Sesame eliciting and safe doses in a large sesame allergic population

Background: Sesame is a significant food allergen causing severe and even fatal reactions. Given its increasing prevalence in western diet, sesame is listed as an allergenic food requiring labeling in the United States and EU. However, data on the population reaction doses to sesame are limited. Methods: All sesame oral food challenges (OFCs), performed either for diagnosis or for threshold identification before the beginning of sesame oral immunotherapy (OIT) between November 2011 and July 2021 in Shamir medical center were analyzed for reaction threshold distribution. Safe-dose challenges with 90–120 min intervals were also analyzed. Results: Two hundred and fifty patients underwent 338 positive OFCs, and additional 158 safe-dose OFCs were performed. The discrete and cumulative protein amounts estimated to elicit an objective reaction in 1% (ED01) of the entire cohort (n = 250) were 0.8 mg (range 0.3–6.3) and 0.7 mg (range 0.1–7.1), respectively, and those for 5% of the population (ED05) were 3.4 mg (range 1.2–20.6) and 4.5 mg (range 1.2–28.8), respectively. Safe-dose OFCs showed similar values of ED01 (0.8, 0.4–7.5 mg) and ED05 (3.4, 1.2–22.9 mg). While doses of ≤1 mg sesame protein elicited oral pruritus in 11.6% of the patients, no objective reaction was documented to this amount in any of the challenges, including safe-dose OFCs. Conclusions: This study provides data on sesame reaction threshold distribution in the largest population of allergic patients studied, with no right or left censored data, and with validation using a safe-dose OFC. It further supports the current methods for ED determination as appropriate for establishing safety precautions for the food industry

Updated threshold dose-distribution data for sesame

Sesame is classified as a “major” food allergen for which mandatory disclosure is required. Understanding reaction thresholds and how these vary within the allergic population is crucial in providing appropriate dietary advice to patients, providing guidance to the food industry, and informing dosing regimens for oral food challenges (FC). However, the largest data series used to derive a threshold dose-distribution for sesame included blinded challenge data from just 40 individuals.1 Data from low-dose, open FC can be used to supplement that from blinded FC, reducing uncertainty in estimating threshold dose-distributions for allergenic foods which otherwise lack sufficient data.2 We, therefore, undertook a systematic search of the literature and performed dose-distribution modelling of individual patient FC data (including open FC) to update estimated eliciting doses for sesame

Peanut Can Be Used as a Reference Allergen for Hazard Characterization in Food Allergen Risk Management: A Rapid Evidence Assessment and Meta-Analysis

Regional and national legislation mandates the disclosure of “priority” allergens when present as an ingredient in foods, but this does not extend to the unintended presence of allergens due to shared production facilities. This has resulted in a proliferation of precautionary allergen (“may contain”) labels (PAL) that are frequently ignored by food-allergic consumers. Attempts have been made to improve allergen risk management to better inform the use of PAL, but a lack of consensus has led to variety of regulatory approaches and nonuniformity in the use of PAL by food businesses. One potential solution would be to establish internationally agreed “reference doses,” below which no PAL would be needed. However, if reference doses are to be used to inform the need for PAL, then it is essential to characterize the hazard associated with these low-level exposures. For peanut, there are now published data relating to over 3000 double-blind, placebo-controlled challenges in allergic individuals, but a similar level of evidence is lacking for other priority allergens. We present the results of a rapid evidence assessment and meta-analysis for the risk of anaphylaxis to a low-level allergen exposure for priority allergens. On the basis of this analysis, we propose that peanut can and should be considered an exemplar allergen for the hazard characterization at a low-level allergen exposure. Resumen: La legislación regional y nacional exige la divulgación de alérgenos "prioritarios" cuando están presentes como ingrediente en los alimentos, pero esto no se extiende a la presencia involuntaria de alérgenos debido a instalaciones de producción compartidas. Esto ha dado lugar a una proliferación de etiquetas de precaución para alérgenos ("pueden contener") (PAL) que los consumidores alérgicos a los alimentos suelen ignorar. Se han hecho intentos para mejorar la gestión del riesgo de alérgenos para informar mejor el uso de PAL, pero la falta de consenso ha llevado a una variedad de enfoques regulatorios y a la falta de uniformidad en el uso de PAL por parte de las empresas alimentarias. Una posible solución sería establecer “dosis de referencia” acordadas internacionalmente, por debajo de las cuales no se necesitaría PAL. Sin embargo, si se van a utilizar dosis de referencia para informar la necesidad de PAL, entonces es esencial caracterizar el peligro asociado con estas exposiciones de bajo nivel. Para el maní, ahora hay datos publicados relacionados con más de 3000 desafíos doble ciego controlados por placebo en individuos alérgicos, pero falta un nivel similar de evidencia para otros alérgenos prioritarios. Presentamos los resultados de una evaluación rápida de la evidencia y un metanálisis del riesgo deanafilaxia a una exposición a alérgenos de bajo nivel para alérgenos prioritarios. Sobre la base de este análisis, proponemos que el cacahuete puede y debe considerarse un alérgeno ejemplar para la caracterización del peligro en una exposición a un alérgeno de bajo nivel.Instituto de Investigación de Tecnología de AlimentosFil: Turner, Paul J. Imperial College London. Instituto Nacional del Corazón y los Pulmones; Reino Unido.Fil: Patel, Nandinee. Imperial College London. Instituto Nacional del Corazón y los Pulmones; Reino Unido.Fil: Ballmer-Weber, Barbara K. Imperial College London. Instituto Nacional del Corazón y los Pulmones; Reino Unido.Fil: Ballmer-Weber, Barbara K. Clínica de Dermatología y Alergología. Kantonsspital; Suiza.Fil: Baumert, Joe L. Imperial College London. Instituto Nacional del Corazón y los Pulmones; Reino Unido.Fil: Blom, W. Marty. Imperial College London. Instituto Nacional del Corazón y los Pulmones; Reino Unido.Fil: Brooke-Taylor, Simon. Imperial College London. Instituto Nacional del Corazón y los Pulmones; Reino Unido.Fil: Brough, Helen. Imperial College London. Instituto Nacional del Corazón y los Pulmones; Reino Unido.Fil: Brough, Helen. King's College London. Departamento de Alergia Pediátrica; Reino Unido.Fil: Campbell, Dianne E. Imperial College London. Instituto Nacional del Corazón y los Pulmones; Reino Unido.Fil: Campbell, Dianne E. Tecnologías DBV. Montrouge; Francia.Fil: Chen, Hongbing. Imperial College London. Instituto Nacional del Corazón y los Pulmones; Reino Unido.Fil: Chinthrajah, R. Sharon. Imperial College London. Instituto Nacional del Corazón y los Pulmones; Reino Unido.Fil: Crevel, René W.R. Imperial College London. Instituto Nacional del Corazón y los Pulmones; Reino Unido.Fil: Dubois, Anthony E.J. Imperial College London. Instituto Nacional del Corazón y los Pulmones; Reino Unido.Fil: Ebisawa, Motohiro. Imperial College London. Instituto Nacional del Corazón y los Pulmones; Reino Unido.Fil: Elizur, Arnon. Imperial College London. Instituto Nacional del Corazón y los Pulmones; Reino Unido.Fil: Elizur, Arnon. Universidad de Tel Aviv. Facultad de Medicina Sackler. Departamento de Pediatría; Israel.Fil: Gerdts, Jennifer D. Imperial College London. Instituto Nacional del Corazón y los Pulmones; Reino Unido.Fil: Gowland, M. Hazel. Imperial College London. Instituto Nacional del Corazón y los Pulmones; Reino Unido.Fil: Houben, Geert F. Imperial College London. Instituto Nacional del Corazón y los Pulmones; Reino Unido.Fil: Hourihane, Jonathan O.B. Imperial College London. Instituto Nacional del Corazón y los Pulmones; Reino Unido.Fil: Knulst, André C. Imperial College London. Instituto Nacional del Corazón y los Pulmones; Reino Unido.Fil: La Vieille, Sébastien. Imperial College London. Instituto Nacional del Corazón y los Pulmones; Reino Unido.Fil: López, María Cristina. Imperial College London. Instituto Nacional del Corazón y los Pulmones; Reino Unido.Fil: Mills, E.N. Clare. Imperial College London. Instituto Nacional del Corazón y los Pulmones; Reino Unido.Fil: Polenta, Gustavo Alberto. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Investigación Tecnología de Alimentos; Argentina.Fil: Polenta, Gustavo Alberto. Imperial College London. Instituto Nacional del Corazón y los Pulmones; Reino Unido.Fil: Purington, Natasha. Imperial College London. Instituto Nacional del Corazón y los Pulmones; Reino Unido.Fil: Said, María. Imperial College London. Instituto Nacional del Corazón y los Pulmones; Reino Unido.Fil: Sampson, Hugh A. Imperial College London. Instituto Nacional del Corazón y los Pulmones; Reino Unido.Fil: Sampson, Hugh A. Escuela de Medicina Icahn. División de Alergia e Inmunología Pediátricasen. Nueva York. Estados Unidos de América.Fil: Schnadt, Sabine. Imperial College London. Instituto Nacional del Corazón y los Pulmones; Reino Unido.Fil: Södergren, Eva. Imperial College London. Instituto Nacional del Corazón y los Pulmones; Reino Unido.Fil: Södergren, Eva. ThermoFisher Scientific; Suecia.Fil: Taylor, Stephen L. Imperial College London. Instituto Nacional del Corazón y los Pulmones; Reino Unido.Fil: Remington, Benjamin C. Imperial College London. Instituto Nacional del Corazón y los Pulmones; Reino Unido.Fil: Remington, Benjamin C. Grupo BV. Consultoría Remington; Holanda

Recipient-independent, high-accuracy FMT-response prediction and optimization in mice and humans

Abstract Background Some microbiota compositions are associated with negative outcomes, including among others, obesity, allergies, and the failure to respond to treatment. Microbiota manipulation or supplementation can restore a community associated with a healthy condition. Such interventions are typically probiotics or fecal microbiota transplantation (FMT). FMT donor selection is currently based on donor phenotype, rather than the anticipated microbiota composition in the recipient and associated health benefits. However, the donor and post-transplant recipient conditions differ drastically. We here propose an algorithm to identify ideal donors and predict the expected outcome of FMT based on donor microbiome alone. We also demonstrate how to optimize FMT for different required outcomes. Results We show, using multiple microbiome properties, that donor and post-transplant recipient microbiota differ widely and propose a tool to predict the recipient post-transplant condition (engraftment success and clinical outcome), using only the donors’ microbiome and, when available, demographics for transplantations from humans to either mice or other humans (with or without antibiotic pre-treatment). We validated the predictor using a de novo FMT experiment highlighting the possibility of choosing transplants that optimize an array of required goals. We then extend the method to characterize a best-planned transplant (bacterial cocktail) by combining the predictor and a generative genetic algorithm (GA). We further show that a limited number of taxa is enough for an FMT to produce a desired microbiome or phenotype. Conclusions Off-the-shelf FMT requires recipient-independent optimized FMT selection. Such a transplant can be from an optimal donor or from a cultured set of microbes. We have here shown the feasibility of both types of manipulations in mouse and human recipients. Video Abstrac

Tumor Necrosis Factor-α from Macrophages Enhances LPS-Induced Clara Cell Expression of Keratinocyte-Derived Chemokine

Tumor necrosis factor (TNF)-α is a cytokine produced by alveolar macrophages in response to LPS in the lung. Clara cells are bronchiolar epithelial cells that produce a variety of proinflammatory cytokines in response to LPS but not to TNF-α. In this study, we examined whether TNF-α affects Clara cell cytokine production in the setting of LPS stimulation. Using a transformed murine Clara cell line (C22), we observed that both LPS and TNF-α induced production of keratinocyte-derived chemokine (KC) and monocyte chemoattractant protein (MCP)-1. We also found that simultaneous LPS and TNF-α stimulation is synergistic for KC production, but additive for MCP-1 production. By using a Transwell coculture system of RAW264.7 macrophages and Clara cells isolated from C57Bl/6 mice, we found that macrophages produce a soluble factor that enhances Clara cell KC production in response to LPS. Cocultures of Clara cells from mice deficient in TNF-α receptors with RAW264.7 macrophages demonstrated that the effect of macrophages on Clara cells is mediated primarily via TNF-α. To determine whether these findings occur in vivo, we treated wild-type and TNF receptor–deficient mice intratracheally with LPS and examined the expression of KC. LPS-treated, TNF receptor–deficient mice showed much less KC mRNA in airway epithelial cells compared with wild-type mice. In contrast, a similar number of KC-expressing cells was seen in the lung periphery. Thus, upregulation of KC by Clara cells in the setting of LPS stimulation is largely dependent on TNF-α originating from alveolar macrophages. These findings shed light on macrophage–Clara cell interactions in regulating the pulmonary inflammatory response to LPS