Fecal Microbiota and Volatile Metabolome Pattern Alterations Precede Late-Onset Meningitis in Preterm Neonates

Nina M Frerichs; Nancy Deianova; Sofia el Manouni el Hassani; Animesh Acharjee; Mohammed Nabil Quraishi; Willem P de Boode; Veerle Cossey; Christian V Hulzebos; Anton H van Kaam; Boris W Kramer; Esther d’Haens; Wouter J de Jonge; Daniel C Vijlbrief; Mirjam M van Weissenbruch; Emma Daulton; Alfian N Wicaksono; James A Covington; Marc A Benninga; Nanne K H de Boer; Johannes B van Goudoever; Hendrik J Niemarkt; Tim G J de Meij

doi:10.1093/infdis/jiae265

Fecal Microbiota and Volatile Metabolome Pattern Alterations Precede Late-Onset Meningitis in Preterm Neonates

Nina M Frerichs^*, Nancy Deianova, Sofia el Manouni el Hassani, Animesh Acharjee^*, Mohammed Nabil Quraishi, Willem P de Boode, Veerle Cossey, Christian V Hulzebos, Anton H van Kaam, Boris W Kramer, Esther d’Haens, Wouter J de Jonge, Daniel C Vijlbrief, Mirjam M van Weissenbruch, Emma Daulton, Alfian N Wicaksono, James A Covington, Marc A Benninga, Nanne K H de Boer, Johannes B van GoudoeverHendrik J Niemarkt, Tim G J de Meij

^*Corresponding author for this work

Cancer and Genomic Sciences

Research output: Contribution to journal › Article › peer-review

Abstract

Background: The fecal microbiota and metabolome are hypothesized to be altered before late-onset neonatal meningitis (LOM), analogous to late-onset sepsis (LOS). The present study aimed to identify fecal microbiota composition and volatile metabolomics preceding LOM.

Methods: Cases and gestational age-matched controls were selected from a prospective, longitudinal preterm cohort study (born <30 weeks’ gestation) at 9 neonatal intensive care units. The microbial composition (16S rRNA sequencing) and volatile metabolome (gas chromatography-ion mobility spectrometry [GC-IMS] and GC-time-of-flight-mass spectrometry [GC-TOF-MS]) were analyzed in fecal samples 1–10 days pre-LOM.

Results: Of 1397 included infants, 21 were diagnosed with LOM (1.5%), and 19 with concomitant LOS (90%). Random forest classification and MaAsLin2 analysis found similar microbiota features contribute to the discrimination of fecal pre-LOM samples versus controls. A random forest model based on 6 microbiota features accurately predicted LOM 1–3 days before diagnosis with an area under the curve (AUC) of 0.88 (n = 147). Pattern recognition analysis by GC-IMS revealed an AUC of 0.70–0.76 (P < .05) in the 3 days pre-LOM (n = 92). No single discriminative metabolites were identified by GC-TOF-MS (n = 66).

Conclusions: Infants with LOM could be accurately discriminated from controls based on preclinical microbiota composition, while alterations in the volatile metabolome were moderately associated with preclinical LOM.

Original language	English
Article number	jiae265
Journal	The Journal of Infectious Diseases
Early online date	23 May 2024
DOIs	https://doi.org/10.1093/infdis/jiae265
Publication status	E-pub ahead of print - 23 May 2024

Keywords

late-onset meningitis
preterm neonates
volatile organic compounds
microbiota analysis
fecal biomarker

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10.1093/infdis/jiae265Licence: Creative Commons: Attribution (CC BY)

H2020_RISE_DNASURF
Tucker, J., Peacock, A. & Horswell, S.
European Commission, European Commission - Management Costs
1/12/17 → 31/05/23
Project: Research

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Frerichs, N. M., Deianova, N., Hassani, S. E. M. E., Acharjee, A., Quraishi, M. N., Boode, W. P. D., Cossey, V., Hulzebos, C. V., Kaam, A. H. V., Kramer, B. W., d’Haens, E., Jonge, W. J. D., Vijlbrief, D. C., Weissenbruch, M. M. V., Daulton, E., Wicaksono, A. N., Covington, J. A., Benninga, M. A., Boer, N. K. H. D., ... Meij, T. G. J. D. (2024). Fecal Microbiota and Volatile Metabolome Pattern Alterations Precede Late-Onset Meningitis in Preterm Neonates. The Journal of Infectious Diseases, Article jiae265. Advance online publication. https://doi.org/10.1093/infdis/jiae265

@article{85f6e88efe264a19a9add7bc43300157,

title = "Fecal Microbiota and Volatile Metabolome Pattern Alterations Precede Late-Onset Meningitis in Preterm Neonates",

abstract = "Background: The fecal microbiota and metabolome are hypothesized to be altered before late-onset neonatal meningitis (LOM), analogous to late-onset sepsis (LOS). The present study aimed to identify fecal microbiota composition and volatile metabolomics preceding LOM.Methods: Cases and gestational age-matched controls were selected from a prospective, longitudinal preterm cohort study (born <30 weeks{\textquoteright} gestation) at 9 neonatal intensive care units. The microbial composition (16S rRNA sequencing) and volatile metabolome (gas chromatography-ion mobility spectrometry [GC-IMS] and GC-time-of-flight-mass spectrometry [GC-TOF-MS]) were analyzed in fecal samples 1–10 days pre-LOM.Results: Of 1397 included infants, 21 were diagnosed with LOM (1.5%), and 19 with concomitant LOS (90%). Random forest classification and MaAsLin2 analysis found similar microbiota features contribute to the discrimination of fecal pre-LOM samples versus controls. A random forest model based on 6 microbiota features accurately predicted LOM 1–3 days before diagnosis with an area under the curve (AUC) of 0.88 (n = 147). Pattern recognition analysis by GC-IMS revealed an AUC of 0.70–0.76 (P < .05) in the 3 days pre-LOM (n = 92). No single discriminative metabolites were identified by GC-TOF-MS (n = 66).Conclusions: Infants with LOM could be accurately discriminated from controls based on preclinical microbiota composition, while alterations in the volatile metabolome were moderately associated with preclinical LOM.",

keywords = "late-onset meningitis, preterm neonates, volatile organic compounds, microbiota analysis, fecal biomarker",

author = "Frerichs, {Nina M} and Nancy Deianova and Hassani, {Sofia el Manouni el} and Animesh Acharjee and Quraishi, {Mohammed Nabil} and Boode, {Willem P de} and Veerle Cossey and Hulzebos, {Christian V} and Kaam, {Anton H van} and Kramer, {Boris W} and Esther d{\textquoteright}Haens and Jonge, {Wouter J de} and Vijlbrief, {Daniel C} and Weissenbruch, {Mirjam M van} and Emma Daulton and Wicaksono, {Alfian N} and Covington, {James A} and Benninga, {Marc A} and Boer, {Nanne K H de} and Goudoever, {Johannes B van} and Niemarkt, {Hendrik J} and Meij, {Tim G J de}",

year = "2024",

month = may,

day = "23",

doi = "10.1093/infdis/jiae265",

language = "English",

journal = "The Journal of Infectious Diseases",

issn = "0022-1899",

publisher = "Oxford University Press",

}

Frerichs, NM, Deianova, N, Hassani, SEME, Acharjee, A, Quraishi, MN, Boode, WPD, Cossey, V, Hulzebos, CV, Kaam, AHV, Kramer, BW, d’Haens, E, Jonge, WJD, Vijlbrief, DC, Weissenbruch, MMV, Daulton, E, Wicaksono, AN, Covington, JA, Benninga, MA, Boer, NKHD, Goudoever, JBV, Niemarkt, HJ & Meij, TGJD 2024, 'Fecal Microbiota and Volatile Metabolome Pattern Alterations Precede Late-Onset Meningitis in Preterm Neonates', The Journal of Infectious Diseases. https://doi.org/10.1093/infdis/jiae265

TY - JOUR

T1 - Fecal Microbiota and Volatile Metabolome Pattern Alterations Precede Late-Onset Meningitis in Preterm Neonates

AU - Frerichs, Nina M

AU - Deianova, Nancy

AU - Hassani, Sofia el Manouni el

AU - Acharjee, Animesh

AU - Quraishi, Mohammed Nabil

AU - Boode, Willem P de

AU - Cossey, Veerle

AU - Hulzebos, Christian V

AU - Kaam, Anton H van

AU - Kramer, Boris W

AU - d’Haens, Esther

AU - Jonge, Wouter J de

AU - Vijlbrief, Daniel C

AU - Weissenbruch, Mirjam M van

AU - Daulton, Emma

AU - Wicaksono, Alfian N

AU - Covington, James A

AU - Benninga, Marc A

AU - Boer, Nanne K H de

AU - Goudoever, Johannes B van

AU - Niemarkt, Hendrik J

AU - Meij, Tim G J de

PY - 2024/5/23

Y1 - 2024/5/23

N2 - Background: The fecal microbiota and metabolome are hypothesized to be altered before late-onset neonatal meningitis (LOM), analogous to late-onset sepsis (LOS). The present study aimed to identify fecal microbiota composition and volatile metabolomics preceding LOM.Methods: Cases and gestational age-matched controls were selected from a prospective, longitudinal preterm cohort study (born <30 weeks’ gestation) at 9 neonatal intensive care units. The microbial composition (16S rRNA sequencing) and volatile metabolome (gas chromatography-ion mobility spectrometry [GC-IMS] and GC-time-of-flight-mass spectrometry [GC-TOF-MS]) were analyzed in fecal samples 1–10 days pre-LOM.Results: Of 1397 included infants, 21 were diagnosed with LOM (1.5%), and 19 with concomitant LOS (90%). Random forest classification and MaAsLin2 analysis found similar microbiota features contribute to the discrimination of fecal pre-LOM samples versus controls. A random forest model based on 6 microbiota features accurately predicted LOM 1–3 days before diagnosis with an area under the curve (AUC) of 0.88 (n = 147). Pattern recognition analysis by GC-IMS revealed an AUC of 0.70–0.76 (P < .05) in the 3 days pre-LOM (n = 92). No single discriminative metabolites were identified by GC-TOF-MS (n = 66).Conclusions: Infants with LOM could be accurately discriminated from controls based on preclinical microbiota composition, while alterations in the volatile metabolome were moderately associated with preclinical LOM.

AB - Background: The fecal microbiota and metabolome are hypothesized to be altered before late-onset neonatal meningitis (LOM), analogous to late-onset sepsis (LOS). The present study aimed to identify fecal microbiota composition and volatile metabolomics preceding LOM.Methods: Cases and gestational age-matched controls were selected from a prospective, longitudinal preterm cohort study (born <30 weeks’ gestation) at 9 neonatal intensive care units. The microbial composition (16S rRNA sequencing) and volatile metabolome (gas chromatography-ion mobility spectrometry [GC-IMS] and GC-time-of-flight-mass spectrometry [GC-TOF-MS]) were analyzed in fecal samples 1–10 days pre-LOM.Results: Of 1397 included infants, 21 were diagnosed with LOM (1.5%), and 19 with concomitant LOS (90%). Random forest classification and MaAsLin2 analysis found similar microbiota features contribute to the discrimination of fecal pre-LOM samples versus controls. A random forest model based on 6 microbiota features accurately predicted LOM 1–3 days before diagnosis with an area under the curve (AUC) of 0.88 (n = 147). Pattern recognition analysis by GC-IMS revealed an AUC of 0.70–0.76 (P < .05) in the 3 days pre-LOM (n = 92). No single discriminative metabolites were identified by GC-TOF-MS (n = 66).Conclusions: Infants with LOM could be accurately discriminated from controls based on preclinical microbiota composition, while alterations in the volatile metabolome were moderately associated with preclinical LOM.

KW - late-onset meningitis

KW - preterm neonates

KW - volatile organic compounds

KW - microbiota analysis

KW - fecal biomarker

U2 - 10.1093/infdis/jiae265

DO - 10.1093/infdis/jiae265

M3 - Article

SN - 0022-1899

JO - The Journal of Infectious Diseases

JF - The Journal of Infectious Diseases

M1 - jiae265

ER -

Fecal Microbiota and Volatile Metabolome Pattern Alterations Precede Late-Onset Meningitis in Preterm Neonates

Abstract

Keywords

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H2020_RISE_DNASURF

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