Ena-DATASET-KNIH-29-03-2021-07:01:51:098-309 - samples
Ontology highlight
ABSTRACT:
PROVIDER: EGAD00001007176 | EGA |
REPOSITORIES: EGA
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Project description:The WHO/IUIS Allergen Nomenclature Database (http://allergen.org) provides up-to-date expert-reviewed data on newly discovered allergens and their unambiguous nomenclature to allergen researchers worldwide. This review discusses the 106 allergens that were accepted by the Allergen Nomenclature Sub-Committee between 01/2019 and 03/2021. Information about protein family membership, patient cohorts, and assays used for allergen characterization is summarized. A first allergenic fungal triosephosphate isomerase, Asp t 36, was discovered in Aspergillus terreus. Plant allergens contained 1 contact, 38 respiratory, and 16 food allergens. Can s 4 from Indian hemp was identified as the first allergenic oxygen-evolving enhancer protein 2 and Cic a 1 from chickpeas as the first allergenic group 4 late embryogenesis abundant protein. Among the animal allergens were 19 respiratory, 28 food, and 3 venom allergens. Important discoveries include Rap v 2, an allergenic paramyosin in molluscs, and Sal s 4 and Pan h 4, allergenic fish tropomyosins. Paramyosins and tropomyosins were previously known mainly as arthropod allergens. Collagens from barramundi, Lat c 6, and salmon, Sal s 6, were the first members from the collagen superfamily added to the database. In summary, the addition of 106 new allergens to the previously listed 930 allergens reflects the continuous linear growth of the allergen database. In addition, 17 newly described allergen sources were included.
| S-EPMC9290965 | biostudies-literature
Project description:BackgroundHLA-E interaction with inhibitory receptor, NKG2A attenuates NK-mediated cytotoxicity. NKG2A overexpression by SARS-CoV-2 exhausts NK cells function, whereas virus-induced down-regulation of MHC-Ia reduces its derived-leader sequence peptide levels required for proper binding of HLA-E to NKG2A. This leads HLA-E to become more complex with viral antigens and delivers them to CD8+ T cells, which facilitates cytolysis of infected cells. Now, the fact that alleles of HLA-E have different levels of expression and affinity for MHC Ia-derived peptide raises the question of whether HLA-E polymorphisms affect susceptibility to COVID-19 or its severity.Methods104 COVID-19 convalescent plasma donors with/without history of hospitalization and 18 blood donors with asymptomatic COVID-19, all were positive for anti-SARS-CoV-2 IgG antibody as well as a group of healthy control including 68 blood donors with negative antibody were subjected to HLA-E genotyping. As a privilege, individuals hadn't been vaccinated against COVID-19 and therefore naturally exposed to the SARS-CoV-2.ResultsThe absence of HLA-E*01:03 allele significantly decreases the odds of susceptibility to SARS-CoV-2 infection [p = 0.044; OR (95 %CI) = 0.530 (0.286 - 0.983)], suggesting that HLA-E*01:01 + HLA-E*01:01 genotype favors more protection against SARS-CoV-2 infection. HLA-E*01:03 + HLA-E*01:03 genotype was also significantly associated with more severe COVID-19 [p = 0.020; 2.606 (1.163 - 5.844) CONCLUSION: Here, our observation about lower susceptibility of HLA-E*01:01 + HLA-E*01:01 genotype to COVID-19 could be clinical evidence in support of some previous studies suggesting that the lower affinity of HLA-E*01:01 to peptides derived from the leader sequence of MHC class Ia may instead shift its binding to virus-derived peptides, which then facilitates target recognition by restricted conventional CD8+ T cells and leads to efficient cytolysis. On the other hand, according to other studies, less reactivity of HLA-E*01:01 with NKG2A abrogates NK cells or T cells inhibition, which may also lead to a greater cytotoxicity against SARS-CoV-2 infected cells compared to HLA-E*01:03. Taken together given HLA-E polymorphisms, the data presented here may be useful in identifying more vulnerable individuals to COVID-19 for better care and management. Especially since along with other risk factors in patients, having HLA-E*01:03 + HLA-E*01:03 genotype may also be associated with the possibility of severe cases of the disease.
| S-EPMC9922572 | biostudies-literature
Project description:SMC08 mRNA-Seq paired end data
| EGAD00001007175 | EGA
Project description:BackgroundThe polymorphisms of classical HLA-Ia and HLA-II loci have been associated with Posner-Schlossman syndrome (PSS) in the southern Chinese population. However, the associations of non-classical HLA-Ib (e.g., HLA-E and HLA-G) loci with PSS have not been reported for in the southern Chinese population. This study aimed to evaluate the associations of the HLA-E and HLA-G loci with PSS in a southern Chinese Han population group.MethodsNinety-seven unrelated patients with PSS and 90 ethnically matched control subjects were recruited from the Shenzhen Eye Hospital in China. The full-length sequences of HLA-E and HLA-G genes were amplified by long-range high-fidelity PCR, and the third exon of the HLA-E gene and the coding region of the HLA-G gene were sequenced.ResultsThe allele frequency of HLA-E*01:03 in patients with PSS was significantly higher than that in the control group (P=0.017, corrected P=0.034, OR =1.66). The genotype frequencies of HLA-E*01:01/01:03 and HLA-E*01:03/01:03 in the PSS group were significantly higher than that in the control group (P=0.027, OR =2.62; P=0.011, OR =3.05; respectively). There were no significant differences in the frequency of HLA-G alleles and genotypes between the two groups (all P>0.05). The haplotype frequency of HLA-E*01:03-G*01:01 in the PSS group was significantly higher than that in the control group (P=0.019, OR =1.63), although this association did not survive the Bonferroni correction (corrected P=0.13).ConclusionsThis study proved for the first time that HLA-E*01:03 and HLA-E*01:03-G*01:01 might be risk factors for PSS.
| S-EPMC6989981 | biostudies-literature
Project description:The emergence and establishment of SARS-CoV-2 variants of interest (VOI) and variants of concern (VOC) highlight the importance of genomic surveillance. We propose a statistical learning strategy (SLS) for identifying and spatiotemporally tracking potentially relevant Spike protein mutations. We analyzed 167,893 Spike protein sequences from US COVID-19 cases (excluding 21,391 sequences from VOI/VOC strains) deposited at GISAID from January 19, 2020 to March 15, 2021. Alignment against the reference Spike protein sequence led to the identification of viral residue variants (VRVs), i.e., residues harboring a substitution compared to the reference strain. Next, generalized additive models were applied to model VRV temporal dynamics, to identify VRVs with significant and substantial dynamics (false discovery rate q-value <0.01; maximum VRV proportion > 10% on at least one day). Unsupervised learning was then applied to hierarchically organize VRVs by spatiotemporal patterns and identify VRV-haplotypes. Finally, homology modelling was performed to gain insight into potential impact of VRVs on Spike protein structure. We identified 90 VRVs, 71 of which have not previously been observed in a VOI/VOC, and 35 of which have emerged recently and are durably present. Our analysis identifies 17 VRVs ∼91 days earlier than their first corresponding VOI/VOC publication. Unsupervised learning revealed eight VRV-haplotypes of 4 VRVs or more, suggesting two emerging strains (B1.1.222 and B.1.234). Structural modeling supported potential functional impact of the D1118H and L452R mutations. The SLS approach equally monitors all Spike residues over time, independently of existing phylogenic classifications, and is complementary to existing genomic surveillance methods.
| S-EPMC8219100 | biostudies-literature
Project description:Fat-adipocyte04 mRNA-Seq paired end data
| EGAD00001007158 | EGA
Project description:Islet-derived-MSC02 mRNA-Seq paired end data
| EGAD00001007161 | EGA
Project description: Not available
| S-EPMC6849947 | biostudies-literature
Project description:SMC05 mRNA-Seq paired end data
| EGAD00001007173 | EGA
Project description:SMC07 mRNA-Seq paired end data
| EGAD00001007174 | EGA