123 research outputs found

    Na+/I- symporter and type 3 iodothyronine deiodinase gene expression in amniotic membrane and placenta and its relationship to maternal thyroid hormones

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    Placental type 3 iodothyronine deiodinase (D3) potentially protects the fetus from the elevated maternal thyroid hormones. Na+/I- symporter (NIS) is a plasma membrane glycoprotein, which mediates active iodide uptake. Our objectives were to establish the distribution of NIS and D3 gene expressions in the placenta and the amniotic membrane and to investigate the relationship between placental D3 and NIS gene expressions and maternal iodine, selenium, and thyroid hormone status. Thyroid hormones, urinary iodine concentration (UIC), and selenium levels were measured in 49 healthy term pregnant women. NIS and D3 gene expressions were studied with the total mRNA RT-PCR method in tissues from maternal placenta (n = 49), fetal placenta (n = 9), and amniotic membrane (n = 9). NIS and D3 gene expressions were shown in the fetal and maternal sides of the placenta and amniotic membrane. Mean blood selenium level was 66 ± 26.5 μg/l, and median UIC was 143 μg/l. We could not demonstrate any statistically significant relationship of spot UIC and blood selenium with NIS and D3 expression (p > 0.05). Positive correlations were found between NIS and thyroxine-binding globulin (TBG) (r = 0.3, p = 0.042) and between D3 and preoperative glucose levels (r = 0.4, p = 0.006). D3 and NIS genes are expressed in term placenta and amniotic membrane; thus, in addition to placenta, amniotic membrane contributes to regulation of maternofetal iodine and thyroid hormone transmission. Further studies are needed to clarify the relationship between maternal glucose levels and placental D3 expression and between TBG and placental NIS expression. © 2013 Springer Science+Business Media New York

    Lichen planus and Hepatitis C: a case-control study

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    BACKGROUND: The association of lichen planus with hepatitis C (HCV) has been widely reported in the literature. However, there are wide geographical variations in the reported prevalence of HCV infection in patients with lichen planus. This study was conducted to determine the frequency of hepatitis C in Iranian patients with lichen planus at Razi hospital, Tehran. METHODS: During the years 1997 and 1998, 146 cases of lichen planus, 78 (53.1%) women and 69 (46.9%) men were diagnosed. They were diagnosed on the basis of the usual clinical features and, if necessary, typical histological findings. The patients were screened for the presence of anti-HCV antibodies by third generation ELISA and liver function tests. We used the results from screening of blood donors for anti HCV (carried out by Iranian Blood Transfusion Organization) for comparison as the control group. RESULTS: Anti-HCV antibodies were detected in seven cases (4.8%). This was significantly higher than that of the blood donors' antibodies (p < 0.001). The odds ratio was 50.37(21.45–112.24). A statistically significant association was demonstrated between erosive lichen planus and HCV infection. Liver function tests were not significantly different between HCV infected and non-infected patients. CONCLUSION: HCV apears to have an etiologic role for lichen planus in Iranian patients. On the other hand, liver function tests are not good screening means for HCV infection

    A multi-scale analysis of bull sperm methylome revealed both species peculiarities and conserved tissue-specific

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    peer-reviewedBackground: Spermatozoa have a remarkable epigenome in line with their degree of specialization, their unique nature and different requirements for successful fertilization. Accordingly, perturbations in the establishment of DNA methylation patterns during male germ cell differentiation have been associated with infertility in several species.Background: Spermatozoa have a remarkable epigenResults: The quantification of DNA methylation at CCGG sites using luminometric methylation assay (LUMA) highlighted the undermethylation of bull sperm compared to the sperm of rams, stallions, mice, goats and men. Total blood cells displayed a similarly high level of methylation in bulls and rams, suggesting that undermethylation of the bovine genome was specific to sperm. Annotation of CCGG sites in different species revealed no striking bias in the distribution of genome features targeted by LUMA that could explain undermethylation of bull sperm. To map DNA methylation at a genome-wide scale, bull sperm was compared with bovine liver, fibroblasts and monocytes using reduced representation bisulfite sequencing (RRBS) and immunoprecipitation of methylated DNA followed by microarray hybridization (MeDIP-chip). These two methods exhibited differences in terms of genome coverage, and consistently, two independent sets of sequences differentially methylated in sperm and somatic cells were identified for RRBS and MeDIP-chip. Remarkably, in the two sets most of the differentially methylated sequences were hypomethylated in sperm. In agreement with previous studies in other species, the sequences that were specifically hypomethylated in bull sperm targeted processes relevant to the germline differentiation program (piRNA metabolism, meiosis, spermatogenesis) and sperm functions (cell adhesion, fertilization), as well as satellites and rDNA repeats. Conclusions: These results highlight the undermethylation of bull spermatozoa when compared with both bovine somatic cells and the sperm of other mammals, and raise questions regarding the dynamics of DNA methylation in bovine male germline. Whether sperm undermethylation has potential interactions with structural variation in the cattle genome may deserve further attention. While bull semen is widely used in artificial insemination, the literature describing DNA methylation in bull spermatozoa is still scarce. The purpose of this study was therefore to characterize the bull sperm methylome relative to both bovine somatic cells and the sperm of other mammals through a multiscale analysis

    Therapeutic targeting of ependymoma as informed by oncogenic enhancer profiling

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    Genomic sequencing has driven precision-based oncology therapy; however, the genetic drivers of many malignancies remain unknown or non-targetable, so alternative approaches to the identification of therapeutic leads are necessary. Ependymomas are chemotherapy-resistant brain tumours, which, despite genomic sequencing, lack effective molecular targets. Intracranial ependymomas are segregated on the basis of anatomical location (supratentorial region or posterior fossa) and further divided into distinct molecular subgroups that reflect differences in the age of onset, gender predominance and response to therapy1,2,3. The most common and aggressive subgroup, posterior fossa ependymoma group A (PF-EPN-A), occurs in young children and appears to lack recurrent somatic mutations2. Conversely, posterior fossa ependymoma group B (PF-EPN-B) tumours display frequent large-scale copy number gains and losses but have favourable clinical outcomes1,3. More than 70% of supratentorial ependymomas are defined by highly recurrent gene fusions in the NF-κB subunit gene RELA (ST-EPN-RELA), and a smaller number involve fusion of the gene encoding the transcriptional activator YAP1 (ST-EPN-YAP1)1,3,4. Subependymomas, a distinct histologic variant, can also be found within the supratetorial and posterior fossa compartments, and account for the majority of tumours in the molecular subgroups ST-EPN-SE and PF-EPN-SE. Here we describe mapping of active chromatin landscapes in 42 primary ependymomas in two non-overlapping primary ependymoma cohorts, with the goal of identifying essential super-enhancer-associated genes on which tumour cells depend. Enhancer regions revealed putative oncogenes, molecular targets and pathways; inhibition of these targets with small molecule inhibitors or short hairpin RNA diminished the proliferation of patient-derived neurospheres and increased survival in mouse models of ependymomas. Through profiling of transcriptional enhancers, our study provides a framework for target and drug discovery in other cancers that lack known genetic drivers and are therefore difficult to treat.This work was supported by an Alex's Lemonade Stand Young Investigator Award (S.C.M.), The CIHR Banting Fellowship (S.C.M.), The Cancer Prevention Research Institute of Texas (S.C.M., RR170023), Sibylle Assmus Award for Neurooncology (K.W.P.), the DKFZ-MOST (Ministry of Science, Technology & Space, Israel) program in cancer research (H.W.), James S. McDonnell Foundation (J.N.R.) and NIH grants: CA154130 (J.N.R.), R01 CA169117 (J.N.R.), R01 CA171652 (J.N.R.), R01 NS087913 (J.N.R.) and R01 NS089272 (J.N.R.). R.C.G. is supported by NIH grants T32GM00725 and F30CA217065. M.D.T. is supported by The Garron Family Chair in Childhood Cancer Research, and grants from the Pediatric Brain Tumour Foundation, Grand Challenge Award from CureSearch for Children’s Cancer, the National Institutes of Health (R01CA148699, R01CA159859), The Terry Fox Research Institute and Brainchild. M.D.T. is also supported by a Stand Up To Cancer St. Baldrick’s Pediatric Dream Team Translational Research Grant (SU2C-AACR-DT1113)

    The sperm factor: paternal impact beyond genes

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    The fact that sperm carry more than the paternal DNA has only been discovered just over a decade ago. With this discovery, the idea that the paternal condition may have direct implications for the fitness of the offspring had to be revisited. While this idea is still highly debated, empirical evidence for paternal effects is accumulating. Male condition not only affects male fertility but also offspring early development and performance later in life. Several factors have been identified as possible carriers of non-genetic information, but we still know little about their origin and function and even less about their causation. I consider four possible non-mutually exclusive adaptive and non-adaptive explanations for the existence of paternal effects in an evolutionary context. In addition, I provide a brief overview of the main non-genetic components found in sperm including DNA methylation, chromatin modifications, RNAs and proteins. I discuss their putative functions and present currently available examples for their role in transferring non-genetic information from the father to the offspring. Finally, I identify some of the most important open questions and present possible future research avenues

    Pan-cancer analysis of whole genomes

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    Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

    Genomic basis for RNA alterations in cancer.

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    Transcript alterations often result from somatic changes in cancer genomes1. Various forms of RNA alterations have been described in cancer, including overexpression2, altered splicing3 and gene fusions4; however, it is difficult to attribute these to underlying genomic changes owing to heterogeneity among patients and tumour types, and the relatively small cohorts of patients for whom samples have been analysed by both transcriptome and whole-genome sequencing. Here we present, to our knowledge, the most comprehensive catalogue of cancer-associated gene alterations to date, obtained by characterizing tumour transcriptomes from 1,188 donors of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA)5. Using matched whole-genome sequencing data, we associated several categories of RNA alterations with germline and somatic DNA alterations, and identified probable genetic mechanisms. Somatic copy-number alterations were the major drivers of variations in total gene and allele-specific expression. We identified 649 associations of somatic single-nucleotide variants with gene expression in cis, of which 68.4% involved associations with flanking non-coding regions of the gene. We found 1,900 splicing alterations associated with somatic mutations, including the formation of exons within introns in proximity to Alu elements. In addition, 82% of gene fusions were associated with structural variants, including 75 of a new class, termed 'bridged' fusions, in which a third genomic location bridges two genes. We observed transcriptomic alteration signatures that differ between cancer types and have associations with variations in DNA mutational signatures. This compendium of RNA alterations in the genomic context provides a rich resource for identifying genes and mechanisms that are functionally implicated in cancer
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