Identification of two new intimin types in atypical enteropathogenic Escherichia coli

Stool specimens of patients with diarrhea or other gastrointestinal alterations who were admitted to Xeral-Calde Hospital (Lugo, Spain) were analyzed for the prevalence of typical and atypical enteropathogenic Escherichia coli (EPEC). Atypical EPEC strains (eae+ bfp–) were detected in 105 (5.2%) of 2015 patients, whereas typical EPEC strains (eae+ bfp+) were identified in only five (0.2%) patients. Atypical EPEC strains were (after Salmonella) the second most frequently recovered enteropathogenic bacteria. In this study, 110 EPEC strains were characterized. The strains belonged to 43 O serogroups and 69 O:H serotypes, including 44 new serotypes not previously reported among human EPEC. However, 29% were of one of three serogroups (O26, O51, and O145) and 33% belonged to eight serotypes (O10:H–, O26:H11, O26:H–, O51:H49, O123:H19, O128:H2, O145:H28, and O145:H–). Only 14 (13%) could be assigned to classical EPEC serotypes. Fifteen intimin types, namely, α1 (6 strains), α2 (4 strains), β1 (34 strains), ξR/β2 (6 strains), γ1 (13 strains), γ2/θ (16 strains), δ/k (5 strains), ε1 (9 strains), νR/ε2 (5 strains), ζ (6 strains), ι1 (1 strain), μR/ι2 (1 strain), νB (1 strain), ξB (1 strain), and ο (2 strains), were detected among the 110 EPEC strains, but none of the strains was positive for intimin types μ1, μ2, λ, or μB. In addition, in atypical EPEC strains of serotypes O10:H–, O84:H–, and O129:H–, two new intimin genes (eae-νB and eae-ο) were identified. These genes showed less than 95% nucleotide sequence identity with existing intimin types. Phylogenetic analysis revealed six groups of closely related intimin genes: (i) α1, α2, ζ, νB, and ο; (ii) ι1 and μR/ι2; (iii) β1, ξR/β2B, δ/β2O, and κ; (iv) ε1, ξB, η1,η2, and νR/ε2; (v) γ1, μB, γ2, and θ; and (vi) λ. These results indicate that atypical EPEC strains belonging to large number of serotypes and with different intimin types might be frequently isolated from human clinical stool samples in Spain. [Int Microbiol 2006; 9(2):103-110

Higher COVID-19 pneumonia risk associated with anti-IFN-α than with anti-IFN-ω auto-Abs in children

We found that 19 (10.4%) of 183 unvaccinated children hospitalized for COVID-19 pneumonia had autoantibodies (auto-Abs) neutralizing type I IFNs (IFN-alpha 2 in 10 patients: IFN-alpha 2 only in three, IFN-alpha 2 plus IFN-omega in five, and IFN-alpha 2, IFN-omega plus IFN-beta in two; IFN-omega only in nine patients). Seven children (3.8%) had Abs neutralizing at least 10 ng/ml of one IFN, whereas the other 12 (6.6%) had Abs neutralizing only 100 pg/ml. The auto-Abs neutralized both unglycosylated and glycosylated IFNs. We also detected auto-Abs neutralizing 100 pg/ml IFN-alpha 2 in 4 of 2,267 uninfected children (0.2%) and auto-Abs neutralizing IFN-omega in 45 children (2%). The odds ratios (ORs) for life-threatening COVID-19 pneumonia were, therefore, higher for auto-Abs neutralizing IFN-alpha 2 only (OR [95% CI] = 67.6 [5.7-9,196.6]) than for auto-Abs neutralizing IFN-. only (OR [95% CI] = 2.6 [1.2-5.3]). ORs were also higher for auto-Abs neutralizing high concentrations (OR [95% CI] = 12.9 [4.6-35.9]) than for those neutralizing low concentrations (OR [95% CI] = 5.5 [3.1-9.6]) of IFN-omega and/or IFN-alpha 2

Understanding uncontrolled severe allergic asthma by integration of omic and clinical data

Background Asthma is a complex, multifactorial disease often linked with sensitization to house dust mites (HDM). There is a subset of patients that does not respond to available treatments, who present a higher number of exacerbations and a worse quality of life. To understand the mechanisms of poor asthma control and disease severity, we aim to elucidate the metabolic and immunologic routes underlying this specific phenotype and the associated clinical features. Methods Eighty-seven patients with a clinical history of asthma were recruited and stratified in 4 groups according to their response to treatment: corticosteroid-controlled (ICS), immunotherapy-controlled (IT), biologicals-controlled (BIO) or uncontrolled (UC). Serum samples were analysed by metabolomics and proteomics; and classifiers were built using machine-learning algorithms. Results Metabolomic analysis showed that ICS and UC groups cluster separately from one another and display the highest number of significantly different metabolites among all comparisons. Metabolite identification and pathway enrichment analysis highlighted increased levels of lysophospholipids related to inflammatory pathways in the UC patients. Likewise, 8 proteins were either upregulated (CCL13, ARG1, IL15 and TNFRSF12A) or downregulated (sCD4, CCL19 and IFNγ) in UC patients compared to ICS, suggesting a significant activation of T cells in these patients. Finally, the machine-learning model built including metabolomic and clinical data was able to classify the patients with an 87.5% accuracy. Conclusions UC patients display a unique fingerprint characterized by inflammatory-related metabolites and proteins, suggesting a pro-inflammatory environment. Moreover, the integration of clinical and experimental data led to a deeper understanding of the mechanisms underlying UC phenotype