Sharing information to reconstruct patient-specific pathways in heterogeneous diseases.

Advances in experimental techniques resulted in abundant genomic, transcriptomic, epigenomic, and proteomic data that have the potential to reveal critical drivers of human diseases. Complementary algorithmic developments enable researchers to map these data onto protein-protein interaction networks and infer which signaling pathways are perturbed by a disease. Despite this progress, integrating data across different biological samples or patients remains a substantial challenge because samples from the same disease can be extremely heterogeneous. Somatic mutations in cancer are an infamous example of this heterogeneity. Although the same signaling pathways may be disrupted in a cancer patient cohort, the distribution of mutations is long-tailed, and many driver mutations may only be detected in a small fraction of patients. We developed a computational approach to account for heterogeneous data when inferring signaling pathways by sharing information across the samples. Our technique builds upon the prize-collecting Steiner forest problem, a network optimization algorithm that extracts pathways from a protein-protein interaction network. We recover signaling pathways that are similar across all samples yet still reflect the unique characteristics of each biological sample. Leveraging data from related tumors improves our ability to recover the disrupted pathways and reveals patient-specific pathway perturbations in breast cancer.

Perturbation biology: inferring signaling networks in cellular systems.

We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology.

Genome-wide analysis identifies a functional association of Tet1 and Polycomb repressive complex 2 in mouse embryonic stem cells.

BACKGROUND: Ten-Eleven Translocation (TETs)proteins mediate the oxidation of 5-methylcytosine (5mC) to 5-hydroxymethylcytosine (5hmC). Tet1 is expressed at high levels in mouse embryonic stem cells (ESCs), where it mediates the induction of 5hmC decoration on gene-regulatory elements. While the function of Tet1 is known, the mechanisms of its specificity remain unclear. RESULTS: We perform a genome-wide comparative analysis of 5hmC in pluripotent ESCs, as well as in differentiated embryonic and adult cells. We find that 5hmC co-localization with Polycomb repressive complex 2 (PRC2) is specific to ESCs and is absent in differentiated cells. Tet1 in ESCs is distributed on bivalent genes in two independent pools: one with Sin3a centered at non-hydroxymethylated transcription start sites and another centered downstream from these sites. This latter pool of Tet1 co-localizes with 5hmC and PRC2. Through co-immunoprecipitation experiments, we show that Tet1 forms a complex with PRC2 specifically in ESCs. Genome-wide analysis of 5hmC profiles in ESCs following knockdown of the PRC2 subunit Suz12 shows a reduction of 5hmC within promoter sequences, specifically at H3K27me3-positive regions of bivalent promoters. CONCLUSIONS: In ESCs, PRC2 recruits Tet1 to chromatin at H3K27me3 positive regions of the genome, with 5hmC enriched in a broad peak centered 455 bp after the transcription start site and dependent on the PRC2 component Suz12. These results suggest that PRC2-dependent recruitment of Tet1 contributes to epigenetic plasticity throughout cell differentiation.

Integrated transcriptional and competitive endogenous RNA networks are cross-regulated in permissive molecular environments.

Competitive endogenous (ce)RNAs cross-regulate each other through sequestration of shared microRNAs and form complex regulatory networks based on their microRNA signature. However, the molecular requirements for ceRNA cross-regulation and the extent of ceRNA networks remain unknown. Here, we present a mathematical mass-action model to determine the optimal conditions for ceRNA activity in silico. This model was validated using phosphatase and tensin homolog (PTEN) and its ceRNA VAMP (vesicle-associated membrane protein)-associated protein A (VAPA) as paradigmatic examples. A computational assessment of the complexity of ceRNA networks revealed that transcription factor and ceRNA networks are intimately intertwined. Notably, we found that ceRNA networks are responsive to transcription factor up-regulation or their aberrant expression in cancer. Thus, given optimal molecular conditions, alterations of one ceRNA can have striking effects on integrated ceRNA and transcriptional networks.

Simultaneous reconstruction of multiple signaling pathways via the prize-collecting steiner forest problem.

Signaling and regulatory networks are essential for cells to control processes such as growth, differentiation, and response to stimuli. Although many "omic" data sources are available to probe signaling pathways, these data are typically sparse and noisy. Thus, it has been difficult to use these data to discover the cause of the diseases and to propose new therapeutic strategies. We overcome these problems and use "omic" data to reconstruct simultaneously multiple pathways that are altered in a particular condition by solving the prize-collecting Steiner forest problem. To evaluate this approach, we use the well-characterized yeast pheromone response. We then apply the method to human glioblastoma data, searching for a forest of trees, each of which is rooted in a different cell-surface receptor. This approach discovers both overlapping and independent signaling pathways that are enriched in functionally and clinically relevant proteins, which could provide the basis for new therapeutic strategies. Although the algorithm was not provided with any information about the phosphorylation status of receptors, it identifies a small set of clinically relevant receptors among hundreds present in the interactome.