Cross-reactive epitopes and their role in food allergy

Allergenic cross-reactivity among food allergens complicates the diagnosis and management of food allergy. This can result in many patients being sensitized (having allergen-specific IgE) to foods without exhibiting clinical reactivity. Some food groups such as shellfish, fish, tree nuts, and peanuts have very high rates of cross-reactivity. In contrast, relatively low rates are noted for grains and milk, whereas many other food families have variable rates of cross-reactivity or are not well studied. Although classical cross-reactive carbohydrate determinants are clinically not relevant, α-Gal in red meat through tick bites can lead to severe reactions. Multiple sensitizations to tree nuts complicate the diagnosis and management of patients allergic to peanut and tree nut. This review discusses cross-reactive allergens and cross-reactive carbohydrate determinants in the major food groups, and where available, describes their B-cell and T-cell epitopes. The clinical relevance of these cross-reactive B-cell and T-cell epitopes is highlighted and their possible impact on allergen-specific immunotherapy for food allergy is discussed

A cell-based high-throughput screening method to directly examine transthyretin amyloid fibril formation at neutral pH

Transthyretin (TTR) is a major amyloidogenic protein associated with hereditary (ATTRm) and nonhereditary (ATTRwt) intractable systemic transthyretin amyloidosis. The pathological mechanisms of ATTR-associated amyloid fibril formation are incompletely understood, and there is a need for identifying compounds that target ATTR. C-terminal TTR fragments are often present in amyloid-laden tissues of most patients with ATTR amyloidosis, and on the basis of in vitro studies, these fragments have been proposed to play important roles in amyloid formation. Here, we found that experimentally-formed aggregates of full-length TTR are cleaved into C-terminal fragments, which were also identified in patients' amyloid-laden tissues and in SH-SY5Y neuronal and U87MG glial cells. We observed that a 5-kDa C-terminal fragment of TTR, TTR81–127, is highly amyloidogenic in vitro, even at neutral pH. This fragment formed amyloid deposits and induced apoptosis and inflammatory gene expression also in cultured cells. Using the highly amyloidogenic TTR81–127 fragment, we developed a cell-based high-throughput screening method to discover compounds that disrupt TTR amyloid fibrils. Screening a library of 1280 off-patent drugs, we identified two candidate repositioning drugs, pyrvinium pamoate and apomorphine hydrochloride. Both drugs disrupted patient-derived TTR amyloid fibrils ex vivo, and pyrvinium pamoate also stabilized the tetrameric structure of TTR ex vivo in patient plasma. We conclude that our TTR81–127–based screening method is very useful for discovering therapeutic drugs that directly disrupt amyloid fibrils. We propose that repositioning pyrvinium pamoate and apomorphine hydrochloride as TTR amyloid-disrupting agents may enable evaluation of their clinical utility for managing ATTR amyloidosis

The whole blood transcriptional regulation landscape in 465 COVID-19 infected samples from Japan COVID-19 Task Force

「コロナ制圧タスクフォース」COVID-19患者由来の血液細胞における遺伝子発現の網羅的解析 --重症度に応じた遺伝子発現の変化には、ヒトゲノム配列の個人差が影響する--. 京都大学プレスリリース. 2022-08-23.Coronavirus disease 2019 (COVID-19) is a recently-emerged infectious disease that has caused millions of deaths, where comprehensive understanding of disease mechanisms is still unestablished. In particular, studies of gene expression dynamics and regulation landscape in COVID-19 infected individuals are limited. Here, we report on a thorough analysis of whole blood RNA-seq data from 465 genotyped samples from the Japan COVID-19 Task Force, including 359 severe and 106 non-severe COVID-19 cases. We discover 1169 putative causal expression quantitative trait loci (eQTLs) including 34 possible colocalizations with biobank fine-mapping results of hematopoietic traits in a Japanese population, 1549 putative causal splice QTLs (sQTLs; e.g. two independent sQTLs at TOR1AIP1), as well as biologically interpretable trans-eQTL examples (e.g., REST and STING1), all fine-mapped at single variant resolution. We perform differential gene expression analysis to elucidate 198 genes with increased expression in severe COVID-19 cases and enriched for innate immune-related functions. Finally, we evaluate the limited but non-zero effect of COVID-19 phenotype on eQTL discovery, and highlight the presence of COVID-19 severity-interaction eQTLs (ieQTLs; e.g., CLEC4C and MYBL2). Our study provides a comprehensive catalog of whole blood regulatory variants in Japanese, as well as a reference for transcriptional landscapes in response to COVID-19 infection

DOCK2 is involved in the host genetics and biology of severe COVID-19

「コロナ制圧タスクフォース」COVID-19疾患感受性遺伝子DOCK2の重症化機序を解明 --アジア最大のバイオレポジトリーでCOVID-19の治療標的を発見--. 京都大学プレスリリース. 2022-08-10.Identifying the host genetic factors underlying severe COVID-19 is an emerging challenge. Here we conducted a genome-wide association study (GWAS) involving 2, 393 cases of COVID-19 in a cohort of Japanese individuals collected during the initial waves of the pandemic, with 3, 289 unaffected controls. We identified a variant on chromosome 5 at 5q35 (rs60200309-A), close to the dedicator of cytokinesis 2 gene (DOCK2), which was associated with severe COVID-19 in patients less than 65 years of age. This risk allele was prevalent in East Asian individuals but rare in Europeans, highlighting the value of genome-wide association studies in non-European populations. RNA-sequencing analysis of 473 bulk peripheral blood samples identified decreased expression of DOCK2 associated with the risk allele in these younger patients. DOCK2 expression was suppressed in patients with severe cases of COVID-19. Single-cell RNA-sequencing analysis (n = 61 individuals) identified cell-type-specific downregulation of DOCK2 and a COVID-19-specific decreasing effect of the risk allele on DOCK2 expression in non-classical monocytes. Immunohistochemistry of lung specimens from patients with severe COVID-19 pneumonia showed suppressed DOCK2 expression. Moreover, inhibition of DOCK2 function with CPYPP increased the severity of pneumonia in a Syrian hamster model of SARS-CoV-2 infection, characterized by weight loss, lung oedema, enhanced viral loads, impaired macrophage recruitment and dysregulated type I interferon responses. We conclude that DOCK2 has an important role in the host immune response to SARS-CoV-2 infection and the development of severe COVID-19, and could be further explored as a potential biomarker and/or therapeutic target

Development of a prediction model of severe reaction in boiled egg challenges

Background: We have proposed a new scoring system (Anaphylaxis SCoring Aichi: ASCA) for a quantitative evaluation of the anaphylactic reaction that is observed in an oral food challenge (OFC). Furthermore, the TS/Pro (Total Score of ASCA/cumulative protein dose) can be a marker to represent the overall severity of a food allergy. We aimed to develop a prediction model for a severe allergic reaction that is provoked in a boiled egg white challenge. Methods: We used two separate datasets to develop and validate the prediction model, respectively. The development dataset included 198 OFCs, that tested positive. The validation dataset prospectively included 140 consecutive OFCs, irrespective of the result.A 'severe reaction' was defined as a TS/Pro higher than 31 (the median score of the development dataset). A multivariate logistic regression analysis was performed to identify the factors associated with a severe reaction and develop the prediction model. Results: The following four factors were independently associated with a severe reaction: ovomucoid specific IgE class (OM-sIgE: 0-6), aged 5 years or over, a complete avoidance of egg, and a total IgE < 1000 IU/mL. Based on these factors, we made a simple scoring prediction model. The model showed good discrimination in a receiver operating characteristic analysis; area under the curve (AUC) = 0.84 in development dataset, AUC = 0.85 in validation dataset. The prediction model significantly improved the AUC in both datasets compared to OM-sIgE alone. Conclusions: This simple scoring prediction model was useful for avoiding risky OFC