24 research outputs found

    Disease-biased and shared characteristics of the immunoglobulin gene repertoires in marginal zone B cell lymphoproliferations.

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    The B cell receptor immunoglobulin (BcR IG) gene repertoires of marginal zone (MZ) lymphoproliferations were analyzed in order to obtain insight into their ontogenetic relationships. Our cohort included cases with MZ lymphomas (n=488) i.e. splenic (SMZL), nodal (NMZL) and extranodal (ENMZL) as well as provisional entities (n=76) according to the World Health Organization classification. The most striking IG gene repertoire skewing was observed in SMZL. However, restrictions were also identified in all other MZ lymphomas studied, particularly ENMZL, with significantly different IG gene distributions depending on the primary site of involvement. Cross-entity comparisons of the MZ IG sequence dataset with a large dataset of IG sequences (MZ-related or not; n=65,837) revealed four major clusters of cases sharing homologous ('public') heavy variable complementarity-determining region 3. These clusters included rearrangements from SMZL, ENMZL (gastric, salivary gland, ocular adnexa), chronic lymphocytic leukemia but also rheumatoid factors and non-malignant spleen MZ cells. In conclusion, different MZ lymphomas display biased immunogenetic signatures indicating distinct antigen exposure histories. The existence of rare public stereotypes raises the intriguing possibility that common, pathogen-triggered, immune-mediated mechanisms, may result in diverse B lymphoproliferations due to targeting versatile progenitor B cells and/or operating in particular microenvironments.This work was supported in part by H2020 “AEGLE, An analytics framework for integrated and personalized healthcare services in Europe”, by the European Union (EU); H2020 No. 692298 project “MEDGENET, Medical Genomics and Epigenomics Network” by the EU; grant AZV 15-30015A from the Ministry of Health of the Czech Republic, and the project CEITEC2020 LQ1601 from the Ministry of Education, Youth, and Sports of the Czech Republic; Bloodwise Research Grant (15019); the Swedish Cancer Society, the Swedish Research Council, the Knut and Alice Wallenberg Foundation, Karolinska Institutet, Stockholm, the Lion’s Cancer Research Foundation, Uppsala, the Marcus Borgström Foundation and Selander’s Foundation, Uppsala

    Polychronidou, Maria

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    Studying the role of dimerization in regulating the activity of Receptor Protein Tyrosine Phosphatase Sigma

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    RPTP-sigma is a member of the LAR-RPTP subfamily that has been associated with neurite outgrowth and neuronal regeneration. In this study, it is reported that dimenzation, seems to play a role in the regulation of RPTP-sigma activity. RPTP-sigma forms homodimers in cells under physiological conditions and antibody induced dimerization of HA-tagged RPTP-sigma leads to changes in the localization of the protein from the plasma membrane to vesicles in the cytoplasm. Furthermore, when N1E-115 cells were used as an in vitro model for studying the effect of RPTP-sigma-dimerization on neurite outgrowth, it was observed that induction of dimerization of the phosphatase allows neurite outgrowth in conditions that are normally non permissive for differentiation of the cells, indicating that dimer formation probably inhibits RPTP-sigma activity. In order to provide useful tools for studying the signaling cascades regulated by the phosphatase, a series of tagged constructs of potential RPTP-sigma interacting proteins were made. Finally, potential interactions between RPTP-sigma and c-MET were investigated

    Determining nuclear shape: The role of farnesylated nuclear membrane proteins

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    Changes in nuclear morphology are observed in diverse developmental processes as well as in pathological conditions. Modification of nuclear membrane and nuclear lamina protein levels results in altered nuclear shapes, as it has been demonstrated in experimental systems ranging from yeast to human cells. The important role of nuclear membrane components in regulating nuclear morphology is additionally highlighted by the abnormally shaped nuclei observed in diseases where nuclear lamina proteins are mutated. Even though the effect of nuclear envelope components on nuclear shape has been thoroughly described, not much is known about the molecular mechanisms that govern these events. In addition to the known role of intermediate filament formation by lamins, here we discuss several mechanisms that might alone or in combination participate in the regulation of nuclear shape observed upon modification of the levels of nuclear membrane and lamina proteins. Based on recent work with the two farnesylated nuclear membrane Drosophila proteins, kugelkern and lamin Dm0, we propose that the direct interaction of farnesylated nuclear membrane proteins with the phospholipid bilayer leads to nuclear envelope deformation. In addition to this mechanism, we suggest that the interaction of nuclear membrane and lamina proteins with cytoskeletal elements and chromatin, and modifications in lipid biosynthesis might also be involved in the formation of abnormally shaped nuclei

    COPS: Detecting Co-Occurrence and Spatial Arrangement of Transcription Factor Binding Motifs in Genome-Wide Datasets

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    <div><p>In multi-cellular organisms, spatiotemporal activity of <em>cis</em>-regulatory DNA elements depends on their occupancy by different transcription factors (TFs). In recent years, genome-wide ChIP-on-Chip, ChIP-Seq and DamID assays have been extensively used to unravel the combinatorial interaction of TFs with <em>cis</em>-regulatory modules (CRMs) in the genome. Even though genome-wide binding profiles are increasingly becoming available for different TFs, single TF binding profiles are in most cases not sufficient for dissecting complex regulatory networks. Thus, potent computational tools detecting statistically significant and biologically relevant TF-motif co-occurrences in genome-wide datasets are essential for analyzing context-dependent transcriptional regulation. We have developed COPS (<u>C</u>o-<u>O</u>ccurrence <u>P</u>attern <u>S</u>earch), a new bioinformatics tool based on a combination of association rules and Markov chain models, which detects co-occurring TF binding sites (BSs) on genomic regions of interest. COPS scans DNA sequences for frequent motif patterns using a Frequent-Pattern tree based data mining approach, which allows efficient performance of the software with respect to both data structure and implementation speed, in particular when mining large datasets. Since transcriptional gene regulation very often relies on the formation of regulatory protein complexes mediated by closely adjoining TF binding sites on CRMs, COPS additionally detects preferred short distance between co-occurring TF motifs. The performance of our software with respect to biological significance was evaluated using three published datasets containing genomic regions that are independently bound by several TFs involved in a defined biological process. In sum, COPS is a fast, efficient and user-friendly tool mining statistically and biologically significant TFBS co-occurrences and therefore allows the identification of TFs that combinatorially regulate gene expression.</p> </div

    Comparison of COPS to two other computational approaches.

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    <p>The plots depict the Matthews Correlation Coefficient (MCC) values as determined using COPS and the published tools CPModule and ModuleDigger, for the co-occurrence patterns Bap/Twi, Tin/Twi, Ase/Pros and Snail/Pros in sequence-sets of increasing size (ranging from 50 to 300 sequences). The MCC values used for evaluating the performance of the different computational tools were calculated as described in the Materials and Methods. The red line illustrates the performance of COPS, the blue line CPModule and the green line ModuleDigger.</p

    Short distance arrangements between BS of known co-regulatory TFs.

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    <p>The table lists the preferred short distance arrangements (Δbp) between the BSs of known co-regulatory TF pairs, as reported by COPS by analyzing different genome-wide datasets (first column). The last column shows the statistical significance (p-value) of the short distance arrangement for each pair of TFBSs. Only pairs with a motif spacing of <20 bp are listed in the table.</p

    COPS flowchart.

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    <p>COPS first scans the input sequences using all known TF binding motifs annotated in open source databases or retrieved from other resources and builds the frequent pattern (FP)-tree. The statistical significance (Z score) of the motif co-occurrences is calculated by comparing the <i>log</i> likelihood score of the frequent pattern to the <i>log</i> likelihood score distribution of the background. The percentage of overlap between the motifs of the pair is also calculated and reported. Additionally, COPS offers the option to calculate the preferred distance between co-occurring TF binding motifs and its statistical significance (Z score).</p

    Observed overlap of the genomic regions bound by the TF Twi and its co-regulators.

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    <p>A-C: The observed overlap (blue bar) between genomic regions bound by Twi and genomic regions bound by its known co-regulator Snail and its candidate co-regulator Ase is depicted in comparison to the expected random overlap (red curve). The overlap of genomic regions bound by Twi in stage 10 to 11 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052055#pone.0052055-Zinzen1" target="_blank">[6]</a> with the genomic regions bound by Twi/Snail/Dorsal in stage 5–7 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052055#pone.0052055-Zeitlinger1" target="_blank">[28]</a> (A), Snail (stage 10 to 11 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052055#pone.0052055-Southall1" target="_blank">[7]</a>) (B) and Ase (stage 10 to 11 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052055#pone.0052055-Southall1" target="_blank">[7]</a>) (C) is shown. A’-C’: Distribution and overlap (red) of Twi- and Twi/Snail/Dorsal- (A’), Twi- and Snail- (B’) and Twi- and Ase- (C’), bound genomic regions. The regions bound by Twi are shown in blue, the regions bound by the co-regulatory TFs in green and the overlapping regions bound by both Twi and the co-regulatory TFs in red.</p
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