<i>SELP</i> Asp603Asn and severe thrombosis in COVID-19 males: implication for anti P-selectin monoclonal antibodies treatment

Fallerini, Chiara; Daga, Sergio; Benetti, Elisa; Picchiotti, Nicola; Zguro, Kristina; Catapano, Francesca; Baroni, Virginia; Lanini, Simone; Bucalossi, Alessandro; Marotta, Giuseppe; Baldassarri, Margherita; Fava, Francesca; Beligni, Giada; Sarno, Laura; Alaverdian, Diana; Palmieri, Maria; Croci, Susanna; Isidori, Andrea M.; Furini, Simone; Frullanti, Elisa; Study, Gen-Covid Multicenter; Renieri, Alessandra; Mari, Francesca

doi:10.1101/2021.05.25.21257803

Cited by 5 publications

(18 citation statements)

References 26 publications

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“…Many more genes are expected to be involved in COVID-19 than the dozens reported thus far. These genes are likely to include a relatively small number of Frontiers in Genetics frontiersin.org core genes and a much larger number of peripheral genes carrying both common and rare variants (Fallerini et al, 2022;Zguro et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

COVID-19 severity: does the genetic landscape of rare variants matter?

et al. 2023

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Rare variants affecting host defense against pathogens may be involved in COVID-19 severity, but most rare variants are not expected to have a major impact on the course of COVID-19. We hypothesized that the accumulation of weak effects of many rare functional variants throughout the exome may contribute to the overall risk in patients with severe disease. This assumption is consistent with the omnigenic model of the relationship between genetic and phenotypic variation in complex traits, according to which association signals tend to spread across most of the genome through gene regulatory networks from genes outside the major pathways to disease-related genes. We performed whole-exome sequencing and compared the burden of rare variants in 57 patients with severe and 29 patients with mild/moderate COVID-19. At the whole-exome level, we observed an excess of rare, predominantly high-impact (HI) variants in the group with severe COVID-19. Restriction to genes intolerant to HI or damaging missense variants increased enrichment for these classes of variants. Among various sets of genes, an increased signal of rare HI variants was demonstrated predominantly for primary immunodeficiency genes and the entire set of genes associated with immune diseases, as well as for genes associated with respiratory diseases. We advocate taking the ideas of the omnigenic model into account in COVID-19 studies.

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Section: Discussionmentioning

confidence: 99%

COVID-19 severity: does the genetic landscape of rare variants matter?

et al. 2023

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“…Lastly, rs6127 is mapped to the SELP gene, which codes for selectin P, a cell adhesion molecule (146). In a recent whole exome sequencing study, the polymorphism was found to be associated with thrombosis and COVID-19 severity in male patients (147). Overall, the COVID-19 related SNPs which fall into the cluster of high allele frequency are located in varying genomic regions, from introns and exons to intergenic regions.…”

Section: Resultsmentioning

confidence: 99%

Semantic and population analysis of the genetic targets related to COVID-19 and its association with genes and diseases

Papageorgiou

Papakonstantinou

Diakou

et al. 2022

Preprint

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SARS-CoV-2 is a coronavirus responsible for one of the most serious, modern worldwide pandemics, with lasting and multi-faceted effects. By late 2021, SARS-CoV-2 has infected more than 180 million people and has killed more than 3 million. The virus gains entrance to human cells through binding to ACE2 via its surface spike protein and causes a complex disease of the respiratory system, termed COVID-19. Vaccination efforts are being made to hinder the viral spread and therapeutics are currently under development. Towards this goal, scientific attention is shifting towards variants and SNPs that affect factors of the disease such as susceptibility and severity. This genomic grammar, tightly related to the dark part of our genome, can be explored through the use of modern methods such as natural language processing. We present a semantic analysis of SARS-CoV-2 related publications, which yielded a repertoire of SNPs, genes and disease ontologies. Population data from the 100Genomes Project were subsequently integrated into the pipeline. Data mining approaches of this scale have the potential to elucidate the complex interaction between COVID-19 pathogenesis and host genetic variation; the resulting knowledge can facilitate the management of high-risk groups and aid the efforts towards precision medicine.

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“…At the contrary, machine learning (ML) approaches offer an innovative tool for managing complex problems by significantly increasing our capacity to identify complex patterns of variations. Using data from the whole exome sequencing (WES), a first line of the ML method, i.e., a LASSO logistic regression, has been applied to extract some thousands of coding genetic features contributing to COVID-19 severity Picchiotti et al (2021); Fallerini et al (2022). Subsequent functional validation of extracted features demonstrated that, in each tested case, the association with severity has a biological basis and suggested hints for adjuvant treatment Benetti et al (2020b); Fallerini et al (2021b,a); Croci et al (2022); Baldassarri et al (2021b,a); Mantovani et al (2022); Monticelli et al (2021).…”

Section: Introductionmentioning

confidence: 99%

“…Subsequent functional validation of extracted features demonstrated that, in each tested case, the association with severity has a biological basis and suggested hints for adjuvant treatment Benetti et al (2020b); Fallerini et al (2021b,a); Croci et al (2022); Baldassarri et al (2021b,a); Mantovani et al (2022); Monticelli et al (2021). Using the extracted features, Fallerini et al (2022) build a severity score named the integrated polygenic score (IPGS), whose performances reached about 75% for both sensitivity and specificity. In this contribution, we want to improve the IPGS severity score performances, with the aim of increasing both metrics and the understanding of biomolecular mechanisms for personalized treatment using innovative ML methods.…”

Section: Introductionmentioning

confidence: 99%

“…In this contribution, we want to improve the IPGS severity score performances, with the aim of increasing both metrics and the understanding of biomolecular mechanisms for personalized treatment using innovative ML methods. More in detail, we start from the same set of coding genetic features contributing to COVID-19 severity, already used in Picchiotti et al (2021);Fallerini et al (2022), to build two new severity scores that take into account the phenotype of the analyzed patients, i.e., the set of their observable characteristics or traits. In particular, we take into account, in the definition of the severity scores, the involvement of single organs in the development of the COVID-19 disease and the age of patients when they contract the virus.…”

Section: Introductionmentioning

confidence: 99%

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Host genetics and COVID-19 severity: increasing the accuracy of latest severity scores by Boolean quantum features

Martelloni,

Turchi,

Fallerini

et al. 2024

Front. Genet.

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The impact of common and rare variants in COVID-19 host genetics has been widely studied. In particular, in Fallerini et al. (Human genetics, 2022, 141, 147–173), common and rare variants were used to define an interpretable machine learning model for predicting COVID-19 severity. First, variants were converted into sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. After that, the Boolean features, selected by these logistic models, were combined into an Integrated PolyGenic Score (IPGS), which offers a very simple description of the contribution of host genetics in COVID-19 severity.. IPGS leads to an accuracy of 55%–60% on different cohorts, and, after a logistic regression with both IPGS and age as inputs, it leads to an accuracy of 75%. The goal of this paper is to improve the previous results, using not only the most informative Boolean features with respect to the genetic bases of severity but also the information on host organs involved in the disease. In this study, we generalize the IPGS adding a statistical weight for each organ, through the transformation of Boolean features into “Boolean quantum features,” inspired by quantum mechanics. The organ coefficients were set via the application of the genetic algorithm PyGAD, and, after that, we defined two new integrated polygenic scores (IPGSph1 and IPGSph2). By applying a logistic regression with both IPGS, (IPGSph2 (or indifferently IPGSph1) and age as inputs, we reached an accuracy of 84%–86%, thus improving the results previously shown in Fallerini et al. (Human genetics, 2022, 141, 147–173) by a factor of 10%.

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SELP Asp603Asn and severe thrombosis in COVID-19 males: implication for anti P-selectin monoclonal antibodies treatment

Cited by 5 publications

References 26 publications

COVID-19 severity: does the genetic landscape of rare variants matter?

COVID-19 severity: does the genetic landscape of rare variants matter?

Semantic and population analysis of the genetic targets related to COVID-19 and its association with genes and diseases

Host genetics and COVID-19 severity: increasing the accuracy of latest severity scores by Boolean quantum features

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