To this end, we present Loregic, a computational method integrating gene expression and regulatory network data, to characterize the cooperativity of regulatory factors.

We attempt to find the gate that best matches each triplet’s observed gene expression pattern across many conditions.

Gene expression is controlled by various gene regulatory factors.

Corruptions of regulatory cooperativity may lead to abnormal gene expression activity such as cancer.

Gene eXpression is a compleX process that achieves both spatial and temporal control through the coordinated action of multiple regulatory factors (RFs) [1—3].

These regulatory factors affecting gene eXpression take several forms, such as transcription factors (TFs), which directly or indirectly bind DNA at promoter and enhancer regions of their target genes, and non-coding RNAs (e.g.

RFs can act as activators or repressors, but ultimately, the target gene eXpression is determined by combining the effects of multiple regulatory factors.

In this study, we use a mathematical model of Dorsal dynamics, fit to experimental data, to determine the ability of the Dorsal gradient to regulate gene expression across the entire dor-sal-ventral axis.

We found that two assumptions are required for the model to match experimental data in both Dorsal distribution and gene expression patterns.

Our model explains the dynamic behavior of the Dorsal gradient at lateral and dorsal positions of the embryo, the ability of Dorsal to regulate gene expression across the entire dorsal-ventral axis, and the robustness of gene expression to stochastic effects.

Using a mathematical model of the Drosophila embryo, we have proposed a solution to this outstanding problem: namely that Cactus, the inhibitor to Dorsal, is present with Dorsal in nuclei across the embryo, which creates a disparity between the gradient measured by fluorescence and the gradient measured by gene expression .

Gene expression model

Signaling through Toll receptors on the ventral side of the embryo causes the dissociation of the dl/Cact complex, and free dl accumulates in the ventral nuclei [5—7] to create a spatial gradient that causes differential gene expression based on multiple gene expression thresholds.

In recent years, detailed measurements of the dl gradient have been performed, potentially allowing us to address the question of how the spatial information carried by the dl gradient results in gene expression [10—12].

These observations left open the question of how a narrow-width dl gradient can specify gene expression domains far beyond its apparent spatial range [10, 12, 16].

A similar method is used to find parameter sets for simulations of gene eXpression , with l = 250, [,4 = 50.

We use the dl/Cact dynamics associated with this set of parameters as an input to the gene eXpression model equations, and allow only the gene eXpression parameters to evolve for 9 different values of the noise parameter, 1], between 0.02 and 0.5.

Application of the methods to detecting circadian rhythms in a metadataset of microarrays that quantify time-dependent gene expression in whole heads of Drosophi/a melanogaster reveals annotations that are enriched among genes with highly asymmetric waveforms.

We apply these methods to a compilation of data on gene eXpression in fruit flies, an important model organism.

We expect these additional waveforms to be more sensitive to asymmetric patterns of gene expression , resulting in discovery of additional rhythmic time series.

Arguably the most well-studied periodic patterns are circadian rhythms: oscillatory changes in gene expression , metabolism, physiology, and behavior with approximately 24-hour (24 h) periods that enable organisms to anticipate and respond to daily changes in their environment, such as nutrient accessibility, temperature, and light [10—13].

The advent of high throughput methods for measuring gene expression now makes transcriptome-wide studies of this nature possible.

As a result, gene expression time series are typically sparsely sampled (e.g., every 2—4 hours (h) in circadian studies), often without multiple measurements per time point, which we refer to here as “replicates”.

The methods that we consider are general and can be applied to detecting periodic behavior in any context, but we describe them here in terms of searching for circadian rhythms in gene expression for clarity.

(A) Z-scored gene expression of genes from the metadataset involved in glutathione metabolism averaged across 24 h and interpolated to every 2 h. (B) Phase and asymmetry distribution of the genes from the metadataset involved in glutathione metabolism.

(A) Z-scored gene expression of genes from the metadataset involved in oxidation reduction averaged across 24 h and interpolated to every 2 h. Black indicates time points Where data were not available (NA).

Peak expression (phase) of these genes is distributed over 24 h. (A) Z-scored gene expression of genes from the metadataset involved in alternative splicing averaged across 24 h and interpolated to every 2 h. (B) Phase and asymmetry distribution of the genes from the 81 Data.

Here we propose signalling entropy, a measure of signalling pathway promiscuity derived from a sample’s genome-wide gene expression profile, as an estimate of the stemness of a tumour sample.

By analysing 3668 breast cancer and 1692 lung adenocarcinoma samples, we further demonstrate that signalling entropy correlates negatively with survival, outperforming leading clinical gene expression based prognostic tools.

Although putative CSCs have been identified by surface marker eXpression for several malignancies, isolated, and demonstrated to be chemotherapeutic resistant [7—11], it remains a significant challenge to obtain a prognostic measure of their abundance from tumour bulk gene eXpression profiles across multiple malignancies.

Embryonic Stem (ES) cell gene eXpression signatures are clear candidates for such a measure and indeed have been demonstrated to be prognostic in breast and lung cancer [12—15].

Specifically, we consider signalling entropy which is computed from the integration of a sample’s genome-wide gene eXpression profile with an interactome, and provides an overall measure of the signalling promiscuity in the sample [16].

Signalling entropy is derived from the integration of a sample’s gene expression profile with a human protein interactome, and provides a rough proxy for the overall level of signalling promiscuity in the sample.

We thus derived a condition for point-wise super-additivity of our measure and then considered a data set of gene expression profiles for 33 distinct adult tissues, representing 528 possible pairwise mixtures [29].

To investigate this relationship, we created an extensive normalized gene expression compendium forthe bacterium Escherichia coli that was further enriched with meta-information through an iterative learning procedure.

Results show that gene expression is an excellent predictor of environmental structure, with multi-class ensemble models achieving balanced accuracy between 70.0% (i3.5%) to 98.3% (i2.3%) for the various characteristics.

Although integrative analysis of multiple microarray gene expression (MAGE) datasets allows to distill the maximum relevant biological information from genomic datasets, the unwanted variation, so-called batch-effects arising from data merged from difference sources has been a major challenge to impede such effort [61].

Our work argues that genome-scale gene expression can be a multipurpose marker for identifying latent, heterogeneous cellular and environmental states and that optimal classification can be achieved with a feature set of a couple hundred genes that might not necessarily have the most pronounced differential expression in the respective conditions.

Categorization of gene expression data

To address this question, we constructed an extensive, annotated gene expression compendium, where we trained Bayesian models for seven distinct classification tasks.

More recently, a probabilistic human tissue and cell type predictor was built based solely on gene expression profiles [28].

In this work, we investigate how well we can predict cellular and environmental state from genome-wide expression, using known gene expression profiles as our only training data.

This E. coli Gene Expression Compendium (EcoGEC), consists of publicly available data that were curated from online public databases such as GEO [30], ArrayExpress [31], SRA [32], SMD [33], M3D [34] and PortEco [35].

When bedGraph format was used in the data, gene expression level was measured in RPKM using the bgrQuantifler program that is part of the RSEQ tool [60].

In addition, gene expression profiling of TEM transitioned to a weak pro-angiogenic phenotype confirmed that TEM are plastic cells and can be reverted to immunological potent monocytes.

We thank the nurses of the breast center (CHUV), Dr. Iulien Dorier (SIB, CIG) for stimulating discussions and the Genomic Technologies Facility (GTF at CIG) for RNA preparation, gene expression profiling and for printing assistance.

We undertook a rigorous experimental design consisting in profiling changes in phenotype, cy-tokine secretion, gene expression and angiogenic activity from the same cell sample in response to treatments.

Gene expression profiling

Boolean modeling of steady state transitions helps in understanding the influence of perturbations on system wide behavior and has been used to identify the key molecular mechanisms controlling gene expression [4,5,6] and regulation [7,8], cell differentiation [9] and signal transduction [10,11,12,13,14,15,16,17,18,19,20].

Finally, gene expression profiling of TEM transitioned to a weak pro-angiogenic phenotype confirmed that TEM infiltrating carcinoma of the breast remain plastic cells that can be reverted from pro-angiogenic and protumoral cells to immunological potent monocytes.

Having identified the critical ligands and pathways controlling TEM plasticity, we next examined in TEM differentiated in vitro Whether differential gene expression might also contribute to the molecular basis of TEM plastic behavior.

To this end, we selected VEGF/TNF-oc, ANG-2/TGF-[3 and PlGF/TGF-B treatments for gene expression profiling using Affimetrix whole genome microarrays, because these treatments were present in 17, 16 and 14, respectively of the 74 links (treatment/receptor/cytokine) retained in TEM regulatory network (S4 Table and Fig.

Enrichment analyses of the gene expression data against known pathways and functional gene categories were conducted as described in Materials and Methods.

From these findings we infer that flexibility peaks could play a functional role as regulatory elements of gene expression for a peculiar set of genes.

However, we must consider that, while the impact of 3’-end sequence on gene expression is well established, the understanding of how its effect is encoded in DNA is limited.

This issue assumes relevant significance for gene evolution and tandem repeats have been considered able to drive transcriptional divergence and to confer evolvability to gene expression [61].

mRNA stability is a key regulatory step controlling gene eXpression and ultimately affects protein levels and function.

The 175 ORFs include genes expressing key components of cell cycle progression and regulation: TUBZ and TUB3 encoding a and fl tubulins, CLB4 and PH 080 encoding cyclins, CDC53 and APC9 encoding respectively the cullin structural protein of SCF complexes and a subunit of the Ana-phase-Promoting Complex/Cyclosome; moreover, AME] , RAD24, RAD59 and SWEI involved in checkpoint maintenance, the F U83, DI G2 and SLT2 encoding MAP-kinases and their regulator BMH1 encoding the major isoform of 14-3-3 proteins.

The connection between flexibility peaks and ORFs could be the evolutionary outcome of modified canonical polyadenylation elements, leading to a differentiated 3’ end processing and gene expression regulation.

Differences in cryptic 3’SS usage due to varying gene expression may contribute to the divergent prognostic implications of SF3BI mutation in various cancers [2,7].

These results suggest that cryptic 3’SS selection may affect gene expression for a subset of genes.

4A) suggests that most genes’ expression are not affected by cryptic 3’SS selection and most cryptic 3’SSs are observed at a low frequency because they are spliced in infrequently compared to their associated canonical 3’SSs.

Differential gene expression

Gene expression was estimated as described above.

We provided these read counts to DESeq2 (v1.2.10, R v3.0.3) and tested for differential gene expression using nbinomWaldTest using cancer type as a covariate for the analysis with different cancers [43].

Gene expression was estimated by summing together the effective counts or FPKM values for all transcripts contained in a gene.

These experiments generated a large data matrix with rows corresponding to gene expression values, and columns corresponding to shRNA perturbations.

PEACS uses a novel computational approach to analyze gene eXpression data from perturbed cellular populations, and can be applied broadly to identify regulators of stem and progenitor cell self-renewal or differentiation.

Thus the gene expression data for each perturbation p is mapped into the space spanned by linear combinations of the first k gene-expression SVD eigenvectors 1/1,.

Microfluidic qPCR was carried out according to the manufacturer’s Protocol (Protocol 37: Fast Gene Expression Analysis Using EvaGreen on the BioMark or BioMark HD System).

For the idealized experiment, gene expression was profiled using standard qPCR and the 17 genes profiled were randomly selected transcription factors expressed by MCFIOA cells and implicated in differentiation.

Lastly, we applied SVD, NMF and ICA to the gene expression matrix to assess the relative performance of these algorithms in identifying changes in cell-state proportions.

We could directly compare gene loadings in the various components with gene expression in the various states because the gene-expression profiles of the pure states were known in our idealized experimental conditions (Fig 2E).

One promising strategy is to identify drugs that, at the transcriptional level, reverse the gene expression signature of a disease.

A major difficulty with this strategy is variability: different gene expression signatures of the same disease or drug treatment can show poor overlap across studies.

Our analyses are based on 21 previously published gene expression signatures of lung cancer obtained from Oncomine [11] and CDIP, the Cancer Data Integration Portal (http://ophid.

On a set of 21 transcriptional signatures of lung cancer, we identified 247 drugs that significantly reverse these pathological gene expression changes (at FDR < 1%).

At an FDR cutoff of 0.01, we find that 247 drugs (out of 1,309 drugs in CMap Build 2) significantly reverse the gene expression changes seen With lung cancer in the full set of 21 lung cancer signatures (Sl Table).

The CMap tool takes as input a set of up-regulated probe sets and a set of down-regulated probe sets, and returns a list of drugs that reverts or mimics those gene eXpression changes.

We used the CMap gene expression profiles from before and after drug treatment to calculate the number of genes differentially expressed in response to a drug, for each of the 1,309 drugs in the collection (see Materials and Methods).

Using yeast gene expression data we show how correlation can be misleading and present proportionality as a valid alternative for relative data.

Using timecourse yeast gene expression data, we show how correlation of relative abundances can lead to conclusions opposite to those drawn from absolute abundances, and that its value changes when different components are included in the analysis.

As far as we are aware, none of the database providers explicitly address whether absolute levels of gene expression were constant across experimental conditions.

The revisitation of this assumption [7] should raise alarm bells about the inferences drawn from many gene expression studies.

To further illustrate how correlation can be misleading we applied it to absolute and relative gene expression data in fission yeast cells deprived of a key nutrient [6].

[6] on the absolute levels of gene expression (i.e., mRNA copies per cell) in fission yeast after cells were deprived of a key nutrient (Fig.

These findings raise intriguing questions as to the molecular mechanisms underlying this proportional regulation, suggesting sophisticated, coordinated control of numerous mRNAs at both transcriptional and post-transcriptional levels of gene expression .

In this respect, we note that GR represses TNFoc induction of IL-8 gene expression by acting after transcription initiation [37].

Note that two cofactors cannot be of the same type at the same step, GR will not repress gene expression if it is an A before the CLS, and the Amin graphs allow GR to act anywhere as a D or as an A after the CLS.

An unresolved question is Whether the underlying mechanism of each transcriptional component is the same or changes With the direction of gene eXpression output (i.e., increase in induction vs. decrease in repression).

Thus, many cofactors are classified as either coactiva-tors or corepressors based solely upon their ability to increase or decrease respectively, the level of steroid-mediated gene eXpression [2,17].

Gene expression involves the binding of molecules, protein, and DNA into complexes that lead to transcription.

This suggests that many transcriptional cofactors/comodulators are mono-functional and similarly modulate basic steps in gene eXpression irrespective of the directional change in gene product levels.

We exploit cap analysis of gene expression (CAGE) time series datasets to directly measure promoter activities overtime.

The FANTOM5 project has recently produced the most comprehensive expression atlas for human and mouse cells, based upon cap analysis of gene expression (CAGE) data [18].

The timing of immediate early and nucleotide binding gene expression is shown in Fig 2A and 2B where it can be seen that in AoSMC-FGFZ, AoSM-C-Ile and MCF7-EGF data the largest proportion of known IEGs is found in the 30-90 min interval when ts values are binned in 30 min intervals (the proportion of clusters annotated to known IEGs is expressed as a percentage of all clusters within each 30 min period according to 1‘5).

These cues are sensed by these cells through changes in imme-diate-early gene expression , and can lead to increased proliferation and migration.

Terms relevant to the immediate-early response included regulation of gene expression , regulation of transcription from RNA polymerase II promoter, regulation of RNA metabolic process and regulation of metabolic process.