Reconstruction And Analysis Of The Molecular Programs Involved In Deciding Mammalian Cell Fate

Cellular function hinges on the ability to process information from the outside environment into specific decisions. Ultimately these processes decide cell fate, whether it be to undergo proliferation, apoptosis, differentiation, migration and other cellular functions. These processes can be thought of as finely tuned programs evolved to maintain robust function in spite of environmental perturbations. Malfunctions in these programs can lead to improper cellular function and various disease states. To develop more effective, personalized and even preventative therapeutics we must attain a better, more detailed, understanding of the programs involved. To this end we have employed mechanistic mathematical modeling to a variety of complex cellular programs. In Chapter 1, we review a variety of computational methods have have been used successfully in different areas of biotechnology. In Chapter 2, we present the software platform UNIVERSAL, which was developed in our lab. UNIVERSAL is an extensible code generation framework for Mac OS X which produces editable, fully commented platform-independent physiochemical model code in several common programming languages from a variety of inputs. UNIVERSAL generates mass-action ODE models of intracellular signal transduction processes and model analysis code, such as adjoint sensitivity balances. We employed the mass-action ODE framework, as generated by UNIVERSAL, commonly throughout the studies presented here. In Chapter 3, we introduce a variety of modeling strategies in the context of EGF-induced Eukaryotic transcription. We demon- strated the ability to make meaningful and statistically consistent model predictions despite considerable parametric uncertainty. In Chapter 4, we constructed a mathematical model to study a mechanism for androgen independent proliferation in prostate cancer. Analysis of the model provided insight into the importance of network components as a function of androgen dependence. Translation became progressively more important in androgen independent cells. Moreover, the analysis suggested that direct targeting of the translational machinery, specifically eIF4E, could be efficacious in androgen independent prostate cancers. In Chapter 5, A mathematical model of RA-induced cell-cycle arrest and differentiation was formulated and tested against BLR1 wild-type (wt) knock-out and knock-in HL-60 cell lines with and without RA. The ensemble of HL-60 models recapitulated the positive feedback between BLR1 and MAPK signaling. We investigated the robustness of the HL-60 network architecture to structural perturbations and generated experimentally testable hypotheses for future study. In Chapter 6, we carried out experimental studies to reduce the structural uncertainty of the HL60 model. Result from the HL-60 model cRaf as the most critical component of the MAPK cascade. To investigate the role of cRaf in RA-induced differentiation we observed the effect of cRaf kinase inhibition. Furthermore, we interrogated a panel of proteins to identify RA responsive cRaf binding partner. We found that cRaf kinase activity was necessary for functional ROS response, but not for RA-induced growth arrest. Based on our findings, we proposed a simplified ontrol architecture for sustained MAPK activation. Computational modeling identified a bistability suggesting that the MAPK activation was self-sustaining. This result was experimentally validated, and could explain previously observed cellular memory effects. Taken together, the results of these studies demonstrated that computational modeling can identify therapeutically relevant targets for human disease such as cancer. Furthermore, we demonstrated the ability of an iterative strategy between computational and experimental analysis to provide insight on key regulator circuits for complex programs involved in deciding cell fate

Analysis of the Molecular Networks in Androgen Dependent and Independent Prostate Cancer Revealed Fragile and Robust Subsystems

Androgen ablation therapy is currently the primary treatment for metastatic prostate cancer. Unfortunately, in nearly all cases, androgen ablation fails to permanently arrest cancer progression. As androgens like testosterone are withdrawn, prostate cancer cells lose their androgen sensitivity and begin to proliferate without hormone growth factors. In this study, we constructed and analyzed a mathematical model of the integration between hormone growth factor signaling, androgen receptor activation, and the expression of cyclin D and Prostate-Specific Antigen in human LNCaP prostate adenocarcinoma cells. The objective of the study was to investigate which signaling systems were important in the loss of androgen dependence. The model was formulated as a set of ordinary differential equations which described 212 species and 384 interactions, including both the mRNA and protein levels for key species. An ensemble approach was chosen to constrain model parameters and to estimate the impact of parametric uncertainty on model predictions. Model parameters were identified using 14 steady-state and dynamic LNCaP data sets taken from literature sources. Alterations in the rate of Prostatic Acid Phosphatase expression was sufficient to capture varying levels of androgen dependence. Analysis of the model provided insight into the importance of network components as a function of androgen dependence. The importance of androgen receptor availability and the MAPK/Akt signaling axes was independent of androgen status. Interestingly, androgen receptor availability was important even in androgen-independent LNCaP cells. Translation became progressively more important in androgen-independent LNCaP cells. Further analysis suggested a positive synergy between the MAPK and Akt signaling axes and the translation of key proliferative markers like cyclin D in androgen-independent cells. Taken together, the results support the targeting of both the Akt and MAPK pathways. Moreover, the analysis suggested that direct targeting of the translational machinery, specifically eIF4E, could be efficacious in androgen-independent prostate cancers

Tissue engineering strategies to bioengineer the ageing skin phenotype in vitro

Human skin ageing is a complex and heterogeneous process, which is influenced by genetically determined intrinsic factors and accelerated by cumulative exposure to extrinsic stressors. In the current world ageing demographic, there is a requirement for a bioengineered ageing skin model, to further the understanding of the intricate molecular mechanisms of skin ageing, and provide a distinct and biologically relevant platform for testing actives and formulations. There have been many recent advances in the development of skin models that recapitulate aspects of the ageing phenotype in vitro. This review encompasses the features of skin ageing, the molecular mechanisms that drive the ageing phenotype, and tissue engineering strategies that have been utilised to bioengineer ageing skin in vitro

Cell Senescence-Independent Changes of Human Skin Fibroblasts with Age

Skin ageing is defined, in part, by collagen depletion and fragmentation that leads to a loss of mechanical tension. This is currently believed to reflect, in part, the accumulation of senescent cells. We compared the expression of genes and proteins for components of the extracellular matrix (ECM) as well as their regulators and found that in vitro senescent cells produced more matrix metalloproteinases (MMPs) than proliferating cells from adult and neonatal donors. This was consistent with previous reports of senescent cells contributing to increased matrix degradation with age; however, cells from adult donors proved significantly less capable of producing new collagen than neonatal or senescent cells, and they showed significantly lower myofibroblast activation as determined by the marker α-SMA. Functionally, adult cells also showed slower migration than neonatal cells. We concluded that the increased collagen degradation of aged fibroblasts might reflect senescence, the reduced collagen production likely reflects senescence-independent processes

Genomic and molecular characterization of preterm birth.

Preterm birth (PTB) complications are the leading cause of long-term morbidity and mortality in children. By using whole blood samples, we integrated whole-genome sequencing (WGS), RNA sequencing (RNA-seq), and DNA methylation data for 270 PTB and 521 control families. We analyzed this combined dataset to identify genomic variants associated with PTB and secondary analyses to identify variants associated with very early PTB (VEPTB) as well as other subcategories of disease that may contribute to PTB. We identified differentially expressed genes (DEGs) and methylated genomic loci and performed expression and methylation quantitative trait loci analyses to link genomic variants to these expression and methylation changes. We performed enrichment tests to identify overlaps between new and known PTB candidate gene systems. We identified 160 significant genomic variants associated with PTB-related phenotypes. The most significant variants, DEGs, and differentially methylated loci were associated with VEPTB. Integration of all data types identified a set of 72 candidate biomarker genes for VEPTB, encompassing genes and those previously associated with PTB. Notably, PTB-associated genes RAB31 and RBPJ were identified by all three data types (WGS, RNA-seq, and methylation). Pathways associated with VEPTB include EGFR and prolactin signaling pathways, inflammation- and immunity-related pathways, chemokine signaling, IFN-γ signaling, and Notch1 signaling. Progress in identifying molecular components of a complex disease is aided by integrated analyses of multiple molecular data types and clinical data. With these data, and by stratifying PTB by subphenotype, we have identified associations between VEPTB and the underlying biology

Mouse Hair Cycle Expression Dynamics Modeled as Coupled Mesenchymal and Epithelial Oscillators

<div><p>The hair cycle is a dynamic process where follicles repeatedly move through phases of growth, retraction, and relative quiescence. This process is an example of temporal and spatial biological complexity. Understanding of the hair cycle and its regulation would shed light on many other complex systems relevant to biological and medical research. Currently, a systematic characterization of gene expression and summarization within the context of a mathematical model is not yet available. Given the cyclic nature of the hair cycle, we felt it was important to consider a subset of genes with periodic expression. To this end, we combined several mathematical approaches with high-throughput, whole mouse skin, mRNA expression data to characterize aspects of the dynamics and the possible cell populations corresponding to potentially periodic patterns. In particular two gene clusters, demonstrating properties of out-of-phase synchronized expression, were identified. A mean field, phase coupled oscillator model was shown to quantitatively recapitulate the synchronization observed in the data. Furthermore, we found only one configuration of positive-negative coupling to be dynamically stable, which provided insight on general features of the regulation. Subsequent bifurcation analysis was able to identify and describe alternate states based on perturbation of system parameters. A 2-population mixture model and cell type enrichment was used to associate the two gene clusters to features of background mesenchymal populations and rapidly expanding follicular epithelial cells. Distinct timing and localization of expression was also shown by RNA and protein imaging for representative genes. Taken together, the evidence suggests that synchronization between expanding epithelial and background mesenchymal cells may be maintained, in part, by inhibitory regulation, and potential mediators of this regulation were identified. Furthermore, the model suggests that impairing this negative regulation will drive a bifurcation which may represent transition into a pathological state such as hair miniaturization.</p></div

Predictions on expression dynamics determined by the two population model.

<p>(<b>A</b>) Histogram of the coefficient of determination (COD) for model estimated expression, shown for all probesets (dark blue background) and probesets identified as low frequency oscillators (LOF, light pink foreground). (<b>B</b>) The magnitude of the t-statistic used to estimate differential expression between the two estimated populations, shown for all probesets (dark blue background) and probesets identified as low frequency oscillators (LOF, light pink foreground). (<b>C</b>) Normalized expression data, , for the two model populations. The left column shows actual expression data and probesets are ordered by the magnitude of the t-statistic. The right column shows expression estimated by the model ordered as in left column.</p

Simulation results from mean field coupled oscillator model.

<p>All curves are calculated by solving EQ 2 (for additional details also see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003914#pcbi.1003914.e135" target="_blank">EQ 11</a>). The magnitude of the first order parameter, shown in red, can be easily calculated from the individual order parameters, and . Here, is related to the first order parameter in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003914#pcbi-1003914-g002" target="_blank">Figure 2</a>, also shown in red (note the subscript was dropped for convenience). (<b>A</b>) Simulation results of for configuration one (config 1, solid) and configuration two (confg 2 dashed). Here config 1 relates to cluster one having negative coupling (). Note that the synchronization was stable only in config 1. We also show the incoherent result when configuration two () was set near, the steady-state value. The top plots show the values of for both cluster 1 (green) and 2 (blue) on the unit circle at time = 1, 12 and 40 days. Note that the clusters are out-of-phase. A movie of the individual oscillators corresponding to configuration 1 is available as Supplementary <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003914#pcbi.1003914.s019" target="_blank">file S2</a>. (<b>B</b>) A simulated bifurcation analysis of the model showing the stable attractors for at different values of (red dots). We note that the simulation results agree with the analytical results of , loss of the incoherent state, and , the upper bound of the wave state. The estimated period of the hair cycle is shown by the dashed line. The values corresponding to the observed hair system are highlighted, note that it is near a critical change in that corresponds to a sharp decrease in the period.</p

Localization of selected candidate genes from the predicted anagen expanding population.

<p>In Situ Hybridization (ISH; RNA) and immunofluorescence (protein) were performed on mouse skin sections taken from telogen (day0) or anagen (Day 16) phases of the hair cycle, determined by Supplementary <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003914#pcbi.1003914.s008" target="_blank">Figure S8A</a>. DAPI was used as a counterstain for cell nuclei (blue). The expression of candidate genes for ISH is seen as bright foci (red and green) in specific cell types. Note comparisons to technical negative control and positive controls in Supplementary <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003914#pcbi.1003914.s009" target="_blank">Figure S9</a>. Foxn1 (red) was identified as a candidate matrix derived cell marker and was used here as a positive control for localization to matrix cells <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003914#pcbi.1003914-Mecklenburg1" target="_blank">[50]</a>. (<b>A</b>) ISH: R1 and 2 shows RNA expression for Ovol1 and Smad6 (green), respectively, which were predicted to be expressed in follicle cell populations that expand during the anagen phase. No expression was observed during the telogen phase (Day 0). (<b>B</b>) Protein staining by immunofluorescence: R1 and 2 shows RNA expression for Ovol1 and Smad6 (green), respectively. Again, no expression was observed during the telogen phase (Day 0).</p