An Andy Y, Acton Erica, Idoko Olubukola T, Shannon Casey P, Blimkie Travis M, Falsafi Reza, Wariri Oghenebrume, Imam Abdulazeez, Dibbasey Tida, Bennike Tue Bjerg, Smolen Kinga K, Diray-Arce Joann, Ben-Othman Rym, Montante Sebastiano, Angelidou Asimenia, Odumade Oludare A, Martino David, Tebbutt Scott J, Levy Ofer, Steen Hanno, Kollmann Tobias R, Kampmann Beate, Hancock Robert E W, Lee Amy H
Centre for Microbial Diseases and Immunity Research, University of British Columbia, Vancouver, Canada.
Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, Canada.
EBioMedicine. 2024 Dec;110:105411. doi: 10.1016/j.ebiom.2024.105411. Epub 2024 Oct 28.
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.
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.
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.
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.
CIHR and NIH/NIAID.
新生儿败血症是一种具有非特异性临床体征的致命疾病，会延误诊断和治疗。仍然需要早期生物标志物来促进及时干预。我们的目标是识别可在临床表现之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。
在两家医院（西非冈比亚）的720名最初健康的足月新生儿中，我们确定了21名在出生后28天内后来因败血症住院的新生儿，分为早发型败血症（EOS，发病≤出生后7天）和晚发型败血症（LOS，发病8 - 28天），12名后来因局部感染住院且无全身受累证据的新生儿，以及33名匹配的健康对照新生儿。在所有新生儿均健康时于出生时以及出生后第一周采集外周血进行RNA测序，以鉴定差异表达基因（DEG）。机器学习方法（sPLS - DA、LASSO）识别出生时表达的可预测后期新生儿败血症发病的基因。
与对照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经有大约1000个DEG。基于这些DEG，开发了一种用于预测出生时EOS的4基因特征（HSPH1、BORA、NCAPG2、PRIM1）（训练AUC = 0.94，敏感性 = 0.93，特异性 = 0.92），并在外部队列中进行了验证（验证AUC = 0.72，敏感性 = 0.83，特异性 = 0.83）。此外，在出生后第一周，EOS扰乱了超过1800个基因的表达，包括那些影响健康对照中观察到的免疫和代谢转变的基因。
尽管出生时看似健康，但后来发生EOS的新生儿在出生时已经有明显的全血基因表达变化，这使得能够开发出一种用于EOS的4基因预测特征。这可以促进新生儿败血症的早期识别和治疗，潜在地减轻其长期后遗症。
加拿大卫生研究院（CIHR）和美国国立卫生研究院/美国国立过敏和传染病研究所（NIH/NIAID）。
