Publications by authors named "Ali Versi"
Background: Lung quantitative computed tomography (qCT) severe asthma clusters have been reported, but their replication and underlying disease mechanisms are unknown. We identified and replicated qCT clusters of severe asthma in two independent asthma cohorts and determined their association with molecular pathways, using radiomultiomics, integrating qCT, multiomics and machine learning/artificial intelligence.
Methods: We used consensus clustering on qCT measurements of airway and lung CT scans, performed in 105 severe asthmatic adults from the U-BIOPRED cohort.
Article Synopsis
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Article Synopsis
- Severe asthma (SA) has various clinical phenotypes linked to a diverse airway microbiome, and a study focused on identifying phenotypes with low microbial diversity.
- Metagenomic sequencing of sputum samples from SA participants identified 51 out of 97 samples with relative dominant species (RDS), with Haemophilus influenzae being the most prevalent.
- The research found that a specific cluster of RDSs associated with Haemophilus influenzae had more severe disease characteristics and indicated a host response linked to neutrophilic inflammation, suggesting potential for antibiotic treatment in this group.
Background: Clustering approaches using single omics platforms are increasingly used to characterise molecular phenotypes of eosinophilic and neutrophilic asthma. Effective integration of multi-omics platforms should lead towards greater refinement of asthma endotypes across molecular dimensions and indicate key targets for intervention or biomarker development.
Objectives: To determine whether multi-omics integration of sputum leads to improved granularity of the molecular classification of severe asthma.
Introduction: The use and generation of gene signatures have been established as a method to define molecular endotypes in complex diseases such as severe asthma. Bioinformatic approaches have now been applied to large omics datasets to define the various co-existing inflammatory and cellular functional pathways driving or characterizing a particular molecular endotype.
Areas Covered: Molecular phenotypes and endotypes of Type 2 inflammatory pathways and also of non-Type 2 inflammatory pathways, such as IL-6 trans-signaling, IL-17 activation, and IL-22 activation, have been defined in the Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes dataset.
Background: Because of altered airway microbiome in asthma, we analysed the bacterial species in sputum of patients with severe asthma.
Methods: Whole genome sequencing was performed on induced sputum from non-smoking (SAn) and current or ex-smoker (SAs/ex) severe asthma patients, mild/moderate asthma (MMA) and healthy controls (HC). Data were analysed by asthma severity, inflammatory status and transcriptome-associated clusters (TACs).
Background: Patients with severe asthma may have a greater risk of dying from COVID-19 disease. Angiotensin converting enzyme-2 (ACE2) and the enzyme proteases, transmembrane protease serine 2 (TMPRSS2) and FURIN, are needed for viral attachment and invasion into host cells.
Methods: We examined microarray mRNA expression of ACE2, TMPRSS2 and FURIN in sputum, bronchial brushing and bronchial biopsies of the European U-BIOPRED cohort.