The Evolution of Virulence in RNA Viruses under a Competition–Colonization Trade-Off

RNA viruses exist in large intra-host populations which display great genotypic and phenotypic diversity. We analyze a model of viral competition between two viruses infecting a constantly replenished cell pool. We assume a trade-off between the ability of the virus to colonize new cells (cell killing rate or virulence) and its local competitiveness (replicative success within coinfected cells). We characterize the conditions that allow for viral spread by means of the basic reproductive number and show that a local coexistence equilibrium exists, which is asymptotically stable. At this equilibrium, the less virulent competitor has a reproductive advantage over the more virulent colonizer reflected by a larger equilibrium population size of the competitor. The equilibria at which one virus outcompetes the other one are unstable, i.e., a second virus is always able to permanently invade. We generalize the two-virus model to multiple viral strains, each displaying a different virulence. To account for the large phenotypic diversity in viral populations, we consider a continuous spectrum of virulences and present a continuum limit of this multiple viral strains model that describes the time evolution of an initial continuous distribution of virulence without mutations. We provide a proof of the existence of solutions of the model equations, analytically assess the properties of stationary solutions, and present numerical approximations of solutions for different initial distributions. Our simulations suggest that initial continuous distributions of virulence evolve toward a distribution that is extremely skewed in favor of competitors. At equilibrium, only the least virulent part of the population survives. The discrepancy of this finding in the continuum limit with the two-virus model is attributed to the skewed equilibrium subpopulation sizes and to the transition to a continuum. Consequently, in viral quasispecies with high virulence diversity, the model predicts collective virulence attenuation. This result may contribute to understanding virulence attenuation, which has been reported in several experimental studies.     

Network Analysis of Breast Cancer Progression and Reversal Using a Tree-Evolving Network Algorithm

The HMT3522 progression series of human breast cells have been used to discover how tissue architecture, microenvironment and signaling molecules affect breast cell growth and behaviors. However, much remains to be elucidated about malignant and phenotypic reversion behaviors of the HMT3522-T4-2 cells of this series. We employed a “pan-cell-state” strategy, and analyzed jointly microarray profiles obtained from different state-specific cell populations from this progression and reversion model of the breast cells using a tree-lineage multi-network inference algorithm, Treegl. We found that different breast cell states contain distinct gene networks. The network specific to non-malignant HMT3522-S1 cells is dominated by genes involved in normal processes, whereas the T4-2-specific network is enriched with cancer-related genes. The networks specific to various conditions of the reverted T4-2 cells are enriched with pathways suggestive of compensatory effects, consistent with clinical data showing patient resistance to anticancer drugs. We validated the findings using an external dataset, and showed that aberrant expression values of certain hubs in the identified networks are associated with poor clinical outcomes. Thus, analysis of various reversion conditions (including non-reverted) of HMT3522 cells using Treegl can be a good model system to study drug effects on breast cancer.     

Altered expression pattern of integrin alphavbeta3 correlates with actin cytoskeleton in primary cultures of human breast cancer

Background

Integrins are transmembrane adhesion receptors that provide the physical link between the actin cytoskeleton and the extracellular matrix. It has been well established that integrins play a major role in various cancer stages, such as tumor growth, progression, invasion and metastasis. In breast cancer, integrin alphavbeta3 has been associated with high malignant potential in cancer cells, signaling the onset of widespread metastasis. Many preclinical breast cancer studies are based on established cell lines, which may not represent the cell behavior and phenotype of the primary tumor of origin, due to undergone genotypic and phenotypic changes. In the present study, short-term primary breast cancer cell cultures were developed. Integrin alphavbeta3 localization was studied in correlation with F-actin cytoskeleton by means of immunofluorescence and immunogold ultrastructural localization. Integrin fluorescence intensities were semi-quantitatively assessed by means of computerized image analysis, while integrin and actin expression was evaluated by Western immunoblotting.

Results

In the primary breast cancer epithelial cells integrin alphavbeta3 immunofluorescence was observed in the marginal cytoplasmic area, whereas in the primary normal breast epithelial cells it was observed in the main cell body, i.e. in the ventrally located perinuclear area. In the former, F-actin cytoskeleton appeared well-formed, consisting of numerous and thicker stress fibers, compared to normal epithelial cells. Furthermore, electron microscopy showed increased integrin alphavbeta3 immunogold localization in epithelial breast cancer cells over the area of stress fibers at the basal cell surface. These findings were verified with Western immunoblotting by the higher expression of integrin beta3 subunit and actin in primary breast cancer cells, revealing their reciprocal relation, in response to the higher motility requirements, determined by the malignant potential of the breast cancer cells.

Conclusion

A model system of primary breast cancer cell cultures was developed, in an effort to maintain the closest resembling environment to the tumor of origin. Using the above system model as an experimental tool the study of breast tumor cell behavior is possible concerning the adhesion capacity and the migrating potential of these cells, as defined by the integrin alphavbeta3 distribution in correlation with F-actin cytoskeleton.     

Bayesian calibration of a stochastic,multiscale agent-based model for predicting in vitro tumor growth

Hybrid multiscale agent-based models (ABMs) are unique in their ability to simulate individual cell interactions and microenvironmental dynamics. Unfortunately, the high computational cost of modeling individual cells, the inherent stochasticity of cell dynamics, and numerous model parameters are fundamental limitations of applying such models to predict tumor dynamics. To overcome these challenges, we have developed a coarse-grained two-scale ABM (cgABM) with a reduced parameter space that allows for an accurate and efficient calibration using a set of time-resolved microscopy measurements of cancer cells grown with different initial conditions. The multiscale model consists of a reaction-diffusion type model capturing the spatio-temporal evolution of glucose and growth factors in the tumor microenvironment (at tissue scale), coupled with a lattice-free ABM to simulate individual cell dynamics (at cellular scale). The experimental data consists of BT474 human breast carcinoma cells initialized with different glucose concentrations and tumor cell confluences. The confluence of live and dead cells was measured every three hours over four days. Given this model, we perform a time-dependent global sensitivity analysis to identify the relative importance of the model parameters. The subsequent cgABM is calibrated within a Bayesian framework to the experimental data to estimate model parameters, which are then used to predict the temporal evolution of the living and dead cell populations. To this end, a moment-based Bayesian inference is proposed to account for the stochasticity of the cgABM while quantifying uncertainties due to limited temporal observational data. The cgABM reduces the computational time of ABM simulations by 93% to 97% while staying within a 3% difference in prediction compared to ABM. Additionally, the cgABM can reliably predict the temporal evolution of breast cancer cells observed by the microscopy data with an average error and standard deviation for live and dead cells being 7.61±2.01 and 5.78±1.13, respectively.     

Oncogene activation induces metabolic transformation resulting in insulin-independence in human breast cancer cells

Normal breast epithelial cells require insulin and EGF for growth in serum-free media. We previously demonstrated that over expression of breast cancer oncogenes transforms MCF10A cells to an insulin-independent phenotype. Additionally, most breast cancer cell lines are insulin-independent for growth. In this study, we investigated the mechanism by which oncogene over expression transforms MCF10A cells to an insulin-independent phenotype. Analysis of the effects of various concentrations of insulin and/or IGF-I on proliferation of MCF10A cells demonstrated that some of the effects of insulin were independent from those of IGF-I, suggesting that oncogene over expression drives a true insulin-independent proliferative phenotype. To test this hypothesis, we examined metabolic functions of insulin signaling in insulin-dependent and insulin-independent cells. HER2 over expression in MCF10A cells resulted in glucose uptake in the absence of insulin at a rate equal to insulin-induced glucose uptake in non-transduced cells. We found that a diverse set of oncogenes induced the same result. To gain insight into how HER2 oncogene signaling affected increased insulin-independent glucose uptake we compared HER2-regulated gene expression signatures in MCF10A and HER2 over expressing MCF10A cells by differential analysis of time series gene expression data from cells treated with a HER2 inhibitor. This analysis identified genes specifically regulated by the HER2 oncogene, including VAMP8 and PHGDH, which have known functions in glucose uptake and processing of glycolytic intermediates, respectively. Moreover, these genes specifically implicated in HER2 oncogene-driven transformation are commonly altered in human breast cancer cells. These results highlight the diversity of oncogene effects on cell regulatory pathways and the importance of oncogene-driven metabolic transformation in breast cancer.     

A polymorphism of the complement regulatory protein MCP (membrane cofactor protein or gp45-70)

Membrane cofactor protein (MCP or gp45-70) is a recently described regulatory glycoprotein of the complement system which binds iC3 or C3b and is present on human platelets, T cells, B cells, monocytes, and mononuclear-derived cell lines. On sodium dodecyl sulfate-polyacrylamide gel electrophoresis, MCP migrates as a doublet with an Mr of the upper band of 63,000 and the lower band of 58,000. The same pattern was found on all cell populations in a given individual and was stable over time. In order to further characterize the two band pattern on sodium dodecyl sulfate-polyacrylamide gel electrophoresis, MCP was isolated by affinity chromatography or immunoprecipitation from 72 healthy unrelated donors. All individuals expressed both bands and, based on the densitometric scanning of gels, three patterns were noted: upper band predominant in 65%, approximately equal distribution of upper and lower bands in 29%, and lower band predominant in 6%. These observed phenotypic frequencies fit with expectations based on Hardy-Weinberg equilibrium for a two-allele system. Family studies also support this model as none of the 26 offspring had a phenotype that deviated from the expected, assuming an autosomal codominant model of inheritance. These results are consistent with a simple two-allele system that controls the expression of the two bands of MCP.     

Unsupervised discovery of dynamic cell phenotypic states from transmitted light movies

Identification of cell phenotypic states within heterogeneous populations, along with elucidation of their switching dynamics, is a central challenge in modern biology. Conventional single-cell analysis methods typically provide only indirect, static phenotypic readouts. Transmitted light images, on the other hand, provide direct morphological readouts and can be acquired over time to provide a rich data source for dynamic cell phenotypic state identification. Here, we describe an end-to-end deep learning platform, UPSIDE (Unsupervised Phenotypic State IDEntification), for discovering cell states and their dynamics from transmitted light movies. UPSIDE uses the variational auto-encoder architecture to learn latent cell representations, which are then clustered for state identification, decoded for feature interpretation, and linked across movie frames for transition rate inference. Using UPSIDE, we identified distinct blood cell types in a heterogeneous dataset. We then analyzed movies of patient-derived acute myeloid leukemia cells, from which we identified stem-cell associated morphological states as well as the transition rates to and from these states. UPSIDE opens up the use of transmitted light movies for systematic exploration of cell state heterogeneity and dynamics in biology and medicine.     

Evidence for a stromal-epithelial “lactate shuttle” in human tumors

Recently, we proposed a new mechanism for understanding the Warburg effect in cancer metabolism. In this new paradigm, cancer-associated fibroblasts undergo aerobic glycolysis, and extrude lactate to “feed” adjacent cancer cells, which then drives mitochondrial biogenesis and oxidative mitochondrial metabolism in cancer cells. Thus, there is vectorial transport of energy-rich substrates from the fibroblastic tumor stroma to anabolic cancer cells. A prediction of this hypothesis is that cancer-associated fibroblasts should express MCT4, a mono-carboxylate transporter that has been implicated in lactate efflux from glycolytic muscle fibers and astrocytes in the brain. To address this issue, we co-cultured MCF7 breast cancer cells with normal fibroblasts. Interestingly, our results directly show that breast cancer cells specifically induce the expression of MCT4 in cancer-associated fibroblasts; MCF7 cells alone and fibroblasts alone, both failed to express MCT4. We also show that the expression of MCT4 in cancer-associated fibroblasts is due to oxidative stress, and can be prevented by pre-treatment with the anti-oxidant N-acetyl-cysteine. In contrast to our results with MCT4, we see that MCT1, a transporter involved in lactate uptake, is specifically upregulated in MCF7 breast cancer cells when co-cultured with fibroblasts. Virtually identical results were also obtained with primary human breast cancer samples. In human breast cancers, MCT4 selectively labels the tumor stroma, e.g., the cancer-associated fibroblast compartment. Conversely, MCT1 was selectively expressed in the epithelial cancer cells within the same tumors. Functionally, we show that overexpression of MCT4 in fibroblasts protects both MCF7 cancer cells and fibroblasts against cell death, under co-culture conditions. Thus, we provide the first evidence for the existence of a stromal-epithelial lactate shuttle in human tumors, analogous to the lactate shuttles that are essential for the normal physiological function of muscle tissue and brain. These data are consistent with the “reverse Warburg effect,” which states that cancer-associated fibroblasts undergo aerobic glycolysis, thereby producing lactate, which is utilized as a metabolic substrate by adjacent cancer cells. In this model, “energy transfer” or “metabolic-coupling” between the tumor stroma and epithelial cancer cells “fuels” tumor growth and metastasis, via oxidative mitochondrial metabolism in anabolic cancer cells. Most importantly, our current findings provide a new rationale and novel strategy for anti-cancer therapies, by employing MCT inhibitors.     

A computational model for populations of dividing cells.

A mathematical model of the cell movements due to cell division is presented. In the model we assume that every cell is a computational object with a given volume, and that the cell pushes the neighbouring cells in order to acquire the space for this volume. The Force that each cell exerts over the other cells is derived from a harmonic arbitrary Potential. The main parameter of the model is the average distance among the cells, that checks if the system is in spatial equilibrium or not. We show that just changing the physical constraints we can model two different systems, a two-dimensional culture on a plate and a three-dimensional early embryo. In both cases the patterns of the cell populations we obtain are similar to the real ones.     

Apical cell protrusions cause vertical deformation of the soft cancer nucleus

Breast cancer nuclei have highly irregular shapes, which are diagnostic and prognostic markers of breast cancer progression. The mechanisms by which irregular cancer nuclear shapes develop are not well understood. Here we report the existence of vertical, apical cell protrusions in cultured MDA-MB-231 breast cancer cells. Once formed, these protrusions persist over time scales of hours and are associated with vertically upward nuclear deformations. They are absent in normal mammary epithelial cells (MCF-10A cells). Microtubule disruption enriched these protrusions preferentially in MDA-MB-231 cells compared with MCF-10A cells, whereas inhibition of nonmuscle myosin II (NMMII) abolished this enrichment. Dynamic confocal imaging of the vertical cell and nuclear shape revealed that the apical cell protrusions form first, and in response, the nucleus deforms and/or subsequently gets vertically extruded into the apical protrusion. Overexpression of lamin A/C in MDA-MB-231 cells reduced nuclear deformation in apical protrusions. These data highlight the role of mechanical stresses generated by moving boundaries, as well as abnormal nuclear mechanics in the development of abnormal nuclear shapes in breast cancer cells.     

FMS-Like Tyrosine Kinase 3 Ligand Treatment Does Not Ameliorate Experimental Rapidly Progressive Glomerulonephritis

Fms-like tyrosine kinase 3-ligand (FL) is a growth factor that may expand dendritic cell and regulatory T cell populations. We hypothesised that FL-induced regulatory T cells would protect mice from experimental rapidly progressive glomerulonephritis. To determine if FL was able to enhance regulatory T cell populations, C57BL/6 mice received 10 days of daily intraperitoneal injections of either FL or phosphate buffered saline. To induce accelerated autologous-phase anti-mouse glomerular basement membrane glomerulonephritis, mice were sensitized to sheep globulin 4 days prior to the induction of glomerulonephritis with sheep anti-mouse glomerular basement membrane globulin, and experiments ended 10 days later. FL was administered before, throughout and during the sensitization phase of this glomerulonephritis model. Renal disease and systemic immunity to the nephritogenic antigen were assessed. FL increased regulatory T cell and plasmacytoid dendritic cell proportions within spleen and lymph nodes. FL administration prior to glomerulonephritis did not protect mice from renal injury. When FL was given throughout the model, FL treated mice had reduced survival, with more interstitial neutrophils and glomerular CD11c+ cells than controls. Systemic immune responses showed increased IL-17A production from splenocytes, with more CD11c+ cells, but reduced plasmacytoid dendritic cell proportions in spleen and lymph nodes, despite increased regulatory T cell proportions. Under homeostatic conditions, FL expanded regulatory T cell and plasmacytoid dendritic cell populations, but FL enhanced systemic inflammatory responses and conventional dendritic cell populations when given during experimental glomerulonephritis, suggesting selective attempts to suppress pathogenic immunity by dendritic cell manipulation may be harmful.     

Segregostat: a novel concept to control phenotypic diversification dynamics on the example of Gram-negative bacteria

Controlling and managing the degree of phenotypic diversification of microbial populations is a challenging task. This task not only requires detailed knowledge regarding diversification mechanisms but also advanced technical set-ups for the real-time analyses and control of population behaviour on single-cell level. In this work, set-up, design and operation of the so called segregostat are described which, in contrast to a traditional chemostat, allows the control of phenotypic diversification of microbial populations over time. Two exemplary case studies will be discussed, i.e. phenotypic diversification dynamics of Eschericia coli and Pseudomonas putida based on outer membrane permeabilization, emphasizing the applicability and versatility of the proposed approach. Upon nutrient limitation, cell population tends to diversify into several subpopulations exhibiting distinct phenotypic features (non-permeabilized and permeabilized cells). Online analysis leads to the determination of the ratio between cells in these two states, which in turn triggers the addition of glucose pulses in order to maintain a predefined diversification ratio. These results prove that phenotypic diversification can be controlled by means of defined pulse-frequency modulation within continuously running bioreactor set-ups. This lays the foundation for systematic studies, not only of phenotypic diversification but also for all processes where dynamics single-cell approaches are required, such as synthetic co-culture processes.     

Generation of Breast Cancer Stem Cells by Steroid Hormones in Irradiated Human Mammary Cell Lines

Exposure to ionizing radiation was shown to result in an increased risk of breast cancer. There is strong evidence that steroid hormones influence radiosensitivity and breast cancer risk. Tumors may be initiated by a small subpopulation of cancer stem cells (CSCs). In order to assess whether the modulation of radiation-induced breast cancer risk by steroid hormones could involve CSCs, we measured by flow cytometry the proportion of CSCs in irradiated breast cancer cell lines after progesterone and estrogen treatment. Progesterone stimulated the expansion of the CSC compartment both in progesterone receptor (PR)-positive breast cancer cells and in PR-negative normal cells. In MCF10A normal epithelial PR-negative cells, progesterone-treatment and irradiation triggered cancer and stemness-associated microRNA regulations (such as the downregulation of miR-22 and miR-29c expression), which resulted in increased proportions of radiation-resistant tumor-initiating CSCs.     

Phenotypic and functional heterogeneity of EBV epitope-specific CD8+ T cells

High frequencies of EBV-specific CD8(+) T cells have been detected during acute EBV infection, yet persistent infection inevitably results. To address this issue, we characterized the phenotype and function of epitope-specific CD8(+) T cell populations from presentation with acute through latent infection. Considerable phenotypic and functional heterogeneity within, as well as between, two different epitope-specific populations was observed over time following acute infection. B7 EBV-encoded nuclear Ag (EBNA)-3A-specific CD8(+) T cells expressed only CD45RO from acute through latent EBV infection. A2 BMLF-1-specific CD8(+) T cells expressed CD45RO during acute infection and either CD45RA or CD45RO during latent EBV infection. This difference in CD45 isoform expression between the two epitope-specific populations did not translate into differences in perforin content, the ability to produce IFN-gamma, or the ability to proliferate in response to Ag in vitro. In individuals with latent EBV infection, the frequencies of A2 BMLF-1- or B7 EBNA-3A-specific CD8(+) T cells that expressed CD45RA, CD45RO, CD62 ligand, CCR7, and perforin were stable over time. However, the expression of CD62 ligand and CCR7 was significantly higher among EBNA-3A-specific CD8(+) T cells than among BMLF-1-specific CD8(+) T cells. Further work is necessary to understand how phenotypic and functional differences between EBV epitope-specific CD8(+) T cells are related to the biology of the virus and to the equilibrium between the virus and the host during persistent infection.     

Markov models for covariate dependence of binary sequences

Suppose that a heterogeneous group of individuals is followed over time and that each individual can be in state 0 or state 1 at each time point. The sequence of states is assumed to follow a binary Markov chain. In this paper we model the transition probabilities for the 0 to 0 and 1 to 0 transitions by two logistic regressions, thus showing how the covariates relate to changes in state. With p covariates, there are 2(p + 1) parameters including intercepts, which we estimate by maximum likelihood. We show how to use transition probability estimates to test hypotheses about the probability of occupying state 0 at time i (i = 2, ..., T) and the equilibrium probability of state 0. These probabilities depend on the covariates. A recursive algorithm is suggested to estimate regression coefficients when some responses are missing. Extensions of the basic model which allow time-dependent covariates and nonstationary or second-order Markov chains are presented. An example shows the model applied to a study of the psychological impact of breast cancer in which women did or did not manifest distress at four time points in the year following surgery.