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. Our aim is to identify potential therapeutic targets in different subtypes of CCA through enhancer profiling. DESIGN: Integrative multiomics enhancer activity profiling of diverse CCA was performed. A panel of diverse CCA cell lines, patient-derived and cell line-derived xenografts were used to study identified enriched pathways and vulnerabilities. NanoString, multiplex immunohistochemistry staining and single-cell spatial transcriptomics were used to explore the immunogenicity of diverse CCA. RESULTS: We identified three distinct groups, associated with different etiologies and unique pathways. Drug inhibitors of identified pathways reduced tumour growth in in vitro and in vivo models. The first group (ESTRO), with mostly fluke-positive CCAs, displayed activation in estrogen signalling and were sensitive to MTOR inhibitors. Another group (OXPHO), with mostly BAP1 and IDH-mutant CCAs, displayed activated oxidative phosphorylation pathways, and were sensitive to oxidative phosphorylation inhibitors. Immune-related pathways were activated in the final group (IMMUN), made up of an immunogenic CCA subtype and CCA with aristolochic acid (AA) mutational signatures. Intratumour differences in AA mutation load were correlated to intratumour variation of different immune cell populations. CONCLUSION: Our study elucidates the mechanisms underlying enhancer dysregulation and deepens understanding of different tumourigenesis processes in distinct CCA subtypes, with potential significant therapeutics and clinical benefits.

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

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. At these loci, MSH2 genomic binding was required for chromatin rewiring, de novo enhancer-promoter interactions, maintenance of histone acetylation levels, and regulation of cell adhesion pathway expression. The chromatin function of MSH2 was independent of its DNA repair catalytic activity but required MSH6, another DNA repair gene, and recruitment to gene loci by the SWI/SNF chromatin remodeler SMARCA4/BRG1. Loss of MSH2 in advanced gastric cancers was accompanied by deficient cell adhesion pathway expression, epithelial-mesenchymal transition, and enhanced tumorigenesis in vitro and in vivo. However, MSH2-deficient gastric cancers also displayed addiction to BAZ1B, a bromodomain-containing family member, and consequent synthetic lethality to bromodomain and extraterminal motif (BET) inhibition. Our results reveal a role for MSH2 in gastric cancer epigenomic regulation and identify BET inhibition as a potential therapy in MSH2-deficient gastric malignancies. SIGNIFICANCE: DNA repair protein MSH2 binds and regulates cell adhesion genes by enabling enhancer-promoter interactions, and loss of MSH2 causes deficient cell adhesion and bromodomain and extraterminal motif inhibitor synthetic lethality in gastric cancer.

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. We first cover how BE modeling differs between traditional datasets and the new data landscape. We also discuss new approaches for measuring and mitigating BEs, including whether a BE is significant enough to warrant correction. Even with the advent of machine learning and artificial intelligence, the increased complexity of next-generation biotechnological data means increased complexities in BE management. We forecast that BEs will not only remain relevant in the age of big data but will become even more important.

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. In this study, the DNA methylation status of EBF1 and mRNA expression levels were analyzed in CCA and normal bile duct (NBD) tissues using a publicly available database of genome-wide association data. The results showed that the DNA methylation of EBF1 promoter region was significantly increased in CCA tissues compared with those of NBD. The degree of methylation was negatively correlated with EBF1 mRNA expression levels. Using methylation-specific PCR technique, the DNA methylation rates of EBF1 promoter region were investigated in CCA tissues (n=72). CCA patients with high methylation rates of EBF1 promoter region in the tumor tissues (54/72) had a poor prognosis. Higher methylation rates of EBF1 promoter region have shown in all CCA cell lines than that of an immortal cholangiocyte cell line (MMNK1). Upon treatment with the DNA methyltransferase inhibitor 5-Aza-dC, increased EBF1 expression levels and reduced DNA methylation rates were observed in CCA cells. Moreover, restoration of EBF1 expression in CCA cells led to inhibition of cell growth, migration and invasion. In addition, RNA sequencing analysis suggested that EBF1 is involved in suppression of numerous pathways in cancer. Taken together, DNA hypermethylation in the EBF1 promoter region suppresses EBF1 expression and induces CCA progression with aggressive clinical outcomes.

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. 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.

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. Single-molecule sequencing and chromosome contact maps enabled assembly of the highly heterozygous durian genome at chromosome-scale resolution. Transcriptomic analysis showed upregulation of sulfur-, ethylene-, and lipid-related pathways in durian fruits. We observed paleopolyploidization events shared by durian and cotton and durian-specific gene expansions in MGL (methionine γ-lyase), associated with production of volatile sulfur compounds (VSCs). MGL and the ethylene-related gene ACS (aminocyclopropane-1-carboxylic acid synthase) were upregulated in fruits concomitantly with their downstream metabolites (VSCs and ethylene), suggesting a potential association between ethylene biosynthesis and methionine regeneration via the Yang cycle. The durian genome provides a resource for tropical fruit biology and agronomy.

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 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' untranslated region deletion as a mechanism of FGFR2 upregulation. Integration of noncoding 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.Significance: Integrated whole-genome and epigenomic analysis of CCA on an international scale identifies new CCA driver genes, noncoding promoter mutations, and structural variants. CCA molecular landscapes differ radically by etiology, underscoring how distinct cancer subtypes in the same organ may arise through different extrinsic and intrinsic carcinogenic processes. Cancer Discov; 7(10); 1116-35. ©2017 AACR.This article is highlighted in the In This Issue feature, p. 1047.

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. Second, many complexes are embedded within highly-connected regions of the PPI network, which makes it difficult to accurately delimit their boundaries. Third, many complexes are small (composed of two or three distinct proteins), so that traditional topological markers such as density are ineffective. RESULTS: We have previously proposed three approaches to address these challenges. First, Supervised Weighting of Composite Networks (SWC) integrates diverse data sources with supervised weighting, and successfully fills in missing co-complex edges in sparse complexes to allow them to be predicted. Second, network decomposition (DECOMP) splits the PPI network into spatially- and temporally-coherent subnetworks, allowing complexes embedded within highly-connected regions to be more clearly demarcated. Finally, Size-Specific Supervised Weighting (SSS) integrates diverse data sources with supervised learning to weight edges in a size-specific manner-of being in a small complex versus a large complex-and improves the prediction of small complexes. Here we integrate these three approaches into a single system. We test the integrated approach on the prediction of yeast and human complexes, and show that it outperforms SWC, DECOMP, or SSS when run individually, achieving the highest precision and recall levels. CONCLUSION: Three groups of protein complexes remain challenging to predict from PPI data: sparse complexes, embedded complexes, and small complexes. Our previous approaches have addressed each of these challenges individually, through data integration, PPI-network decomposition, and supervised learning. Here we integrate these approaches into a single complex-discovery system, which improves the prediction of all three types of challenging complexes. With our approach, protein complexes can be more accurately and comprehensively predicted, allowing a clearer elucidation of the modular machinery of the cell.

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). We evaluate in depth the performance of these methods on PPI datasets from yeast, and highlight their limitations and challenges, in particular at detecting sparse and small or sub-complexes and discerning overlapping complexes. We describe methods for integrating diverse information including expression profiles and 3D structures of proteins with PPI networks to understand the dynamics of complex formation, for instance, of time-based assembly of complex subunits and formation of fuzzy complexes from intrinsically disordered proteins. Finally, we discuss methods for identifying dysfunctional complexes in human diseases, an application that is proving invaluable to understand disease mechanisms and to discover novel therapeutic targets. We hope this review aptly commemorates a decade of research on computational prediction of complexes and constitutes a valuable reference for further advancements in this exciting area.

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. This makes it difficult to accurately delimit the boundaries of such complexes. Second, many condition- and location-specific PPIs are not detected, leading to sparsely-connected complexes that cannot be picked out by clustering algorithms. Third, the majority of complexes are small complexes (made up of two or three proteins), which are extra sensitive to the effects of extraneous edges and missing co-complex edges. We show that many existing complex-discovery algorithms have trouble predicting such complexes, and show that our insight into the disparity between the static interactome and dynamic protein complexes can be used to improve the performance of complex discovery.

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. First, the PPI network is weighted by size-specific supervised weighting (SSS), which integrates heterogeneous data and their topological features with an overall topological isolatedness feature. SSS uses a naive-Bayes maximum-likelihood model to weight the edges with two posterior probabilities: that of being in a small complex, and of being in a large complex. The second stage, Extract, analyzes the SSS-weighted network to extract putative small complexes and scores them by cohesiveness-weighted density, which incorporates both small-co-complex and large-co-complex weights of edges within and surrounding the complexes. We test our approach on the prediction of yeast and human small complexes, and demonstrate that our approach attains higher precision and recall than some popular complex prediction algorithms. Furthermore, our approach generates a greater number of novel predictions with higher quality in terms of functional coherence.

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. 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. RESULTS: We 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. CONCLUSIONS: The 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.

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. Furthermore, many transient interactions are detected between proteins that are not from the same complex, while not all proteins from the same complex may actually interact. As a result, predicted complexes often do not match true complexes well, and many true complexes go undetected. RESULTS: We address these challenges by integrating PPI data with other heterogeneous data sources to construct a composite protein network, and using a supervised maximum-likelihood approach to weight each edge based on its posterior probability of belonging to a complex. We then use six different clustering algorithms, and an aggregative clustering strategy, to discover complexes in the weighted network. We test our method on Saccharomyces cerevisiae and Homo sapiens, and show that complex discovery is improved: compared to previously proposed supervised and unsupervised weighting approaches, our method recalls more known complexes, achieves higher precision at all recall levels, and generates novel complexes of greater functional similarity. Furthermore, our maximum-likelihood approach allows learned parameters to be used to visualize and evaluate the evidence of novel predictions, aiding human judgment of their credibility. CONCLUSIONS: Our approach integrates multiple data sources with supervised learning to create a weighted composite protein network, and uses six clustering algorithms with an aggregative clustering strategy to discover novel complexes. We show improved performance over previous approaches in terms of precision, recall, and number and quality of novel predictions. We present and visualize two novel predicted complexes in yeast and human, and find external evidence supporting these predictions.

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. As a result, predicted complexes often contain many spuriously included proteins, precluding them from matching true complexes. RESULTS: We propose two methods to tackle this problem: (1) The localization GO term decomposition method: We utilize cellular component Gene Ontology (GO) terms to decompose PPI networks into several smaller networks such that the proteins in each decomposed network are annotated with the same cellular component GO term. (2) The hub removal method: This method is based on the observation that hub proteins are more likely to fuse clusters that correspond to different complexes. To avoid this, we remove hub proteins from PPI networks, and then apply a complex discovery algorithm on the remaining PPI network. The removed hub proteins are added back to the generated clusters afterwards. We tested the two methods on the yeast PPI network downloaded from BioGRID. Our results show that these methods can improve the performance of several complex discovery algorithms significantly. Further improvement in performance is achieved when we apply them in tandem. CONCLUSIONS: The performance of complex discovery algorithms is hindered by the fact that not all the interactions in a PPI network take place at the same time. We tackle this problem by using localization GO terms or hubs to decompose a PPI network before complex discovery, which achieves considerable improvement.