Biology subjects

Glass, K.

Publications and source records attributed to Glass, K..

At least 37 records · Page 2Linked to original sources

bioRxiv2021

ETV2 regulates PARP-1 binding protein to induce ER stress-mediated cell death in tuberin-deficient cells

Lymphangioleiomyomatosis (LAM) is a rare progressive disease, characterized by mutations in the tuberous sclerosis complex genes (Tsc1 or Tsc2), and hyperactivation of mechanistic target of rapamycin complex 1 (mTORC1). The effectiveness of mTORC1 inhibitors is limited by their lack of cytotoxic effects. Here, we report that E26 transformation specific (ETS) Variant Transcription Factor 2 (ETV2) is a critical regulator of Tsc2-deficient cell survival. Nuclear localization of ETV2 in Tsc2-deficient cells is mTORC1-independent and is enhanced by spleen tyrosine kinase (Syk) inhibition. In the nucleus, ETV2 transcriptionally regulates poly(ADP-ribose) polymerase 1 binding protein (PARPBP), a coregulator of transcription, mRNA and protein expression. Silencing of ETV2 or PARPBP in Tsc2-deficient cells induced ER-stress and increased cell death in vitro and in vivo. We also found ETV2 expression in human cells with loss of heterozygosity for TSC2 lending support to the translational relevance of our findings. In conclusion, we report a novel signaling axis unique to Syk-inhibition is mTORC1-independent and promotes a cytocidal response in Tsc2-deficient cells, and therefore, maybe a potential alternative therapeutic target in LAM.

cell biology↗

bioRxiv2021

Reconstructing Sample-Specific Networks using LIONESS

We recently developed LIONESS, a method to estimate sample-specific networks based on the output of an aggregate network reconstruction approach. In this manuscript, we describe how to apply LIONESS to different network reconstruction algorithms and data types. We highlight how decisions related to data preprocessing may affect the output networks, discuss expected outcomes, and give examples of how to analyze and compare single sample networks.

systems biology↗

bioRxiv2021

gpuZoo: Cost-effective estimation of gene regulatory networks using the Graphics Processing Unit

Gene regulatory network inference allows for the study of transcriptional control to identify the alteration of cellular processes in human diseases. Our group has developed several tools to model a variety of regulatory processes, including transcriptional (PANDA, SPIDER) and post-transcriptional (PUMA) gene regulation, and gene regulation in individual samples (LIONESS). These methods work by performing repeated operations on data matrices in order to integrate information across multiple lines of biological evidence. This limits their use for large-scale genomic studies due to the associated high computational burden. To address this limitation, we developed gpuZoo, which includes GPU-accelerated implementations of these algorithms. The runtime of the gpuZoo implementation in MATLAB and Python is up to 61 times faster and 28 times less expensive than the multi-core CPU implementation of the same methods. gpuZoo takes advantage of the modern multi-GPU device architecture to build a population of sample-specific gene regulatory networks with similar runtime and cost improvements by combining GPU acceleration with an efficient on-line derivation. Taken together, gpuZoo allows parallel and on-line gene regulatory network inference in large-scale genomic studies with cost-effective performance. gpuZoo is available in MATLAB through the netZooM package https://github.com/netZoo/netZooM and in Python through the netZooPy package https://github.com/netZoo/netZooPy.

systems biology↗

bioRxiv2021

GRAND: A database of gene regulatory network models across human conditions

Gene regulation plays a fundamental role in shaping tissue identity, function, and response to perturbation. Regulatory processes are controlled by complex networks of interacting elements, including transcription factors, miRNAs and their target genes. The structure of these networks helps to determine phenotypes and can ultimately influence the development of disease or response to therapy. We developed GRAND (https://grand.networkmedicine.org) as a database for gene regulatory network models that can be compared between biological states, or used to predict which drugs produce changes in regulatory network structure. The database includes 12,468 genome-scale networks covering 36 human tissues, 28 cancers, 1,378 unperturbed cell lines, as well as 173,013 TF and gene targeting scores for 2,858 small molecule-induced cell line perturbation paired with phenotypic information. GRAND allows the networks to be queried using phenotypic information and visualized using a variety of interactive tools. In addition, it includes a web application that matches disease states to potentially therapeutic small molecule drugs using regulatory network properties. Graphical abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY Modeling gene regulation across human conditions integrates cancer tissues and cell lines, small molecules, and normal tissue networks. C_FIG_DISPLAY

genomics↗

bioRxiv2021

Adjustment of spurious correlations in co-expression measurements from RNA-Sequencing data

1Gene co-expression measurements are widely used in computational biology to identify coordinated expression patterns across a group of samples, which may indicate that these genes are controlled by the same transcriptional regulatory program, or involved in common biological processes. Gene co-expression is generally estimated from RNA-Sequencing data, which are commonly normalized to remove technical variability. Here, we demonstrate that certain normalization methods, in particular quantile-based methods, can introduce false-positive associations between genes, and that this can consequently hamper downstream co-expression network analysis. Quantile-based normalization can, however, be extremely powerful. In particular when preprocessing large-scale heterogeneous data, quantile-based normalization methods such as smooth quantile normalization can be applied to remove technical variability while maintaining global differences in expression for samples with different biological attributes. We therefore developed SNAIL, a normalization method based on smooth quantile normalization specifically designed for modeling of co-expression measurements. We show that SNAIL avoids formation of false-positive associations in co-expression as well as in downstream network analyses. Using SNAIL, one can avoid arbitrary gene filtering and retain associations to genes that only express in small subgroups of samples. This highlights the methods potential future impact on network modeling and other association-based approaches in large-scale heterogeneous data.

bioinformatics↗

bioRxiv2021

Predicting genotype-specific gene regulatory networks

Understanding how each persons unique genotype influences their individual patterns of gene regulation has the potential to improve our understanding of human health and development and to refine genotype-specific disease risk assessments and treatments. However, the effects of genetic variants are not typically considered when constructing gene regulatory networks, despite the fact that many disease-associated genetic variants are thought to have regulatory effects, including the disruption of transcription factor (TF) binding. We developed EGRET (Estimating the Genetic Regulatory Effect on TFs), which infers a genotype-specific gene regulatory network (GRN) for each individual in a study population. EGRET begins by constructing a genotype-informed TF-gene prior network derived using TF motif predictions, eQTL data, individual genotypes, and the predicted effects of genetic variants on TF binding. It then uses message passing to integrate this prior network with gene expression and TF protein-protein interaction data to produce a refined, genotype-specific regulatory network. We used EGRET to infer GRNs for two blood-derived cell lines and identified genotype-associated, cell-line specific regulatory differences that we subsequently validated using allele-specific expression, chromatin accessibility QTLs, and differential ChIP-seq TF binding. We also inferred EGRET GRNs for three cell types from each of 119 individuals and identified cell type-specific regulatory differences associated with diseases related to those cell types. EGRET is, to our knowledge, the first method that infers networks that reflect individual genetic variation in a way that provides insight into genetic regulatory associations that drive complex phenotypes. EGRET is available through the Network Zoo R package (netZooR v0.9; netzoo.github.io).

systems biology↗

bioRxiv2020

Constructing Gene Regulatory Networks using Epigenetic Data

The biological processes that drive cellular function can be represented by a complex network of interactions between regulators (transcription factors) and their targets (genes). A cells epigenetic state plays an important role in mediating these interactions, primarily by influencing chromatin accessibility. However, effectively leveraging epigenetic information when constructing regulatory networks remains a challenge. We developed SPIDER, which incorporates epigenetic information (DNase-Seq) into a message passing framework in order to estimate gene regulatory networks. We validated SPIDERs predictions using ChlP-Seq data from ENCODE and found that SPIDER networks were more accurate than other publicly available, epigenetically informed regulatory networks as well as networks based on methods that leverage epigenetic data to predict transcription factor binding sites. SPIDER was also able to improve the detection of cell line specific regulatory interactions. Notably, SPIDER can recover ChlP-seq verified transcription factor binding events in the regulatory regions of genes that do not have a corresponding sequence motif. Constructing biologically interpretable, epigenetically informed networks using SPIDER will allow us to better understand gene regulation as well as aid in the identification of cell-specific drivers and biomarkers of cellular phenotypes.

systems biology↗

bioRxiv2020

Genomic signatures of the unjamming transition in compressed human bronchial epithelial cells

Epithelial tissue has the capacity to switch from a collective phase that is quiescent, solidlike and non-migratory to one that is dynamic, fluid-like and migratory. In certain physiological and pathophysiological contexts this phenotypic switch has been attributed not to the well-known epithelial-to-mesenchymal transition, EMT, but rather to the recently discovered unjamming transition, UJT. UJT has been characterized thus far mainly at functional and morphological levels whereas underlying genome-wide molecular events remain largely unexplored. Using primary human bronchial epithelial cells and one well-defined trigger of UJT -mechanical compression- here we combine temporal RNA-Seq data and Protein-Protein Interaction networks to provide the first genome-wide analysis of UJT. Our results show that compression induces a multiphasic transcriptional response characterized by an early activation of genes regulating the membrane and actomyosin structure, and a delayed activation of genes regulating the extracellular matrix and cellmatrix interactions. This biphasic response is mediated by a cascade of signaling processes that promotes actin polymerization through the recruitment of integrin-ECM adhesive complexes and promotes increased cellular motility through activation of AP-1 transcription factors via ERK and JNK pathways. These findings, taken together, show that the UJT program is not the result of any single signaling pathway but rather comprises a coordinated interplay of downstream pathways including development, fate selection, energy metabolism, cytoskeletal reorganization, and adhesive interaction with extracellular matrix.

genomics↗

bioRxiv2020

Histone demethylome map reveals combinatorial gene regulatory functions in embryonic stem cells

Epigenetic regulators and transcription factors establish distinct regulatory networks for gene regulation to maintain the embryonic stem cell (ESC) state. Although much has been learned regarding individual epigenetic regulators, their combinatorial functions remain elusive. Here, we report previously unknown combinatorial functions of histone demethylases (HDMs) in gene regulation of mouse ESCs. Generation of a histone demethylome (HDMome) map of 20 well-characterized HDMs based on their genome-wide binding revealed co-occupancy of HDMs in different combinations: KDM1A-KDM4B-KDM6A and JARID2-KDM2B-KDM4A-KDM4C-KDM5B largely co-occupy at enhancers and promoters, respectively. Mechanistic studies uncover that KDM1A-KDM6A combinatorially modulates P300/H3K27ac, H3K4me2 deposition and OCT4 recruitment that directs the OCT4/CORE regulatory network for target gene expression; while co-operative actions of JARID2-KDM2B-KDM4A-KDM4C-KDM5B control H2AK119ub1 and bivalent marks of polycomb-repressive complexes that facilitate the PRC regulatory network for target gene repression. Thus, combinatorial functions of HDMs differentially impact gene expression programs in mESCs.

genomics↗

bioRxiv2019

PUMA: PANDA Using MicroRNA Associations

Conventional methods to analyze genomic data do not make use of the interplay between multiple factors, such as between microRNAs (miRNAs) and the mRNA transcripts they regulate, and thereby often fail to identify the cellular processes that are unique to specific tissues. We developed PUMA (PANDA Using MicroRNA Associations), a computational tool that uses message passing to integrate a prior network of miRNA target predictions with protein-protein interaction and target gene co-expression information to model genome-wide gene regulation by miRNAs. We applied PUMA to 38 tissues from the Genotype-Tissue Expression (GTEx) project, integrating RNA-Seq data with two different miRNA target predictions priors, built on predictions from TargetScan and miRanda, respectively. We found that while target predictions obtained from these two different resources are considerably different, PUMA captures similar tissue-specific miRNA-target gene regulatory interactions in the different network models. Furthermore, tissue-specific functions of miRNAs, which we identified by analyzing their regulatory profiles and which we made available through a Shiny app (https://kuijjer.shinyapps.io/puma_gtex/), are highly similar between networks modeled on the two target prediction resources. This indicates that PUMA consistently captures important tissue-specific regulatory processes of miRNAs. In addition, using PUMA we identified miRNAs regulating important tissue-specific processes that, when mutated, may result in disease development in the same tissue. PUMA is available in C++, MATLAB, and Python code on GitHub (https://github.com/kuijjerlab/PUMA and https://github.com/kuijjerlab/PyPuma).

systems biology↗

bioRxiv2019

Integrated transcriptomic correlation network analysis identifies COPD molecular determinants

Chronic obstructive pulmonary disease (COPD) is a heterogeneous and complex syndrome. Network-based analysis implemented by SWIM software can be exploited to identify key molecular switches - called \"switch genes\" - for disease. Genes contributing to common biological processes or define given cell types are frequently co-regulated and co-expressed, giving rise to expression network modules. Consistently, we found that the COPD correlation network built by SWIM consists of three well-characterized modules: one populated by switch genes, all up-regulated in COPD cases and related to the regulation of immune response, inflammatory response, and hypoxia (like TIMP1, HIF1A, SYK, LY96, BLNK and PRDX4); one populated by well-recognized immune signature genes, all up-regulated in COPD cases; one where the GWAS genes AGER and CAVIN1 are the most representative module genes, both down-regulated in COPD cases. Interestingly, 70% of AGER negative interactors are switch genes including PRDX4, whose activation strongly correlates with the activation of known COPD GWAS interactors SERPINE2, CD79A, and POUF2AF1. These results suggest that SWIM analysis can identify key network modules related to complex diseases like COPD.

bioinformatics↗

bioRxiv2019

The periphery and the core properties explain the omnigenic model in the human interactome

Understanding the connectivity patterns of genes in a localized disease neighborhood or disease module in a molecular interaction network (interactome) is a key step toward advancing the knowledge about molecular mechanisms underlying a complex disease. In this work, we introduce a framework that detects peripheral and core regions of a disease in the human interactome. We leverage gene expression data on 104 diseases and analyze the connectivity of differentially expressed genes (quantified by a p-value < 0.05) and their topological membership in the network to distinguish between peripheral and core genes. Per definition, peripheral and core genes are topologically different and we show that they also differ biologically. Core genes are more enriched for Genome-wide association study (GWAS) and Online Mendelian Inheritance in Man (OMIM) data, whereas peripheral genes are more shared across different disease states and their overlap helps predict disease proximity in the human interactome. Based on this observation, we propose a flower model to explain the organization of genes in the human interactome, with core genes of different diseases as the petals and the peripheral genes as the (shared) stem. We show that this network model is an important step towards finding novel drug targets and improving disease classification. Overall, we were able to demonstrate how perturbations percolate through the human interactome and contribute to peripheral and core regions, an important novel feature of the omnigenic model.

systems biology↗

bioRxiv2019

Modulating the unfolded protein response: Impacts of radiation on the response of prostate cancer cells to ONC201

Prostate cancer (PCa) is the most common non-cutaneous cancer in men and a notable cause of cancer mortality when it metastasises. Localised disease is mostly treated with surgery or radiotherapy. As PCa develops and treatment resistance emerges, the unfolded protein response (UPR) arises as an important adaptive biology co-amplifying with key cancer drivers [1]. The UPR can be cytoprotective but when acutely activated can lead to cell death. In this study we sought to enhance the acute activation of the UPR using radiation and ONC201, previously reported to be an UPR activator [2]. We found that treating PCa cells with ONC201 quickly increases the expression of components in all arms of the UPR - ATF4, ATF6 and IRE1-XBP1 - culminating in the subsequent cell death. During this time window between UPR activation and cell death we tested the priming effect of short-term administration of ONC201 on radiation responses. Pre-treatment with ONC201 for 24 hours prior to irradiation led to enhanced cytotoxicity compared to radiation alone assessed by cell viability and clonogenic assays. With priming, RNA-Seq analysis showed a sustained suppression of transcripts encoding cell cycle regulators as well as components of the DNA damage response pathways. Phenotypically this was reflected in enhanced cell cycle arrest and induction of necrosis and apoptosis. Furthermore, we demonstrated that short-term administration of inhibitors of cell cycle regulators (Dinaciclib and BI2536), could replicate this priming effect. Thus, we propose future studies to assess the impact of the short-term administration of drugs targeting the UPR and cell cycle regulation to enhance radiotherapy response.

cancer biology↗

bioRxiv2019

lionessR: single-sample network reconstruction in R

We recently developed LIONESS (Linear Interpolation to Obtain Network Estimates for Single Samples), a method that can be used together with network reconstruction algorithms to extract networks for individual samples in a population. LIONESS was originally made available as a function within the PANDA (Passing Attributes between Networks for Data Assimilation) regulatory network reconstruction framework. In this application note, we describe lionessR, an R implementation of LIONESS that can be applied to any network reconstruction method in R that outputs a complete, weighted adjacency matrix. As an example, we use lionessR to model single-sample co-expression networks on a bone cancer dataset, and show how lionessR can be used to identify differential co-expression between two groups of patients.\n\nAvailability and implementationThe lionessR open source R package, which includes a vignette of the application, is freely available at https://github.com/mararie/lionessR.\n\nContactmarieke.kuijjer@ncmm.uio.no

bioinformatics↗

bioRxiv2018

New Statistical Methods for Constructing Robust Differential Correlation Networks

The interplay among microRNAs (miRNAs) plays an important role in the developments of complex human diseases. Co-expression networks can characterize the interactions among miRNAs. Differential correlation network is a powerful tool to investigate the differences of co-expression networks between cases and controls. To construct a differential correlation network, the Fishers Z-transformation test is usually used. However, the Fishers Z-transformation test requires the normality assumption, the violation of which would result in inflated Type I error rate. Several bootstrapping-based improvements for Fishers Z test have been proposed. However, these methods are too computationally intensive to be used to construct differential correlation networks for high-throughput genomic data. In this article, we proposed six novel robust equal-correlation tests that are computationally efficient. The systematic simulation studies and a real microRNA data analysis showed that one of the six proposed tests (ST5) overall performed better than other methods.

bioinformatics↗

bioRxiv2018

Gene regulatory network analysis identifies sex-linked differences in colon cancer drug metabolism processes

Significant sex differences are observed in colon cancer, and understanding these differences is essential to advance disease prevention, diagnosis, and treatment. Males have a higher risk of developing colon cancer and a lower survival rate than women. However, the molecular features that drive these sex differences are poorly understood. We used both transcript-based and gene regulatory network methods to analyze RNA-Seq data from The Cancer Genome Atlas for 445 patients with colon cancer. We compared gene expression between tumors in men and women and found no significant sex differences except for sex-chromosome genes. We then inferred patient-specific gene regulatory networks, and found significant regulatory differences between males and females, with drug and xenobiotics metabolism via cytochrome P450 pathways more strongly targeted in females. This finding was validated in a dataset that included 1,193 patients from five independent studies. While targeting of the drug metabolism pathway did not change the overall survival for males treated with adjuvant chemotherapy, females with greater targeting had an increase in 10-year overall survival probability, with 89% (95% CI: 78%-100%) survival compared to 61% (95% CI: 45%-82%) for women with lower targeting, respectively (p=0.034). Our network analysis uncovered patterns of transcriptional regulation that differentiate male and female colon cancer. Most importantly, targeting of the drug metabolism pathway was predictive of survival in women who received adjuvant chemotherapy. This network-based approach can be used to investigate the molecular features that drive sex differences in other cancers and complex diseases.

systems biology↗

bioRxiv2017

Understanding Tissue-specific Gene Regulation

Although all human tissues carry out common processes, tissues are distinguished by gene expres-sion patterns, implying that distinct regulatory programs control tissue-specificity. In this study, we investigate gene expression and regulation across 38 tissues profiled in the Genotype-Tissue Expression project. We find that network edges (transcription factor to target gene connections) have higher tissue-specificity than network nodes (genes) and that regulating nodes (transcription factors) are less likely to be expressed in a tissue-specific manner as compared to their targets (genes). Gene set enrichment analysis of network targeting also indicates that regulation of tissue-specific function is largely independent of transcription factor expression. In addition, tissue-specific genes are not highly targeted in their corresponding tissue-network. However, they do assume bottleneck positions due to variability in transcription factor targeting and the influence of non-canonical regulatory interactions. These results suggest that tissue-specificity is driven by context-dependent regulatory paths, providing transcriptional control of tissue-specific processes.

genomics↗

bioRxiv2016

Estimating Drivers of Cell State Transitions Using Gene Regulatory Network Models

Specific cellular states are often associated with distinct gene expression patterns. These states are plastic, changing during development, or in the transition from health to disease. One relatively simple extension of this concept is to recognize that we can classify different cell-types by their active gene regulatory networks and that, consequently, transitions between cellular states can be modeled by changes in these underlying regulatory networks. Here we describe MONSTER, MOdeling Network State Transitions from Expression and Regulatory data, a regression-based method for inferring transcription factor drivers of cell state conditions at the gene regulatory network level. As a demonstration, we apply MONSTER to four different studies of chronic obstructive pulmonary disease to identify transcription factors that alter the network structure as the cell state progresses toward the disease-state. Our results demonstrate that MONSTER can find strong regulatory signals that persist across studies and tissues of the same disease and that are not detectable using conventional analysis methods based on differential expression. An R package implementing MONSTER is available at github.com/QuackenbushLab/MONSTER.

systems biology↗