On entropy and information in gene interaction networks

MOTIVATION

Modern biological experiments often produce candidate lists of genes presumably related to the studied phenotype. One can ask if the gene list as a whole makes sense in the context of existing knowledge: Are the genes in the list reasonably related to each other or do they look like a random assembly? There are also situations when one wants to know if two or more gene sets are closely related. Gene enrichment tests based on counting the number of genes two sets have in common are adequate if we presume that two genes are related only when they are in fact identical. If by related we mean well connected in the interaction network space, we need a new measure of relatedness for gene sets.

RESULTS

We derive entropy, interaction information and mutual information for gene sets on interaction networks, starting from a simple phenomenological model of a living cell. Formally, the model describes a set of interacting linear harmonic oscillators in thermal equilibrium. Because the energy function is a quadratic form of the degrees of freedom, entropy and all other derived information quantities can be calculated exactly. We apply these concepts to estimate the probability that genes from several independent genome-wide association studies are not mutually informative; to estimate the probability that two disjoint canonical metabolic pathways are not mutually informative; and to infer relationships among human diseases based on their gene signatures. We show that the present approach is able to predict observationally validated relationships not detectable by gene enrichment methods. The converse is also true; the two methods are therefore complementary.

AVAILABILITY AND IMPLEMENTATION

The functions defined in this paper are available in an R package, gsia, available for download at https://github.com/ucsd-ccbb/gsia.

Abstract P6-06-01: Analyzing the physical and functional protein interaction landscape of breast cancer

A key unanswered question in cancer genetics is how different mutations, dispersed across a multitude of genes, elicit similar pathology and patient outcomes. The answer may lie in understanding the molecular networks and protein complexes (i.e. signaling pathways, chromatin architecture, etc) in cancer and mapping mutated genes into the complexes and pathways in which they function. Determining how systematic interaction networks are wired in cancer cells and how different mutations perturb these networks will guide the search for new cancer genes and provide a platform for integrating patient data to make biological and clinical predictions more accurate. The goal of this study is to uncover the comprehensive protein-protein interaction networks and pathways in various breast cancer subtypes to better understand how mutated cancer genes and genomes hijack and re-wire pathways and complexes during the course of breast tumorigenesis.

Here we catalog protein-protein interactions for 40 genes recurrently mutated in breast cancer, using affinity purification and mass spectrometry. To identify co-associated proteins, cDNA clones expressing each protein were tagged with 3xFLAG at either N or C-terminus and introduced into MCF10A (non-tumorigenic “healthy” control), MCF7 (luminal A subtype), and MDA-MB-231 (claudin-low) cells using doxycycline-inducible lentiviral vectors. For proteins with prevalent pathogenic mutations (e.g. PIK3CA-H1047R, BRCA1-C61G), mutant cDNA clones were also analyzed in parallel. Our interaction network reveals subtype and mutation-specific protein-protein interactions, many of which are not previously reported. Given that genes encoding components of a protein complex or a biological pathway often share similar phenotype upon genetic perturbation, we genetically knocked out genes interacting with DNA damage response (DDR) proteins using CRISPR/Cas9, and found multiple novel interacting genes whose knockout results in significant PARPi (olaparib) and/or cisplatin sensitivity. This result not only functionally validates the physical protein interactions, but also demonstrates that our interactome mapping approach can helps identify newdruggable vulnerabilities in cancer cells.

We anticipate the breast cancer interactome study will uncover aberrant pathways and protein complexes uniquely operating in breast cancer cells, and thus pinpoint proteins that may potentially serve as distinct biomarkers or therapeutic targets for tumors having the same or similar subtypes and/or genomic mutations.

Citation Format: Kim M, Kim K, Tutuncuoglu B, Soucheray M, Swaney D, Zheng F, Park J, O'Leary P, Coppé J-P, van 't Veer L, Ashworth A, Ideker T, Krogan N. Analyzing the physical and functional protein interaction landscape of breast cancer [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P6-06-01.

Transcriptional repression of IFNβ1 by ATF2 confers melanoma resistance to therapy

The resistance of melanoma to current treatment modalities represents a major obstacle for durable therapeutic response, and thus, the elucidation of mechanisms of resistance is urgently needed. The crucial functions of Activating Transcription Factor-2 (ATF2) in the development and therapeutic resistance of melanoma have been previously reported, although the precise underlying mechanisms remain unclear. Here, we report a protein kinase C epsilon (PKCε)- and Activating Transcription Factor-2 (ATF2)-mediated mechanism that facilitates resistance by transcriptionally repressing the expression of IFNβ1 and downstream type-I IFN signaling, which is otherwise induced upon exposure to chemotherapy. Treatment of early stage melanomas expressing low levels of PKCε with chemotherapies relieves its transcriptional repression of IFNB1, resulting in impaired S-phase progression, a senescence-like phenotype, and increased cell death. This response is lost in late stage metastatic melanomas expressing high levels of PKCε. Notably, nuclear ATF2 and low expression of IFNβ1 in melanoma tumor samples correlates with poor patient responsiveness to biochemotherapy or neoadjuvant IFN-α2a. Conversely, cytosolic ATF2 and induction of IFNβ1 coincides with therapeutic responsiveness. Collectively, we identify an IFNβ1-dependent, cell autonomous mechanism that contributes to the therapeutic resistance of melanoma via the PKCε-ATF2 regulatory axis.

A new approach to decoding life: systems biology

Systems biology studies biological systems by systematically perturbing them (biologically, genetically, or chemically); monitoring the gene, protein, and informational pathway responses; integrating these data; and ultimately, formulating mathematical models that describe the structure of the system and its response to individual perturbations. The emergence of systems biology is described, as are several examples of specific systems approaches.

Testing for differentially-expressed genes by maximum-likelihood analysis of microarray data

Although two-color fluorescent DNA microarrays are now standard equipment in many molecular biology laboratories, methods for identifying differentially expressed genes in microarray data are still evolving. Here, we report a refined test for differentially expressed genes which does not rely on gene expression ratios but directly compares a series of repeated measurements of the two dye intensities for each gene. This test uses a statistical model to describe multiplicative and additive errors influencing an array experiment, where model parameters are estimated from observed intensities for all genes using the method of maximum likelihood. A generalized likelihood ratio test is performed for each gene to determine whether, under the model, these intensities are significantly different. We use this method to identify significant differences in gene expression among yeast cells growing in galactose-stimulating versus non-stimulating conditions and compare our results with current approaches for identifying differentially-expressed genes. The effect of sample size on parameter optimization is also explored, as is the use of the error model to compare the within- and between-slide intensity variation intrinsic to an array experiment.

Integrated genomic and proteomic analyses of a systematically perturbed metabolic network

We demonstrate an integrated approach to build, test, and refine a model of a cellular pathway, in which perturbations to critical pathway components are analyzed using DNA microarrays, quantitative proteomics, and databases of known physical interactions. Using this approach, we identify 997 messenger RNAs responding to 20 systematic perturbations of the yeast galactose-utilization pathway, provide evidence that approximately 15 of 289 detected proteins are regulated posttranscriptionally, and identify explicit physical interactions governing the cellular response to each perturbation. We refine the model through further iterations of perturbation and global measurements, suggesting hypotheses about the regulation of galactose utilization and physical interactions between this and a variety of other metabolic pathways.

Negative selection: a method for obtaining low-abundance cDNAs using high-density cDNA clone arrays

The identification of the entire complement of genes expressed in a cell, tissue, or organism provides a framework for understanding biological properties and establishes a tool set for subsequent functional studies. The large-scale sequencing of randomly selected clones from cDNA libraries has been successfully employed as a method for identifying a large fraction of these expressed genes. However, this approach is limited by the inherent redundancy of cellular transcripts reflecting widely variant levels of gene transcription. As a result, a high percentage of transcript duplications are encountered as the number of sequenced clones accrues. To address this problem, we have developed a negative hybridization selection method that employs the hybridization of complex cDNA probes to high-density arrays of cDNA clones and the subsequent selection of clones with a null or low hybridization signal. This approach was applied to a cDNA library constructed from normal human prostate tissue and resulted in the reduction of highly expressed prostate cDNAs from 6.8 to 0.57% with an overall decline in clone redundancy from 33 to 11%. The selected clones also reflected a more diverse cDNA population, with 89% of the clones representing distinctly different cDNAs compared with 67% of the randomly selected clones. This method compares favorably with cDNA library re-association normalization approaches and offers several distinct advantages, including the flexibility to use previously prepared libraries, and the ability to employ an iterative screening approach for continued accrual of cDNAs representing rare transcripts.

Effect of electrode position on outcome of low-energy intracardiac cardioversion of atrial fibrillation

The aim of this study was to evaluate the new method of low-energy, catheter-based intracardiac cardioversion in patients with chronic atrial fibrillation (AF) and to compare 2 different lead positions. Accordingly, we prospectively studied 80 consecutive patients with chronic AF (9.8 +/- 7.9 months) who were randomly assigned to undergo internal cardioversion either via defibrillation electrodes placed in the right atrium and coronary sinus (coronary sinus group) or via defibrillation electrodes placed in the right atrium and left pulmonary artery (pulmonary artery group). Intracardiac shocks were delivered by an external defibrillator synchronized to the QRS complex. After conversion, all patients were treated orally with sotalol (mean daily dose, 189 +/- 63 mg/day). For conversion to sinus rhythm, the overall mean energy requirement was 5.6 +/- 3.1 J. In the coronary sinus group, cardioversion was achieved in 35 of 38 patients at a mean energy level of 4.1 +/- 2.3 J (range 1.0 to 9.9), and in the pulmonary artery group in 39 of 42 patients with 7.2 +/- 3.1 J (range 2.5 to 14.8). Although there was no difference with regard to success rate, the energy differed significantly between the 2 groups (p < 0.01). Mean lead impedance was 56.4 +/- 7.0 omega and 54.6 +/- 8.5 omega, respectively (p = NS). No serious complications were observed in either lead group. At a mean follow-up of 14.2 +/- 7.0 months, 54% and 56%, respectively, of patients who had been converted successfully remained in sinus rhythm. Thus, low-energy biphasic shocks delivered between the right atrium and coronary sinus or pulmonary artery are equally effective for cardioversion of patients with chronic AF. The energy requirements for conversion from a pulmonary artery electrode position are higher than for the coronary sinus position.