Predictive gene expression signature diagnoses neonatal sepsis before clinical presentation

Andy Y An; Erica Acton; Olubukola T Idoko; Casey P Shannon; Travis M Blimkie; Reza Falsafi; Oghenebrume Wariri; Abdulazeez Imam; Tida Dibbasey; Tue Bjerg Bennike; Kinga K Smolen; Joann Diray-Arce; Rym Ben-Othman; Sebastiano Montante; Asimenia Angelidou; Oludare A Odumade; David Martino; Scott J Tebbutt; Ofer Levy; Hanno Steen; Tobias R Kollmann; Beate Kampmann; Robert E W Hancock; Amy H Lee; EPIC Consortium

doi:10.1016/j.ebiom.2024.105411

Predictive gene expression signature diagnoses neonatal sepsis before clinical presentation

Andy Y An, Erica Acton, Olubukola T Idoko, Casey P Shannon, Travis M Blimkie, Reza Falsafi, Oghenebrume Wariri, Abdulazeez Imam, Tida Dibbasey, Tue Bjerg Bennike, Kinga K Smolen, Joann Diray-Arce, Rym Ben-Othman, Sebastiano Montante, Asimenia Angelidou, Oludare A Odumade, David Martino, Scott J Tebbutt, Ofer Levy, Hanno SteenTobias R Kollmann, Beate Kampmann, Robert E W Hancock, Amy H Lee, EPIC Consortium

Research output: Contribution to journal › Journal article › Research › peer-review

Abstract

BACKGROUND: Neonatal sepsis is a deadly disease with non-specific clinical signs, delaying diagnosis and treatment. There remains a need for early biomarkers to facilitate timely intervention. Our objective was to identify neonatal sepsis gene expression biomarkers that could predict sepsis at birth, prior to clinical presentation.

METHODS: Among 720 initially healthy full-term neonates in two hospitals (The Gambia, West Africa), we identified 21 newborns who were later hospitalized for sepsis in the first 28 days of life, split into early-onset sepsis (EOS, onset ≤7 days of life) and late-onset sepsis (LOS, onset 8-28 days of life), 12 neonates later hospitalized for localized infection without evidence of systemic involvement, and 33 matched control neonates who remained healthy. RNA-seq was performed on peripheral blood collected at birth when all neonates were healthy and also within the first week of life to identify differentially expressed genes (DEGs). Machine learning methods (sPLS-DA, LASSO) identified genes expressed at birth that predicted onset of neonatal sepsis at a later time.

FINDINGS: Neonates who later developed EOS already had ∼1000 DEGs at birth when compared to control neonates or those who later developed a localized infection or LOS. Based on these DEGs, a 4-gene signature (HSPH1, BORA, NCAPG2, PRIM1) for predicting EOS at birth was developed (training AUC = 0.94, sensitivity = 0.93, specificity = 0.92) and validated in an external cohort (validation AUC = 0.72, sensitivity = 0.83, and specificity = 0.83). Additionally, during the first week of life, EOS disrupted expression of >1800 genes including those influencing immune and metabolic transitions observed in healthy controls.

INTERPRETATION: Despite appearing healthy at birth, neonates who later developed EOS already had distinct whole blood gene expression changes at birth, which enabled the development of a 4-gene predictive signature for EOS. This could facilitate early recognition and treatment of neonatal sepsis, potentially mitigating its long-term sequelae.

FUNDING: CIHR and NIH/NIAID.

Original language	English
Article number	105411
Journal	EBioMedicine
Volume	110
ISSN	2352-3964
DOIs	https://doi.org/10.1016/j.ebiom.2024.105411
Publication status	Published - Dec 2024

Bibliographical note

Keywords

Neonatal sepsis
Transcriptomics
Predictive biomarkers
Ontogeny
Machine learning

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An, A. Y., Acton, E., Idoko, O. T., Shannon, C. P., Blimkie, T. M., Falsafi, R., Wariri, O., Imam, A., Dibbasey, T., Bennike, T. B., Smolen, K. K., Diray-Arce, J., Ben-Othman, R., Montante, S., Angelidou, A., Odumade, O. A., Martino, D., Tebbutt, S. J., Levy, O., ... EPIC Consortium (2024). Predictive gene expression signature diagnoses neonatal sepsis before clinical presentation. EBioMedicine, 110, Article 105411. https://doi.org/10.1016/j.ebiom.2024.105411

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title = "Predictive gene expression signature diagnoses neonatal sepsis before clinical presentation",

abstract = "BACKGROUND: Neonatal sepsis is a deadly disease with non-specific clinical signs, delaying diagnosis and treatment. There remains a need for early biomarkers to facilitate timely intervention. Our objective was to identify neonatal sepsis gene expression biomarkers that could predict sepsis at birth, prior to clinical presentation.METHODS: Among 720 initially healthy full-term neonates in two hospitals (The Gambia, West Africa), we identified 21 newborns who were later hospitalized for sepsis in the first 28 days of life, split into early-onset sepsis (EOS, onset ≤7 days of life) and late-onset sepsis (LOS, onset 8-28 days of life), 12 neonates later hospitalized for localized infection without evidence of systemic involvement, and 33 matched control neonates who remained healthy. RNA-seq was performed on peripheral blood collected at birth when all neonates were healthy and also within the first week of life to identify differentially expressed genes (DEGs). Machine learning methods (sPLS-DA, LASSO) identified genes expressed at birth that predicted onset of neonatal sepsis at a later time.FINDINGS: Neonates who later developed EOS already had ∼1000 DEGs at birth when compared to control neonates or those who later developed a localized infection or LOS. Based on these DEGs, a 4-gene signature (HSPH1, BORA, NCAPG2, PRIM1) for predicting EOS at birth was developed (training AUC = 0.94, sensitivity = 0.93, specificity = 0.92) and validated in an external cohort (validation AUC = 0.72, sensitivity = 0.83, and specificity = 0.83). Additionally, during the first week of life, EOS disrupted expression of >1800 genes including those influencing immune and metabolic transitions observed in healthy controls.INTERPRETATION: Despite appearing healthy at birth, neonates who later developed EOS already had distinct whole blood gene expression changes at birth, which enabled the development of a 4-gene predictive signature for EOS. This could facilitate early recognition and treatment of neonatal sepsis, potentially mitigating its long-term sequelae.FUNDING: CIHR and NIH/NIAID.",

keywords = "Neonatal sepsis, Transcriptomics, Predictive biomarkers, Ontogeny, Machine learning",

author = "An, {Andy Y} and Erica Acton and Idoko, {Olubukola T} and Shannon, {Casey P} and Blimkie, {Travis M} and Reza Falsafi and Oghenebrume Wariri and Abdulazeez Imam and Tida Dibbasey and Bennike, {Tue Bjerg} and Smolen, {Kinga K} and Joann Diray-Arce and Rym Ben-Othman and Sebastiano Montante and Asimenia Angelidou and Odumade, {Oludare A} and David Martino and Tebbutt, {Scott J} and Ofer Levy and Hanno Steen and Kollmann, {Tobias R} and Beate Kampmann and Hancock, {Robert E W} and Lee, {Amy H} and {EPIC Consortium}",

year = "2024",

month = dec,

doi = "10.1016/j.ebiom.2024.105411",

language = "English",

volume = "110",

journal = "EBioMedicine",

issn = "2352-3964",

publisher = "Elsevier",

}

An, AY, Acton, E, Idoko, OT, Shannon, CP, Blimkie, TM, Falsafi, R, Wariri, O, Imam, A, Dibbasey, T, Bennike, TB, Smolen, KK, Diray-Arce, J, Ben-Othman, R, Montante, S, Angelidou, A, Odumade, OA, Martino, D, Tebbutt, SJ, Levy, O, Steen, H, Kollmann, TR, Kampmann, B, Hancock, REW, Lee, AH & EPIC Consortium 2024, 'Predictive gene expression signature diagnoses neonatal sepsis before clinical presentation', EBioMedicine, vol. 110, 105411. https://doi.org/10.1016/j.ebiom.2024.105411

TY - JOUR

T1 - Predictive gene expression signature diagnoses neonatal sepsis before clinical presentation

AU - An, Andy Y

AU - Acton, Erica

AU - Idoko, Olubukola T

AU - Shannon, Casey P

AU - Blimkie, Travis M

AU - Falsafi, Reza

AU - Wariri, Oghenebrume

AU - Imam, Abdulazeez

AU - Dibbasey, Tida

AU - Bennike, Tue Bjerg

AU - Smolen, Kinga K

AU - Diray-Arce, Joann

AU - Ben-Othman, Rym

AU - Montante, Sebastiano

AU - Angelidou, Asimenia

AU - Odumade, Oludare A

AU - Martino, David

AU - Tebbutt, Scott J

AU - Levy, Ofer

AU - Steen, Hanno

AU - Kollmann, Tobias R

AU - Kampmann, Beate

AU - Hancock, Robert E W

AU - Lee, Amy H

AU - EPIC Consortium

PY - 2024/12

Y1 - 2024/12

N2 - BACKGROUND: Neonatal sepsis is a deadly disease with non-specific clinical signs, delaying diagnosis and treatment. There remains a need for early biomarkers to facilitate timely intervention. Our objective was to identify neonatal sepsis gene expression biomarkers that could predict sepsis at birth, prior to clinical presentation.METHODS: Among 720 initially healthy full-term neonates in two hospitals (The Gambia, West Africa), we identified 21 newborns who were later hospitalized for sepsis in the first 28 days of life, split into early-onset sepsis (EOS, onset ≤7 days of life) and late-onset sepsis (LOS, onset 8-28 days of life), 12 neonates later hospitalized for localized infection without evidence of systemic involvement, and 33 matched control neonates who remained healthy. RNA-seq was performed on peripheral blood collected at birth when all neonates were healthy and also within the first week of life to identify differentially expressed genes (DEGs). Machine learning methods (sPLS-DA, LASSO) identified genes expressed at birth that predicted onset of neonatal sepsis at a later time.FINDINGS: Neonates who later developed EOS already had ∼1000 DEGs at birth when compared to control neonates or those who later developed a localized infection or LOS. Based on these DEGs, a 4-gene signature (HSPH1, BORA, NCAPG2, PRIM1) for predicting EOS at birth was developed (training AUC = 0.94, sensitivity = 0.93, specificity = 0.92) and validated in an external cohort (validation AUC = 0.72, sensitivity = 0.83, and specificity = 0.83). Additionally, during the first week of life, EOS disrupted expression of >1800 genes including those influencing immune and metabolic transitions observed in healthy controls.INTERPRETATION: Despite appearing healthy at birth, neonates who later developed EOS already had distinct whole blood gene expression changes at birth, which enabled the development of a 4-gene predictive signature for EOS. This could facilitate early recognition and treatment of neonatal sepsis, potentially mitigating its long-term sequelae.FUNDING: CIHR and NIH/NIAID.

AB - BACKGROUND: Neonatal sepsis is a deadly disease with non-specific clinical signs, delaying diagnosis and treatment. There remains a need for early biomarkers to facilitate timely intervention. Our objective was to identify neonatal sepsis gene expression biomarkers that could predict sepsis at birth, prior to clinical presentation.METHODS: Among 720 initially healthy full-term neonates in two hospitals (The Gambia, West Africa), we identified 21 newborns who were later hospitalized for sepsis in the first 28 days of life, split into early-onset sepsis (EOS, onset ≤7 days of life) and late-onset sepsis (LOS, onset 8-28 days of life), 12 neonates later hospitalized for localized infection without evidence of systemic involvement, and 33 matched control neonates who remained healthy. RNA-seq was performed on peripheral blood collected at birth when all neonates were healthy and also within the first week of life to identify differentially expressed genes (DEGs). Machine learning methods (sPLS-DA, LASSO) identified genes expressed at birth that predicted onset of neonatal sepsis at a later time.FINDINGS: Neonates who later developed EOS already had ∼1000 DEGs at birth when compared to control neonates or those who later developed a localized infection or LOS. Based on these DEGs, a 4-gene signature (HSPH1, BORA, NCAPG2, PRIM1) for predicting EOS at birth was developed (training AUC = 0.94, sensitivity = 0.93, specificity = 0.92) and validated in an external cohort (validation AUC = 0.72, sensitivity = 0.83, and specificity = 0.83). Additionally, during the first week of life, EOS disrupted expression of >1800 genes including those influencing immune and metabolic transitions observed in healthy controls.INTERPRETATION: Despite appearing healthy at birth, neonates who later developed EOS already had distinct whole blood gene expression changes at birth, which enabled the development of a 4-gene predictive signature for EOS. This could facilitate early recognition and treatment of neonatal sepsis, potentially mitigating its long-term sequelae.FUNDING: CIHR and NIH/NIAID.

KW - Neonatal sepsis

KW - Transcriptomics

KW - Predictive biomarkers

KW - Ontogeny

KW - Machine learning

UR - http://www.scopus.com/inward/record.url?scp=85209951043&partnerID=8YFLogxK

U2 - 10.1016/j.ebiom.2024.105411

DO - 10.1016/j.ebiom.2024.105411

M3 - Journal article

C2 - 39472236

SN - 2352-3964

VL - 110

JO - EBioMedicine

JF - EBioMedicine

M1 - 105411

ER -

Predictive gene expression signature diagnoses neonatal sepsis before clinical presentation

Abstract

Bibliographical note

Keywords

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