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bioRxiv2018

High-resolution mapping of cancer cell networks using co-functional interactions

Powerful new technologies for perturbing genetic elements have expanded the study of genetic interactions in model systems ranging from yeast to human cell lines. However, technical artifacts can confound signal across genetic screens and limit the immense potential of parallel screening approaches. To address this problem, we devised a novel PCA-based method for eliminating these artifacts and bolstering sensitivity and specificity for detection of genetic interactions. Applying this strategy to a set of >300 whole genome CRISPR screens, we report ~1 million pairs of correlated \"co-functional\" genes that provide finer-scale information about cell compartments, biological pathways, and protein complexes than traditional gene sets. Lastly, we employed a gene community detection approach to implicate core genes for cancer growth and compress signal from functionally related genes in the same community into a single score. This work establishes new algorithms for probing cancer cell networks and motivates the acquisition of further CRISPR screen data across diverse genotypes and cell types to further resolve the complexity of cell signaling processes.

genomics↗

bioRxiv2018

Pan-cancer analysis of transcriptional metabolic dysregulation using The Cancer Genome Atlas

Understanding the levels of metabolic dysregulation in different disease settings is vital for the safe and effective incorporation of metabolism-targeted therapeutics in the clinic. Using transcriptomic data from 10,704 tumor and normal samples from The Cancer Genome Atlas, across 26 disease sites, we developed a novel bioinformatics pipeline that distinguishes tumor from normal tissues, based on differential gene expression for 114 metabolic pathways. This pathway dysregulation was confirmed in separate patient populations, further demonstrating the robustness of this approach. A bootstrapping simulation was then applied to assess whether these alterations were biologically meaningful, rather than expected by chance. We provide distinct examples of the types of analysis that can be accomplished with this tool to understand cancer specific metabolic dysregulation, highlighting novel pathways of interest in both common and rare disease sites. Utilizing a pathway mapping approach to understand patterns of metabolic flux, differential drug sensitivity, can accurately be predicted. Further, the identification of Master Metabolic Transcriptional Regulators, whose expression was highly correlated with pathway gene expression, explains why metabolic differences exist in different disease sites. We demonstrate these also have the ability to segregate patient populations and predict responders to different metabolism-targeted therapeutics.

bioinformatics↗

bioRxiv2017

Fractional Dynamics in Bioscience and Biomedicine and the Physics of Cancer

Almost all phenomena and structures in nature exhibit some degrees of fractionality or fractality. Fractional calculus and fractal theory are two interrelated concepts. In this article we study the memory effects in nature and particularly in biological structures. Based on this fact that natural way to incorporate memory effects in the modeling of various phenomena and dealing with complexities is using of fractional calculus, in this article we present different examples in various branch of science from cosmology to biology and we investigate this idea that are we able to describe all of such these phenomena using the well-know and powerful tool of fractional calculus. In particular we focus on fractional calculus approach as an effective tool for better understanding of physics of living systems and organism and especially physics of cancer.

biophysics↗

bioRxiv2016

UNCROSS: Filtering of high-frequency cross-talk in 16S amplicon reads

Next-generation amplicon sequencing is widely used for surveying biological diversity in applications such as microbial metagenomics, immune system repertoire analysis and targeted tumor sequencing of cancer-associated genes. In such studies, assignment of reads to incorrect samples (cross-talk) is a well-documented problem that is rarely considered in practice. By considering unexpected OTUs in artificial (mock) samples, I estimate that cross-talk occurred for ~2% of the reads in one Illumina GAIIx run and eleven Illumina MiSeq runs targeting 16S ribosomal RNA. I also describe UNCROSS, an algorithm for detecting and filtering cross-talk in OTU tables.

bioinformatics↗

bioRxiv2018

MechRNA: prediction of lncRNA mechanisms from RNA-RNA and RNA-protein interactions

MotivationLong non-coding RNAs (lncRNAs) are defined as transcripts longer than 200 nucleotides that do not get translated into proteins. Often these transcripts are processed (spliced, capped, polyadenylated) and some are known to have important biological functions. However, most lncRNAs have unknown or poorly understood functions. Nevertheless, because of their potential role in cancer, lncRNAs are receiving a lot of attention, and the need for computational tools to predict their possible mechanisms of action is more than ever. Fundamentally, most of the known lncRNA mechanisms involve RNA-RNA and/or RNA-protein interactions. Through accurate predictions of each kind of interaction and integration of these predictions, it is possible to elucidate potential mechanisms for a given lncRNA.\n\nApproachHere we introduce MechRNA, a pipeline for corroborating RNA-RNA interaction prediction and protein binding prediction for identifying possible lncRNA mechanisms involving specific targets or on a transcriptome-wide scale. The first stage uses a version of IntaRNA2 with added functionality for efficient prediction of RNA-RNA interactions with very long input sequences, allowing for large-scale analysis of lncRNA interactions with little or no loss of optimality. The second stage integrates protein binding information pre-computed by GraphProt, for both the lncRNA and the target. The final stage involves inferring the most likely mechanism for each lncRNA/target pair. This is achieved by generating candidate mechanisms from the predicted interactions, the relative locations of these interactions and correlation data, followed by selection of the most likely mechanistic explanation using a combined p-value.\n\nResultsWe applied MechRNA on a number of recently identified cancer-related lncRNAs (PCAT1, PCAT29, ARLnc1) and also on two well-studied lncRNAs (PCA3 and 7SL). This led to the identification of hundreds of high confidence potential targets for each lncRNA and corresponding mechanisms. These predictions include the known competitive mechanism of 7SL with HuR for binding on the tumor suppressor TP53, as well as mechanisms expanding what is known about PCAT1 and ARLn1 and their targets BRCA2 and AR, respectively. For PCAT1-BRCA2, the mechanism involves competitive binding with HuR, which we confirmed using HuR immunoprecipitation assays.\n\nAvailabilityMechRNA is available for download at https://bitbucket.org/compbio/mechrna\n\nContactbackofen@informatik.uni-freiburg.de, cenksahi@indiana.edu\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2018

UNCROSS2: identification of cross-talk in 16S rRNA OTU tables

Next-generation amplicon sequencing is widely used for surveying biological diversity in applications such as microbial metagenomics, immune system repertoire analysis and targeted tumor sequencing of cancer-associated genes. In such studies, assignment of reads to incorrect samples (cross-talk) is a well-documented problem that is rarely considered in practice. Here, I describe UNCROSS2, an algorithm designed to detect and filter cross-talk in OTU tables generated by next-generation sequencing of the 16S ribosomal RNA gene. On eight published datasets, cross-talk rates are estimated to range from 0.4% to 1.5% mis-assigned reads. On a mock community test, UNCROSS2 identifies spurious counts due to cross-talk with sensitivity [~]80% to 90% and error rate from [~]1% to [~]20%, but it is not clear whether the accuracy of the algorithm is sufficient to decisively improve diversity rates in practice.

bioinformatics↗

bioRxiv2018

Master regulators of signaling pathways coordinate key processes of embryonic development in breast cancer

Signal transduction pathways allow cells to respond to environmental cues and can induce intracellular changes. In some contexts, like embryonic development, signal transduction plays crucial roles in cell fate determination and differentiation, while in developed organisms some of this processes contribute in the maintenance of the structural integrity of tissues.\n\nTumor cells are recognized as having deregulated signaling which leads to a series of abnormal behaviors known as the hallmarks of cancer. Although gene regulation is often viewed as the last step in signal transduction, transcriptional regulation of the components of a pathway may impact in the long term deregulation observed in tumors. The study of gene regulatory networks centered around genes of the signal transduction pathways allows the identification of transcriptional regulators with the greatest influence over the signal transduction gene signature, also denominated Master Regulators.\n\nIn this work we identify, the master regulators that regulate the expression of genes of 25 relevant pathways grouped in KEGG within the category of signal transduction in a breast cancer dataset. For this purpose we implemented a modified MARINa algorithm that identifies, from a network of regulons, those that possess more differentially expressed genes related to the process to be studied. We identified CLOCK, TSHZ2, HOXA2, MEIS2, HOXA3, HAND2, HOXA5, TBX18, PEG3 and GLI2 as the top 10 master regulators of signaling pathways in breast cancer. Nine of them are recognized for taking part in embryonic development associated processes.\n\nIndividual enrichment GO biological function for each TMR regulons showed to be significantly enriched in embryonic development related processes. Hedgehog signaling pathway was shown as enriched and also highly deregulated. The genes of the HOXA family are shared among most of the TMRs. Overall, this suggests the importance of the aberrant reprogramming of mechanisms present during embryonic development, being coopted in favor of tumor development.

systems biology↗

bioRxiv2018

RISC: robust integration of single-cell RNA-seq datasets with different extents of cell cluster overlap

Single cell RNA-seq (scRNA-seq) has remarkably advanced our understanding of cellular heterogeneity and dynamics in tissue development, diseases, and cancers. Integrated data analysis can often uncover molecular and cellular links among individual datasets and thus provide new biological insights, such as developmental relationship. Due to differences in experimental platforms and biological sample batches, the integration of multiple scRNA-seq datasets is challenging. To address this, we developed a novel computational method for robust integration of scRNA-seq (RISC) datasets using principal component regression (PCR). Because of the natural compatibility of eigenvectors between PCR model and dimension reduction, RISC can accurately integrate scRNA-seq datasets and avoid over-integration. Compared to existing software, RISC shows particular improvement in integrating datasets that contain cells of the same types (more accurately clusters) but at distinct functional states. To demonstrate the value of RISC in finding small groups of cells common between otherwise heterogenous datasets, we applied it to scRNA-seq datasets of normal and malignant cells and successfully identified small clusters of cells in healthy kidney tissues that may be related to the origin of renal tumors.

bioinformatics↗

bioRxiv2018

Cross-talk between Hippo and Wnt signalling pathways in intestinal crypts: insightsfrom an agent-based model

Intestinal crypts are responsible for the total cell renewal of the lining of the intestines, occurring every 4-7 days in humans. This rapid turnover is governed by the complex interplay between signalling pathways and the cell cycle within individual cells in the crypts. The role of Wnt signalling in governing cell proliferation and differentiation in the intestinal crypt has been extensively studied, with increased signalling found towards the lower regions of the crypt. Recent studies have shown that the Wnt signalling gradient found within the crypt may arise as a result of division-based spreading from a Wnt reservoir at the crypt base. The discovery of the Hippo pathways involvement in maintaining crypt homeostasis is more recent; a mechanistic understanding of Hippo pathway dynamics, and its possible cross-talk with the Wnt pathway, remains lacking. To explore how the interplay between these pathways may control crypt homeostasis and dysplasia we developed and analysed a mathematical model coupling an ordinary differential equation description of Wnt and Hippo signalling with a cell-based description of cell movement, proliferation and contact inhibition. Furthermore, we compared an imposed Wnt gradient with a division-based Wnt gradient model. Our results suggest that Hippo signalling affects the Wnt pathway by reducing the presence of free cytoplasmic {beta}-catenin, causing cell cycle arrest. This process slows the rate of crypt turnover, previously attributed only to mutations in Wnt pathway components commonly observed in colorectal cancers. We also show that a division-based spreading of Wnt can form a Wnt gradient, resulting in proliferative dynamics comparable to imposed-gradient models. This suggests that imposed-gradient models are a reasonable approximation of experimentally observed Wnt gradients, however they provide little insight into the source of Wnt. Finally, a simulated APC double mutant, with misregulated Wnt and Hippo signalling activity, is predicted to be capable of completing a more rapid monoclonal conversion of the crypt than a Wnt-only mutant.\n\nAuthor summaryColorectal cancer is the fourth leading cause of cancer death worldwide. It is generally established that colorectal cancer occurs upon disruption of the dynamics of crypts, tubular indentations lining the walls of the gastrointestinal tract that are responsible for renewal of the intestinal epithelium. Thus, understanding the mechanisms that regulate intestinal crypt dynamics is instrumental to understand how colorectal cancer can emerge. We used mathematical modelling to explore how crypt dynamics emerge from the interactions across several biological scales, including mutations that cause an abnormal cell response to changes at the cell or tissue level, cell proliferation and motility within the crypt. Our model describes, in each cell within the crypt, levels and dynamics of genes involved in two signalling pathways - the canonical Wnt and Hippo pathways - relevant for cell proliferation and adhesion. In simulations, we predict how the crosstalk between these pathways affects crypt dynamics in both healthy and dysplastic cases. Furthermore, our simulations match recent biological evidence of a novel mechanism, based on cell division, causing heterogeneous and graded localisation of different cell types (dividing or not) within the crypt. Our results suggest the possibility of combined disease treatment, targeting both the Wnt and Hippo pathways.

systems biology↗

bioRxiv2014

Reverse enGENEering of regulatory networks from Big Data: a guide for a biologist

Omics technologies enable unbiased investigation of biological systems through massively parallel sequence acquisition or molecular measurements, bringing the life sciences into the era of Big Data. A central challenge posed by such omics datasets is how to transform this data into biological knowledge. For example, how to use this data to answer questions such as: which functional pathways are involved in cell differentiation? Which genes should we target to stop cancer? Network analysis is a powerful and general approach to solve this problem consisting of two fundamental stages, network reconstruction and network interrogation. Herein, we provide an overview of network analysis including a step by step guide on how to perform and use this approach to investigate a biological question. In this guide, we also include the software packages that we and others employ for each of the steps of a network analysis workflow.

Systems Biology↗

bioRxiv2017

A Multi-Stage Representation Of Cell Proliferation As A Markov Process

The stochastic simulation algorithm commonly known as Gillespies algorithm (originally derived for modelling well-mixed systems of chemical reactions) is now used ubiquitously in the modelling of biological processes in which stochastic effects play an important role. In well mixed scenarios at the sub-cellular level it is often reasonable to assume that times between successive reaction/interaction events are exponentially distributed and can be appropriately modelled as a Markov process and hence simulated by the Gillespie algorithm. However, Gillespies algorithm is routinely applied to model biological systems for which it was never intended. In particular, processes in which cell proliferation is important (e.g. embryonic development, cancer formation) should not be simulated naively using the Gillespie algorithm since the history-dependent nature of the cell cycle breaks the Markov process. The variance in experimentally measured cell cycle times is far less than in an exponential cell cycle time distribution with the same mean.\n\nHere we suggest a method of modelling the cell cycle that restores the memoryless property to the system and is therefore consistent with simulation via the Gillespie algorithm. By breaking the cell cycle into a number of independent exponentially distributed stages we can restore the Markov property at the same time as more accurately approximating the appropriate cell cycle time distributions. The consequences of our revised mathematical model are explored analytically as far as possible. We demonstrate the importance of employing the correct cell cycle time distribution by recapitulating the results from two models incorporating cellular proliferation (one spatial and one non-spatial) and demonstrating that changing the cell cycle time distribution makes quantitative and qualitative differences to the outcome of the models. Our adaptation will allow modellers and experimentalists alike to appropriately represent cellular proliferation - vital to the accurate modelling of many biological processes - whilst still being able to take advantage of the power and efficiency of the popular Gillespie algorithm.

cell biology↗

bioRxiv2017

Cell cycle time series gene expression data encoded as cyclic attractors in Hopfield systems

Modern time series gene expression and other omics data sets have enabled unprecedented resolution of the dynamics of cellular processes such as cell cycle and response to pharmaceutical compounds. In anticipation of the proliferation of time series data sets in the near future, we use the Hopfield model, a recurrent neural network based on spin glasses, to model the dynamics of cell cycle in HeLa (human cervical cancer) and S. cerevisiae cells. We study some of the rich dynamical properties of these cyclic Hopfield systems, including the ability of populations of simulated cells to recreate experimental expression data and the effects of noise on the dynamics. Next, we use a genetic algorithm to identify sets of genes which, when selectively inhibited by local external fields representing gene silencing compounds such as kinase inhibitors, disrupt the encoded cell cycle. We find, for example, that inhibiting the set of four kinases BRD4, MAPK1, NEK7, and YES1 in HeLa cells causes simulated cells to accumulate in the M phase. Finally, we suggest possible improvements and extensions to our model.\n\nAuthor SummaryCell cycle - the process in which a parent cell replicates its DNA and divides into two daughter cells - is an upregulated process in many forms of cancer. Identifying gene inhibition targets to regulate cell cycle is important to the development of effective therapies. Although modern high throughput techniques offer unprecedented resolution of the molecular details of biological processes like cell cycle, analyzing the vast quantities of the resulting experimental data and extracting actionable information remains a formidable task. Here, we create a dynamical model of the process of cell cycle using the Hopfield model (a type of recurrent neural network) and gene expression data from human cervical cancer cells and yeast cells. We find that the model recreates the oscillations observed in experimental data. Tuning the level of noise (representing the inherent randomness in gene expression and regulation) to the \"edge of chaos\" is crucial for the proper behavior of the system. We then use this model to identify potential gene targets for disrupting the process of cell cycle. This method could be applied to other time series data sets and used to predict the effects of untested targeted perturbations.

systems biology↗

bioRxiv2018

Genome-wide Methylation Patterns Under Caloric Restriction in Daphnia magna.

The degradation of epigenetic control with age is associated with progressive diseases of ageing, including cancers, immunodeficiency and diabetes. Reduced caloric intake slows the effects of aging and age-related diseases, a process likely to be mediated by the impact of caloric restriction on epigenetic factors such as DNA methylation. We used whole genome bisulphite sequencing to study how DNA methylation patterns change with diet in a small invertebrate, the crustacean Daphnia magna. Daphnia show the classic response of longer life under CR, and they reproduce clonally, which permits the study of epigenetic changes in the absence of genetic variation. Global CpG methylation was 0.7-0.9%, and there was no difference in overall methylation levels between normal and calorie restricted replicates. However, 453 regions were differentially methylated (DMRs) between the normally fed and calorie restricted (CR) replicates. Of these 61% were hypomethylated in the CR group, and 39% were hypermethylated in the CR group. Gene Ontogeny (GO) term enrichment of hyper and hypo-methylated genes showed significant over- and under-representation in three molecular function terms and four biological process GO terms. Notable among these were kinase and phosphorylation activity, which have a well-known functional link to cancers.

genomics↗

bioRxiv2017

Reference-free deconvolution of DNA methylation signatures identifies common differentially methylated gene regions on 1p36 across breast cancer subtypes

Breast cancer is a complex disease and studying DNA methylation (DNAm) in tumors is complicated by disease heterogeneity. We compared DNAm in breast tumors with normal-adjacent breast samples from The Cancer Genome Atlas (TCGA). We constructed models stratified by tumor stage and PAM50 molecular subtype and performed cell-type reference-free deconvolution on each model. We identified nineteen differentially methylated gene regions (DMGRs) in early stage tumors across eleven genes (AGRN, C1orf170, FAM41C, FLJ39609, HES4, ISG15, KLHL17, NOC2L, PLEKHN1, SAMD11, WASH5P). These regions were consistently differentially methylated in every subtype and all implicated genes are localized on chromosome 1p36.3. We also validated seventeen DMGRs in an independent data set. Identification and validation of shared DNAm alterations across tumor subtypes in early stage tumors advances our understanding of common biology underlying breast carcinogenesis and may contribute to biomarker development. We also provide evidence on the importance and potential function of 1p36 in cancer.

genomics↗

bioRxiv2017

High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow

BackgroundCancer is a complex, multiscale dynamical system, with interactions between tumor cells and non-cancerous host systems. Therapies act on this combined cancer-host system, sometimes with unexpected results. Systematic investigation of mechanistic computational models can augment traditional laboratory and clinical studies, helping identify the factors driving a treatments success or failure. However, given the uncertainties regarding the underlying biology, these multiscale computational models can take many potential forms, in addition to encompassing high-dimensional parameter spaces. Therefore, the exploration of these models is computationally challenging. We propose that integrating two existing technologies--one to aid the construction of multiscale agent-based models, the other developed to enhance model exploration and optimization--can provide a computational means for high-throughput hypothesis testing, and eventually, optimization.\n\nResultsIn this paper, we introduce a high throughput computing (HTC) framework that integrates a mechanistic 3-D multicellular simulator (PhysiCell) with an extreme-scale model exploration platform (EMEWS) to investigate high-dimensional parameter spaces. We show early results in applying PhysiCell-EMEWS to 3-D cancer immunotherapy and show insights on therapeutic failure. We describe a generalized PhysiCell-EMEWS workflow for high-throughput cancer hypothesis testing, where hundreds or thousands of mechanistic simulations are compared against data-driven error metrics to perform hypothesis optimization.\n\nConclusionsWhile key notational and computational challenges remain, mechanistic agent-based models and high-throughput model exploration environments can be combined to systematically and rapidly explore key problems in cancer. These high-throughput computational experiments can improve our understanding of the underlying biology, drive future experiments, and ultimately inform clinical practice.

systems biology↗

bioRxiv2017

STAR Chimeric Post For Rapid Detection Of Circular RNA And Fusion Transcripts

MotivationThe biological relevance of chimeric RNA alignments is now well established. Chimera arising as chromosomal fusions are often drivers of cancer, and recently discovered circular RNA are only now being characterized. While software already exists for fusion discovery and quantitation, high false positive rates and high run-times hamper scalable fusion discovery on large datasets. Furthermore, very little software is available for circular RNA detection and quantification.\n\nResultsHere we present STAR Chimeric Post (STARChip), a novel software package that processes chimeric alignments from the STAR aligner and produces annotated circular RNA and high precision fusions in a rapid, efficient, and scalable manner that is appropriate for high dimensional medical omics datasets.\n\nAvailability and ImplementationSTARChip is available at https://github.com/LosicLab/STARChip\n\nContactsbojan.losic@mssm.edu or nicholas.kipp.akers@gmail.com\n\nSupplementary InformationSupplementary figures and tables are available online.

bioinformatics↗

bioRxiv2017

Vimentin Plays A Functional Role In Mammary Gland Regeneration

In the mammary gland, vimentin intermediate filaments are expressed in stromal cells and in basal epithelial cell populations including gland-reconstituting mammary stem cells (MaSC), with largely undefined functions. Here, we studied how vimentin deficiency affects mouse mammary gland development. Our results demonstrate that in adult vimentin knockout mice (Vim-/-) mammary ductal outgrowth is delayed. The adult Vim-/- glands are characterised by dilated ducts, an imbalance in the proportion of basal to luminal mammary epithelial cells and a reduction in cells expressing Slug (Snai2), an established MaSC regulator. All of these features are indicative of reduced progenitor cell activity. Accordingly, isolated Vim-/- mammary epithelial cells display reduced capacity to form mammospheres, and altered organoid structure, compared to wt counterparts, when plated in a 3D matrix in vitro. Importantly, altered basal epithelial cell number translates into defects in Vim-/- mammary gland regeneration in vivo in cleared fat pad transplantation studies. Furthermore, we show that vimentin contributes to stem-like cell properties in triple negative MDA-MB-231 breast cancer cells, wherein vimentin depletion reduces tumorsphere formation and alters expression of breast cancer stem cell-associated surface markers. Together, our findings identify vimentin as a positive regulator of stemness in the developing mouse mammary gland and in breast cancer cells.

developmental biology↗

bioRxiv2017

DNA Replication Modulates R-loop Levels to Maintain Genome Stability

Conflicts between transcription and replication are a potent source of DNA damage. The transcription machinery has the potential to aggravate such conflicts by generating co-transcriptional R-loops as an additional barrier to replication fork progression. Here, we investigate the influence of conflict orientation and R-loop formation on genome stability in human cells using a defined episomal system. This approach reveals that head-on (HO) and co-directional (CD) conflicts induce distinct DNA damage responses. Unexpectedly, the replisome acts as an orientation-dependent regulator of R-loop levels, reducing R-loops in the CD orientation but promoting their formation in the HO orientation. Replication stress and deregulated origin firing increase the number of HO collisions leading to genome-destabilizing R-loops. Our findings not only uncover an intrinsic function of the replisome in R-loop homeostasis, but also suggest a mechanistic basis for genome instability associated with deregulated DNA replication, which is observed in many disease states, including cancer.

cell biology↗