The helper component-proteinase (HC-Pro) is a multifunctional protein found among potyviruses. With respect to its silencing suppressor function, small RNA binding appears to be the major activity of HC-Pro. HC-Pro could also exhibit other suppressor activities. HC-Pro may inhibit the Hua Enhancer 1 (HEN1) activity. There is indirect evidence showing that either transient or stable expression of HC-Pro in plants results in an increase of non-methylated small RNAs. Here, we demonstrated that recombinant Zucchini yellow mosaic virus (ZYMV) HC-Pro inhibited the methyltransferase activity of HEN1 in vitro. Moreover, we found that the HC-Pro FINK mutant, which has lost small RNA-binding activity, inhibited HEN1 activity, while the truncated proteins and total soluble bacterial proteins did not. Using the ELISA-binding assay, we provided evidence that the HC-Pro FRNK wild-type and HC-Pro FINK both bound to HEN1, with HC-Pro FRNK binding stronger than HC-Pro FINK. Motif mapping analysis revealed that the amino acids located between positions 139 and 320 of ZYMV HC-Pro were associated with HEN1 interaction.
Introduction Amplicon deep-sequencing using second-generation sequencing technology is an innovative molecular diagnostic technique and enables a highly-sensitive detection of mutations. As an international consortium we had investigated previously the robustness, precision, and reproducibility of 454 amplicon next-generation sequencing (NGS) across 10 laboratories from 8 countries (Leukemia, 2011;25:1840-8). Aims In Phase II of the study, we established distinct working groups for various hematological malignancies, i.e. acute myeloid leukemia (AML), acute lymphoblastic leukemia (ALL), chronic lymphocytic leukemia (CLL), chronic myelogenous leukemia (CML), myelodysplastic syndromes (MDS), myeloproliferative neoplasms (MPN), and multiple myeloma. Currently, 27 laboratories from 13 countries are part of this research consortium. In total, 74 gene targets were selected by the working groups and amplicons were developed for a NGS deep-sequencing assay (454 Life Sciences, Branford, CT). A data analysis pipeline was developed to standardize mutation interpretation both for accessing raw data (Roche Amplicon Variant Analyzer, 454 Life Sciences) and variant interpretation (Sequence Pilot, JSI Medical Systems, Kippenheim, Germany). Results We will report on the design, standardization, quality control aspects, landscape of mutations, as well as the prognostic and predictive utility of this assay in a cohort of 8,867 cases. Overall, 1,146 primer sequences were designed and tested. In detail, for example in AML, 924 cases had been screened for CEBPA mutations. RUNX1 mutations were analyzed in 1,888 cases applying the deep-sequencing read counts to study the stability of such mutations at relapse and their utility as a biomarker to detect residual disease. Analyses of DNMT3A (n=1,041) were focused to perform landscape investigations and to address the prognostic relevance. Additionally, this working group is focusing on TET2, ASXL1, and TP53 analyses. A novel prognostic model is being developed allowing stratification of AML into prognostic subgroups based on molecular markers only. In ALL, 1,124 pediatric and adult cases have been screened, including 763 assays for TP53 mutations both at diagnosis and relapse of ALL. Pediatric and adult leukemia expert labs developed additional content to study the mutation incidence of other B and T lineage markers such as IKZF1, JAK2, IL7R, PAX5, EP300, LEF1, CRLF2, PHF6, WT1, JAK1, PTEN, AKT1, IL7R, NOTCH1, CREBBP, or FBXW7. Further, the molecular landscape of CLL is changing rapidly. As such, a separate working group focused on analyses including NOTCH1, SF3B1, MYD88, XPO1, FBXW7 and BIRC3. Currently, 922 cases were screened to investigate the range of mutational burden of NOTCH1 mutations for their prognostic relevance. In MDS, RUNX1 mutation analyses were performed in 977 cases. The prognostic relevance of TP53 mutations in MDS was assessed in additional 327 cases, including isolated deletions of chromosome 5q. Next, content was developed targeting genes of the cellular splicing component, e.g. SF3B1, SRSF2, U2AF1, and ZRSR2. In BCR-ABL1-negative MPN, nine genes of interest (JAK2, MPL, TET2, CBL, KRAS, EZH2, IDH1, IDH2, ASXL1) have been analyzed in a cohort of 155 primary myelofibrosis cases searching for novel somatic mutations and addressing their relevance for disease progression and leukemia transformation. Moreover, an assay was developed and applied to CMML cases allowing the simultaneous analysis of 25 leukemia-associated target genes in a single sequencing run using just 20 ng of starting DNA. Finally, nine laboratories are studying CML, applying ultra-deep sequencing of the BCR-ABL1 tyrosine kinase domain. Analyses were performed on 615 cases investigating the dynamics of expansion of mutated clones under various tyrosine kinase inhibitor therapies. Conclusion Molecular characterization of hematological malignancies today requires high diagnostic sensitivity and specificity. As part of the IRON-II study, a network of laboratories analyzed a variety of disease entities applying amplicon-based NGS assays. Importantly, the consortium not only standardized assay design for disease-specific panels, but also achieved consensus on a common data analysis pipeline for mutation interpretation. Distinct working groups have been forged to address scientific tasks and in total 8,867 cases had been analyzed thus far. Disclosures: Kohlmann: Roche Diagnostics: Honoraria; MLL Munich Leukemia Laboratory: Employment. Martinelli:Roche Diagnostics: Research Support Other. Alikian:Roche Diagnostics: Research Support Other. Auber:Roche Diagnostics: Research Support Other. Belickova:Roche Diagnostics: Research Support Other. Bronzini:Roche Diagnostics: Research Support Other. Cazzaniga:Roche Diagnostics: Research Support Other. Chiaretti:Roche Diagnostics: Research Support Other. Ernst:Roche Diagnostics: Research Support Other. Fuellgrabe:Roche Diagnostics: Research Support Other. Gabriel:Roche Diagnostics: Research Support Other. Hernandez:Roche Diagnostics: Research Support Other. Jansen:Roche Diagnostics: Research Support Other. Iacobucci:Roche Diagnostics: Research Support Other. Lion:Roche Diagnostics: Research Support Other. Lode:Roche Diagnostics: Research Support Other. Martinez-Lopez:Roche Diagnostics: Research Support Other. Mills:Roche Diagnostics: Research Support Other. Mossner:Roche Diagnostics: Research Support Other. Machova Polakova:Roche Diagnostics: Research Support Other. Porret:Roche Diagnostics: Research Support Other. Pospisilova:Roche Diagnostics: Research Support Other. Preudhomme:Roche Diagnostics: Research Support Other. Sayitoglu:Roche Diagnostics: Research Support Other. Soverini:Roche Diagnostics: Research Support Other. Spinelli:Roche Diagnostics: Research Support Other. Thiede:Roche Diagnostics: Research Support Other. Vandenberghe:Roche Diagnostics: Research Support Other. Yeoh:Roche Diagnostics: Research Support Other. Hochhaus:Roche Diagnostics: Research Support Other. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership; Roche Diagnostics: Research Funding.
High-throughput sequencing technologies are widely used to analyse genomic variants or rare mutational events in different fields of genomic research, with a fast development of new or adapted platforms and technologies, enabling amplicon-based analysis of single target genes or even whole genome sequencing within a short period of time. Each sequencing platform is characterized by well-defined types of errors, resulting from different steps in the sequencing workflow. Here we describe a universal method to prepare amplicon libraries that can be used for sequencing on different high-throughput sequencing platforms. We have sequenced distinct exons of the CREB binding protein (CREBBP) gene and analysed the output resulting from three major deep-sequencing platforms. platform-specific errors were adjusted according to the result of sequence analysis from the remaining platforms. Additionally, bioinformatic methods are described to determine platform dependent errors. Summarizing the results we present a platform-independent cost-efficient and timesaving method that can be used as an alternative to commercially available sample-preparation kits.
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