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Modeling Macrophage Polarization and Its Effect on Cancer Treatment Success

Open Journal of Immunology 2018年第2期8卷 36-80页

作者： valentin morales Luis Soto-Ortiz Department of Engineering and Technologies East Los Angeles College Monterey Park USA Department of Mathematics East Los Angeles College Monterey Park USA

Positive feedback loops drive immune cell polarization toward a pro-tumor phenotype that accentuates immunosuppression and tumor angiogenesis. This phenotypic switch leads to the escape of cancer cells from immune destruction. These positive feedback loops are generated by cytokines such as TGF-β, Interleukin-10 and Interleukin-4, which are responsible for the polarization of monocytes and M1 macrophages into pro-tumor M2 macrophages, and the polarization of naive helper T cells intopro-tumor Th2 cells. In this article, we present a deterministic ordinary differential equation (ODE) model that includes key cellular interactions and cytokine signaling pathways that lead to immune cell polarization in the tumor microenvironment. The model was used to simulate various cancer treatments in silico. We identified combination therapies that consist of M1 macrophages or Th1 helper cells, coupled with an anti-angiogenic treatment, that are robust with respect to immune response strength, initial tumor size and treatment resistance. We also identified IL-4 and IL-10 as the targets that should be neutralized in order to make these combination treatments robust with respect to immune cell polarization. The model simulations confirmed a hypothesis based on published experimental evidence that a polarization into the M1 and Th1 phenotypes to increase the M1-to-M2 and Th1-to-Th2 ratios plays a significant role in treatment success. Our results highlight the importance of immune cell reprogramming as a viable strategy to eradicate a highly vascularized tumor when the strength of the immune response is characteristically weak and cell polarization to the pro-tumor phenotype has occurred.

关键词： Macrophage Polarization Feedback Loops Immunosuppression Mathematical Modeling Cancer Therapy

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Maize Gene Regulatory Network for Phenolic Metabolism

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Molecular Plant 2017年第3期10卷 498-515页

作者： Fan Yang Wei Li Nan Jiang Haidong Yu Kengo Morohashi Wilberforce Zachary Oumal Daniel E. morales-Mantilla Fabio Andres Gomez-Canol Eric Mukundi Luis Daniel Prada-Salcedo Roberto Alers Velazquez Jasmin valentin Maria Katherine Mejia-Guerra John Gray Andrea I. Doseff Erich Grotewold Center for Applied Sciences (CAPS) Department of Molecular Genetics Department of Physiology and Cell Biology Heart and Lung Research Institute Molecular Cellular and Developmental Biology (MCDB) Graduate Program Success in Graduate Education (SiGuE) Program The Ohio State University Columbus OH 43210 USA Department of Biological Sciences University of Toledo Toledo OH 43560 USA

The translation of the genotype into phenotype, represented for example by the expression of genes encod- ing enzymes required for the biosynthesis of phytochemicals that are important for interaction of plants with the environment, is largely carried out by transcription factors （TFs） that recognize specific cis-regulatory elements in the genes that they control. TFs and their target genes are organized in gene regulatory net- works （GRNs）, and thus uncovering GRN architecture presents an important biological challenge necessary to explain gene regulation. Linking TFs to the genes they control, central to understanding GRNs, can be car- ried out using gene- or TF-centered approaches. In this study, we employed a gene-centered approach uti- lizing the yeast one-hybrid assay to generate a network of protein-DNA interactions that participate in the transcriptional control of genes involved in the biosynthesis of maize phenolic compounds including gen- eral phenylpropanoids, lignins, and flavonoids. We identified 1100 protein-DNA interactions involving 54 phenolic gene promoters and 568 TFs. A set of 11 TFs recognized 10 or more promoters, suggesting a role in coordinating pathway gene expression. The integration of the gene-centered network with informa- tion derived from TF-centered approaches provides a foundation for a phenolics GRN characterized by in- terlaced feed-forward loops that link developmental regulators with biosynthetic genes.

关键词： phenylpropanoid flavonoid yeast one-hybrid chromatin immunoprecipitation

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