Search

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. Agency for Science, Technology and Research (A*STAR) Ministry of Education (MOE) National Medical Research Council (NMRC) National Research Foundation (NRF) This study was supported by National Medical Research Council grants NMRC/STaR/0026/2015, MOH-000967-00 and OFLCG18May-0003, and A*ccelerate GAP fund ETPL/15-R15 GAP-0021 (to P. Tan) and MOE tier 2 grant (MOE2017-T2-1-105) and NMRC CS-IRG grant (NMRC/CIRG/1481/2017 to S. Li) and SCISSOR (A*STAR IAF-PP) H18/01/a0/020. Funding was also provided by Cancer Science Institute of Singapore, NUS, under the National Research Foundation Singapore and the Singapore Ministry of Education under its Research Centers of Excellence initiative, and block funding from Duke-NUS Medical School. A.M. Nargund is also supported by St. Baldrick's Foundation Research Award. R. Sundar is supported by a National Medical Research Council (NMRC) Fellowship, Singapore. M.J. Fullwood is supported by the RNA Biology Center at the Cancer Science Institute of Singapore, NUS, as part of funding under the Singapore Ministry of Education Academic Research Fund Tier 3 awarded to Daniel Tenen (MOE2014-T3-1-006) and the National Research Foundation Singapore and the Singapore Ministry of Education under its Research Centers of Excellence initiative.

Summary of Patients and Clinical Data.

Supplementary Figure 1. Unsupervised Clustering on CCA samples. Supplementary Figure 2. Alterations Found in CCA Clusters. Supplementary Figure 3. MAP2K4 Homozygous Deletions and ERBB2 Amplifications. Supplementary Figure 4. Alterations Related to Structural Variations Found in CCAs. Supplementary Figure 5. FIREFLY Analysis of Pathways Systematically Dysregulated by Somatic Promoter Mutations that Alter Transcription Factor Binding. Supplementary Figure 6. Epigenetic Clusters and Mutation Signatures.

Structural Variations across 71 WGS CCAs.

Summary of CCA Alterations.

Further details of methods, in addition to the supplementary figure and table legends.

Alterations in CCA Clusters.

Coverage Statistics and List of Genes for Targeted Sequencing.

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.

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. Ministry of Education (MOE) National Research Foundation (NRF) Submitted/Accepted version This research/project is supported by the National Research Foundation, Singapore under its Industry Alignment Fund – Prepositioning (IAF-PP) Funding Initiative. W.W.B.G. and L.W. acknowledge support from a Ministry of Education, Singapore, AcRF Tier 2 award (MOE2019-T2-1-042). C.Y. and L.W. acknowledge support from an AI Singapore grant (AISG-100E-2019-028).

Introduction Many cancers involve the participation of rare cell populations that may only be found in a subset of patients. Single-cell RNA sequencing (scRNA-seq) can identify distinct cell populations across multiple samples with batch normalization used to reduce processing-based effects between samples. However, aggressive normalization obscures rare cell populations, which may be erroneously grouped with other cell types. There is a need for conservative batch normalization that maintains the biological signal necessary to detect rare cell populations. MapBatch We designed a batch normalization tool, MapBatch, based on two principles: an autoencoder trained with a single sample learns the underlying gene expression structure of cell types without batch effect; and an ensemble model combines multiple autoencoders, allowing the use of multiple samples for training. Each autoencoder is trained on one sample, learning a projection into the biological space S representing the real expression differences between cells in that sample (Figure 1a, middle). When other samples are projected into S, the projection reduces expression differences orthogonal to S, while preserving differences along S. The reverse projection transforms the data back into gene space at the autoencoder's output, sans expression differences orthogonal to S (Figure 1a, right). Since batch-based technical differences are not represented in S, this transformation selectively removes batch effect between samples, while preserving biological signal. The autoencoder output thus represents normalized expression data, conditioned on the training sample. To incorporate multiple samples into training, MapBatch uses an ensemble of autoencoders, each trained with a single sample (Figure 1b). We train with a minimal number of samples necessary to cover the different cell populations in the dataset. We implement regularization using dropout and noise layers, and an a priori feature extraction layer using KEGG gene modules. The autoencoders' outputs are concatenated for downstream analysis. For visualization and clustering, we use the top principal components of the concatenated outputs. For differential expression (DE), we perform DE on each of the gene matrices output by each model, then take the result with the lowest P-value. To test MapBatch, we generated a synthetic dataset based on 7 batches of publicly available PBMC data. For each batch we simulated rare cell populations by selecting one of three cell types to perturb by up and down-regulating 40 genes in 0.5%-2% of the cells (Figure 1c). We simulated additional batch effect by scaling each gene in each batch with a scaling factor. Upon visualization and clustering, cells grouped largely by batch (Figure 1d). After batch normalization, cells grouped by cell type rather than batch, and all three perturbed cell populations were successfully delineated (Figure 1e). DE between each perturbed population and its mother cells accurately retrieved the perturbed genes, showing that normalization maintained real expression differences (Figure 1e). In contrast, three methods tested Seurat (Stuart et al., 2019), Harmony (Korsunsky et al., 2019), and Liger (Welch et al., 2019) could only derive a subset of the perturbed populations (Figures 1f-h). MapBatch identifies rare populations in multiple myeloma (MM) We used MapBatch to process bone marrow scRNA-seq data from 14 MM samples and 2 healthy controls. After batch normalization, unsupervised clustering identified 20 clusters, which we annotated using MapCell (Koh & Hoon, 2019) (Figures 2a, 2b). We identified 3 small clusters of cells that could not be reliably annotated, comprising less than 1% of total cells and found in only a subset of patients (Figures 2c, 2d). As validation, we observed that these cells were present in distinct clusters in individual samples using their uncorrected expression data, providing evidence that these clusters were not driven by batch effect nor MapBatch (Figure 2e). Conclusion Batch normalization of scRNA-seq data involves a trade-off between minimizing batch effect and maximizing the remaining biological signal. While most methods lean towards the former, MapBatch maintains more biological signal for downstream analysis, enabling the discovery of previously difficult to find cell populations. Figure 1 Figure 1. Disclosures Xu: Proteona Pte Ltd: Current Employment. Scolnick: Proteona Pte Ltd: Current holder of individual stocks in a privately-held company. Huo: Proteona Pte Ltd: Ended employment in the past 24 months. Lovci: Proteona Pte Ltd: Current Employment. Chng: Amgen: Honoraria, Research Funding; Abbvie: Honoraria; Janssen: Honoraria, Research Funding; Novartis: Honoraria; Celgene: Honoraria, Research Funding.

Background Multiple Myeloma (MM) is an incurable plasma cell (PC) malignancy and high risk MM remains an unmet clinical need. Translocation 4;14 occurs in 15% of MM and is associated with an adverse prognosis. A deeper understanding of the biology and immune micro-environment of t(4;14) MM is necessary for the development of effective targeted therapies. Single Cell multi-omics provides a new tool for phenotypic characterization of MM. Here we used Proteona's ESCAPE™ single cell multi-omics platform to study a cohort of patients with t(4;14) MM. Methods Diagnostic bone marrow (BM) samples from 13 patients with t(4;14) MM (one of whom had samples at diagnosis and relapse) were analysed using the ESCAPE™ platform from Proteona which simultaneously measures gene and cell surface protein expression of 65 proteins in single cells. Cryopreserved BM samples were stained with antibodies and subsequently sorted on CD138 expression. The CD138 positive and negative fractions were recombined at a 1:1 ratio for analysis using the 10x Genomics 3' RNAseq kit. Resulting data were analyzed with Proteona's MapSuite™ single cell analytics platform. In particular, Mapcell was used to annotate the cells and MapBatch was used for batch normalization in order to preserve rare cell populations. Results Patients had a median age of 63 years and received novel agent-based induction. Median progression free and overall survival (PFS and OS) were 22 and 34 months respectively. We first analyzed serial BM samples from an individual patient that were taken at diagnosis and relapse following bortezomib based treatment. The PCs in this patient showed variations in gene expression between diagnosis and relapse (Fig 1A), including the reduction of HIST1H2BG expression, which has previously been correlated with resistance to bortezomib. Subsequent analysis of the immune cells identified a shift in the ratio of T cells to CD14 monocytes from 5.7 at diagnosis to 0.6 at relapse suggesting a major change in the BM immune micro-environment in response to therapy. Next, we analyzed the malignant PCs of the diagnostic samples. As expected, MMSET (NSD2) was overexpressed in all PCs compared to normal PCs, while FGFR3 expression could be categorized into no expression of FGFR3, low expression (80% of cells expressing FGFR3) (Fig 1B). No gene or protein expression patterns within the PCs were identified that correlated with PFS or OS in this cohort. Finally, we analyzed the immune micro-environment in the diagnostic samples (Fig 1C). While there was no overall discernable pattern of cell types present, one cluster of cells, annotated as 'unknown' cell type, suggested a small population of cells that had not been previously annotated in published single cell RNA-seq data. The cells were CD45+ and CD138 - both at the protein and RNA level, suggesting they are not plasma cells. We tested if the number of the 'unknown' cells in each sample correlated with PFS, but there was no significant correlation. We then used these cells to derive a gene signature profile which was expressed in most of the cells in the 'unknown' cluster as well as a minor fraction of cells in other clusters including some PCs. The number of cells expressing the gene signature negatively correlated with PFS, with samples containing more cells expressing the signature having a lower PFS than samples with fewer signature positive cells (Fig 2). The correlation remained significant whether we included PCs in the analysis or not, but was not significant amongst only the PC population, suggesting that the cells responsible for the correlation are from the immune micro-environment. Conclusions We present the first application of single cell multi-omic immune profiling in high-risk MM and demonstrate that t(4;14) is a phenotypically heterogenous disease. While no consistent gene or protein expression patterns were identified within the malignant cell population, we did identify gene expression changes in a relapsed patient sample that may reflect key alterations in the PCs responsible for therapy resistance. In addition, we identified a gene signature expressed in a rare population of non-plasma cells that significantly correlated with PFS in this patient cohort. These data highlight the potential of single cell multi-omic analysis to identify immune micro-environmental signatures that correlate with response to therapy in t(4;14) MM. Figure 1 Figure 1. Disclosures Scolnick: Proteona Pte Ltd: Current holder of individual stocks in a privately-held company. Huo: Proteona Pte Ltd: Ended employment in the past 24 months. Xu: Proteona Pte Ltd: Current Employment. Chng: Amgen: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Novartis: Honoraria; Abbvie: Honoraria.

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., info:eu-repo/semantics/published

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 organization 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 challenges faced by these methods, in particular detection of sparse and small or sub- complexes and discerning of 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., Comment: 1 Table

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.

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.

In recent years, personalized mobile phone ringtones have been in growing demand. This paper describes auto-DJ, which uses the phone owners' voices in software DJ's performances for creating their own personalized ringtones, with a focus on its scratched sound synthesis module. Scratching is the primary technique for playing the turntable as a musical instrument - making "new" sounds from records by changing the rate of playing them with hand movements. A scratched sound synthesizer turns sound clips into scratches, each of which comprises one or more strokes. We synthesize them by resampling using pitch deviation envelopes which are derived from those matched for original scratched sounds. Our score representation allows musicians to describe scratches, strokes, and their acoustic characteristics in a musical and concise notation. The scratched sound synthesizer currently produces three types of scratches. Despite the small repertoire, we have successfully created realistic and expressive performances.

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.

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.

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.

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.