11 research outputs found

    Genetic landscape in Russian patients with familial left ventricular noncompaction

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    BackgroundLeft ventricular noncompaction (LVNC) cardiomyopathy is a disorder that can be complicated by heart failure, arrhythmias, thromboembolism, and sudden cardiac death. The aim of this study is to clarify the genetic landscape of LVNC in a large cohort of well-phenotyped Russian patients with LVNC, including 48 families (n=214).MethodsAll index patients underwent clinical examination and genetic analysis, as well as family members who agreed to participate in the clinical study and/or in the genetic testing. The genetic testing included next generation sequencing and genetic classification according to ACMG guidelines.ResultsA total of 55 alleles of 54 pathogenic and likely pathogenic variants in 24 genes were identified, with the largest number in the MYH7 and TTN genes. A significant proportion of variants −8 of 54 (14.8%) −have not been described earlier in other populations and may be specific to LVNC patients in Russia. In LVNC patients, the presence of each subsequent variant is associated with increased odds of having more severe LVNC subtypes than isolated LVNC with preserved ejection fraction. The corresponding odds ratio is 2.77 (1.37 −7.37; p <0.001) per variant after adjustment for sex, age, and family.ConclusionOverall, the genetic analysis of LVNC patients, accompanied by cardiomyopathy-related family history analysis, resulted in a high diagnostic yield of 89.6%. These results suggest that genetic screening should be applied to the diagnosis and prognosis of LVNC patients

    High Mutability of the Tumor Suppressor Genes RASSF1 and RBSP3 (CTDSPL) in Cancer

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    BACKGROUND:Many different genetic alterations are observed in cancer cells. Individual cancer genes display point mutations such as base changes, insertions and deletions that initiate and promote cancer growth and spread. Somatic hypermutation is a powerful mechanism for generation of different mutations. It was shown previously that somatic hypermutability of proto-oncogenes can induce development of lymphomas. METHODOLOGY/PRINCIPAL FINDINGS:We found an exceptionally high incidence of single-base mutations in the tumor suppressor genes RASSF1 and RBSP3 (CTDSPL) both located in 3p21.3 regions, LUCA and AP20 respectively. These regions contain clusters of tumor suppressor genes involved in multiple cancer types such as lung, kidney, breast, cervical, head and neck, nasopharyngeal, prostate and other carcinomas. Altogether in 144 sequenced RASSF1A clones (exons 1-2), 129 mutations were detected (mutation frequency, MF = 0.23 per 100 bp) and in 98 clones of exons 3-5 we found 146 mutations (MF = 0.29). In 85 sequenced RBSP3 clones, 89 mutations were found (MF = 0.10). The mutations were not cytidine-specific, as would be expected from alterations generated by AID/APOBEC family enzymes, and appeared de novo during cell proliferation. They diminished the ability of corresponding transgenes to suppress cell and tumor growth implying a loss of function. These high levels of somatic mutations were found both in cancer biopsies and cancer cell lines. CONCLUSIONS/SIGNIFICANCE:This is the first report of high frequencies of somatic mutations in RASSF1 and RBSP3 in different cancers suggesting it may underlay the mutator phenotype of cancer. Somatic hypermutations in tumor suppressor genes involved in major human malignancies offer a novel insight in cancer development, progression and spread

    Analysis of forces that determine helix formation in α-proteins

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    A model for prediction of α-helical regions in amino acid sequences has been tested on the mainly-α protein structure class. The modeling represents the construction of a continuous hypothetical α-helical conformation for the whole protein chain, and was performed using molecular mechanics tools. The positive prediction of α-helical and non-α-helical pentapeptide fragments of the proteins is 79%. The model considers only local interactions in the polypeptide chain without the influence of the tertiary structure. It was shown that the local interaction defines the α-helical conformation for 85% of the native α-helical regions. The relative energy contributions to the energy of the model were analyzed with the finding that the van der Waals component determines the formation of α-helices. Hydrogen bonds remain at constant energy independently whether α-helix or non-α-helix occurs in the native protein, and do not determine the location of helical regions. In contrast to existing methods, this approach additionally permits the prediction of conformations of side chains. The model suggests the correct values for ~60% of all χ-angles of α-helical residues

    NotI flanking sequences: a tool for gene discovery and verification of the human genome

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    A set of 22 551 unique human NotI flanking sequences (16.2 Mb) was generated. More than 40% of the set had regions with significant similarity to known proteins and expressed sequences. The data demonstrate that regions flanking NotI sites are less likely to form nucleosomes efficiently and resemble promoter regions. The draft human genome sequence contained 55.7% of the NotI flanking sequences, Celera’s database contained matches to 57.2% of the clones and all public databases (including non-human and previously sequenced NotI flanks) matched 89.2% of the NotI flanking sequences (identity ≄90% over at least 50 bp, data from December 2001). The data suggest that the shotgun sequencing approach used to generate the draft human genome sequence resulted in a bias against cloning and sequencing of NotI flanks. A rough estimation (based primarily on chromosomes 21 and 22) is that the human genome contains 15 000–20 000 NotI sites, of which 6000–9000 are unmethylated in any particular cell. The results of the study suggest that the existing tools for computational determination of CpG islands fail to identify a significant fraction of functional CpG islands, and unmethylated DNA stretches with a high frequency of CpG dinucleotides can be found even in regions with low CG content

    NotI Microarrays: Novel Epigenetic Markers for Early Detection and Prognosis of High Grade Serous Ovarian Cancer

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    Chromosome 3-specific NotI microarray (NMA) containing 180 clones with 188 genes was used in the study to analyze 18 high grade serous ovarian cancer (HGSOC) samples and 7 benign ovarian tumors. We aimed to find novel methylation-dependent biomarkers for early detection and prognosis of HGSOC. Thirty five NotI markers showed frequency of methylation/deletion more or equal to 17%. To check the results of NMA hybridizations several samples for four genes (LRRC3B, THRB, ITGA9 and RBSP3 (CTDSPL)) were bisulfite sequenced and confirmed the results of NMA hybridization. A set of eight biomarkers: NKIRAS1/RPL15, THRB, RBPS3 (CTDSPL), IQSEC1, NBEAL2, ZIC4, LOC285205 and FOXP1, was identified as the most prominent set capable to detect both early and late stages of ovarian cancer. Sensitivity of this set is equal to (72 ± 11)% and specificity (94 ± 5)%. Early stages represented the most complicated cases for detection. To distinguish between Stages I + II and Stages III + IV of ovarian cancer the most perspective set of biomarkers would include LOC285205, CGGBP1, EPHB1 and NKIRAS1/RPL15. The sensitivity of the set is equal to (80 ± 13)% and the specificity is (88 ± 12)%. Using this technique we plan to validate this panel with new epithelial ovarian cancer samples and add markers from other chromosomes

    Measurement of the Branching Fraction of B0→J/ψπ0B^{0} \rightarrow J/\psi \pi^{0} Decays

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    International audienceThe ratio of branching fractions between B0→J/ψπ0B^{0} \rightarrow J/\psi \pi^{0} and B+→J/ψK∗+B^{+} \rightarrow J/\psi K^{*+} decays is measured with proton-proton collision data collected by the LHCb experiment, corresponding to an integrated luminosity of 9 fb−1^{-1}. The measured value is BB0→J/ψπ0BB+→J/ψK∗+=(1.153±0.053±0.048)×10−2\frac{\mathcal{B}_{B^{0} \rightarrow J/\psi \pi^{0}}}{\mathcal{B}_{B^{+} \rightarrow J/\psi K^{*+}}} = (1.153 \pm 0.053 \pm 0.048 ) \times 10^{-2}, where the first uncertainty is statistical and the second is systematic. The branching fraction for B0→J/ψπ0B^{0} \rightarrow J/\psi \pi^{0} decays is determined using the branching fraction of the normalisation channel, resulting in BB0→J/ψπ0=(1.670±0.077±0.069±0.095)×10−5\mathcal{B}_{B^{0} \rightarrow J/\psi \pi^{0}} = (1.670 \pm 0.077 \pm 0.069 \pm 0.095) \times 10^{-5}, where the last uncertainty corresponds to that of the external input. This result is consistent with the current world average value and competitive with the most precise single measurement to date

    Transverse polarisation measurement of Λ\Lambda hyperons in ppNe collisions at sNN\sqrt{s_{NN}}=68.4 GeV with the LHCb detector

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    A measurement of the transverse polarization of the Λ\Lambda and Λˉ\bar{\Lambda}hyperons in ppNe fixed-target collisions at sNN\sqrt{s_{NN}}=68.4 GeV is presented using data collected by the LHCb detector. The polarization is studied using the decay Λ→pπ−\Lambda \rightarrow p \pi^- together with its charge conjugated process, the integrated values measured are PΛ=0.029±0.019 (stat)±0.012 (syst) , P_{\Lambda} = 0.029 \pm 0.019 \, (\rm{stat}) \pm 0.012 \, (\rm{syst}) \, , PΛˉ=0.003±0.023 (stat)±0.014 (syst)  P_{\bar{\Lambda}} = 0.003 \pm 0.023 \, (\rm{stat}) \pm 0.014 \,(\rm{syst}) \, Furthermore, the results are shown as a function of the Feynman xx variable, transverse momentum, pseudorapidity and rapidity of the hyperons, and are compared with previous measurements.A measurement of the transverse polarization of the Λ\Lambda and Λˉ\bar{\Lambda} hyperons in ppNe fixed-target collisions at sNN\sqrt{s_{NN}} = 68.4 GeV is presented using data collected by the LHCb detector. The polarization is studied using the decay Λ→pπ−\Lambda \rightarrow p \pi^- together with its charge conjugated process, the integrated values measured are PΛ=0.029±0.019 (stat)±0.012 (syst) , P_{\Lambda} = 0.029 \pm 0.019 \, (\rm{stat}) \pm 0.012 \, (\rm{syst}) \, , PΛˉ=0.003±0.023 (stat)±0.014 (syst) . P_{\bar{\Lambda}} = 0.003 \pm 0.023 \, (\rm{stat}) \pm 0.014 \,(\rm{syst}) \,. Furthermore, the results are shown as a function of the Feynman~xx~variable, transverse momentum, pseudorapidity and rapidity of the hyperons, and are compared with previous measurements
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