Integrative multiomics enhancer activity profiling identifies therapeutic vulnerabilities in cholangiocarcinoma of different etiologies.

Objectives: Cholangiocarcinoma (CCA) is a heterogeneous malignancy with high mortality and dismal prognosis, and an urgent clinical need for new therapies. Knowledge of the CCA epigenome is largely limited to aberrant DNA methylation. Dysregulation of enhancer activities has been identified to affect carcinogenesis and leveraged for new therapies but is uninvestigated in CCA.

Chromatin Rewiring by Mismatch Repair Protein MSH2 Alters Cell Adhesion Pathways and Sensitivity to BET Inhibition in Gastric Cancer.

Unlabelled: Mutations in the DNA mismatch repair gene MSH2 are causative of microsatellite instability (MSI) in multiple cancers. Here, we discovered that besides its well-established role in DNA repair, MSH2 exerts a novel epigenomic function in gastric cancer. Unbiased CRISPR-based mass spectrometry combined with genome-wide CRISPR functional screening revealed that in early-stage gastric cancer MSH2 genomic binding is not randomly distributed but rather is associated specifically with tumor-associated super-enhancers controlling the expression of cell adhesion genes.

Are batch effects still relevant in the age of big data?

Batch effects (BEs) are technical biases that may confound analysis of high-throughput biotechnological data. BEs are complex and effective mitigation is highly context-dependent. In particular, the advent of high-resolution technologies such as single-cell RNA sequencing presents new challenges.

Promoter hypermethylation of early B cell factor 1 (EBF1) is associated with cholangiocarcinoma progression.

DNA hypermethylation in a promoter region causes gene silencing via epigenetic changes. We have previously reported that early B cell factor 1 (EBF1) was down-regulated in cholangiocarcinoma (CCA) tissues and related to tumor progression. Thus, we hypothesized that the DNA hypermethylation of EBF1 promoter would suppress EBF1 expression in CCA and induce its progression.

Thermal proximity coaggregation for system-wide profiling of protein complex dynamics in cells.

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.

The draft genome of tropical fruit durian (Durio zibethinus).

Durian (Durio zibethinus) is a Southeast Asian tropical plant known for its hefty, spine-covered fruit and sulfury and onion-like odor. Here we present a draft genome assembly of D. zibethinus, representing the third plant genus in the Malvales order and first in the Helicteroideae subfamily to be sequenced.

Whole-Genome and Epigenomic Landscapes of Etiologically Distinct Subtypes of Cholangiocarcinoma.

Cholangiocarcinoma (CCA) is a hepatobiliary malignancy exhibiting high incidence in countries with endemic liver-fluke infection. We analyzed 489 CCAs from 10 countries, combining whole-genome (71 cases), targeted/exome, copy-number, gene expression, and DNA methylation information. Integrative clustering defined 4 CCA clusters-fluke-positive CCAs (clusters 1/2) are enriched in amplifications and mutations; conversely, fluke-negative CCAs (clusters 3/4) exhibit high copy-number alterations and / expression, or epigenetic mutations () and /-related gene rearrangements.

Prediction of problematic complexes from PPI networks: sparse, embedded, and small complexes.

Background: The prediction of protein complexes from high-throughput protein-protein interaction (PPI) data remains an important challenge in bioinformatics. Three groups of complexes have been identified as problematic to discover. First, many complexes are sparsely connected in the PPI network, and do not form dense clusters that can be derived by clustering algorithms.

Methods for protein complex prediction and their contributions towards understanding the organisation, function and dynamics of complexes.

Complexes of physically interacting proteins constitute fundamental functional units responsible for driving biological processes within cells. A faithful reconstruction of the entire set of complexes is therefore essential to understand the functional organisation of cells. In this review, we discuss the key contributions of computational methods developed till date (approximately between 2003 and 2015) for identifying complexes from the network of interacting proteins (PPI network).

From the static interactome to dynamic protein complexes: Three challenges.

Protein interactions and complexes behave in a dynamic fashion, but this dynamism is not captured by interaction screening technologies, and not preserved in protein-protein interaction (PPI) networks. The analysis of static interaction data to derive dynamic protein complexes leads to several challenges, of which we identify three. First, many proteins participate in multiple complexes, leading to overlapping complexes embedded within highly-connected regions of the PPI network.

Discovery of small protein complexes from PPI networks with size-specific supervised weighting.

The prediction of small complexes (consisting of two or three distinct proteins) is an important and challenging subtask in protein complex prediction from protein-protein interaction (PPI) networks. The prediction of small complexes is especially susceptible to noise (missing or spurious interactions) in the PPI network, while smaller groups of proteins are likelier to take on topological characteristics of real complexes by chance. We propose a two-stage approach, SSS and Extract, for discovering small complexes.

Stringent DDI-based prediction of H. sapiens-M. tuberculosis H37Rv protein-protein interactions.

Background: H. sapiens-M. tuberculosis H37Rv protein-protein interaction (PPI) data are very important information to illuminate the infection mechanism of M.

Supervised maximum-likelihood weighting of composite protein networks for complex prediction.

Background: Protein complexes participate in many important cellular functions, so finding the set of existent complexes is essential for understanding the organization and regulation of processes in the cell. With the availability of large amounts of high-throughput protein-protein interaction (PPI) data, many algorithms have been proposed to discover protein complexes from PPI networks. However, such approaches are hindered by the high rate of noise in high-throughput PPI data, including spurious and missing interactions.

Decomposing PPI networks for complex discovery.

Background: Protein complexes are important for understanding principles of cellular organization and functions. With the availability of large amounts of high-throughput protein-protein interactions (PPI), many algorithms have been proposed to discover protein complexes from PPI networks. However, existing algorithms generally do not take into consideration the fact that not all the interactions in a PPI network take place at the same time.