scholarly journals Previously Derived Host Gene Expression Classifiers Identify Bacterial and Viral Etiologies of Acute Febrile Respiratory Illness in a South Asian Population

2020 ◽  
Vol 7 (6) ◽  
Author(s):  
L Gayani Tillekeratne ◽  
Sunil Suchindran ◽  
Emily R Ko ◽  
Elizabeth A Petzold ◽  
Champica K Bodinayake ◽  
...  

Abstract Background Pathogen-based diagnostics for acute respiratory infection (ARI) have limited ability to detect etiology of illness. We previously showed that peripheral blood-based host gene expression classifiers accurately identify bacterial and viral ARI in cohorts of European and African descent. We determined classifier performance in a South Asian cohort. Methods Patients ≥15 years with fever and respiratory symptoms were enrolled in Sri Lanka. Comprehensive pathogen-based testing was performed. Peripheral blood ribonucleic acid was sequenced and previously developed signatures were applied: a pan-viral classifier (viral vs nonviral) and an ARI classifier (bacterial vs viral vs noninfectious). Results Ribonucleic acid sequencing was performed in 79 subjects: 58 viral infections (36 influenza, 22 dengue) and 21 bacterial infections (10 leptospirosis, 11 scrub typhus). The pan-viral classifier had an overall classification accuracy of 95%. The ARI classifier had an overall classification accuracy of 94%, with sensitivity and specificity of 91% and 95%, respectively, for bacterial infection. The sensitivity and specificity of C-reactive protein (>10 mg/L) and procalcitonin (>0.25 ng/mL) for bacterial infection were 100% and 34%, and 100% and 41%, respectively. Conclusions Previously derived gene expression classifiers had high predictive accuracy at distinguishing viral and bacterial infection in South Asian patients with ARI caused by typical and atypical pathogens.

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S633-S634
Author(s):  
Rachael E Mahle ◽  
Sunil Suchindran ◽  
Ricardo Henao ◽  
Julie M Steinbrink ◽  
Thomas W Burke ◽  
...  

Abstract Background Difficulty distinguishing bacterial and viral infections contributes to excess antibiotic use. A host response strategy overcomes many limitations of pathogen-based tests, but depends on a functional immune system. This approach may therefore be limited in immunocompromised (IC) hosts. Here, we evaluated a host response test in IC subjects, which has not been extensively studied in this manner. Methods An 81-gene signature was measured using qRT-PCR in previously enrolled IC subjects (chemotherapy, solid organ transplant, immunomodulatory agents, AIDS) with confirmed bacterial infection, viral infection, or non-infectious illness (NI). A regularized logistic regression model estimated the likelihood of bacterial, viral, and noninfectious classes. Clinical adjudication was the reference standard. Results A host gene expression model trained in a cohort of 136 immunocompetent subjects (43 bacterial, 41 viral, and 52 NI) had an overall accuracy of 84.6% for the diagnosis of bacterial vs. non-bacterial infection and 80.8% for viral vs. non-viral infection. The model was validated in an independent cohort of 134 IC subjects (64 bacterial, 28 viral, 42 NI). The overall accuracy was 73.9% for bacterial infection (p=0.03 vs. training cohort) and 75.4% for viral infection (p=0.27). Test utility could be improved by reporting probability ranges. For example, results divided into probability quartiles would allow the highest quartile to be used to rule in infection and the lowest to rule out infection. For IC subjects in the lowest quartile, the test had 90.1% and 96.4% sensitivity for bacterial and viral infection, respectively. For the highest quartile, the test had 91.4% and 84.0% specificity for bacterial and viral infection, respectively. The type or number of immunocompromising conditions did not impact performance. Illness Etiology Probabilities Conclusion A host gene expression test discriminated bacterial, viral, and non-infectious etiologies at a lower overall accuracy in IC patients compared to immunocompetent patients, though this difference was only significant for bacterial vs non-bacterial disease. With modified interpretive criteria, a host response strategy may offer clinically useful and complementary diagnostic information for IC patients. Disclosures Thomas W. Burke, PhD, Predigen, Inc (Consultant) Geoffrey S. Ginsburg, MD PhD, Predigen, Inc (Shareholder, Other Financial or Material Support) Christopher W. Woods, MD, MPH, FIDSA, Predigen, Inc (Shareholder, Other Financial or Material Support) Ephraim L. Tsalik, MD, MHS, PhD, FIDSA, Predigen, Inc (Scientific Research Study Investigator, Shareholder, Other Financial or Material Support)


2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S629-S630
Author(s):  
Nicholas Bodkin ◽  
Melissa H Ross ◽  
Ricardo Henao ◽  
Ephraim L Tsalik

Abstract Background Host gene expression has emerged as a promising diagnostic strategy to discriminate bacterial and viral infection. Multiple gene signatures of varying size and complexity have been developed in various clinical populations. However, there has been no systematic comparison of these signatures. It is also unclear how these signatures apply to different clinical populations. This meta-analysis examined 19 published signatures, validated in 49 publicly available datasets for a total of 4750 patients. The objectives were to understand how the signatures compared to each other with respect to composition and performance, and to evaluate their performance in different patient subgroups. Methods Signatures were characterized with respect to size, platform, and discovery population. For each of the 19 signatures, we ran leave-one-out cross-validation to generate AUCs for bacterial classification and viral classification. We then applied dataset-specific thresholds to generate accuracies, pooling patients across datasets. Results Signature performance varied significantly with a median AUC across all validation datasets ranging from 0.55 to 0.94 for bacterial classification and 0.79 to 0.96 for viral classification. Signature size varied (1- 341 genes) with smaller signatures generally performing more poorly for both bacterial classification (P < .001) and for viral classification (P = 0.02). Viral infection was easier to diagnose than bacterial infection (85% vs. 80% overall accuracy, respectively; P < .001). Host gene expression classifiers performed more poorly in children < 12-years compared to those older than 12-years for both bacterial infection (77% vs. 83%, respectively; P < .001) and for viral infection (82% vs. 89%, respectively; P < .001). We did not observe differences based on illness severity as defined by ICU care for either bacterial or viral infections. Conclusion We observed significant differences among gene expression signatures for bacterial/viral discrimination, though these were not due to variations in the discovery methods or populations. Signature size directly correlated with test performance, which was generally better for the diagnosis of viral infection and in populations >12-years. Disclosures Ephraim L. Tsalik, MD, MHS, PhD, Predigen (Shareholder, Other Financial or Material Support, Founder)


Author(s):  
Ian S Jaffe ◽  
Anja K Jaehne ◽  
Eugenia Quackenbush ◽  
Emily R Ko ◽  
Emanuel P Rivers ◽  
...  

Abstract Background Difficulty discriminating bacterial from viral infections drives antibacterial misuse. Host gene expression tests discriminate bacterial and viral etiologies, but their clinical utility has not been evaluated. Methods Host gene expression and procalcitonin levels were measured in 582 Emergency Department participants with suspected infection. We also recorded clinician diagnosis, and clinician-recommended treatment. These four diagnostic strategies were compared to clinical adjudication as the reference. To estimate the clinical impact of host gene expression, we calculated the change in overall net benefit (∆NB, the difference in net benefit comparing one diagnostic strategy to a reference) across a range of prevalence estimates while factoring in the clinical significance of false positive and negative errors. Results Gene expression correctly classified bacterial, viral, or non-infectious illness in 74.1% of subjects, similar to the other strategies. Clinical diagnosis and clinician-recommended treatment revealed a bias toward overdiagnosis of bacterial infection resulting in high sensitivity (92.6% and 94.5%, respectively), but poor specificity (67.2% and 58.8%, respectively) resulting in a 33.3% rate of inappropriate antibacterial use. Gene expression offered a more balanced sensitivity (79.0%) and specificity (80.7%), which corresponded to a statistically significant improvement in average weighted accuracy (79.9% vs. 71.5% for procalcitonin and 76.3% for clinician-recommended treatment, p<0.0001 for both). Consequently, host gene expression had greater net benefit in diagnosing bacterial infection than clinician-recommended treatment (∆NB=6.4%) and procalcitonin (∆NB=17.4%). Conclusions Host gene expression-based tests to distinguish bacterial and viral infection can facilitate appropriate treatment, improving patient outcomes and mitigating the antibacterial resistance crisis.


Author(s):  
Rachael E Mahle ◽  
Sunil Suchindran ◽  
Ricardo Henao ◽  
Julie M Steinbrink ◽  
Thomas W Burke ◽  
...  

Abstract Background Host gene expression has emerged as a complementary strategy to pathogen detection tests for the discrimination of bacterial and viral infection. The impact of immunocompromise on host response tests remains unknown. We evaluated a host response test discriminating bacterial, viral, and non-infectious conditions in immunocompromised subjects. Methods An 81-gene signature was measured using RT-PCR in subjects with immunocompromise (chemotherapy, solid organ transplant, immunomodulatory agents, AIDS) with bacterial infection, viral infection, or noninfectious illness. A regularized logistic regression model trained in immunocompetent subjects was used to estimate the likelihood of each class in immunocompromised subjects. Results Accuracy in the 136-subject immunocompetent training cohort was 84.6% for bacterial vs. non-bacterial discrimination and 80.8% for viral vs. non-viral discrimination. Model validation in 134 immunocompromised subjects showed overall accuracy of 73.9% for bacterial infection (p=0.04 relative to immunocompetent subjects) and 75.4% for viral infection (p=0.30). A scheme reporting results by quartile improved test utility. The highest probability quartile ruled-in bacterial and viral infection with 91.4% and 84.0% specificity, respectively. The lowest probability quartile ruled-out infection with 90.1% and 96.4% sensitivity for bacterial and viral infection, respectively. Performance was independent of the type or number of immunocompromising conditions. Conclusion A host gene expression test discriminated bacterial, viral, and non-infectious etiologies at a lower overall accuracy in immunocompromised patients compared to immunocompetent patients, though this difference was only significant for bacterial infection classification. With modified interpretive criteria, a host response strategy may offer clinically useful diagnostic information for patients with immunocompromise.


2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Zhang-Wei Liu ◽  
Nan Zhao ◽  
Yin-Na Su ◽  
Shan-Shan Chen ◽  
Xin-Jian He

An amendment to this paper has been published and can be accessed via a link at the top of the paper.


1990 ◽  
pp. 701-708 ◽  
Author(s):  
C. Sengupta-Gopalan ◽  
E. Estabrook ◽  
H. Gambliel ◽  
W. Nirunsuksiri ◽  
H. Richter

mBio ◽  
2021 ◽  
Vol 12 (3) ◽  
Author(s):  
Lauren E. Fuess ◽  
Stijn den Haan ◽  
Fei Ling ◽  
Jesse N. Weber ◽  
Natalie C. Steinel ◽  
...  

ABSTRACT Commensal microbial communities have immense effects on their vertebrate hosts, contributing to a number of physiological functions, as well as host fitness. In particular, host immunity is strongly linked to microbiota composition through poorly understood bi-directional links. Gene expression may be a potential mediator of these links between microbial communities and host function. However, few studies have investigated connections between microbiota composition and expression of host immune genes in complex systems. Here, we leverage a large study of laboratory-raised fish from the species Gasterosteus aculeatus (three-spined stickleback) to document correlations between gene expression and microbiome composition. First, we examined correlations between microbiome alpha diversity and gene expression. Our results demonstrate robust positive associations between microbial alpha diversity and expression of host immune genes. Next, we examined correlations between host gene expression and abundance of microbial taxa. We identified 15 microbial families that were highly correlated with host gene expression. These families were all tightly correlated with host expression of immune genes and processes, falling into one of three categories—those positively correlated, negatively correlated, and neutrally related to immune processes. Furthermore, we highlight several important immune processes that are commonly associated with the abundance of these taxa, including both macrophage and B cell functions. Further functional characterization of microbial taxa will help disentangle the mechanisms of the correlations described here. In sum, our study supports prevailing hypotheses of intimate links between host immunity and gut microbiome composition. IMPORTANCE Here, we document associations between host gene expression and gut microbiome composition in a nonmammalian vertebrate species. We highlight associations between expression of immune genes and both microbiome diversity and abundance of specific microbial taxa. These findings support other findings from model systems which have suggested that gut microbiome composition and host immunity are intimately linked. Furthermore, we demonstrate that these correlations are truly systemic; the gene expression detailed here was collected from an important fish immune organ (the head kidney) that is anatomically distant from the gut. This emphasizes the systemic impact of connections between gut microbiota and host immune function. Our work is a significant advancement in the understanding of immune-microbiome links in nonmodel, natural systems.


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