Protocol to use TopNet for gene regulatory network modeling using gene expression data from perturbation experiments.

Inference of gene regulatory networks from gene perturbation experiments is the most reliable approach for investigating interdependence between genes. Here, we describe the initial gene perturbations, expression measurements, and preparation steps, followed by network modeling using TopNet. Summarization and visualization of the estimated networks and optional genetic testing of dependencies revealed by the network model are demonstrated.

Gene network modeling via TopNet reveals functional dependencies between diverse tumor-critical mediator genes.

Malignant cell transformation and the underlying reprogramming of gene expression require the cooperation of multiple oncogenic mutations. This cooperation is reflected in the synergistic regulation of non-mutant downstream genes, so-called cooperation response genes (CRGs). CRGs affect diverse hallmark features of cancer cells and are not known to be functionally connected.

Multiple imputation and direct estimation for qPCR data with non-detects.

Background: Quantitative real-time PCR (qPCR) is one of the most widely used methods to measure gene expression. An important aspect of qPCR data that has been largely ignored is the presence of non-detects: reactions failing to exceed the quantification threshold and therefore lacking a measurement of expression. While most current software replaces these non-detects with a value representing the limit of detection, this introduces substantial bias in the estimation of both absolute and differential expression.

Bone marrow mesenchymal stromal cells from acute myelogenous leukemia patients demonstrate adipogenic differentiation propensity with implications for leukemia cell support.

Bone marrow mesenchymal stromal cells (MSCs) constitute one of the important components of the hematopoietic microenvironmental niche. In vivo studies have shown that depletion of marrow MSCs resulted in reduction of hematopoietic stem cell content, and there is in vitro evidence that marrow MSCs are able to support leukemia progenitor cell proliferation and survival and provide resistance to cytotoxic therapies. How MSCs from leukemia marrow differ from normal counterparts and how they are influenced by the presence of leukemia stem and progenitor cells are still incompletely understood.

Developmental alcohol exposure impairs synaptic plasticity without overtly altering microglial function in mouse visual cortex.

Fetal alcohol spectrum disorder (FASD), caused by gestational ethanol (EtOH) exposure, is one of the most common causes of non-heritable and life-long mental disability worldwide, with no standard treatment or therapy available. While EtOH exposure can alter the function of both neurons and glia, it is still unclear how EtOH influences brain development to cause deficits in sensory and cognitive processing later in life. Microglia play an important role in shaping synaptic function and plasticity during neural circuit development and have been shown to mount an acute immunological response to EtOH exposure in certain brain regions.

On non-detects in qPCR data.

Motivation: Quantitative real-time PCR (qPCR) is one of the most widely used methods to measure gene expression. Despite extensive research in qPCR laboratory protocols, normalization and statistical analysis, little attention has been given to qPCR non-detects-those reactions failing to produce a minimum amount of signal.

Results: We show that the common methods of handling qPCR non-detects lead to biased inference.

On the underlying assumptions of threshold Boolean networks as a model for genetic regulatory network behavior.

Boolean networks (BoN) are relatively simple and interpretable models of gene regulatory networks. Specifying these models with fewer parameters while retaining their ability to describe complex regulatory relationships is an ongoing methodological challenge. Additionally, extending these models to incorporate variable gene decay rates, asynchronous gene response, and synergistic regulation while maintaining their Markovian nature increases the applicability of these models to genetic regulatory networks (GRN).

Gene sets identified with oncogene cooperativity analysis regulate in vivo growth and survival of leukemia stem cells.

Leukemia stem cells (LSCs) represent a biologically distinct subpopulation of myeloid leukemias, with reduced cell cycle activity and increased resistance to therapeutic challenge. To better characterize key properties of LSCs, we employed a strategy based on identification of genes synergistically dysregulated by cooperating oncogenes. We hypothesized that such genes, termed "cooperation response genes" (CRGs), would represent regulators of LSC growth and survival.

Synergistic response to oncogenic mutations defines gene class critical to cancer phenotype.

Understanding the molecular underpinnings of cancer is of critical importance to the development of targeted intervention strategies. Identification of such targets, however, is notoriously difficult and unpredictable. Malignant cell transformation requires the cooperation of a few oncogenic mutations that cause substantial reorganization of many cell features and induce complex changes in gene expression patterns.