The Mitotic Checkpoint Complex Requires an Evolutionary Conserved Cassette to Bind and Inhibit Active APC/C

The Spindle Assembly Checkpoint (SAC) ensures genomic stability by preventing sister chromatid separation until all chromosomes are attached to the spindle. It catalyzes the production of the Mitotic Checkpoint Complex (MCC), which inhibits Cdc20 to inactivate the Anaphase Promoting Complex/Cyclosome (APC/C). Here we show that two Cdc20-binding motifs in BubR1 of the recently identified ABBA motif class are crucial for the MCC to recognize active APC/C-Cdc20. Mutating these motifs eliminates MCC binding to the APC/C, thereby abolishing the SAC and preventing cells from arresting in response to microtubule poisons. These ABBA motifs flank a KEN box to form a cassette that is highly conserved through evolution, both in the arrangement and spacing of the ABBA-KEN-ABBA motifs, and association with the amino-terminal KEN box required to form the MCC. We propose that the ABBA-KEN-ABBA cassette holds the MCC onto the APC/C by binding the two Cdc20 molecules in the MCC-APC/C complex.This work was supported by an SFI Starting Investigator Research Grant (13/SIRG/2193) to N.E.D. and a CR UK Programme grant C29/A13678 to J.P. J.P. acknowledges the financial support of Wellcome Trust Grant 092096 and CR UK Grant C6946/A14492 core support to the Gurdon Institute

Finding motif pairs in the interactions between heterogeneous proteins via bootstrapping and boosting

<p>Abstract</p> <p>Background</p> <p>Supervised learning and many stochastic methods for predicting protein-protein interactions require both negative and positive interactions in the training data set. Unlike positive interactions, negative interactions cannot be readily obtained from interaction data, so these must be generated. In protein-protein interactions and other molecular interactions as well, taking all non-positive interactions as negative interactions produces too many negative interactions for the positive interactions. Random selection from non-positive interactions is unsuitable, since the selected data may not reflect the original distribution of data.</p> <p>Results</p> <p>We developed a bootstrapping algorithm for generating a negative data set of arbitrary size from protein-protein interaction data. We also developed an efficient boosting algorithm for finding interacting motif pairs in human and virus proteins. The boosting algorithm showed the best performance (84.4% sensitivity and 75.9% specificity) with balanced positive and negative data sets. The boosting algorithm was also used to find potential motif pairs in complexes of human and virus proteins, for which structural data was not used to train the algorithm. Interacting motif pairs common to multiple folds of structural data for the complexes were proven to be statistically significant. The data set for interactions between human and virus proteins was extracted from BOND and is available at <url>http://virus.hpid.org/interactions.aspx</url>. The complexes of human and virus proteins were extracted from PDB and their identifiers are available at <url>http://virus.hpid.org/PDB_IDs.html</url>.</p> <p>Conclusion</p> <p>When the positive and negative training data sets are unbalanced, the result via the prediction model tends to be biased. Bootstrapping is effective for generating a negative data set, for which the size and distribution are easily controlled. Our boosting algorithm could efficiently predict interacting motif pairs from protein interaction and sequence data, which was trained with the balanced data sets generated via the bootstrapping method.</p

Cancer symptom awareness and barriers to symptomatic presentation in England – Are we clear on cancer?

Background: Low cancer awareness may contribute to delayed diagnosis and poor cancer survival. We aimed to quantify socio-demographic differences in cancer symptom awareness and barriers to symptomatic presentation in the English population. Methods: Using a uniquely large data set (n=49?270), we examined the association of cancer symptom awareness and barriers to presentation with age, gender, marital status and socio-economic position (SEP), using logistic regression models to control for confounders. Results: The youngest and oldest, the single and participants with the lowest SEP recognised the fewest cancer symptoms, and reported most barriers to presentation. Recognition of nine common cancer symptoms was significantly lower, and embarrassment, fear and difficulties in arranging transport to the doctor’s surgery were significantly more common in participants living in the most deprived areas than in the most affluent areas. Women were significantly more likely than men to both recognise common cancer symptoms and to report barriers. Women were much more likely compared with men to report that fear would put them off from going to the doctor. Conclusions: Large and robust socio-demographic differences in recognition of some cancer symptoms, and perception of some barriers to presentation, highlight the need for targeted campaigns to encourage early presentation and improve cancer outcomes

The combined effect of smoking tobacco and drinking alcohol on cause-specific mortality: a 30 year cohort study

&lt;p&gt;&lt;b&gt;Background:&lt;/b&gt; Smoking and consuming alcohol are both related to increased mortality risk. Their combined effects on cause-specific mortality were investigated in a prospective cohort study.&lt;/p&gt; &lt;p&gt;&lt;b&gt;Methods:&lt;/b&gt; Participants were 5771 men aged 35-64, recruited during 1970-73 from various workplaces in Scotland. Data were obtained from a questionnaire and a screening examination. Causes of death were all cause, coronary heart disease (CHD), stroke, alcohol-related, respiratory and smoking-related cancer. Participants were divided into nine groups according to their smoking status (never, ex or current) and reported weekly drinking (none, 1-14 units and 15 or more). Cox proportional hazards models were used to obtain relative rates of mortality, adjusted for age and other risk factors.&lt;/p&gt; &lt;p&gt;&lt;b&gt;Results:&lt;/b&gt; In 30 years of follow-up, 3083 men (53.4%) died. Compared with never smokers who did not drink, men who both smoked and drank 15+ units/week had the highest all-cause mortality (relative rate = 2.71 (95% confidence interval 2.31-3.19)). Relative rates for CHD mortality were high for current smokers, with a possible protective effect of some alcohol consumption in never smokers. Stroke mortality increased with both smoking and alcohol consumption. Smoking affected respiratory mortality with little effect of alcohol. Adjusting for a wide range of confounders attenuated the relative rates but the effects of alcohol and smoking still remained. Premature mortality was particularly high in smokers who drank 15 or more units, with a quarter of the men not surviving to age 65. 30% of men with manual occupations both smoked and drank 15+ units/week compared with only 13% with non-manual ones.&lt;/p&gt; &lt;p&gt;&lt;b&gt;Conclusions:&lt;/b&gt; Smoking and drinking 15+ units/week was the riskiest behaviour for all causes of death.&lt;/p&gt

The effects of clinical task interruptions on subsequent performance of a medication pre-administration task

There is a surge of research exploring the role of task interruptions in the manifestation of primary task errors both in controlled experimental settings, and safety critical workplaces such as healthcare. Despite such research providing valuable insights into the disruptive properties of task interruption, and, the importance of considering the likely disruptive consequences of clinical task interruptions in healthcare environments, there is an urgent need for an approach that best mimics complex working environments such as healthcare, whilst allowing better control over experimental variables with minimal constraints. We propose that this can be achieved with ecologically sensitive experimental tasks designed to have high levels of experimental control so that theoretical as well as practical parameters and factors can be tested. We developed a theoretically and ecologically informed procedural memory-based task - the CAMROSE Medication Pre-Administration Task. Results revealed significantly more sequence errors were made on low, moderate and high complex conditions compared to no interruption condition. There was no significant difference in non-sequence errors. Findings reveal the importance of developing ecologically valid tasks to explore non-observable characteristics of clinical task interruptions. Both theoretical and possible practical implications are discussed

ELM-the eukaryotic linear motif resource in 2020

The eukaryotic linear motif (ELM) resource is a repository of manually curated experimentally validated short linear motifs (SLiMs). Since the initial release almost 20 years ago, ELM has become an indispensable resource for the molecular biology community for investigating functional regions in many proteins. In this update, we have added 21 novel motif classes, made major revisions to 12 motif classes and added >400 new instances mostly focused on DNA damage, the cytoskeleton, SH2-binding phosphotyrosine motifs and motif mimicry by pathogenic bacterial effector proteins. The current release of the ELM database contains 289 motif classes and 3523 individual protein motif instances manually curated from 3467 scientific publications. ELM is available at: http://elm.eu.org