Towards a functional understanding of cell growth dynamics in shoot meristem stem-cell niche

Shoot apical meristems (SAMs) harbor a set of stem-cells which supply cells for the development of all above-ground structures. A precise spatio-temporal control of growth patterns in stem-cells and the differentiating progeny is critical to maintain a stable set of stem-cells. In recent years, an array of approaches including molecular genetics, transient perturbations, live-imaging, image processing and mathematical modeling have been employed to study the cellular dynamics. In this article, we highlight recent studies that link cell–cell communication mechanisms to cell mechanics and overall growth control that govern stem-cell homeostasis and morphogenesis in SAMs.     

A complex systems approach to Arabidopsis root stem-cell niche developmental mechanisms: from molecules, to networks, to morphogenesis

Recent reports have shown that the molecular mechanisms involved in root stem-cell niche development in Arabidopsis thaliana are complex and contain several feedback loops and non-additive interactions that need to be analyzed using computational and formal approaches. Complex systems cannot be understood in terms of the behavior of their isolated components, but they emerge as a consequence of largely non-linear interactions among their components. The study of complex systems has provided a useful approach for the exploration of system-level characteristics and behaviors of the molecular networks involved in cell differentiation and morphogenesis during development. We analyzed the complex molecular networks underlying stem-cell niche patterning in the A. thaliana root in terms of some of the key dynamic traits of complex systems: self-organization, modularity and structural properties. We use these analyses to integrate the available root stem-cell niche molecular mechanisms data and postulate novel hypotheses, missing components and interactions and explain apparent contradictions in the literature.     

Mathematical modelling of the influence of heat shock proteins on cancer invasion of tissue

Tumour cell invasion is crucial for cancer metastasis, which is the main cause of cancer mortality. An important group of proteins involved in cancer invasion are the Heat Shock Proteins (HSPs). According to experimental data, inhibition of one of these proteins, Hsp90, slows down cancer cells while they are invading tissue, but does not affect the synthesis of matrix metalloproteinases (MMP2 and MMP9), which are very important for cancer metastasis, acting as extracellular matrix (ECM) degrading enzymes. To test different biological hypotheses regarding how precisely Hsp90 influences tumour invasion, in this paper we use a model of solid tumour growth which accounts for the interactions between Hsp90 dynamics and the migration of cancer cells and, alternatively, between Hsp90 dynamics and the synthesis of matrix degrading enzymes (MDEs). The model consists of a system of reaction-diffusion-taxis partial differential equations describing interactions between cancer cells, MDE, and the host tissue (ECM). Using numerical simulations we investigate the effects of the administration of Hsp90 inhibitors on the dynamics of tumour invasion. Alternative mechanisms of reduction of cancer invasiveness result in different simulated patterns of the invading tumour cells. Therefore, predictions of the model suggest experiments which might be performed to develop a deeper understanding of the tumour invasion process.     

Inhibition of Escherichia coli biofilm formation by self-assembled monolayers of functional alkanethiols on gold

Bacterial biofilms cause serious problems, such as antibiotic resistance and medical device-related infections. To further understand bacterium-surface interactions and to develop efficient control strategies, self-assembled monolayers (SAMs) of alkanethiols presenting different functional groups on gold films were analyzed to determine their resistance to biofilm formation. Escherichia coli was labeled with green florescence protein, and its biofilm formation on SAM-modified surfaces was monitored by confocal laser scanning microscopy. The three-dimensional structures of biofilms were analyzed with the COMSTAT software to obtain information about biofilm thickness and surface coverage. SAMs presenting methyl, L-gulonamide (a sugar alcohol tethered with an amide bond), and tri(ethylene glycol) (TEG) groups were tested. Among these, the TEG-terminated SAM was the most resistant to E. coli biofilm formation; e.g., it repressed biofilm formation by E. coli DH5alpha by 99.5% +/- 0.1% for 1 day compared to the biofilm formation on a bare gold surface. When surfaces were patterned with regions consisting of methyl-terminated SAMs surrounded by TEG-terminated SAMs, E. coli formed biofilms only on methyl-terminated patterns. Addition of TEG as a free molecule to growth medium at concentrations of 0.1 and 1.0% also inhibited biofilm formation, while TEG at concentrations up to 1.5% did not have any noticeable effects on cell growth. The results of this study suggest that the reduction in biofilm formation on surfaces modified with TEG-terminated SAMs is a result of multiple factors, including the solvent structure at the interface, the chemorepellent nature of TEG, and the inhibitory effect of TEG on cell motility.     

Crypt dynamics and colorectal cancer: advances in mathematical modelling

Mathematical modelling forms a key component of systems biology, offering insights that complement and stimulate experimental studies. In this review, we illustrate the role of theoretical models in elucidating the mechanisms involved in normal intestinal crypt dynamics and colorectal cancer. We discuss a range of modelling approaches, including models that describe cell proliferation, migration, differentiation, crypt fission, genetic instability, APC inactivation and tumour heterogeneity. We focus on the model assumptions, limitations and applications, rather than on the technical details. We also present a new stochastic model for stem-cell dynamics, which predicts that, on average, APC inactivation occurs more quickly in the stem-cell pool in the absence of symmetric cell division. This suggests that natural niche succession may protect stem cells against malignant transformation in the gut. Finally, we explain how we aim to gain further understanding of the crypt system and of colorectal carcinogenesis with the aid of multiscale models that cover all levels of organization from the molecular to the whole organ.     

Emergent behaviors from a cellular automaton model for invasive tumor growth in heterogeneous microenvironments

Understanding tumor invasion and metastasis is of crucial importance for both fundamental cancer research and clinical practice. In vitro experiments have established that the invasive growth of malignant tumors is characterized by the dendritic invasive branches composed of chains of tumor cells emanating from the primary tumor mass. The preponderance of previous tumor simulations focused on non-invasive (or proliferative) growth. The formation of the invasive cell chains and their interactions with the primary tumor mass and host microenvironment are not well understood. Here, we present a novel cellular automaton (CA) model that enables one to efficiently simulate invasive tumor growth in a heterogeneous host microenvironment. By taking into account a variety of microscopic-scale tumor-host interactions, including the short-range mechanical interactions between tumor cells and tumor stroma, degradation of the extracellular matrix by the invasive cells and oxygen/nutrient gradient driven cell motions, our CA model predicts a rich spectrum of growth dynamics and emergent behaviors of invasive tumors. Besides robustly reproducing the salient features of dendritic invasive growth, such as least-resistance paths of cells and intrabranch homotype attraction, we also predict nontrivial coupling between the growth dynamics of the primary tumor mass and the invasive cells. In addition, we show that the properties of the host microenvironment can significantly affect tumor morphology and growth dynamics, emphasizing the importance of understanding the tumor-host interaction. The capability of our CA model suggests that sophisticated in silico tools could eventually be utilized in clinical situations to predict neoplastic progression and propose individualized optimal treatment strategies.     

Automated tracking of stem cell lineages of Arabidopsis shoot apex using local graph matching

Shoot apical meristems (SAMs) of higher plants harbor stem‐cell niches. The cells of the stem‐cell niche are organized into spatial domains of distinct function and cell behaviors. A coordinated interplay between cell growth dynamics and changes in gene expression is critical to ensure stem‐cell homeostasis and organ differentiation. Exploring the causal relationships between cell growth patterns and gene expression dynamics requires quantitative methods to analyze cell behaviors from time‐lapse imagery. Although technical breakthroughs in live‐imaging methods have revealed spatio‐temporal dynamics of SAM‐cell growth patterns, robust computational methods for cell segmentation and automated tracking of cells have not been developed. Here we present a local graph matching‐based method for automated‐tracking of cells and cell divisions of SAMs of Arabidopsis thaliana. The cells of the SAM are tightly clustered in space which poses a unique challenge in computing spatio‐temporal correspondences of cells. The local graph‐matching principle efficiently exploits the geometric structure and topology of the relative positions of cells in obtaining spatio‐temporal correspondences. The tracker integrates information across multiple slices in which a cell may be properly imaged, thus providing robustness to cell tracking in noisy live‐imaging datasets. By relying on the local geometry and topology, the method is able to track cells in areas of high curvature such as regions of primordial outgrowth. The cell tracker not only computes the correspondences of cells across spatio‐temporal scale, but it also detects cell division events, and identifies daughter cells upon divisions, thus allowing automated estimation of cell lineages from images captured over a period of 72 h. The method presented here should enable quantitative analysis of cell growth patterns and thus facilitating the development of in silico models for SAM growth.     

Untangling Brain-Wide Dynamics in Consciousness by Cross-Embedding

Brain-wide interactions generating complex neural dynamics are considered crucial for emergent cognitive functions. However, the irreducible nature of nonlinear and high-dimensional dynamical interactions challenges conventional reductionist approaches. We introduce a model-free method, based on embedding theorems in nonlinear state-space reconstruction, that permits a simultaneous characterization of complexity in local dynamics, directed interactions between brain areas, and how the complexity is produced by the interactions. We demonstrate this method in large-scale electrophysiological recordings from awake and anesthetized monkeys. The cross-embedding method captures structured interaction underlying cortex-wide dynamics that may be missed by conventional correlation-based analysis, demonstrating a critical role of time-series analysis in characterizing brain state. The method reveals a consciousness-related hierarchy of cortical areas, where dynamical complexity increases along with cross-area information flow. These findings demonstrate the advantages of the cross-embedding method in deciphering large-scale and heterogeneous neuronal systems, suggesting a crucial contribution by sensory-frontoparietal interactions to the emergence of complex brain dynamics during consciousness.     

Tracking adult stem cells

The maintenance of stem-cell-driven tissue homeostasis requires a balance between the generation and loss of cell mass. Adult stem cells have a close relationship with the surrounding tissue--known as their niche--and thus, stem-cell studies should preferably be performed in a physiological context, rather than outside their natural environment. The mouse is an attractive model in which to study adult mammalian stem cells, as numerous experimental systems and genetic tools are available. In this review, we describe strategies commonly used to identify and functionally characterize adult stem cells in mice and discuss their potential, limitations and interpretations, as well as how they have informed our understanding of adult stem-cell biology. An accurate interpretation of physiologically relevant stem-cell assays is crucial to identify adult stem cells and elucidate how they self-renew and give rise to differentiated progeny.     

Dynamic Circadian Protein–Protein Interaction Networks Predict Temporal Organization of Cellular Functions

Essentially all biological processes depend on protein–protein interactions (PPIs). Timing of such interactions is crucial for regulatory function. Although circadian (∼24-hour) clocks constitute fundamental cellular timing mechanisms regulating important physiological processes, PPI dynamics on this timescale are largely unknown. Here, we identified 109 novel PPIs among circadian clock proteins via a yeast-two-hybrid approach. Among them, the interaction of protein phosphatase 1 and CLOCK/BMAL1 was found to result in BMAL1 destabilization. We constructed a dynamic circadian PPI network predicting the PPI timing using circadian expression data. Systematic circadian phenotyping (RNAi and overexpression) suggests a crucial role for components involved in dynamic interactions. Systems analysis of a global dynamic network in liver revealed that interacting proteins are expressed at similar times likely to restrict regulatory interactions to specific phases. Moreover, we predict that circadian PPIs dynamically connect many important cellular processes (signal transduction, cell cycle, etc.) contributing to temporal organization of cellular physiology in an unprecedented manner.     

Substrate-mediated nucleic acid delivery from self-assembled monolayers

Substrate-mediated nucleic acid (NA) delivery involves the immobilization of NAs or NA delivery vehicles to biomaterials for localized transfection of cells. Self-assembled monolayers (SAMs) offer an easy system to immobilize delivery vectors. SAMs form well-defined surfaces; therefore, the effect of surface composition on vector immobilization and transfection efficiency can also be studied. To date, the most effective SAM-mediated delivery systems have utilized nonspecific interactions for immobilization; however, systems that rely on specific interactions between vector and surface can impart higher control of spatial and/or temporal delivery. This review summarizes systems that use both specific and nonspecific interactions for gene delivery from SAMs; highlights progress and remaining challenges; and explores other specific recognition modalities that might be employed for future applications in surface-mediated NA delivery.     

Competition coefficients in a marine epibenthic assemblage depend on spatial structure

We investigated the importance of the spatial context of interactions in a multispecies marine epibenthic assemblage with respect to the outcomes of interspecific interactions, neighbour-specific growth rates, and the dynamics of spatial and mean-field models of the system. We compared the outcomes of interactions and overgrowth rates of pair-wise combinations of species in spatially simplified contrived interactions with the same combinations in an unmanipulated assemblage. While estimates of neighbour-specific growth rates were similar in both sets of interactions, the probability of a species winning a particular interaction was strongly dependent on whether the interaction was contrived or occurred in the unmanipulated assemblage. The dynamics of a spatial model and its mean-field equivalent parameterised from estimates of interaction outcome and neighbour-specific growth from contrived interactions were significantly different to the dynamics of models based on estimates of interaction outcome and neighbour-specific growth obtained from non-manipulated assemblages. Differences in the dynamics of models based on parameters from unmanipulated and contrived interactions are primarily due to differences in outcomes of interspecific interactions, while fluctuations in growth rates contribute to the variability around these dynamics. Our results suggest that conclusions about interspecific interactions and community dynamics examined in simplified spatial associations (e.g. in manipulative experiments) is likely to be limited to assemblages with a similarly simplified spatial structure, which is an unlikely occurrence in nature.     

Characterization of S-adenosylmethionine synthetases in soybean under flooding and drought stresses

Soybean is stress-sensitive crop that exhibits markedly reduced growth under flooding and drought conditions. Three S-adenosylmethionine synthetases (SAMs) proteins were identified as flooding and drought responsive proteins in soybean using a proteomic technique. To better understand the role of these SAMs proteins in soybean under flooding and drought stresses, temporal, organ, and stress specificities were examined at mRNA and enzyme activity levels. The activity of SAMs decreased in response to the flooding, however, it was not significantly changed by NaCl, cold, gibberellic acid, and calcium in soybean roots. The activity of SAMs was induced in roots and hypocotyls under drought. The mRNA expression of the S-adenosylmethionine synthetase (SAMs) family was down-regulated in root tips and roots under the flooding and the drought, and SAMs 1 and SAMs 2 were down-regulated in roots under both stresses. A gene 1-aminocyclopropane-1-carboxylate synthase was up-regulated in root tips, roots, and hypocotyls under drought, however, it was not changed in root tips and roots under the flooding. In addition, 1-aminocyclopropane-1-carboxylate oxidase was induced in root tips under flooding and drought. These results suggest that SAMs was involved in the response to the flooding and drought and it might affect ethylene biosynthesis in soybean.     

The molecular repertoire of the 'almighty' stem cell

Stem cells share the defining characteristics of self-renewal, which maintains or expands the stem-cell pool, and multi-lineage differentiation, which generates and regenerates tissues. Stem-cell self-renewal and differentiation are influenced by the convergence of intrinsic cellular signals and extrinsic microenvironmental cues from the surrounding stem-cell niche, but the specific signals involved are poorly understood. Recently, several studies have sought to identify the genetic mechanisms that underlie the stem-cell phenotype. Such a molecular road map of stem-cell function should lead to an understanding of the true potential of stem cells.     

A single spacer nucleotide determines the specificities of two mRNA regulatory proteins

Regulation of messenger RNA is crucial in many contexts, including development, memory and cell growth. The 3' untranslated region is a rich repository of regulatory elements that bind proteins and microRNAs. Here we focus on PUF proteins, an important family of mRNA regulatory proteins crucial in stem-cell proliferation, pattern formation and synaptic plasticity. We show that two Caenorhabditis elegans PUF proteins, FBF and PUF-8, differ in RNA-binding specificity. FBF requires the presence of a single 'extra' nucleotide in the middle of an eight-nucleotide site, whereas PUF-8 requires its absence. A discrete protein segment is responsible for the difference. We propose that a structural distortion in the central region of FBF imposes the requirement for the additional nucleotide and that this mode of PUF specificity may be common. We suggest that new specificities can be designed and selected using the PUF scaffold.     

Task and content modulate amygdala-hippocampal connectivity in emotional retrieval

The ability to remember emotional events is crucial for adapting to biologically and socially significant situations. Little is known, however, about the nature of the neural interactions supporting the integration of mnemonic and emotional information. Using fMRI and dynamic models of effective connectivity, we examined regional neural activity and specific interactions between brain regions during a contextual memory retrieval task. We independently manipulated emotional context and relevance of retrieved emotional information to task demands. We show that retrieval of emotionally valenced contextual information is associated with enhanced connectivity from hippocampus to amygdala, structures crucially involved with encoding of emotional events. When retrieval of emotional information is relevant to current behavior, amygdala-hippocampal connectivity increases bidirectionally, under modulatory influences from orbitofrontal cortex, a region implicated in representation of affective value and behavioral guidance. Our findings demonstrate that both memory content and behavioral context impact upon large scale neuronal dynamics underlying emotional retrieval.     

Chipping away at 'stemness'

Global gene-expression analyses of human embryonic stem cells confirm the involvement of some known genes in stem-cell function and identify some new candidate regulators of stem-cell growth. Support remains elusive, however, for the concept of 'stemness' - a pattern of expression of genes that is common to all stem cells.     

The role of pleiotrophin and β-catenin in fetal lung development

Mammalian lung development is a complex biological process, which is temporally and spatially regulated by growth factors, hormones, and extracellular matrix proteins. Abnormal changes of these molecules often lead to impaired lung development, and thus pulmonary diseases. Epithelial-mesenchymal interactions are crucial for fetal lung development. This paper reviews two interconnected pathways, pleiotrophin and Wnt/β-catenin, which are involved in fibroblast and epithelial cell communication during fetal lung development.     

A family of laminin-related proteins controlling ectodermal differentiation in Drosophila.

It is shown that the proteins encoded by the tumor suppressor fat gene, the neurogenic slit gene and crumbs gene of Drosophila contain domains homologous with modules identified previously in laminin A. These proteins of Drosophila have a number of features in common: they have large extracellular regions containing laminin A modules linked to epidermal growth factor-like domains, and they are all involved in cell-cell interactions that are crucial for correct morphogenesis of ectodermal tissues (development of midline neuroepithelia, organizationof epithelial tissues etc.). It is suggested that the laminin A-type modules of these proteins play important roles in the interactions that control ectodermal differentiation.