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1.  A bioinformatics approach reveals novel interactions of the OVOL transcription factors in the regulation of epithelial – mesenchymal cell reprogramming and cancer progression 
BMC Systems Biology  2014;8:29.
Background
Mesenchymal to Epithelial Transition (MET) plasticity is critical to cancer progression, and we recently showed that the OVOL transcription factors (TFs) are critical regulators of MET. Results of that work also posed the hypothesis that the OVOLs impact MET in a range of cancers. We now test this hypothesis by developing a model, OVOL Induced MET (OI-MET), and sub-model (OI-MET-TF), to characterize differential gene expression in MET common to prostate cancer (PC) and breast cancer (BC).
Results
In the OI-MET model, we identified 739 genes differentially expressed in both the PC and BC models. For this gene set, we found significant enrichment of annotation for BC, PC, cancer, and MET, as well as regulation of gene expression by AP1, STAT1, STAT3, and NFKB1. Focusing on the target genes for these four TFs plus the OVOLs, we produced the OI-MET-TF sub-model, which shows even greater enrichment for these annotations, plus significant evidence of cooperation among these five TFs. Based on known gene/drug interactions, we prioritized targets in the OI-MET-TF network for follow-on analysis, emphasizing the clinical relevance of this work. Reflecting these results back to the OI-MET model, we found that binding motifs for the TF pair AP1/MYC are more frequent than expected and that the AP1/MYC pair is significantly enriched in binding in cancer models, relative to non-cancer models, in these promoters. This effect is seen in both MET models (solid tumors) and in non-MET models (leukemia). These results are consistent with our hypothesis that the OVOLs impact cancer susceptibility by regulating MET, and extend the hypothesis to include mechanisms not specific to MET.
Conclusions
We find significant evidence of the OVOL, AP1, STAT1, STAT3, and NFKB1 TFs having important roles in MET, and more broadly in cancer. We prioritize known gene/drug targets for follow-up in the clinic, and we show that the AP1/MYC TF pair is a strong candidate for intervention.
doi:10.1186/1752-0509-8-29
PMCID: PMC4008156  PMID: 24612742
Metastasis; Migration; Tumor progression; Systems biology; Transcription factors; Signal transduction; Therapeutics
2.  The identification of gene expression profiles associated with progression of human diabetic neuropathy 
Brain  2011;134(11):3222-3235.
Diabetic neuropathy is a common complication of diabetes. While multiple pathways are implicated in the pathophysiology of diabetic neuropathy, there are no specific treatments and no means to predict diabetic neuropathy onset or progression. Here, we identify gene expression signatures related to diabetic neuropathy and develop computational classification models of diabetic neuropathy progression. Microarray experiments were performed on 50 samples of human sural nerves collected during a 52-week clinical trial. A series of bioinformatics analyses identified differentially expressed genes and their networks and biological pathways potentially responsible for the progression of diabetic neuropathy. We identified 532 differentially expressed genes between patient samples with progressing or non-progressing diabetic neuropathy, and found these were functionally enriched in pathways involving inflammatory responses and lipid metabolism. A literature-derived co-citation network of the differentially expressed genes revealed gene subnetworks centred on apolipoprotein E, jun, leptin, serpin peptidase inhibitor E type 1 and peroxisome proliferator-activated receptor gamma. The differentially expressed genes were used to classify a test set of patients with regard to diabetic neuropathy progression. Ridge regression models containing 14 differentially expressed genes correctly classified the progression status of 92% of patients (P < 0.001). To our knowledge, this is the first study to identify transcriptional changes associated with diabetic neuropathy progression in human sural nerve biopsies and describe their potential utility in classifying diabetic neuropathy. Our results identifying the unique gene signature of patients with progressive diabetic neuropathy will facilitate the development of new mechanism-based diagnostics and therapies.
doi:10.1093/brain/awr228
PMCID: PMC3212712  PMID: 21926103
biomarkers; diabetic neuropathy; classification model; sural nerve; gene expression
3.  LRpath analysis reveals common pathways dysregulated via DNA methylation across cancer types 
BMC Genomics  2012;13:526.
Background
The relative contribution of epigenetic mechanisms to carcinogenesis is not well understood, including the extent to which epigenetic dysregulation and somatic mutations target similar genes and pathways. We hypothesize that during carcinogenesis, certain pathways or biological gene sets are commonly dysregulated via DNA methylation across cancer types. The ability of our logistic regression-based gene set enrichment method to implicate important biological pathways in high-throughput data is well established.
Results
We developed a web-based gene set enrichment application called LRpath with clustering functionality that allows for identification and comparison of pathway signatures across multiple studies. Here, we employed LRpath analysis to unravel the commonly altered pathways and other gene sets across ten cancer studies employing DNA methylation data profiled with the Illumina HumanMethylation27 BeadChip. We observed a surprising level of concordance in differential methylation across multiple cancer types. For example, among commonly hypomethylated groups, we identified immune-related functions, peptidase activity, and epidermis/keratinocyte development and differentiation. Commonly hypermethylated groups included homeobox and other DNA-binding genes, nervous system and embryonic development, and voltage-gated potassium channels. For many gene sets, we observed significant overlap in the specific subset of differentially methylated genes. Interestingly, fewer DNA repair genes were differentially methylated than expected by chance.
Conclusions
Clustering analysis performed with LRpath revealed tightly clustered concepts enriched for differential methylation. Several well-known cancer-related pathways were significantly affected, while others were depleted in differential methylation. We conclude that DNA methylation changes in cancer tend to target a subset of the known cancer pathways affected by genetic aberrations.
doi:10.1186/1471-2164-13-526
PMCID: PMC3505188  PMID: 23033966
4.  Transcriptional Profiling of Diabetic Neuropathy in the BKS db/db Mouse 
Diabetes  2011;60(7):1981-1989.
OBJECTIVE
A better understanding of the molecular mechanisms underlying the development and progression of diabetic neuropathy (DN) is essential for the design of mechanism-based therapies. We examined changes in global gene expression to define pathways regulated by diabetes in peripheral nerve.
RESEARCH DESIGN AND METHODS
Microarray data for 24-week-old BKS db/db and db/+ mouse sciatic nerve were analyzed to define significantly differentially expressed genes (DEGs); DEGs were further analyzed to identify regulated biological processes and pathways. Expression profile clustering was performed to identify coexpressed DEGs. A set of coexpressed lipid metabolism genes was used for promoter sequence analysis.
RESULTS
Gene expression changes are consistent with structural changes of axonal degeneration. Pathways regulated in the db/db nerve include lipid metabolism, carbohydrate metabolism, energy metabolism, peroxisome proliferator–activated receptor signaling, apoptosis, and axon guidance. Promoter sequences of lipid metabolism–related genes exhibit evidence of coregulation of lipid metabolism and nervous system development genes.
CONCLUSIONS
Our data support existing hypotheses regarding hyperglycemia-mediated nerve damage in DN. Moreover, our analyses revealed a possible coregulation mechanism connecting hyperlipidemia and axonal degeneration.
doi:10.2337/db10-1541
PMCID: PMC3121428  PMID: 21617178
5.  Characterizing the dynamics of CD4+ T cell priming within a lymph node1 
Generating adaptive immunity after infection or immunization requires physical interaction within a lymph node (LN) T-zone between antigen-bearing dendritic cells (DCs) and rare cognate T cells. Many fundamental questions remain regarding the dynamics of DC-CD4+ T cell interactions leading to priming. For example, it is not known how the production of primed CD4+ T cells relates to the numbers of cognate T cells, antigen-bearing DCs, or peptide-MHCII level on the DC.
To address these questions, we developed an agent-based model of a LN to examine the relationships among cognate T cell frequency, DC density, parameters characterizing DC-T interactions and the output of primed T cells. We found that the output of primed CD4+ T cells is linearly related to cognate frequency, but non-linearly related to the number of antigen-bearing DCs present during infection. This addresses the applicability of two photon microscopy studies to understanding actual infection dynamics, as these types of experiments increase the cognate frequency by orders of magnitude as compared to physiologic levels. We found a trade-off between the quantity of peptide-MHCII on the surface of individual DCs and number of antigen-bearing DCs present in the LN in contributing to the production of primed CD4+ T cells. Interestingly, pMHCII half-life plays a minor, although still significant, role in determining CD4+ T cell priming, unlike the primary role that has been suggested for CD8+ T cell priming. Finally, we identify several pathogen-targeted mechanisms that, if altered in their efficiency, can significantly effect the generation of primed CD4+ T cells.
doi:10.4049/jimmunol.0903117
PMCID: PMC3153313  PMID: 20154206
infection; pMHC; priming; binding; agent-based model; computer simulation
6.  Literature-based discovery of diabetes- and ROS-related targets 
BMC Medical Genomics  2010;3:49.
Background
Reactive oxygen species (ROS) are known mediators of cellular damage in multiple diseases including diabetic complications. Despite its importance, no comprehensive database is currently available for the genes associated with ROS.
Methods
We present ROS- and diabetes-related targets (genes/proteins) collected from the biomedical literature through a text mining technology. A web-based literature mining tool, SciMiner, was applied to 1,154 biomedical papers indexed with diabetes and ROS by PubMed to identify relevant targets. Over-represented targets in the ROS-diabetes literature were obtained through comparisons against randomly selected literature. The expression levels of nine genes, selected from the top ranked ROS-diabetes set, were measured in the dorsal root ganglia (DRG) of diabetic and non-diabetic DBA/2J mice in order to evaluate the biological relevance of literature-derived targets in the pathogenesis of diabetic neuropathy.
Results
SciMiner identified 1,026 ROS- and diabetes-related targets from the 1,154 biomedical papers (http://jdrf.neurology.med.umich.edu/ROSDiabetes/). Fifty-three targets were significantly over-represented in the ROS-diabetes literature compared to randomly selected literature. These over-represented targets included well-known members of the oxidative stress response including catalase, the NADPH oxidase family, and the superoxide dismutase family of proteins. Eight of the nine selected genes exhibited significant differential expression between diabetic and non-diabetic mice. For six genes, the direction of expression change in diabetes paralleled enhanced oxidative stress in the DRG.
Conclusions
Literature mining compiled ROS-diabetes related targets from the biomedical literature and led us to evaluate the biological relevance of selected targets in the pathogenesis of diabetic neuropathy.
doi:10.1186/1755-8794-3-49
PMCID: PMC2988702  PMID: 20979611

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