In a recent study published in Nature Microbiology, researchers developed an integrated metagenomics of host-microbe plasma to facilitate the diagnosis of sepsis.
Study: integrated host-microbe plasma metagenomics for the diagnosis of sepsis in a prospective cohort of critically ill adults. Image credit: Kateryna Kon/Shutterstock
background
Sepsis accounts for 20% of all deaths worldwide and 20% to 50% of hospital deaths in the United States. For timely and effective antibiotic therapy crucial for sepsis survival, early detection and identification of microbial infections is required. However, etiological pathogens are not identified in more than 30% of cases. It is essential to distinguish sepsis from non-infectious systemic disorders, as they often appear clinically similar during hospitalization.
About the study
In the present study, the researchers created a sepsis diagnostic tool that combined host transcriptional profiling together with broad-range pathogen identification.
In two tertiary care hospitals, the team conducted a prospective observational review of critically ill adult patients admitted to the intensive care unit (ICU) of the emergency department (ED). Patients were divided into five subgroups based on the presence or absence of sepsis. These patients included those who had: (1) clinically adjudicated sepsis as well as a confirmed bacterial bloodstream infection (SepsisBSI); (2) clinically adjudicated sepsis, as well as a confirmed non-blood infection (Sepsisnon-BSI); (3) suspected sepsis characterized by negative clinical microbiological tests (Sepsissuspected); (4) patients who have no evidence of sepsis and an explanation for their critical illness (no sepsis); or (5) patients with an indeterminate status (Indeterm).
Using ribonucleic acid (RNA) sequencing in whole blood samples, the team first examined transcriptional variations between patients with clinically and microbiologically proven sepsis and those without symptoms of infection. A technique called gene set enrichment analysis (GSEA) detects groups of genes within a data set with related biological functions.
A differential gene expression (DE) study was performed between the SepsisBSI and Sepsisnon-BSI groups to further identify variations between sepsis patients with bloodstream infections versus peripheral sites. The team developed a universal sepsis diagnostic classifier based on whole blood gene expression patterns in response to the practical requirement to diagnose sepsis in SepsisBSI and Sepsisnon-BSI patients. The team used a bagged support vector machine (bSVM) learning strategy to pick the genes that most successfully differentiated patients with sepsis (SepsisBSI and Sepsisnon-BSI) and those without sepsis (No-sepsis ).
An average of 2.3 × 107 reads were obtained after sequencing RNA from patients obtained with available plasma samples. In addition, DE analysis was performed to determine whether a biologically plausible signal could be used to differentiate patients who had and did not have sepsis.
results
Exacerbation of heart failure, overdose/intoxication, cardiac arrest, and pulmonary embolism were the most frequently diagnosed conditions in the non-sepsis group. Regardless of subgroup, the majority of patients required vasopressor support and mechanical ventilation. SepsisBSI and Sepsisnon-BSI patients who had demonstrated sepsis did not differ from non-sepsis patients with respect to age, sex, race, ethnicity, APACHE III score, immunocompromise, l ‘intubation status, the maximum white blood cell count or 28. – daily mortality. In the group of patients without sepsis, all but one patient demonstrated two or more criteria for systemic inflammatory response syndrome (SIRS).
The study also revealed downregulation of pathways related to ribosomal RNA processing and translation along with upregulation of genes involved in innate immune signaling and neutrophil degranulation in patients with sepsis. Using the DE analysis, the team found 5,227 genes. The Sepsisnon-BSI cohort showed enrichment in genes associated with defensins, antimicrobial peptides, and G alpha signaling, as well as other pathways. On the other hand, the SepsisBSI cohort showed enrichment in genes associated with immunoregulatory interactions between non-lymphoid and lymphoid cells and CD28 signaling, among other functions.
The bSVM model showed an average cross-validation area under the receiver operating characteristic (ROC) curve (AUC) of 0.81. Samples with transcript counts below the quality control (QC) threshold had a lower mean input mass than samples with sufficient counts.
Interestingly, several differentially expressed genes have been identified as sepsis biomarkers, including increased human leukocyte repressed CD177 isotype DR (HLA-DRA), indicating a biologically significant transcriptome signature of the plasma RNA In the Sepsisnon-BSI group, next-generation metagenomic sequencing (mNGS) of plasma deoxyribonucleic acid (DNA) revealed three of eight bacterial urinary tract infection (UTI) pathogens and two of 25 bacterial urinary tract infection pathogens lower respiratory tract infection (LRTI). None of the three patients with severe colitis caused by C. difficile had this pathogen. In eight of 73 patients with proven sepsis, additional potential bacterial pathogens not identified by culture were found.
conclusion
Overall, the study findings demonstrated that reliable diagnosis of sepsis is facilitated by combining host gene expression profiling with metagenomic pathogen identification from plasma nucleic acid. Future research is required to verify and evaluate the therapeutic utility of this culture-independent diagnostic strategy.
Journal reference:
- Kalantar, K., Neyton, L., Abdelghany, M., Mick, E., Jauregui, A., & Caldera, S. et al. (2022). Integrated host-microbe plasma metagenomics for the diagnosis of sepsis in a prospective cohort of critically ill adults. Microbiology of nature. doi: 10.1038/s41564-022-01237-2