Project description:Global endemic infections, such as leptospirosis, rickettsial diseases, and dengue infections present diagnostic challenges, posing a dilemma for antibiotic stewardship worldwide. The goal of this project was to identify accurate transcriptional classifiers able to discriminate between bacterial and viral illness of global pathogens.
Project description:Pneumonia remains the leading cause of death in children under five, but existing diagnostic methods frequently lead to either insufficient or excessive treatments. Our study aims to bridge this gap by identifying host transcriptomic biomarkers in the blood of children with confirmed viral or bacterial pneumonia. Using RNA sequencing, we identified and validated in an independent cohort a 5-transcript signature that accurately differentiates bacterial from viral pneumonia. This signature has considerable potential to improve diagnostic accuracy for pediatric pneumonia, minimizing delays in diagnosis and avoiding unnecessary treatments, with the possibility of significantly impacting clinical practice. a strand specific library preparation was completed using NEBNext® Ultra™ II mRNA kit (NEB) and NEB rRNA/globin depletion probes following manufacturer’s recommendations. Individual libraries were normalized using Qubit, pooled together and diluted. The sequencing was performed using a 150 paired-end configuration in a Novaseq6000 platform. Quality control of raw data was carried out using FastQC, alignment and read counting were performed using STAR, alignment filtering was done with SAMtools and read counting was carried out using FeatureCounts. RNAseq data was processed for batch correction using control samples and COMBAT-Seq package.
Project description:Background: Distinguishing between bacterial and viral lower respiratory tract infections (LRTI) in hospitalized patients remains challenging. Transcriptional profiling is a promising tool for improving diagnosis in LRTI. Methods: We performed whole blood transcriptional analysis in a cohort of 118 adult patients (median [IQR] age, 61 [50-76] years) hospitalized with bacterial, viral or viral-bacterial LRTI, and 40 age-matched healthy controls (60 [46-70] years). We applied class comparisons, modular analysis and class prediction algorithms to identify distinct biosignatures for bacterial and viral LRTI, which were validated in an independent group of patients. Results: Patients were classified as bacterial (B, n=22), viral (V, n=71) and bacterial-viral LRTI (BV, n=25) based on comprehensive microbiologic testing. Compared with healthy controls statistical group comparisons (p<0.01; with multiple test corrections) identified 3,376 differentially expressed genes in patients with B-LRTI; 2,391 in V-LRTI, and 2,628 in BV-LRTI. Independent of etiologic pathogen, patients with LRTI demonstrated overexpression of innate immunity and underexpression of adaptive immunity genes. Patients with B-LRTI showed significant overexpression of inflammation (B>BV>V) and neutrophils (B>BV>V) while those with V-LRTI displayed significantly greater overexpression of interferon genes (V>BV>B). The K-Nearest Neighbors (K-NN) algorithm identified 10 classifier genes that discriminated patients with bacterial vs viral LRTI with 97% [95%CI: 84-100] sensitivity and 92% [77-98] specificity. In comparison, procalcitonin classified bacterial vs viral LRTI with 38% [18-62] sensitivity and 91% [76-98] specificity. Conclusions: Transcriptional profiling can be used as a helpful tool for the diagnosis of adults hospitalized with LRTI. 158 samples, no replicates; bacterial LRTI n=22, viral LRTI n=71, bacterial-viral coinfections n=25, and healthy controls n=40
Project description:Pneumonia stands as the primary cause of death among children under five, with its onset attributed to a broad spectrum of microorganisms. Diagnosis poses an ongoing challenge, relying on clinical or microbiological criteria, often resulting in delayed and inaccurate treatment and unnecessary therapy. Our research focuses on identifying host transcriptomic biomarkers in the blood of children affected by viral and bacterial pneumonia, alongside healthy controls. The main goal is to establish a gene-expression signature enhancing disease diagnosis and management. We conducted an analysis of a total of 192 whole blood samples, comprising 38 controls and 154 viral and bacterial pneumonia patients recruited through the EUCLIDS clinical network. Our investigation identified 5,486 differentially expressed genes (DEGs) when comparing blood RNA from pneumonia patients with healthy controls. Functional enrichment analysis highlighted pathways related to the immune system response, encompassing neutrophil degranulation, humoral immune response, and various inflammatory pathways. In the comparative analysis of gene expression between viral and bacterial pneumonia patients, we identified 272 DEGs. Gene set analysis revealed a significant difference in pathway enrichment for the immune response, contingent on the over-regulation of gene sets in viral or bacterial pneumonias. Furthermore, we identified a 5-transcript host signature specifically designed to distinguish between viral and bacterial pediatric pneumonia (FAM20A, BAG3, TDRD9, MXRA7 and KLF14; AUC: 0.95 [0.88–1.00]), pseudo-validated in a cohort including probable bacterial and viral patients (AUC: 0.87 [0.77–0.97]). This signature holds the potential to enhance the accuracy of previously described general transcript-based signatures for viral and bacterial infections.
Project description:Viral infections are among the most common causes for fever without an apparent source (FWS) in young children; however, many febrile children are treated with antibiotics despite the absence of bacterial infection. Adenovirus, human herpesvirus 6 (HHV-6) and enterovirus are detected in children with FWS more often than other viral species. Virus and bacteria interact with pattern recognition receptors in circulating blood leukocytes and trigger specific host transcriptional programs that mediate immune response, and unique transcriptional signatures may be ascertained to discriminate between viral and bacterial causes for children with FWS. Microarray analyses were conducted on peripheral blood samples obtained from 51 pediatric patients with confirmed adenovirus, human herpesvirus 6 (HHV-6), enterovirus or bacterial infection. Whole blood transcriptional profiles could clearly distinguish febrile children from healthy controls, and febrile children with viral infections from afebrile children carrying the same virus. Molecular pathways regulating host immune response were the most affected in febrile children with infection. Pattern recognition programs were prominently activated in all febrile children with infection, while differential activation of transcriptional programs was observed among viral species. Interferon signaling pathway was uniquely activated in children with febrile viral infection, while a different set of pathways was uniquely activated in children with bacterial infection. Transcriptional signatures were identified and classified febrile children with viral or bacterial infection with 87% overall accuracy, an improvement from the current clinical practice of deducing from white blood cell (WBC) count status. Similar degree of accuracy was observed when we validated the signature probes on data sets from an independent study with different microarray platforms. The current study confirms the clinical utility of blood transcriptional analysis, suggests the composition of transcriptional signatures which can be used to ascertain the infectious etiology of febrile young children without an apparent source, thus limit the overuse of antibiotics on febrile children presenting with this common clinical complaint. Total RNA samples extracted from whole blood of young children were processed for hybridization onto Illumina Human-HT12 version 4 beadchips, and differential expression of the transcripts was analyzed between sick children with either viral or bacterial infection and healthy children.
Project description:A pressing clinical challenge is identifying the etiologic basis of acute respiratory illness. Without reliable diagnostics, the uncertainty associated with this clinical entity leads to a significant, inappropriate use of antibacterials. Use of host peripheral blood gene expression data to classify individuals with bacterial infection, viral infection, or non-infection represents a complementary diagnostic approach. Patients with respiratory tract infection along with ill, non-infected controls were enrolled through the emergency department or undergraduate student health services. Whole blood was obtained to generate gene expression profiles. These profiles were then used to generate signatures of bacterial acute respiratory infection, viral acute respiratory infection, and non-infectious illness. 273 subjects were ascertained for this analysis. This included 88 patients with non-infectious illness, 115 with viral acute respiratory infection, and 70 with bacterial acute respiratory infection. Samples were obtained at the time of enrollment, which was at initial clinical presentation. Total RNA was extracted from human blood using the PAXgene Blood RNA Kit. Microarray data were generated using the GeneChip Human Genome U133A 2.0 Array. Microarrays were generated in two microarray batches with seven overlapping samples giving rise to 280 total microarray experiments.
Project description:Background: Distinguishing between bacterial and viral lower respiratory tract infections (LRTI) in hospitalized patients remains challenging. Transcriptional profiling is a promising tool for improving diagnosis in LRTI. Methods: We performed whole blood transcriptional analysis in a cohort of 118 adult patients (median [IQR] age, 61 [50-76] years) hospitalized with bacterial, viral or viral-bacterial LRTI, and 40 age-matched healthy controls (60 [46-70] years). We applied class comparisons, modular analysis and class prediction algorithms to identify distinct biosignatures for bacterial and viral LRTI, which were validated in an independent group of patients. Results: Patients were classified as bacterial (B, n=22), viral (V, n=71) and bacterial-viral LRTI (BV, n=25) based on comprehensive microbiologic testing. Compared with healthy controls statistical group comparisons (p<0.01; with multiple test corrections) identified 3,376 differentially expressed genes in patients with B-LRTI; 2,391 in V-LRTI, and 2,628 in BV-LRTI. Independent of etiologic pathogen, patients with LRTI demonstrated overexpression of innate immunity and underexpression of adaptive immunity genes. Patients with B-LRTI showed significant overexpression of inflammation (B>BV>V) and neutrophils (B>BV>V) while those with V-LRTI displayed significantly greater overexpression of interferon genes (V>BV>B). The K-Nearest Neighbors (K-NN) algorithm identified 10 classifier genes that discriminated patients with bacterial vs viral LRTI with 97% [95%CI: 84-100] sensitivity and 92% [77-98] specificity. In comparison, procalcitonin classified bacterial vs viral LRTI with 38% [18-62] sensitivity and 91% [76-98] specificity. Conclusions: Transcriptional profiling can be used as a helpful tool for the diagnosis of adults hospitalized with LRTI.
Project description:Viral infections are among the most common causes for fever without an apparent source (FWS) in young children; however, many febrile children are treated with antibiotics despite the absence of bacterial infection. Adenovirus, human herpesvirus 6 (HHV-6) and enterovirus are detected in children with FWS more often than other viral species. Virus and bacteria interact with pattern recognition receptors in circulating blood leukocytes and trigger specific host transcriptional programs that mediate immune response, and unique transcriptional signatures may be ascertained to discriminate between viral and bacterial causes for children with FWS. Microarray analyses were conducted on peripheral blood samples obtained from 51 pediatric patients with confirmed adenovirus, human herpesvirus 6 (HHV-6), enterovirus or bacterial infection. Whole blood transcriptional profiles could clearly distinguish febrile children from healthy controls, and febrile children with viral infections from afebrile children carrying the same virus. Molecular pathways regulating host immune response were the most affected in febrile children with infection. Pattern recognition programs were prominently activated in all febrile children with infection, while differential activation of transcriptional programs was observed among viral species. Interferon signaling pathway was uniquely activated in children with febrile viral infection, while a different set of pathways was uniquely activated in children with bacterial infection. Transcriptional signatures were identified and classified febrile children with viral or bacterial infection with 87% overall accuracy, an improvement from the current clinical practice of deducing from white blood cell (WBC) count status. Similar degree of accuracy was observed when we validated the signature probes on data sets from an independent study with different microarray platforms. The current study confirms the clinical utility of blood transcriptional analysis, suggests the composition of transcriptional signatures which can be used to ascertain the infectious etiology of febrile young children without an apparent source, thus limit the overuse of antibiotics on febrile children presenting with this common clinical complaint.