Project description:BackgroundViruses and bacteria from the nasopharynx are capable of causing community-acquired pneumonia (CAP), which can be difficult to diagnose. We aimed to investigate whether shifts in the composition of these nasopharyngeal microbial communities can be used as diagnostic biomarkers for CAP in adults.MethodsWe collected nasopharyngeal swabs from adult CAP patients and controls without infection in a prospective multicenter case-control study design. We generated bacterial and viral profiles using 16S ribosomal RNA gene sequencing and multiplex polymerase chain reaction (PCR), respectively. Bacterial, viral, and clinical data were subsequently used as inputs for extremely randomized trees classification models aiming to distinguish subjects with CAP from healthy controls.ResultsWe enrolled 117 cases and 48 control subjects. Cases displayed significant beta diversity differences in nasopharyngeal microbiota (P = .016, R2 = .01) compared to healthy controls. Our extremely randomized trees classification models accurately discriminated CAP caused by bacteria (area under the curve [AUC] .83), viruses (AUC .95) or mixed origin (AUC .81) from healthy control subjects. We validated this approach using a dataset of nasopharyngeal samples from 140 influenza patients and 38 controls, which yielded highly accurate (AUC .93) separation between cases and controls.ConclusionsRelative proportions of different bacteria and viruses in the nasopharynx can be leveraged to diagnose CAP and identify etiologic agent(s) in adult patients. Such data can inform the development of a microbiota-based diagnostic panel used to identify CAP patients and causative agents from nasopharyngeal samples, potentially improving diagnostic specificity, efficiency, and antimicrobial stewardship practices.
Project description:BACKGROUND: 50% to 80% of asthma exacerbations are precipitated by viral upper respiratory tract infections (RTI), yet the influence of viral pathogen diversity on asthma outcomes is poorly understood due to the limited scope and throughput of conventional viral detection methods. METHODS: We investigated the capability of the Virochip, a DNA microarray-based viral detection platform, to characterize the viral diversity in RTIs in asthmatic and non-asthmatic adults. RESULTS: The Virochip detected viruses in a higher proportion of samples (65%) than culture isolation (17%), while exhibiting high concordance (98%), sensitivity (97%) and specificity (98%) with pathogen-specific PCR. A similar spectrum of viruses was identified in the RTIs from each patient subgroup; however, unexpected diversity among the coronaviruses (HCoVs) and HRVs was revealed. All but one of the HCoVs corresponded to the newly-recognized HCoV-NL63 and HCoV-HKU1 viruses, and over 20 different serotypes of HRVs were detected, including a set of 5 divergent isolates that form a distinct genetic subgroup. CONCLUSIONS: The Virochip can detect both known and novel variants of viral pathogens present in RTIs. Given the diversity detected here, larger scale studies will be necessary to determine if particular substrains of viruses confer an elevated risk of asthma exacerbation This SuperSeries is composed of the SubSeries listed below.
Project description:Severe LRIs are a major cause of infant/child hospitalization and a risk factors for asthma pathogenisis. Innate immune interactions with microbial pathogens, esp. in early life, underpin risk. We aimed to characterize cord blood mononuclear cell (CBMC) responses to activation of micriobial recognition receptors (TLRs 3, 4, & 7) and identify response pattern variations associated withsLRI susceptibility in infancy.
Project description:BackgroundLower respiratory tract infections (LRTIs) are a leading cause of childhood morbidity and mortality. Potentially pathogenic organisms are present in the respiratory tract in both symptomatic and asymptomatic children, but their presence does not necessarily indicate disease. We aimed to assess the concordance between upper and lower respiratory tract microbiota during LRTIs and the use of nasopharyngeal microbiota to discriminate LRTIs from health.MethodsFirst, we did a prospective study of children aged between 4 weeks and 5 years who were admitted to the paediatric intensive care unit (PICU) at Wilhelmina Children's Hospital (Utrecht, Netherlands) for a WHO-defined LRTI requiring mechanical ventilation. We obtained paired nasopharyngeal swabs and deep endotracheal aspirates from these participants (the so-called PICU cohort) between Sept 10, 2013, and Sept 4, 2016. We also did a matched case-control study (1:2) with the same inclusion criteria in children with LRTIs at three Dutch teaching hospitals and in age-matched, sex-matched, and time-matched healthy children recruited from the community. Nasopharyngeal samples were obtained at admission for cases and during home visits for controls. Data for child characteristics were obtained by questionnaires and from pharmacy printouts and medical charts. We used quantitative PCR and 16S rRNA-based sequencing to establish viral and bacterial microbiota profiles, respectively. We did sparse random forest classifier analyses on the bacterial data, viral data, metadata, and the combination of all three datasets to distinguish cases from controls.Findings29 patients were enrolled in the PICU cohort. Intra-individual concordance in terms of viral microbiota profiles (96% agreement [95% CI 93-99]) and bacterial microbiota profiles (58 taxa with a median Pearson's r 0·93 [IQR 0·62-0·99]; p<0·05 for all 58 taxa) was high between nasopharyngeal and endotracheal aspirate samples, supporting the use of nasopharyngeal samples as proxy for lung microbiota during LRTIs. 154 cases and 307 matched controls were prospectively recruited to our case-control cohort. Individually, bacterial microbiota (area under the curve 0·77), viral microbiota (0·70), and child characteristics (0·80) poorly distinguished health from disease. However, a classification model based on combined bacterial and viral microbiota plus child characteristics distinguished children with LRTIs from their matched controls with a high degree of accuracy (area under the curve 0·92).InterpretationOur data suggest that the nasopharyngeal microbiota can serve as a valid proxy for lower respiratory tract microbiota in childhood LRTIs, that clinical LRTIs in children result from the interplay between microbiota and host characteristics, rather than a single microorganism, and that microbiota-based diagnostics could improve future diagnostic and treatment protocols.FundingSpaarne Gasthuis, University Medical Center Utrecht, and the Netherlands Organization for Scientific Research.
Project description:BackgroundAcute respiratory tract infections are commonly caused by viruses in children. The differences in clinical data and outcome between single and multiple viral infections in hospitalized children were analyzed.MethodsWe retrospectively reviewed the medical records of hospitalized children who had fever and a xTAG Respiratory Virus Panel (RVP) test over a 2-year period. The clinical data were analyzed and compared between single and multiple viral infections. Viral etiologies in upper and lower respiratory infections were analyzed and compared.ResultsA total of 442 patients were enrolled. Patients with positive viral detection (N = 311) had a significantly lower rate of leukocytosis (p = 0.03), less evidence of bacterial infection (p = 0.004), and shorter duration of hospitalization (p = 0.019) than those with negative viral detection. The age of patients with multiple viral infections was younger than those with single viral infection; however, there were no significant differences in duration of fever, antibiotics treatment and hospitalization between these two groups. The most commonly identified virus was human rhinovirus. About 27% (n = 83) of patients had multiple viral infections. Overall, the highest percentage of human bocavirus infection was detected in multiple viral infections (79%). Lower respiratory tract infection (LRTI) was independently associated with multiple viral infections (p = 0.022), respiratory syncytial virus (RSV) infection (p = 0.001) and longer hospitalization duration (p = 0.011).ConclusionMultiple viral infections were associated with younger age and a higher risk of developing LRTI. However, multiple viral infections did not predict a worse disease outcome. More studies are needed to unveil the interplay between the hosts and different viruses in multiple viral infections.
Project description:BACKGROUND:The occurrence of respiratory tract viral infections in patients with primary hypogammaglobulinemia has not been studied. OBJECTIVE:We conducted a prospective 12-month follow-up study of respiratory tract infections in 12 adult patients with primary hypogammaglobulinemia. METHODS:Nasal swab samples and induced sputum samples were taken at the onset of acute respiratory tract infection and every 3 months thereafter. Samples were tested for bacteria and viruses. PCR tests were performed for 15 respiratory tract viruses. In case the results for rhinovirus were positive, follow-up nasal swab samples were taken every 2 weeks until rhinoviral PCR results became negative. Patients completed symptom diaries, which were collected every month. The spouses of the patients served as healthy control subjects. RESULTS:During the 12-month period, the 12 patients had 65 episodes of acute respiratory tract infections, and the 11 spouses had 12 acute episodes (P < .001). Respiratory tract viruses were found in sputum in 54% of the infections. Rhinovirus was the most common virus. In more than half of our patients, rhinoviral PCR results stayed positive for more than 2 months. The most long-acting persistence with the same rhinovirus was 4 months. CONCLUSIONS:Despite adequate immunoglobulin replacement therapy, patients with primary hypogammaglobulinemia have increased susceptibility to respiratory tract viral infections. Rhinoviral infections are frequent and prolonged.
Project description:Molecular analysis of respiratory viruses and the host response to both infection and vaccination have transformed our understanding of these ubiquitous pathogens. Polymerase chain reaction for the rapid and accurate diagnosis of viral infections has led to a better understanding of the epidemiology and impact of many common respiratory viruses and resulted in better patient care. Over the past decade a number of new respiratory viruses including human metapneumovirus and new coronaviruses have been discovered using molecular techniques such as random primer amplification, pan-viral array and next generation sequencing. Analysis of the host transcriptional response during respiratory viral infection using in-vitro, animal models and natural and experimental human challenge have furthered the understanding of the mechanisms and predictors of severe disease and may identify potential therapeutic targets to prevent and ameliorate illness.
Project description:BackgroundLower respiratory tract infection (LRTI) is one of the leading cause of death in children under 5 years old around the world between 1980 and 2016. Distinguishing between viral and bacterial infection is challenging when children suffered from LRTI in the absence of pathogen detection. The aim of our study is to analyze the difference of serum β2-microglobulin (β2-MG) between viral LRTI and bacterial LRTI in children.MethodsThis retrospective study included children with LRTI caused by a single pathogen from Yancheng Third People's Hospital, Yancheng, China, between January 1, 2016 and December 31, 2019. Participants were divided into the younger group (1 year old ≤ age < 3 years old) and the older group (3 years old ≤ age < 5 years old) for subgroup analysis.ResultsA total of 475 children with LRTI caused by common respiratory pathogens were identified. In the younger group as well as the older group, the serum level of β2-MG in respiratory syncytial virus, influenza A virus and influenza B virus groups were significantly increased compared to that in the Mycoplasma pneumoniae group. Compared with Streptococcus pneumoniae infection group, the serum β2-MG level of respiratory syncytial virus, influenza A virus and influenza B virus groups were significantly higher in children between 1 and 3 years old.ConclusionsThe serum β2-MG may distinguish viral infection from bacterial infection in children with LRTI.
Project description:Viruses are the most frequent cause of respiratory disease in children. However, despite the advanced diagnostic methods currently in use, in 20 to 50% of respiratory samples a specific pathogen cannot be detected. In this work, we used a metagenomic approach and deep sequencing to examine respiratory samples from children with lower and upper respiratory tract infections that had been previously found negative for 6 bacteria and 15 respiratory viruses by PCR. Nasal washings from 25 children (out of 250) hospitalized with a diagnosis of pneumonia and nasopharyngeal swabs from 46 outpatient children (out of 526) were studied. DNA reads for at least one virus commonly associated to respiratory infections was found in 20 of 25 hospitalized patients, while reads for pathogenic respiratory bacteria were detected in the remaining 5 children. For outpatients, all the samples were pooled into 25 DNA libraries for sequencing. In this case, in 22 of the 25 sequenced libraries at least one respiratory virus was identified, while in all other, but one, pathogenic bacteria were detected. In both patient groups reads for respiratory syncytial virus, coronavirus-OC43, and rhinovirus were identified. In addition, viruses less frequently associated to respiratory infections were also found. Saffold virus was detected in outpatient but not in hospitalized children. Anellovirus, rotavirus, and astrovirus, as well as several animal and plant viruses were detected in both groups. No novel viruses were identified. Adding up the deep sequencing results to the PCR data, 79.2% of 250 hospitalized and 76.6% of 526 ambulatory patients were positive for viruses, and all other children, but one, had pathogenic respiratory bacteria identified. These results suggest that at least in the type of populations studied and with the sampling methods used the odds of finding novel, clinically relevant viruses, in pediatric respiratory infections are low.
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