Untargeted metabolomic profiling in children identifies novel pathways in asthma and atopy.

BACKGROUND: Asthma and other atopic disorders can present with varying clinical phenotypes marked by differential metabolomic manifestations and enriched biological pathways. OBJECTIVE: We sought to identify these unique metabolomic profiles in atopy and asthma. METHODS: We analyzed baseline nonfasted plasma samples from a large multisite pediatric population of 470 children aged <13 years from 3 different sites in the United States and France. Atopy positivity (At+) was defined as skin prick test result of ≥3 mm and/or specific IgE ≥ 0.35 IU/mL and/or total IgE ≥ 173 IU/mL. Asthma positivity (As+) was based on physician diagnosis. The cohort was divided into 4 groups of varying combinations of asthma and atopy, and 6 pairwise analyses were conducted to best assess the differential metabolomic profiles between groups. RESULTS: Two hundred ten children were classified as At-As-, 42 as At+As-, 74 as At-As+, and 144 as At+As+. Untargeted global metabolomic profiles were generated through ultra-high-performance liquid chromatography-tandem mass spectroscopy. We applied 2 independent machine learning classifiers and short-listed 362 metabolites as discriminant features. Our analysis showed the most diverse metabolomic profile in the At+As+/At-As- comparison, followed by the At-As+/At-As- comparison, indicating that asthma is the most discriminant condition associated with metabolomic changes. At+As+ metabolomic profiles were characterized by higher levels of bile acids, sphingolipids, and phospholipids, and lower levels of polyamine, tryptophan, and gamma-glutamyl amino acids. CONCLUSION: The At+As+ phenotype displays a distinct metabolomic profile suggesting underlying mechanisms such as modulation of host-pathogen and gut microbiota interactions, epigenetic changes in T-cell differentiation, and lower antioxidant properties of the airway epithelium.

Disease diagnostics using machine learning of immune receptors.

Clinical diagnosis typically incorporates physical examination, patient history, and various laboratory tests and imaging studies, but makes limited use of the human system's own record of antigen exposures encoded by receptors on B cells and T cells. We analyzed immune receptor datasets from 593 individuals to develop MAchine Learning for Immunological Diagnosis (Mal-ID) , an interpretive framework to screen for multiple illnesses simultaneously or precisely test for one condition. This approach detects specific infections, autoimmune disorders, vaccine responses, and disease severity differences. Human-interpretable features of the model recapitulate known immune responses to SARS-CoV-2, Influenza, and HIV, highlight antigen-specific receptors, and reveal distinct characteristics of Systemic Lupus Erythematosus and Type-1 Diabetes autoreactivity. This analysis framework has broad potential for scientific and clinical interpretation of human immune responses.

Combining avidin with CD63 improves basophil activation test accuracy in classifying peanut allergy.

BACKGROUND: Conventional basophil activation tests (BATs) measure basophil activation by the increased expression of CD63. Previously, fluorophore-labeled avidin, a positively-charged molecule, was found to bind to activated basophils, which tend to expose negatively charged granule constituents during degranulation. This study further compares avidin versus CD63 as basophil activation biomarkers in classifying peanut allergy. METHODS: Seventy subjects with either a peanut allergy (N = 47), a food allergy other than peanut (N = 6), or no food allergy (N = 17) were evaluated. We conducted BATs in response to seven peanut extract (PE) concentrations (0.01-10,000 ng/mL) and four control conditions (no stimulant, anti-IgE, fMLP (N-formylmethionine-leucyl-phenylalanine), and anti-FcεRI). We measured avidin binding and CD63 expression on basophils with flow cytometry. We evaluated logistic regression and XGBoost models for peanut allergy classification and feature identification. RESULTS: Avidin binding was correlated with CD63 expression. Both markers discriminated between subjects with and without a peanut allergy. Although small by percentage, an avidin+ /CD63- cell subset was found in all allergic subjects tested, indicating that the combination of avidin and CD63 could allow a more comprehensive identification of activated basophils. Indeed, we obtained the best classification accuracy (97.8% sensitivity, 96.7% specificity) by combining avidin and CD63 across seven PE doses. Similar accuracy was obtained by combining PE dose of 10,000 ng/mL for avidin and PE doses of 10 and 100 ng/mL for CD63. CONCLUSIONS: Avidin and CD63 are reliable BAT activation markers associated with degranulation. Their combination enhances the identification of activated basophils and improves the classification accuracy of peanut allergy.

Subduing the influence of PCR inhibitors on amplifying aged, degraded, and low copy number DNA: PCR enhancer cocktail-p and rescue PCR.

PCR inhibitors are a formidable problem to the study of aged, degraded, and/or low copy number DNA. As a result, there is a need to find alternate methods that ameliorate the efficacy of PCR. In this study, we attempted to use genetic methods to identify the species of salmonid (Oncorhynchus spp.) remains recovered from archaeological sites along the Feather River located in northern California, United States. In the process of doing so, we compared the efficacy of a PCR enhancer cocktail called "PEC-P" and a reagent rich PCR recipe called "rescue PCR" over standard PCR. Across all treatments (full concentration and 1:10 dilute eluates subjected to standard PCR, PEC-P, and rescue PCR) species identification was possible for 74 of 93 archaeological fish specimens (79.6%). Overall, six of the 93 samples (6.5%) consistently yielded species identification across all treatments. The species of ten specimens (10.8%) were uniquely identified from amplicons produced with either PEC-P or rescue PCR or both. Notably, the species of seven samples (7.5%) were uniquely identified with standard PCR over the alternative treatments. Considering both full concentration and 1:10 dilute eluates (N = 186), standard PCR performed as well as PEC-P (p = 0.1451) and rescue (p = 0.6753). Yet, considering results from full concentration eluates alone (N = 93), PEC-P (60.2%) outperformed both standard PCR (44.1%; p = 0.0277) and rescue PCR (40.9%; p = 0.0046). Stochasticity observed in our study cautions us against choosing a "best" performing method of those explored here and suggests their respective potentials to improve success may be sample dependent. When working with samples compromised by PCR inhibitors, it is useful to have alternative methodologies for subduing the problem. Both PEC-P and rescue PCR represent useful alternative methods for the study of aged, degraded, and/or low copy number DNA samples compromised by PCR inhibitors.