In Vivo Assessment of the Impact of Regional Intracranial Atherosclerotic Lesions on Brain Arterial 3D Hemodynamics

BACKGROUND AND PURPOSE

Intracranial atherosclerosis induces hemodynamic disturbance, which is not well-characterized, particularly in cerebral flow redistribution. We aimed to characterize the impact of regional stenotic lesions on intracranial hemodynamics by using 4D flow MR imaging.

MATERIALS AND METHODS

4D flow MR imaging was performed in 22 symptomatic patients (mean age, 68.4 ± 14.2 years) with intracranial stenosis (ICA, n = 7; MCA, n = 9; basilar artery, n = 6) and 10 age-appropriate healthy volunteers (mean age, 60.7 ± 8.1 years). 3D blood flow patterns were visualized by using time-integrated pathlines. Blood flow and peak velocity asymmetry indices were compared between patients and healthy volunteers in 4 prespecified arteries: ICAs, MCAs, and anterior/posterior cerebral arteries.

RESULTS

3D blood flow pathlines demonstrated flow redistribution across cerebral arteries in patients with unilateral intracranial stenosis. For patients with ICA stenosis compared with healthy volunteers, significantly lower flow and peak velocities were identified in the ipsilateral ICA (P = .001 and P = .001) and MCA (P < .001 and P = .001), but higher flow, in the ipsilateral PCA (P < .001). For patients with MCA stenosis, significantly lower flow and peak velocities were observed in the ipsilateral ICA (P = .009 and P = .045) and MCA (P < .001 and P = .005), but significantly higher flow was found in the ipsilateral posterior cerebral artery (P = .014) and anterior cerebral artery (P = .006). The asymmetry indices were not significantly different between patients with basilar artery stenosis and the healthy volunteers.

CONCLUSIONS

Regional intracranial atherosclerotic lesions not only alter distal arterial flow but also significantly affect ipsilateral collateral arterial hemodynamics.

Coordination of signaling and tissue mechanics during morphogenesis of murine intestinal villi: a role for mitotic cell rounding

Efficient digestion and absorption of nutrients by the intestine requires a very large apical surface area, a feature that is enhanced by the presence of villi, fingerlike epithelial projections that extend into the lumen. Prior to villus formation, the epithelium is a thick pseudostratified layer. In mice, villus formation begins at embryonic day (E)14.5, when clusters of mesenchymal cells form just beneath the thick epithelium. At this time, analysis of the flat lumenal surface reveals a regular pattern of short apical membrane invaginations that form in regions of the epithelium that lie in between the mesenchymal clusters. Apical invaginations begin in the proximal intestine and spread distally, deepening with time. Interestingly, mitotically rounded cells are frequently associated with these invaginations. These mitotic cells are located at the tips of the invaginating membrane (internalized within the epithelium), rather than adjacent to the apical surface. Further investigation of epithelial changes during membrane invagination reveals that epithelial cells located between mesenchymal clusters experience a circumferential compression, as epithelial cells above each cluster shorten and widen. Using a computational model, we examined whether such forces are sufficient to cause apical invaginations. Simulations and in vivo data reveal that proper apical membrane invagination involves intraepithelial compressive forces, mitotic cell rounding in the compressed regions and apico-basal contraction of the dividing cell. Together, these data establish a new model that explains how signaling events intersect with tissue forces to pattern apical membrane invaginations that define the villus boundaries.

RhoC GTPase Is a Potent Regulator of Glutamine Metabolism and N-Acetylaspartate Production in Inflammatory Breast Cancer Cells

Inflammatory breast cancer (IBC) is an extremely lethal cancer that rapidly metastasizes. Although the molecular attributes of IBC have been described, little is known about the underlying metabolic features of the disease. Using a variety of metabolic assays, including (13)C tracer experiments, we found that SUM149 cells, the primary in vitro model of IBC, exhibit metabolic abnormalities that distinguish them from other breast cancer cells, including elevated levels of N-acetylaspartate, a metabolite primarily associated with neuronal disorders and gliomas. Here we provide the first evidence of N-acetylaspartate in breast cancer. We also report that the oncogene RhoC, a driver of metastatic potential, modulates glutamine and N-acetylaspartate metabolism in IBC cells in vitro, revealing a novel role for RhoC as a regulator of tumor cell metabolism that extends beyond its well known role in cytoskeletal rearrangement.

A collection of intrinsic disorder characterizations from eukaryotic proteomes

Intrinsically disordered proteins and protein regions lack a stable three-dimensional structure under physiological conditions. Several proteomic investigations of intrinsic disorder have been performed to date and have found disorder to be prevalent in eukaryotic proteomes. Here we present descriptive statistics of intrinsic disorder features for ten model eukaryotic proteomes that have been calculated from computational disorder prediction algorithms. The data descriptor also provides consensus disorder annotations as well as additional physical parameters relevant to protein disorder, and further provides protein existence information for all proteins included in our analysis. The complete datasets can be downloaded freely, and it is envisaged that they will be updated periodically with new proteomes and protein disorder prediction algorithms. These datasets will be especially useful for assessing protein disorder, and conducting novel analyses that advance our understanding of intrinsic disorder and protein structure.

Connecting the dots: the effects of macromolecular crowding on cell physiology

The physicochemical properties of cellular environments with a high macromolecular content have been systematically characterized to explain differences observed in the diffusion coefficients, kinetics parameters, and thermodynamic properties of proteins inside and outside of cells. However, much less attention has been given to the effects of macromolecular crowding on cell physiology. Here, we review recent findings that shed some light on the role of crowding in various cellular processes, such as reduction of biochemical activities, structural reorganization of the cytoplasm, cytoplasm fluidity, and cellular dormancy. We conclude by presenting some unresolved problems that require the attention of biophysicists, biochemists, and cell physiologists. Although it is still underappreciated, macromolecular crowding plays a critical role in life as we know it.

Quantitative proteome-based guidelines for intrinsic disorder characterization

Intrinsically disordered proteins fail to adopt a stable three-dimensional structure under physiological conditions. It is now understood that many disordered proteins are not dysfunctional, but instead engage in numerous cellular processes, including signaling and regulation. Disorder characterization from amino acid sequence relies on computational disorder prediction algorithms. While numerous large-scale investigations of disorder have been performed using these algorithms, and have offered valuable insight regarding the prevalence of protein disorder in many organisms, critical proteome-based descriptive statistical guidelines that would enable the objective assessment of intrinsic disorder in a protein of interest remain to be established. Here we present a quantitative characterization of numerous disorder features using a rigorous non-parametric statistical approach, providing expected values and percentile cutoffs for each feature in ten eukaryotic proteomes. Our estimates utilize multiple ab initio disorder prediction algorithms grounded on physicochemical principles. Furthermore, we present novel threshold values, specific to both the prediction algorithms and the proteomes, defining the longest primary sequence length in which the significance of a continuous disordered region can be evaluated on the basis of length alone. The guidelines presented here are intended to improve the interpretation of disorder content and continuous disorder predictions from the proteomic point of view.

Villification in the mouse: Bmp signals control intestinal villus patterning

In the intestine, finger-like villi provide abundant surface area for nutrient absorption. During murine villus development, epithelial Hedgehog (Hh) signals promote aggregation of subepithelial mesenchymal clusters that drive villus emergence. Clusters arise first dorsally and proximally and spread over the entire intestine within 24 h, but the mechanism driving this pattern in the murine intestine is unknown. In chick, the driver of cluster pattern is tensile force from developing smooth muscle, which generates deep longitudinal epithelial folds that locally concentrate the Hh signal, promoting localized expression of cluster genes. By contrast, we show that in mouse, muscle-induced epithelial folding does not occur and artificial deformation of the epithelium does not determine the pattern of clusters or villi. In intestinal explants, modulation of Bmp signaling alters the spatial distribution of clusters and changes the pattern of emerging villi. Increasing Bmp signaling abolishes cluster formation, whereas inhibiting Bmp signaling leads to merged clusters. These dynamic changes in cluster pattern are faithfully simulated by a mathematical model of a Turing field in which an inhibitor of Bmp signaling acts as the Turing activator. In vivo, genetic interruption of Bmp signal reception in either epithelium or mesenchyme reveals that Bmp signaling in Hh-responsive mesenchymal cells controls cluster pattern. Thus, unlike in chick, the murine villus patterning system is independent of muscle-induced epithelial deformation. Rather, a complex cocktail of Bmps and Bmp signal modulators secreted from mesenchymal clusters determines the pattern of villi in a manner that mimics the spread of a self-organizing Turing field.

Estimation of the lag time in a subsequent monomer addition model for fibril elongation

The lag time for dock–lock fibril elongation can be estimated from kinetic parameters.

Loss of the Ubiquitin-conjugating Enzyme UBE2W Results in Susceptibility to Early Postnatal Lethality and Defects in Skin, Immune, and Male Reproductive Systems

UBE2W ubiquitinates N termini of proteins rather than internal lysine residues, showing a preference for substrates with intrinsically disordered N termini. The in vivo functions of this intriguing E2, however, remain unknown. We generated Ube2w germ line KO mice that proved to be susceptible to early postnatal lethality without obvious developmental abnormalities. Although the basis of early death is uncertain, several organ systems manifest changes in Ube2w KO mice. Newborn Ube2w KO mice often show altered epidermal maturation with reduced expression of differentiation markers. Mirroring higher UBE2W expression levels in testis and thymus, Ube2w KO mice showed a disproportionate decrease in weight of these two organs (~50%), suggesting a functional role for UBE2W in the immune and male reproductive systems. Indeed, Ube2w KO mice displayed sustained neutrophilia accompanied by increased G-CSF signaling and testicular vacuolation associated with decreased fertility. Proteomic analysis of a vulnerable organ, presymptomatic testis, showed a preferential accumulation of disordered proteins in the absence of UBE2W, consistent with the view that UBE2W preferentially targets disordered polypeptides. These mice further allowed us to establish that UBE2W is ubiquitously expressed as a single isoform localized to the cytoplasm and that the absence of UBE2W does not alter cell viability in response to various stressors. Our results establish that UBE2W is an important, albeit not essential, protein for early postnatal survival and normal functioning of multiple organ systems.

Development, growth and maintenance of β-cell mass: models are also part of the story

Pancreatic β-cells in the islets of Langerhans play a crucial role in regulating glucose homeostasis in the circulation. Loss of β-cell mass or function due to environmental, genetic and immunological factors leads to the manifestation of diabetes mellitus. The mechanisms regulating the dynamics of pancreatic β-cell mass during normal development and diabetes progression are complex. To fully unravel such complexity, experimental and clinical approaches need to be combined with mathematical and computational models. In the natural sciences, mathematical and computational models have aided the identification of key mechanisms underlying the behavior of systems comprising multiple interacting components. A number of mathematical and computational models have been proposed to explain the development, growth and death of pancreatic β-cells. In this review, we discuss some of these models and how their predictions provide novel insight into the mechanisms controlling β-cell mass during normal development and diabetes progression. Lastly, we discuss a handful of the major open questions in the field.

Metabolic network motifs can provide novel insights into evolution: The evolutionary origin of Eukaryotic organelles as a case study

Phylogenetic trees are typically constructed using genetic and genomic data, and provide robust evolutionary relationships of species from the genomic point of view. We present an application of network motif mining and analysis of metabolic pathways that when used in combination with phylogenetic trees can provide a more complete picture of evolution. By using distributions of three-node motifs as a proxy for metabolic similarity, we analyze the ancestral origin of Eukaryotic organelles from the metabolic point of view to illustrate the application of our motif mining and analysis network approach. Our analysis suggests that the hypothesis of an early proto-Eukaryote could be valid. It also suggests that a δ- or ϵ-Proteobacteria may have been the endosymbiotic partner that gave rise to modern mitochondria. Our evolutionary analysis needs to be extended by building metabolic network reconstructions of species from the phylum Crenarchaeota, which is considered to be a possible archaeal ancestor of the eukaryotic cell. In this paper, we also propose a methodology for constructing phylogenetic trees that incorporates metabolic network signatures to identify regions of genomically-estimated phylogenies that may be spurious. We find that results generated from our approach are consistent with a parallel phylogenetic analysis using the method of feature frequency profiles.

Surveying the floodgates: estimating protein flux into the endoplasmic reticulum lumen in Saccharomyces cerevisiae

Endoplasmic reticulum resident proteins, along with all proteins traveling through the secretory pathway must enter endoplasmic reticulum lumen through membrane-embedded translocons. In Saccharomyces cerevisiae the heterotrimeric endoplasmic reticulum translocon is composed of the Sec61p, Sss1p, and Sbh1p core subunits. While the involvement of various molecules associated with the Sec61 complex has been thoroughly characterized, little attention has been given to the overall flux through these channels. In this work we carried out a meta-analysis to estimate the average and absolute flux of proteins into the endoplasmic reticulum lumen. We estimate an average of 460 proteins enter the endoplasmic reticulum every second, with an absolute minimum and maximum flux of 78 and 3700 molecules per second, respectively. With current technologies limiting the ability to obtain accurate measurements of these events, our estimates shed light on the flow of protein entering the endoplasmic reticulum lumen.

Inferring the Effects of Honokiol on the Notch Signaling Pathway in SW480 Colon Cancer Cells

In a tumor cell, the development of acquired therapeutic resistance and the ability to survive in extracellular environments that differ from the primary site are the result of molecular adaptations in potentially highly plastic molecular networks. The accurate prediction of intracellular networks in a tumor remains a difficult problem in cancer informatics. In order to make truly rational patient-driven therapeutic decisions, it will be critical to develop methodologies that can accurately infer the molecular circuitry in the cells of a specific tumor. Despite enormous heterogeneity, cellular networks elicit deterministic digital-like responses. We discuss the use and limitations of methodologies that model molecular networks in cancer cells as a digital circuit. We also develop a network model of Notch signaling in colon cancer using a novel reverse engineering logic-based method and published western blot data to elucidate the interactions likely present in the circuits of the SW480 colon cancer cell line. Within this framework, we make predictions related to the role that honokiol may be playing as an anti-cancer drug.

Competitive inhibition reaction mechanisms for the two-step model of protein aggregation

We propose three new reaction mechanisms for competitive inhibition of protein aggregation for the two-step model of protein aggregation. The first mechanism is characterized by the inhibition of native protein, the second is characterized by the inhibition of aggregation-prone protein and the third mechanism is characterized by the mixed inhibition of native and aggregation-prone proteins. Rate equations are derived for these mechanisms, and a method is described for plotting kinetic results to distinguish these three types of inhibitors. The derived rate equations provide a simple way of estimating the inhibition constant of native or aggregation-prone protein inhibitors in protein aggregation. The new approach is used to estimate the inhibition constants of different peptide inhibitors of insulin aggregation.

Unraveling the contribution of pancreatic beta-cell suicide in autoimmune type 1 diabetes

In type 1 diabetes, an autoimmune disease mediated by autoreactive T-cells that attack insulin-secreting pancreatic beta-cells, it has been suggested that disease progression may additionally require protective mechanisms in the target tissue to impede such auto-destructive mechanisms. We hypothesize that the autoimmune attack against beta-cells causes endoplasmic reticulum stress by forcing the remaining beta-cells to synthesize and secrete defective insulin. To rescue beta-cell from the endoplasmic reticulum stress, beta-cells activate the unfolded protein response to restore protein homeostasis and normal insulin synthesis. Here we investigate the compensatory role of unfolded protein response by developing a multi-state model of type 1 diabetes that takes into account beta-cell destruction caused by pathogenic autoreactive T-cells and apoptosis triggered by endoplasmic reticulum stress. We discuss the mechanism of unfolded protein response activation and how it counters beta-cell extinction caused by an autoimmune attack and/or irreversible damage by endoplasmic reticulum stress. Our results reveal important insights about the balance between beta-cell destruction by autoimmune attack (beta-cell homicide) and beta-cell apoptosis by endoplasmic reticulum stress (beta-cell suicide). It also provides an explanation as to why the unfolded protein response may not be a successful therapeutic target to treat type 1 diabetes.

Optimal experimental design to estimate statistically significant periods of oscillations in time course data

We investigated commonly used methods (Autocorrelation, Enright, and Discrete Fourier Transform) to estimate the periodicity of oscillatory data and determine which method most accurately estimated periods while being least vulnerable to the presence of noise. Both simulated and experimental data were used in the analysis performed. We determined the significance of calculated periods by applying these methods to several random permutations of the data and then calculating the probability of obtaining the period's peak in the corresponding periodograms. Our analysis suggests that the Enright method is the most accurate for estimating the period of oscillatory data. We further show that to accurately estimate the period of oscillatory data, it is necessary that at least five cycles of data are sampled, using at least four data points per cycle. These results suggest that the Enright method should be more widely applied in order to improve the analysis of oscillatory data.

Network representations and methods for the analysis of chemical and biochemical pathways

Systems biologists increasingly use network representations to investigate biochemical pathways and their dynamic behaviours. In this critical review, we discuss four commonly used network representations of chemical and biochemical pathways. We illustrate how some of these representations reduce network complexity but result in the ambiguous representation of biochemical pathways. We also examine the current theoretical approaches available to investigate the dynamic behaviour of chemical and biochemical networks. Finally, we describe how the critical chemical and biochemical pathways responsible for emergent dynamic behaviour can be identified using network mining and functional mapping approaches.

Unraveling intestinal stem cell behavior with models of crypt dynamics

The definition, regulation and function of intestinal stem cells (ISCs) has been hotly debated. Recent discoveries have started to clarify the nature of ISCs, but many questions remain. This review discusses the current advances and controversies of ISC biology as well as theoretical compartmental models that have been coupled with in vivo experimentation to investigate the mechanisms of ISC dynamics during homeostasis, tumorigenesis, repair and development. We conclude our review by discussing the key lingering questions in the field and proposing how many of these questions can be addressed using both compartmental models and experimental techniques.

Validity of the Michaelis-Menten equation--steady-state or reactant stationary assumption: that is the question

The Michaelis-Menten equation is generally used to estimate the kinetic parameters, V and K(M), when the steady-state assumption is valid. Following a brief overview of the derivation of the Michaelis-Menten equation for the single-enzyme, single-substrate reaction, a critical review of the criteria for validity of the steady-state assumption is presented. The application of the steady-state assumption makes the implicit assumption that there is an initial transient during which the substrate concentration remains approximately constant, equal to the initial substrate concentration, while the enzyme-substrate complex concentration builds up. This implicit assumption is known as the reactant stationary assumption. This review presents evidence showing that the reactant stationary assumption is distinct from and independent of the steady-state assumption. Contrary to the widely believed notion that the Michaelis-Menten equation can always be applied under the steady-state assumption, the reactant stationary assumption is truly the necessary condition for validity of the Michaelis-Menten equation to estimate kinetic parameters. Therefore, the application of the Michaelis-Menten equation only leads to accurate estimation of kinetic parameters when it is used under experimental conditions meeting the reactant stationary assumption. The criterion for validity of the reactant stationary assumption does not require the restrictive condition of choosing a substrate concentration that is much higher than the enzyme concentration in initial rate experiments.

The circadian clock in oral health and diseases

Most physiological processes in mammals display circadian rhythms that are driven by the endogenous circadian clock. This clock is comprised of a central component located in the hypothalamic suprachiasmatic nucleus and subordinate clocks in peripheral tissues. Circadian rhythms sustain 24-hour oscillations of a large number of master genes controlling the correct timing and synchronization of diverse physiological and metabolic processes within our bodies. This complex regulatory network provides an important communication link between our brain and several peripheral organs and tissues. At the molecular level, circadian oscillations of gene expression are regulated by a family of transcription factors called "clock genes". Dysregulation of clock gene expression results in diverse human pathological conditions, including autoimmune diseases and cancer. There is increasing evidence that the circadian clock affects tooth development, salivary gland and oral epithelium homeostasis, and saliva production. This review summarizes current knowledge of the roles of clock genes in the formation and maintenance of oral tissues, and discusses potential links between "oral clocks" and diseases such as head and neck cancer and Sjögren's syndrome.

Integration of steady-state and temporal gene expression data for the inference of gene regulatory networks

We develop a new regression algorithm, cMIKANA, for inference of gene regulatory networks from combinations of steady-state and time-series gene expression data. Using simulated gene expression datasets to assess the accuracy of reconstructing gene regulatory networks, we show that steady-state and time-series data sets can successfully be combined to identify gene regulatory interactions using the new algorithm. Inferring gene networks from combined data sets was found to be advantageous when using noisy measurements collected with either lower sampling rates or a limited number of experimental replicates. We illustrate our method by applying it to a microarray gene expression dataset from human umbilical vein endothelial cells (HUVECs) which combines time series data from treatment with growth factor TNF and steady state data from siRNA knockdown treatments. Our results suggest that the combination of steady-state and time-series datasets may provide better prediction of RNA-to-RNA interactions, and may also reveal biological features that cannot be identified from dynamic or steady state information alone. Finally, we consider the experimental design of genomics experiments for gene regulatory network inference and show that network inference can be improved by incorporating steady-state measurements with time-series data.

Circadian rhythms regulate amelogenesis

Ameloblasts, the cells responsible for making enamel, modify their morphological features in response to specialized functions necessary for synchronized ameloblast differentiation and enamel formation. Secretory and maturation ameloblasts are characterized by the expression of stage-specific genes which follows strictly controlled repetitive patterns. Circadian rhythms are recognized as key regulators of the development and diseases of many tissues including bone. Our aim was to gain novel insights on the role of clock genes in enamel formation and to explore the potential links between circadian rhythms and amelogenesis. Our data shows definitive evidence that the main clock genes (Bmal1, Clock, Per1 and Per2) oscillate in ameloblasts at regular circadian (24 h) intervals both at RNA and protein levels. This study also reveals that the two markers of ameloblast differentiation i.e. amelogenin (Amelx; a marker of secretory stage ameloblasts) and kallikrein-related peptidase 4 (Klk4, a marker of maturation stage ameloblasts) are downstream targets of clock genes. Both, Amelx and Klk4 show 24h oscillatory expression patterns and their expression levels are up-regulated after Bmal1 over-expression in HAT-7 ameloblast cells. Taken together, these data suggest that both the secretory and the maturation stages of amelogenesis might be under circadian control. Changes in clock gene expression patterns might result in significant alterations of enamel apposition and mineralization.

Follow-the-leader cell migration requires biased cell-cell contact and local microenvironmental signals

Directed cell migration often involves at least two types of cell motility that include multicellular streaming and chain migration. However, what is unclear is how cell contact dynamics and the distinct microenvironments through which cells travel influence the selection of one migratory mode or the other. The embryonic and highly invasive neural crest (NC) are an excellent model system to study this question since NC cells have been observed in vivo to display both of these types of cell motility. Here, we present data from tissue transplantation experiments in chick and in silico modeling that test our hypothesis that cell contact dynamics with each other and the microenvironment promote and sustain either multicellular stream or chain migration. We show that when premigratory cranial NC cells (at the pre-otic level) are transplanted into a more caudal region in the head (at the post-otic level), cells alter their characteristic stream behavior and migrate in chains. Similarly, post-otic NC cells migrate in streams after transplantation into the pre-otic hindbrain, suggesting that local microenvironmental signals dictate the mode of NC cell migration. Simulations of an agent-based model (ABM) that integrates the NC cell behavioral data predict that chain migration critically depends on the interplay of biased cell-cell contact and local microenvironment signals. Together, this integrated modeling and experimental approach suggests new experiments and offers a powerful tool to examine mechanisms that underlie complex cell migration patterns.

Intracranial 4D flow MRI: toward individualized assessment of arteriovenous malformation hemodynamics and treatment-induced changes

BACKGROUND AND PURPOSE

Arteriovenous malformations are an important etiology of hemorrhagic stroke. However, current imaging modalities and risk do not provide insights into individual AVM hemodynamics and its role in pathophysiology. The aims of this study are to determine whether intracranial 4D flow MR imaging can provide insights into arteriovenous malformation hemodynamics independent of the Spetzler-Martin grade and to report the changes in flow observed during staged embolization.

MATERIALS AND METHODS

Intracranial 3D blood flow was assessed in 20 patients with AVM (age = 39 ± 15 years, Spetzler-Martin grade ranging from 1-4) with the use of 4D flow MR imaging (temporal resolution = 45 ms, spatial resolution = [1.2-1.6mm](3)). AVM hemodynamics were visualized by means of time-integrated 3D pathlines depicting the AVM arterial feeding and venous draining patterns over the cardiac cycle. Analysis included the grading of feeding and draining velocities on a 3-point scale (0 = low <25 cm/s, 1 = medium <50 cm/s, 2 = high >50 cm/s). For 4 of 20 patients undergoing 4D flow MR imaging follow-up after staged embolization, peak velocities were quantified in arterial feeders, draining veins, the sagittal sinus, and contralateral arteries.

RESULTS

In 50% of the cases with Spetzler-Martin grade >2, heterogeneous flow (velocity grade differences >1) was found across arteries and veins. Velocities in draining veins increased from Spetzler-Martin grade = 1 (grading = 0.5 ± 0.6) to Spetzler-Martin grade ≥3 (1.1 ± 0.6), whereas arterial velocities were similar (1.7 ± 0.6 versus 1.5 ± 0.6). In the postembolization subgroup of 4 patients, 4D flow MR imaging demonstrated successively more compact AVM and redistribution of velocities. Changes in arterial and venous velocities during treatment were highly different among individuals.

CONCLUSIONS

Spetzler-Martin grade does not reflect differences in 3D AVM arterial and venous hemodynamics, and an individual assessment of AVM hemodynamics may be needed for improved lesion characterization. Four-dimensional flow MR imaging may have the potential to monitor and guide embolization treatment planning.

Abstract 5239: Unraveling the complex regulatory relationship between PI3K signaling and metabolic transformation in breast cancer

Cancer cells exhibit a metabolic phenotype characterized by high rates of glucose uptake and lactate production, known as the Warburg effect. While the Warburg effect and normal proliferative metabolism appear similar, important molecular differences exist. We hypothesize that molecular and metabolic drivers of the Warburg effect can be modulated to impede cancer proliferation without substantial effects on normal tissue growth. Intracellular networks exhibit a variety of emergent non-linear behaviors and, as a result, the use of experimental intuition alone will not be enough to identify these drivers. Using a combination of experimental and theoretical methods, we developed a model of breast cancer progression that includes metabolism and the phosphatidylinositol-3 kinase (PI3K) signaling pathway, an important regulator of carbon metabolism. A key component of our model is a detailed logic network of molecular interactions associated with PI3K signaling as well as regulatory connections to central carbon metabolism, including the ATP/AMP ratio, GLUT receptor activation, hexokinase activation, and changes in the catalytic activity of pyruvate kinase. To validate our model, a series of phospho Western blot analyses were performed using a normal-like breast cell line and a diverse set of breast cancer cell lines exposed to PI3K pathway inhibitors. From these data, a series of predictive network models were constructed representing distinct stages of breast cancer progression. We also generated detailed metabolic flux maps for each cell line using metabolic flux analysis (MFA), a method that relies on carbon-13 tracers, mass-spectrometry, and measurements of extracellular flux to infer intracellular flux. In agreement with recent studies, we found an increase in the reductive carboxylation of glutamine derived alpha-ketoglutarate in cells constitutively adapted to hypoxia. We also identified a potentially important metabolic vulnerability in aggressive breast cancers. Moreover, we found important PI3K network differences at the RNA and protein levels, some of which were isoform specific. Together our data indicate that very different system-level properties are associated with distinct stages of breast cancer progression and metabolic transformation. Our model is suitable for performing in silico molecular perturbations to predict a normal as well as tumor level response to a targeted therapy or combination of therapies. Our approach also serves as a prototype for the use of systems biology methods in personalized medicine where molecular and metabolic data collected from a patient's biopsied tumor is input into a predictive model designed to develop a strategic treatment plan for the patient. The use of predictive models to integrate data from an individual patient will have a profound impact on cancer care decisions and patient outcomes in the future.

Citation Format: Michelle L. Wynn, Megan Egbert, Lauren D. Van Wassenhove, Zhi Fen Wu, Firas Midani, Charles Evans, Charles F. Burant, Santiago Schnell, Sofia D. Merajver. Unraveling the complex regulatory relationship between PI3K signaling and metabolic transformation in breast cancer. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5239. doi:10.1158/1538-7445.AM2013-5239

Abstract 5223: Elucidating the complex cross-talk between the MAPK and PIK3 pathways

Targeted molecular inhibitors have emerged as a leading anti-cancer strategy; however, despite promising pre-clinical data, many targeted inhibitors induce undesirable off-target effects in the clinic. The large number of off-target effects associated with molecular inhibitors was recently termed the ‘‘whack a mole problem’’ because inhibiting one molecular target often unintentionally activates another molecule. It is increasingly clear that the high incidence of off-target effects associated with targeted inhibitors is related to the complex interactions and emergent behaviors inherent to the highly complex and dysregulated intracellular networks of cancer. Both the mitogen activated protein kinase (MAPK) and phosphatidylinositol-3 kinase (PI3K) pathways are known to be dysregulated in cancer. In previous work, we and others have demonstrated that the MAPK pathway promotes motility, invasion, and angiogenic factors while the PI3K pathway plays an important role in controlling anchorage independent growth. In addition, the PI3K pathway plays an essential role in stimulating glucose metabolism and the Warburg effect. We hypothesize that robust interactions exist between these two pathways that influence efficacy and potentially also acquired resistance to targeted therapies. Using a combination of experimental and theoretical techniques, we developed a predictive network model linking growth factor signaling to the MAPK and PI3K pathways as well as to glucose metabolism. Specifically, we constructed a logic-based network of the cross-talk between MAPK and PI3K signaling that relied on a detailed literature survey to identify known molecular interactions as well as proposed interactions and regulatory feedback connections in the literature. We next performed a set of experiments using a normal-like breast epithelial cell line and a series of pathway specific inhibitors with and without growth factor stimulation to validate our model. Finally, we repeated these experiments using a diverse set of breast cancer cell lines and integrated this data to produce a series of cancer networks representative of different stages of breast cancer progression. Our model was able to recapitulate both our own experimental data and published data in the literature using a smaller subset of regulatory feedback mechanisms than we started with. Together, our results suggest that some proposed interactions and feedback mechanisms attributed to MAPK and PI3K cross-talk in the literature may not be valid.

Citation Format: Megan E. Egbert, Michelle L. Wynn, Zhi Fen Wu, Santiago Schnell, Sofia D. Merajver. Elucidating the complex cross-talk between the MAPK and PIK3 pathways. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5223. doi:10.1158/1538-7445.AM2013-5223

Logic-based models in systems biology: a predictive and parameter-free network analysis method

Highly complex molecular networks, which play fundamental roles in almost all cellular processes, are known to be dysregulated in a number of diseases, most notably in cancer. As a consequence, there is a critical need to develop practical methodologies for constructing and analysing molecular networks at a systems level. Mathematical models built with continuous differential equations are an ideal methodology because they can provide a detailed picture of a network's dynamics. To be predictive, however, differential equation models require that numerous parameters be known a priori and this information is almost never available. An alternative dynamical approach is the use of discrete logic-based models that can provide a good approximation of the qualitative behaviour of a biochemical system without the burden of a large parameter space. Despite their advantages, there remains significant resistance to the use of logic-based models in biology. Here, we address some common concerns and provide a brief tutorial on the use of logic-based models, which we motivate with biological examples.

Network motifs provide signatures that characterize metabolism

Motifs are repeating patterns that determine the local properties of networks. In this work, we characterized all 3-node motifs using enzyme commission numbers of the International Union of Biochemistry and Molecular Biology to show that motif abundance is related to biochemical function. Further, we present a comparative analysis of motif distributions in the metabolic networks of 21 species across six kingdoms of life. We found the distribution of motif abundances to be similar between species, but unique across cellular organelles. Finally, we show that motifs are able to capture inter-species differences in metabolic networks and that molecular differences between some biological species are reflected by the distribution of motif abundances in metabolic networks.

More than Skew: Asymmetric Wave Propagation in a Reaction-Diffusion-Convection System

Convection-induced instability in reaction-diffusion systems produces complicated patterns of oscillations behind propagating wavefronts. We transform the system twice: into lambda-omega form, then into polar variables. We find analytical estimates for the wavefront speed which we confirm numerically. Our previous work examined a simpler system [E. H. Flach, S. Schnell, and J. Norbury, Phys. Rev. E 76, 036216 (2007)]; the onset of instability is qualitatively different in numerical solutions of this system. We modify our estimates and connect the two different behaviours. Our estimate explains how the Turing instability fits with pattern found in reaction-diffusion-convection systems. Our results can have important applications to the pattern formation analysis of biological systems.

Molecular and circadian controls of ameloblasts

Stage-specific expression of ameloblast-specific genes is controlled by differential expression of transcription factors. In addition, ameloblasts follow daily rhythms in their main activities (i.e. enamel protein secretion and enamel mineralization). This time-related control is orchestrated by oscillations of clock proteins involved in the regulation of circadian rhythms. Our aim was to identify the potential links between daily rhythms and developmental controls of ameloblast differentiation. The effects of the transcription factors distal-less homeobox 3 (Dlx3) and runt-related transcription factor 2 (Runx2), and the clock gene nuclear receptor subfamily 1, group D, member 1 (Nr1d1), on secretory and maturation ameloblasts [using stage-specific markers amelogenin (Amelx), enamelin (Enam), and kallikrein-related peptidase 4 (Klk4)] were evaluated in the HAT-7 ameloblast cell line. Amelx and Enam steady-state mRNA expression levels were down-regulated in Runx2 over-expressing cells and up-regulated in Dlx3 over-expressing cells. In contrast, Klk4 mRNA was up-regulated by both Dlx3 and Runx2. Furthermore, a temporal and spatial relationship between clock genes and ameloblast differentiation markers was detected. Of interest, clock genes not only affected rhythmic expression of ameloblast-specific genes but also influenced the expression of Runx2. Multiscale mathematical modeling is being explored to further understand the temporal and developmental controls of ameloblast differentiation. Our study provides novel insights into the regulatory mechanisms sustaining ameloblast differentiation.

Computational modelling of cell chain migration reveals mechanisms that sustain follow-the-leader behaviour

Follow-the-leader chain migration is a striking cell migratory behaviour observed during vertebrate development, adult neurogenesis and cancer metastasis. Although cell–cell contact and extracellular matrix (ECM) cues have been proposed to promote this phenomenon, mechanisms that underlie chain migration persistence remain unclear. Here, we developed a quantitative agent-based modelling framework to test mechanistic hypotheses of chain migration persistence. We defined chain migration and its persistence based on evidence from the highly migratory neural crest model system, where cells within a chain extend and retract filopodia in short-lived cell contacts and move together as a collective. In our agent-based simulations, we began with a set of agents arranged as a chain and systematically probed the influence of model parameters to identify factors critical to the maintenance of the chain migration pattern. We discovered that chain migration persistence requires a high degree of directional bias in both lead and follower cells towards the target. Chain migration persistence was also promoted when lead cells maintained cell contact with followers, but not vice-versa. Finally, providing a path of least resistance in the ECM was not sufficient alone to drive chain persistence. Our results indicate that chain migration persistence depends on the interplay of directional cell movement and biased cell–cell contact.

Unraveling the complex regulatory relationships between metabolism and signal transduction in cancer

Cancer cells exhibit an altered metabolic phenotype, known as the Warburg effect, which is characterized by high rates of glucose uptake and glycolysis, even under aerobic conditions. The Warburg effect appears to be an intrinsic component of most cancers and there is evidence linking cancer progression to mutations, translocations, and alternative splicing of genes that directly code for or have downstream effects on key metabolic enzymes. Many of the same signaling pathways are routinely dysregulated in cancer and a number of important oncogenic signaling pathways play important regulatory roles in central carbon metabolism. Unraveling the complex regulatory relationship between cancer metabolism and signaling requires the application of systems biology approaches. Here we discuss computational approaches for modeling protein signal transduction and metabolism as well as how the regulatory relationship between these two important cellular processes can be combined into hybrid models.

A graphical user interface for a method to infer kinetics and network architecture (MIKANA)

One of the main challenges in the biomedical sciences is the determination of reaction mechanisms that constitute a biochemical pathway. During the last decades, advances have been made in building complex diagrams showing the static interactions of proteins. The challenge for systems biologists is to build realistic models of the dynamical behavior of reactants, intermediates and products. For this purpose, several methods have been recently proposed to deduce the reaction mechanisms or to estimate the kinetic parameters of the elementary reactions that constitute the pathway. One such method is MIKANA: Method to Infer Kinetics And Network Architecture. MIKANA is a computational method to infer both reaction mechanisms and estimate the kinetic parameters of biochemical pathways from time course data. To make it available to the scientific community, we developed a Graphical User Interface (GUI) for MIKANA. Among other features, the GUI validates and processes an input time course data, displays the inferred reactions, generates the differential equations for the chemical species in the pathway and plots the prediction curves on top of the input time course data. We also added a new feature to MIKANA that allows the user to exclude a priori known reactions from the inferred mechanism. This addition improves the performance of the method. In this article, we illustrate the GUI for MIKANA with three examples: an irreversible Michaelis–Menten reaction mechanism; the interaction map of chemical species of the muscle glycolytic pathway; and the glycolytic pathway of Lactococcus lactis. We also describe the code and methods in sufficient detail to allow researchers to further develop the code or reproduce the experiments described. The code for MIKANA is open source, free for academic and non-academic use and is available for download (Information S1).

A model of threshold behavior reveals rescue mechanisms of bystander proteins in conformational diseases

Conformational diseases result from the failure of a specific protein to fold into its correct functional state. The misfolded proteins can lead to the toxic aggregation of proteins. Protein misfolding in conformational diseases often displays a threshold behavior characterized by a sudden shift between nontoxic to toxic levels of misfolded proteins. In some conformational diseases, evidence suggests that misfolded proteins interact with bystander proteins (unfolded and native folded proteins), eliciting a misfolded phenotype. These bystander isomers would follow their normal physiological pathways in absence of misfolded proteins. In this article, we present a general mechanism of bystander and misfolded protein interaction which we have used to investigate how the threshold behavior in protein misfolding is triggered in conformational diseases. Using a continuous flow reactor model of the endoplasmic reticulum, we found that slight changes in the bystander protein residence time in the endoplasmic reticulum or the ratio of basal misfolded to bystander protein inflow rates can trigger the threshold behavior in protein misfolding. Our analysis reveals three mechanisms to rescue bystander proteins in conformational diseases. The results of our model can now help direct experiments to understand the threshold behavior and develop therapeutic strategies targeting the modulation of conformational diseases.

Responses of methanotrophic activity in soils and cultures to water stress

Diffusive gas transport at high water contents and physiological water stress at low water contents limited atmospheric methane consumption rates during experimental manipulations of soil water content and water potential. Maximum rates of atmospheric methane consumption occurred at a soil water content of 25% (grams per gram [dry weight]) and a water potential of about -0.2 MPa. In contrast, uptake rates were highest at a water content of 38% and a water potential of -0.03 MPa when methane was initially present at 200 ppm. Uptake rates of atmospheric and elevated methane decreased when water potentials were reduced by adding either ionic or nonionic solutes to soils with a fixed water content. Uptake rates during these manipulations were lower when sodium chloride or potassium chloride was used to adjust water potential rather than sucrose. The response of methane consumption by soils to water potential was somewhat less pronounced than the response of methanotrophic cultures (e.g., Methylosinus trichosporium OB3b, Methylomonas rubra [= M. methanica], an isolate from a freshwater peat, and an isolate from an intertidal marine mudflat). However, unlike soils, methanotrophic cultures exhibited a stronger adverse response to nonionic solutes than to sodium chloride.