Mechanisms governing the pioneering and redistribution capabilities of the non-classical pioneer PU.1

Establishing gene regulatory networks during differentiation or reprogramming requires master or pioneer transcription factors (TFs) such as PU.1, a prototype master TF of hematopoietic lineage differentiation. To systematically determine molecular features that control its activity, here we analyze DNA-binding in vitro and genome-wide in vivo across different cell types with native or ectopic PU.1 expression. Although PU.1, in contrast to classical pioneer factors, is unable to access nucleosomal target sites in vitro, ectopic induction of PU.1 leads to the extensive remodeling of chromatin and redistribution of partner TFs. De novo chromatin access, stable binding, and redistribution of partner TFs both require PU.1's N-terminal acidic activation domain and its ability to recruit SWI/SNF remodeling complexes, suggesting that the latter may collect and distribute co-associated TFs in conjunction with the non-classical pioneer TF PU.1

Causal Modeling of Cancer-Stromal Communication Identifies PAPPA as a Novel Stroma-Secreted Factor Activating NFκB Signaling in Hepatocellular Carcinoma

Inter-cellular communication with stromal cells is vital for cancer cells. Molecules involved in the communication are potential drug targets. To identify them systematically, we applied a systems level analysis that combined reverse network engineering with causal effect estimation. Using only observational transcriptome profiles we searched for paracrine factors sending messages from activated hepatic stellate cells (HSC) to hepatocellular carcinoma (HCC) cells. We condensed these messages to predict ten proteins that, acting in concert, cause the majority of the gene expression changes observed in HCC cells. Among the 10 paracrine factors were both known and unknown cancer promoting stromal factors, the former including Placental Growth Factor (PGF) and Periostin (POSTN), while Pregnancy-Associated Plasma Protein A (PAPPA) was among the latter. Further support for the predicted effect of PAPPA on HCC cells came from both in vitro studies that showed PAPPA to contribute to the activation of NFκB signaling, and clinical data, which linked higher expression levels of PAPPA to advanced stage HCC. In summary, this study demonstrates the potential of causal modeling in combination with a condensation step borrowed from gene set analysis [Model-based Gene Set Analysis (MGSA)] in the identification of stromal signaling molecules influencing the cancer phenotype

HCC protein network regulated by HSCs.

<p>HSC-induced changes in HCC gene expression were mapped on the BioGRID interactome of protein-protein and protein-gene interactions and the largest regulated sub-network was identified. Components of several oncogenic signaling pathways are regulated, NFκB pathway members, TGF-beta/SMAD3 and Map-kinases. Moreover, anti-apoptosis (BIRC3) and motility-related (RND1) genes can be found. Colors indicate logarithmic fold changes (base 2) of the genes upon conditioned media incubation. Red denotes repression; green induction of the gene after incubation with HSC conditioned media.</p

Scheme of the HSC-HCC network used in causal modeling.

<p>The network consists of three types of genes, cellular HSC genes (yellow), secreted HSC gene products (red) and HCC ‘target’ genes (blue). Individual genes are represented by nodes. Black arrows indicate dependencies among genes that were estimated from gene expression data. These can be directional, i.e. the expression level of a gene impacts the expression level of another downstream gene; or un-directed, i.e. the causal gene could not be inferred. Genes upstream of a particular gene are denoted as parents (e.g. x3 and x4 are parents of x8, and x3, x4, x7 and x8 are parents of x12). Secreted HSC gene products can be parents of other HSC genes. In contrast, HCC genes were excluded in network estimation because they cannot impact HSC genes in the chosen experimental setup. Green dashed arrows indicate estimated causal effects of secreted HSC genes on HCC cell genes. Causal effects that are stable across sub-sampling runs are reported, e.g. x10 has stable causal effects on y1, y2 and y3, whereas x13 has no stable effect on any HCC gene.</p

Most influential stromal regulators.

<p>Subset of secreted HSC gene products which best describe the expression changes observed in conditioned HCC samples. symbol: gene symbol, ensembl gene ID: ensembl gene identifier, set size: number of HCC genes influenced by HSC gene product, probability: probability from MGSA that the target gene set is active (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004293#sec011" target="_blank">Materials and Methods</a>).</p><p>Most influential stromal regulators.</p

Differentially expressed genes with large variance across HCC samples.

<p>HCC cells were stimulated with conditioned media from HSCs from 15 different human donors (Hep_1-Hep_15) while control samples (ctrl1-4) were incubated with plain medium. Of the significant differentially expressed genes upon incubation with conditioned media, only the ones with large variation across HCC samples are shown (for details please see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004293#sec011" target="_blank">Material and Methods</a>). Expression data was scaled to mean = 0 and standard deviation = 1, such that negative values (blue) indicate lower expression in the sample compared to the mean and positive values (yellow) higher expression in the sample compared to the mean.</p

PAPPA expression in HSCs and HCC tissues.

<p>PAPPA protein levels in conditioned media, correlation of protein and mRNA levels, and correlation with collagen. A. PAPPA levels in conditioned media of HSCs from 15 different human donors. B. Correlation of PAPPA protein levels and mRNA levels in HSCs from 15 different human donors. C. Correlation of PAPPA and collagen I (COL1A1) mRNA expression in 51 human HCC tissues.</p

Overview of the experimental and computational approach to identify secreted factors of HSCs regulating HCC gene expression.

<p>Conditioned medium of primary human HSC (n = 15) was transferred onto human Hep3B HCC cells. Gene expression data of HSC and HCC cells were filtered to reduce the dimensionality of the data and to build cause-and-effect (target) matrices. These served as input for the IDA algorithm which estimates causal effects for each cause on each target gene. Causal effects that were stable across sub-sampling runs (i.e. that were stable with respect to small perturbations of the data) were retained and subjected to Model-based Gene Set Analysis (MGSA) to extract a sparse set of HSC genes influencing HCC cell gene expression.</p

Correlation of HSC secreted PAPPA levels with NFκB activation in conditioned HCC.

<p>A. Correlation of HSC-CM induced NFκB activity in HCC cells (relative to NFκB activity in cells stimulated with control medium) with PAPPA levels in HSC-CM (n = 15). B. HCC cells were incubated with recombinant human PAPPA protein (PAPPA) either in CM from HCSs from 2 different human donors (CM1 and CM2) or control medium (ctr.). Furthermore, cells were stimulated with CM1, CM2 or control medium alone. After 4h stimulation, cellular extracts were analyzed with Western blot analysis for phosphorylated p65 and IkB-alpha. Analysis of actin expression demonstrated equal loading.</p

PAPPA expression in human HCC tissues of different tumor stages.

<p>PAPPA mRNA expression levels in human HCC tissues (n = 52) of tumor stages I (n = 12), II (n = 19) and III (n = 21). One-way ANOVA shows a significant effect (p = 0.008) of tumor stage on PAPPA mRNA expression level.</p