response and energy metabolism of monocytes from patients with septic shock. Sci Rep, 11, 2021, 15149.
Singh, V., Tripathi, A., Dutta, R., Proteomic approaches to decipher mechanisms underlying pathogenesis in multiple sclerosis patients. Proteomics, 19, 2019, e1800335.
De Benedetti, F., Prencipe, G., Bracaglia, C., Marasco, E., Grom, A.A., Targeting interferon-γ in hyperinflammation: opportunities and challenges. Nat Rev Rheumatol 17 (2021), 678–691.
Cifani, P., Kentsis, A., Towards comprehensive and quantitative proteomics for diagnosis and therapy of human disease. Proteomics, 17, 2017, 10.1002/pmic.201600079.
Unwin, R.D., Quantification of proteins by iTRAQ. Methods Mol Biol 658 (2010), 205–215.
Thompson, A., Schäfer, J., Kuhn, K., Kienle, S., Schwarz, J., Schmidt, G., et al. Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal Chem 75 (2003), 1895–1904.
Li, M., Ren, R., Yan, M., Chen, S., Chen, C., Yan, J., Identification of novel biomarkers for sepsis diagnosis via serum proteomic analysis using iTRAQ-2D-LC-MS/MS. J Clin Lab Anal, 36, 2022, e24142.
Su, L., Pan, P., Yan, P., Long, Y., Zhou, X., Wang, X., et al. Role of vimentin in modulating immune cell apoptosis and inflammatory responses in sepsis. Sci Rep, 9, 2019, 5747.
Su, L., Zhou, R., Liu, C., Wen, B., Xiao, K., Kong, W., et al. Urinary proteomics analysis for sepsis biomarkers with iTRAQ labeling and two-dimensional liquid chromatography–tandem mass spectrometry. J Trauma Acute Care Surg 74 (2013), 940–945.
Levitt, J.E., Rogers, A.J., Proteomic study of acute respiratory distress syndrome: current knowledge and implications for drug development. Expert Rev Proteomics 13 (2016), 457–469.
Berbers, R.M., Drylewicz, J., Ellerbroek, P.M., van Montfrans, J.M., Dalm, V.A.S.H., van Hagen, P.M., et al. Targeted proteomics reveals inflammatory pathways that classify immune dysregulation in common variable immunodeficiency. J Clin Immunol 41 (2021), 362–373.
David, P., Hansen, F.J., Bhat, A., Weber, G.F., An overview of proteomic methods for the study of “cytokine storms.”. Expert Rev Proteomics 18 (2021), 83–91.
Demichev, V., Tober-Lau, P., Nazarenko, T., Lemke, O., Kaur Aulakh, S., Whitwell, H.J., et al. A proteomic survival predictor for COVID-19 patients in intensive care. PLOS Digit Health, 1, 2022, e0000007.
Liu, X., Abad, L., Chatterjee, L., Cristea, I.M., Varjosalo, M., Mapping protein–protein interactions by mass spectrometry. Mass Spectrom Rev, 2025.
Rajamäki, K., Keskitalo, S., Seppänen, M., Kuismin, O., Vähäsalo, P., Trotta, L., et al. Haploinsufficiency of A20 impairs protein–protein interactome and leads into caspase-8–dependent enhancement of NLRP3 inflammasome activation. RMD Open, 4, 2018, e000740.
Liu, X., Salokas, K., Tamene, F., Jiu, Y., Weldatsadik, R.G., Öhman, T., et al. An AP-MS– and BioID-compatible MAC-tag enables comprehensive mapping of protein interactions and subcellular localizations. Nat Commun, 9, 2018, 1188.
Kaustio, M., Haapaniemi, E., Göös, H., Hautala, T., Park, G., Syrjänen, J., et al. Damaging heterozygous mutations in NFKB1 lead to diverse immunologic phenotypes. J Allergy Clin Immunol 140 (2017), 782–796.
Nurmi, K., Silventoinen, K., Keskitalo, S., Rajamäki, K., Kouri, V.P., Kinnunen, M., et al. Truncating NFKB1 variants cause combined NLRP3 inflammasome activation and type I interferon signaling and predispose to necrotizing fasciitis. Cell Rep Med, 5, 2024, 101503.
Gerritsen, J.S., White, F.M., Phosphoproteomics: a valuable tool for uncovering molecular signaling in cancer cells. Expert Rev Proteomics 18 (2021), 661–674.
Thingholm, T.E., Jensen, O.N., Enrichment and characterization of phosphopeptides by immobilized metal affinity chromatography (IMAC) and mass spectrometry. Methods Mol Biol 527 (2009), 47–56.
Chen, L., Guan, W.J., Qiu, Z.E., Xu, J.B., Bai, X., Hou, X.C., et al. SARS-CoV-2 nucleocapsid protein triggers hyperinflammation via protein–protein interaction-mediated intracellular Cl− accumulation in respiratory epithelium. Signal Transduct Target Ther, 7, 2022, 255.
Xu, S., Pan, X., Mao, L., Pan, H., Xu, W., Hu, Y., et al. Phospho-Tyr705 of STAT3 is a therapeutic target for sepsis through regulating inflammation and coagulation. Cell Commun Signal, 18, 2020, 104.
Świerkot, J., Nowak, B., Czarny, A., Zaczyńska, E., Sokolik, R., Madej, M., et al. The activity of JAK/STAT and NF-κB in patients with rheumatoid arthritis. Adv Clin Exp Med 25 (2016), 709–717.
Bouhaddou, M., Memon, D., Meyer, B., White, K.M., Rezelj, V.V., Correa Marrero, M., et al. The global phosphorylation landscape of SARS-CoV-2 infection. Cell 182 (2020), 685–712.e19.
Weintz, G., Olsen, J.V., Frühauf, K., Niedzielska, M., Amit, I., Jantsch, J., et al. The phosphoproteome of Toll-like receptor–activated macrophages. Mol Syst Biol, 6, 2010, 371.
Gui, Y., Yu, Y., Wang, W., Wang, Y., Lu, H., Mozdzierz, S., et al. Proteome characterization of liver–kidney comorbidity after microbial sepsis. FASEB J, 38, 2024, e23597.
Lin, Y.H., Platt, M.P., Fu, H., Gui, Y., Wang, Y., Gonzalez-Juarbe, N., et al. Global proteome and phosphoproteome characterization of sepsis-induced kidney injury. Mol Cell Proteomics 19 (2020), 2030–2047.
Deutsch, E.W., Omenn, G.S., Sun, Z., Maes, M., Pernemalm, M., Palaniappan, K.K., et al. Advances and utility of the human plasma proteome. J Proteome Res 20 (2021), 5241–5263.
Tulling, A.J., Holierhoek, M.G., Jansen-Hoogendijk, A.M., Hoste, L., Haerynck, F., Tavernier, S.J., et al. Serum proteomics reveals hemophagocytic lymphohistiocytosis-like phenotype in a subset of patients with multisystem inflammatory syndrome in children. Clin Immunol, 264, 2024, 110252.
Sacco, K., Castagnoli, R., Vakkilainen, S., Liu, C., Delmonte, O.M., Oguz, C., et al. Immunopathological signatures in multisystem inflammatory syndrome in children and pediatric COVID-19. Nat Med 28 (2022), 1050–1062.
Budnik, B., Levy, E., Harmange, G., Slavov, N., SCoPE-MS: mass spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation. Genome Biol, 19, 2018, 161.
Tracey, L.J., An, Y., Justice, M.J., CyTOF: an emerging technology for single-cell proteomics in the mouse. Curr Protoc, 1, 2021, e118.
Brodie, T.M., Tosevski, V., High-dimensional single-cell analysis with mass cytometry. Curr Protoc Immunol 118 (2017), 5.11.1–5.11.25.
Hartmann, F.J., Bendall, S.C., Immune monitoring using mass cytometry and related high-dimensional imaging approaches. Nat Rev Rheumatol 16 (2020), 87–99.
Specht, H., Emmott, E., Petelski, A.A., Huffman, R.G., Perlman, D.H., Serra, M., et al. Single-cell proteomic and transcriptomic analysis of macrophage heterogeneity using SCoPE2. Genome Biol, 22, 2021, 50.
Giudice, G., Petsalaki, E., Proteomics and phosphoproteomics in precision medicine: applications and challenges. Brief Bioinform 20 (2019), 767–777.
Feyaerts, D., Hédou, J., Gillard, J., Chen, H., Tsai, E.S., Peterson, L.S., et al. Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19. Cell Rep Med, 3, 2022, 100680.
Fuchs, S., Sawas, N., Staedler, N., Schubert, D.A., D'Andrea, A., Zeiser, R., et al. High-dimensional single-cell proteomics analysis identifies immune checkpoint signatures and therapeutic targets in ulcerative colitis. Eur J Immunol 49 (2019), 462–475.
Gisbert, J.P., Chaparro, M., Clinical usefulness of proteomics in inflammatory bowel disease: a comprehensive review. J Crohns Colitis 13 (2019), 374–384.
Messner, C.B., Demichev, V., Wendisch, D., Michalick, L., White, M., Freiwald, A., et al. Ultra-high–throughput clinical proteomics reveals classifiers of COVID-19 infection. Cell Syst 11 (2020), 11–24.e4.
Demichev, V., Tober-Lau, P., Lemke, O., Nazarenko, T., Thibeault, C., Whitwell, H., et al. A time-resolved proteomic and prognostic map of COVID-19. Cell Syst 12 (2021), 780–794.e7.
Patel, H., Ashton, N.J., Dobson, R.J.B., Andersson, L.M., Yilmaz, A., Blennow, K., et al. Proteomic blood profiling in mild, severe and critical COVID-19 patients. Sci Rep, 11, 2021, 6357.
Shu, T., Ning, W., Wu, D., Xu, J., Han, Q., Huang, M., et al. Plasma proteomics identify biomarkers and pathogenesis of COVID-19. Immunity 53 (2020), 1108–1122.e5.
Ramaswamy, A., Brodsky, N.N., Sumida, T.S., Comi, M., Asashima, H., Hoehn, K.B., et al. Immune dysregulation and autoreactivity correlate with disease severity in SARS-CoV-2-associated multisystem inflammatory syndrome in children. Immunity 54 (2021), 1083–1095.e7.
Bons, J.A.P., van Dieijen-Visser, M.P., Wodzig, W.K.W.H., Clinical proteomics in chronic inflammatory diseases: a review. Proteomics Clin Appl 1 (2007), 1123–1133.
Crea, F., Inflammation, targeted proteomics, and microvascular dysfunction: the new frontiers of ischaemic heart disease. Eur Heart J 43 (2022), 1517–1520.
Fabian, O., Bajer, L., Drastich, P., Harant, K., Sticova, E., Daskova, N., et al. A current state of proteomics in adult and pediatric inflammatory bowel diseases: a systematic search and review. Int J Mol Sci, 24, 2023, 9386.
Mihai, S., Codrici, E., Popescu, I.D., Enciu, A.M., Albulescu, L., Necula, L.G., et al. Inflammation-related mechanisms in chronic kidney disease prediction, progression, and outcome. J Immunol Res, 2018, 2018, 2180373.
Shen, B., Yi, X., Sun, Y., Bi, X., Du, J., Zhang, C., et al. Proteomic and metabolomic characterization of COVID-19 patient sera. Cell 182 (2020), 59–72.e15.
Overmyer, K.A., Shishkova, E., Miller, I.J., Balnis, J., Bernstein, M.N., Peters-Clarke, T.M., et al. Large-scale multi-omic analysis of COVID-19 severity. Cell Syst 12 (2021), 23–40.e7.
Nie, X., Qian, L., Sun, R., Huang, B., Dong, X., Xiao, Q., et al. Multi-organ proteomic landscape of COVID-19 autopsies. Cell 184 (2021), 775–791.e14.
Kaneko, T., Ezra, S., Abdo, R., Voss, C., Zhong, S., Liu, X., et al. Kinome and phosphoproteome reprogramming underlies the aberrant immune responses in critically ill COVID-19 patients. Clin Proteomics, 21, 2024, 13.
Nguyen, A., Yaffe, M.B., Proteomics and systems biology approaches to signal transduction in sepsis. Crit Care Med 31 (2003), S1–S6.
Rajczewski, A.T., Jagtap, P.D., Griffin, T.J., An overview of technologies for MS-based proteomics-centric multi-omics. Expert Rev Proteomics 19 (2022), 165–181.
Johnson, C.H., Ivanisevic, J., Siuzdak, G., Metabolomics: beyond biomarkers and towards mechanisms. Nat Rev Mol Cell Biol 17 (2016), 451–459.
Azad, R.K., Shulaev, V., Metabolomics technology and bioinformatics for precision medicine. Brief Bioinform 20 (2019), 1957–1971.
Tannahill, G.M., Curtis, A.M., Adamik, J., Palsson-McDermott, E.M., McGettrick, A.F., Goel, G., et al. Succinate is an inflammatory signal that induces IL-1β through HIF-1α. Nature 496 (2013), 238–242.
Bambouskova, M., Gorvel, L., Lampropoulou, V., Sergushichev, A., Loginicheva, E., Johnson, K., et al. Electrophilic properties of itaconate and derivatives regulate the IκBζ-ATF3 inflammatory axis. Nature 556 (2018), 501–504.
Mills, E.L., Ryan, D.G., Prag, H.A., Dikovskaya, D., Menon, D., Zaslona, Z., et al. Itaconate is an anti-inflammatory metabolite that activates Nrf2 via alkylation of KEAP1. Nature 556 (2018), 113–117.
Qing, H., Desrouleaux, R., Israni-Winger, K., Mineur, Y.S., Fogelman, N., Zhang, C., et al. Origin and function of stress-induced IL-6 in murine models. Cell 182 (2020), 372–387.e14.
Chan, K.R., Gan, E.S., Chan, C.Y.Y., Liang, C., Low, J.Z.H., Zhang, S.L.X., et al. Metabolic perturbations and cellular stress underpin susceptibility to symptomatic live-attenuated yellow fever infection. Nat Med 25 (2019), 1218–1224.
Dean DA, Klechka L, Hossain E, Parab AR, Eaton K, Hinsdale M, et al. Spatial metabolomics reveals localized impact of influenza virus infection on the lung tissue metabolome. mSystems. 2022;7:e0035322.
Song, J.W., Lam, S.M., Fan, X., Cao, W.J., Wang, S.Y., Tian, H., et al. Omics-driven systems interrogation of metabolic dysregulation in COVID-19 pathogenesis. Cell Metab 32 (2020), 188–202.e5.
Weiner, J. 3rd, Maertzdorf, J., Sutherland, J.S., Duffy, F.J., Thompson, E., Suliman, S., et al. Metabolite changes in blood predict the onset of tuberculosis. Nat Commun, 9, 2018, 5208.
Abdrabou, W., Dieng, M.M., Diawara, A., Sermé, S.S., Almojil, D., Sombié, S., et al. Metabolome modulation of the host adaptive immunity in human malaria. Nat Metab 3 (2021), 1001–1016.
Yu, N., Peng, C., Chen, W., Sun, Z., Zheng, J., Zhang, S., et al. Circulating metabolomic signature in generalized pustular psoriasis blunts monocyte hyperinflammation by triggering amino acid response. Front Immunol, 12, 2021, 739514.
Xiao, N., Nie, M., Pang, H., Wang, B., Hu, J., Meng, X., et al. Integrated cytokine and metabolite analysis reveals immunometabolic reprogramming in COVID-19 patients with therapeutic implications. Nat Commun, 12, 2021, 1618.
Sun, J., Peters, M., Yu, L.R., Vijay, V., Bidarimath, M., Agrawal, M., et al. Untargeted metabolomics and lipidomics in COVID-19 patient plasma reveals disease severity biomarkers. Metabolomics, 21, 2024, 3.
Clark, I.A., Alleva, L.M., Budd, A.C., Cowden, W.B., Understanding the role of inflammatory cytokines in malaria and related diseases. Travel Med Infect Dis 6 (2008), 67–81.
Kranidioti, E., Ricaño-Ponce, I., Antonakos, N., Kyriazopoulou, E., Kotsaki, A., Tsangaris, I., et al. Modulation of metabolomic profile in sepsis according to the state of immune activation. Crit Care Med 52 (2024), e536–e544.
Han, T.L., Yang, Y., Zhang, H., Law, K.P., Analytical challenges of untargeted GC-MS–based metabolomics and the critical issues in selecting the data processing strategy. F1000Res, 6, 2017, 967.
Tautenhahn, R., Cho, K., Uritboonthai, W., Zhu, Z., Patti, G.J., Siuzdak, G., An accelerated workflow for untargeted metabolomics using the METLIN database. Nat Biotechnol 30 (2012), 826–828.
Wishart, D.S., Feunang, Y.D., Marcu, A., Guo, A.C., Liang, K., Vázquez-Fresno, R., et al. HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res 46 (2018), D608–D617.
Burel, J.G., Apte, S.H., Doolan, D.L., Systems approaches towards molecular profiling of human immunity. Trends Immunol 37 (2016), 53–67.
Bakker, O.B., Aguirre-Gamboa, R., Sanna, S., Oosting, M., Smeekens, S.P., Jaeger, M., et al. Integration of multi-omics data and deep phenotyping enables prediction of cytokine responses. Nat Immunol 19 (2018), 776–786.
Koeken, V.A.C.M., van Crevel, R., Netea, M.G., Li, Y., Resolving trained immunity with systems biology. Eur J Immunol 51 (2021), 773–784.
Lukan, N., “Cytokine storm,” not only in COVID-19 patients. Mini-review. Immunol Lett 228 (2020), 38–44.
Tisoncik, J.R., Korth, M.J., Simmons, C.P., Farrar, J., Martin, T.R., Katze, M.G., Into the eye of the cytokine storm. Microbiol Mol Biol Rev 76 (2012), 16–32.
Karki, R., Kanneganti, T.D., The “cytokine storm”: molecular mechanisms and therapeutic prospects. Trends Immunol 42 (2021), 681–705.
Leisman, D.E., Ronner, L., Pinotti, R., Taylor, M.D., Sinha, P., Calfee, C.S., et al. Cytokine elevation in severe and critical COVID-19: a rapid systematic review, meta-analysis, and comparison with other inflammatory syndromes. Lancet Respir Med 8 (2020), 1233–1244.
Ramilowski, J.A., Goldberg, T., Harshbarger, J., Kloppmann, E., Lizio, M., Satagopam, V.P., et al. A draft network of ligand-receptor–mediated multicellular signalling in human. Nat Commun, 6, 2015, 7866.
Zheng, G.X.Y., Terry, J.M., Belgrader, P., Ryvkin, P., Bent, Z.W., Wilson, R., et al. Massively parallel digital transcriptional profiling of single cells. Nat Commun, 8, 2017, 14049.
Reyes, M., Filbin, M.R., Bhattacharyya, R.P., Sonny, A., Mehta, A., Billman, K., et al. Plasma from patients with bacterial sepsis or severe COVID-19 induces suppressive myeloid cell production from hematopoietic progenitors in vitro. Sci Transl Med, 13, 2021, eabe9599.
Tohme, S., Yazdani, H.O., Sud, V., Loughran, P., Huang, H., Zamora, R., et al. Computational analysis supports IL-17A as a central driver of neutrophil extracellular trap–mediated injury in liver ischemia reperfusion. J Immunol 202 (2019), 268–277.
Azhar, N., Namas, R.A., Almahmoud, K., Zaaqoq, A., Malak, O.A., Barclay, D., et al. A putative “chemokine switch” that regulates systemic acute inflammation in humans. Sci Rep, 11, 2021, 9703.
Jung, S., Potapov, I., Chillara, S., Del Sol, A., Leveraging systems biology for predicting modulators of inflammation in patients with COVID-19. Sci Adv, 7, 2021.
Alivernini, S., MacDonald, L., Elmesmari, A., Finlay, S., Tolusso, B., Gigante, M.R., et al. Distinct synovial tissue macrophage subsets regulate inflammation and remission in rheumatoid arthritis. Nat Med 26 (2020), 1295–1306.
Amadori, L., Calcagno, C., Fernandez, D.M., Koplev, S., Fernandez, N., Kaur, R., et al. Systems immunology-based drug repurposing framework to target inflammation in atherosclerosis. Nat Cardiovasc Res 2 (2023), 550–571.
Tao, W., Concepcion, A.N., Vianen, M., Marijnissen, A.C.A., Lafeber, F.P.G.J., Radstake, T.R.D.J., et al. Multiomics and machine learning accurately predict clinical response to adalimumab and etanercept therapy in patients with rheumatoid arthritis. Arthritis Rheumatol 73 (2021), 212–222.
Zeng, X., Zhu, S., Lu, W., Liu, Z., Huang, J., Zhou, Y., et al. Target identification among known drugs by deep learning from heterogeneous networks. Chem Sci 11 (2020), 1775–1797.
Subramanian, A., Narayan, R., Corsello, S.M., Peck, D.D., Natoli, T.E., Lu, X., et al. A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell 171 (2017), 1437–1452.e17.
Belyaeva, A., Cammarata, L., Radhakrishnan, A., Squires, C., Yang, K.D., Shivashankar, G.V., et al. Causal network models of SARS-CoV-2 expression and aging to identify candidates for drug repurposing. Nat Commun, 12, 2021, 1024.
Bludau, I., Aebersold, R., Proteomic and interactomic insights into the molecular basis of cell functional diversity. Nat Rev Mol Cell Biol 21 (2020), 327–340.
Aebersold, R., Mann, M., Mass-spectrometric exploration of proteome structure and function. Nature 537 (2016), 347–355.
Pomyen, Y., Wanichthanarak, K., Poungsombat, P., Fahrmann, J., Grapov, D., Khoomrung, S., Deep metabolome: applications of deep learning in metabolomics. Comput Struct Biotechnol J 18 (2020), 2818–2825.
Sen, P., Lamichhane, S., Mathema, V.B., McGlinchey, A., Dickens, A.M., Khoomrung, S., et al. Deep learning meets metabolomics: a methodological perspective. Brief Bioinform 22 (2021), 1531–1542.
