Predictive gene expression signature diagnoses neonatal sepsis before clinical presentation.

IF 9.7 1区医学 Q1 MEDICINE, RESEARCH & EXPERIMENTAL

EBioMedicine Pub Date : 2024-10-24 DOI:10.1016/j.ebiom.2024.105411

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

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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.

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预测性基因表达特征可在临床表现前诊断新生儿败血症。

背景：新生儿败血症是一种致命的疾病，其临床症状无特异性，延误了诊断和治疗。目前仍需要早期生物标志物来促进及时干预。我们的目的是鉴定可在临床表现前预测出生时败血症的新生儿败血症基因表达生物标志物：方法：在两家医院（西非冈比亚）的 720 名最初健康的足月新生儿中，我们发现了 21 名在出生后 28 天内因败血症住院的新生儿（分为早发型败血症（EOS，发病时间≤出生后 7 天）和晚发型败血症（LOS，发病时间为出生后 8-28 天））、12 名因局部感染住院但无全身受累证据的新生儿以及 33 名保持健康的匹配对照组新生儿。对所有新生儿出生时和出生后第一周内采集的外周血进行了RNA-seq分析，以确定差异表达基因（DEG）。机器学习方法（sPLS-DA、LASSO）确定了出生时表达的基因，这些基因可预测新生儿败血症的发病时间：研究结果：与对照组新生儿或后来发生局部感染或LOS的新生儿相比，后来发生EOS的新生儿在出生时已有1000个DEGs。在这些 DEGs 的基础上，建立了预测出生时 EOS 的 4 个基因特征（HSPH1、BORA、NCAPG2、PRIM1）（训练 AUC = 0.94、灵敏度 = 0.93、特异性 = 0.92），并在外部队列中进行了验证（验证 AUC = 0.72、灵敏度 = 0.83、特异性 = 0.83）。此外，在新生儿出生后的第一周，EOS干扰了超过1800个基因的表达，其中包括在健康对照组中观察到的影响免疫和代谢转换的基因：尽管新生儿在出生时看起来很健康，但后来出现 EOS 的新生儿在出生时就已经出现了明显的全血基因表达变化，这使得我们能够开发出 EOS 的 4 个基因预测特征。这有助于早期识别和治疗新生儿败血症，从而减轻其长期后遗症：基金：CIHR 和 NIH/NIAID。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

EBioMedicine Biochemistry, Genetics and Molecular Biology-General Biochemistry,Genetics and Molecular Biology

CiteScore

17.70

自引率

0.90%

发文量

579

审稿时长

5 weeks

期刊介绍： eBioMedicine is a comprehensive biomedical research journal that covers a wide range of studies that are relevant to human health. Our focus is on original research that explores the fundamental factors influencing human health and disease, including the discovery of new therapeutic targets and treatments, the identification of biomarkers and diagnostic tools, and the investigation and modification of disease pathways and mechanisms. We welcome studies from any biomedical discipline that contribute to our understanding of disease and aim to improve human health.