18 research outputs found

    HuR/ELAVL1 drives malignant peripheral nerve sheath tumor growth and metastasis

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    Cancer cells can develop a strong addiction to discrete molecular regulators, which control the aberrant gene expression programs that drive and maintain the cancer phenotype. Here, we report the identification of the RNA-binding protein HuR/ELAVL1 as a central oncogenic driver for malignant peripheral nerve sheath tumors (MPNSTs), which are highly aggressive sarcomas that originate from cells of the Schwann cell lineage. HuR was found to be highly elevated and bound to a multitude of cancer-associated transcripts in human MPNST samples. Accordingly, genetic and pharmacological inhibition of HuR had potent cytostatic and cytotoxic effects on tumor growth, and strongly suppressed metastatic capacity in vivo. Importantly, we linked the profound tumorigenic function of HuR to its ability to simultaneously regulate multiple essential oncogenic pathways in MPNST cells, including the Wnt/β-catenin, YAP/TAZ, RB/E2F, and BET pathways, which converge on key transcriptional networks. Given the exceptional dependency of MPNST cells on HuR for survival, proliferation, and dissemination, we propose that HuR represents a promising therapeutic target for MPNST treatment

    Clonal chromosomal mosaicism and loss of chromosome Y in elderly men increase vulnerability for SARS-CoV-2

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    The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, COVID-19) had an estimated overall case fatality ratio of 1.38% (pre-vaccination), being 53% higher in males and increasing exponentially with age. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, we found 133 cases (1.42%) with detectable clonal mosaicism for chromosome alterations (mCA) and 226 males (5.08%) with acquired loss of chromosome Y (LOY). Individuals with clonal mosaic events (mCA and/or LOY) showed a 54% increase in the risk of COVID-19 lethality. LOY is associated with transcriptomic biomarkers of immune dysfunction, pro-coagulation activity and cardiovascular risk. Interferon-induced genes involved in the initial immune response to SARS-CoV-2 are also down-regulated in LOY. Thus, mCA and LOY underlie at least part of the sex-biased severity and mortality of COVID-19 in aging patients. Given its potential therapeutic and prognostic relevance, evaluation of clonal mosaicism should be implemented as biomarker of COVID-19 severity in elderly people. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, individuals with clonal mosaic events (clonal mosaicism for chromosome alterations and/or loss of chromosome Y) showed an increased risk of COVID-19 lethality

    Genetic diversity of HLA system in two populations from Morelos, Mexico: Cuernavaca and rural Morelos

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    We studied HLA class I (HLA-A, -B) and class II (HLA-DRB1, -DQB1) alleles by PCR-SSP based typing in 112 Mexicans from the state of Morelos living in the city of Cuernavaca (N = 82) and rural communities (N = 30), to obtain information regarding allelic and haplotypic frequencies. The most frequent haplotypes in Morelos include seven Native American, one European, one African and one Asian haplotype. Admixture estimates revealed that the main genetic components in Morelos are Native American (60.43 ± 2.22 by ML; 53.57 of Native American haplotypes) and European (39.58 ± 3.70 by ML; 27.68 of European haplotypes), and a virtually absent African genetic component (0.00 ± 4.93 by ML; but 11.16 of African haplotypes)

    Genetic diversity of HLA system in six populations from Mexico City Metropolitan Area, Mexico: Mexico City North, Mexico City South, Mexico City East, Mexico City West, Mexico City Center and rural Mexico City

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    We studied HLA class I (HLA-A, -B) and class II (HLA-DRB1, -DQB1) alleles by PCR-SSP based typing in 1217 Mexicans from the Mexico City Metropolitan Area living in the northern (N = 751), southern (N = 52), eastern (N = 79), western (N = 33), and central (N = 152) Mexico City, and rural communities (N = 150), to obtain information regarding allelic and haplotypic frequencies. We found that the most frequent haplotypes include 11 Native American haplotypes. Admixture estimates revealed that the main genetic components are Native American (63.85 ± 1.55 by ML; 57.19 of Native American haplotypes) and European (28.53 ± 3.13 by ML; 28.40 of European haplotypes), and a less apparent African genetic component (7.61 ± 1.96 by ML; 7.17 of African haplotypes)

    Genetic diversity of HLA system in two populations from Hidalgo, Mexico: Pachuca and rural Hidalgo

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    We studied HLA class I (HLA-A, -B) and class II (HLA-DRB1, -DQB1) alleles by PCR-SSP based typing in 122 Mexicans from the state of Hidalgo living in the city of Pachuca (N = 41) and rural communities (N = 81), to obtain information regarding allelic and haplotypic frequencies. We find that the most frequent haplotypes in Hidalgo include eight Native American and one European haplotypes. Admixture estimates revealed that the main genetic components in Hidalgo are Native American (58.93 ± 2.16 by ML; 54.51 of Native American haplotypes) and European (32.49 ± 2.88 by ML; 28.69 of European haplotypes), and a relatively high African genetic component (8.58 ± 0.93 by ML; 6.97 of African haplotypes)

    Liver Fibrosis, Host Genetic and Hepatitis C Virus Related Parameters as Predictive Factors of Response to Therapy against Hepatitis C Virus in HIV/HCV Coinfected Patients

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    OBJECTIVE: To establish the role of liver fibrosis as a predictive tool of response to pegylated interferon alpha (Peg-IFN) and ribavirin (RBV) treatment in human immunodeficiency (HIV)/hepatitis C virus (HCV) coinfected patients, in addition to recognized predictive factors (HCV load, HCV genotype, IL-28B polymorphism). PATIENTS AND METHODS: A sample of 267 HIV/HCV coinfected patients was treated with Peg-IFN and RBV. Predictive factors of rapid (RVR) and sustained (SVR) virological response were analyzed. Independent variables were age, sex, IL28B, -238 TNF-α and -592 IL-10 polymorphisms, HCV genotype, HCV-RNA levels, significant fibrosis or cirrhosis and CD4+ T cell count. RESULTS: Patients infected by HCV genotype 1 (n = 187) showed RVR and SVR in 12% and 39% of cases, respectively. The parameters associated with RVR were IL28B genotype CC and plasma HCV-RNA levels <600,000 IU/ml. Advanced liver fibrosis was negatively associated with SVR in patients without RVR. A SVR was obtained in 42% of subjects with HCV genotype 4, and the independent factors associated with SVR were IL28B genotype CC and an HCV-RNA <600,000 IU/ml. A SVR was obtained in 66% of patients with HCV genotypes 2/3; in this case, the independent parameter associated with SVR was the absence of significant liver fibrosis. TNF-α and IL-10 polymorphisms were not associated with SVR, although a significantly higher percentage of -238 TNF-α genotype GG was detected in patients with significant liver fibrosis. CONCLUSIONS: In HIV/HCV coinfected patients with HCV genotypes 1 or 4, RVR, mainly influenced by genotype IL28B and HCV-RNA levels, reliably predicted SVR after 4 weeks of therapy with Peg-IFN plus RBV. In patients infected by HCV genotype 3, an elevated relapse rate compromised the influence of RVR on SVR. Relapses were related to the presence of advanced liver fibrosis. Liver cirrhosis was associated with a -238 TNF-α polymorphism in these patients

    The immunogenetic diversity of the HLA system in Mexico correlates with underlying population genetic structure

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    We studied HLA class I (HLA-A, -B) and class II (HLA-DRB1, -DQB1) allele groups and alleles by PCR-SSP based typing in a total of 15,318 mixed ancestry Mexicans from all the states of the country divided into 78 sample sets, providing information regarding allelic and haplotypic frequencies and their linkage disequilibrium, as well as admixture estimates and genetic substructure. We identified the presence of 4268 unique HLA extended haplotypes across Mexico and find that the ten most frequent (HF > 1%) HLA haplotypes with significant linkage disequilibrium (Δ’≥0.1) in Mexico (accounting for 20% of the haplotypic diversity of the country) are of primarily Native American ancestry (A*02~B*39~DRB1*04~DQB1*03:02, A*02~B*35~DRB1*08~DQB1*04, A*68~B*39~DRB1*04~DQB1*03:02, A*02~B*35~DRB1*04~DQB1*03:02, A*24~B*39~DRB1*14~DQB1*03:01, A*24~B*35~DRB1*04~DQB1*03:02, A*24~B*39~DRB1*04~DQB1*03:02, A*02~B*40:02~DRB1*04~DQB1*03:02, A*68~B*35~DRB1*04~DQB1*03:02, A*02~B*15:01~DRB1*04~DQB1*03:02). Admixture estimates obtained by a maximum likelihood method using HLA-A/-B/-DRB1 as genetic estimators revealed that the main genetic components in Mexico as a whole are Native American (ranging from 37.8% in the northern part of the country to 81.5% in the southeastern region) and European (ranging from 11.5% in the southeast to 62.6% in northern Mexico). African admixture ranged from 0.0 to 12.7% not following any specific pattern. We were able to detect three major immunogenetic clusters correlating with genetic diversity and differential admixture within Mexico: North, Central and Southeast, which is in accordance with previous reports using genome-wide data. Our findings provide insights into the population immunogenetic substructure of the whole country and add to the knowledge of mixed ancestry Latin American population genetics, important for disease association studies, detection of demographic signatures on population variation and improved allocation of public health resources.1 Introduction 2 Subjects, materials and methods 2.1 Subjects 2.2 HLA typing 2.3 Statistical analysis 2.3.1 HLA allelic and haplotypic diversity 2.3.2 Admixture proportions calculations 2.3.3 Genetic diversity and genetic substructure assessment 3 Results 3.1 HLA allele groups 3.2 Haplotypic diversity 3.3 Admixture estimates 3.4 Genetic diversity and genetic substructure assessment 4 Discussion 4.1 Admixture estimates in Mexican populations and immunogenetic diversity 4.2 The Native American immunogenetic component in Mexican populations 4.3 Implications of the study of alleles and haplotypes of the HLA system in Mexican populations and final considerations 5 Conclusio
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