Role of CD44 in clear cell renal cell carcinoma invasiveness after antiangiogenic treatment

Treballs Finals de Grau de Farmàcia, Facultat de Farmàcia, Universitat de Barcelona, 2017. Tutor/a: Joan Carles Rodríguez Rubio.[eng] During last century, big effort to understand the biochemical basis of cancer was carried out. One of the principal branches of these cancer investigations used drugs to prevent the formation of new blood vessels –process called angiogenesis– responsible for the nutrients supply of the tumour. These drugs are generally called antiangiogenics. It was discovered that some types of tumour have or develop resistance to these drugs when treatment was long enough. For that reason, mechanisms of resistance, aggressiveness, invasion and/or metastasis after the treatment are nowadays relevant to study. Recently, a protein that could be involved in the increased invasiveness of tumour cells after the antiangiogenic treatment appeared. This project collects some evidence that indicates that this protein, called CD44, might play a role in the increased invasion after antiangiogenic treatment in mouse models of renal carcinoma.[cat] Durant l’últim segle, s’ha fet un gran esforç per aprofundir en la basant bioquímica de la investigació contra el càncer. Una de les branques principals d’aquesta investigació utilitza fàrmacs que prevenen la formació de nous vasos sanguinis –procés anomenat angiogènesis- encarregats de nodrir el tumor. Aquests fàrmacs es diuen generalment antiangiogènics. S’ha descobert que alguns tipus de tumor tenen o desenvolupen resistència a aquests fàrmacs quan el tractament és prou llarg. Per aquesta raó, actualment s’està investigant profundament quins són els mecanismes pels quals apareix aquesta resistència, així com també perquè els tumors es tornen més agressius, invasius i/o metastàtics després del tractament. Recentment s’ha descobert una proteïna que podria estar involucrada en l’augment de la invasivitat de les cèl·lules tumorals després del tractament antiangiogènic. Aquest treball recull algunes de les evidències que apunten cap al paper de la proteïna CD44 en l’increment de la invasió tumoral post-tractament amb fàrmacs antiangiogènics en models ratolins de càncer renal

Fraction of unbound subunits.

<p>We calculated the predicted number of unbound proteins in a cell by subtracting the number of protein complexes from total number for each protein. a, Fraction of free proteins in yeast plotted together with the number of their interactions based on the Collins PPI network [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004424#pcbi.1004424.ref023" target="_blank">23</a>]. b, fraction of free proteins in human and the number of their interactions in the considered PPI dataset [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004424#pcbi.1004424.ref010" target="_blank">10</a>].</p

Summary of quantitative predictions of protein complex abundances in yeast.

<p>Only a few protein complex abundances are available in the literature. We summarized these, providing also a short explanation of how these were estimated. We compared SiComPre predictions against the trivial method of predicting protein complex abundances using the average of the abundance of the constitutive subunits. SiComPre predictions show a better agreement to experimental data compared to predictions based on the protein abundance averages. The predicted abundances were rescaled by squaring the value predicted from the simulation to re-establish the linear dependence between SiComPre predictions and experimental data.</p><p>Summary of quantitative predictions of protein complex abundances in yeast.</p

Variations in SiComPre predicted anaphase promoting complexes.

<p>The predicted structures of the APC complex in yeast, human and human after Bortezomib treatment. The reported overlap scores were calculated by comparing to the reference protein complexes discussed above. The lower score observed for the yeast is due to the larger APC complex size found in yeast [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004424#pcbi.1004424.ref009" target="_blank">9</a>].</p

Relationship between predicted refined complexes (RCs).

<p>a, In the left part of this figure, nodes represent all the predicted RCs, edges represent overlap between RCs. Connected protein complexes share components above a threshold overlap (≥ 0.1). Node size corresponds to the number of proteins in the complex and the colour represents the quantitative prediction with darker colour meaning higher abundance. Some of these connected components match the same reference complex with every node representing a complex variant. In the right part of this figure, we merged all variant refined complexes that could be associated with the RSC complex, the colour depth of the nodes represent how many times a protein has been observed in a SC that match one of these RCs. In this case edges represent interactions between proteins found in the initial PPI dataset [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004424#pcbi.1004424.ref023" target="_blank">23</a>]. The three set of proteins with coloured background are named according to the corresponding reference complexes [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004424#pcbi.1004424.ref009" target="_blank">9</a>]. All the proteins of the reference RSC complex are found by SiComPre except YBL006C and YGR275W. These form a dense region with higher abundance corresponding to the core complex and a less dense auxiliary complex attached to it. Two of the proteins in the less dense region match the reference complex of ISW1a, suggesting a strong interaction between these two complexes. b, The core RSC complex and its attachments according to Gavin et al. [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004424#pcbi.1004424.ref005" target="_blank">5</a>] compared with the RSC complex predicted by SiComPre. Blue nodes are core proteins, while all the others are attachments according to Gavin et al [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004424#pcbi.1004424.ref005" target="_blank">5</a>]. Colour indicate whether they are predicted by SiComPre either to be in RSC complex (purple), ISW1a complex (yellow), new module (pink) or not bounded to RSC (orange). Edges represent interactions according to the initial PPI database [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004424#pcbi.1004424.ref023" target="_blank">23</a>].</p

Budding yeast protein complexes predicted by SiComPre.

<p>Structure of many refined predicted complexes after dropping small abundance complexes. The colours are chosen according to the best matching reference complex. The legend shows some of the most well-known complexes (the full list can be found in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004424#pcbi.1004424.s001" target="_blank">S1 Table</a>). Similarly coloured RCs match a single reference complex. These RCs can be considered different variants of the same complex.</p

Protein coverage summary and number of predicted complexes.

<p>PPI datasets used in this study contain 1622 and 3006 proteins for yeast and human respectively [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004424#pcbi.1004424.ref023" target="_blank">23</a>]. In yeast, all of these proteins can be found in the abundance datasets [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004424#pcbi.1004424.ref003" target="_blank">3</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004424#pcbi.1004424.ref016" target="_blank">16</a>], while in human only 88% of proteins in the PPI network have data on protein abundances [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004424#pcbi.1004424.ref010" target="_blank">10</a>]. More information is known about domains in yeast than in human [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004424#pcbi.1004424.ref021" target="_blank">21</a>]. The final yeast model contains 84% of the interactions and 91% of proteins from the initial PPI network. The human model contains 67% of the interactions and 76% of the proteins of the PPI network (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004424#pcbi.1004424.t001" target="_blank">Table 1</a>). The missing proteins and interactions are due to the lack of DDI interactions or shared GO function between two proteins of the same interaction (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004424#pcbi.1004424.s007" target="_blank">S1 Text</a> for additional details about model generation). The whole pipeline generated 657 complexes in yeast from which 248 do not match any known complexes [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004424#pcbi.1004424.ref009" target="_blank">9</a>]. From the human data we predicted 1158 complexes and 890 of these cannot be associated to any CORUM complex.</p><p>Protein coverage summary and number of predicted complexes.</p

Le degré de grammaticalisation de HAVE : application d’une méthodologie développée par Joan Bybee et William Pagliuca

International audienc

Additional file 4: of The role of breast-feeding in infant immune system: a systems perspective on the intestinal microbiome

Microbial abundance tables. (CSV 14 kb

The correlation between gemcitabine efficacy and the ratio of dCK and RR in the simulations.

<p>The correlation between gemcitabine efficacy measured as and the ratio of dCK and RR, given with with respect to simulations with our model.</p