Computational identification of ubiquitylation sites from protein sequences

<p>Abstract</p> <p>Background</p> <p>Ubiquitylation plays an important role in regulating protein functions. Recently, experimental methods were developed toward effective identification of ubiquitylation sites. To efficiently explore more undiscovered ubiquitylation sites, this study aims to develop an accurate sequence-based prediction method to identify promising ubiquitylation sites.</p> <p>Results</p> <p>We established an ubiquitylation dataset consisting of 157 ubiquitylation sites and 3676 putative non-ubiquitylation sites extracted from 105 proteins in the UbiProt database. This study first evaluates promising sequence-based features and classifiers for the prediction of ubiquitylation sites by assessing three kinds of features (amino acid identity, evolutionary information, and physicochemical property) and three classifiers (support vector machine, <it>k</it>-nearest neighbor, and NaïveBayes). Results show that the set of used 531 physicochemical properties and support vector machine (SVM) are the best kind of features and classifier respectively that their combination has a prediction accuracy of 72.19% using leave-one-out cross-validation.</p> <p>Consequently, an informative physicochemical property mining algorithm (IPMA) is proposed to select an informative subset of 531 physicochemical properties. A prediction system UbiPred was implemented by using an SVM with the feature set of 31 informative physicochemical properties selected by IPMA, which can improve the accuracy from 72.19% to 84.44%. To further analyze the informative physicochemical properties, a decision tree method C5.0 was used to acquire if-then rule-based knowledge of predicting ubiquitylation sites. UbiPred can screen promising ubiquitylation sites from putative non-ubiquitylation sites using prediction scores. By applying UbiPred, 23 promising ubiquitylation sites were identified from an independent dataset of 3424 putative non-ubiquitylation sites, which were also validated by using the obtained prediction rules.</p> <p>Conclusion</p> <p>We have proposed an algorithm IPMA for mining informative physicochemical properties from protein sequences to build an SVM-based prediction system UbiPred. UbiPred can predict ubiquitylation sites accompanied with a prediction score each to help biologists in identifying promising sites for experimental verification. UbiPred has been implemented as a web server and is available at <url>http://iclab.life.nctu.edu.tw/ubipred</url>.</p

Widespread Contribution of Gdf7 Lineage to Cerebellar Cell Types and Implications for Hedgehog-Driven Medulloblastoma Formation

The roof plate is a specialized embryonic midline tissue of the central nervous system that functions as a signaling center regulating dorsal neural patterning. In the developing hindbrain, roof plate cells express Gdf7 and previous genetic fate mapping studies showed that these cells contribute mostly to non-neural choroid plexus epithelium. We demonstrate here that constitutive activation of the Sonic hedgehog signaling pathway in the Gdf7 lineage invariably leads to medulloblastoma. Lineage tracing analysis reveals that Gdf7-lineage cells not only are a source of choroid plexus epithelial cells, but are also present in the cerebellar rhombic lip and contribute to a subset of cerebellar granule neuron precursors, the presumed cell-of-origin for Sonic hedgehog-driven medulloblastoma. We further show that Gdf7-lineage cells also contribute to multiple neuronal and glial cell types in the cerebellum, including glutamatergic granule neurons, unipolar brush cells, Purkinje neurons, GABAergic interneurons, Bergmann glial cells, and white matter astrocytes. These findings establish hindbrain roof plate as a novel source of diverse neural cell types in the cerebellum that is also susceptible to oncogenic transformation by deregulated Sonic hedgehog signaling

Determination of cytokine protein levels in oral secretions in patients undergoing radiotherapy for head and neck malignancies

Abstract Background Cytokines may be elevated in tumor and normal tissues following irradiation. Cytokine expression in these tissues may predict for toxicity or tumor control. The purpose of this pilot study was to determine the feasibility of measuring local salivary cytokine levels using buccal sponges in patients receiving chemo-radiation for head and neck malignancies. Patients and methods 11 patients with epithelial malignancies of the head and neck were recruiting to this study. All patients received radiotherapy to the head and neck region with doses ranging between 60 – 67.5 Gy. Chemotherapy was delivered concurrently with radiation in all patients. Salivary samples were obtained from high dose and low dose regions prior to treatment and at three intervals during treatment for assessment of cytokine levels (IL-4, IL-6, IL-8, IL-10, EGF, MCP-1, TNF-α, and VEGF). Results Cytokine levels were detectable in the salivary samples. Salivary cytokine levels of IL-4, IL-6, IL-8, EGF, MCP-1, TNF- α , and VEGF were higher in the high dose region compared to the low dose region at all time points (p  Conclusion Assessment of salivary cytokine levels may provide a novel method to follow local cytokine levels during radiotherapy and may provide a mechanism to study cytokine levels in a regional manner.</p

RTOG Sarcoma Radiation Oncologists Reach Consensus on Gross Tumor Volume and Clinical Target Volume on Computed Tomographic Images for Preoperative Radiotherapy of Primary Soft Tissue Sarcoma of Extremity in Radiation Therapy Oncology Group Studies

To develop a Radiation Therapy Oncology Group (RTOG) atlas delineating gross tumor volume (GTV) and clinical target volume (CTV) to be used for preoperative radiotherapy of primary extremity soft tissue sarcoma (STS). A consensus meeting was held during the RTOG meeting in January 2010 to reach agreement about GTV and CTV delineation on computed tomography (CT) images for preoperative radiotherapy of high-grade large extremity STS. Data were presented to address the local extension of STS. Extensive discussion ensued to develop optimal criteria for GTV and CTV delineation on CT images. A consensus was reached on appropriate CT-based GTV and CTV. The GTV is gross tumor defined by T1 contrast-enhanced magnetic resonance images. Fusion of magnetic resonance and images is recommended to delineate the GTV. The CTV for high-grade large STS typically includes the GTV plus 3-cm margins in the longitudinal directions. If this causes the field to extend beyond the compartment, the field can be shortened to include the end of a compartment. The radial margin from the lesion should be 1.5 cm, including any portion of the tumor not confined by an intact fascial barrier, bone, or skin surface. The consensus on GTV and CTV for preoperative radiotherapy of high-grade large extremity STS is available as web-based images and in a descriptive format through the RTOG. This is expected to improve target volume consistency and allow for rigorous evaluation of the benefits and risks of such treatment

Variation in the Gross Tumor Volume and Clinical Target Volume for Preoperative Radiotherapy of Primary Large High-Grade Soft Tissue Sarcoma of the Extremity Among RTOG Sarcoma Radiation Oncologists

To evaluate variability in the definition of preoperative radiotherapy gross tumor volume (GTV) and clinical target volume (CTV) delineated by sarcoma radiation oncologists. Extremity sarcoma planning CT images along with the corresponding diagnostic MRI from two patients were distributed to 10 Radiation Therapy Oncology Group sarcoma radiation oncologists with instructions to define GTV and CTV using standardized guidelines. The CT data with contours were then returned for central analysis. Contours representing statistically corrected 95% (V95) and 100% (V100) agreement were computed for each structure. For the GTV, the minimum, maximum, mean (SD) volumes (mL) were 674, 798, 752 ± 35 for the lower extremity case and 383, 543, 447 ± 46 for the upper extremity case. The volume (cc) of the union, V95 and V100 were 882, 761, and 752 for the lower, and 587, 461, and 455 for the upper extremity, respectively. The overall GTV agreement was judged to be almost perfect in both lower and upper extremity cases (kappa = 0.9 [ p < 0.0001] and kappa = 0.86 [ p < 0.0001]). For the CTV, the minimum, maximum, mean (SD) volumes (mL) were 1145, 1911, 1605 ± 211 for the lower extremity case and 637, 1246, 1006 ± 180 for the upper extremity case. The volume (cc) of the union, V95, and V100 were 2094, 1609, and 1593 for the lower, and 1533, 1020, and 965 for the upper extremity cases, respectively. The overall CTV agreement was judged to be almost perfect in the lower extremity case (kappa = 0.85 [ p < 0.0001]) but only substantial in the upper extremity case (kappa = 0.77 [ p < 0.0001]). Almost perfect agreement existed in the GTV of these two representative cases. Tshere was no significant disagreement in the CTV of the lower extremity, but variation in the CTV of upper extremity was seen, perhaps related to the positional differences between the planning CT and the diagnostic MRI