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Transcriptome profiles discriminate between Gram-positive and Gram-negative sepsis in preterm neonates



Genome-wide expression profiles have been previously employed as clinical research diagnostic tools for newborn sepsis. We aimed to determine if transcriptomic profiles could discriminate between Gram-positive and Gram-negative bacterial sepsis in preterm infants.


Prospective, observational, double-cohort study was conducted in very low birth weight infants with clinical signs and culture-positive sepsis. Blood samples were collected when clinical signs became apparent. Total RNA was processed for transcriptomic analysis. Results were validated by both reverse-transcription polymerase chain reaction and a mathematical model.


We included 25 septic preterm infants, 17 with Gram-positive and 8 with Gram-negative bacteria. The principal component analysis identified these two clusters of patients. We performed a predictive model based on 21 genes that showed an area under the receiver-operating characteristic curve of 1. Eight genes were overexpressed in Gram-positive septic infants: CD37, CSK, MAN2B2, MGAT1, MOB3A, MYO9B, SH2D3C, and TEP1. The most significantly overexpressed pathways were related to metabolic and immunomodulating responses that translated into an equilibrium between pro- and anti-inflammatory responses.


The transcriptomic profile allowed identification of whether the causative agent was Gram-positive or Gram-negative bacteria. The overexpression of genes such as CD37 and CSK, which control cytokine production and cell survival, could explain the better clinical outcome in sepsis caused by Gram-positive bacteria.


  • Transcriptomic profiles not only enable an early diagnosis of sepsis in very low birth weight infants but also discriminate between Gram-positive and Gram-negative bacteria as causative agents.

  • The overexpression of some genes related to cytokine production and cell survival could explain the better clinical outcome in sepsis caused by Gram-positive bacteria, and could lead us to a future, targeted therapy.

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Fig. 1: Tridimensional principal component analysis (PCA) mean centering and scaling based on the complete genome.
Fig. 2: Discriminative model between gram-positive and gram-negative sepsis.
Fig. 3: Boxplots represent mean and error standard of mRNA expression (2-ΔΔCt) in gram-positive and gram-negative sepsis for each gene.
Fig. 4: Model explaining changes of overexpressed genes in Gram-positive versus Gram-negative neonates (in red) linked with significant biological processes shown in Table 4.


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M.C. acknowledges PI18/01292 grant by the Health Research Institute Carlos III (Spanish Ministry of Science, Universities and Innovation; Kingdom of Spain).

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A.P., M.C., I.L.-C., and A.P.-L. designed the data collection instruments, recruited the patients, collected data, and reviewed and revised the manuscript. J.M.M., J.K., S.L.-P., J.D.P.-R., and E.S. carried out the analyses, and reviewed and revised the manuscript. M.C., E.S., and M.V. conceptualized and designed the study, coordinated and supervised data collection, and critically reviewed the manuscript for important intellectual content. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

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Correspondence to María Cernada.

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Cernada, M., Pinilla-González, A., Kuligowski, J. et al. Transcriptome profiles discriminate between Gram-positive and Gram-negative sepsis in preterm neonates. Pediatr Res 91, 637–645 (2022).

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