Model Of Gene Regulation Research Articles

Discovering and Constructing ceRNA-miRNA-Target Gene Regulatory Networks during Anther Development in Maize.

The “competing endogenous RNA (ceRNA) hypothesis” has recently been proposed for a new type of gene regulatory model in many organisms. Anther development is a crucial biological process in plant reproduction, and its gene regulatory network (GRN) has been gradually revealed during the past two decades. However, it is still unknown whether ceRNAs contribute to anther development and sexual reproduction in plants. We performed RNA and small RNA sequencing of anther tissues sampled at three developmental stages in two maize lines. A total of 28,233 stably transcribed loci, 61 known and 51 potentially novel microRNAs (miRNAs) were identified from the transcriptomes. Predicted ceRNAs and target genes were found to conserve in sequences of recognition sites where their corresponding miRNAs bound. We then reconstructed 79 ceRNA-miRNA-target gene regulatory networks consisting of 51 known miRNAs, 28 potentially novel miRNAs, 619 ceRNA-miRNA pairs, and 869 miRNA-target gene pairs. More than half of the regulation pairs showed significant negative correlations at transcriptional levels. Several well-studied miRNA-target gene pairs associated with plant flower development were located in some networks, including miR156-SPL, miR159-MYB, miR160-ARF, miR164-NAC, miR172-AP2, and miR319-TCP pairs. Six target genes in the networks were found to be orthologs of functionally confirmed genes participating in anther development in plants. Our results provide an insight that the ceRNA-miRNA-target gene regulatory networks likely contribute to anther development in maize. Further functional studies on a number of ceRNAs, miRNAs, and target genes will facilitate our deep understanding on mechanisms of anther development and sexual plants reproduction.

Transcript degradation and codon usage regulate gene expression in a lytic phage.

Many viral genomes are small, containing only single- or double-digit numbers of genes and relatively few regulatory elements. Yet viruses successfully execute complex regulatory programs as they take over their host cells. Here, we propose that some viruses regulate gene expression via a carefully balanced interplay between transcription, translation, and transcript degradation. As our model system, we employ bacteriophage T7, whose genome of approximately sixty genes is well annotated and for which there is a long history of computational models of gene regulation. We expand upon prior modeling work by implementing a stochastic gene expression simulator that tracks individual transcripts, polymerases, ribosomes, and ribonucleases participating in the transcription, translation, and transcript-degradation processes occurring during a T7 infection. By combining this detailed mechanistic modeling of a phage infection with high-throughput gene expression measurements of several strains of bacteriophage T7, evolved and engineered, we can show that both the dynamic interplay between transcription and transcript degradation, and between these two processes and translation, appear to be critical components of T7 gene regulation. Our results point to targeted degradation as a generic gene regulation strategy that may have evolved in many other viruses. Further, our results suggest that detailed mechanistic modeling may uncover the biological mechanisms at work in both evolved and engineered virus variants.

Promoter-dependent nuclear RNA degradation ensures cell cycle-specific gene expression

Cell cycle progression depends on phase-specific gene expression. Here we show that the nuclear RNA degradation machinery plays a lead role in promoting cell cycle-dependent gene expression by triggering promoter-dependent co-transcriptional RNA degradation. Single molecule quantification of RNA abundance in different phases of the cell cycle indicates that relative curtailment of gene expression in certain phases is attained even when transcription is not completely inhibited. When nuclear ribonucleases are deleted, transcription of the Saccharomyces cerevisiae G1-specific axial budding gene AXL2 is detected throughout the cell cycle and its phase-specific expression is lost. Promoter replacement abolished cell cycle-dependent RNA degradation and rendered the RNA insensitive to the deletion of nuclear ribonucleases. Together the data reveal a model of gene regulation whereby RNA abundance is controlled by promoter-dependent induction of RNA degradation.

Clinically Advanced p38 Inhibitors Suppress DUX4 Expression in Cellular and Animal Models of Facioscapulohumeral Muscular Dystrophy.

Facioscapulohumeral muscular dystrophy (FSHD) is characterized by misexpression of the double homeobox 4 (DUX4) developmental transcription factor in mature skeletal muscle, where it is responsible for muscle degeneration. Preventing expression of DUX4 mRNA is a disease-modifying therapeutic strategy with the potential to halt or reverse the course of disease. We previously reported that agonists of the β-2 adrenergic receptor suppress DUX4 expression by activating adenylate cyclase to increase cAMP levels. Efforts to further explore this signaling pathway led to the identification of p38 mitogen-activated protein kinase as a major regulator of DUX4 expression. In vitro experiments demonstrate that clinically advanced p38 inhibitors suppress DUX4 expression in FSHD type 1 and 2 myoblasts and differentiating myocytes in vitro with exquisite potency. Individual small interfering RNA-mediated knockdown of either p38α or p38β suppresses DUX4 expression, demonstrating that each kinase isoform plays a distinct requisite role in activating DUX4 Finally, p38 inhibitors effectively suppress DUX4 expression in a mouse xenograft model of human FSHD gene regulation. These data support the repurposing of existing clinical p38 inhibitors as potential therapeutics for FSHD. The surprise finding that p38α and p38β isoforms each independently contribute to DUX4 expression offers a unique opportunity to explore the utility of p38 isoform-selective inhibitors to balance efficacy and safety in skeletal muscle. We propose p38 inhibition as a disease-modifying therapeutic strategy for FSHD. SIGNIFICANCE STATEMENT: Facioscapulohumeral muscular dystrophy (FSHD) currently has no treatment options. This work provides evidence that repurposing a clinically advanced p38 inhibitor may provide the first disease-modifying drug for FSHD by suppressing toxic DUX4 expression, the root cause of muscle degeneration in this disease.

Transcriptional Bursts in a Nonequilibrium Model for Gene Regulation by Supercoiling

We analyze transcriptional bursting within a stochastic nonequilibrium model, which accounts for the coupling between the dynamics of DNA supercoiling and gene transcription. We find a clear signature of bursty transcription when there is a separation between the timescales of transcription initiation and supercoiling dissipation (the latter may either be diffusive or mediated by topological enzymes, such as type I or type II topoisomerases). In multigenic DNA domains, we observe either bursty transcription or transcription waves; the type of behavior can be selected for by controlling gene activity and orientation. In the bursty phase, the statistics of supercoiling fluctuations at the promoter are markedly non-Gaussian.

Bayesian Estimation for Stochastic Gene Expression Using Multifidelity Models

The finite state projection (FSP) approach to solving the chemical master equation has enabled successful inference of discrete stochastic models to predict single-cell gene regulation dynamics. Unfortunately, the FSP approach is highly computationally intensive for all but the simplest models, an issue that is highly problematic when parameter inference and uncertainty quantification takes enormous numbers of parameter evaluations. To address this issue, we propose two new computational methods for the Bayesian inference of stochastic gene expression parameters given single-cell experiments. We formulate and verify an adaptive delayed acceptance Metropolis-Hastings (ADAMH) algorithm to utilize with reduced Krylov-basis projections of the FSP. We then introduce an extension of the ADAMH into a hybrid scheme that consists of an initial phase to construct a reduced model and a faster second phase to sample from the approximate posterior distribution determined by the constructed model. We test and compare both algorithms to an adaptive Metropolis algorithm with full FSP-based likelihood evaluations on three example models and simulated data to show that the new ADAMH variants achieve substantial speedup in comparison to the full FSP approach. By reducing the computational costs of parameter estimation, we expect the ADAMH approach to enable efficient data-driven estimation for more complex gene regulation models.

The LhrC sRNAs control expression of T cell-stimulating antigen TcsA in Listeria monocytogenes by decreasing tcsA mRNA stability

ABSTRACTThe bacterial pathogen Listeria monocytogenes encodes seven homologous small regulatory RNAs, named the LhrC family of sRNAs. The LhrCs are highly induced under infection-relevant conditions and are known to inhibit the expression of multiple target mRNAs encoding virulence-associated surface proteins. In all cases studied so far, the LhrCs use their CU-rich regions for base pairing to complementary AG-rich sequences of the ribosomal binding site (RBS) of specific target mRNAs. Consequently, LhrC-mRNA interaction results in inhibition of translation followed by mRNA degradation, corresponding to the canonical model for sRNA-mediated gene regulation in bacteria. Here, we demonstrate that the LhrC sRNAs employ a different regulatory mechanism when acting to down-regulate the expression of tcsA, encoding a T cell-stimulating antigen. In this case, LhrC base pairs to an AG-rich site located well upstream of the RBS in tcsA mRNA. Using an in vitro translation assay, we found that LhrC could not prevent the ribosome from translating the tcsA messenger. Rather, the LhrC sRNAs act to decrease the half-life of tcsA mRNA in vivo. Importantly, LhrC-mediated destabilization of tcsA mRNA relies on an intact LhrC binding site near the 5´-end of the tcsA mRNA and occurs independently of translation. Based on these findings, we propose an alternative mechanism for LhrC-mediated control in L. monocytogenes that relies solely on sRNA-induced degradation of a target mRNA.

Multi-study inference of regulatory networks for more accurate models of gene regulation.

Gene regulatory networks are composed of sub-networks that are often shared across biological processes, cell-types, and organisms. Leveraging multiple sources of information, such as publicly available gene expression datasets, could therefore be helpful when learning a network of interest. Integrating data across different studies, however, raises numerous technical concerns. Hence, a common approach in network inference, and broadly in genomics research, is to separately learn models from each dataset and combine the results. Individual models, however, often suffer from under-sampling, poor generalization and limited network recovery. In this study, we explore previous integration strategies, such as batch-correction and model ensembles, and introduce a new multitask learning approach for joint network inference across several datasets. Our method initially estimates the activities of transcription factors, and subsequently, infers the relevant network topology. As regulatory interactions are context-dependent, we estimate model coefficients as a combination of both dataset-specific and conserved components. In addition, adaptive penalties may be used to favor models that include interactions derived from multiple sources of prior knowledge including orthogonal genomics experiments. We evaluate generalization and network recovery using examples from Bacillus subtilis and Saccharomyces cerevisiae, and show that sharing information across models improves network reconstruction. Finally, we demonstrate robustness to both false positives in the prior information and heterogeneity among datasets.

NextPBM: a platform to study cell-specific transcription factor binding and cooperativity.

High-throughput (HT) in vitro methods for measuring protein-DNA binding have become invaluable for characterizing transcription factor (TF) complexes and modeling gene regulation. However, current methods do not utilize endogenous proteins and, therefore, do not quantify the impact of cell-specific post-translational modifications (PTMs) and cooperative cofactors. We introduce the HT nextPBM (nuclear extract protein-binding microarray) approach to study DNA binding of native cellular TFs that accounts for PTMs and cell-specific cofactors. We integrate immune-depletion and phosphatase treatment steps into our nextPBM pipeline to characterize the impact of cofactors and phosphorylation on TF binding. We analyze binding of PU.1/SPI1 and IRF8 from human monocytes, delineate DNA-sequence determinants for their cooperativity, and show how PU.1 affinity correlates with enhancer status and the presence of cooperative and collaborative cofactors. We describe how nextPBMs, and our accompanying computational framework, can be used to discover cell-specific cofactors, screen for synthetic cooperative DNA elements, and characterize TF cooperativity.

Thermodynamic model of gene regulation for the Or59b olfactory receptor in Drosophila.

Complex eukaryotic promoters normally contain multiple cis-regulatory sequences for different transcription factors (TFs). The binding patterns of the TFs to these sites, as well as the way the TFs interact with each other and with the RNA polymerase (RNAp), lead to combinatorial problems rarely understood in detail, especially under varying epigenetic conditions. The aim of this paper is to build a model describing how the main regulatory cluster of the olfactory receptor Or59b drives transcription of this gene in Drosophila. The cluster-driven expression of this gene is represented as the equilibrium probability of RNAp being bound to the promoter region, using a statistical thermodynamic approach. The RNAp equilibrium probability is computed in terms of the occupancy probabilities of the single TFs of the cluster to the corresponding binding sites, and of the interaction rules among TFs and RNAp, using experimental data of Or59b expression to tune the model parameters. The model reproduces correctly the changes in RNAp binding probability induced by various mutation of specific sites and epigenetic modifications. Some of its predictions have also been validated in novel experiments.

VITAMIN A, RETINOIC ACID AND TAMIBAROTENE, A FRONT TOWARD ITS ADVANCES: A REVIEW

Vitamin A and its derivative retinoic acid (13-cis RA, 9-cis RA, all-trans RA) and recent tamibarotene have been shown a broad variety of biological actives in human, such as vision, embryonic development, cell growth and cellular differentiation and immune function. These precise functions of RA are mediated by their retinoic acid receptors (RARs). In the past five decades, retinoic acid (RA) proved therapeutic benefits in cancer prevention, in skin diseases and in acute promyelocytic leukemia (APL).The elucidation of the molecular basis of vitamin A acid and its retinoid pharmacology in APL has been illustrated in several publications, the detail molecular model of gene regulation had also been proposed by Zhu in earlier 90s. A molecular model is further revised. As an approach to APL treatment, one possible the action of retinoic acid (RA), A consensus sequence (TCAGGTCA motif ) has been postulated for thyroid hormone (TRE) and retinoic acid responsive element (RARE)-containing in the promoter region of target genes. High dose of RA-RARE-PML/RARa complexes in intracellular localization appears to relieve repressors from DNA-bound receptor, including the dissociation of co-re pressor complexes N-CoR, SMRT and HDACs from PML-RARa or PML-RARa/RXR. Also release PML/RARa -mediated transcription repression. This transcriptional derepression occurs at RARa target gene promoter. Consequentially, PML-RARa chimera converted receptor from a repressor to a RA-dependent activator of transcription. The resulting pml-RARA oncoprotein proteolytic degradation occurs through the autophagy-lysosome pathway and the ubiquitin SUMO-proteasome system(UPS) as well as caspase 3, or lysosomal protease (cathepsin D) enzyme or/and EI-like ubiquitin-activating enzyme(UBEIL) induction. Accordingly the expression level of PML-RARa downregulated. PML protein relocalizes into the wild-type nuclear body (PML-NB) configuration or a truncated PML-RARa fusion fragment detected or/and the wild-type RAR upregulated. An effect is to relieve the blockade of pml/RARa-mediated RA dependent promyelocytic differentiation, and retinoic acid (9-cid RA,ATRA, Am80) in APL therapy. Here, oncogenic pml/RARa as constitutive transcriptional repressor that block myeloid differentiation at promyelocytic phenotype. RA can overcome the transcrptional repressor activity of pml/RARa. The oncogenic pml/RARa uncovers a pathogenic role in leukemogenesis of APL through blocking promyelocytic differentiation. This oncogenic receptor derivative pml/ RARa chimera is locked in their "off" regular mode thereby constitutively repressing transcription of target genes or key enzymes (such as AP-1, PTEN, DAPK2,UP.1, p21WAF/CCKN1A）that are critical for differentiation of hematopoietic cells. This is first described in eukaryotes.

Modeling Cellular Differentiation and Reprogramming with Gene Regulatory Networks.

Gene expression regulation is a fundamental cellular process that enables robust functioning of cells. How different genes interact among themselves to coordinate and maintain the overall gene expression profile observed in a cell is a key question in cellular biology. However, the immense complexity arising due to the scale and the nature of gene-gene interactions often hinders obtaining a global understanding of gene regulation. In this regard, network models of gene regulation based on gene-gene interactions, commonly referred to as gene regulatory networks (GRNs), serve important purpose of describing the overall interactions within a cell and provide a systematic approach to study their global behavior. In particular, in the context of cellular differentiation and reprogramming, where regulated changes in gene expression play a crucial role, precise knowledge of a cell type-specific GRN can enable control of the eventual cell fates with potential clinical applications. In this chapter, we describe our computational methodologies that we have tailor-made with purpose of applications to cell fate control. Briefly, we introduce the process of cellular differentiation and reprogramming, describe GRNs and common strategies to model them, and, finally, introduce the concept of determinants of cellular reprogramming and differentiation. In the Methods section, we elaborate on the different steps involved in the computational pipeline, including initial gene expression data processing, characterization of prior knowledge network, algorithm to remove non-cell type-specific edges, topological characterization of the inferred network, and Boolean network simulations to mimic cellular transitions. Finally, we provide a strategy to identify determinants of cellular reprogramming and differentiation based on the proposed computational methods.

A Boolean Network Approach to Estrogen Transcriptional Regulation

Gene expression governs important biological processes such as the cell’s growth cycle and its response to environmental signals. Alterations of this complex network of transcriptional interactions often lead to unstable expression states and disease. Estrogen is a sex hormone known for its roles in cell proliferation. Its expression has been involved in several physiological functions such as regulating the menstrual and reproduction cycles in women. Altered expression states where estrogen levels are atypically high have been associated with an increased incidence of breast, ovarian, and cervix cancer. To better understand the implications of deregulation of the estrogen and estrogen receptor regulatory networks, in this work we generated a dynamical model of gene regulation of the estrogen receptor transcription network based on known regulatory interactions. By using an adaptation to classical Boolean Networks dynamics we identified proliferative and antiproliferative gene expression states of the network and also to identify key players that promote these altered states when perturbed. We also modeled how pairwise gene alterations may contribute to shifts between these two proliferative states and found that the coordinated subexpression of E2F1 and SMAD4 is the most important combination in terms of promoting proliferative states in the network.

A Simple and Flexible Computational Framework for Inferring Sources of Heterogeneity from Single-Cell Dynamics.

Single-cell time-lapse data provide the means for disentangling sources of cell-to-cell and intra-cellular variability, a key step for understanding heterogeneity in cell populations. However, single-cell analysis with dynamic models is a challenging open problem: current inference methods address only single-gene expression or neglect parameter correlations. We report on a simple, flexible, and scalable method for estimating cell-specific and population-average parameters of non-linear mixed-effects models of cellular networks, demonstrating its accuracy with a published model and dataset. We also propose sensitivity analysis for identifying which biological sub-processes quantitatively and dynamically contribute to cell-to-cell variability. Our application to endocytosis in yeast demonstrates that dynamic models of realistic size can be developed for the analysis of single-cell data and that shifting the focus from single reactions or parameters to nuanced and time-dependent contributions of sub-processes helps biological interpretation. Generality and simplicity of the approach will facilitate customized extensions for analyzing single-cell dynamics.

Computing intrinsic noise of the genetic regulation modeled by Hill functions

Intrinsic noise embedded in stochastic gene regulation due to low copy number of species has been studied using the approach of theoretical modeling and computational simulation, including the standard methods of stochastic simulation algorithm (SSA) and linear noise approximation (LNA). At average cell level, Hill functions are widely used as a compact format to represent gene regulation involving multi-transcription-factor binding and cooperativity. Heuristic SSA and LNA methods (hSSA and hLNA) have been applied to study stochastic models employing Hill functions. It is however unclear which modeling and simulation method is suitable to characterize intrinsic noise of Hill-type gene regulation with sufficient accuracy and computational efficiency. In this work, we perform noise analysis of two gene regulatory models represented by second-order activating and inhibitory Hill functions, seeking to evaluate the performance of five existing noise modeling methods. Specifically, SSA and LNA are applied to the full models that are expanded from the Hill functions containing only elementary reactions, while hSSA and hLNA are applied to reduced models where the Hill function is heuristically used. In addition, we characterize intrinsic noise using the slow-scale LNA (ssLNA) method that is recently proposed to deal with models with both fast and slow time scales. Using SSA as ground truth, we find that hSSA and hLNA underestimate the level of intrinsic noise in the Hill-type models, despite of high computational efficiency. The ssLNA approach calculates noise with comparable accuracy as SSA and LNA, while requesting much less computational resources. In addition, the chemical Langevin equation (CLE) under the same slow-scale framework simulates single-cell stochastic trajectories as accurately as SSA yet with significantly lower computational demands. This study shows that ssLNA complemented by slow-scale CLE offers a computational platform that out-performs the other four methods in characterizing intrinsic stochasticity of the Hill-type genetic models.

Gene Regulatory Network Modeling of Macrophage Differentiation Corroborates the Continuum Hypothesis of Polarization States.

Macrophages derived from monocyte precursors undergo specific polarization processes which are influenced by the local tissue environment: classically activated (M1) macrophages, with a pro-inflammatory activity and a role of effector cells in Th1 cellular immune responses, and alternatively activated (M2) macrophages, with anti-inflammatory functions and involved in immunosuppression and tissue repair. At least three different subsets of M2 macrophages, namely, M2a, M2b, and M2c, are characterized in the literature based on their eliciting signals. The activation and polarization of macrophages is achieved through many, often intertwined, signaling pathways. To describe the logical relationships among the genes involved in macrophage polarization, we used a computational modeling methodology, namely, logical (Boolean) modeling of gene regulation. We integrated experimental data and knowledge available in the literature to construct a logical network model for the gene regulation driving macrophage polarization to the M1, M2a, M2b, and M2c phenotypes. Using the software GINsim and BoolNet, we analyzed the network dynamics under different conditions and perturbations to understand how they affect cell polarization. Dynamic simulations of the network model, enacting the most relevant biological conditions, showed coherence with the observed behavior of in vivo macrophages. The model could correctly reproduce the polarization toward the four main phenotypes as well as to several hybrid phenotypes, which are known to be experimentally associated to physiological and pathological conditions. We surmise that shifts among different phenotypes in the model mimic the hypothetical continuum of macrophage polarization, with M1 and M2 being the extremes of an uninterrupted sequence of states. Furthermore, model simulations suggest that anti-inflammatory macrophages are resilient to shift back to the pro-inflammatory phenotype.

Linguistic modelling of gene regulation: Translation from the language of experiments to the language of modelling

Abstract Understanding of gene regulation mechanisms is one of the main goals in systems biology of development. In this communication we introduce a biologically oriented approach to model gene regulation by explicitly involving all the available empirical data. The method is derived by further developing the family of diverse approaches known as fuzzy linguistic modelling (FLM) of gene action. FLM makes it possible to formulate in simple rules the known details of diverse and hierarchical gene-regulatory organization, i.e., to reword experimental conclusions formulated in the language of experimentalists into the language of model designers.

Identification of parameters in systems biology

We consider the inverse problem for the identification of the finite dimensional set of parameters for systems of nonlinear ordinary differential equations (ODEs) arising in systems biology. A numerical method which combines Bellman’s quasilinearization with sensitivity analysis and Tikhonov’s regularization is implemented. We apply the method to various biological models such as the classical Lotka–Volterra system, bistable switch model in genetic regulatory networks, gene regulation and repressilator models from synthetic biology. The numerical results and application to real data demonstrate the quadratic convergence.

Evaluation of Mixotrophy-Associated Gene Expression in Two Species of Polar Marine Algae

Mixotrophic flagellates can comprise significant proportions of plankton biomass in marine ecosystems. Despite the growing recognition of the importance of this ecological strategy, and the identification of major environmental factors controlling phagotrophic behavior (light and nutrients), the physiological and molecular mechanisms underlying mixotrophic behavior are still unclear. In this study, we performed RNA-Seq transcriptomic analysis for two mixotrophic prasinophytes, Micromonas polaris and Pyramimonas tychotreta, under dissolved nutrient regimes that altered their ingestion of bacteria prey. Though the strains examined were polar isolates, both belong to genera with widespread distribution. Our aim was to characterize the transcriptomes of these two non-model phytoflagellates, identify transcripts consistent with phagotrophic activity and assess their differential expression in response to nutrient stress. De novo assembly of the transcriptomes yielded large numbers of novel coding transcripts with no known match within public databases. A summary of the transcripts by Gene Ontology terms showed many expected expression patterns, including genes involved in photosynthetic pathways and enzymes implicated in nutrient uptake pathways. Searches of KEGG databases identified several genes associated with intra-cellular digestive pathways actively transcribed in both prasinophytes. Differential expression analysis showed a larger response in P. tychotreta, where 23,373 genes were up-regulated and 1,752 were down-regulated in the low nutrient treatment when phagotrophy was enhanced. In contrast, in M. polaris, low nutrient treatments resulted in up-regulation of 314 transcripts while down-regulating 371. With respect to phagotrophic-related expression, 37 genes were co-expressed in both P. tychotreta and M. polaris, and although the response was less pronounced in M. polaris, it is consistent with differences in observed ingestion behavior. This study presents the first genomic data for Pyramimonas tychotreta, and also contributes to the limited available data for Micromonas polaris. Furthermore, it provides insight into the presence of genes associated with phagocytosis within the Prasinophyceae and contributes to the understanding of potential target genes required for the construction of a complete model of gene regulation of phagocytic behavior in algae.

Transcriptional and post-transcriptional regulation of viral gene expression in the gamma-herpesvirus Kaposi's sarcoma-associated herpesvirus.

Kaposi's sarcoma-associated herpesvirus (KSHV), the etiological agent of the AIDS-associated tumor Kaposi's sarcoma, is a complex virus that expresses ~90 proteins in a regulated temporal cascade during its replication cycle. Although KSHV relies on cellular machinery for gene expression, it also uses specialized regulators to control nearly every step of the process. In this review we discuss the current understanding of KSHV gene regulation. High-throughput sequencing and a new robust system to mutate KSHV have paved the way for comprehensive studies of KSHV gene expression, leading to the characterization of new viral factors that control late gene expression and post-transcriptional steps of gene regulation. They have also revealed key aspects of chromatin-based control of gene expression in the latent and lytic cycle. The combination of mutant analysis and high-throughput sequencing will continue to expand our model of KSHV gene regulation and point to potential new targets for anti-KSHV drugs.