A human B-cell interactome identifies MYB and FOXM1 as master regulators of proliferation in germinal centers

Assembly of a mixed interaction network specific to human B cells.Identification and validation of master regulators of germinal center reaction.MYB and FOXM1 are synergistic master regulators of proliferation in germinal center B cells and control a new protein complex involving replication and mitotic-related genes

Correction: Inferring Protein Modulation from Gene Expression Data Using Conditional Mutual Information.

[This corrects the article DOI: 10.1371/journal.pone.0109569.]

Correction: Inferring Protein Modulation from Gene Expression Data Using Conditional Mutual Information

[This corrects the article DOI: 10.1371/journal.pone.0109569.]

Inferring protein modulation from gene expression data using conditional mutual information.

Systematic, high-throughput dissection of causal post-translational regulatory dependencies, on a genome wide basis, is still one of the great challenges of biology. Due to its complexity, however, only a handful of computational algorithms have been developed for this task. Here we present CINDy (Conditional Inference of Network Dynamics), a novel algorithm for the genome-wide, context specific inference of regulatory dependencies between signaling protein and transcription factor activity, from gene expression data. The algorithm uses a novel adaptive partitioning methodology to accurately estimate the full Condition Mutual Information (CMI) between a transcription factor and its targets, given the expression of a signaling protein. We show that CMI analysis is optimally suited to dissecting post-translational dependencies. Indeed, when tested against a gold standard dataset of experimentally validated protein-protein interactions in signal transduction networks, CINDy significantly outperforms previous methods, both in terms of sensitivity and precision

Systematic, network-based characterization of therapeutic target inhibitors.

A large fraction of the proteins that are being identified as key tumor dependencies represent poor pharmacological targets or lack clinically-relevant small-molecule inhibitors. Availability of fully generalizable approaches for the systematic and efficient prioritization of tumor-context specific protein activity inhibitors would thus have significant translational value. Unfortunately, inhibitor effects on protein activity cannot be directly measured in systematic and proteome-wide fashion by conventional biochemical assays. We introduce OncoLead, a novel network based approach for the systematic prioritization of candidate inhibitors for arbitrary targets of therapeutic interest. In vitro and in vivo validation confirmed that OncoLead analysis can recapitulate known inhibitors as well as prioritize novel, context-specific inhibitors of difficult targets, such as MYC and STAT3. We used OncoLead to generate the first unbiased drug/regulator interaction map, representing compounds modulating the activity of cancer-relevant transcription factors, with potential in precision medicine

Alternative three-way network topologies including a Transcription Factor (TF), a Target gene (Tg) and a Modulator gene (M).

<p>(A) depicts the independent regulation of the target gene by a modulator and a TF; (B) describes a three-way interaction between the TF, the target gene and the modulator.</p

Default parameters used for running MINDy.

<p>NA: Not applicable.</p><p>Default parameters used for running MINDy.</p

Schematic representations of the CINDy algorithm.

<p>A collection of gene expression profiles is required to calculate Conditional Mutual Information between lists of modulators, transcription factors and putative target genes, with the final output of inferred modulation events.</p