Optimal management of lower respiratory tract infection relies on distinguishing infectious from noninfectious etiologies and identifying the microbiologic cause if applicable. This process is ...complicated by overlapping clinical symptoms and the colonizing lung microbiota. In a recent issue of the JCI, Mick, Tsitsiklis, and colleagues apply RNA-Seq to tracheal aspirates from critically ill children and demonstrate how integration of the host response with microbial identification results in a harmonious and accurate diagnostic classifier. Though promising, there are numerous barriers to realizing a combined host and pathogen diagnostic.
Improved risk stratification and prognosis prediction in sepsis is a critical unmet need. Clinical severity scores and available assays such as blood lactate reflect global illness severity with ...suboptimal performance, and do not specifically reveal the underlying dysregulation of sepsis. Here, we present prognostic models for 30-day mortality generated independently by three scientific groups by using 12 discovery cohorts containing transcriptomic data collected from primarily community-onset sepsis patients. Predictive performance is validated in five cohorts of community-onset sepsis patients in which the models show summary AUROCs ranging from 0.765-0.89. Similar performance is observed in four cohorts of hospital-acquired sepsis. Combining the new gene-expression-based prognostic models with prior clinical severity scores leads to significant improvement in prediction of 30-day mortality as measured via AUROC and net reclassification improvement index These models provide an opportunity to develop molecular bedside tests that may improve risk stratification and mortality prediction in patients with sepsis.
SARS-CoV-2 infection has been shown to trigger a wide spectrum of immune responses and clinical manifestations in human hosts. Here, we sought to elucidate novel aspects of the host response to ...SARS-CoV-2 infection through RNA sequencing of peripheral blood samples from 46 subjects with COVID-19 and directly comparing them to subjects with seasonal coronavirus, influenza, bacterial pneumonia, and healthy controls. Early SARS-CoV-2 infection triggers a powerful transcriptomic response in peripheral blood with conserved components that are heavily interferon-driven but also marked by indicators of early B-cell activation and antibody production. Interferon responses during SARS-CoV-2 infection demonstrate unique patterns of dysregulated expression compared to other infectious and healthy states. Heterogeneous activation of coagulation and fibrinolytic pathways are present in early COVID-19, as are IL1 and JAK/STAT signaling pathways, which persist into late disease. Classifiers based on differentially expressed genes accurately distinguished SARS-CoV-2 infection from other acute illnesses (auROC 0.95 95% CI 0.92-0.98). The transcriptome in peripheral blood reveals both diverse and conserved components of the immune response in COVID-19 and provides for potential biomarker-based approaches to diagnosis.
Acute respiratory infections caused by bacterial or viral pathogens are among the most common reasons for seeking medical care. Despite improvements in pathogen-based diagnostics, most patients ...receive inappropriate antibiotics. Host response biomarkers offer an alternative diagnostic approach to direct antimicrobial use. This observational cohort study determined whether host gene expression patterns discriminate noninfectious from infectious illness and bacterial from viral causes of acute respiratory infection in the acute care setting. Peripheral whole blood gene expression from 273 subjects with community-onset acute respiratory infection (ARI) or noninfectious illness, as well as 44 healthy controls, was measured using microarrays. Sparse logistic regression was used to develop classifiers for bacterial ARI (71 probes), viral ARI (33 probes), or a noninfectious cause of illness (26 probes). Overall accuracy was 87% (238 of 273 concordant with clinical adjudication), which was more accurate than procalcitonin (78%, P < 0.03) and three published classifiers of bacterial versus viral infection (78 to 83%). The classifiers developed here externally validated in five publicly available data sets (AUC, 0.90 to 0.99). A sixth publicly available data set included 25 patients with co-identification of bacterial and viral pathogens. Applying the ARI classifiers defined four distinct groups: a host response to bacterial ARI, viral ARI, coinfection, and neither a bacterial nor a viral response. These findings create an opportunity to develop and use host gene expression classifiers as diagnostic platforms to combat inappropriate antibiotic use and emerging antibiotic resistance.
Compare three host response strategies to distinguish bacterial and viral etiologies of acute respiratory illness (ARI).
In this observational cohort study, procalcitonin, a 3-protein panel (CRP, ...IP-10, TRAIL), and a host gene expression mRNA panel were measured in 286 subjects with ARI from four emergency departments. Multinomial logistic regression and leave-one-out cross validation were used to evaluate the protein and mRNA tests.
The mRNA panel performed better than alternative strategies to identify bacterial infection: AUC 0.93 vs. 0.83 for the protein panel and 0.84 for procalcitonin (P<0.02 for each comparison). This corresponded to a sensitivity and specificity of 92% and 83% for the mRNA panel, 81% and 73% for the protein panel, and 68% and 87% for procalcitonin, respectively. A model utilizing all three strategies was the same as mRNA alone. For the diagnosis of viral infection, the AUC was 0.93 for mRNA and 0.84 for the protein panel (p<0.05). This corresponded to a sensitivity and specificity of 89% and 82% for the mRNA panel, and 85% and 62% for the protein panel, respectively.
A gene expression signature was the most accurate host response strategy for classifying subjects with bacterial, viral, or non-infectious ARI.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
OBJECTIVES:To find and validate generalizable sepsis subtypes using data-driven clustering.
DESIGN:We used advanced informatics techniques to pool data from 14 bacterial sepsis transcriptomic ...datasets from eight different countries (n = 700).
SETTING:Retrospective analysis.
SUBJECTS:Persons admitted to the hospital with bacterial sepsis.
INTERVENTIONS:None.
MEASUREMENTS AND MAIN RESULTS:A unified clustering analysis across 14 discovery datasets revealed three subtypes, which, based on functional analysis, we termed “Inflammopathic, Adaptive, and Coagulopathic.” We then validated these subtypes in nine independent datasets from five different countries (n = 600). In both discovery and validation data, the Adaptive subtype is associated with a lower clinical severity and lower mortality rate, and the Coagulopathic subtype is associated with higher mortality and clinical coagulopathy. Further, these clusters are statistically associated with clusters derived by others in independent single sepsis cohorts.
CONCLUSIONS:The three sepsis subtypes may represent a unifying framework for understanding the molecular heterogeneity of the sepsis syndrome. Further study could potentially enable a precision medicine approach of matching novel immunomodulatory therapies with septic patients most likely to benefit.
Optimal management of lower respiratory tract infection relies on distinguishing infectious from noninfectious etiologies and identifying the microbiologic cause if applicable. This process is ...complicated by overlapping clinical symptoms and the colonizing lung microbiota. In a recent issue of theJCI, Mick, Tsitsiklis, and colleagues apply RNA-Seq to tracheal aspirates from critically ill children and demonstrate how integration of the host response with microbial identification results in a harmonious and accurate diagnostic classifier. Though promising, there are numerous barriers to realizing a combined host and pathogen diagnostic.
Acute graft-versus-host disease (aGVHD) is the leading cause of morbidity and mortality after allogeneic hematopoietic cell transplantation (HCT). Approximately 35% to 50% of HCT recipients develop ...aGVHD; however, there are no validated diagnostic and predictive blood biomarkers for aGVHD in clinical use. Here, we show that plasma samples from aGVHD patients have a distinct microRNA (miRNA) expression profile. We found that 6 miRNAs (miR-423, miR-199a-3p, miR-93*, miR-377, miR-155, and miR-30a) were significantly upregulated in the plasma of aGVHD patients (n = 116) when compared with non-GVHD patients (n = 52) in training and validation phases. We have developed a model including 4 miRNAs (miR-423, miR-199a-3p, miR-93*, and miR-377) that can predict the probability of aGVHD with an area under the curve of 0.80. Moreover, these elevated miRNAs were detected before the onset of aGVHD (median = 16 days before diagnosis). In addition, the levels of these miRNAs were positively associated with aGVHD severity, and high expression of the miRNA panel was associated with poor overall survival. Furthermore, the miRNA signature for aGVHD was not detected in the plasma of lung transplant or nontransplant sepsis patients. Our results have identified a specific plasma miRNA signature that may serve as an independent biomarker for the prediction, diagnosis, and prognosis of aGVHD.
Key Points
Recent advances in the field of infectious disease diagnostics have given rise to a number of host- and pathogen-centered diagnostic approaches. Most diagnostic approaches in contemporary infectious ...disease focus on pathogen detection and characterization. Host-focused diagnostics have recently emerged and are based on detecting the activation of biological pathways that are highly specific to the type of infecting pathogen (e.g., viral, bacterial, protozoan, fungal). Although this progress is encouraging, it is unlikely that any single diagnostic platform will fully address the clinician's need for actionable data with short turnaround times in all settings.
Emerging pandemic infectious threats, inappropriate antibacterial use contributing to multidrug resistance, and increased morbidity and mortality from diagnostic delays all contribute to a need for ...improved diagnostics in the field of infectious diseases. Historically, diagnosis of infectious diseases has relied on pathogen detection; however, a novel concept to improve diagnostics in infectious diseases relies instead on the detection of changes in patterns of gene expression in circulating white blood cells in response to infection. Alterations in peripheral blood gene expression in the infected state are robust and reproducible, yielding diagnostic and prognostic information to help facilitate patient treatment decisions.
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