Structures of two major histocompatibility complex class I genes of the rainbow trout (Oncorhynchus mykiss)

Abstract. Here we describe two rainbow trout major histocompatibility complex (MHC) class I genes characterized from 5 phage genomic clones prepared from a single fish. Clone GC71 contains all exons except a leader peptide-encoding exon. An open reading frame is maintained, and thus the gene MhcOnmy-U71 could be expressed in this individual. The class I gene found on clone GC41 lacks exons encoding the leader peptide and cytoplasmic domain. This gene, MhcOnmy-U41p, is a pseudogene due to a deletion in the !2 domain-encoding exon causing premature termination. Both the Onmy-U71 and Onmy-U41p genes are distinguished by long introns between the exons encoding the !1 and !2 domains. Clone GC41 also contains the 3' exons of the LMP7/PSMB8 gene encoding the ânterferon-induced proteosome subunit of rainbow trout

Unique haplotypes of co-segregating major histocompatibility class II A and class II B alleles in Atlantic salmon (Salmo salar) give rise to diverse class II genotypes

Sequence-based typing of a breeding population (G1) consisting of 84 Atlantic salmon individuals revealed the presence of 7 Sasa-DAA and 7 Sasa-DAB expressed alleles. Subsequent typing of 1,182 individuals belonging to 33 families showed that Sasa-DAA and Sasa-DAB segregate as haplotypes. In total seven unique haplotypes were established, with frequencies in the population studied ranging from 0.01 to 0.49. Each haplotype is characterized by a unique minisatellite marker size embedded in the 3' untranslated region of the Sasa-DAA gene. These data corroborate the fact that Atlantic salmon express a single class II locus, consisting of tightly linked class II A and class B genes. The seven haplotypes give rise to 15 genotypes with frequencies varying between 0.01 and 0.23; 21 class II homozygous individuals were present in the G1 population. We also studied the frequency distribution in another breeding population (G4, n=374) using the minisatellite marker. Only one new marker size was present, suggesting the presence of one new class II haplotype. The marker frequency distribution in the G4 population differed markedly from the G1 population. The genomic organizations of two Sasa-DAA and Sasa-DAB alleles were determined, and supported the notion that these alleles belong to the same locus. In contrast to other studies of salmonid class II sequences, phylogenetic analyses of brown trout and Atlantic class II A and class II B sequences provided support for trans-species polymorphis

Modes of salmonid MHC class I and II evolution differ from the primate paradigm

Rainbow trout (Oncorhynchus mykiss) and brown trout (Salmo trutta) represent two salmonid genera separated for 15-20 million years. cDNA sequences were determined for the classical MHC class I heavy chain gene UBA and the MHC class II β-chain gene DAB from 15 rainbow and 10 brown trout. Both genes are highly polymorphic in both species and diploid in expression. The MHC class I alleles comprise several highly divergent lineages that are represented in both species and predate genera separation. The class II alleles are less divergent, highly species specific, and probably arose after genera separation. The striking difference in salmonid MHC class I and class II evolution contrasts with the situation in primates, where lineages of class II alleles have been sustained over longer periods of time relative to class I lineages. The difference may arise because salmonid MHC class I and II genes are not linked, whereas in mammals they are closely linked. A prevalent mechanism for evolving new MHC class I alleles in salmonids is recombination in intron II that shuffles α1 and α2 domains into different combinations

Dynamics of the coastal zone

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At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients. © The Author(s) 2024