Transcription Factor Activity Profiles Research Articles

Multilevel proteomic analyses reveal molecular diversity between diffuse-type and intestinal-type gastric cancer

Diffuse-type gastric cancer (DGC) and intestinal-type gastric cancer (IGC) are the major histological types of gastric cancer (GC). The molecular mechanism underlying DGC and IGC differences are poorly understood. In this research, we carry out multilevel proteomic analyses, including proteome, phospho-proteome, and transcription factor (TF) activity profiles, of 196 cases covering DGC and IGC in Chinese patients. Integrative proteogenomic analysis reveals ARIDIA mutation associated with opposite prognostic effects between DGC and IGC, via diverse influences on their corresponding proteomes. Systematical comparison and consensus clustering analysis identify three subtypes of DGC and IGC, respectively, based on distinct patterns of the cell cycle, extracellular matrix organization, and immune response-related proteins expression. TF activity-based subtypes demonstrate that the disease progressions of DGC and IGC were regulated by SWI/SNF and NFKB complexes. Furthermore, inferred immune cell infiltration and immune clustering show Th1/Th2 ratio is an indicator for immunotherapeutic effectiveness, which is validated in an independent GC anti-PD1 therapeutic patient group. Our multilevel proteomic analyses enable a more comprehensive understanding of GC and can further advance the precision medicine.

Iterative sub-network component analysis enables reconstruction of large scale genetic networks.

BackgroundNetwork component analysis (NCA) became a popular tool to understand complex regulatory networks. The method uses high-throughput gene expression data and a priori topology to reconstruct transcription factor activity profiles. Current NCA algorithms are constrained by several conditions posed on the network topology, to guarantee unique reconstruction (termed compliancy). However, the restrictions these conditions pose are not necessarily true from biological perspective and they force network size reduction, pruning potentially important components.ResultsTo address this, we developed a novel, Iterative Sub-Network Component Analysis (ISNCA) for reconstructing networks at any size. By dividing the initial network into smaller, compliant subnetworks, the algorithm first predicts the reconstruction of each subntework using standard NCA algorithms. It then subtracts from the reconstruction the contribution of the shared components from the other subnetwork. We tested the ISNCA on real, large datasets using various NCA algorithms. The size of the networks we tested and the accuracy of the reconstruction increased significantly. Importantly, FOXA1, ATF2, ATF3 and many other known key regulators in breast cancer could not be incorporated by any NCA algorithm because of the necessary conditions. However, their temporal activities could be reconstructed by our algorithm, and therefore their involvement in breast cancer could be analyzed.ConclusionsOur framework enables reconstruction of large gene expression data networks, without reducing their size or pruning potentially important components, and at the same time rendering the results more biological plausible. Our ISNCA method is not only suitable for prediction of key regulators in cancer studies, but it can be applied to any high-throughput gene expression data.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0768-9) contains supplementary material, which is available to authorized users.

Dynamic transcription factor activity profiles reveal key regulatory interactions during megakaryocytic and erythroid differentiation.

The directed differentiation toward erythroid (E) or megakaryocytic (MK) lineages by the MK-E progenitor (MEP) could enhance the ex vivo generation of red blood cells and platelets for therapeutic transfusions. The lineage choice at the MEP bifurcation is controlled in large part by activity within the intracellular signal transduction network, the output of which determines the activity of transcription factors (TFs) and ultimately gene expression. Although many TFs have been implicated, E or MK differentiation is a complex process requiring multiple days, and the dynamics of TF activities during commitment and terminal maturation are relatively unexplored. Herein, we applied a living cell array for the large-scale, dynamic quantification of TF activities during MEP bifurcation. A panel of hematopoietic TFs (GATA-1, GATA-2, SCL/TAL1, FLI-1, NF-E2, PU.1, c-Myb) was characterized during E and MK differentiation of bipotent K562 cells. Dynamic TF activity profiles associated with differentiation towards each lineage were identified, and validated with previous reports. From these activity profiles, we show that GATA-1 is an important hub during early hemin- and PMA-induced differentiation, and reveal several characteristic TF interactions for E and MK differentiation that confirm regulatory mechanisms documented in the literature. Additionally, we highlight several novel TF interactions at various stages of E and MK differentiation. Furthermore, we investigated the mechanism by which nicotinamide (NIC) promoted terminal MK maturation using an MK-committed cell line, CHRF-288-11 (CHRF). Concomitant with its enhancement of ploidy, NIC strongly enhanced the activity of three TFs with known involvement in terminal MK maturation: FLI-1, NF-E2, and p53. Dynamic profiling of TF activity represents a novel tool to complement traditional assays focused on mRNA and protein expression levels to understand progenitor cell differentiation.

ROBNCA: robust network component analysis for recovering transcription factor activities

Network component analysis (NCA) is an efficient method of reconstructing the transcription factor activity (TFA), which makes use of the gene expression data and prior information available about transcription factor (TF)-gene regulations. Most of the contemporary algorithms either exhibit the drawback of inconsistency and poor reliability, or suffer from prohibitive computational complexity. In addition, the existing algorithms do not possess the ability to counteract the presence of outliers in the microarray data. Hence, robust and computationally efficient algorithms are needed to enable practical applications. We propose ROBust Network Component Analysis (ROBNCA), a novel iterative algorithm that explicitly models the possible outliers in the microarray data. An attractive feature of the ROBNCA algorithm is the derivation of a closed form solution for estimating the connectivity matrix, which was not available in prior contributions. The ROBNCA algorithm is compared with FastNCA and the non-iterative NCA (NI-NCA). ROBNCA estimates the TF activity profiles as well as the TF-gene control strength matrix with a much higher degree of accuracy than FastNCA and NI-NCA, irrespective of varying noise, correlation and/or amount of outliers in case of synthetic data. The ROBNCA algorithm is also tested on Saccharomyces cerevisiae data and Escherichia coli data, and it is observed to outperform the existing algorithms. The run time of the ROBNCA algorithm is comparable with that of FastNCA, and is hundreds of times faster than NI-NCA. The ROBNCA software is available at http://people.tamu.edu/∼amina/ROBNCA

Transcription Factor Networks in Invasion-Promoting Breast Carcinoma-Associated Fibroblasts

Carcinoma-associated fibroblasts (CAFs) contribute to both tumor growth and cancer progression. In this report, we applied an emerging transcription factor (TF) activity array to fibroblasts to capture the activity of the intracellular signaling network and to define a signature that distinguishes mammary CAFs from normal mammary fibroblasts. Normal fibroblasts that restrained cancer cell invasion developed into an invasion-promoting CAF phenotype through exposure to conditioned medium from MDA-MB-231 breast cancer cells. A myofibroblast-like CAF cell line expressing high levels of smooth muscle actin was compared to normal mammary fibroblasts before and after induction. Comparison of TF activity profiles for all three fibroblast types identified a TF activity signature common to CAFs which included activation of reporters for TFs ELK1, GATA1, retinoic acid receptor (RAR), serum response factor (SRF), and vitamin D receptor (VDR). Additionally, CAFs resembling myofibroblasts, relative to normal fibroblasts, had elevated activation corresponding to NF-kappaB, RUNX2, and YY1, and distinct activity patterns for several differentiation-related TF reporters. Induction of CAFs by exposure of normal fibroblasts to conditioned medium from MDA-MB-231 cells resulted in increased activation of reporters for HIF1, several STAT TFs, and proliferation-related TFs such as AP1. Myofibroblast-like CAFs and induced normal mammary fibroblasts promoted invasion of breast cancer cells by distinct mechanisms, consistent with their distinct patterns of TF activation. The TF activity profiles of CAF subtypes provide an overview of intracellular signaling associated with the induction of a pro-invasive stroma, and provide a mechanistic link between the microenvironmental stimuli and phenotypic response.

Stage-Associated Dynamic Activity Profile of Transcription Factors in Nasopharyngeal Carcinoma Progression Based on Protein/DNA Array Analysis

Transcription factors (TFs) are crucial modulators of gene regulation during the development and progression of tumors. We previously reported the activation of TFs in nasopharyngeal carcinoma (NPC) cell lines. In this study, we explored the activity profiles of TFs in Protein/DNA array data of a 12-tissue independent set and a 13-tissue pooled set of NPC that included different clinical stages. TFs associated with tumor progression were revealed using a generalized linear model-based regression analysis. Immunohistochemical analysis of clinical NPC samples was used to validate the results of array analysis. We identified 26 TFs that showed increased activities. Of these 26 TFs, 16 were correlated with clinical stages. Activity changes of AP2 and ATF/CREB were confirmed by electrophoretic mobility shift assay (EMSA), and increased expression of AP2α, β, γ, ATF2, and ATF1 in nuclei of tumor cells was associated with clinical stages. In addition, the expressions of AP2α, ATF2, and ATF1 were correlated with those of their target genes (epithelia growth factor receptor (EGFR) and matrix metalloproteinase 2 (MMP-2), respectively). This study provides data and valuable clues that can be used to further investigate the laws of gene transcription regulation in NPC and to identify suitable targets for the development of TF-targeted antitumor agents.

Profiling and comparing transcription factors activated in non‐metastatic and metastatic nasopharyngeal carcinoma cells

Transcription factors (TFs) are modulators of gene expression that are critically important in the establishment and progression of human cancers. In the current study, the activity profiles of TFs in a normal nasopharyngeal epithelial cell line and in nasopharyngeal carcinoma (NPC) cell lines were studied using oligonucleotide array-based TF assays. Compared to the normal epithelial cell line NP69, nine TFs in the non-metastatic NPC cell line (6-10B) and eight TFs in a metastatic NPC cell line (5-8F) were upregulated. Among upregulated TFs, Sp1, AP2, and ATF/CREB families exhibited relatively high activities in NPC cell lines. Transcription levels of Sp1, ATF-1, ATF-2, AP2alpha, AP2gamma, and CREB1 were higher in 5-8F cells than in 6-10B cells. In addition, higher expression of the Sp1 target genes MMP-9 and VEGF was observed in 5-8F cells. Sp1 silencing reduced VEGF and MMP-9 expression. Inhibition of Sp1 expression and activity in 5-8F cells by mithramycin resulted in downregulated expression and secretion of MMP-9 and VEGF, concomitant with inhibition of cell migration and invasion. These results suggest that dynamic changes in TF activities occur in NPC cells and that these changes may play important roles in regulating the expression of genes associated with the development and progression of NPC.

Reconstruction of Escherichia coli transcriptional regulatory networks via regulon-based associations

BackgroundNetwork reconstruction methods that rely on covariance of expression of transcription regulators and their targets ignore the fact that transcription of regulators and their targets can be controlled differently and/or independently. Such oversight would result in many erroneous predictions. However, accurate prediction of gene regulatory interactions can be made possible through modeling and estimation of transcriptional activity of groups of co-regulated genes.ResultsIncomplete regulatory connectivity and expression data are used here to construct a consensus network of transcriptional regulation in Escherichia coli (E. coli). The network is updated via a covariance model describing the activity of gene sets controlled by common regulators. The proposed model-selection algorithm was used to annotate the likeliest regulatory interactions in E. coli on the basis of two independent sets of expression data, each containing many microarray experiments under a variety of conditions. The key regulatory predictions have been verified by an experiment and literature survey. In addition, the estimated activity profiles of transcription factors were used to describe their responses to environmental and genetic perturbations as well as drug treatments.ConclusionInformation about transcriptional activity of documented co-regulated genes (a core regulon) should be sufficient for discovering new target genes, whose transcriptional activities significantly co-vary with the activity of the core regulon members. Our ability to derive a highly significant consensus network by applying the regulon-based approach to two very different data sets demonstrated the efficiency of this strategy. We believe that this approach can be used to reconstruct gene regulatory networks of other organisms for which partial sets of known interactions are available.

Detection of eQTL modules mediated by activity levels of transcription factors

Studies of gene expression quantitative trait loci (eQTL) in different organisms have shown the existence of eQTL hot spots: each being a small segment of DNA sequence that harbors the eQTL of a large number of genes. Two questions of great interest about eQTL hot spots arise: (1) which gene within the hot spot is responsible for the linkages, i.e. which gene is the quantitative trait gene (QTG)? (2) How does a QTG affect the expression levels of many genes linked to it? Answers to the first question can be offered by available biological evidence or by statistical methods. The second question is harder to address. One simple situation is that the QTG encodes a transcription factor (TF), which regulates the expression of genes linked to it. However, previous results have shown that TFs are not overrepresented in the eQTL hot spots. In this article, we consider the scenario that the propagation of genetic perturbation from a QTG to other linked genes is mediated by the TF activity. We develop a procedure to detect the eQTL modules (eQTL hot spots together with linked genes) that are compatible with this scenario. We first detect 27 eQTL modules from a yeast eQTL data, and estimate TF activity profiles using the method of Yu and Li (2005). Then likelihood ratio tests (LRTs) are conducted to find 760 relationships supporting the scenario of TF activity mediation: (DNA polymorphism --> cis-linked gene --> TF activity --> downstream linked gene). They are organized into 4 eQTL modules: an amino acid synthesis module featuring a cis-linked gene LEU2 and the mediating TF Leu3; a pheromone response module featuring a cis-linked gene GPA1 and the mediating TF Ste12; an energy-source control module featuring two cis-linked genes, GSY2 and HAP1, and the mediating TF Hap1; a mitotic exit module featuring four cis-linked genes, AMN1, CSH1, DEM1 and TOS1, and the mediating TF complex Ace2/Swi5. Gene Ontology is utilized to reveal interesting functional groups of the downstream genes in each module. Our methods are implemented in an R package: eqtl.TF, which includes source codes and relevant data. It can be freely downloaded at http://www.stat.ucla.edu/~sunwei/software.htm. http://www.stat.ucla.edu/~sunwei/yeast_eQTL_TF/supplementary.pdf.

Transition of Escherichia coli from Aerobic to Micro-aerobic Conditions Involves Fast and Slow Reacting Regulatory Components

Understanding life at a systems level is a major aim of biology. The bacterium Escherichia coli offers one of the best opportunities to achieve this goal. It is a metabolically versatile bacterium able to respond to changes in oxygen availability. This ability is a crucial component of its lifestyle, allowing it to thrive in aerobic external environments and under the oxygen-starved conditions of a host gut. The controlled growth conditions of chemostat culture were combined with transcript profiling to investigate transcriptome dynamics during the transition from aerobic to micro-aerobic conditions. In addition to predictable changes in transcripts encoding proteins of central metabolism, the abundances of transcripts involved in homeostasis of redox-reactive metals (Cu and Fe), and cell envelope stress were significantly altered. To gain further insight into the responses of the regulatory networks, the activities of key transcription factors during the transition to micro-aerobic conditions were inferred using a probabilistic modeling approach, which revealed that the response of the direct oxygen sensor FNR was rapid and overshot, whereas the indirect oxygen sensor ArcA reacted more slowly. Similarly, the cell envelope stress sensors RpoE and CpxR reacted rapidly and more slowly, respectively. Thus, it is suggested that combining rapid and slow reacting components in regulatory networks might be a feature of systems in which a signal is perceived by two or more functionally related transcription factors controlling overlapping regulons.

Factor analysis for gene regulatory networks and transcription factor activity profiles.

BackgroundMost existing algorithms for the inference of the structure of gene regulatory networks from gene expression data assume that the activity levels of transcription factors (TFs) are proportional to their mRNA levels. This assumption is invalid for most biological systems. However, one might be able to reconstruct unobserved activity profiles of TFs from the expression profiles of target genes. A simple model is a two-layer network with unobserved TF variables in the first layer and observed gene expression variables in the second layer. TFs are connected to regulated genes by weighted edges. The weights, known as factor loadings, indicate the strength and direction of regulation. Of particular interest are methods that produce sparse networks, networks with few edges, since it is known that most genes are regulated by only a small number of TFs, and most TFs regulate only a small number of genes.ResultsIn this paper, we explore the performance of five factor analysis algorithms, Bayesian as well as classical, on problems with biological context using both simulated and real data. Factor analysis (FA) models are used in order to describe a larger number of observed variables by a smaller number of unobserved variables, the factors, whereby all correlation between observed variables is explained by common factors. Bayesian FA methods allow one to infer sparse networks by enforcing sparsity through priors. In contrast, in the classical FA, matrix rotation methods are used to enforce sparsity and thus to increase the interpretability of the inferred factor loadings matrix. However, we also show that Bayesian FA models that do not impose sparsity through the priors can still be used for the reconstruction of a gene regulatory network if applied in conjunction with matrix rotation methods. Finally, we show the added advantage of merging the information derived from all algorithms in order to obtain a combined result.ConclusionMost of the algorithms tested are successful in reconstructing the connectivity structure as well as the TF profiles. Moreover, we demonstrate that if the underlying network is sparse it is still possible to reconstruct hidden activity profiles of TFs to some degree without prior connectivity information.