The Accuracy of 16S rRNA Polymerase Chain Reaction for the Diagnosis of Neonatal Sepsis: A Meta-Analysis

16S rDNA Sequencing for Bacterial Identification in Preterm Infants with Suspected Early-Onset Neonatal Sepsis

BACKGROUND

The high prevalence of suspected early-onset neonatal sepsis among preterm infants leads to immediate antibiotic administration upon admission. Notably, most blood cultures for suspected early-onset neonatal sepsis do not yield a causative pathogen. This study aimed to assess polymerase chain reaction (PCR) targeting the variable region V4 of the 16S ribosomal gene (16S rDNA) and Sanger sequencing for bacterial identification in preterm infants with suspected early-onset neonatal sepsis.

METHODS

Therefore, this prospective study was conducted. Preterm infants with suspected early-onset neonatal sepsis were included in this study. The three groups were formed based on the risk of infection and clinical sepsis. Blood samples were collected upon admission to the neonatal unit for culture and molecular analysis. PCR amplification and subsequent Sanger sequencing of the V4 region of the 16S rDNA were performed.

RESULTS

Twenty-eight patients were included in this study. Blood cultures were negative in 100% of the patients. Amplification and sequencing of the V4 region identified bacterial genera in 19 patients across distinct groups. The predominant taxonomically identified genus was Pseudomonas.

CONCLUSIONS

Amplifying the 16S rDNA variable region through PCR and subsequent Sanger sequencing in preterm neonates with suspected early-onset neonatal sepsis can enhance the identification of microbial species that cause infection, especially in negative cultures.

Application of Advanced Molecular Methods to Study Early-Onset Neonatal Sepsis

Early-onset sepsis (EOS) is a global health issue, considered one of the primary causes of neonatal mortality. Diagnosis of EOS is challenging because its clinical signs are nonspecific, and blood culture, which is the current gold-standard diagnostic tool, has low sensitivity. Commonly used biomarkers for sepsis diagnosis, including C-reactive protein, procalcitonin, and interleukin-6, lack specificity for infection. Due to the disadvantages of blood culture and other common biomarkers, ongoing efforts are directed towards identifying innovative molecular approaches to diagnose neonates at risk of sepsis. This review aims to gather knowledge and recent research on these emerging molecular methods. PCR-based techniques and unrestricted techniques based on 16S rRNA sequencing and 16S-23S rRNA gene interspace region sequencing offer several advantages. Despite their potential, these approaches are not able to replace blood cultures due to several limitations; however, they may prove valuable as complementary tests in neonatal sepsis diagnosis. Several microRNAs have been evaluated and have been proposed as diagnostic biomarkers in EOS. T2 magnetic resonance and bioinformatic analysis have proposed potential biomarkers of neonatal sepsis, though further studies are essential to validate these findings.

The accuracy of soluble urokinase-type plasminogen activator receptor for the diagnosis of neonatal sepsis: a meta-analysis

Background

Neonatal sepsis is one of the major causes of morbidity and mortality in newborns. However, atypical clinical manifestations and symptoms make the early diagnosis of neonatal sepsis a challenge. Relatively high-serum soluble urokinase-type plasminogen activator receptor (suPAR) has been implicated as a diagnostic biomarker for adult sepsis. Therefore, the meta-analysis is intended to explore the diagnostic value of suPAR for neonatal sepsis.

Methods

The PubMed, Cochrane Library, Embase, Web of Science, China National Knowledge Infrastructure, China Biological Medicine Disk, and Wanfang databases were retrieved from inception to 31 December 2022 to collect diagnostic accuracy studies about suPAR for neonatal sepsis. Two reviewers independently screened the literature, extracted data, and assessed the risk of bias in the included studies using the quality assessment of diagnostic accuracy studies-2 (QUADAS-2) tool. Then, a meta-analysis was performed using Stata 15.0 software.

Results

A total of six articles involving eight studies were included. The results of the meta-analysis showed that the pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 0.89 [95%CI (0.83-0.93)], 0.94 [95%CI (0.77-0.98)], 14 [95%CI (3.5-55.2)], 0.12 [95%CI (0.08-0.18)], and 117 [95%CI (24-567)], respectively. The area under the curve (AUC) of summary receiver operator characteristic (SROC) curves was 0.92 [95%CI (0.90-0.94)]. Sensitivity analysis confirmed the stability of the results, and publication bias was not observed. Fagan's nomogram results demonstrated the clinical availability of the findings.

Conclusion

Current evidence suggests that suPAR has potential diagnostic value for neonatal sepsis. Owing to the limited quality of the included studies, more high-quality studies are needed to verify the above conclusion.

Identification of a Four-Gene Signature for Diagnosing Paediatric Sepsis

Aim

Early diagnosis of paediatric sepsis is crucial for the proper treatment of children and reduction of hospitalization and mortality. Biomarkers are a convenient and effective method for diagnosing any disease. However, huge differences among the studies reporting biomarkers for diagnosing sepsis have limited their clinical application. Therefore, in this study, we aimed to evaluate the diagnostic value of key genes involved in paediatric sepsis based on the data of the Gene Expression Omnibus database.

Methods

We used the GSE119217 dataset to identify differentially expressed genes (DEGs) between patients with and without paediatric sepsis. The most relevant gene modules of paediatric sepsis were screened through the weighted gene coexpression network analysis (WGCNA). Common genes (CGs) were found between DEGs and WGCNA. Genes with a potential diagnostic value in paediatric sepsis were selected from the CGs using least absolute shrinkage and selection operator regression and support vector machine recursive feature elimination. The principal component analysis, receiver operating characteristic curves, and C-index were used to verify the diagnostic value of the identified genes in six other independent sepsis datasets. Subsequently, a meta-analysis of the selected genes was performed to evaluate the value of these genes as biomarkers in paediatric sepsis.

Results

A total of 41 CGs were selected from the GSE119217 dataset. A four-gene signature composed of ANXA3, CD177, GRAMD1C, and TIGD3 effectively distinguished patients with paediatric sepsis from those in the control group. The signature was verified using six other independent datasets. In addition, the meta-analysis results showed that the pooled sensitivity, specificity, and area under the curve values were 1.00, 0.98, and 1.00, respectively.

Conclusion

The four-gene signature can be used as new biomarkers to distinguish patients with paediatric sepsis from healthy individuals.