Human Respiratory Syncytial Virus-Induced Immune Signature of Infection Revealed by Transcriptome Analysis of Clinical Pediatric Nasopharyngeal Swab Samples

Epidemiological and phylogenetic assessment of human respiratory syncytial virus among pediatric patients presenting acute respiratory infections in Shiraz, Iran during 2015-2016

Background and Objectives

The pediatric population worldwide bears a significant morbidity and death burden due to acute respiratory infections (ARIs). Human Orthopneumovirus, sometimes referred to as the Human Respiratory Syncytial Virus (HRSV), is one of the main causes of ARIs in infants. The main goal of this study was to identify the genetic diversity of HRSV strains that were circulating in the Iranian population at a certain time of year.

Materials and Methods

Two hundred youngsters less than 12 years old with acute respiratory infections had samples taken from their throat and pharynx secretions. Then, external and hemi-nested PCR were employed, using specific primers targeting the G gene region to detect HRSV. Subsequently, nine randomly selected positive samples were subjected to sequencing. The results were then compared with reference strains cataloged in GeneBank, and phylogenetic tree was constructed using Chromes and MEGA7.

Results

Out of 200 samples, 34 were identified as containing HRSV. Subgroup A was predominant, accounting for 61.76% of cases, followed by subgroup BA (35.29%) and subgroup B (2.94%). Phylogenetic analysis revealed five samples associated with subtype B and four with genotype A. Genomic analysis showed three samples under the GA2 subgroup and one under GA1 for subtype A, and four samples in subgroup BA and one in GB2 for subtype B.

Conclusion

In this study, subgroup A strains, particularly genotype GA2, exhibited a higher prevalence compared to subgroup B strains during the specific period under investigation, shedding light on the genetic landscape of HRSV in this region.

Characterization of the internal working-life exposome using minimally and non-invasive sampling methods - a narrative review

During recent years, we are moving away from the 'one exposure, one disease'-approach in occupational settings and towards a more comprehensive approach, taking into account the totality of exposures during a life course by using an exposome approach. Taking an exposome approach however is accompanied by many challenges, one of which, for example, relates to the collection of biological samples. Methods used for sample collection in occupational exposome studies should ideally be minimally invasive, while at the same time sensitive, and enable meaningful repeated sampling in a large population and over a longer time period. This might be hampered in specific situations e.g., people working in remote areas, during pandemics or with flexible work hours. In these situations, using self-sampling techniques might offer a solution. Therefore, our aim was to identify existing self-sampling techniques and to evaluate the applicability of these techniques in an occupational exposome context by conducting a literature review. We here present an overview of current self-sampling methodologies used to characterize the internal exposome. In addition, the use of different biological matrices was evaluated and subdivided based on their level of invasiveness and applicability in an occupational exposome context. In conclusion, this review and the overview of self-sampling techniques presented herein can serve as a guide in the design of future (occupational) exposome studies while circumventing sample collection challenges associated with exposome studies.

Identification of immune-related genes in diagnosing retinopathy of prematurity with sepsis through bioinformatics analysis and machine learning

Background: There is increasing evidence indicating that immune system dysregulation plays a pivotal role in the pathogenesis of retinopathy of prematurity (ROP) and sepsis. This study aims to identify key diagnostic candidate genes in ROP with sepsis. Methods: We obtained publicly available data on ROP and sepsis from the gene expression omnibus database. Differential analysis and weighted gene correlation network analysis (WGCNA) were performed to identify differentially expressed genes (DEGs) and key module genes. Subsequently, we conducted functional enrichment analysis to gain insights into the biological functions and pathways. To identify immune-related pathogenic genes and potential mechanisms, we employed several machine learning algorithms, including Support Vector Machine Recursive Feature Elimination (SVM-RFE), Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest (RF). We evaluated the diagnostic performance using nomogram and Receiver Operating Characteristic (ROC) curves. Furthermore, we used CIBERSORT to investigate immune cell dysregulation in sepsis and performed cMAP analysis to identify potential therapeutic drugs. Results: The sepsis dataset comprised 352 DEGs, while the ROP dataset had 307 DEGs and 420 module genes. The intersection between DEGs for sepsis and module genes for ROP consisted of 34 genes, primarily enriched in immune-related pathways. After conducting PPI network analysis and employing machine learning algorithms, we pinpointed five candidate hub genes. Subsequent evaluation using nomograms and ROC curves underscored their robust diagnostic potential. Immune cell infiltration analysis revealed immune cell dysregulation. Finally, through cMAP analysis, we identified some small molecule compounds that have the potential for sepsis treatment. Conclusion: Five immune-associated candidate hub genes (CLEC5A, KLRB1, LCN2, MCEMP1, and MMP9) were recognized, and the nomogram for the diagnosis of ROP with sepsis was developed.

Interactions Between Severe Acute Respiratory Syndrome Coronavirus 2 Replication and Major Respiratory Viruses in Human Nasal Epithelium

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), along with extensive nonpharmacological interventions, have profoundly altered the epidemiology of major respiratory viruses. Some studies have described virus-virus interactions, particularly manifested by viral interference mechanisms at different scales. However, our knowledge of the interactions between SARS-CoV-2 and other respiratory viruses remains incomplete. Here, we studied the interactions between SARS-CoV-2 and several respiratory viruses (influenza, respiratory syncytial virus, human metapneumovirus, and human rhinovirus) in a reconstituted human epithelial airway model, exploring different scenarios affecting the sequence and timing of coinfections. We show that the virus type and sequence of infections are key factors in virus-virus interactions, the primary infection having a determinant role in the immune response to the secondary infection.

Crucial Genes in Aortic Dissection Identified by Weighted Gene Coexpression Network Analysis

Background

Aortic dissection (AD) is a lethal vascular disease with high mortality and morbidity. Though AD clinical pathology is well understood, its molecular mechanisms remain unclear. Specifically, gene expression profiling helps illustrate the potential mechanism of aortic dissection in terms of gene regulation and its modification by risk factors. This study was aimed at identifying the genes and molecular mechanisms in aortic dissection through bioinformatics analysis.

Method

Nine patients with AD and 10 healthy controls were enrolled. The gene expression in peripheral mononuclear cells was profiled through next-generation RNA sequencing. Analyses including differential expressed gene (DEG) via DEGseq, weighted gene coexpression network (WGCNA), and VisANT were performed to identify crucial genes associated with AD. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was also utilized to analyze Gene Ontology (GO).

Results

DEG analysis revealed that 1,113 genes were associated with AD. Of these, 812 genes were markedly reduced, whereas 301 genes were highly expressed, in AD patients. DEGs were rich in certain categories such as MHC class II receptor activity, MHC class II protein complex, and immune response genes. Gene coexpression networks via WGCNA identified 3 gene hub modules, with one positively and 2 negatively correlated with AD, respectively. Specifically, module 37 was the most strongly positively correlated with AD with a correlation coefficient of 0.72. Within module 37, five hub genes (AGFG1, MCEMP1, IRAK3, KCNE1, and CLEC4D) displayed high connectivity and may have clinical significance in the pathogenesis of AD.

Conclusion

Our analysis provides the possible association of specific genes and gene modules for the involvement of the immune system in aortic dissection. AGFG1, MCEMP1, IRAK3, KCNE1, and CLEC4D in module M37 were highly connected and strongly linked with AD, suggesting that these genes may help understand the pathogenesis of aortic dissection.

Novel Biomarkers Differentiating Viral from Bacterial Infection in Febrile Children: Future Perspectives for Management in Clinical Praxis

Differentiating viral from bacterial infections in febrile children is challenging and often leads to an unnecessary use of antibiotics. There is a great need for more accurate diagnostic tools. New molecular methods have improved the particular diagnostics of viral respiratory tract infections, but defining etiology can still be challenging, as certain viruses are frequently detected in asymptomatic children. For the detection of bacterial infections, time consuming cultures with limited sensitivity are still the gold standard. As a response to infection, the immune system elicits a cascade of events, which aims to eliminate the invading pathogen. Recent studies have focused on these host–pathogen interactions to identify pathogen-specific biomarkers (gene expression profiles), or “pathogen signatures”, as potential future diagnostic tools. Other studies have assessed combinations of traditional bacterial and viral biomarkers (C-reactive protein, interleukins, myxovirus resistance protein A, procalcitonin, tumor necrosis factor-related apoptosis-inducing ligand) to establish etiology. In this review we discuss the performance of such novel diagnostics and their potential role in clinical praxis. In conclusion, there are several promising novel biomarkers in the pipeline, but well-designed randomized controlled trials are needed to evaluate the safety of using these novel biomarkers to guide clinical decisions.