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bioRxiv2018

A multiscale model of epigenetic heterogeneity reveals the kinetic routes of pathological cell fate reprogramming

The inherent capacity of somatic cells to switch their phenotypic status in response to damage stimuli in vivo might have a pivotal role in ageing and cancer. However, how the entry-exit mechanisms of phenotype reprogramming are established remains poorly understood. In an attempt to elucidate such mechanisms, we herein introduce a stochastic model of combined epigenetic regulation (ER)-gene regulatory network (GRN) to study the plastic phenotypic behaviours driven by ER heterogeneity. Furthermore, based on the existence of multiple scales, we formulate a method for stochastic model reduction, from which we derive an efficient hybrid simulation scheme that allows us to deal with such complex systems. Our analysis of the coupled system reveals a regime of tristability in which pluripotent stem-like and differentiated steady-states coexist with a third indecisive state. Crucially, ER heterogeneity of differentiation genes is for the most part responsible for conferring abnormal robustness to pluripotent stem-like states. We then formulate epigenetic heterogeneity-based strategies capable of unlocking and facilitating the transit from differentiation-refractory (pluripotent stem-like) to differentiation-primed epistates. The application of the hybrid numerical method validated the likelihood of such switching involving solely kinetic changes in epigenetic factors. Our results suggest that epigenetic heterogeneity regulates the mechanisms and kinetics of phenotypic robustness of cell fate reprogramming. The occurrence of tunable switches capable of modifying the nature of cell fate reprogramming from pathological to physiological might pave the way for new therapeutic strategies to regulate reparative reprogramming in ageing and cancer.\n\nAuthor summaryCertain modifications of the structure and functioning of the protein/DNA complex called chromatin can allow adult, fully differentiated cells to adopt a stem cell-like pluripotent state in a purely epigenetic manner, not involving changes in the underlying DNA sequence. Such reprogramming-like phenomena may constitute an innate reparative route through which human tissues respond to injury and could also serve as a novel regenerative strategy in human pathological situations in which tissue or organ repair is impaired. However, it should be noted that in vivo reprogramming would be capable of maintaining tissue homeostasis provided the acquisition of pluripotency features is strictly transient and accompanied by an accurate replenishment of the specific cell types being lost. Crucially, an excessive reprogramming to pluripotency in the absence of controlled re-differentiation would impair the repair or the replacement of damaged cells, thereby promoting pathological alterations of cell fate. A mechanistic understanding of how the degree of chromatin plasticity dictates the reparative versus pathological behaviour of in vivo reprogramming to rejuvenate aged tissues while preventing tumorigenesis is urgently needed, including especially the intrinsic epigenetic heterogeneity of the tissue resident cells being reprogrammed. We here introduce a novel method that mathematically captures how epigenetic heterogeneity is actually the driving force that governs the routes and kinetics to entry into and exit from a pathological pluripotent-like state. Moreover, our approach computationally validates the likelihood of unlocking chronic, unrestrained pluripotent states and drive their differentiation down the correct path by solely manipulating the intensity and direction of few epigenetic control switches. Our approach could inspire new therapeutic approaches based on in vivo cell reprogramming for efficient tissue regeneration and rejuvenation and cancer treatment.

systems biology↗

bioRxiv2018

ATR function is indispensable to allow proper mammalian follicle development.

Mammalian female fertility relies on the proper development of follicles. Right after birth in mouse, oocytes associate with somatic ovarian cells to form follicles. These follicles grow during adult lifetime to produce viable gametes. In this study, we analyzed the role of the ATM and rad3-related (ATR) kinase in mouse oogenesis and folliculogenesis using a hypomorphic mutation of the Atr gene (Murga et al., 2009). Female mice homozygote for this allele have been reported to be sterile. Our data show that female meiotic prophase is not grossly altered when ATR levels are reduced. However, follicle development is majorly compromised since Atr mutant ovaries present a decrease of growing follicles. Comprehensive analysis of follicular cell death and proliferation suggest that wild-type levels of ATR are required to achieve optimal follicular development. Altogether, these findings suggest that reduced ATR expression causes sterility due to defects in follicular progression rather than in meiotic recombination. We discuss the implication of these findings for the use of ATR inhibitors as anti-cancer drugs and its possible side effects on female fertility.

cell biology↗

bioRxiv2015

Cancer Classification by Correntropy-Based Sparse Compact Incremental Learning Machine

Cancer prediction is of great importance and significance and it is crucial to provide researchers and scientists with novel, accurate and robust computational tools for this issue. Recent technologies such as Microarray and Next Generation Sequencing have paved the way for computational methods and techniques to play critical roles in this regard. Many important problems in cell biology require the dense nonlinear interactions between functional modules to be considered. The importance of computer simulation in understanding cellular processes is now widely accepted, and a variety of simulation algorithms useful for studying certain subsystems have been designed. In this article, a Sparse Compact Incremental Learning Machine (SCILM) is proposed for cancer classification problem on microarray gene expression data which take advantage of Correntropy cost that makes it robust against diverse noises and outliers. Moreover, since SCILM uses l1-norm of the weights, it has sparseness which can be applied for gene selection purposes as well. Finally, due to compact structure, the proposed method is capable of performing classification tasks in all of the cases with only one neuron in its hidden layer. The experimental analysis is performed on 26 well known microarray datasets regarding diverse kinds of cancers and the results show that the proposed method not only achieved significantly high accuracy but also because of its sparseness, final connectivity weights determined the value and effectivity of each gene regarding the corresponding cancer.

Bioinformatics↗

bioRxiv2018

Pathway maps enable straightforward yet customized and semi-automated yet insightful analyses of omics data

To explore the molecular processes underlying some biological theme of interest based on public data, gene lists are used herein as input for the construction of annotated pathway maps, employing Cytoscape apps, and then high-throughput (\"omics\") gene expression data are overlaid onto these maps. Seeded with a published set of marker genes of the senescence-associated secretory phenotype and the genes of the cellular senescence KEGG pathway, a gene/protein interaction network and annotated clusters (a \"pathway map\") of cellular senescence are derived. The map can be amended, by adding some application-specific genes, and overlaid with gene expression data describing cellular senescence of fibroblasts and with disease-related gene expression data associated with prostate and pancreatic cancer, and with ischemic stroke, allowing insights into the role of cellular senescence in disease. Some gene expression data are derived from the \"Biomarker Benchmark repository\". The pathway map approach can be followed in principle for any biological theme of interest, fostering much-needed independence from the investigator-biased expert networks usually used for overlaying gene expression data.

genomics↗

bioRxiv2017

A general and powerful stage-wise testing procedure for differential expression and differential transcript usage

BackgroundReductions in sequencing cost and innovations in expression quantification have prompted an emergence of RNA-seq studies with complex designs and data analysis at transcript resolution. These applications involve multiple hypotheses per gene, leading to challenging multiple testing problems. Conventional approaches provide separate top-lists for every contrast and false discovery rate (FDR) control at individual hypothesis level. Hence, they fail to establish proper gene-level error control, which compromises downstream validation experiments. Tests that aggregate individual hypotheses are more powerful and provide gene-level FDR control, but in the RNA-seq literature no methods are available for post-hoc analysis of individual hypotheses.\n\nResultsWe introduce a two-stage procedure that leverages the increased power of aggregated hypothesis tests while maintaining high biological resolution by post-hoc analysis of genes passing the screening hypothesis. Our method is evaluated on simulated and real RNA-seq experiments. It provides gene-level FDR control in studies with complex designs while boosting power for interaction effects without compromising the discovery of main effects. In a differential transcript usage/expression context, stage-wise testing gains power by aggregating hypotheses at the gene level, while providing transcript-level assessment of genes passing the screening stage. Finally, a prostate cancer case study highlights the relevance of combining gene with transcript level results.\n\nConclusionStage-wise testing is a general paradigm that can be adopted whenever individual hypotheses can be aggregated. In our context, it achieves an optimal middle ground between biological resolution and statistical power while providing gene-level FDR control, which is beneficial for downstream biological interpretation and validation.

bioinformatics↗

bioRxiv2018

Characterizing functional consequences of DNA copy number alterations in breast and ovarian tumors by spaceMap

MotivationWe propose a novel conditional graphical model -- spaceMap -- to construct gene regulatory networks from multiple types of high dimensional omic profiles. A motivating application is to characterize the perturbation of DNA copy number alterations (CNA) on downstream protein levels in tumors. Through a penalized multivariate regression framework, spaceMap jointly models high dimensional protein levels as responses and high dimensional CNA as predictors. In this setup, spaceMap infers an undirected network among proteins together with a directed network encoding how CNA perturb the protein network. spaceMap can be applied to learn other types of regulatory relationships from high dimensional molecular pro-files, especially those exhibiting hub structures.\n\nResultsSimulation studies show spaceMap has greater power in detecting regulatory relationships over competing methods. Additionally, spaceMap includes a network analysis toolkit for biological interpretation of inferred networks. We applied spaceMap to the CNA, gene expression and proteomics data sets from CPTAC-TCGA breast (n=77) and ovarian (n=174) cancer studies. Each cancer exhibited disruption of ion transmembrane transport and regulation from RNA polymerase II promoter by CNA events unique to each cancer. Moreover, using protein levels as a response yields a more functionally-enriched network than using RNA expressions in both cancer types. The network results also help to pinpoint crucial cancer genes and provide insights on the functional consequences of important CNA in breast and ovarian cancers.\n\nAvailabilityThe R package spaceMap -- including vignettes and documentation -- is hosted at https://topherconley.github.io/spacemap

bioinformatics↗

bioRxiv2018

Stabilized Independent Component Analysis outperforms other methods in finding reproducible signals in tumoral transcriptomes

MotivationMatrix factorization methods are widely exploited in order to reduce dimensionality of transcriptomic datasets to the action of few hidden factors (metagenes). Applying such methods to similar independent datasets should yield reproducible inter-series outputs, though it was never demonstrated yet.\n\nResultsWe systematically test state-of-art methods of matrix factorization on several transcriptomic datasets of the same cancer type. Inspired by concepts of evolutionary bioinformatics, we design a new framework based on Reciprocally Best Hit (RBH) graphs in order to benchmark the methods reproducibility. We show that a particular protocol of application of Independent Component Analysis (ICA), accompanied by a stabilisation procedure, leads to a significant increase in the inter-series output reproducibility. Moreover, we show that the signals detected through this method are systematically more interpretable than those of other state-of-art methods. We developed a user-friendly tool BIODICA for performing the Stabilized ICA-based RBH meta-analysis. We apply this methodology to the study of colorectal cancer (CRC) for which 14 independent publicly available transcriptomic datasets can be collected. The resulting RBH graph maps the landscape of interconnected factors that can be associated to biological processes or to technological artefacts. These factors can be used as clinical biomarkers or robust and tumor-type specific transcriptomic signatures of tumoral cells or tumoral microenvironment. Their intensities in different samples shed light on the mechanistic basis of CRC molecular subtyping.\n\nAvailabilityThe BIODICA tool is available from https://github.com/LabBandSB/BIODICA.\n\nContactlaura.cantini@curie.fr and andrei.zinovyev@curie.fr\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

systems biology↗

bioRxiv2017

OneD: increasing reproducibility of Hi-C Samples with abnormal karyotypes

The three-dimensional conformation of genomes is an essential component of their biological activity. The advent of the Hi-C technology enabled an unprecedented progress in our understanding of genome structures. However, Hi-C is subject to systematic biases that can compromise downstream analyses. Several strategies have been proposed to remove those biases, but the issue of abnormal karyotypes received little attention. Many experiments are performed in cancer cell lines, which typically harbor large-scale copy number variations that create visible defects on the raw Hi-C maps. The consequences of these widespread artifacts on the normalized maps are mostly unexplored. We observed that current normalization methods are not robust to the presence of large-scale copy number variations, potentially obscuring biological differences and enhancing batch effects. To address this issue, we developed an alternative approach designed to take into account chromosomal abnormalities. The method, called OneD, increases reproducibility among replicates of Hi-C samples with abnormal karyotype, outperforming previous methods significantly. On normal karyotypes, OneD fared equally well as state-of-the-art methods, making it a safe choice for Hi-C normalization. OneD is fast and scales well in terms of computing resources for resolutions up to 1 kbp. OneD is implemented as an R package available at http://www.github.com/qenvio/dryhic.

bioinformatics↗

bioRxiv2018

EXD2 and WRN exonucleases are required for interstrand crosslink repair in Drosophila

Interstrand crosslinks (ICLs) present a major threat to genome integrity, preventing both the correct transcription of active chromatin and complete replication of the genome. This is exploited in genotoxic chemotherapy where ICL induction is used to kill highly proliferative cancer cells. Repair of ICLs involves a complex interplay of numerous proteins, including those in the Fanconi anemia (FA) pathway, though alternative and parallel pathways have been postulated. Here, we investigate the role of the 3-5 exonuclease, EXD2, and the highly related WRN exonuclease (implicated in premature ageing human Werner syndrome), in repair of interstrand crosslinks in the fruit fly, Drosophila melanogaster. We find that flies mutant for EXD2 (DmEXD2) have elevated rates of genomic instability resulting from chromosome breakage and loss of the resulting acentric fragments, in contrast to WRN exonuclease (DmWRNexo) mutants where excess homologous recombination is the principal mechanism of genomic instability. Most notably, we demonstrate that proliferating larval neuroblasts mutant for either DmWRNexo or DmEXD2 are deficient in repair of DNA interstrand crosslinks caused by diepoxybutane or mitomycin C, strongly suggesting that each nuclease individually plays a role in repair of ICLs in flies. These findings have significant implications not only for understanding the complex process of ICL repair in humans, but also for enhancing cancer therapies that rely on ICL induction, with caveats for cancer therapy in Werner syndrome and Fanconi anemia patients.

cell biology↗

bioRxiv2018

Umbelliprenin isolated from Ferula sinkiangensis inhibits tumor growth and migration through the disturbance of Wnt signaling pathway in gastric cancer

The traditional herb medicine Ferula sinkiangensis K. M. Shen (F. sinkiangensis) has been used to treat stomach disorders in Xinjiang District for centuries. Umbelliprenin is the effective component isolated from F. sinkiangensis which is particularly found in plants of the family Ferula. We previously reported the promising effects of Umbelliprenin against gastric cancer cells, but its anti-migration effect remained unknown. Here we investigated the anti-migration effect and mechanism of Umbelliprenin in human gastric cancer cells. In SRB assay, Umbelliprenin showed cytotoxic activities in the gastric cancer cell lines AGS and BGC-823 in a dose-and-time-dependent manner, while it showed lower cytotoxic activity in the normal gastric epithelium cell line GES-1. During transwell, scratch and colony assays, the migration of tumor cells was inhibited by Umbelliprenin treatment. The expression levels of the Wnt-associated signaling pathway proteins were analyzed with western blots, and the results showed that Umbelliprenin decreased the expression levels of proteins of the Wnt signalling pathway, such as Wnt-2, {beta}-catenin, GSK-3{beta}, p-GSK-3{beta}, Survivin and c-myc. The translocation of {beta}-catenin to the nucleus was also inhibited by Umbelliprenin treatment. In TCF reporter assay, the transcriptional activity of T-cell factor/lymphoid enhancer factor (TCF/LEF) was decreased after Umbelliprenin treatment. The in vivo results suggested that Umbelliprenin induced little to no harm in the lung, heart and kidney. Overall, these data provided evidence that Umbelliprenin may inhibit the growth, invasion and migration of gastric cancer cells by disturbing the Wnt signaling pathway.

molecular biology↗

bioRxiv2017

MuClone: Somatic mutation detection and classification through probabilistic integration of clonal population structure

Accurate detection and classification of somatic single nucleotide variants (SNVs) is important in defining the clonal composition of human cancers. Existing tools are prone to miss low prevalence mutations and methods for classification of mutations into clonal groups across the whole genome are underdeveloped. Increasing interest in deciphering clonal population dynamics over multiple samples in time or anatomic space from the same patient is resulting in whole genome sequence (WGS) data from phylogenetically related samples. With the access to this data, we posited that injecting clonal structure information into the inference of mutations from multiple samples would improve mutation detection.\n\nWe developed MuClone: a novel statistical framework for simultaneous detection and classification of mutations across multiple tumour samples of a patient from whole genome or exome sequencing data. The key advance lies in incorporating prior knowledge about the cellular prevalences of clones to improve the performance of detecting mutations, particularly low prevalence mutations. We evaluated MuClone through synthetic and real data from spatially sampled ovarian cancers. Results support the hypothesis that clonal information improves sensitivity in detecting somatic mutations without compromising specificity. In addition, MuClone classifies mutations across whole genomes of multiple samples into biologically meaningful groups, providing additional phylogenetic insights and enhancing the study of WGS-derived clonal dynamics.

bioinformatics↗

bioRxiv2018

Increasing consensus of context-specific metabolic models by integrating data-inferred cell functions

Genome-scale metabolic models provide a valuable context for analyzing data from diverse high-throughput experimental techniques. Models can quantify the activities of diverse pathways and cellular functions. Since some metabolic reactions are only catalyzed in specific environments, several algorithms exist that build context-specific models. However, these methods make differing assumptions that influence the content and associated predictive capacity of resulting models, such that model content varies more due to methods used than cell types. Here we overcome this problem with a novel framework for inferring the metabolic functions of a cell before model construction. For this, we curated a list of metabolic tasks and developed a framework to infer the activity of these functionalities from transcriptomic data. We protected the data-inferred tasks during the implementation of diverse context-specific model extraction algorithms for 44 cancer cell lines. We show that the protection of data-inferred metabolic tasks decreases the variability of models across extraction methods. Furthermore, resulting models better capture the actual biological variability across cell lines. This study highlights the potential of using biological knowledge, inferred from omics data, to obtain a better consensus between existing extraction algorithms. It further provides guidelines for the development of the next-generation of data contextualization methods.

systems biology↗

bioRxiv2015

Combining Dependent P-values with an Empirical Adaptation of Brown’s Method

Motivation: Combining P-values from multiple statistical tests is a common exercise in bioinformatics. However, this procedure is non-trivial for dependent P-values. Here we discuss an empirical adaptation of Browns Method (an extension of Fishers Method) for combining dependent P-values which is appropriate for the correlated data sets found in high-throughput biological experiments.\n\nResults: We show that Fishers Method is biased when used on dependent sets of P-values with both simulated data and gene expression data from The Cancer Genome Atlas (TCGA). When applied on the same data sets, the Empirical Browns Method provides a better null distribution and a more conservative result.\n\nAvailability: The Empirical Browns Method is available in Python, R, and MATLAB and can be obtained from https://github.com/IlyaLab/CombiningDependentPvaluesUsingEBM.1

Bioinformatics↗

bioRxiv2017

Extracting Intercellular Signaling Network Of Cancer Tissues Using Ligand-Receptor Expression Patterns From Whole-Tumor And Single-Cell Transcriptomes

Many behaviors of cancer, such as progression, metastasis and drug resistance etc., cannot be fully understood by genetic mutations or intracellular signaling alone. Instead, they are emergent properties of cell community that forms a tumor. Studies of tumor heterogeneity reveal that many cancer behaviors critically depend on the intercellular communication mediated by secreted signaling ligands and their cognate receptors that take place between cancer cells and stromal cells. Owing to systematic cancer omics efforts, we studied such cell-cell interactions using data from cancer transcriptome database. We curated a list of more than 2,500 ligand-receptor pairs and developed a method to identify their enrichment in tumors from TCGA pancancer data and to build a cell interaction network from single-cell data for the case of melanoma. Using the specificity of the ligand-receptor interaction and their expressions measured in individual cells, we built a map of a cell-cell communication network which indicate what signal is exchanged between which cell types. Such networks establish a new formal phenotypes which are embodied by the cell communication structure - it may offer new opportunities to identify the molecular signatures which may influence cancer cell behaviors by changing cell population dynamics.

systems biology↗

bioRxiv2017

Network integration of multi-tumour omics data suggests novel targeting strategies

We characterize different tumour types in the search for multi-tumour drug targets, in particular aiming for drug repurposing or novel drug combinations. Starting from 11 tumour types from The Cancer Genome Atlas, we obtain three clusters based on transcriptomic correlation profiles. A network-based analysis, integrating gene expression profiles and protein interactions of cancer-related genes, allowed us to define three cluster-specific signatures, with genes belonging to NF-B signaling, chromosomal instability, ubiquitin-proteasome system, DNA metabolism, and apoptosis biological processes. These signatures have been characterized by different approaches based on mutational, pharmacological and clinical evidences, demonstrating the validity of our selection. Moreover, we defined new pharmacological strategies validated by in vitro experiments that showed inhibition of cell growth in two tumour cell lines, with significant synergistic effect. Our study thus provides a list of genes and pathways with the potential to be used, singularly or in combination, for the design of novel treatment strategies.

systems biology↗

bioRxiv2017

WebMeV: A Cloud Platform for Analyzing and Visualizing Cancer Genomic Data

Although large, complex genomic data sets are increasingly easy to generate, and the number of publicly available data sets in cancer and other diseases is rapidly growing, the lack of intuitive, easy to use analysis tools has remained a barrier to the effective use of such data. WebMeV (https://mev.tm4.org) is an open-source, web-based tool that gives users access to sophisticated tools for analysis of RNA-Seq and other data in an interface designed to democratize data access. WebMeV combines cloud-based technologies with a simple user interface to allow users to access large public data sets such as that from The Cancer Genome Atlas (TCGA) or to upload their own. The interface allows users to visualize data and to apply advanced data mining analysis methods to explore the data and draw biologically meaningful conclusions. We provide an overview of WebMeV and demonstrate two simple use cases that illustrate the value of putting data analysis in the hands of those looking to explore the underlying biology of the systems being studied.

bioinformatics↗

bioRxiv2017

Progesterone receptor maintains estrogen receptor gene expression by regulating DNA methylation in hormone-free breast cancer cells

Breast cancer prognosis and response to endocrine therapy strongly depends on the expression of the estrogen and progesterone receptors (ER and PR, respectively). Although much is known about ER gene (ESR1) regulation after hormonal stimulation, how it is regulated in hormone-free condition is not fully understood. We used ER-/PR-positive breast cancer cells to investigate the role of PR in ESR1 regulation in the absence of hormones. We show that PR binds to the low-methylated ESR1 promoter and maintains both gene expression and DNA methylation of the ESR1 locus in hormone-deprived breast cancer cells. Depletion of PR reduces ESR1 expression, with a concomitant increase in gene promoter methylation. The high amount of methylation in the ESR1 promoter of PR-depleted cells persists after the stable re-expression of PR and inhibits PR binding to this genomic region. As a consequence, the rescue of PR expression in PR-depleted cells is insufficient to restore ESR1 expression. Consistently, DNA methylation impedes PR binding to consensus progesterone responsive elements. These findings contribute to understanding the complex crosstalk between PR and ER and suggest that the analysis of ESR1 promoter methylation in breast cancer cells can help to design more appropriate targeted therapies for breast cancer patients.

molecular biology↗

bioRxiv2018

K-Ras G-domain binding with signaling lipid phosphoinositides: PIP2 association, orientation, function

Ras genes are potent drivers of human cancers, with mutated K-Ras4B being the most abundant isoform. Targeted inhibition of oncogenic gene products is considered the holy grail of present-day cancer therapy, and recent discoveries of small molecule inhibitors for K-Ras4B greatly benefited from a deeper understanding of the protein structure and dynamics of the GTPase. Since interactions with biological membranes are key for Ras function, the details of Ras - lipid interactions have become a major focus of study, especially since it is becoming clear that such interactions not only involve the Ras C-terminus for lipid anchoring, but also the G-protein domain. Here we investigated the interaction between K-Ras4B with the signaling lipid phosphatidyl inositol (4,5) phosphate (PIP2) using NMR spectroscopy and molecular dynamics simulations, complemented by biophysical and cell biology assays. We discovered that the {beta}2 and {beta}3 strands as well as helices 4 and 5 of the GTPase G-domain bind to PIP2, and that these secondary structural elements employ specific residues for these interactions. These likely occur in two orientation states of the protein relative to the membrane. Importantly, we found that some of these residues, which are known to be oncogenic when mutated (D47K, D92N, K104M and D126N), are critical for K-Ras-mediated transformation of fibroblast cells, while not substantially affecting basal and assisted nucleotide hydrolysis and exchange. We further showed that mutation K104M can indeed abolish localization of mutant K-Ras to the plasma membrane. These findings suggest that specific G-domain residues play an important, previously-unknown role in regulating Ras function by mediating interactions with membrane PIP2 lipids. Thus, a detailed description of the novel K-Ras-PIP2 binding surfaces is likely to inform the future design of therapeutic reagents.

biophysics↗