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Benchmarking transcriptional host response signatures for infection diagnosis.

ABSTRACT: Identification of host transcriptional response signatures has emerged as a new paradigm for infection diagnosis. For clinical applications, signatures must robustly detect the pathogen of interest without cross-reacting with unintended conditions. To evaluate the performance of infectious disease signatures, we developed a framework that includes a compendium of 17,105 transcriptional profiles capturing infectious and non-infectious conditions and a standardized methodology to assess robustness and cross-reactivity. Applied to 30 published signatures of infection, the analysis showed that signatures were generally robust in detecting viral and bacterial infections in independent data. Asymptomatic and chronic infections were also detectable, albeit with decreased performance. However, many signatures were cross-reactive with unintended infections and aging. In general, we found robustness and cross-reactivity to be conflicting objectives, and we identified signature properties associated with this trade-off. The data compendium and evaluation framework developed here provide a foundation for the development of signatures for clinical application. A record of this paper's transparent peer review process is included in the supplemental information.

SUBMITTER: Chawla DG

PROVIDER: S-EPMC9768893 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Benchmarking transcriptional host response signatures for infection diagnosis.

Chawla Daniel G DG Cappuccio Antonio A Tamminga Andrea A Sealfon Stuart C SC Zaslavsky Elena E Kleinstein Steven H SH

Cell systems 20221201 12

Identification of host transcriptional response signatures has emerged as a new paradigm for infection diagnosis. For clinical applications, signatures must robustly detect the pathogen of interest without cross-reacting with unintended conditions. To evaluate the performance of infectious disease signatures, we developed a framework that includes a compendium of 17,105 transcriptional profiles capturing infectious and non-infectious conditions and a standardized methodology to assess robustness ...[more]

PMID: 36549274

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Project description:BackgroundThe World Health Organization (WHO) and Foundation for Innovative New Diagnostics (FIND) have published target product profiles (TPPs) calling for non-sputum-based diagnostic tests for the diagnosis of active tuberculosis (ATB) disease and for predicting the progression from latent tuberculosis infection (LTBI) to ATB. A large number of host-derived blood-based gene-expression biomarkers for diagnosis of patients with ATB have been proposed to date, but none have been implemented in clinical settings. The focus of this study is to directly compare published gene signatures for diagnosis of patients with ATB across a large, diverse list of publicly available gene expression datasets, and evaluate their performance against the WHO/FIND TPPs.Methods and findingsWe searched PubMed, Gene Expression Omnibus (GEO), and ArrayExpress in June 2018. We included all studies irrespective of study design and enrollment criteria. We found 16 gene signatures for the diagnosis of ATB compared to other clinical conditions in PubMed. For each signature, we implemented a classification model as described in the corresponding original publication of the signature. We identified 24 datasets containing 3,083 transcriptome profiles from whole blood or peripheral blood mononuclear cell samples of healthy controls or patients with ATB, LTBI, or other diseases from 14 countries in GEO. Using these datasets, we calculated weighted mean area under the receiver operating characteristic curve (AUROC), specificity at 90% sensitivity, and negative predictive value (NPV) for each gene signature across all datasets. We also compared the diagnostic odds ratio (DOR), heterogeneity in DOR, and false positive rate (FPR) for each signature using bivariate meta-analysis. Across 9 datasets of patients with culture-confirmed diagnosis of ATB, 11 signatures had weighted mean AUROC > 0.8, and 2 signatures had weighted mean AUROC ≤ 0.6. All but 2 signatures had high NPV (>98% at 2% prevalence). Two gene signatures achieved the minimal WHO TPP for a non-sputum-based triage test. When including datasets with clinical diagnosis of ATB, there was minimal reduction in the weighted mean AUROC and specificity of all but 3 signatures compared to when using only culture-confirmed ATB data. Only 4 signatures had homogeneous DOR and lower FPR when datasets with clinical diagnosis of ATB were included; other signatures either had heterogeneous DOR or higher FPR or both. Finally, 7 of 16 gene signatures predicted progression from LTBI to ATB 6 months prior to sputum conversion with positive predictive value > 6% at 2% prevalence. Our analyses may have under- or overestimated the performance of certain ATB diagnostic signatures because our implementation may be different from the published models for those signatures. We re-implemented published models because the exact models were not publicly available.ConclusionsWe found that host-response-based diagnostics could accurately identify patients with ATB and predict individuals with high risk of progression from LTBI to ATB prior to sputum conversion. We found that a higher number of genes in a signature did not increase the accuracy of the signature. Overall, the Sweeney3 signature performed robustly across all comparisons. Our results provide strong evidence for the potential of host-response-based diagnostics in achieving the WHO goal of ending tuberculosis by 2035, and host-response-based diagnostics should be pursued for clinical implementation.

Project description:BACKGROUND:During infection by intracellular pathogens, a highly complex interplay occurs between the infected cell trying to degrade the invader and the pathogen which actively manipulates the host cell to enable survival and proliferation. Many intracellular pathogens pose important threats to human health and major efforts have been undertaken to better understand the host-pathogen interactions that eventually determine the outcome of the infection. Over the last decades, the unicellular eukaryote Dictyostelium discoideum has become an established infection model, serving as a surrogate macrophage that can be infected with a wide range of intracellular pathogens. In this study, we use high-throughput RNA-sequencing to analyze the transcriptional response of D. discoideum when infected with Mycobacterium marinum and Legionella pneumophila. The results were compared to available data from human macrophages. RESULTS:The majority of the transcriptional regulation triggered by the two pathogens was found to be unique for each bacterial challenge. Hallmark transcriptional signatures were identified for each infection, e.g. induction of endosomal sorting complexes required for transport (ESCRT) and autophagy genes in response to M. marinum and inhibition of genes associated with the translation machinery and energy metabolism in response to L. pneumophila. However, a common response to the pathogenic bacteria was also identified, which was not induced by non-pathogenic food bacteria. Finally, comparison with available data sets of regulation in human monocyte derived macrophages shows that the elicited response in D. discoideum is in many aspects similar to what has been observed in human immune cells in response to Mycobacterium tuberculosis and L. pneumophila. CONCLUSIONS:Our study presents high-throughput characterization of D. discoideum transcriptional response to intracellular pathogens using RNA-seq. We demonstrate that the transcriptional response is in essence distinct to each pathogen and that in many cases, the corresponding regulation is recapitulated in human macrophages after infection by mycobacteria and L. pneumophila. This indicates that host-pathogen interactions are evolutionary conserved, derived from the early interactions between free-living phagocytic cells and bacteria. Taken together, our results strengthen the use of D. discoideum as a general infection model.

Project description:BackgroundAcute sinusitis (AS) is a frequent cause of antibiotic prescriptions in children. Distinguishing bacterial AS from common viral upper respiratory infections (URIs) is crucial to prevent unnecessary antibiotic use but is challenging with current diagnostic methods. Despite its speed and cost, untargeted RNA sequencing of clinical samples from children with suspected AS has the potential to overcome several limitations of other methods. In addition, RNA-seq may reveal novel host-response biomarkers for development of future diagnostic assays that distinguish bacterial from viral infections. There are however no available RNA-seq datasets of pediatric AS that provide a comprehensive view of both pathogen etiology and host immune response.MethodsHere, we performed untargeted RNA-seq (metatranscriptomics) of nasopharyngeal samples from 221 children with AS and performed a comprehensive analysis of pathogen etiology and the impact of bacterial and viral infections on host immune responses. Accuracy of RNA-seq-based pathogen detection was evaluated by comparison with culture tests for three common bacterial pathogens and qRT-PCR tests for 12 respiratory viruses. Host gene expression patterns were explored to identify potential host responses that distinguish bacterial from viral infections.ResultsRNA-seq-based pathogen detection showed high concordance with culture or qRT-PCR, showing 87%/81% sensitivity (sens) / specificity (spec) for detecting three AS-associated bacterial pathogens, and 86%/92% (sens/spec) for detecting 12 URI-associated viruses, respectively. RNA-seq also detected an additional 22 pathogens not tested for clinically and identified plausible pathogens in 11/19 (58%) of cases where no organism was detected by culture or qRT-PCR. We reconstructed genomes of 196 viruses across the samples including novel strains of coronaviruses, respiratory syncytial virus, and enterovirus D68, which provide useful genomic data for ongoing pathogen surveillance programs. By analyzing host gene expression, we identified host-response signatures that differentiate bacterial and viral infections, revealing hundreds of candidate gene biomarkers for future diagnostic assays.ConclusionsOur study provides a one-of-kind dataset that profiles the interplay between pathogen infection and host responses in pediatric AS and URI. It reveals bacterial and viral-specific host responses that could enable new diagnostic approaches and demonstrates the potential of untargeted RNA-seq in diagnostic analysis of AS and URI.

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Benchmarking transcriptional host response signatures for infection diagnosis.

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Benchmarking transcriptional host response signatures for infection diagnosis.

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