Gene signatures from scRNA-seq accurately quantify mast cells in biopsies in asthma

Respiratory disease, characterized by changes in the cells of the lung, can affect molecular phenotype of cells and the intercellular interactions, resulting in a disbalance in the relative proportions of individual cell types. Understanding these changes is essential to understand the pathophysiology of lung disease. Conventional 'bulk' RNA-sequencing (RNA-seq), analyzing the entire transcriptome of the tissue sample, provides information about average expression levels of each gene in the mixed cell population; whereas it does not consider the cellular heterogeneity in samples composed of more than one cell type 1 . Single-cell RNA-seq (scRNA-seq) assesses the transcriptome of a complex biological sample with single-cell resolution, allowing identification of the relative frequency of discrete cell-types and analysis of their transcriptomes 1 . Nevertheless, analyzing the transcriptomic signature in large numbers of patients by scRNA-Seq is currently limited by its high costs. Mast cells are key regulatory cells driving the inflammatory process in asthma2 . Since they can be quantified by immunohistochemical staining for validation purposes, we used mast cells as an example of a rare cell population to assess the validity of our deconvolution approach. Recently, a number of bulk RNA-seq deconvolution methods have become available 3 , for instance of two deconvolution methods, namely support vector regression (SVR) 4 , the machine-learning method implemented in CYBERSORT, and Non-Negative Least Square (NNLS) 5 , using a matrix of cell-type selective genes identified with AutoGeneSc 6 . Both approaches are designed to estimate relative proportion of the main, common cell types present in the sample. When we used these methods to estimate the number of mast cells, we found a poor correlation with the number of mast cells stained by immunohistochemistry in the biopsies, suggesting the CIBERSORT and NNLS are less reliable in the case of rare cell types. We explored the possibility to use scRNA-Seq data from small numbers of subjects to specifically interrogate the relative cell type frequency of a rare cell population in a bulk RNA-Seq dataset obtained from a large asthma cohort

COL4A3 is degraded in allergic asthma and degradation predicts response to anti-IgE therapy.

BACKGROUND Asthma is a heterogeneous syndrome substantiating the urgent requirement for endotype-specific biomarkers. Dysbalance of fibrosis and fibrolysis in asthmatic lung tissue leads to reduced levels of the inflammation-protective collagen 4 (COL4A3). OBJECTIVE To delineate the degradation of COL4A3 in allergic airway inflammation and evaluate the resultant product as a biomarker for anti-IgE therapy response. METHODS The serological COL4A3 degradation marker C4Ma3 (Nordic Bioscience, Denmark) and serum cytokines were measured in the ALLIANCE cohort (pediatric cases/controls: 134/35; adult cases/controls: 149/31). Exacerbation of allergic airway disease in mice was induced by sensitising to OVA, challenge with OVA aerosol and instillation of poly(cytidylic-inosinic). Fulacimstat (chymase inhibitor, Bayer) was used to determine the role of mast cell chymase in COL4A3 degradation. Patients with cystic fibrosis (CF, n=14) and CF with allergic broncho-pulmonary aspergillosis (ABPA, n=9) as well as severe allergic, uncontrolled asthmatics (n=19) were tested for COL4A3 degradation. Omalizumab (anti-IgE) treatment was assessed by the Asthma Control Test. RESULTS Serum levels of C4Ma3 were increased in asthma in adults and children alike and linked to a more severe, exacerbating allergic asthma phenotype. In an experimental asthma mouse model, C4Ma3 was dependent on mast cell chymase. Serum C4Ma3 was significantly elevated in CF plus ABPA and at baseline predicted the success of the anti-IgE therapy in allergic, uncontrolled asthmatics (diagnostic odds ratio 31.5). CONCLUSION C4Ma3 level depend on lung mast cell chymase and are increased in a severe, exacerbating allergic asthma phenotype. C4Ma3 may serve as a novel biomarker to predict anti-IgE therapy response

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

Plasmid-Encoded Transferable mecB-Mediated Methicillin Resistance in Staphylococcus aureus

During cefoxitin-based nasal screening, phenotypically categorized methicillin-resistant Staphylococcus aureus (MRSA) was isolated and tested negative for the presence of the mecA and mecC genes as well as for the SCCmec-orfX junction region. The isolate was found to carry a mecB gene previously described for Macrococcus caseolyticus but not for staphylococcal species. The gene is flanked by β-lactam regulatory genes similar to mecR, mecI, and blaZ and is part of an 84.6-kb multidrug-resistance plasmid that harbors genes encoding additional resistances to aminoglycosides (aacA-aphD, aphA, and aadK) as well as macrolides (ermB) and tetracyclines (tetS). This further plasmidborne β-lactam resistance mechanism harbors the putative risk of acceleration or reacceleration of MRSA spread, resulting in broad ineffectiveness of β-lactams as a main therapeutic application against staphylococcal infections

Detection of mecA-and mecC-positive methicillin-resistant staphylococcus aureus (MRSA) isolates by the new Xpert MRSA Gen 3 PCR assay

An advanced methicillin-resistant Staphylococcus aureus (MRSA) detection PCR approach targeting SCCmec-orfX along with mecA and mecC was evaluated for S. aureus and coagulase-negative staphylococci. The possession of mecA and/or mecC was correctly confirmed in all cases. All methicillin-susceptible S. aureus strains (n=98; including staphylococcal cassette chromosome mec element [SCCmec] remnants) and 98.1% of the MRSA strains (n=160, including 10 mecC-positive MRSA) were accurately categorized.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Identification of PsA phenotypes with machine learning analytics using data from two phase III clinical trials of guselkumab in a bio-naïve population of patients with PsA

Objectives Psoriatic arthritis (PsA) phenotypes are typically defined by their clinical components, which may not reflect patients' overlapping symptoms. This post hoc analysis aimed to identify hypothesis-free PsA phenotype clusters using machine learning to analyse data from the phase III DISCOVER-1/DISCOVER-2 clinical trials. Methods Pooled data from bio-naïve patients with active PsA receiving guselkumab 100 mg every 8/4 weeks were retrospectively analysed. Non-negative matrix factorisation was applied as an unsupervised machine learning technique to identify PsA phenotype clusters; baseline patient characteristics and clinical observations were input features. Minimal disease activity (MDA), disease activity index for psoriatic arthritis (DAPSA) low disease activity (LDA) and DAPSA remission at weeks 24 and 52 were evaluated. Results Eight clusters (n=661) were identified: cluster 1 (feet dominant), cluster 2 (male, overweight, psoriasis dominant), cluster 3 (hand dominant), cluster 4 (dactylitis dominant), cluster 5 (enthesitis, large joints), cluster 6 (enthesitis, small joints), cluster 7 (axial dominant) and cluster 8 (female, obese, large joints). At week 24, MDA response was highest in cluster 2 and lowest in clusters 3, 5 and 6; at week 52, it was highest in cluster 2 and lowest in cluster 5. At weeks 24 and 52, DAPSA LDA and remission were highest in cluster 2 and lowest in clusters 4 and 6, respectively. All clusters improved with guselkumab treatment over 52 weeks. Conclusions Unsupervised machine learning identified eight PsA phenotype clusters with significant differences in demographics, clinical features and treatment responses. In the future, such data could help support individualised treatment decisions

Molecular Epidemiology of Methicillin-Susceptible and Methicillin-Resistant Staphylococcus aureus in Wild, Captive and Laboratory Rats: Effect of Habitat on the Nasal S. aureus Population

Rats are a reservoir of human- and livestock-associated methicillin-resistant Staphylococcus aureus (MRSA). However, the composition of the natural S. aureus population in wild and laboratory rats is largely unknown. Here, 144 nasal S. aureus isolates from free-living wild rats, captive wild rats and laboratory rats were genotyped and profiled for antibiotic resistances and human-specific virulence genes. The nasal S. aureus carriage rate was higher among wild rats (23.4%) than laboratory rats (12.3%). Free-living wild rats were primarily colonized with isolates of clonal complex (CC) 49 and CC130 and maintained these strains even in husbandry. Moreover, upon livestock contact, CC398 isolates were acquired. In contrast, laboratory rats were colonized with many different S. aureus lineages—many of which are commonly found in humans. Five captive wild rats were colonized with CC398-MRSA. Moreover, a single CC30-MRSA and two CC130-MRSA were detected in free-living or captive wild rats. Rat-derived S. aureus isolates rarely harbored the phage-carried immune evasion gene cluster or superantigen genes, suggesting long-term adaptation to their host. Taken together, our study revealed a natural S. aureus population in wild rats, as well as a colonization pressure on wild and laboratory rats by exposure to livestock- and human-associated S. aureus, respectively.Peer Reviewe