A comparison of host response strategies to distinguish bacterial and viral infection

Objectives Compare three host response strategies to distinguish bacterial and viral etiologies of acute respiratory illness (ARI). Methods 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. Results 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. Conclusions A gene expression signature was the most accurate host response strategy for classifying subjects with bacterial, viral, or non-infectious ARI.

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1330. Evaluation of Multiple Host Response-Based Strategies to Classify Acute Respiratory Illness

Open Forum Infectious Diseases ◽

10.1093/ofid/ofz360.1194 ◽

2019 ◽

Vol 6 (Supplement_2) ◽

pp. S481-S481

Author(s):

Melissa H Ross ◽

Ricardo Henao ◽

Thomas W Burke ◽

Micah T McClain ◽

Geoffrey S Ginsburg ◽

...

Keyword(s):

Gene Expression ◽

Host Response ◽

Multinomial Logistic Regression ◽

Gene Expression Signature ◽

Respiratory Illness ◽

Optimal Cutoff ◽

Antibiotic Overuse ◽

Response Strategies ◽

Leave One Out ◽

Research Grant

Abstract Background 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.

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1226. Performance of a Host Response Test for Bacterial/Viral Discrimination in Immunocompromised Patients

Open Forum Infectious Diseases ◽

10.1093/ofid/ofaa439.1411 ◽

2020 ◽

Vol 7 (Supplement_1) ◽

pp. S633-S634

Author(s):

Rachael E Mahle ◽

Sunil Suchindran ◽

Ricardo Henao ◽

Julie M Steinbrink ◽

Thomas W Burke ◽

...

Keyword(s):

Gene Expression ◽

Bacterial Infection ◽

Viral Infection ◽

Host Response ◽

Host Gene ◽

Response Strategy ◽

Solid Organ ◽

Host Gene Expression ◽

Material Support ◽

Response Test

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)

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Validation of a host gene expression test for bacterial/viral discrimination in immunocompromised hosts

Clinical Infectious Diseases ◽

10.1093/cid/ciab043 ◽

2021 ◽

Author(s):

Rachael E Mahle ◽

Sunil Suchindran ◽

Ricardo Henao ◽

Julie M Steinbrink ◽

Thomas W Burke ◽

...

Keyword(s):

Gene Expression ◽

Bacterial Infection ◽

Viral Infection ◽

Host Response ◽

Pathogen Detection ◽

Host Gene ◽

Response Strategy ◽

Solid Organ ◽

Host Gene Expression ◽

The Impact

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.

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Unmasking cellular response of a bloom-forming alga to virus infection by resolving expression profiling at a single-cell level

10.1101/186981 ◽

2017 ◽

Author(s):

Shilo Rosenwasser ◽

Miguel J. Frada ◽

David Pilzer ◽

Ron Rotkopf ◽

Assaf Vardi

Keyword(s):

Gene Expression ◽

Viral Infection ◽

Single Cell ◽

Host Defense ◽

Host Response ◽

Expression Profiles ◽

Life Cycles ◽

Viral Gene ◽

Single Cell Level ◽

Cell Level

AbstractMarine viruses are major evolutionary and biogeochemical drivers of microbial life in the ocean. Host response to viral infection typically includes virus-induced rewiring of metabolic network to supply essential building blocks for viral assembly, as opposed to activation of anti-viral host defense. Nevertheless, there is a major bottleneck to accurately discern between viral hijacking strategies and host defense responses when averaging bulk population response. Here we use Emiliania huxleyi, a bloom-forming alga and its specific virus (EhV), as one of the most ecologically important host-virus model system in the ocean. Using automatic microfluidic setup to capture individual algal cells, we quantified host and virus gene expression on a single-cell resolution during the course of infection. We revealed high heterogeneity in viral gene expression among individual cells. Simultaneous measurements of expression profiles of host and virus genes at a single-cell level allowed mapping of infected cells into newly defined infection states and uncover a yet unrecognized early phase in host response that occurs prior to viral expression. Intriguingly, resistant cells emerged during viral infection, showed unique expression profiles of metabolic genes which can provide the basis for discerning between viral resistant and sensitive cells within heterogeneous populations in the marine environment. We propose that resolving host-virus arms race at a single-cell level will provide important mechanistic insights into viral life cycles and will uncover host defense strategies.

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Inflammation, Adenoma and Cancer: Objective Classification of Colon Biopsy Specimens with Gene Expression Signature

Disease Markers ◽

10.1155/2008/586721 ◽

2008 ◽

Vol 25 (1) ◽

pp. 1-16 ◽

Cited By ~ 61

Author(s):

Orsolya Galamb ◽

Balázs Györffy ◽

Ferenc Sipos ◽

Sándor Spisák ◽

Anna Mária Németh ◽

...

Keyword(s):

Gene Expression ◽

Discriminant Analysis ◽

Gene Expression Signature ◽

Transglutaminase 2 ◽

Stepwise Discriminant Analysis ◽

Lipocalin 2 ◽

Bowel Diseases ◽

Set Up ◽

Leave One Out

Gene expression analysis of colon biopsies using high-density oligonucleotide microarrays can contribute to the understanding of local pathophysiological alterations and to functional classification of adenoma (15 samples), colorectal carcinomas (CRC) (15) and inflammatory bowel diseases (IBD) (14). Total RNA was extracted, amplified and biotinylated from frozen colonic biopsies. Genome-wide gene expression profile was evaluated by HGU133plus2 microarrays and verified by RT-PCR. We applied two independent methods for data normalization and used PAM for feature selection. Leave one-out stepwise discriminant analysis was performed. Top validated genes included collagenIVα1, lipocalin-2, calumenin, aquaporin-8 genes in CRC; CD44, met proto-oncogene, chemokine ligand-12, ADAM-like decysin-1 and ATP-binding casette-A8 genes in adenoma; and lipocalin-2, ubiquitin D and IFITM2 genes in IBD. Best differentiating markers between Ulcerative colitis and Crohn's disease were cyclin-G2; tripartite motif-containing-31; TNFR shedding aminopeptidase regulator-1 and AMICA. The discriminant analysis was able to classify the samples in overall 96.2% using 7 discriminatory genes (indoleamine-pyrrole-2,3-dioxygenase, ectodermal-neural cortex, TIMP3, fucosyltransferase-8, collectin sub-family member 12, carboxypeptidase D, and transglutaminase-2). Using routine biopsy samples we successfully performed whole genomic microarray analysis to identify discriminative signatures. Our results provide further insight into the pathophysiological background of colonic diseases. The results set up data warehouse which can be mined further.

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Expression Signature of E2F1 and Its Associated Genes Predict Superficial to Invasive Progression of Bladder Tumors

Journal of Clinical Oncology ◽

10.1200/jco.2009.25.0977 ◽

2010 ◽

Vol 28 (16) ◽

pp. 2660-2667 ◽

Cited By ~ 160

Author(s):

Ju-Seog Lee ◽

Sun-Hee Leem ◽

Sang-Yeop Lee ◽

Sang-Cheol Kim ◽

Eun-Sung Park ◽

...

Keyword(s):

Gene Expression ◽

Bladder Cancer ◽

Cancer Progression ◽

Gene Expression Data ◽

Gene Expression Signature ◽

Molecular Signature ◽

Expression Data ◽

Bladder Tumors ◽

Expression Signature ◽

Leave One Out

Purpose In approximately 20% of patients with superficial bladder tumors, the tumors progress to invasive tumors after treatment. Current methods of predicting the clinical behavior of these tumors prospectively are unreliable. We aim to identify a molecular signature that can reliably identify patients with high-risk superficial tumors that are likely to progress to invasive tumors. Patients and Methods Gene expression data were collected from tumor specimens from 165 patients with bladder cancer. Various statistical methods, including leave-one-out cross-validation methods, were applied to identify a gene expression signature that could predict the likelihood of progression to invasive tumors and to test the robustness of the expression signature in an independent cohort. The robustness of the gene expression signature was validated in an independent (n = 353) cohort. Results Supervised analysis of gene expression data revealed a gene expression signature that is strongly associated with invasive bladder tumors. A molecular classifier based on this gene expression signature correctly predicted the likelihood of progression of superficial tumor to invasive tumor. Conclusion We present a molecular signature that can predict, at diagnosis, the likelihood of bladder cancer progression and, possibly, lead to improvements in patient therapy.

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Rapid, Sample-to-Answer Host Gene Expression Test to Diagnose Viral Infection

Open Forum Infectious Diseases ◽

10.1093/ofid/ofz466 ◽

2019 ◽

Cited By ~ 1

Author(s):

Ephraim L Tsalik ◽

AyeAye Khine ◽

Abdossamad Talebpour ◽

Alaleh Samiei ◽

Vilcy Parmar ◽

...

Keyword(s):

Gene Expression ◽

Viral Infection ◽

Whole Blood ◽

Host Response ◽

Human Subjects ◽

Host Gene ◽

Host Gene Expression ◽

Suspected Infection ◽

Gene Expression Signatures ◽

Infectious Illness

Abstract Background 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 tests have yet been developed to accomplish this. Here, Qvella’s FAST™ HR process was developed to quantify previously identified host gene-expression signatures in whole blood in <45 minutes. Methods Whole blood was collected from 128 human subjects (mean age 47, range 18-88) with clinically adjudicated, microbiologically confirmed viral infection, bacterial infection, non-infectious illness, or healthy controls. Stabilized mRNA was released from cleaned and stabilized RNA-surfactant complexes using e-lysisTM, an electrical process providing a qRT-PCR-ready sample. Threshold cycle values (CT) for 10 host response targets were normalized to HPRT1 expression, a control mRNA. The transcripts in the signature were specifically chosen to discriminate viral from non-viral infection (bacterial, non-infectious 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 vs. non-viral infection (up to 5.0 normalized CT difference). Classification of 128 subjects into viral or non-viral etiologies demonstrated 90.6% overall accuracy compared to 82.0% for procalcitonin (p=0.06). FASTTM 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.

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