Cholangiocarcinoma (CCA) is a hepatobiliary malignancy exhibiting high incidence in countries with endemic liver-fluke infection. We analysed 489 CCAs from 10 countries, combining whole-genome (71 cases), targeted/exome, copy-number, gene expression, and DNA methylation information. Integrative clustering defined four CCA clusters – Fluke-Positive CCAs (Clusters 1/2) are enriched in ERBB2 amplifications and TP53 mutations, conversely Fluke-Negative CCAs (Clusters 3/4) exhibit high copy-number alterations and PD-1/PD-L2 expression, or epigenetic mutations (IDH1/2, BAP1) and FGFR/PRKA-related gene rearrangements. Whole-genome analysis highlighted FGFR2 3′UTR deletion as a mechanism of FGFR2 upregulation. Integration of non-coding promoter mutations with protein-DNA binding profiles demonstrates pervasive modulation of H3K27me3-associated sites in CCA. Clusters 1 and 4 exhibit distinct DNA hypermethylation patterns targeting either CpG islands or shores – mutation signature and subclonality analysis suggests that these reflect different mutational pathways. Our results exemplify how genetics, epigenetics and environmental carcinogens can interplay across different geographies to generate distinct molecular subtypes of cancer.

Proteins differentially interact with each other across cellular states and conditions, but an efficient proteome-wide strategy to monitor them is lacking. We report the application of thermal proximity coaggregation (TPCA) for high-throughput intracellular monitoring of protein complex dynamics. Significant TPCA signatures observed among well-validated protein-protein interactions correlate positively with interaction stoichiometry and are statistically observable in more than 350 annotated human protein complexes. Using TPCA, we identified many complexes without detectable differential protein expression, including chromatin-associated complexes, modulated in S phase of the cell cycle. Comparison of six cell lines by TPCA revealed cell-specific interactions even in fundamental cellular processes. TPCA constitutes an approach for system-wide studies of protein complexes in nonengineered cells and tissues and might be used to identify protein complexes that are modulated in diseases.

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In certain tasks such as pursuit and evasion, multiple agents need to coordinate their behavior to achieve a common goal. An interesting question is, how can such behavior be best evolved? A powerful approach is to control the agents with neural networks, coevolve them in separate subpopulations, and test them together in the common task. In this paper, such a method, called Multi-Agent ESP (Enforced SubPopulations), is proposed and demonstrated in a prey-capture task. First, the approach is shown more efficient than evolving a single central controller for all agents. Second, cooperation is found to be most efficient through stigmergy, i.e. through role-based responses to the environment, rather than direct communication between the agents. Together these results suggest that role-based cooperation is an effective strategy in certain multi-agent domains. [ This paper is a revision of

BackgroundH. sapiens-M. tuberculosis H37Rv protein-protein interaction (PPI) data are very important information to illuminate the infection mechanism of M. tuberculosis H37Rv. But current H. sapiens-M. tuberculosis H37Rv PPI data are very scarce. This seriously limits the study of the interaction between this important pathogen and its host H. sapiens. Computational prediction of H. sapiens-M. tuberculosis H37Rv PPIs is an important strategy to fill in the gap. Domain-domain interaction (DDI) based prediction is one of the frequently used computational approaches in predicting both intra-species and inter-species PPIs. However, the performance of DDI-based host-pathogen PPI prediction has been rather limited.ResultsWe develop a stringent DDI-based prediction approach with emphasis on (i) differences between the specific domain sequences on annotated regions of proteins under the same domain ID and (ii) calculation of the interaction strength of predicted PPIs based on the interacting residues in their interaction interfaces.We compare our stringent DDI-based approach to a conventional DDI-based approach for predicting PPIs based on gold standard intra-species PPIs and coherent informative Gene Ontology terms assessment. The assessment results show that our stringent DDI-based approach achieves much better performance in predicting PPIs than the conventional approach. Using our stringent DDI-based approach, we have predicted a small set of reliable H. sapiens-M. tuberculosis H37Rv PPIs which could be very useful for a variety of related studies.We also analyze the H. sapiens-M. tuberculosis H37Rv PPIs predicted by our stringent DDI-based approach using cellular compartment distribution analysis, functional category enrichment analysis and pathway enrichment analysis. The analyses support the validity of our prediction result. Also, based on an analysis of the H. sapiens-M. tuberculosis H37Rv PPI network predicted by our stringent DDI-based approach, we have discovered some important properties of domains involved in host-pathogen PPIs. We find that both host and pathogen proteins involved in host-pathogen PPIs tend to have more domains than proteins involved in intra-species PPIs, and these domains have more interaction partners than domains on proteins involved in intra-species PPI.ConclusionsThe stringent DDI-based prediction approach reported in this work provides a stringent strategy for predicting host-pathogen PPIs. It also performs better than a conventional DDI-based approach in predicting PPIs. We have predicted a small set of accurate H. sapiens-M. tuberculosis H37Rv PPIs which could be very useful for a variety of related studies.