Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Derivation of a metabolic signature associated with bacterial meningitis in infants



Diagnosis of bacterial meningitis (BM) is challenging in newborn infants. Presently, biomarkers of BM have limited diagnostic accuracy. Analysis of cerebrospinal fluid (CSF) metabolites may be a useful diagnostic tool in BM.


In a nested case–control study, we examined >400 metabolites in CSF of uninfected infants and infants with culture-confirmed BM using gas and liquid chromatography mass spectrometry. Preterm and full-term infants in a Level III or IV Neonatal Intensive Care Unit were prospectively enrolled when evaluated for serious bacterial infection.


Over 200 CSF metabolites significantly differed in uninfected infants and infants with BM. Using machine learning, we found that as few as 6 metabolites distinguished infants with BM from uninfected infants in this pilot cohort. Further analysis demonstrated three metabolites associated with Group B Streptococcal meningitis.


We report the first comprehensive metabolic analysis of CSF in infants with BM. In our pilot cohort, we derived a metabolic signature that predicted the presence or absence of BM, irrespective of gestational age, postnatal age, sex, race and ethnicity, presence of neurosurgical hardware, white blood cell count in CSF, and red blood cell contamination in CSF. Metabolic analysis may aid diagnosis of BM and facilitate clinical decision-making in infants.


  • In a pilot cohort, metabolites in cerebrospinal fluid distinguished infants with bacterial meningitis from uninfected infants.

  • We report the first comprehensive metabolic analysis of cerebrospinal fluid in infants with bacterial meningitis.

  • Our findings may be used to improve diagnosis of bacterial meningitis and to offer mechanistic insights into the pathophysiology of bacterial meningitis in infants.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Many individual metabolites in CSF differ between infants with bacterial meningitis and uninfected infants.
Fig. 2: A focused metabolic signature associated with BM in our cohort of infants.
Fig. 3: Secondary metabolites associated with Group B Streptococcal meningitis.
Fig. 4: Families of metabolites distinguish subjects with BM from uninfected infants.


  1. 1.

    Okike, I. O. et al. Incidence, etiology, and outcome of bacterial meningitis in infants aged <90 days in the United kingdom and Republic of Ireland: prospective, enhanced, national population-based surveillance. Clin. Infect. Dis. 59, e150–e157 (2014).

    CAS  PubMed  Google Scholar 

  2. 2.

    Lawn, J. E., Cousens, S., Zupan, J. & Lancet Neonatal Survival Steering Team 4 Million neonatal deaths: when? Where? Why?. Lancet 365, 891–900 (2005).

    PubMed  Google Scholar 

  3. 3.

    Biondi, E. A. et al. Prevalence of bacteremia and bacterial meningitis in febrile neonates and infants in the second month of life. JAMA Netw. Open 2, e190874–12 (2019).

    PubMed  PubMed Central  Google Scholar 

  4. 4.

    Mann, K. & Jackson, M. A. Meningitis. Pediatr. Rev. 29, 417–429, quiz 430 (2008).

  5. 5.

    Libster, R. et al. Long-term outcomes of Group B Streptococcal meningitis. Pediatrics 130, e8–e15 (2012).

    PubMed  Google Scholar 

  6. 6.

    Seale, A. C. et al. Neonatal severe bacterial infection impairment estimates in South Asia, sub-Saharan Africa, and Latin America for 2010. Pediatr. Res. 74, 73–85 (2013).

    PubMed  PubMed Central  Google Scholar 

  7. 7.

    Heath, P. T. & Okike, I. O. Neonatal bacterial meningitis: an update. Paediatr. Child Health 20, 526–530 (2010).

    Google Scholar 

  8. 8.

    Thomson, J. et al. Cerebrospinal fluid reference values for young infants undergoing lumbar puncture. Pediatrics 141, e20173405 (2018).

    PubMed  Google Scholar 

  9. 9.

    Garges, H. P. et al. Neonatal meningitis: what is the correlation among cerebrospinal fluid cultures, blood cultures, and cerebrospinal fluid parameters? Pediatrics 117, 1094–1100 (2006).

    PubMed  Google Scholar 

  10. 10.

    Srinivasan, L., Kilpatrick, L., Shah, S. S., Abbasi, S. & Harris, M. C. Cerebrospinal fluid cytokines in the diagnosis of bacterial meningitis in infants. Pediatr. Res. 80, 566–572 (2016).

    CAS  PubMed  Google Scholar 

  11. 11.

    Wei, T.-T. et al. Diagnostic accuracy of procalcitonin in bacterial meningitis versus nonbacterial meningitis: a systematic review and meta-analysis. Medicine 95, e3079 (2016).

    PubMed  PubMed Central  Google Scholar 

  12. 12.

    Langley, R. J. et al. An integrated clinico-metabolomic model improves prediction of death in sepsis. Sci. Transl. Med. 5, 195ra95–195ra95 (2013).

    PubMed  PubMed Central  Google Scholar 

  13. 13.

    Rogers, A. J. et al. Metabolomic derangements are associated with mortality in critically ill adult patients. PLoS ONE 9, e87538–7 (2014).

    PubMed  PubMed Central  Google Scholar 

  14. 14.

    Coen, M., O’Sullivan, M., Bubb, W. A., Kuchel, P. W. & Sorrell, T. Proton nuclear magnetic resonance-based metabonomics for rapid diagnosis of meningitis and ventriculitis. Clin. Infect. Dis. 41, 1582–1590 (2005).

    CAS  PubMed  Google Scholar 

  15. 15.

    Gordon, S. M., Srinivasan, L. & Harris, M. C. Neonatal meningitis: overcoming challenges in diagnosis, prognosis, and treatment with omics. Front. Pediatr. 5, 139 (2017).

    PubMed  PubMed Central  Google Scholar 

  16. 16.

    Evans, A. M., DeHaven, C. D., Barrett, T., Mitchell, M. & Milgram, E. Integrated, nontargeted ultrahigh performance liquid chromatography/electrospray ionization tandem mass spectrometry platform for the identification and relative quantification of the small-molecule complement of biological systems. Anal. Chem. 81, 6656–6667 (2009).

    CAS  PubMed  Google Scholar 

  17. 17.

    Langley, R. J. et al. Integrative ‘omic’ analysis of experimental bacteremia identifies a metabolic signature that distinguishes human sepsis from systemic inflammatory response syndromes. Am. J. Respir. Crit. Care Med. 190, 445–455 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    PubMed  PubMed Central  Google Scholar 

  19. 19.

    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B (Methodol.) 57, 289–300 (1995).

    Google Scholar 

  20. 20.

    Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).

    Google Scholar 

  21. 21.

    Kuhn, M. Building predictive models in R using the caret package. J. Stat. Softw. 28, (2008).

  22. 22.

    Liaw, A. & Wiener, M. Classification and regression by randomForest. R News 2, 18–22 (2002).

    Google Scholar 

  23. 23.

    Chong, J. & Xia, J. MetaboAnalystR: an R package for flexible and reproducible analysis of metabolomics data. Bioinformatics 34, 4313–4314 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Melville, J. M. & Moss, T. J. M. The immune consequences of preterm birth. Front. Neurosci. 7, 79 (2013).

    PubMed  PubMed Central  Google Scholar 

  25. 25.

    Volpe, J. J., Kinney, H. C., Jensen, F. E. & Rosenberg, P. A. The developing oligodendrocyte: key cellular target in brain injury in the premature infant. Int. J. Dev. Neurosci. 29, 423–440 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    De Sanctis, A. G., Killian, J. A. & Garcia, T. Lactic acid of spinal fluid in meningitis: practical diagnostic and prognostic value. Am. J. Dis. Child. 46, 239–249 (1933).

    Google Scholar 

  27. 27.

    Huy, N. T. et al. Cerebrospinal fluid lactate concentration to distinguish bacterial from aseptic meningitis: a systemic review and meta-analysis. Crit. Care 14, R240 (2010).

    PubMed  PubMed Central  Google Scholar 

  28. 28.

    Srinivasan, L., Harris, M. C. & Shah, S. S. Lumbar puncture in the neonate: challenges in decision making and interpretation. Semin. Perinatol. 36, 445–453 (2012).

    PubMed  Google Scholar 

  29. 29.

    Mason, S. Cerebrospinal fluid amino acid profiling of pediatric cases with tuberculous meningitis. Front. Neurosci. 11, 534 (2017).

    PubMed  PubMed Central  Google Scholar 

  30. 30.

    Buryakova, A. V. & Sytinsky, I. A. Amino acid composition of cerebrospinal fluid in acute neuroinfections in children. Arch. Neurol. 32, 28–31 (1975).

    CAS  PubMed  Google Scholar 

  31. 31.

    Kuroda, H. et al. Cerebrospinal fluid GABA levels in various neurological and psychiatric diseases. J. Neurol. Neurosurg. Psychiatry 45, 257–260 (1982).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Spranger, M. et al. Excess glutamate in the cerebrospinal fluid in bacterial meningitis. J. Neurol. Sci. 143, 126–131 (1996).

    CAS  PubMed  Google Scholar 

  33. 33.

    Mason, S., Reinecke, C. J. & Solomons, R. Cerebrospinal fluid amino acid profiling of pediatric cases with tuberculous meningitis. Front. Neurosci. 11, 248–248 (2017).

    Google Scholar 

  34. 34.

    Kanehisa, M. & Goto, S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Wippel, C. et al. Bacterial cytolysin during meningitis disrupts the regulation of glutamate in the brain, leading to synaptic damage. PLoS Pathog. 9, e1003380 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Leib, S. L., Kim, Y. S., Ferriero, D. M. & Täuber, M. G. Neuroprotective effect of excitatory amino acid antagonist kynurenic acid in experimental bacterial meningitis. J. Infect. Dis. 173, 166–171 (1996).

    CAS  PubMed  Google Scholar 

  37. 37.

    Ben-Ari, Y., Khazipov, R., Leinekugel, X., Caillard, O. & Gaiarsa, J. L. GABAA, NMDA and AMPA receptors: a developmentally regulated ‘ménage à trois’. Trends Neurosci. 20, 523–529 (1997).

    CAS  PubMed  Google Scholar 

  38. 38.

    Kölker, S. et al. NMDA receptor activation and respiratory chain complex V inhibition contribute to neurodegeneration in d-2-hydroxyglutaric aciduria. Eur. J. Neurosci. 16, 21–28 (2002).

    PubMed  Google Scholar 

  39. 39.

    Choi, D. W. & Rothman, S. M. The role of glutamate neurotoxicity in hypoxic-ischemic neuronal death. Annu. Rev. Neurosci. 13, 171–182 (1990).

    CAS  PubMed  Google Scholar 

  40. 40.

    Barks, J. D. & Silverstein, F. S. Excitatory amino acids contribute to the pathogenesis of perinatal hypoxic-ischemic brain injury. Brain Pathol. 2, 235–243 (1992).

    CAS  PubMed  Google Scholar 

  41. 41.

    Pu, Y. et al. Increased detectability of alpha brain glutamate/glutamine in neonatal hypoxic-ischemic encephalopathy. AJNR Am. J. Neuroradiol. 21, 203–212 (2000).

    CAS  PubMed  Google Scholar 

  42. 42.

    Wisnowski, J. L. et al. The effects of therapeutic hypothermia on cerebral metabolism in neonates with hypoxic-ischemic encephalopathy: an in vivo 1H-MR spectroscopy study. J. Cereb. Blood Flow Metab. 36, 1075–1086 (2016).

    CAS  PubMed  Google Scholar 

  43. 43.

    Idrissi, El,A. & Trenkner, E. Growth factors and taurine protect against excitotoxicity by stabilizing calcium homeostasis and energy metabolism. J. Neurosci. 19, 9459–9468 (1999).

    PubMed  PubMed Central  Google Scholar 

  44. 44.

    Wiswell, T. E., Baumgart, S., Gannon, C. M. & Spitzer, A. R. No lumbar puncture in the evaluation for early neonatal sepsis: will meningitis be missed? Pediatrics 95, 803–806 (1995).

    CAS  PubMed  Google Scholar 

  45. 45.

    Stoll, B. J. et al. To tap or not to tap: high likelihood of meningitis without sepsis among very low birth weight infants. Pediatrics 113, 1181–1186 (2004).

    PubMed  Google Scholar 

Download references


S.M.G. received support for this work from the American Academy of Pediatrics, Children’s Hospital of Philadelphia, and NIH grant T32 AI 118684. L.S. and M.C.H. received support for this work from The Foerderer Awards at the Children’s Hospital of Philadelphia.

Author information




S.M.G., L.S., and M.C.H. designed the study, acquired and analyzed data, and drafted the article. D.M.T., S.R.M., and D.D.F. analyzed data and critically revised the article. M.A.T. and A.H. acquired data. S.A. and J.C.F. acquired data and critically revised the article.

Corresponding author

Correspondence to Scott M. Gordon.

Ethics declarations

Competing interests

The authors declare no competing interests.

Patient consent statement

Informed consent was obtained from families prior to analysis of infant CSF samples and data acquisition.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information


Supplementary Information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Gordon, S.M., Srinivasan, L., Taylor, D.M. et al. Derivation of a metabolic signature associated with bacterial meningitis in infants. Pediatr Res 88, 184–191 (2020).

Download citation

Further reading


Quick links