Transcriptional Network Model Research Articles

Lineage-Selective Dependencies in Pediatric Cancers.

The quest for effective cancer therapeutics has traditionally centered on targeting mutated or overexpressed oncogenic proteins. However, challenges arise in cancers with low mutational burden or when the mutated oncogene is not conventionally targetable, which are common situations in childhood cancers. This obstacle has sparked large-scale unbiased screens to identify collateral genetic dependencies crucial for cancer cell growth. These screens have revealed promising targets for therapeutic intervention in the form of lineage-selective dependency genes, which may have an expanded therapeutic window compared to pan-lethal dependencies. Many lineage-selective dependencies regulate gene expression and are closely tied to the developmental origins of pediatric tumors. Placing lineage-selective dependencies in a transcriptional network model is helpful for understanding their roles in driving malignant cell behaviors. Here, we discuss the identification of lineage-selective dependencies and how two transcriptional models, core regulatory circuits and gene regulatory networks, can serve as frameworks for understanding their individual and collective actions, particularly in cancers affecting children and young adults.

Learning in Transcriptional Network Models: Computational Discovery of Pathway-Level Memory and Effective Interventions.

Trainability, in any substrate, refers to the ability to change future behavior based on past experiences. An understanding of such capacity within biological cells and tissues would enable a particularly powerful set of methods for prediction and control of their behavior through specific patterns of stimuli. This top-down mode of control (as an alternative to bottom-up modification of hardware) has been extensively exploited by computer science and the behavioral sciences; in biology however, it is usually reserved for organism-level behavior in animals with brains, such as training animals towards a desired response. Exciting work in the field of basal cognition has begun to reveal degrees and forms of unconventional memory in non-neural tissues and even in subcellular biochemical dynamics. Here, we characterize biological gene regulatory circuit models and protein pathways and find them capable of several different kinds of memory. We extend prior results on learning in binary transcriptional networks to continuous models and identify specific interventions (regimes of stimulation, as opposed to network rewiring) that abolish undesirable network behavior such as drug pharmacoresistance and drug sensitization. We also explore the stability of created memories by assessing their long-term behavior and find that most memories do not decay over long time periods. Additionally, we find that the memory properties are quite robust to noise; surprisingly, in many cases noise actually increases memory potential. We examine various network properties associated with these behaviors and find that no one network property is indicative of memory. Random networks do not show similar memory behavior as models of biological processes, indicating that generic network dynamics are not solely responsible for trainability. Rational control of dynamic pathway function using stimuli derived from computational models opens the door to empirical studies of proto-cognitive capacities in unconventional embodiments and suggests numerous possible applications in biomedicine, where behavior shaping of pathway responses stand as a potential alternative to gene therapy.

High-resolution temporal transcriptome sequencing unravels ERF and WRKY as the master players in the regulatory networks underlying sesame responses to waterlogging and recovery

Major crops are generally sensitive to waterlogging, but our limited understanding of the waterlogging gene regulatory network hinders the efforts to develop waterlogging-tolerant cultivars. We generated high-resolution temporal transcriptome data from root of two contrasting sesame genotypes over a 48 h period waterlogging and drainage treatments. Three distinct chronological transcriptional phases were identified, including the early-waterlogging, late-waterlogging and drainage responses. We identified 47 genes representing the core waterlogging-responsive genes. Waterlogging/drainage-induced transcriptional changes were mainly driven by ERF and WRKY transcription factors (TF). The major difference between the two genotypes resides in the early transcriptional phase. A chronological transcriptional network model predicting putative causal regulations between TFs and downstream waterlogging-responsive genes was constructed and some interactions were validated through yeast one-hybrid assay. Overall, this study unveils the architecture and dynamic regulation of the waterlogging/drainage response in a non-model crop and helps formulate new hypotheses on stress sensing, signaling and sophisticated adaptive responses.

Transcriptional deregulation underlying the pathogenesis of small cell lung cancer.

The discovery of recurrent alterations in genes encoding transcription regulators and chromatin modifiers is one of the most important recent developments in the study of the small cell lung cancer (SCLC) genome. With advances in models and analytical methods, the field of SCLC biology has seen remarkable progress in understanding the deregulated transcription networks linked to the tumor development and malignant progression. This review will discuss recent discoveries on the roles of RB and P53 family of tumor suppressors and MYC family of oncogenes in tumor initiation and development. It will also describe the roles of lineage-specific factors in neuroendocrine (NE) cell differentiation and homeostasis and the roles of epigenetic alterations driven by changes in NFIB and chromatin modifiers in malignant progression and chemoresistance. These recent findings have led to a model of transcriptional network in which multiple pathways converge on regulatory regions of crucial genes linked to tumor development. Validation of this model and characterization of target genes will provide critical insights into the biology of SCLC and novel strategies for tumor intervention.

The effect of inhibition of PP1 and TNFα signaling on pathogenesis of SARS coronavirus.

BackgroundThe complex interplay between viral replication and host immune response during infection remains poorly understood. While many viruses are known to employ anti-immune strategies to facilitate their replication, highly pathogenic virus infections can also cause an excessive immune response that exacerbates, rather than reduces pathogenicity. To investigate this dichotomy in severe acute respiratory syndrome coronavirus (SARS-CoV), we developed a transcriptional network model of SARS-CoV infection in mice and used the model to prioritize candidate regulatory targets for further investigation.ResultsWe validated our predictions in 18 different knockout (KO) mouse strains, showing that network topology provides significant predictive power to identify genes that are important for viral infection. We identified a novel player in the immune response to virus infection, Kepi, an inhibitory subunit of the protein phosphatase 1 (PP1) complex, which protects against SARS-CoV pathogenesis. We also found that receptors for the proinflammatory cytokine tumor necrosis factor alpha (TNFα) promote pathogenesis, presumably through excessive inflammation.ConclusionsThe current study provides validation of network modeling approaches for identifying important players in virus infection pathogenesis, and a step forward in understanding the host response to an important infectious disease. The results presented here suggest the role of Kepi in the host response to SARS-CoV, as well as inflammatory activity driving pathogenesis through TNFα signaling in SARS-CoV infections. Though we have reported the utility of this approach in bacterial and cell culture studies previously, this is the first comprehensive study to confirm that network topology can be used to predict phenotypes in mice with experimental validation.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-016-0336-6) contains supplementary material, which is available to authorized users.

Differential expression of transcriptional regulatory units in the prefrontal cortex of patients with bipolar disorder: potential role of early growth response gene 3.

Bipolar disorder (BD) is a severe mental illness with a strong genetic component. Despite its high degree of heritability, current genetic studies have failed to reveal individual loci of large effect size. In lieu of focusing on individual genes, we investigated regulatory units (regulons) in BD to identify candidate transcription factors (TFs) that regulate large groups of differentially expressed genes. Network-based approaches should elucidate the molecular pathways governing the pathophysiology of BD and reveal targets for potential therapeutic intervention. The data from a large-scale microarray study was used to reconstruct the transcriptional associations in the human prefrontal cortex, and results from two independent microarray data sets to obtain BD gene signatures. The regulatory network was derived by mapping the significant interactions between known TFs and all potential targets. Five regulons were identified in both transcriptional network models: early growth response 3 (EGR3), TSC22 domain family, member 4 (TSC22D4), interleukin enhancer-binding factor 2 (ILF2), Y-box binding protein 1 (YBX1) and MAP-kinase-activating death domain (MADD). With a high stringency threshold, the consensus across tests was achieved only for the EGR3 regulon. We identified EGR3 in the prefrontal cortex as a potential key target, robustly repressed in both BD signatures. Considering that EGR3 translates environmental stimuli into long-term changes in the brain, disruption in biological pathways involving EGR3 may induce an impaired response to stress and influence on risk for psychiatric disorders, particularly BD.

Biosynthesis of milk fat, protein, and lactose: roles of transcriptional and posttranscriptional regulation

The demand for high-quality milk is increasing worldwide. The efficiency of milk synthesis can be improved by taking advantage of the accumulated knowledge of the transcriptional and posttranscriptional regulation of genes coding for proteins involved in the synthesis of fat, protein, and lactose in the mammary gland. Research in this area is relatively new, but data accumulated in the last 10 years provide a relatively clear picture. Milk fat synthesis appears to be regulated, at least in bovines, by an interactive network between SREBP1, PPARγ, and LXRα, with a potential role for other transcription factors, such as Spot14, ChREBP, and Sp1. Milk protein synthesis is highly regulated by insulin, amino acids, and amino acid transporters via transcriptional and posttranscriptional routes, with the insulin-mTOR pathway playing a central role. The transcriptional regulation of lactose synthesis is still poorly understood, but it is clear that glucose transporters play an important role. They can also cooperatively interact with amino acid transporters and the mTOR pathway. Recent data indicate the possibility of nutrigenomic interventions to increase milk fat synthesis by feeding long-chain fatty acids and milk protein synthesis by feeding amino acids. We propose a transcriptional network model to account for all available findings. This model encompasses a complex network of proteins that control milk synthesis with a cross talk between milk fat, protein, and lactose regulation, with mTOR functioning as a central hub.

Active enhancer positions can be accurately predicted from chromatin marks and collective sequence motif data.

BackgroundTranscriptional regulation in multi-cellular organisms is a complex process involving multiple modular regulatory elements for each gene. Building whole-genome models of transcriptional networks requires mapping all relevant enhancers and then linking them to target genes. Previous methods of enhancer identification based either on sequence information or on epigenetic marks have different limitations stemming from incompleteness of each of these datasets taken separately.ResultsIn this work we present a new approach for discovery of regulatory elements based on the combination of sequence motifs and epigenetic marks measured with ChIP-Seq. Our method uses supervised learning approaches to train a model describing the dependence of enhancer activity on sequence features and histone marks. Our results indicate that using combination of features provides superior results to previous approaches based on either one of the datasets. While histone modifications remain the dominant feature for accurate predictions, the models based on sequence motifs have advantages in their general applicability to different tissues. Additionally, we assess the relevance of different sequence motifs in prediction accuracy showing that even tissue-specific enhancer activity depends on multiple motifs.ConclusionsBased on our results, we conclude that it is worthwhile to include sequence motif data into computational approaches to active enhancer prediction and also that classifiers trained on a specific set ofenhancers can generalize with significant accuracy beyond the training set.

Network based elucidation of drug response: from modulators to targets.

Network-based drug discovery aims at harnessing the power of networks to investigate the mechanism of action of existing drugs, or new molecules, in order to identify innovative therapeutic treatments. In this review, we describe some of the most recent advances in the field of network pharmacology, starting with approaches relying on computational models of transcriptional networks, then moving to protein and signaling network models and concluding with “drug networks”. These networks are derived from different sources of experimental data, or literature-based analysis, and provide a complementary view of drug mode of action. Molecular and drug networks are powerful integrated computational and experimental approaches that will likely speed up and improve the drug discovery process, once fully integrated into the academic and industrial drug discovery pipeline.

Modeling transcriptional networks regulating secondary growth and wood formation in forest trees

The complex interactions among the genes that underlie a biological process can be modeled and presented as a transcriptional network, in which genes (nodes) and their interactions (edges) are shown in a graphical form similar to a wiring diagram. A large number of genes have been identified that are expressed during the radial woody growth of tree stems (secondary growth), but a comprehensive understanding of how these genes interact to influence woody growth is currently lacking. Modeling transcriptional networks has recently been made tractable by next-generation sequencing-based technologies that can comprehensively catalog gene expression and transcription factor-binding genome-wide, but has not yet been extensively applied to undomesticated tree species or woody growth. Here we discuss basic features of transcriptional networks, approaches for modeling biological networks, and examples of biological network models developed for forest trees to date. We discuss how transcriptional network research is being developed in the model forest tree genus, Populus, and how this research area can be further developed and applied. Transcriptional network models for forest tree secondary growth and wood formation could ultimately provide new predictive models to accelerate hypothesis-driven research and develop new breeding applications.

Transcriptional network predicts viral set point during acute HIV-1 infection

HIV-1-infected individuals with higher viral set points progress to AIDS more rapidly than those with lower set points. Predicting viral set point early following infection can contribute to our understanding of early control of HIV-1 replication, to predicting long-term clinical outcomes, and to the choice of optimal therapeutic regimens. In a longitudinal study of 10 untreated HIV-1-infected patients, we used gene expression profiling of peripheral blood mononuclear cells to identify transcriptional networks for viral set point prediction. At each sampling time, a statistical analysis inferred the optimal transcriptional network that best predicted viral set point. We then assessed the accuracy of this transcriptional model by predicting viral set point in an independent cohort of 10 untreated HIV-1-infected patients from Malawi. The gene network inferred at time of enrollment predicted viral set point 24 weeks later in the independent Malawian cohort with an accuracy of 87.5%. As expected, the predictive accuracy of the networks inferred at later time points was even greater, exceeding 90% after week 4. The composition of the inferred networks was largely conserved between time points. The 12 genes comprising this dynamic signature of viral set point implicated the involvement of two major canonical pathways: interferon signaling (p<0.0003) and membrane fraction (p<0.02). A silico knockout study showed that HLA-DRB1 and C4BPA may contribute to restricting HIV-1 replication. Longitudinal gene expression profiling of peripheral blood mononuclear cells from patients with acute HIV-1 infection can be used to create transcriptional network models to early predict viral set point with a high degree of accuracy.

A Meta-Analysis Reveals the Commonalities and Differences in Arabidopsis thaliana Response to Different Viral Pathogens

Understanding the mechanisms by which plants trigger host defenses in response to viruses has been a challenging problem owing to the multiplicity of factors and complexity of interactions involved. The advent of genomic techniques, however, has opened the possibility to grasp a global picture of the interaction. Here, we used Arabidopsis thaliana to identify and compare genes that are differentially regulated upon infection with seven distinct (+)ssRNA and one ssDNA plant viruses. In the first approach, we established lists of genes differentially affected by each virus and compared their involvement in biological functions and metabolic processes. We found that phylogenetically related viruses significantly alter the expression of similar genes and that viruses naturally infecting Brassicaceae display a greater overlap in the plant response. In the second approach, virus-regulated genes were contextualized using models of transcriptional and protein-protein interaction networks of A. thaliana. Our results confirm that host cells undergo significant reprogramming of their transcriptome during infection, which is possibly a central requirement for the mounting of host defenses. We uncovered a general mode of action in which perturbations preferentially affect genes that are highly connected, central and organized in modules.

An Integrated Framework to Model Cellular Phenotype as a Component of Biochemical Networks

Identification of regulatory molecules in signaling pathways is critical for understanding cellular behavior. Given the complexity of the transcriptional gene network, the relationship between molecular expression and phenotype is difficult to determine using reductionist experimental methods. Computational models provide the means to characterize regulatory mechanisms and predict phenotype in the context of gene networks. Integrating gene expression data with phenotypic data in transcriptional network models enables systematic identification of critical molecules in a biological network. We developed an approach based on fuzzy logic to model cell budding in Saccharomyces cerevisiae using time series expression microarray data of the cell cycle. Cell budding is a phenotype of viable cells undergoing division. Predicted interactions between gene expression and phenotype reflected known biological relationships. Dynamic simulation analysis reproduced the behavior of the yeast cell cycle and accurately identified genes and interactions which are essential for cell viability.

Our evolving knowledge of the transcriptional landscape

The development of a genome-scale approach to identification of the 5' ends of capped mRNAs (CAGE) has given new insights into many aspects of mammalian RNApolII transcription control. They include the identification of the minimal initiator motif, the different types of proximal promoter architecture, the promoters of noncoding RNAs, the transcription of retrotransposons, and the extensive impact of alternative promoters on the proteome. CAGE also offers applications as a form of expression profiling that measures promoter use, allowing more precise development of transcriptional network models.

How to Build Transcriptional Network Models of Mammalian Pattern Formation

BackgroundGenetic regulatory networks of sequence specific transcription factors underlie pattern formation in multicellular organisms. Deciphering and representing the mammalian networks is a central problem in development, neurobiology, and regenerative medicine. Transcriptional networks specify intermingled embryonic cell populations during pattern formation in the vertebrate neural tube. Each embryonic population gives rise to a distinct type of adult neuron. The homeodomain transcription factor Lbx1 is expressed in five such populations and loss of Lbx1 leads to distinct respecifications in each of the five populations.Methodology/Principal FindingsWe have purified normal and respecified pools of these five populations from embryos bearing one or two copies of the null Lbx1GFP allele, respectively. Microarrays were used to show that expression levels of 8% of all transcription factor genes were altered in the respecified pool. These transcription factor genes constitute 20–30% of the active nodes of the transcriptional network that governs neural tube patterning. Half of the 141 regulated nodes were located in the top 150 clusters of ultraconserved non-coding regions. Generally, Lbx1 repressed genes that have expression patterns outside of the Lbx1-expressing domain and activated genes that have expression patterns inside the Lbx1-expressing domain.Conclusions/SignificanceConstraining epistasis analysis of Lbx1 to only those cells that normally express Lbx1 allowed unprecedented sensitivity in identifying Lbx1 network interactions and allowed the interactions to be assigned to a specific set of cell populations. We call this method ANCEA, or active node constrained epistasis analysis, and think that it will be generally useful in discovering and assigning network interactions to specific populations. We discuss how ANCEA, coupled with population partitioning analysis, can greatly facilitate the systematic dissection of transcriptional networks that underlie mammalian patterning.

Single-Cell Gene-Expression Analysis by Quantitative RT-PCR

The problem of development has long been one of the key issues in biology. With stem-cell therapies on the horizon, the “reverse engineering” of developmental programs promises to become a task of great practical significance. We now understand the general schemes by which transcriptional networks regulate cellular differentiation and morphogenesis. These genetic circuits function as complex state machines which, over the course of development, undergo sequenced transitions that bring cells to specific end states. A variety of different gene-expression assays can be used to follow these transitions. The sensitivity of the assays now in common use limits the resolution with which we can follow the activity of genetic-regulatory networks. This thesis describes two projects aimed at refining an established gene-profiling method, quantitative RT-PCR, so that it can be used to profile transcriptional-network states cross-sectionally within developing cell populations at single-cell resolution. Two advanced qRT-PCR protocols were developed to support these projects, one based on microfluidic “digital PCR,” the other based on multiplexed “preamplification PCR.” These protocols were used to measure transcription-factor expression in hematopoietic progenitor cells, and to evaluate the effects of aging on the stability of gene regulation. In their current form, the digital PCR and preamplification techniques will permit the analysis of perhaps a few hundred to a few thousand cells in a single-cell survey. By combining microfluidic-chip assays with the preamplification method, it will soon be possible to scale up to the analysis of many thousands of cells, while profiling many different transcription factors in each individual cell. This should facilitate the modeling of the transcriptional networks which control cellular differentiation as we press forward into the era of “tissue engineering."

Models from experiments: combinatorial drug perturbations of cancer cells

We present a novel method for deriving network models from molecular profiles of perturbed cellular systems. The network models aim to predict quantitative outcomes of combinatorial perturbations, such as drug pair treatments or multiple genetic alterations. Mathematically, we represent the system by a set of nodes, representing molecular concentrations or cellular processes, a perturbation vector and an interaction matrix. After perturbation, the system evolves in time according to differential equations with built-in nonlinearity, similar to Hopfield networks, capable of representing epistasis and saturation effects. For a particular set of experiments, we derive the interaction matrix by minimizing a composite error function, aiming at accuracy of prediction and simplicity of network structure. To evaluate the predictive potential of the method, we performed 21 drug pair treatment experiments in a human breast cancer cell line (MCF7) with observation of phospho-proteins and cell cycle markers. The best derived network model rediscovered known interactions and contained interesting predictions. Possible applications include the discovery of regulatory interactions, the design of targeted combination therapies and the engineering of molecular biological networks.