Epithelial-to-mesenchymal transition (EMT) is a developmental process hijacked by cancer cells to leave the primary tumor site, invade surrounding tissue, and establish distant metastases. A hallmark of EMT is the loss of E-cadherin expression, and one major signal for the induction of EMT is transforming growth factor beta (TGFβ, which is dysregulated in up to 40% of hepatocellular carcinoma (HCC). We have constructed an EMT network of 70 nodes and 135 edges by integrating the signaling pathways involved in developmental EMT and known dysregulations in invasive HCC. We then used discrete dynamic modeling to understand the dynamics of the EMT network driven by TGFβ. Our network model recapitulates known dysregulations during the induction of EMT and predicts the activation of the Wnt and Sonic hedgehog (SHH) signaling pathways during this process. We show, across multiple murine (P2E and P2M) and human HCC cell lines (Huh7, PLC/PRF/5, HLE, and HLF), that the TGFβ signaling axis is a conserved driver of mesenchymal phenotype HCC and confirm that Wnt and SHH signaling are induced in these cell lines. Furthermore, we identify by network analysis eight regulatory feedback motifs that stabilize the EMT process and show that these motifs involve cross-talk among multiple major pathways. Our model will be useful in identifying potential therapeutic targets for the suppression of EMT, invasion and metastasis in HCC.

Epithelial-to-mesenchymal transition (EMT) is a developmental process hijacked by cancer cells to leave the primary tumor site, invade surrounding tissue and establish distant metastases. A hallmark of EMT is the loss of E-cadherin expression, and one major signal for the induction of EMT is transforming growth factor beta (TGFβ), which is dysregulated in up to 40% of hepatocellular carcinoma (HCC). We aim to identify network perturbations that suppress TGFβ-driven EMT, with the goal of suppressing invasive properties of cancer cells. We use a systems-level Boolean dynamic model of EMT to systematically screen individual and combination perturbations (inhibition or constitutive activation of up to four nodes). We use a recently developed network control approach to understand the mechanism through which the combinatorial interventions suppress EMT. We test the results of our in silico analysis using siRNA. Our model predicts that targeting key elements of feedback loops in combination with the SMAD complex is more effective than suppressing the SMAD complex alone. We demonstrate experimentally that expression of a majority of these elements is enriched in mesenchymal relative to epithelial phenotype HCC cell lines. An siRNA screen of the predicted combinations confirms that many targeting strategies suppress TGFβ-driven EMT measured by E-cadherin expression and cell migration. Our analysis reveals that some perturbations give rise to hybrid states intermediate to the epithelial and mesenchymal states. Our results indicate that EMT is driven by an interconnected signaling network and many apparently successful single interventions may lead to steady states that are in-between epithelial and mesenchymal states. As these putative hybrid or partial EMT states may retain invasive properties, our results suggest that combinatorial therapies are necessary to fully suppress invasive properties of tumor cells.

We present a novel methodology to construct a Boolean dynamic model from time series metagenomic information and integrate this modeling with genome-scale metabolic network reconstructions to identify metabolic underpinnings for microbial interactions. We apply this in the context of a critical health issue: clindamycin antibiotic treatment and opportunistic Clostridium difficile infection. Our model recapitulates known dynamics of clindamycin antibiotic treatment and C. difficile infection and predicts therapeutic probiotic interventions to suppress C. difficile infection. Genome-scale metabolic network reconstructions reveal metabolic differences between community members and are used to explore the role of metabolism in the observed microbial interactions. In vitro experimental data validate a key result of our computational model, that B. intestinihominis can in fact slow C. difficile growth.

Large granular lymphocyte (LGL) leukemia is a spectrum of rare lymphoproliferative diseases of T lymphocytes and natural killer cells. These diseases frequently present with splenomegaly, neutropenia, and autoimmune diseases like rheumatoid arthritis. LGL leukemia is more commonly of a chronic, indolent nature; however, rarely, they have an aggressive course. LGL leukemia is thought to arise from chronic antigen stimulation, which drives long term cell survival through the activation of survival signaling pathways and suppression of pro-apoptotic signals. These include Jak-Stat, Mapk, Pi3k-Akt, sphingolipid, and IL-15/Pdgf signaling. Treatment traditionally includes immunosuppression with low dose methotrexate, cyclophosphamide, and other immunosuppressive agents; however, prospective and retrospective studies reveal very limited success. New studies surrounding Jak-Stat signaling suggest this may reveal new avenues for LGL leukemia therapeutics.

Targeted drugs disrupting proteins that are dysregulated in cancer have emerged as promising treatments because of their specificity to cancer cell aberrations and thus their improved side effect profile. However, their success remains limited, largely due to existing or emergent therapy resistance. We suggest that this is due to limited understanding of the entire relevant cellular landscape. A class of mathematical models called discrete dynamic network models can be used to understand the integrated effect of an individual tumor's aberrations. We review the recent literature on discrete dynamic models of cancer and highlight their predicted therapeutic strategies. We believe dynamic network modeling can be used to drive treatment decision-making in a personalized manner to direct improved treatments in cancer.

BackgroundThe incorporation of annotated sequence information from multiple related species in commonly used databases (Ensembl, Flybase, Saccharomyces Genome Database, Wormbase, etc.) has increased dramatically over the last few years. This influx of information has provided a considerable amount of raw material for evaluation of evolutionary relationships. To aid in the process, we have developed JCoDA (Java Codon Delimited Alignment) as a simple-to-use visualization tool for the detection of site specific and regional positive/negative evolutionary selection amongst homologous coding sequences.ResultsJCoDA accepts user-inputted unaligned or pre-aligned coding sequences, performs a codon-delimited alignment using ClustalW, and determines the dN/dS calculations using PAML (Phylogenetic Analysis Using Maximum Likelihood, yn00 and codeml) in order to identify regions and sites under evolutionary selection. The JCoDA package includes a graphical interface for Phylip (Phylogeny Inference Package) to generate phylogenetic trees, manages formatting of all required file types, and streamlines passage of information between underlying programs. The raw data are output to user configurable graphs with sliding window options for straightforward visualization of pairwise or gene family comparisons. Additionally, codon-delimited alignments are output in a variety of common formats and all dN/dS calculations can be output in comma-separated value (CSV) format for downstream analysis. To illustrate the types of analyses that are facilitated by JCoDA, we have taken advantage of the well studied sex determination pathway in nematodes as well as the extensive sequence information available to identify genes under positive selection, examples of regional positive selection, and differences in selection based on the role of genes in the sex determination pathway.ConclusionsJCoDA is a configurable, open source, user-friendly visualization tool for performing evolutionary analysis on homologous coding sequences. JCoDA can be used to rapidly screen for genes and regions of genes under selection using PAML. It can be freely downloaded at http://www.tcnj.edu/~nayaklab/jcoda.

We present the epithelial-to-mesenchymal transition (EMT) from two perspectives: experimental/ technological and theoretical. We review the state of the current understanding of the regulatory networks that underlie EMT in three physiological contexts: embryonic development, wound healing, and metastasis. We describe the existing experimental systems and manipulations used to better understand the molecular participants and factors that influence EMT and metastasis. We review the mathematical models of the regulatory networks involved in EMT, with a particular emphasis on the network motifs (such as coupled feedback loops) that can generate intermediate hybrid states between the epithelial and mesenchymal states. Ultimately, the understanding gained about these networks should be translated into methods to control phenotypic outcomes, especially in the context of cancer therapeutic strategies. We present emerging theories of how to drive the dynamics of a network toward a desired dynamical attractor (e.g. an epithelial cell state) and emerging synthetic biology technologies to monitor and control the state of cells.

Backgroundc-Met, a high-affinity receptor for Hepatocyte Growth Factor (HGF), plays a critical role in tumor growth, invasion, and metastasis. Hepatocellular carcinoma (HCC) patients with activated HGF/c-Met signaling have a significantly worse prognosis. Targeted therapies using c-Met tyrosine kinase inhibitors are currently in clinical trials for HCC, although receptor tyrosine kinase inhibition in other cancers has demonstrated early success. Unfortunately, therapeutic effect is frequently not durable due to acquired resistance.MethodsWe utilized the human MHCC97-H c-Met positive (c-Met+) HCC cell line to explore the compensatory survival mechanisms that are acquired after c-Met inhibition. MHCC97-H cells with stable c-Met knockdown (MHCC97-H c-Met KD cells) were generated using a c-Met shRNA vector with puromycin selection and stably transfected scrambled shRNA as a control. Gene expression profiling was conducted, and protein expression was analyzed to characterize MHCC97-H cells after blockade of the c-Met oncogene. A high-throughput siRNA screen was performed to find putative compensatory survival proteins, which could drive HCC growth in the absence of c-Met. Findings from this screen were validated through subsequent analyses.ResultsWe have previously demonstrated that treatment of MHCC97-H cells with a c-Met inhibitor, PHA665752, results in stasis of tumor growth in vivo. MHCC97-H c-Met KD cells demonstrate slower growth kinetics, similar to c-Met inhibitor treated tumors. Using gene expression profiling and siRNA screening against 873 kinases and phosphatases, we identified ErbB3 and TGF-α as compensatory survival factors that are upregulated after c-Met inhibition. Suppressing these factors in c-Met KD MHCC97-H cells suppresses tumor growth in vitro. In addition, we found that the PI3K/Akt signaling pathway serves as a negative feedback signal responsible for the ErbB3 upregulation after c-Met inhibition. Furthermore, in vitro studies demonstrate that combination therapy with PHA665752 and Gefitinib (an EGFR inhibitor) significantly reduced cell viability and increased apoptosis compared with either PHA665752 or Gefitinib treatment alone.Conclusionc-Met inhibition monotherapy is not sufficient to eliminate c-Met+ HCC tumor growth. Inhibition of both c-Met and EGFR oncogenic pathways provides superior suppression of HCC tumor growth. Thus, combination of c-Met and EGFR inhibition may represent a superior therapeutic regimen for c-Met+ HCC.