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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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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↗

bioRxiv2018

TuBA: Tunable Biclustering Algorithm Reveals Clinically Relevant Tumor Transcriptional Profiles in Breast Cancer

BackgroundTraditional clustering approaches for gene expression data are not well adapted to address the complexity and heterogeneity of tumors, where small sets of genes may be aberrantly co-expressed in specific subsets of tumors. Biclustering algorithms that perform local clustering on subsets of genes and conditions help address this problem. We propose a graph-based Tunable Biclustering Algorithm (TuBA) based on a novel pairwise proximity measure, examining the relationship of samples at the extremes of genes expression profiles to identify similarly altered signatures. ResultsTuBAs predictions are consistent in 3,940 Breast Invasive Carcinoma (BRCA) samples from three independent sources, employing different technologies for measuring gene expression (RNASeq and Microarray). Over 60% of biclusters identified independently in each dataset had significant agreement in their gene sets, as well as similar clinical implications. About 50% of biclusters were enriched in the ER-/HER2- (or basal-like) subtype, while more than 50% were associated with transcriptionally active copy number changes. Biclusters representing gene co-expression patterns in stromal tissue were also identified in tumor specimens. ConclusionTuBA offers a simple biclustering method that can identify biologically relevant gene co-expression signatures not captured by traditional unsupervised clustering approaches. It complements biclustering approaches that are designed to identify constant or coherent submatrices in gene expression datasets, and outperforms them in identifying a multitude of altered transcriptional profiles that are associated with observed genomic heterogeneity of diseased states in breast cancer, both within and across tumor subtypes, a promising step in understanding disease heterogeneity, and a necessary first step in individualized therapy.

systems biology↗

bioRxiv2017

A Map Of Genetic Interactions In Cancer Cells

Cancer genomes often harbor hundreds of molecular aberrations. Such genetic variants can be drivers or passengers of tumorigenesis and, as a side effect, create new vulnerabilities for potential therapeutic exploitation. To systematically identify genotype-dependent vulnerabilities and synthetic lethal interactions, forward genetic screens in different genetic backgrounds have been conducted. We devised MINGLE, a computational framework that integrates CRISPR/Cas9 screens originating from many different libraries and laboratories to build genetic interaction maps. It builds on analytical approaches that were established for genetic network discovery in model organisms. We applied this method to integrate and analyze data from 85 CRISPR/Cas9 screens in human cancer cell lines combining functional data with information on genetic variants to explore the relationships of more than 2.1 million gene-background relationships. In addition to known dependencies, our analysis identified new genotype-specific vulnerabilities of cancer cells. Experimental validation of predicted vulnerabilities associated with aberrant Wnt/{beta}-catenin signaling identified GANAB and PRKCSH as new positive regulators of Wnt/{beta}-catenin signaling. By clustering genes with similar genetic interaction profiles, we drew the largest genetic network in cancer cells to date. Our scalable approach highlights how diverse genetic screens can be integrated to systematically build informative maps of genetic interactions in cancer, which can grow dynamically as more data is included.

systems biology↗

bioRxiv2017

Post-translational regulation of metabolism in fumarate hydratase deficient cancer cells

Deregulated signal transduction pathways and energy metabolism are hallmarks of cancer and both play a fundamental role in the process of tumorigenesis. While it is increasingly recognised that signalling and metabolism are highly interconnected, the underpinning mechanisms of their co-regulation are still largely unknown. Here we designed and acquired proteomics, phosphoproteomics, and metabolomics experiments in fumarate hydratase (FH) deficient cells and developed a computational modelling approach to identify putative regulatory phosphorylation-sites of metabolic enzymes. We identified previously reported functionally relevant phosphosites and potentially novel regulatory residues in enzymes of the central carbon metabolism. In particular, we show that pyruvate dehydrogenase (PDHA1) enzymatic activity is inhibited by increased phosphorylation in FH-deficient cells. Our work provides a novel approach to investigate how post-translational modifications of enzymes regulate metabolism and could have important implications for understanding the metabolic transformation of FH-deficient cancers.

systems biology↗

bioRxiv2017

The activation loop residue serine 173 of S.pombe Chk1 kinase is critical for the response to DNA replication stress

Why the DNA damage checkpoint kinase Chk1 protects the genome of lower and higher eukaryotic cells differentially is still unclear. Mammalian Chk1 regulates replication origins, safeguards DNA replication forks and promotes fork progression. Conversely, yeast Chk1 acts only in G1 and G2. We report here that the mutation of serine 173 (S173A) in the activation loop of fission yeast Chk1 abolishes the G1-M and S-M checkpoints without affecting the G2-M arrest. Although Chk1-S173A is fully phosphorylated at serine 345 by the DNA damage sensor Rad3 (ATR) when DNA replication forks break, cells fail to stop the cell cycle. Mutant cells are uniquely sensitive to the DNA alkylation agent methyl- methanesulfate (MMS). This MMS sensitivity is genetically linked with the lagging strand DNA polymerase delta. Chk1-S173A is also unable to block mitosis when the G1 transcription factor Cdc10 is impaired. Serine 173 is equivalent to lysine 166 in human Chk1, an amino acid important for substrate specificity. We conclude that the removal of serine 173 impairs the phosphorylation of a Chk1 target that is important to protect cells from DNA replication stress.\n\nSummary statementMutation of serine-173 in the activation loop of Chk1 kinase may promote cancer as it abolishes the response to genetic alterations that arise while chromosomes are being copied.

cell biology↗

bioRxiv2017

Computational investigation of biological and technical variability in high throughput phenotyping and cell line identification

High-throughput cell profiling experiments are characterizing cell phenotype under a broad variety of microenvironmental and therapeutic conditions. However, biological and technical variability are contributing to wide ranges of reported parameter values, even for standard cell lines grown in identical conditions. In this paper, we develop a mathematical model of cell proliferation assays that account for biological and technical variability and limitations of the experimental platforms, including (1) cell confluency effects, (2) biological variability and technical errors in pipetting, (3) biological variability in proliferation characteristics, (4) technical variability and uncertainty in measurement timing, (5) cell counting errors, and (6) the impact of limited temporal sampling. We use this model to create synthetic datasets with growth rates and measurement times typical of cancer cell cultures, and investigate the impact of the initial cell seeding density and the common practice of fitting exponential growth curves to three cell count measurements. We find that the combined sources of variability mask the sub-exponential growth characteristics of the synthetic datasets, and that researchers profiling the same cell lines under different seeding characteristics can find significant (p < 0.05) differences in the measured growth rates. Even seeding the cells at 1% of the confluent limit can cause significant (p < 0.05) differences in the measured growth rate from the ground truth. We explored the effect of reducing errors in each part of the virtual experimental system, and found the best improvements from reducing timing errors, reducing cell counting errors, or reducing the interval between measurements (to reduce the inaccuracy of the exponential growth assumption when fitting curves). Reducing biological variability and pipetting errors had the least impact, because any improvements are still masked by cell counting errors. We close with a discussion of recommended practices for high-throughput cell phenotyping and cell line identification systems.

cell biology↗

bioRxiv2017

Integrating TARA Oceans Datasets Using Unsupervised Multiple Kernel Learning

Recent high-throughput sequencing advances have expanded the breadth of available omics datasets and the integrated analysis of multiple datasets obtained on the same samples has allowed to gain important insights in a wide range of applications. However, the integration of various sources of information remains a challenge for systems biology since produced datasets are often of heterogeneous types, with the need of developing generic methods to take their different specificities into account.\n\nWe propose a multiple kernel framework that allows to integrate multiple datasets of various types into a single exploratory analysis. Several solutions are provided to learn either a consensus meta-kernel or a meta-kernel that preserves the original topology of the datasets. We applied our framework to analyse two public multi-omics datasets. First, the multiple metagenomic datasets, collected during the TARA Oceans expedition, was explored to demonstrate that our method is able to retrieve previous findings in a single KPCA as well as to provide a new image of the sample structures when a larger number of datasets are included in the analysis. To perform this analysis, a generic procedure is also proposed to improve the interpretability of the kernel PCA in regards with the original data. Second, the multi-omics breast cancer datasets, provided by The Cancer Genome Atlas, is analysed using a kernel Self-Organizing Maps with both single and multi-omics strategies. The comparison of this two approaches demonstrates the benefit of our integration method to improve the representation of the studied biological system.\n\nProposed methods are available in the R package mixKernel, released on CRAN. It is fully compatible with the mixOmics package and a tutorial describing the approach can be found on mixOmics web site http://mixomics.org/mixkernel/.

bioinformatics↗

bioRxiv2017

Clay-induced DNA double-strand breaks underlay genetic diversity, antibiotic resistance and molecular basis of asbestosis

Some natural clays and synthetic nanofibres present in the environment have a severe impact on human health. After several decades of research, the molecular mechanism of how asbestos induce cancers is not well understood. Different fibres, including asbestos, can penetrate the membrane and introduce DNA in both, bacterial and eukaryotic cells. Incubating Escherichia coli with sepiolite, a clayey material, and asbestos under friction forces, both fibres cause double-strand breaks in bacteria. Since antibiotics and clays are used together in animal husbandry, the mutagenic effect of these fibres might constitute a pathway to antibiotic resistance due to the friction provided by peristalsis of the gut from farm animals in addition to the previously proposed horizontal gene transfer. Moreover, we raise the possibility that the same mechanism could generate bacteria diversity in natural scenarios with a role in the evolution of species. Finally, we provide a new model on how asbestos may promote mutagenesis and cancer based on the observed mechanical genotoxicity.

evolutionary biology↗