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Derivation of a metabolic signature associated with bacterial meningitis in infants.

ABSTRACT: BACKGROUND: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. METHODS: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. RESULTS: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. CONCLUSIONS: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. IMPACT: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.

SUBMITTER: Gordon SM

PROVIDER: S-EPMC7390682 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Derivation of a metabolic signature associated with bacterial meningitis in infants.

Gordon Scott M SM Srinivasan Lakshmi L Taylor Deanne M DM Master Stephen R SR Tremoglie Marissa A MA Hankeova Adriana A Flannery Dustin D DD Abbasi Soraya S Fitzgerald Julie C JC Harris Mary C MC

Pediatric research 20200302 2

<h4>Background</h4>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.<h4>Methods</h4>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 Neonata ...[more]

PMID: 32120377

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Project description:BACKGROUND: Bacterial meningitis (BM) is an infectious disease that results in high mortality and morbidity. Despite efficacious antibiotic therapy, neurological sequelae are often observed in patients after disease. Currently, the main challenge in BM treatment is to develop adjuvant therapies that reduce the occurrence of sequelae. In recent papers published by our group, we described the associations between the single nucleotide polymorphisms (SNPs) AADAT +401C?>?T, APEX1 Asn148Glu, OGG1 Ser326Cys and PARP1 Val762Ala and BM. In this study, we analyzed the associations between the SNPs TNF -308G?>?A, TNF -857C?>?T, IL-8 -251A?>?T and BM and investigated gene-gene interactions, including the SNPs that we published previously. METHODS: The study was conducted with 54 BM patients and 110 healthy volunteers (as the control group). The genotypes were investigated via primer-introduced restriction analysis-polymerase chain reaction (PIRA-PCR) or polymerase chain reaction-based restriction fragment length polymorphism (PCR-RFLP) analysis. Allelic and genotypic frequencies were also associated with cytokine and chemokine levels, as measured with the x-MAP method, and cell counts. We analyzed gene-gene interactions among SNPs using the generalized multifactor dimensionality reduction (GMDR) method. RESULTS: We did not find significant association between the SNPs TNF -857C?>?T and IL-8 -251A?>?T and the disease. However, a higher frequency of the variant allele TNF -308A was observed in the control group, associated with changes in cytokine levels compared to individuals with wild type genotypes, suggesting a possible protective role. In addition, combined inter-gene interaction analysis indicated a significant association between certain genotypes and BM, mainly involving the alleles APEX1 148Glu, IL8 -251 T and AADAT +401 T. These genotypic combinations were shown to affect cyto/chemokine levels and cell counts in CSF samples from BM patients. CONCLUSIONS: In conclusion, this study revealed a significant association between genetic variability and altered inflammatory responses, involving important pathways that are activated during BM. This knowledge may be useful for a better understanding of BM pathogenesis and the development of new therapeutic approaches.

Dataset Information

Derivation of a metabolic signature associated with bacterial meningitis in infants.

Publications

Derivation of a metabolic signature associated with bacterial meningitis in infants.

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