Commonly disrupted pathways in brain and kidney in a pig model of systemic endotoxemia.
Acute kidney injury
Brain impairment
Bulk RNA-seq
Immune regulation
Immune system
Infection
LPS
Lipopolysaccharide
Organ dysfunction
Pig model
Porcine model
Sepsis
Septic
Shock
Systemic inflammation
Transcriptome
Journal
Journal of neuroinflammation
ISSN: 1742-2094
Titre abrégé: J Neuroinflammation
Pays: England
ID NLM: 101222974
Informations de publication
Date de publication:
04 Jan 2024
04 Jan 2024
Historique:
received:
03
07
2023
accepted:
19
12
2023
medline:
5
1
2024
pubmed:
5
1
2024
entrez:
4
1
2024
Statut:
epublish
Résumé
Sepsis is a life-threatening state that arises due to a hyperactive inflammatory response stimulated by infection and rarely other insults (e.g., non-infections tissue injury). Although changes in several proinflammatory cytokines and signals are documented in humans and small animal models, far less is known about responses within affected tissues of large animal models. We sought to understand the changes that occur during the initial stages of inflammation by administering intravenous lipopolysaccharide (LPS) to Yorkshire pigs and assessing transcriptomic alterations in the brain, kidney, and whole blood. Robust transcriptional alterations were found in the brain, with upregulated responses enriched in inflammatory pathways and downregulated responses enriched in tight junction and blood vessel functions. Comparison of the inflammatory response in the pig brain to a similar mouse model demonstrated some overlapping changes but also numerous differences, including oppositely dysregulated genes between species. Substantial changes also occurred in the kidneys following LPS with several enriched upregulated pathways (cytokines, lipids, unfolded protein response, etc.) and downregulated gene sets (tube morphogenesis, glomerulus development, GTPase signal transduction, etc.). We also found significant dysregulation of genes in whole blood that fell into several gene ontology categories (cytokines, cell cycle, neutrophil degranulation, etc.). We observed a strong correlation between the brain and kidney responses, with significantly shared upregulated pathways (cytokine signaling, cell death, VEGFA pathways) and downregulated pathways (vasculature and RAC1 GTPases). In summary, we have identified a core set of shared genes and pathways in a pig model of systemic inflammation.
Identifiants
pubmed: 38178237
doi: 10.1186/s12974-023-03002-6
pii: 10.1186/s12974-023-03002-6
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
9Subventions
Organisme : NIH HHS
ID : NS084974
Pays : United States
Organisme : NIH HHS
ID : AG062556
Pays : United States
Organisme : NIH HHS
ID : AG06211
Pays : United States
Organisme : NIH HHS
ID : NS094137
Pays : United States
Organisme : NIH HHS
ID : AG057997
Pays : United States
Organisme : NIH HHS
ID : AG062077
Pays : United States
Organisme : NIH HHS
ID : NS110435
Pays : United States
Organisme : NIH HHS
ID : AG047327
Pays : United States
Organisme : NIH HHS
ID : AG049992
Pays : United States
Informations de copyright
© 2023. The Author(s).
Références
Riedemann NC, Guo R-F, Ward PA. The enigma of sepsis. J Clin Invest. 2003;112:460–7.
pubmed: 12925683
pmcid: 171398
doi: 10.1172/JCI200319523
Starr ME, Saito H. Sepsis in old age: review of human and animal studies. Aging Dis. 2014;5:126–36.
pubmed: 24729938
pmcid: 3966671
Mayr FB, Yende S, Angus DC. Epidemiology of severe sepsis. Virulence. 2014;5:4–11.
pubmed: 24335434
doi: 10.4161/viru.27372
Angus DC, et al. Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care. Crit Care Med. 2001;29:1303–10.
pubmed: 11445675
doi: 10.1097/00003246-200107000-00002
Beltrán-García J, et al. Characterization of early peripheral immune responses in patients with sepsis and septic shock. Biomedicines. 2022;10:525.
pubmed: 35327327
pmcid: 8945007
doi: 10.3390/biomedicines10030525
Chakraborty RK, Burns B. Systemic inflammatory response syndrome. St. Petersburg: StatPearls Publishing; 2021.
Cecconi M, Evans L, Levy M, Rhodes A. Sepsis and septic shock. Lancet. 2018;392:75–87.
pubmed: 29937192
doi: 10.1016/S0140-6736(18)30696-2
Hirayama I, et al. Changes in carbon dioxide production and oxygen uptake evaluated using indirect calorimetry in mechanically ventilated patients with sepsis. Crit Care. 2021;25:416.
pubmed: 34863262
pmcid: 8645073
doi: 10.1186/s13054-021-03830-z
Peerapornratana S, Manrique-Caballero CL, Gómez H, Kellum JA. Acute kidney injury from sepsis: current concepts, epidemiology, pathophysiology, prevention and treatment. Kidney Int. 2019;96:1083–99.
pubmed: 31443997
pmcid: 6920048
doi: 10.1016/j.kint.2019.05.026
Zarbock A, Gomez H, Kellum JA. Sepsis-induced acute kidney injury revisited: pathophysiology, prevention and future therapies. Curr Opin Crit Care. 2014;20:588–95.
pubmed: 25320909
pmcid: 4495653
doi: 10.1097/MCC.0000000000000153
Rånby M, Bergsdorf N, Pohl G, Wallén P. Isolation of two variants of native one-chain tissue plasminogen activator. FEBS Lett. 1982;146:289–92.
pubmed: 6890472
doi: 10.1016/0014-5793(82)80936-8
Poston JT, Koyner JL. Sepsis associated acute kidney injury. BMJ. 2019;364:k4891.
pubmed: 30626586
pmcid: 6890472
doi: 10.1136/bmj.k4891
Liu J, Xie H, Ye Z, Li F, Wang L. Rates, predictors, and mortality of sepsis-associated acute kidney injury: a systematic review and meta-analysis. BMC Nephrol. 2020;21:318.
pubmed: 32736541
pmcid: 7393862
doi: 10.1186/s12882-020-01974-8
Kuwabara S, Goggins E, Okusa MD. The pathophysiology of sepsis-associated AKI. Clin J Am Soc Nephrol. 2022;17:1050–69.
pubmed: 35764395
pmcid: 9269625
doi: 10.2215/CJN.00850122
Gaieski DF, Edwards JM, Kallan MJ, Carr BG. Benchmarking the incidence and mortality of severe sepsis in the United States. Crit Care Med. 2013;41:1167–74.
pubmed: 23442987
doi: 10.1097/CCM.0b013e31827c09f8
Kumar G, et al. Nationwide trends of severe sepsis in the 21st Century (2000–2007). Chest. 2011;140:1223–31.
pubmed: 21852297
doi: 10.1378/chest.11-0352
Kang SS, et al. Lipocalin-2 protects the brain during inflammatory conditions. Mol Psychiatry. 2018;23:344–50.
pubmed: 28070126
doi: 10.1038/mp.2016.243
Horiguchi H, et al. Innate immunity in the persistent inflammation, immunosuppression, and catabolism syndrome and its implications for therapy. Front Immunol. 2018;9:595.
pubmed: 29670613
pmcid: 5893931
doi: 10.3389/fimmu.2018.00595
Wiersinga WJ, Leopold SJ, Cranendonk DR, van der Poll T. Host innate immune responses to sepsis. Virulence. 2014;5:36–44.
pubmed: 23774844
doi: 10.4161/viru.25436
Jaffer U, Wade RG, Gourlay T. Cytokines in the systemic inflammatory response syndrome: a review. HSR Proc Intensive Care Cardiovasc Anesth. 2010;2:161–75.
pubmed: 23441054
pmcid: 3484588
Gofton TE, Young GB. Sepsis-associated encephalopathy. Nat Rev Neurol. 2012;8:557–66.
pubmed: 22986430
doi: 10.1038/nrneurol.2012.183
Semmler A, et al. Persistent cognitive impairment, hippocampal atrophy and EEG changes in sepsis survivors. J Neurol Neurosurg Psychiatry. 2013;84:62–9.
pubmed: 23134661
doi: 10.1136/jnnp-2012-302883
Hotchkiss RS, Monneret G, Payen D. Sepsis-induced immunosuppression: from cellular dysfunctions to immunotherapy. Nat Rev Immunol. 2013;13:862–74.
pubmed: 24232462
pmcid: 4077177
doi: 10.1038/nri3552
Iwashyna TJ, Ely EW, Smith DM, Langa KM. Long-term cognitive impairment and functional disability among survivors of severe sepsis. JAMA. 2010;304:1787–94.
pubmed: 20978258
pmcid: 3345288
doi: 10.1001/jama.2010.1553
Pipanmekaporn T, et al. Incidence and risk factors of delirium in multi-center Thai surgical intensive care units: a prospective cohort study. J Intensive Care Med. 2015;3:53.
doi: 10.1186/s40560-015-0118-z
Dhondt L, et al. The development of a juvenile porcine augmented renal clearance model through continuous infusion of lipopolysaccharides: an exploratory study. Front Vet Sci. 2021;8:639771.
pubmed: 33996970
pmcid: 8116505
doi: 10.3389/fvets.2021.639771
Terenina E, et al. Time course study of the response to LPS targeting the pig immune gene networks. BMC Genomics. 2017;18:988.
pubmed: 29273011
pmcid: 5741867
doi: 10.1186/s12864-017-4363-5
Bush SJ, et al. Species-specificity of transcriptional regulation and the response to lipopolysaccharide in mammalian macrophages. Front Cell Dev Biol. 2020;8:661.
pubmed: 32793601
pmcid: 7386301
doi: 10.3389/fcell.2020.00661
Lewis AJ, Seymour CW, Rosengart MR. Current murine models of sepsis. Surg Infect. 2016;17:385–93.
doi: 10.1089/sur.2016.021
Liu B, et al. Transcriptomic analysis and laboratory experiments reveal potential critical genes and regulatory mechanisms in sepsis-associated acute kidney injury. Ann Transl Med. 2022;10:737.
pubmed: 35957725
pmcid: 9358506
doi: 10.21037/atm-22-845
Walters EM, Wells KD, Bryda EC, Schommer S, Prather RS. Swine models, genomic tools and services to enhance our understanding of human health and diseases. Lab Anim. 2017;46:167–72.
doi: 10.1038/laban.1215
Pabst R. The pig as a model for immunology research. Cell Tissue Res. 2020;380:287–304.
pubmed: 32356014
pmcid: 7223737
doi: 10.1007/s00441-020-03206-9
Simchick G, et al. Pig brains have homologous resting-state networks with human brains. Brain Connect. 2019;9:566–79.
pubmed: 31115245
pmcid: 6727477
doi: 10.1089/brain.2019.0673
Sauleau P, Lapouble E, Val-Laillet D, Malbert C-H. The pig model in brain imaging and neurosurgery. Animal. 2009;3:1138–51.
pubmed: 22444844
doi: 10.1017/S1751731109004649
Janosevic D, et al. The orchestrated cellular and molecular responses of the kidney to endotoxin define a precise sepsis timeline. Elife. 2021;10:e62270.
pubmed: 33448928
pmcid: 7810465
doi: 10.7554/eLife.62270
Tiba MH, et al. A comprehensive assessment of multi-system responses to a renal inoculation of uropathogenic E. coli in swine. PLoS ONE. 2020;15:e0243577.
pubmed: 33306742
pmcid: 7732124
doi: 10.1371/journal.pone.0243577
van Lier D, Kox M, Pickkers P. Promotion of vascular integrity in sepsis through modulation of bioactive adrenomedullin and dipeptidyl peptidase 3. J Intern Med. 2021;289:792–806.
pubmed: 33381880
doi: 10.1111/joim.13220
Gilchrist M, et al. Activating transcription factor 3 is a negative regulator of allergic pulmonary inflammation. J Exp Med. 2008;205:2349–57.
pubmed: 18794337
pmcid: 2556774
doi: 10.1084/jem.20072254
Kwon J-W, et al. Activating transcription factor 3 represses inflammatory responses by binding to the p65 subunit of NF-κB. Sci Rep. 2015;5:14470.
pubmed: 26412238
pmcid: 4585983
doi: 10.1038/srep14470
Carow B, Rottenberg ME. SOCS3, a major regulator of infection and inflammation. Front Immunol. 2014;5:58.
pubmed: 24600449
pmcid: 3928676
doi: 10.3389/fimmu.2014.00058
de Chaves Souza JA, Nogueira AVB. SOCS3 expression correlates with severity of inflammation, expression of proinflammatory cytokines, and activation of STAT3 and p38 MAPK in LPS-induced inflammation in vivo. Mediat Inflamm. 2013. https://doi.org/10.1155/2013/650812 .
doi: 10.1155/2013/650812
Arnold JS, et al. Tissue-specific roles of Tbx1 in the development of the outer, middle and inner ear, defective in 22q11DS patients. Hum Mol Genet. 2006;15:1629–39.
pubmed: 16600992
doi: 10.1093/hmg/ddl084
Ghosh TK, Brook JD, Wilsdon A. T-Box genes in human development and disease. Curr Top Dev Biol. 2017;122:383–415.
pubmed: 28057271
doi: 10.1016/bs.ctdb.2016.08.006
Spinner MA, et al. GATA2 deficiency: a protean disorder of hematopoiesis, lymphatics, and immunity. Blood. 2014;123:809–21.
pubmed: 24227816
pmcid: 3916876
doi: 10.1182/blood-2013-07-515528
Wosik K, et al. Angiotensin II controls occludin function and is required for blood-brain barrier maintenance: relevance to multiple sclerosis. J Neurosci. 2007;27:9032–42.
pubmed: 17715340
pmcid: 6672193
doi: 10.1523/JNEUROSCI.2088-07.2007
Barichello T, Generoso JS, Collodel A, Petronilho F, Dal-Pizzol F. The blood–brain barrier dysfunction in sepsis. Tissue Barriers. 2021;9:1840912.
pubmed: 33319634
doi: 10.1080/21688370.2020.1840912
Sekino N, Selim M, Shehadah A. Sepsis-associated brain injury: underlying mechanisms and potential therapeutic strategies for acute and long-term cognitive impairments. J Neuroinflammation. 2022;19:101.
pubmed: 35488237
pmcid: 9051822
doi: 10.1186/s12974-022-02464-4
Bai Y, et al. Tuberous sclerosis complex protein 2-independent activation of mTORC1 by human Cytomegalovirus pUL38. J Virol. 2015;89:7625–35.
pubmed: 25972538
pmcid: 4505643
doi: 10.1128/JVI.01027-15
Kazi AA, Pruznak AM, Frost RA, Lang CH. Sepsis-induced alterations in protein-protein interactions within mTOR complex 1 and the modulating effect of leucine on muscle protein synthesis. Shock. 2011;35:117–25.
pubmed: 20577146
pmcid: 2995824
doi: 10.1097/SHK.0b013e3181ecb57c
Sharpe AH, Wherry EJ, Ahmed R, Freeman GJ. The function of programmed cell death 1 and its ligands in regulating autoimmunity and infection. Nat Immunol. 2007;8:239–45.
pubmed: 17304234
doi: 10.1038/ni1443
Catrysse L, Vereecke L, Beyaert R, van Loo G. A20 in inflammation and autoimmunity. Trends Immunol. 2014;35:22–31.
pubmed: 24246475
doi: 10.1016/j.it.2013.10.005
Ma A, Malynn BA. A20: linking a complex regulator of ubiquitylation to immunity and human disease. Nat Rev Immunol. 2012;12:774–85.
pubmed: 23059429
pmcid: 3582397
doi: 10.1038/nri3313
Kuriakose T, Zheng M, Neale G, Kanneganti T-D. IRF1 is a transcriptional regulator of ZBP1 promoting NLRP3 inflammasome activation and cell death during influenza virus infection. J Immunol. 2018;200:1489–95.
pubmed: 29321274
doi: 10.4049/jimmunol.1701538
Daniels JR, et al. Discovery of novel proteomic biomarkers for the prediction of kidney recovery from dialysis-dependent AKI patients. Kidney360. 2021;2:1716–27.
pubmed: 34913041
pmcid: 8670726
doi: 10.34067/KID.0002642021
Barker G, et al. Lipid and lipoprotein dysregulation in sepsis: clinical and mechanistic insights into chronic critical illness. J Clin Med Res. 2021;10:1693.
Metzing UB, et al. Endoplasmic reticulum stress and the unfolded protein response in skeletal muscle of subjects suffering from peritoneal sepsis. Sci Rep. 2022;12:504.
pubmed: 35017615
pmcid: 8752775
doi: 10.1038/s41598-021-04517-9
Xu K, et al. Blood vessel tubulogenesis requires Rasip1 regulation of GTPase signaling. Dev Cell. 2011;20:526–39.
pubmed: 21396893
pmcid: 3078994
doi: 10.1016/j.devcel.2011.02.010
Kemp SS, et al. Molecular basis for pericyte-induced capillary tube network assembly and maturation. Front Cell Dev Biol. 2022;10:943533.
pubmed: 36072343
pmcid: 9441561
doi: 10.3389/fcell.2022.943533
Rossi MT, et al. Molecular framework of mouse endothelial cell dysfunction during inflammation: a proteomics approach. Int J Mol Sci. 2022;23:8399.
pubmed: 35955534
pmcid: 9369400
doi: 10.3390/ijms23158399
Wang N, Wu R, Comish PB, Kang R, Tang D. Pharmacological modulation of BET family in sepsis. Front Pharmacol. 2021;12:642294.
pubmed: 33776776
pmcid: 7990776
doi: 10.3389/fphar.2021.642294
Carlson DE, Chiu WC, Fiedler SM, Hoffman GE. Central neural distribution of immunoreactive Fos and CRH in relation to plasma ACTH and corticosterone during sepsis in the rat. Exp Neurol. 2007;205:485–500.
pubmed: 17462630
pmcid: 1950276
doi: 10.1016/j.expneurol.2007.03.015
Pan W, et al. Brain interleukin-15 in neuroinflammation and behavior. Neurosci Biobehav Rev. 2013;37:184–92.
pubmed: 23201098
doi: 10.1016/j.neubiorev.2012.11.009
Popoff MR. Bacterial factors exploit eukaryotic Rho GTPase signaling cascades to promote invasion and proliferation within their host. Small GTPases. 2014;5:e983863.
doi: 10.4161/sgtp.28209
da Hahmeyer MLS, da Silva-Santos JE. Rho-proteins and downstream pathways as potential targets in sepsis and septic shock: what have we learned from basic research. Cells. 2021;10:1844.
pubmed: 34440613
pmcid: 8391638
doi: 10.3390/cells10081844
Sennlaub F, et al. CCR2(+) monocytes infiltrate atrophic lesions in age-related macular disease and mediate photoreceptor degeneration in experimental subretinal inflammation in Cx3cr1 deficient mice. EMBO Mol Med. 2013;5:1775–93.
pubmed: 24142887
pmcid: 3840491
doi: 10.1002/emmm.201302692
Xu J, Ganguly A, Zhao J, Ivey M, Lopez R. CCR2 signaling promotes brain infiltration of inflammatory monocytes and contributes to neuropathology during cryptococcal meningoencephalitis. MBio. 2021. https://doi.org/10.1128/mBio.01076-21 .
doi: 10.1128/mBio.01076-21
pubmed: 35164557
pmcid: 8689562
Salminen A, et al. Activation of innate immunity system during aging: NF-kB signaling is the molecular culprit of inflamm-aging. Ageing Res Rev. 2008;7:83–105.
pubmed: 17964225
doi: 10.1016/j.arr.2007.09.002
Faure E, Sieling T. Bacterial lipopolysaccharide activates NF-κB through Toll-like receptor 4 (TLR-4) in cultured human dermal endothelial cells: differential expression of TLR-4 and TLR-2 in endothelial cells. Boll Soc Ital Biol Sper. 2000. https://doi.org/10.1074/jbc.275.15.11058 .
doi: 10.1074/jbc.275.15.11058
Bhattacharyya S, Dudeja PK, Tobacman JK. Lipopolysaccharide activates NF-kappaB by TLR4-Bcl10-dependent and independent pathways in colonic epithelial cells. Am J Physiol Gastrointest Liver Physiol. 2008;295:G784–90.
pubmed: 18718996
doi: 10.1152/ajpgi.90434.2008
Abraham E. Nuclear factor-kappaB and its role in sepsis-associated organ failure. J Infect Dis. 2003. https://doi.org/10.1086/374750 .
doi: 10.1086/374750
pubmed: 12792853
Caraballo C, Jaimes F. Organ dysfunction in sepsis: an ominous trajectory from infection to death. Yale J Biol Med. 2019;92:629–40.
pubmed: 31866778
pmcid: 6913810
Ritchie ME, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43:e47.
pubmed: 25605792
pmcid: 4402510
doi: 10.1093/nar/gkv007
Pimentel H, Bray NL, Puente S, Melsted P, Pachter L. Differential analysis of RNA-seq incorporating quantification uncertainty. Nat Methods. 2017;14:687–90.
pubmed: 28581496
doi: 10.1038/nmeth.4324
Andrews S. FastQC: a quality control tool for high throughput sequence data. 2010.
Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016;32:3047–8.
pubmed: 27312411
pmcid: 5039924
doi: 10.1093/bioinformatics/btw354
Bushnell B. BBMap: a fast, accurate, splice-aware aligner. Berkeley: Lawrence Berkeley National Lab (LBNL); 2014.
Warr A, et al. An improved pig reference genome sequence to enable pig genetics and genomics research. Gigascience. 2020. https://doi.org/10.1101/668921 .
doi: 10.1101/668921
pubmed: 32543654
pmcid: 7448572
Olney KC, Brotman SM, Andrews JP, Valverde-Vesling VA, Wilson MA. Reference genome and transcriptome informed by the sex chromosome complement of the sample increase ability to detect sex differences in gene expression from RNA-Seq data. Biol Sex Differ. 2020;11:42.
pubmed: 32693839
pmcid: 7374973
doi: 10.1186/s13293-020-00312-9
Dobin A, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15–21.
pubmed: 23104886
doi: 10.1093/bioinformatics/bts635
Bray NL, Pimentel H, Melsted P, Pachter L. Erratum: Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol. 2016;34:888.
pubmed: 27504780
doi: 10.1038/nbt0816-888d
Hoffman GE, Schadt EE. variancePartition: interpreting drivers of variation in complex gene expression studies. BMC Bioinf. 2016;17:483.
doi: 10.1186/s12859-016-1323-z
Law CW, Chen Y, Shi W, Smyth GK. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 2014;15:R29.
pubmed: 24485249
pmcid: 4053721
doi: 10.1186/gb-2014-15-2-r29
Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40.
pubmed: 19910308
doi: 10.1093/bioinformatics/btp616
Chen Y, Lun ATL, Smyth GK. From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline. F1000Res. 2016;5:1438.
pubmed: 27508061
pmcid: 4934518
Robinson MD, Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 2010;11:R25.
pubmed: 20196867
pmcid: 2864565
doi: 10.1186/gb-2010-11-3-r25
Liu R, et al. Why weight? Combining voom with estimates of sample quality improves power in RNA-seq analyses. Nucleic Acids Res. 2015;43:97.
doi: 10.1093/nar/gkv412
Zhou Y, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019;10:1523.
pubmed: 30944313
pmcid: 6447622
doi: 10.1038/s41467-019-09234-6