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image of Integrating Transcriptomic Data and Mendelian Randomization Analyses Reveals Potentially Novel Sepsis-related Targets

Abstract

Background

Sepsis remains a leading cause of global morbidity and mortality.

Objective

To identify candidate biomarkers that may be mechanistically related to the pathogenesis of sepsis.

Methods

The Gene Expression Omnibus database was leveraged to identify differentially expressed genes (DEGs) between the healthy control and septicemia groups. Genes causally related to sepsis were probed through the integration of GWAS and expression quantitative trait loci (eQTL) data in a two-sample Mendelian randomization (MR) analysis. A set of key sepsis-related genes was then selected based on the overlap between these putative causal genes and the DEGs. These genes were then subjected to enrichment analyses, testing set validation, and analyses of their expression dynamics in clinical samples.

Results

An examination of the overlap between 228 sepsis-related DEGs identified in the training dataset and 275 candidate causal genes linked to sepsis derived from the MR analysis led to the selection of four overlapping (SLC22A15, IL5RA, HDC, and SLC46A2) that may play a key role in sepsis. Enrichment analyses indicated that these genes were involved in the regulation of histidine metabolism and immune/inflammatory responses. In immune cell infiltration analyses, these genes were positively correlated with inflammatory response activation and the suppression of adaptive immunity. Consistent findings were obtained through qPCR verification in clinical samples.

Conclusion

These results offer potential insight into the mechanisms that govern septicemia and thus suggest a promising series of candidates that may be amenable to targeting to prevent or treat sepsis more effectively.

© 2025 Bentham Science Publishers
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2025-04-29
2025-09-10

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References

  1. Willmann K. Moita L.F. Physiologic disruption and metabolic reprogramming in infection and sepsis. Cell Metab. 2024 36 5 927 946 10.1016/j.cmet.2024.02.013 38513649
    [Google Scholar]
  2. Shankar-Hari M. Calandra T. Soares M.P. Bauer M. Wiersinga W.J. Prescott H.C. Knight J.C. Baillie K.J. Bos L.D.J. Derde L.P.G. Finfer S. Hotchkiss R.S. Marshall J. Openshaw P.J.M. Seymour C.W. Venet F. Vincent J.L. Le Tourneau C. Maitland-van der Zee A.H. McInnes I.B. van der Poll T. Reframing sepsis immunobiology for translation: Towards informative subtyping and targeted immunomodulatory therapies. Lancet Respir. Med. 2024 12 4 323 336 10.1016/S2213‑2600(23)00468‑X 38408467
    [Google Scholar]
  3. Hotchkiss R.S. Monneret G. Payen D. Sepsis-induced immunosuppression: From cellular dysfunctions to immunotherapy. Nat. Rev. Immunol. 2013 13 12 862 874 10.1038/nri3552 24232462
    [Google Scholar]
  4. Chen L. Hua J. He X. Co-expression network analysis identifies potential candidate hub genes in severe influenza patients needing invasive mechanical ventilation. BMC Genomics 2022 23 1 703 10.1186/s12864‑022‑08915‑9 36243706
    [Google Scholar]
  5. Liu Y. Cao Y. Mediating role of blood metabolites in the relationship between immune cell traits and sepsis: A Mendelian randomization and mediation analysis. Inflamm. Res. 2025 74 1 5 10.1007/s00011‑024‑01971‑9 39762630
    [Google Scholar]
  6. Sun L. Zhang C. Song P. Zhong X. Xie B. Huang Y. Hu Y. Xu X. Lei X. Hypertension and 28-day mortality in sepsis patients: An observational and mendelian randomization study. Heart Lung 2025 70 147 156 10.1016/j.hrtlng.2024.11.020 39671847
    [Google Scholar]
  7. Russell J.A. Meyer N.J. Walley K.R. Use of Mendelian randomization to better understand and treat sepsis. Intensive Care Med. 2022 48 11 1638 1641 10.1007/s00134‑022‑06778‑y 36104530
    [Google Scholar]
  8. Krishnamoorthy S. Li G.H.Y. Cheung C.L. Transcriptome-wide summary data-based Mendelian randomization analysis reveals 38 novel genes associated with severe COVID-19. J. Med. Virol. 2023 95 1 e28162 10.1002/jmv.28162 36127160
    [Google Scholar]
  9. Battle A. Brown C.D. Engelhardt B.E. Montgomery S.B. GTEx Consortium Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group Statistical Methods groups—Analysis Working Group Enhancing GTEx (eGTEx) groups NIH Common Fund NIH/NCI NIH/NHGRI NIH/NIMH NIH/NIDA Biospecimen Collection Source Site—NDRI Biospecimen Collection Source Site—RPCI Biospecimen Core Resource—VARI Brain Bank Repository—University of Miami Brain Endowment Bank Leidos Biomedical—Project Management ELSI Study Genome Browser Data Integration &Visualization—EBI Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz Lead analysts Laboratory, Data Analysis &Coordinating Center (LDACC) NIH program management Biospecimen collection Pathology eQTL manuscript working group Genetic effects on gene expression across human tissues. Nature 2017 550 7675 204 213 10.1038/nature24277 29022597
    [Google Scholar]
  10. Liyanarachi K.V. Flatby H. Hallan S. Asvold B.O. Damas J.K. Rogne T. Uromodulin and risk of upper urinary tract infections: A mendelian randomization study. Am. J. Kidney Dis. 2025 85 5 570 576e1 10.1053/j.ajkd.2024.11.007
    [Google Scholar]
  11. Bosmann M. Ward P.A. The inflammatory response in sepsis. Trends Immunol. 2013 34 3 129 136 10.1016/j.it.2012.09.004 23036432
    [Google Scholar]
  12. Liu D. Huang S.Y. Sun J.H. Zhang H.C. Cai Q.L. Gao C. Li L. Cao J. Xu F. Zhou Y. Guan C.X. Jin S.W. Deng J. Fang X.M. Jiang J.X. Zeng L. Sepsis-induced immunosuppression: Mechanisms, diagnosis and current treatment options. Mil. Med. Res. 2022 9 1 56 10.1186/s40779‑022‑00422‑y 36209190
    [Google Scholar]
  13. Geiger H. de Haan G. Florian M.C. The ageing haematopoietic stem cell compartment. Nat. Rev. Immunol. 2013 13 5 376 389 10.1038/nri3433 23584423
    [Google Scholar]
  14. Boomer J.S. To K. Chang K.C. Takasu O. Osborne D.F. Walton A.H. Bricker T.L. Jarman S.D. II Kreisel D. Krupnick A.S. Srivastava A. Swanson P.E. Green J.M. Hotchkiss R.S. Immunosuppression in patients who die of sepsis and multiple organ failure. JAMA 2011 306 23 2594 2605 10.1001/jama.2011.1829 22187279
    [Google Scholar]
  15. Moriguchi T. Takai J. Histamine and histidine decarboxylase: Immunomodulatory functions and regulatory mechanisms. Genes Cells 2020 25 7 443 449 10.1111/gtc.12774 32394600
    [Google Scholar]
  16. Hattori M. Yamazaki M. Ohashi W. Tanaka S. Hattori K. Todoroki K. Fujimori T. Ohtsu H. Matsuda N. Hattori Y. Critical role of endogenous histamine in promoting end-organ tissue injury in sepsis. Intensive Care Med. Exp. 2016 4 1 36 10.1186/s40635‑016‑0109‑y 27822777
    [Google Scholar]
  17. Hori Y. Nihei Y. Kurokawa Y. Kuramasu A. Makabe-Kobayashi Y. Terui T. Doi H. Satomi S. Sakurai E. Nagy A. Watanabe T. Ohtsu H. Accelerated clearance of Escherichia coli in experimental peritonitis of histamine-deficient mice. J. Immunol. 2002 169 4 1978 1983 10.4049/jimmunol.169.4.1978 12165523
    [Google Scholar]
  18. Neugebauer E. Lorenz W. Rixen D. Stinner B. Sauer S. Dietz W. Histamine release in sepsis. Crit. Care Med. 1996 24 10 1670 1677 10.1097/00003246‑199610000‑00012 8874304
    [Google Scholar]
  19. Kuroda K. Ishii K. Mihara Y. Kawanoue N. Wake H. Mori S. Yoshida M. Nishibori M. Morimatsu H. Histidine-rich glycoprotein as a prognostic biomarker for sepsis. Sci. Rep. 2021 11 1 10223 10.1038/s41598‑021‑89555‑z 33986340
    [Google Scholar]
  20. Nishibori M. Wake H. Morimatsu H. Histidine-rich glycoprotein as an excellent biomarker for sepsis and beyond. Crit. Care 2018 22 1 209 10.1186/s13054‑018‑2127‑5 30119699
    [Google Scholar]
  21. Wake H. Histidine-rich glycoprotein modulates the blood-vascular system in septic condition. Acta Med. Okayama 2019 73 5 379 382 31649362
    [Google Scholar]
  22. Qian F. Jiang X. Chai R. Liu D. The roles of solute carriers in auditory function. Front. Genet. 2022 13 823049 10.3389/fgene.2022.823049 35154281
    [Google Scholar]
  23. Paik D. Monahan A. Caffrey D.R. Elling R. Goldman W.E. Silverman N. SLC46 family transporters facilitate cytosolic innate immune recognition of monomeric peptidoglycans. J. Immunol. 2017 199 1 263 270 10.4049/jimmunol.1600409 28539433
    [Google Scholar]
  24. Ablasser A. Goldeck M. Cavlar T. Deimling T. Witte G. Röhl I. Hopfner K.P. Ludwig J. Hornung V. cGAS produces a 2′-5′-linked cyclic dinucleotide second messenger that activates STING. Nature 2013 498 7454 380 384 10.1038/nature12306 23722158
    [Google Scholar]
  25. Carozza J.A. Böhnert V. Nguyen K.C. Skariah G. Shaw K.E. Brown J.A. Rafat M. von Eyben R. Graves E.E. Glenn J.S. Smith M. Li L. Extracellular cGAMP is a cancer-cell-produced immunotransmitter involved in radiation-induced anticancer immunity. Nat. Cancer 2020 1 2 184 196 10.1038/s43018‑020‑0028‑4 33768207
    [Google Scholar]
  26. Cordova A.F. Ritchie C. Böhnert V. Li L. Human SLC46A2 is the dominant cGAMP importer in extracellular cgamp-sensing macrophages and monocytes. ACS Cent. Sci. 2021 7 6 1073 1088 10.1021/acscentsci.1c00440 34235268
    [Google Scholar]
  27. Bharadwaj R. Lusi C.F. Mashayekh S. Nagar A. Subbarao M. Kane G.I. Wodzanowski K.A. Brown A.R. Okuda K. Monahan A. Paik D. Nandy A. Anonick M.V. Goldman W.E. Kanneganti T.D. Orzalli M.H. Grimes C.L. Atukorale P.U. Silverman N. Methotrexate suppresses psoriatic skin inflammation by inhibiting muropeptide transporter SLC46A2 activity. Immunity 2023 56 5 998 1012.e8 10.1016/j.immuni.2023.04.001 37116499
    [Google Scholar]
  28. Yee S.W. Giacomini K.M. Emerging roles of the human solute carrier 22 family. Drug Metab. Dispos. 2022 50 9 1193 1210 10.1124/dmd.121.000702 34921098
    [Google Scholar]
  29. Yee S.W. Buitrago D. Stecula A. Ngo H.X. Chien H.C. Zou L. Koleske M.L. Giacomini K.M. Deorphaning a solute carrier 22 family member, SLC22A15, through functional genomic studies. FASEB J. 2020 34 12 15734 15752 10.1096/fj.202001497R 33124720
    [Google Scholar]
  30. Zhang P. Azad P. Engelhart D.C. Haddad G.G. Nigam S.K. SLC22 transporters in the fly renal system regulate response to oxidative stress in vivo. Int. J. Mol. Sci. 2021 22 24 13407 10.3390/ijms222413407 34948211
    [Google Scholar]
  31. Serrano-del Valle A. Anel A. Naval J. Marzo I. Immunogenic cell death and immunotherapy of multiple myeloma. Front. Cell Dev. Biol. 2019 7 50 10.3389/fcell.2019.00050 31041312
    [Google Scholar]
  32. Scibek J.J. Evergren E. Zahn S. Canziani G.A. Van Ryk D. Chaiken I.M. Biosensor analysis of dynamics of interleukin 5 receptor subunit βc interaction with IL5:IL5Rα complexes. Anal. Biochem. 2002 307 2 258 265 10.1016/S0003‑2697(02)00043‑X 12202242
    [Google Scholar]
  33. Xu C. Gao M. Zhang J. Fu Y. IL5RA as an immunogenic cell death-related predictor in progression and therapeutic response of multiple myeloma. Sci. Rep. 2023 13 1 8528 10.1038/s41598‑023‑35378‑z 37236993
    [Google Scholar]
  34. Gorski S.A. Lawrence M.G. Hinkelman A. Spano M.M. Steinke J.W. Borish L. Teague W.G. Braciale T.J. Expression of IL-5 receptor alpha by murine and human lung neutrophils. PLoS One 2019 14 8 e0221113 10.1371/journal.pone.0221113 31415658
    [Google Scholar]
  35. Gorski S.A. Hahn Y.S. Braciale T.J. Group 2 innate lymphoid cell production of IL-5 is regulated by NKT cells during influenza virus infection. PLoS Pathog. 2013 9 9 e1003615 10.1371/journal.ppat.1003615 24068930
    [Google Scholar]
  36. Mesnil C. Raulier S. Paulissen G. Xiao X. Birrell M.A. Pirottin D. Janss T. Starkl P. Ramery E. Henket M. Schleich F.N. Radermecker M. Thielemans K. Gillet L. Thiry M. Belvisi M.G. Louis R. Desmet C. Marichal T. Bureau F. Lung-resident eosinophils represent a distinct regulatory eosinophil subset. J. Clin. Invest. 2016 126 9 3279 3295 10.1172/JCI85664 27548519
    [Google Scholar]
  37. Salomão R. Ferreira B.L. Salomão M.C. Santos S.S. Azevedo L.C.P. Brunialti M.K.C. Sepsis: evolving concepts and challenges. Braz. J. Med. Biol. Res. 2019 52 4 e8595 10.1590/1414‑431x20198595 30994733
    [Google Scholar]
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Integrating Transcriptomic Data and Mendelian Randomization Analyses Reveals Potentially Novel Sepsis-related Targets
Bentham Science Publishers ; https://doi.org/10.2174/0109298673370740250403141421
/content/journals/cmc/10.2174/0109298673370740250403141421
/content/journals/cmc/10.2174/0109298673370740250403141421
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  • Article Type:     Research Article
Keywords: immune responses ; mendelian randomization ; differentially expressed genes ; key gene ; Sepsis ; histidine metabolism

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