Transcriptional network dynamics in macrophage activation

Mechanisms establishing TLR4-responsive activation states of inflammatory response genes

Precise control of the innate immune response is required for resistance to microbial infections and maintenance of normal tissue homeostasis. Because this response involves coordinate regulation of hundreds of genes, it provides a powerful biological system to elucidate the molecular strategies that underlie signal- and time-dependent transitions of gene expression. Comprehensive genome-wide analysis of the epigenetic and transcription status of the TLR4-induced transcriptional program in macrophages suggests that Toll-like receptor 4 (TLR4)-dependent activation of nearly all immediate/early- (I/E) and late-response genes results from a sequential process in which signal-independent factors initially establish basal levels of gene expression that are then amplified by signal-dependent transcription factors. Promoters of I/E genes are distinguished from those of late genes by encoding a distinct set of signal-dependent transcription factor elements, including TATA boxes, which lead to preferential binding of TBP and basal enrichment for RNA polymerase II immediately downstream of transcriptional start sites. Global nuclear run-on (GRO) sequencing and total RNA sequencing further indicates that TLR4 signaling markedly increases the overall rates of both transcriptional initiation and the efficiency of transcriptional elongation of nearly all I/E genes, while RNA splicing is largely unaffected. Collectively, these findings reveal broadly utilized mechanisms underlying temporally distinct patterns of TLR4-dependent gene activation required for homeostasis and effective immune responses.

The innate immune response is a complex biological program that is configured to allow host cells to rapidly respond to infection and tissue injury. An essential feature of this response is the sequential activation of large numbers of genes that play roles in amplification of the initial inflammatory response, exert anti-microbial activities, and initiate acquired immunity. Here, we use a combination of genome-wide approaches to characterize the basal and activated states of promoters that drive the expression of genes that are turned on at immediate/early or late times in macrophages following their stimulation with a mimetic of bacterial infection. These studies identify genetically encoded features that establish basal levels of expression and distinct temporal profiles of signal-dependent gene activation required for effective immune responses. The general features of immediate/early and late genes defined by these studies are likely to be instructive for understanding how other high-magnitude, temporally orchestrated programs of gene expression are established.

Systematic clustering of transcription start site landscapes

Genome-wide, high-throughput methods for transcription start site (TSS) detection have shown that most promoters have an array of neighboring TSSs where some are used more than others, forming a distribution of initiation propensities. TSS distributions (TSSDs) vary widely between promoters and earlier studies have shown that the TSSDs have biological implications in both regulation and function. However, no systematic study has been made to explore how many types of TSSDs and by extension core promoters exist and to understand which biological features distinguish them. In this study, we developed a new non-parametric dissimilarity measure and clustering approach to explore the similarities and stabilities of clusters of TSSDs. Previous studies have used arbitrary thresholds to arrive at two general classes: broad and sharp. We demonstrated that in addition to the previous broad/sharp dichotomy an additional category of promoters exists. Unlike typical TATA-driven sharp TSSDs where the TSS position can vary a few nucleotides, in this category virtually all TSSs originate from the same genomic position. These promoters lack epigenetic signatures of typical mRNA promoters and a substantial subset of them are mapping upstream of ribosomal protein pseudogenes. We present evidence that these are likely mapping errors, which have confounded earlier analyses, due to the high similarity of ribosomal gene promoters in combination with known G addition bias in the CAGE libraries. Thus, previous two-class separations of promoter based on TSS distributions are motivated, but the ultra-sharp TSS distributions will confound downstream analyses if not removed.

Metabolic labeling of RNA uncovers principles of RNA production and degradation dynamics in mammalian cells

Cellular RNA levels are determined by the interplay of RNA production, processing and degradation. However, because most studies of RNA regulation do not distinguish the separate contributions of these processes, little is known about how they are temporally integrated. Here we combine metabolic labeling of RNA at high temporal resolution with advanced RNA quantification and computational modeling to estimate RNA transcription and degradation rates during the response of mouse dendritic cells to lipopolysaccharide. We find that changes in transcription rates determine the majority of temporal changes in RNA levels, but that changes in degradation rates are important for shaping sharp 'peaked' responses. We used sequencing of the newly transcribed RNA population to estimate temporally constant RNA processing and degradation rates genome wide. Degradation rates vary significantly between genes and contribute to the observed differences in the dynamic response. Certain transcripts, including those encoding cytokines and transcription factors, mature faster. Our study provides a quantitative approach to study the integrative process of RNA regulation.

Uncovering the transcriptional circuitry in skeletal muscle regeneration

Skeletal muscle has a remarkable ability to regenerate after repeated and complete destruction of the tissue. The healing phases for an injured muscle undergo an activation program controlled by a dynamically inducible transcriptional regulatory network. Mapping a complex mammalian transcriptional network is confronted by significant challenges and requires the integration of multiple experimental data types. In this work we present a system approach to describe the transcriptional circuitry during skeletal muscle regeneration using time-course expression data and motif scanning information. Time-lagged correlation analysis was utilized to evaluate the transcription factor (TF) → target associations. Our analysis identified six TFs that potentially play a central role throughout the regeneration process. Four of them have previously been described to be important for muscle regeneration and differentiation. The remaining two TFs are identified as novel regulators that may have a role in the regeneration process. We hope that our work may provide useful clues to help accelerate the recovery process in injured skeletal muscle.

Gene expression profiles of RAW264.7 macrophages stimulated with preparations of LPS differing in isolation and purity

Lipopolysaccharide is a major component of the cell wall of Gram-negative bacteria and a potent stimulator of innate immune response via TLR4. Studies on the LPS action both in vivo and in vitro have used different preparations of LPS, including ultra-pure LPS (LIST) and a less pure but less expensive form (Sigma) isolated from Escherichia coli serotype O111:B4. The difference between the effects of these compounds has not been well studied although this information is important in understanding TLR stimulation. In this study, we compared response of RAW264.7 macrophage cells treated LIST or Sigma LPS for 6 h and 24 h. Gene expression data were analyzed to identify specific genes and pathways that are in common and unique to the two LPS preparations. Seven hundred fifty-five genes were differentially expressed at 6 h in response to Sigma LPS and 973 were differentially expressed following LIST LPS treatment, with 503 in common. At 24 h, Sigma LPS induced or repressed 901 genes while 1646 genes were differentially regulated by LIST LPS treatment; 701 genes were shared by two forms of LPS. Although considerably more genes were differentially expressed in response to LIST LPS, similar molecular pathways and transcriptional networks were activated by the two LPS preparations. We also treated bone marrow-derived macrophages (BMMs) from three strains of mice with different concentrations of LIST and Sigma LPS and showed that BMMs produced more IL-6 and TNF-α in response to LIST LPS at low LPS concentrations but, at higher LPS concentrations, more cytokines were produced in response to stimulation by Sigma LPS. Together, these findings suggest that, despite activation of similar molecular pathways by LIST and Sigma LPS preparations, residual protein impurities in the Sigma LPS preparation may nevertheless influence the transcriptional profile attributed to TLR4 stimulation.

The mononuclear phagocyte system of the pig as a model for understanding human innate immunity and disease

The biology of cells of the mononuclear phagocyte system has been studied extensively in the mouse. Studies of the pig as an experimental model have commonly been consigned to specialist animal science journals. In this review, we consider some of the many ways in which the innate immune systems of humans differ from those of mice, the ways that pigs may address the shortcomings of mice as models for the study of macrophage differentiation and activation in vitro, and the biology of sepsis and other pathologies in the living animal. With the completion of the genome sequence and the characterization of many key regulators and markers, the pig has emerged as a tractable model of human innate immunity and disease that should address the limited, predictive value of rodents in preclinical studies.

Using Systems Biology Approaches to Predict New Players in the Innate Immune System

Toll-like receptors (TLRs) are critical players in the innate immune response to pathogens. However, transcriptional regulatory mechanisms in the TLR activation pathways are still relatively poorly characterized. To address this question, the author of this chapter applied a systematic approach to predict transcription factors that temporally regulate differentially expressed genes under diverse TLR stimuli. Time-course microarray data were selected from mouse bone marrow-derived macrophages stimulated by six TLR agonists. Differentially regulated genes were clustered on the basis of their dynamic behavior. The author then developed a computational method to identify positional overlapping transcription factor (TF) binding sites in each cluster, so as to predict possible TFs that may regulate these genes. A second microarray dataset, on wild-type, Myd88-/- and Trif-/- macrophages stimulated by lipopolysaccharide (LPS), was used to provide supporting evidence on this combined approach. Overall, the author was able to identify known TLR TFs, as well as to predict new TFs that may be involved in TLR signaling.

Global analysis of the eukaryotic pathways and networks regulated by Salmonella typhimurium in mouse intestinal infection in vivo

BACKGROUND

Acute enteritis caused by Salmonella is a public health concern. Salmonella infection is also known to increase the risk of inflammatory bowel diseases and cancer. Therefore, it is important to understand how Salmonella works in targeting eukaryotic pathways in intestinal infection. However, the global physiological function of Salmonella typhimurium in intestinal mucosa in vivo is unclear. In this study, a whole genome approach combined with bioinformatics assays was used to investigate the in vivo genetic responses of the mouse colon to Salmonella. We focused on the intestinal responses in the early stage (8 hours) and late stage (4 days) after Salmonella infection.

RESULTS

Of the 28,000 genes represented on the array, our analysis of mRNA expression in mouse colon mucosa showed that a total of 856 genes were expressed differentially at 8 hours post-infection. At 4 days post-infection, a total of 7558 genes were expressed differentially. 23 differentially expressed genes from the microarray data was further examined by real-time PCR. Ingenuity Pathways Analysis identified that the most significant pathway associated with the differentially expressed genes in 8 hours post-infection is oxidative phosphorylation, which targets the mitochondria. At the late stage of infection, a series of pathways associated with immune and inflammatory response, proliferation, and apoptosis were identified, whereas the oxidative phosphorylation was shut off. Histology analysis confirmed the biological role of Salmonella, which induced a physiological state of inflammation and proliferation in the colon mucosa through the regulation of multiple signaling pathways. Most of the metabolism-related pathways were targeted by down-regulated genes, and a general repression process of metabolic pathways was observed. Network analysis supported IFN-γ and TNF-α function as mediators of the immune/inflammatory response for host defense against pathogen.

CONCLUSION

Our study provides novel genome-wide transcriptional profiling data on the mouse colon mucosa's response to the Salmonella typhimurium infection. Building the pathways and networks of interactions between these genes help us to understand the complex interplay in the mice colon during Salmonella infection, and further provide new insights into the molecular cascade, which is mobilized to combat Salmonella-associated colon infection in vivo.

Differential functional genomic effects of anti-inflammatory phytocompounds on immune signaling

BACKGROUND

Functional comparative genomic analysis of the cellular immunological effects of different anti-inflammatory phytocompounds is considered as a helpful approach to distinguish the complex and specific bioactivities of candidate phytomedicines. Using LPS-stimulated THP-1 monocytes, we characterize here the immunomodulatory activities of three single phytocompounds (emodin, shikonin, and cytopiloyne) and a defined phytocompound mixture extracted from Echinacea plant (BF/S+L/Ep) by focused DNA microarray analysis of selected immune-related genes.

RESULTS

Shikonin and emodin significantly inhibited the early expression (within 0.5 h) of approximately 50 genes, notably cytokines TNF-α, IL-1β and IL-4, chemokines CCL4 and CCL8, and inflammatory modulators NFATC3 and PTGS2. In contrast, neither cytopiloyne nor BF/S+L/Ep inhibited the early expression of these 50 genes, but rather inhibited most late-stage expression (~12 h) of another immune gene subset. TRANSPATH database key node analysis identified the extracellular signal-regulated kinase (ERK) 1/2 activation pathway as the putative target of BF/S+L/Ep and cytopiloyne. Western blot confirmed that delayed inactivation of the ERK pathway was indeed demonstrable for these two preparations during the mid-stage (1 to 4 h) of LPS stimulation. We further identified ubiquitin pathway regulators, E6-AP and Rad23A, as possible key regulators for emodin and shikonin, respectively.

CONCLUSION

The current focused DNA microarray approach rapidly identified important subgenomic differences in the pattern of immune cell-related gene expression in response to specific anti-inflammatory phytocompounds. These molecular targets and deduced networks may be employed as a guide for classifying, monitoring and manipulating the molecular and immunological specificities of different anti-inflammatory phytocompounds in key immune cell systems and for potential pharmacological application.

The phosphoproteome of toll-like receptor-activated macrophages

Recognition of microbial danger signals by toll-like receptors (TLR) causes re-programming of macrophages. To investigate kinase cascades triggered by the TLR4 ligand lipopolysaccharide (LPS) on systems level, we performed a global, quantitative and kinetic analysis of the phosphoproteome of primary macrophages using stable isotope labelling with amino acids in cell culture, phosphopeptide enrichment and high-resolution mass spectrometry. In parallel, nascent RNA was profiled to link transcription factor (TF) phosphorylation to TLR4-induced transcriptional activation. We reproducibly identified 1850 phosphoproteins with 6956 phosphorylation sites, two thirds of which were not reported earlier. LPS caused major dynamic changes in the phosphoproteome (24% up-regulation and 9% down-regulation). Functional bioinformatic analyses confirmed canonical players of the TLR pathway and highlighted other signalling modules (e.g. mTOR, ATM/ATR kinases) and the cytoskeleton as hotspots of LPS-regulated phosphorylation. Finally, weaving together phosphoproteome and nascent transcriptome data by in silico promoter analysis, we implicated several phosphorylated TFs in primary LPS-controlled gene expression.

Notch signaling determines the M1 versus M2 polarization of macrophages in antitumor immune responses

Macrophages are important tumor-infiltrating cells and play pivotal roles in tumor growth and metastasis. Macrophages participate in immune responses to tumors in a polarized manner: classic M1 macrophages produce interleukin (IL) 12 to promote tumoricidal responses, whereas M2 macrophages produce IL10 and help tumor progression. The mechanisms governing macrophage polarization are unclear. Here, we show that the M2-like tumor-associated macrophages (TAM) have a lower level of Notch pathway activation in mouse tumor models. Forced activation of Notch signaling increased M1 macrophages which produce IL12, no matter whether M1 or M2 inducers were applied. When Notch signaling was blocked, the M1 inducers induced M2 response in the expense of M1. Macrophages deficient in canonical Notch signaling showed TAM phenotypes. Forced activation of Notch signaling in macrophages enhanced their antitumor capacity. We further show that RBP-J-mediated Notch signaling regulates the M1 versus M2 polarization through SOCS3. Therefore, Notch signaling plays critical roles in the determination of M1 versus M2 polarization of macrophages, and compromised Notch pathway activation will lead to the M2-like TAMs. These results provide new insights into the molecular mechanisms of macrophage polarization and shed light on new therapies for cancers through the modulation of macrophage polarization through the Notch signaling.

Transcriptome analysis reveals a major impact of JAK protein tyrosine kinase 2 (Tyk2) on the expression of interferon-responsive and metabolic genes

Background

Tyrosine kinase 2 (Tyk2), a central component of Janus kinase/signal transducer and activator of transcription (JAK/STAT) signaling, has major effects on innate immunity and inflammation. Mice lacking Tyk2 are resistant to endotoxin shock induced by lipopolysaccharide (LPS), and Tyk2 deficient macrophages fail to efficiently induce interferon α/β after LPS treatment. However, how Tyk2 globally regulates transcription of downstream target genes remains unknown. Here we examine the regulatory role of Tyk2 in basal and inflammatory transcription by comparing gene expression profiles of peritoneal macrophages from Tyk2 mutant and wildtype control mice that were either kept untreated or exposed to LPS for six hours.

Results

Untreated Tyk2-deficient macrophages exhibited reduced expression of immune response genes relative to wildtype, in particular those that contain interferon response elements (IRF/ISRE), whereas metabolic genes showed higher expression. Upon LPS challenge, IFN-inducible genes (including those with an IRF/ISRE transcription factor binding-site) were strongly upregulated in both Tyk2 mutant and wildtype cells and reached similar expression levels. In contrast, metabolic gene expression was strongly decreased in wildtype cells upon LPS treatment, while in Tyk2 mutant cells the expression of these genes remained relatively unchanged, which exaggerated differences already present at the basal level. We also identified several 5'UR transcription factor binding-sites and 3'UTR regulatory elements that were differentially induced between Tyk2 deficient and wildtype macrophages and that have not previously been implicated in immunity.

Conclusions

Although Tyk2 is essential for the full LPS response, its function is mainly required for baseline expression but not LPS-induced upregulation of IFN-inducible genes. Moreover, Tyk2 function is critical for the downregulation of metabolic genes upon immune challenge, in particular genes involved in lipid metabolism. Together, our findings suggest an important regulatory role for Tyk2 in modulating the relationship between immunity and metabolism.

Increased nucleolar localization of SpiA3G in classically but not alternatively activated macrophages

Macrophages play a key role in innate immune response to pathogens and in tissue homeostasis, inflammation and repair. A serpin A3G (SpiA3G) is highly induced in classically activated macrophages. We show increased localization of SpiA3G in the nucleolus and co-localization with cathepsin L, upon classical, but not alternative activation of macrophages. Despite the increased expression of cathepsin L in the nuclei of classically activated macrophages, no cathepsin activity was detected. Since only pro-inflammatory, but not anti-inflammatory stimuli induce increased nucleolar localization of SpiA3G, we propose that SpiA3g translocation into the nucleolus is important in host defense against pathogens.

Functional clustering and lineage markers: insights into cellular differentiation and gene function from large-scale microarray studies of purified primary cell populations

Very large microarray datasets showing gene expression across multiple tissues and cell populations provide a window on the transcriptional networks that underpin the differences in functional activity between biological systems. Clusters of co-expressed genes provide lineage markers, candidate regulators of cell function and, by applying the principle of guilt by association, candidate functions for genes of currently unknown function. We have analysed a dataset comprising pure cell populations from hemopoietic and non-hemopoietic cell types (http://biogps.gnf.org). Using a novel network visualisation and clustering approach, we demonstrate that it is possible to identify very tight expression signatures associated specifically with embryonic stem cells, mesenchymal cells and hematopoietic lineages. Selected examples validate the prediction that gene function can be inferred by co-expression. One expression cluster was enriched in phagocytes, which, alongside endosome-lysosome constituents, contains genes that may make up a 'pathway' for phagocyte differentiation. Promoters of these genes are enriched for binding sites for the ETS/PU.1 and MITF families. Another cluster was associated with the production of a specific extracellular matrix, with high levels of gene expression shared by cells of mesenchymal origin (fibroblasts, adipocytes, osteoblasts and myoblasts). We discuss the limitations placed upon such data by the presence of alternative promoters with distinct tissue specificity within many protein-coding genes.

Antiviral response dictated by choreographed cascade of transcription factors

The dendritic cell (DC) is a master regulator of immune responses. Pathogenic viruses subvert normal immune function in DCs through the expression of immune antagonists. Understanding how these antagonists interact with the host immune system requires knowledge of the underlying genetic regulatory network that operates during an uninhibited antiviral response. To isolate and identify this network, we studied DCs infected with Newcastle disease virus, which is able to stimulate innate immunity and DC maturation through activation of RIG-I signaling, but lacks the ability to evade the human IFN response. To analyze this experimental model, we developed a new approach integrating genome-wide expression kinetics and time-dependent promoter analysis. We found that the genetic program underlying the antiviral cell-state transition during the first 18 h postinfection could be explained by a single convergent regulatory network. Gene expression changes were driven by a stepwise multifactor cascading control mechanism, where the specific transcription factors controlling expression changed over time. Within this network, most individual genes were regulated by multiple factors, indicating robustness against virus-encoded immune evasion genes. In addition to effectively recapitulating current biological knowledge, we predicted, and validated experimentally, antiviral roles for several novel transcription factors. More generally, our results show how a genetic program can be temporally controlled through a single regulatory network to achieve the large-scale genetic reprogramming characteristic of cell-state transitions.

Regulatory interdependence of myeloid transcription factors revealed by Matrix RNAi analysis

The knockdown of 78 transcription factors in differentiating human THP-1 cells using matrix RNAi reveals their interdependence

Background

With the move towards systems biology, we need sensitive and reliable ways to determine the relationships between transcription factors and their target genes. In this paper we analyze the regulatory relationships between 78 myeloid transcription factors and their coding genes by using the matrix RNAi system in which a set of transcription factor genes are individually knocked down and the resultant expression perturbation is quantified.

Results

Using small interfering RNAs we knocked down the 78 transcription factor genes in monocytic THP-1 cells and monitored the perturbation of the expression of the same 78 transcription factors and 13 other transcription factor genes as well as 5 non-transcription factor genes by quantitative real-time RT-PCR, thereby building a 78 × 96 matrix of perturbation and measurement. This approach identified 876 cases where knockdown of one transcription factor significantly affected the expression of another (from a potential 7,488 combinations). Our study also revealed cell-type-specific transcriptional regulatory networks in two different cell types.

Conclusions

By considering whether the targets of a given transcription factor are naturally up- or downregulated during phorbol 12-myristate 13-acetate-induced differentiation, we could classify these edges as pro-differentiative (229), anti-differentiative (76) or neither (571) using expression profiling data obtained in the FANTOM4 study. This classification analysis suggested that several factors could be involved in monocytic differentiation, while others such as MYB and the leukemogenic fusion MLL-MLLT3 could help to maintain the initial undifferentiated state by repressing the expression of pro-differentiative factors or maintaining expression of anti-differentiative factors.

Combined genome-wide expression profiling and targeted RNA interference in primary mouse macrophages reveals perturbation of transcriptional networks associated with interferon signalling

Background

Interferons (IFNs) are potent antiviral cytokines capable of reprogramming the macrophage phenotype through the induction of interferon-stimulated genes (ISGs). Here we have used targeted RNA interference to suppress the expression of a number of key genes associated with IFN signalling in murine macrophages prior to stimulation with interferon-gamma. Genome-wide changes in transcript abundance caused by siRNA activity were measured using exon-level microarrays in the presence or absence of IFNγ.

Results

Transfection of murine bone-marrow derived macrophages (BMDMs) with a non-targeting (control) siRNA and 11 sequence-specific siRNAs was performed using a cationic lipid transfection reagent (Lipofectamine2000) prior to stimulation with IFNγ. Total RNA was harvested from cells and gene expression measured on Affymetrix GeneChip Mouse Exon 1.0 ST Arrays. Network-based analysis of these data revealed six siRNAs to cause a marked shift in the macrophage transcriptome in the presence or absence IFNγ. These six siRNAs targeted the Ifnb1, Irf3, Irf5, Stat1, Stat2 and Nfkb2 transcripts. The perturbation of the transcriptome by the six siRNAs was highly similar in each case and affected the expression of over 600 downstream transcripts. Regulated transcripts were clustered based on co-expression into five major groups corresponding to transcriptional networks associated with the type I and II IFN response, cell cycle regulation, and NF-KB signalling. In addition we have observed a significant non-specific immune stimulation of cells transfected with siRNA using Lipofectamine2000, suggesting use of this reagent in BMDMs, even at low concentrations, is enough to induce a type I IFN response.

Conclusion

Our results provide evidence that the type I IFN response in murine BMDMs is dependent on Ifnb1, Irf3, Irf5, Stat1, Stat2 and Nfkb2, and that siRNAs targeted to these genes results in perturbation of key transcriptional networks associated with type I and type II IFN signalling and a suppression of macrophage M1 polarization.

Enabling a systems biology approach to immunology: focus on innate immunity

Immunity is not simply the product of a series of discrete linear signalling pathways; rather it is comprised of a complex set of integrated responses arising from a dynamic network of thousands of molecules subject to multiple influences. Its behaviour often cannot be explained or predicted solely by examining its components. Here, we review recently developed resources for the systems-level investigation of immunity. Although innate immunity is emphasized here, its considerable overlap with adaptive immunity makes many of these resources relevant to both arms of the immune response. We discuss recent studies implementing these approaches and illustrate the potential of systems biology to generate novel insights into the complexities of innate immunity.

Emergent genome-wide control in wildtype and genetically mutated lipopolysaccarides-stimulated macrophages

Large-scale gene expression studies have mainly focused on highly expressed and 'discriminatory' genes to decipher key regulatory processes. Biological responses are consequence of the concerted action of gene regulatory network, thus, limiting our attention to genes having the most significant variations is insufficient for a thorough understanding of emergent whole genome response. Here we comprehensively analyzed the temporal oligonucleotide microarray data of lipopolysaccharide (LPS) stimulated macrophages in 4 genotypes; wildtype, Myeloid Differentiation factor 88 (MyD88) knockout (KO), TIR-domain-containing adapter-inducing interferon-beta (TRIF) KO and MyD88/TRIF double KO (DKO). Pearson correlations computed on the whole genome expression between different genotypes are extremely high (>0.98), indicating a strong co-regulation of the entire expression network. Further correlation analyses reveal genome-wide response is biphasic, i) acute-stochastic mode consisting of small number of sharply induced immune-related genes and ii) collective mode consisting of majority of weakly induced genes of diverse cellular processes which collectively adjust their expression level. Notably, temporal correlations of a small number of randomly selected genes from collective mode show scalability. Furthermore, in collective mode, the transition from large scatter in expression distributions for single ORFs to smooth linear lines emerges as an organizing principle when grouping of 50 ORFs and above. With this emergent behavior, the role of MyD88, TRIF and novel MyD88, TRIF-independent processes for gene induction can be linearly superposed to decipher quantitative whole genome differential control of transcriptional and mRNA decay machineries. Our work demonstrates genome-wide co-regulated responses subsequent to specific innate immune stimulus which have been largely neglected.

Differentiation and heterogeneity in the mononuclear phagocyte system

Cells of the mononuclear phagocyte system (MPS) are found in large numbers in every organ of the body, where they contribute to innate and acquired immunity and homeostasis. This review considers the locations of MPS cells, surface markers that distinguish subsets of monocytes and macrophages, the pathways of MPS differentiation, and the growth factors and transcription factors that guide them. Although the number of MPS sub-populations that can be defined is infinite, the features that unite the MPS remain compelling. Those features clearly include antigen-presenting dendritic cells within the MPS and argue against any basis for separating them from macrophages.

Transcriptional regulatory networks in haematopoiesis

The coordinated expression of genes lies at the heart of developmental programmes, with complex regulatory networks controlling the spatial and temporal aspects of gene expression. Haematopoiesis (blood formation) has long served as a model process for studying the specification and subsequent differentiation of stem cells and represents the best characterised adult stem cell system. In this review, we outline how the integration of experimental and computational approaches as applied to haematopoiesis has resulted in some of the most advanced models of transcriptional regulatory networks in mammals.

Shaping segments: Hox gene function in the genomic age

Despite decades of research, morphogenesis along the various body axes remains one of the major mysteries in developmental biology. A milestone in the field was the realisation that a set of closely related regulators, called Hox genes, specifies the identity of body segments along the anterior-posterior (AP) axis in most animals. Hox genes have been highly conserved throughout metazoan evolution and code for homeodomain-containing transcription factors. Thus, they exert their function mainly through activation or repression of downstream genes. However, while much is known about Hox gene structure and molecular function, only a few target genes have been identified and studied in detail. Our knowledge of Hox downstream genes is therefore far from complete and consequently Hox-controlled morphogenesis is still poorly understood. Genome-wide approaches have facilitated the identification of large numbers of Hox downstream genes both in Drosophila and vertebrates, and represent a crucial step towards a comprehensive understanding of how Hox proteins drive morphological diversification. In this review, we focus on the role of Hox genes in shaping segmental morphologies along the AP axis in Drosophila, discuss some of the conclusions drawn from analyses of large target gene sets and highlight methods that could be used to gain a more thorough understanding of Hox molecular function. In addition, the mechanisms of Hox target gene regulation are considered with special emphasis on recent findings and their implications for Hox protein specificity in the context of the whole organism.

Identification of transcripts with enriched expression in the developing and adult pancreas

BACKGROUND

Despite recent advances, the transcriptional hierarchy driving pancreas organogenesis remains largely unknown, in part due to the paucity of comprehensive analyses. To address this deficit we generated ten SAGE libraries from the developing murine pancreas spanning Theiler stages 17-26, making use of available Pdx1 enhanced green fluorescent protein (EGFP) and Neurog3 EGFP reporter strains, as well as tissue from adult islets and ducts.

RESULTS

We used a specificity metric to identify 2,536 tags with pancreas-enriched expression compared to 195 other mouse SAGE libraries. We subsequently grouped co-expressed transcripts with differential expression during pancreas development using K-means clustering. We validated the clusters first using quantitative real time PCR and then by analyzing the Theiler stage 22 pancreas in situ hybridization staining patterns of over 600 of the identified genes using the GenePaint database. These were then categorized into one of the five expression domains within the developing pancreas. Based on these results we identified a cascade of transcriptional regulators expressed in the endocrine pancreas lineage and, from this, we developed a predictive regulatory network describing beta-cell development.

CONCLUSION

Taken together, this work provides evidence that the SAGE libraries generated here are a valuable resource for continuing to elucidate the molecular mechanisms regulating pancreas development. Furthermore, our studies provide a comprehensive analysis of pancreas development, and insights into the regulatory networks driving this process are revealed.

Deep cap analysis gene expression (CAGE): genome-wide identification of promoters, quantification of their expression, and network inference

In cap analysis gene expression (CAGE), short ( approximately 20 nucleotide) sequence tags originating from the 5' end of full-length mRNAs are sequenced to identify transcription events on a genome-wide scale. The rapid increase in the throughput of present-day sequencers provides much deeper CAGE tag sequencing, where CAGE tags can be found multiple times for each mRNA in a given experiment. CAGE tag counts can then be used to reliably estimate the cellular concentration of the corresponding mRNA. In contrast to microarray and SAGE expression profiling, CAGE identifies the location of each transcription start site in addition to its expression level. This makes it possible for us to infer a genome-wide network of transcriptional regulation by searching the promoter region surrounding each CAGE-defined transcription start site for potential transcription factor binding sites. Hence, deep CAGE is a unique tool for the construction of a promoter-based network of transcriptional regulation. CAGE-based expression profiling also allows us to identify dynamic promoter usage in time-course experiments and the specific promoter regulated by a given transcription factor in disruption experiments. The sheer size of the short-tag datasets produced by modern sequencers spurs a need for new software development to handle the amount of data generated by next-generation sequencers. In addition, new visualization methods will be needed to represent a promoter-based transcriptional network.

Modeling promoter grammars with evolving hidden Markov models

MOTIVATION

Describing and modeling biological features of eukaryotic promoters remains an important and challenging problem within computational biology. The promoters of higher eukaryotes in particular display a wide variation in regulatory features, which are difficult to model. Often several factors are involved in the regulation of a set of co-regulated genes. If so, promoters can be modeled with connected regulatory features, where the network of connections is characteristic for a particular mode of regulation.

RESULTS

With the goal of automatically deciphering such regulatory structures, we present a method that iteratively evolves an ensemble of regulatory grammars using a hidden Markov Model (HMM) architecture composed of interconnected blocks representing transcription factor binding sites (TFBSs) and background regions of promoter sequences. The ensemble approach reduces the risk of overfitting and generally improves performance. We apply this method to identify TFBSs and to classify promoters preferentially expressed in macrophages, where it outperforms other methods due to the increased predictive power given by the grammar.

AVAILABILITY

The software and the datasets are available from http://modem.ucsd.edu/won/eHMM.tar.gz

Uncovering a macrophage transcriptional program by integrating evidence from motif scanning and expression dynamics

Macrophages are versatile immune cells that can detect a variety of pathogen-associated molecular patterns through their Toll-like receptors (TLRs). In response to microbial challenge, the TLR-stimulated macrophage undergoes an activation program controlled by a dynamically inducible transcriptional regulatory network. Mapping a complex mammalian transcriptional network poses significant challenges and requires the integration of multiple experimental data types. In this work, we inferred a transcriptional network underlying TLR-stimulated murine macrophage activation. Microarray-based expression profiling and transcription factor binding site motif scanning were used to infer a network of associations between transcription factor genes and clusters of co-expressed target genes. The time-lagged correlation was used to analyze temporal expression data in order to identify potential causal influences in the network. A novel statistical test was developed to assess the significance of the time-lagged correlation. Several associations in the resulting inferred network were validated using targeted ChIP-on-chip experiments. The network incorporates known regulators and gives insight into the transcriptional control of macrophage activation. Our analysis identified a novel regulator (TGIF1) that may have a role in macrophage activation.

Macrophages play a vital role in host defense against infection by recognizing pathogens through pattern recognition receptors, such as the Toll-like receptors (TLRs), and mounting an immune response. Stimulation of TLRs initiates a complex transcriptional program in which induced transcription factor genes dynamically regulate downstream genes. Microarray-based transcriptional profiling has proved useful for mapping such transcriptional programs in simpler model organisms; however, mammalian systems present difficulties such as post-translational regulation of transcription factors, combinatorial gene regulation, and a paucity of available gene-knockout expression data. Additional evidence sources, such as DNA sequence-based identification of transcription factor binding sites, are needed. In this work, we computationally inferred a transcriptional network for TLR-stimulated murine macrophages. Our approach combined sequence scanning with time-course expression data in a probabilistic framework. Expression data were analyzed using the time-lagged correlation. A novel, unbiased method was developed to assess the significance of the time-lagged correlation. The inferred network of associations between transcription factor genes and co-expressed gene clusters was validated with targeted ChIP-on-chip experiments, and yielded insights into the macrophage activation program, including a potential novel regulator. Our general approach could be used to analyze other complex mammalian systems for which time-course expression data are available.

Systems biology of transcription control in macrophages

The study of the mammalian immune system offers many advantages to systems biologists. The cellular components of the mammalian immune system are experimentally tractable; they can be isolated or differentiated from in vivo and ex vivo sources and have an essential role in health and disease. For these reasons, the major effectors cells of the innate immune system, macrophages, have been a particular focus in international genome and transcriptome consortia. Genome-scale analysis of the transcriptome, and transcription initiation has enabled the construction of predictive models of transcription control in macrophages that identify the points of control (the major nodes of networks) and the ways in which they interact.

The Innate Immune Database (IIDB)

Background

As part of a National Institute of Allergy and Infectious Diseases funded collaborative project, we have performed over 150 microarray experiments measuring the response of C57/BL6 mouse bone marrow macrophages to toll-like receptor stimuli. These microarray expression profiles are available freely from our project web site . Here, we report the development of a database of computationally predicted transcription factor binding sites and related genomic features for a set of over 2000 murine immune genes of interest. Our database, which includes microarray co-expression clusters and a host of web-based query, analysis and visualization facilities, is available freely via the internet. It provides a broad resource to the research community, and a stepping stone towards the delineation of the network of transcriptional regulatory interactions underlying the integrated response of macrophages to pathogens.

Description

We constructed a database indexed on genes and annotations of the immediate surrounding genomic regions. To facilitate both gene-specific and systems biology oriented research, our database provides the means to analyze individual genes or an entire genomic locus. Although our focus to-date has been on mammalian toll-like receptor signaling pathways, our database structure is not limited to this subject, and is intended to be broadly applicable to immunology. By focusing on selected immune-active genes, we were able to perform computationally intensive expression and sequence analyses that would currently be prohibitive if applied to the entire genome. Using six complementary computational algorithms and methodologies, we identified transcription factor binding sites based on the Position Weight Matrices available in TRANSFAC. For one example transcription factor (ATF3) for which experimental data is available, over 50% of our predicted binding sites coincide with genome-wide chromatin immnuopreciptation (ChIP-chip) results. Our database can be interrogated via a web interface. Genomic annotations and binding site predictions can be automatically viewed with a customized version of the Argo genome browser.

Conclusion

We present the Innate Immune Database (IIDB) as a community resource for immunologists interested in gene regulatory systems underlying innate responses to pathogens. The database website can be freely accessed at .

Towards defining the nuclear proteome

Direct evidence is reported for 2,568 mammalian proteins within the nuclear proteome, consisting of at least 14% of the entire proteome.

Background

The nucleus is a complex cellular organelle and accurately defining its protein content is essential before any systematic characterization can be considered.

Results

We report direct evidence for 2,568 mammalian proteins within the nuclear proteome: the nuclear subcellular localization of 1,529 proteins based on a high-throughput subcellular localization protocol of full-length proteins and an additional 1,039 proteins for which clear experimental evidence is documented in published literature. This is direct evidence that the nuclear proteome consists of at least 14% of the entire proteome. This dataset was used to evaluate computational approaches designed to identify additional nuclear proteins.

Conclusion

This represents direct experimental evidence that the nuclear proteome consists of at least 14% of the entire proteome. This high-quality nuclear proteome dataset was used to evaluate computational approaches designed to identify additional nuclear proteins. Based on this analysis, researchers can determine the stringency and types of lines of evidence they consider to infer the size and complement of the nuclear proteome.

Integrated approaches to uncovering transcription regulatory networks in mammalian cells

Integrative systems biology has emerged as an exciting research approach in molecular biology and functional genomics that involves the integration of genomics, proteomics, and metabolomics datasets. These endeavors establish a systematic paradigm by which to interrogate, model, and iteratively refine our knowledge of the regulatory events within a cell. Here we review the latest technologies available to collect high-throughput measurements of a cellular state as well as the most successful methods for the integration and interrogation of these measurements. In particular we will focus on methods available to infer transcription regulatory networks in mammals.

Constructing the landscape of the mammalian transcriptome

The principal route to understanding the biological significance of the genome sequence comes from discovery and characterization of that portion of the genome that is transcribed into RNA products. We now know that this ;transcriptome' is unexpectedly complex and its precise definition in any one species requires multiple technical approaches and an ability to work on a very large scale. A key step is the development of technologies able to capture snapshots of the complexity of the various kinds of RNA generated by the genome. As the human, mouse and other model genome sequencing projects approach completion, considerable effort has been focused on identifying and annotating the protein-coding genes as the principal output of the genome. In pursuing this aim, several key technologies have been developed to generate large numbers and highly diverse sets of full-length cDNAs and their variants. However, the search has identified another hidden transcriptional universe comprising a wide variety of non-protein coding RNA transcripts. Despite initial scepticism, various experiments and complementary technologies have demonstrated that these RNAs are dynamically transcribed and a subset of them can act as sense-antisense RNAs, which influence the transcriptional output of the genome. Recent experimental evidence suggests that the list of non-protein coding RNAs is still largely incomplete and that transcription is substantially more complex even than currently thought.

Transcription factor expression in lipopolysaccharide-activated peripheral-blood-derived mononuclear cells

Transcription factors play a key role in integrating and modulating biological information. In this study, we comprehensively measured the changing abundances of mRNAs over a time course of activation of human peripheral-blood-derived mononuclear cells ("macrophages") with lipopolysaccharide. Global and dynamic analysis of transcription factors in response to a physiological stimulus has yet to be achieved in a human system, and our efforts significantly advanced this goal. We used multiple global high-throughput technologies for measuring mRNA levels, including massively parallel signature sequencing and GeneChip microarrays. We identified 92 of 1,288 known human transcription factors as having significantly measurable changes during our 24-h time course. At least 42 of these changes were previously unidentified in this system. Our data demonstrate that some transcription factors operate in a functional range below 10 transcripts per cell, whereas others operate in a range three orders of magnitude greater. The highly reproducible response of many mRNAs indicates feedback control. A broad range of activation kinetics was observed; thus, combinatorial regulation by small subsets of transcription factors would permit almost any timing input to cis-regulatory elements controlling gene transcription.

Deciphering modular and dynamic behaviors of transcriptional networks

The coordinated and dynamic modulation or interaction of genes or proteins acts as an important mechanism used by a cell in functional regulation. Recent studies have shown that many transcriptional networks exhibit a scale-free topology and hierarchical modular architecture. It has also been shown that transcriptional networks or pathways are dynamic and behave only in certain ways and controlled manners in response to disease development, changing cellular conditions, and different environmental factors. Moreover, evolutionarily conserved and divergent transcriptional modules underline fundamental and species-specific molecular mechanisms controlling disease development or cellular phenotypes. Various computational algorithms have been developed to explore transcriptional networks and modules from gene expression data. In silico studies have also been made to mimic the dynamic behavior of regulatory networks, analyzing how disease or cellular phenotypes arise from the connectivity or networks of genes and their products. Here, we review the recent development in computational biology research on deciphering modular and dynamic behaviors of transcriptional networks, highlighting important findings. We also demonstrate how these computational algorithms can be applied in systems biology studies as on disease, stem cells, and drug discovery.

Mammalian RNA polymerase II core promoters: insights from genome-wide studies

The identification and characterization of mammalian core promoters and transcription start sites is a prerequisite to understanding how RNA polymerase II transcription is controlled. New experimental technologies have enabled genome-wide discovery and characterization of core promoters, revealing that most mammalian genes do not conform to the simple model in which a TATA box directs transcription from a single defined nucleotide position. In fact, most genes have multiple promoters, within which there are multiple start sites, and alternative promoter usage generates diversity and complexity in the mammalian transcriptome and proteome. Promoters can be described by their start site usage distribution, which is coupled to the occurrence of cis-regulatory elements, gene function and evolutionary constraints. A comprehensive survey of mammalian promoters is a major step towards describing and understanding transcriptional control networks.

T. Systems biology of innate immunity

Systems Biology has emerged as an exciting research approach in molecular biology and functional genomics that involves a systematic use of genomic, proteomic, and metabolomic technologies for the construction of network-based models of biological processes. These endeavors, collectively referred to as systems biology establish a paradigm by which to systematically interrogate, model, and iteratively refine our knowledge of the regulatory events within a cell. Here, we present a new systems approach, integrating DNA and transcript expression information, specifically designed to identify transcriptional networks governing the macrophage immune response to lipopolysaccharide (LPS). Using this approach, we are not only able to infer a global macrophage transcriptional network, but also time-specific sub-networks that are dynamically active across the LPS response. We believe that our system biological approach could be useful for identifying other complex networks mediating immunological responses.

PU.1 and ICSBP control constitutive and IFN-gamma-regulated Tlr9 gene expression in mouse macrophages

Macrophages are activated by unmethylated CpG-containing DNA (CpG DNA) via TLR9. IFN-gamma and LPS can synergize with CpG DNA to enhance proinflammatory responses in murine macrophages. Here, we show that LPS and IFN-gamma up-regulated Tlr9 mRNA in murine bone marrow-derived macrophages (BMM). The ability of LPS and IFN-gamma to induce Tlr9 mRNA expression in BMM was dependent on the presence of the growth factor, CSF-1, which is constitutively present in vivo. However, there were clear differences in mechanisms of Tlr9 mRNA induction. LPS stimulation rapidly removed the CSF-1 receptor (CSF-1R) from the cell surface, thereby blocking CSF-1-mediated transcriptional repression and indirectly inducing Tlr9 mRNA expression. By contrast, IFN-gamma activated the Tlr9 promoter directly and only marginally affected cell surface CSF-1R expression. An approximately 100-bp proximal promoter of the murine Tlr9 gene was sufficient to confer basal and IFN-gamma-inducible expression in RAW264.7 cells. A composite IFN regulatory factor (IRF)/PU.1 site upon the major transcription start site was identified. Mutation of the binding sites for PU.1 or IRF impaired basal promoter activity, but only the IRF-binding site was required for IFN-gamma induction. The mRNA expression of the IRF family member IFN consensus-binding protein [(ICSBP)/IRF8] was coregulated with Tlr9 in macrophages, and constitutive and IFN-gamma-inducible Tlr9 mRNA expression was reduced in ICSBP-deficient BMM. This study therefore characterizes the regulation of mouse Tlr9 expression and defines a molecular mechanism by which IFN-gamma amplifies mouse macrophage responses to CpG DNA.

Perturbations to uncover gene networks

After the major achievements of the DNA sequencing projects, an equally important challenge now is to uncover the functional relationships among genes (i.e. gene networks). It has become increasingly clear that computational algorithms are crucial for extracting meaningful information from the massive amount of data generated by high-throughput genome-wide technologies. Here, we summarise how systems identification algorithms, originating from physics and control theory, have been adapted for use in biology. We also explain how experimental perturbations combined with genome-wide measurements are being used to uncover gene networks. Perturbation techniques could pave the way for identifying gene networks in more complex settings such as multifactorial diseases and for improving the efficacy of drug evaluation.

Mechanisms of transcriptional regulation underlying temporal integration of signals

How cells convert the duration of signals into differential adaptation of gene expression is a poorly understood issue. Signal-induced immediate-early gene (IEG) expression couples early signals to late expression of downstream <target> genes. Here we study how kinetic features of the IEG-<target> system allow temporal integration of stimuli in a pancreatic beta cell model of metabolic stimulation. Gene expression profiling revealed that beta cells produce drastically different transcriptional outputs in response to different stimuli durations. Noteworthy, most genes (87%) regulated by a sustained stimulation (4 h) were not regulated by a transient stimulation (1 h followed by 3 h without stimulus). We analyzed the induction kinetics of several previously identified IEGs and <targets>. IEG expression persisted as long as stimulation was maintained, but was rapidly lost upon stimuli removal, abolishing the delayed <target> induction. The molecular mechanisms coupling the duration of stimuli to quantitative <target> transcription were demonstrated for the AP-1 transcription factor. In conclusion, we propose that the network composed of IEGs and their <targets> dynamically functions to convert signal inputs of different durations into quantitative differences in global transcriptional adaptation. These findings provide a novel and more comprehensive view of dynamic gene regulation.

The complexity of the mammalian transcriptome

A comprehensive understanding of protein and regulatory networks is strictly dependent on the complete description of the transcriptome of cells. After the determination of the genome sequence of several mammalian species, gene identification is based on in silico predictions followed by evidence of transcription. Conservative estimates suggest that there are about 20,000 protein-encoding genes in the mammalian genome. In the last few years the combination of full-length cDNA cloning, cap-analysis gene expression (CAGE) tag sequencing and tiling arrays experiments have unveiled unexpected additional complexities in the transcriptome. Here we describe the current view of the mammalian transcriptome focusing on transcripts diversity, the growing non-coding RNA world, the organization of transcriptional units in the genome and promoter structures. In-depth analysis of the brain transcriptome has been challenging due to the cellular complexity of this organ. Here we present a computational analysis of CAGE data from different regions of the central nervous system, suggesting distinctive mechanisms of brain-specific transcription.