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  • 1
    In: Open Forum Infectious Diseases, Oxford University Press (OUP), Vol. 6, No. Supplement_2 ( 2019-10-23), p. S902-S902
    Abstract: The etiology of pneumonia is difficult to diagnose, with typical bacterial, atypical bacterial, and viral infections being the most common. However, diagnostics that discriminate these infectious etiologies are limited. We, therefore, focused on the host response to identify possible diagnostic markers and better understand these infections. However, atypical bacterial pneumonia is challenging to identify in humans precisely because of this diagnostic difficulty. Therefore, we utilized murine models to define host response differences between typical bacterial, atypical bacterial, and viral pneumonia. Methods Mice were intranasally inoculated with S. pneumoniae (n = 38), M. pneumoniae (n = 27), H1N1 pr8 (n = 19), or saline as a control (n = 42). RNA was extracted from peripheral blood collected at 24, 48, 72, 120, or 168 hours and subjected to microarray analysis. Diagnostic signatures were generated using lasso logistic regression and accuracy was assessed using nested leave-one-out cross-validation with feature selection repeated within each iteration. Differentially expressed genes were used to perform gene set enrichment analysis. These murine-derived signatures were externally validated in silico in 487 human subjects found across 5 publicly available data sets. Results We generated pathogen-specific murine disease signatures that performed with 91–100% accuracy. Pathway analysis revealed that animals with pneumococcal pneumonia had a robust immune response by 48 hours that continued to 72 hours post-infection. In contrast, animals infected with M. pneumoniae did not show evidence of a strong immune response until 72-hours post-infection. Additionally, the immune response to M. pneumoniae bared greater similarity to the viral response than it did to the host pneumococcal response. H1N1-infected mice showed an anti-viral response at 120 hours that resolved by 168 hours post-infection. The AUC values resulting from independent human validation of our murine signatures ranged from 89 to 98%. Conclusion There are discrete host responses to typical bacterial, atypical bacterial, and viral etiologies of pneumonia in mice. These signatures validate well in humans, highlighting the conserved nature of the host response to these pathogen classes. Disclosures Ephraim L. Tsalik, MD MHS PhD, Immunexpress: Consultant; Predigen, Inc.: Officer or Board Member, Research Grant.
    Type of Medium: Online Resource
    ISSN: 2328-8957
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2019
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  • 2
    In: Genome Medicine, Springer Science and Business Media LLC, Vol. 6, No. 11 ( 2014-11)
    Type of Medium: Online Resource
    ISSN: 1756-994X
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2014
    detail.hit.zdb_id: 2484394-5
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  • 3
    In: Open Forum Infectious Diseases, Oxford University Press (OUP), Vol. 5, No. suppl_1 ( 2018-11-26), p. S587-S587
    Type of Medium: Online Resource
    ISSN: 2328-8957
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2018
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  • 4
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2020
    In:  Open Forum Infectious Diseases Vol. 7, No. Supplement_1 ( 2020-12-31), p. S629-S630
    In: Open Forum Infectious Diseases, Oxford University Press (OUP), Vol. 7, No. Supplement_1 ( 2020-12-31), p. S629-S630
    Abstract: 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 & lt; .001) and for viral classification (P = 0.02). Viral infection was easier to diagnose than bacterial infection (85% vs. 80% overall accuracy, respectively; P & lt; .001). Host gene expression classifiers performed more poorly in children & lt; 12-years compared to those older than 12-years for both bacterial infection (77% vs. 83%, respectively; P & lt; .001) and for viral infection (82% vs. 89%, respectively; P & lt; .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 & gt;12-years. Disclosures Ephraim L. Tsalik, MD, MHS, PhD, Predigen (Shareholder, Other Financial or Material Support, Founder)
    Type of Medium: Online Resource
    ISSN: 2328-8957
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2020
    detail.hit.zdb_id: 2757767-3
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  • 5
    In: Open Forum Infectious Diseases, Oxford University Press (OUP), Vol. 7, No. Supplement_1 ( 2020-12-31), p. S633-S634
    Abstract: 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)
    Type of Medium: Online Resource
    ISSN: 2328-8957
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2020
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  • 6
    In: Open Forum Infectious Diseases, Oxford University Press (OUP), Vol. 5, No. suppl_1 ( 2018-11-26), p. S588-S588
    Type of Medium: Online Resource
    ISSN: 2328-8957
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2018
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  • 7
    In: Open Forum Infectious Diseases, Oxford University Press (OUP), Vol. 5, No. suppl_1 ( 2018-11-26), p. S586-S586
    Type of Medium: Online Resource
    ISSN: 2328-8957
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2018
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  • 8
    In: Open Forum Infectious Diseases, Oxford University Press (OUP), Vol. 7, No. 6 ( 2020-06-01)
    Abstract: 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 ( & gt;10 mg/L) and procalcitonin ( & gt;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.
    Type of Medium: Online Resource
    ISSN: 2328-8957
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2020
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  • 9
    In: Open Forum Infectious Diseases, Oxford University Press (OUP), Vol. 6, No. 11 ( 2019-11-01)
    Abstract: Distinguishing bacterial, viral, or other etiologies of acute illness is diagnostically challenging with significant implications for appropriate antimicrobial use. Host gene expression offers a promising approach, although no clinically useful test has been developed yet to accomplish this. Here, Qvella’s FAST HR (Richmond Hill, Ontario, Canada) process was developed to quantify previously identified host gene expression signatures in whole blood in & lt;45 minutes. Method Whole blood was collected from 128 human subjects (mean age 47, range 18–88) with clinically adjudicated, microbiologically confirmed viral infection, bacterial infection, noninfectious illness, or healthy controls. Stabilized mRNA was released from cleaned and stabilized RNA-surfactant complexes using e-lysis, an electrical process providing a quantitative real-time reverse transcription polymerase chain reaction-ready sample. Threshold cycle values (CT) for 10 host response targets were normalized to hypoxanthine phosphoribosyltransferase 1 expression, a control mRNA. The transcripts in the signature were specifically chosen to discriminate viral from nonviral infection (bacterial, noninfectious illness, or healthy). Classification accuracy was determined using cross-validated sparse logistic regression. Results Reproducibility of mRNA quantification was within 1 cycle as compared to the difference seen between subjects with viral versus nonviral infection (up to 5.0 normalized CT difference). Classification of 128 subjects into viral or nonviral etiologies demonstrated 90.6% overall accuracy compared to 82.0% for procalcitonin (P = .06). FAST HR achieved rapid and accurate measurement of the host response to viral infection in less than 45 minutes. Conclusions These results demonstrate the ability to translate host gene expression signatures to clinical platforms for use in patients with suspected infection. Clinical Trials Registration NCT00258869.
    Type of Medium: Online Resource
    ISSN: 2328-8957
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2019
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  • 10
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2019
    In:  Open Forum Infectious Diseases Vol. 6, No. Supplement_2 ( 2019-10-23), p. S481-S481
    In: Open Forum Infectious Diseases, Oxford University Press (OUP), Vol. 6, No. Supplement_2 ( 2019-10-23), p. S481-S481
    Abstract: Host response-based diagnostics are an alternative to pathogen-based tests. Host response strategies include proteomic and transcriptomic approaches. Here, we compare three host response strategies for ARI diagnosis: Procalcitonin (PCT), a 3-protein panel, and an mRNA panel. Methods PCT, a 3-protein panel (CRP, IP-10, TRAIL), and a host gene expression mRNA panel were measured in a cohort of 286 participants presenting to one of the four Emergency Departments with ARI due to bacterial (n = 47), viral (n = 162), or noninfectious (n = 77) etiologies. Multinomial logistic regression and leave-one-out cross-validation were used to train and evaluate the protein and mRNA panels. Performance characteristics were calculated for each method, and their combination, for the ability to discriminate bacterial vs. non-bacterial infection and viral vs. nonviral infection. PCT was not evaluated for viral vs. nonviral discrimination since it does not discriminate viral and noninfectious etiologies. McNemar’s test was used to compare overall accuracy of mRNA and protein panels. Results For discriminating bacterial vs. non-bacterial etiologies, the mRNA panel had an AUC of 0.93 vs. 0.83 for both the protein panel and PCT. A model utilizing all three strategies was the same as mRNA alone. Using previously established cutoffs, overall accuracy was similar between mRNA and protein panels, but the protein panel had widely discordant sensitivity (43%) and specificity (92%). When selecting an optimal cutoff for the protein panel that balanced the two (82% and 73%, respectively), the mRNA panel had a significantly greater overall accuracy (P 〈 0.001). Similar results were found when discriminating viral vs. non-viral subjects: the mRNA panel (AUC = 0.93) outperformed the protein panel (AUC = 0.84). Combining the mRNA and protein panels was equivalent to the mRNA panel alone. Conclusion A host-based gene expression signature is the most effective platform for classifying subjects with bacterial, viral, or noninfectious ARI. A gene expression approach, when translated to a clinically available platform, may facilitate diagnosis and clinical management of acute infectious diseases, mitigating antibiotic overuse. Disclosures Ephraim L. Tsalik, MD, MHS, PhD, Immunexpress: Consultant; Predigen, Inc.: Officer or Board Member, Research Grant.
    Type of Medium: Online Resource
    ISSN: 2328-8957
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2019
    detail.hit.zdb_id: 2757767-3
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