Network Inference Algorithms Elucidate Nrf2 Regulation of Mouse Lung Oxidative Stress

A variety of cardiovascular, neurological, and neoplastic conditions have been associated with oxidative stress, i.e., conditions under which levels of reactive oxygen species (ROS) are elevated over significant periods. Nuclear factor erythroid 2-related factor (Nrf2) regulates the transcription of several gene products involved in the protective response to oxidative stress. The transcriptional regulatory and signaling relationships linking gene products involved in the response to oxidative stress are, currently, only partially resolved. Microarray data constitute RNA abundance measures representing gene expression patterns. In some cases, these patterns can identify the molecular interactions of gene products. They can be, in effect, proxies for protein–protein and protein–DNA interactions. Traditional techniques used for clustering coregulated genes on high-throughput gene arrays are rarely capable of distinguishing between direct transcriptional regulatory interactions and indirect ones. In this study, newly developed information-theoretic algorithms that employ the concept of mutual information were used: the Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNE), and Context Likelihood of Relatedness (CLR). These algorithms captured dependencies in the gene expression profiles of the mouse lung, allowing the regulatory effect of Nrf2 in response to oxidative stress to be determined more precisely. In addition, a characterization of promoter sequences of Nrf2 regulatory targets was conducted using a Support Vector Machine classification algorithm to corroborate ARACNE and CLR predictions. Inferred networks were analyzed, compared, and integrated using the Collective Analysis of Biological Interaction Networks (CABIN) plug-in of Cytoscape. Using the two network inference algorithms and one machine learning algorithm, a number of both previously known and novel targets of Nrf2 transcriptional activation were identified. Genes predicted as novel Nrf2 targets include Atf1, Srxn1, Prnp, Sod2, Als2, Nfkbib, and Ppp1r15b. Furthermore, microarray and quantitative RT-PCR experiments following cigarette-smoke-induced oxidative stress in Nrf2+/+ and Nrf2−/− mouse lung affirmed many of the predictions made. Several new potential feed-forward regulatory loops involving Nrf2, Nqo1, Srxn1, Prdx1, Als2, Atf1, Sod1, and Park7 were predicted. This work shows the promise of network inference algorithms operating on high-throughput gene expression data in identifying transcriptional regulatory and other signaling relationships implicated in mammalian disease

The synthetic triterpenoid RTA dh404 (CDDO-dhTFEA) restores endothelial function impaired by reduced Nrf2 activity in chronic kidney disease

AbstractChronic kidney disease (CKD) is associated with endothelial dysfunction and accelerated cardiovascular disease, which are largely driven by systemic oxidative stress and inflammation. Oxidative stress and inflammation in CKD are associated with and, in part, due to impaired activity of the cytoprotective transcription factor Nrf2. RTA dh404 is a synthetic oleanane triterpenoid compound which potently activates Nrf2 and inhibits the pro-inflammatory transcription factor NF-κB. This study was designed to test the effects of RTA dh404 on endothelial function, inflammation, and the Nrf2-mediated antioxidative system in the aorta of rats with CKD induced by 5/6 nephrectomy. Sham-operated rats served as controls. Subgroups of CKD rats were treated orally with RTA dh404 (2mg/kg/day) or vehicle for 12 weeks. The aortic rings from untreated CKD rats exhibited a significant reduction in the acetylcholine-induced relaxation response which was restored by RTA dh404 administration. Impaired endothelial function in the untreated CKD rats was accompanied by significant reduction of Nrf2 activity (nuclear translocation) and expression of its cytoprotective target genes, as well as accumulation of nitrotyrosine and upregulation of NAD(P)H oxidases, 12-lipoxygenase, MCP-1, and angiotensin II receptors in the aorta. These abnormalities were ameliorated by RTA dh404 administration, as demonstrated by the full or partial restoration of the expression of all the above analytes to sham control levels. Collectively, the data demonstrate that endothelial dysfunction in rats with CKD induced by 5/6 nephrectomy is associated with impaired Nrf2 activity in arterial tissue, which can be reversed with long term administration of RTA dh404

Transcriptome Profiles of Carcinoma-in-Situ and Invasive Non-Small Cell Lung Cancer as Revealed by SAGE

Non-small cell lung cancer (NSCLC) presents as a progressive disease spanning precancerous, preinvasive, locally invasive, and metastatic lesions. Identification of biological pathways reflective of these progressive stages, and aberrantly expressed genes associated with these pathways, would conceivably enhance therapeutic approaches to this devastating disease.Through the construction and analysis of SAGE libraries, we have determined transcriptome profiles for preinvasive carcinoma-in-situ (CIS) and invasive squamous cell carcinoma (SCC) of the lung, and compared these with expression profiles generated from both bronchial epithelium, and precancerous metaplastic and dysplastic lesions using Ingenuity Pathway Analysis. Expression of genes associated with epidermal development, and loss of expression of genes associated with mucociliary biology, are predominant features of CIS, largely shared with precancerous lesions. Additionally, expression of genes associated with xenobiotic metabolism/detoxification is a notable feature of CIS, and is largely maintained in invasive cancer. Genes related to tissue fibrosis and acute phase immune response are characteristic of the invasive SCC phenotype. Moreover, the data presented here suggests that tissue remodeling/fibrosis is initiated at the early stages of CIS. Additionally, this study indicates that alteration in copy-number status represents a plausible mechanism for differential gene expression in CIS and invasive SCC.This study is the first report of large-scale expression profiling of CIS of the lung. Unbiased expression profiling of these preinvasive and invasive lesions provides a platform for further investigations into the molecular genetic events relevant to early stages of squamous NSCLC development. Additionally, up-regulated genes detected at extreme differences between CIS and invasive cancer may have potential to serve as biomarkers for early detection

Network biomarkers, interaction networks and dynamical network biomarkers in respiratory diseases

Identification and validation of interaction networks and network biomarkers have become more critical and important in the development of disease-specific biomarkers, which are functionally changed during disease development, progression or treatment. The present review headlined the definition, significance, research and potential application for network biomarkers, interaction networks and dynamical network biomarkers (DNB). Disease-specific interaction networks, network biomarkers, or DNB have great significance in the understanding of molecular pathogenesis, risk assessment, disease classification and monitoring, or evaluations of therapeutic responses and toxicities. Protein-based DNB will provide more information to define the differences between the normal and pre-disease stages, which might point to early diagnosis for patients. Clinical bioinformatics should be a key approach to the identification and validation of disease-specific biomarkers

Discovery and Mechanistic Studies of Novel Redox Modulators for Treatment of Pancreatic Cancer

Pancreatic cancer remains a devastating disease and conventional chemotherapy shows modest efficacy because of drug resistance and systemic toxicity. The reprogramming of energy metabolism and oxidative stress are two hallmarks of cancer, and redox modulators have been developed as an attractive approach to treat cancer. At low or moderate levels, reactive oxygen species (ROS) serve as signaling molecules to mediate cellular functions; while at high levels, ROS induce oxidation of lipids, proteins, and DNA, ultimately leading to cell death. In this dissertation project, I aimed to identify novel redox modulators and provide a preclinical characterization of their mechanisms of action (MOAs) in pancreatic cancer cells. Through lead optimization of a previously studied quinazolinedione-based redox modulator, we identified QD394 with significant cytotoxicity in pancreatic cancer cells. Bru-seq technique and clustering analysis revealed remarkably similar post-treatment transcriptomic profiles between QD394 and napabucasin. Both compounds inhibited STAT3 phosphorylation, induced DNA damage, increased cellular ROS, and decreased the GSH/GSSG ratio. Moreover, QD394 caused an iron- and ROS-dependent, GPX4-mediated cell death, suggesting ferroptosis as a major mechanism. QD394 also decreased the expression of mitochondrial proteins, including LRPPRC and PNPT1 involved in mitochondrial RNA catabolic processes. A derivative QD394-Me was synthesized with improved plasma stability and reduced toxicity in mice compared to QD394. These results demonstrate that QD394 and QD394-Me represent novel ROS-inducing drug-like compounds warranting further development for the treatment of pancreatic cancer. Mito-Chlor, a mitochondrial-targeting triphenylphosphonium derivative of the nitrogen mustard chlorambucil, was identified to inhibit transcription of the mitochondrial genome through Bru-seq analysis, which is similar to a new ROS inducer SQD1 featuring a styrylquinoline-5, 8-dione core. Both Mito-Chlor and SQD1 decreased the mRNA levels of mitochondrial genes. However, only Mito-Chlor reduced their protein expression, and interfered with mitochondria membrane potential and oxidative phosphorylation. Both compounds increased cellular and mitochondrial ROS and stimulated similar downstream signaling related to oxidative stress and AP-1 transcription factors. These results establish SQD1 and Mito-Chlor as novel mitochondrial transcription inhibitors and redox modulators that may be applied to study cancer cell death related to mitochondrial function and redox signaling. Finally, a medium-throughput phenotypic screen of 20,000 diverse drug-like compounds produced a quinolin-chlorobenzothioate, QCBT7, as a potent hit with submicromolar cytotoxicity in cancer cells. Its structure is similar to 8-quinolinethiol hydrochloride (8TQ), a proteasome inhibitor. Proteasome inhibitors have shown anticancer efficacy. As a more stable derivative of 8TQ, QCBT7 caused the accumulation of ubiquitylated proteins, indicating its proteasome inhibitory activity. Additionally, QCBT7 increased the expression of a set of genes (PFKFB4, CHOP, HMOX1, and SLC7A11) at both nascent RNA and protein levels, similar to the known proteasome inhibitors MG132 and ixazomib. We have also identified PFKFB4 as a potential biomarker of proteasome inhibitors that can be used to monitor treatment response. Together, this study discovers that QCBT7 induces proteasome inhibition, hypoxic response, endoplasmic reticulum stress, and glycolysis, leading to cell death. In summary, the work as a whole provides a detailed characterization of redox modulators and their effects on cell death, mitochondria, or proteasome activity. We also identify novel ROS-related genes and pathways that could be beneficial for pancreatic cancer therapeutics. This thesis contributes to the overall understanding of ROS signaling in pancreatic cancer and the validity of ROS-modulating therapies. This collective work provides the foundation to improve the redox modulators discovered for testing in vivo.PHDMedicinal ChemistryUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163190/1/shuaihu_1.pd

FAIR and bias-free network modules for mechanism-based disease redefinitions

Even though chronic diseases are the cause of 60% of all deaths around the world, the underlying causes for most of them are not fully understood. Hence, diseases are defined based on organs and symptoms, and therapies largely focus on mitigating symptoms rather than cure. This is also reflected in the most commonly used disease classifications. The complex nature of diseases, however, can be better defined in terms of networks of molecular interactions. This research applies the approaches of network medicine – a field that uses network science for identifying and treating diseases – to multiple diseases with highly unmet medical need such as stroke and hypertension. The results show the success of this approach to analyse complex disease networks and predict drug targets for different conditions, which are validated through preclinical experiments and are currently in human clinical trials

An integrative, multi-scale, genome-wide model reveals the phenotypic landscape of Escherichia coli.

Given the vast behavioral repertoire and biological complexity of even the simplest organisms, accurately predicting phenotypes in novel environments and unveiling their biological organization is a challenging endeavor. Here, we present an integrative modeling methodology that unifies under a common framework the various biological processes and their interactions across multiple layers. We trained this methodology on an extensive normalized compendium for the gram-negative bacterium Escherichia coli, which incorporates gene expression data for genetic and environmental perturbations, transcriptional regulation, signal transduction, and metabolic pathways, as well as growth measurements. Comparison with measured growth and high-throughput data demonstrates the enhanced ability of the integrative model to predict phenotypic outcomes in various environmental and genetic conditions, even in cases where their underlying functions are under-represented in the training set. This work paves the way toward integrative techniques that extract knowledge from a variety of biological data to achieve more than the sum of their parts in the context of prediction, analysis, and redesign of biological systems