Addressing barriers in comprehensiveness, accessibility, reusability, interoperability and reproducibility of computational models in systems biology.

Computational models are often employed in systems biology to study the dynamic behaviours of complex systems. With the rise in the number of computational models, finding ways to improve the reusability of these models and their ability to reproduce virtual experiments becomes critical. Correct and effective model annotation in community-supported and standardised formats is necessary for this improvement. Here, we present recent efforts toward a common framework for annotated, accessible, reproducible and interoperable computational models in biology, and discuss key challenges of the field.

Tissue Forge: Interactive biological and biophysics simulation environment.

Tissue Forge is an open-source interactive environment for particle-based physics, chemistry and biology modeling and simulation. Tissue Forge allows users to create, simulate and explore models and virtual experiments based on soft condensed matter physics at multiple scales, from the molecular to the multicellular, using a simple, consistent interface. While Tissue Forge is designed to simplify solving problems in complex subcellular, cellular and tissue biophysics, it supports applications ranging from classic molecular dynamics to agent-based multicellular systems with dynamic populations. Tissue Forge users can build and interact with models and simulations in real-time and change simulation details during execution, or execute simulations off-screen and/or remotely in high-performance computing environments. Tissue Forge provides a growing library of built-in model components along with support for user-specified models during the development and application of custom, agent-based models. Tissue Forge includes an extensive Python API for model and simulation specification via Python scripts, an IPython console and a Jupyter Notebook, as well as C and C++ APIs for integrated applications with other software tools. Tissue Forge supports installations on 64-bit Windows, Linux and MacOS systems and is available for local installation via conda.

Sequential changes in cellular properties accompanying amniote somite formation.

Somites are transient structures derived from the pre-somitic mesoderm (PSM), involving mesenchyme-to-epithelial transition (MET) where the cells change their shape and polarize. Using Scanning electron microscopy (SEM), immunocytochemistry and confocal microscopy, we study the progression of these events along the tail-to-head axis of the embryo, which mirrors the progression of somitogenesis (younger cells located more caudally). SEM revealed that PSM epithelialization is a gradual process, which begins much earlier than previously thought, starting with the dorsalmost cells, then the medial ones, and then, simultaneously, the ventral and lateral cells, before a somite fully separates from the PSM. The core (internal) cells of the PSM and somites never epithelialize, which suggests that the core cells could be 'trapped' within the somitocoele after cells at the surfaces of the PSM undergo MET. Three-dimensional imaging of the distribution of the cell polarity markers PKCζ, PAR3, ZO1, the Golgi marker GM130 and the apical marker N-cadherin reveal that the pattern of polarization is distinctive for each marker and for each surface of the PSM, but the order of these events is not the same as the progression of cell elongation. These observations challenge some assumptions underlying existing models of somite formation.

Iron Deposition in the Brain After Aneurysmal Subarachnoid Hemorrhage.

BACKGROUND: After aneurysmal subarachnoid hemorrhage (SAH), thrombus forms over the cerebral cortex and releases hemoglobin. When extracellular, hemoglobin is toxic to neurones. High local hemoglobin concentration overwhelms the clearance capacity of macrophages expressing the hemoglobin-haptoglobin scavenger receptor CD163. We hypothesized that iron is deposited in the cortex after SAH and would associate with outcome. METHODS: Two complementary cross-sectional studies were conducted. Postmortem brain tissue from 39 SAH (mean postictal interval of 9 days) and 22 control cases was studied with Perls' staining for iron and immunolabeling for CD163, ADAM17 (a disintegrin and metallopeptidase domain 17), CD68, and Iba1 (ionized calcium binding adaptor molecule 1). In parallel, to study the persistence of cortical iron and its relationship to clinical outcome, we conducted a susceptibility-weighted imaging study of 21 SAH patients 6 months postictus and 10 control individuals. RESULTS: In brain tissue from patients dying soon after SAH, the distribution of iron deposition followed a gradient that diminished with distance from the brain surface. Iron was located intracellularly (mainly in macrophages, and occasionally in microglia, neurones, and glial cells) and extracellularly. Microglial activation and motility markers were increased after SAH, with a similar inward diminishing gradient. In controls, there was a positive correlation between CD163 and iron, which was lost after SAH. In SAH survivors, iron-sensitive imaging 6 months post-SAH confirmed persistence of cortical iron, related to the size and location of the blood clot immediately after SAH, and associated with cognitive outcome. CONCLUSIONS: After SAH, iron deposits in the cortical gray matter in a pattern that reflects proximity to the brain surface and thrombus and is related to cognitive outcome. These observations support therapeutic manoeuvres which prevent the permeation of hemoglobin into the cortex after SAH.

A multiscale multicellular spatiotemporal model of local influenza infection and immune response.

Respiratory viral infections pose a serious public health concern, from mild seasonal influenza to pandemics like those of SARS-CoV-2. Spatiotemporal dynamics of viral infection impact nearly all aspects of the progression of a viral infection, like the dependence of viral replication rates on the type of cell and pathogen, the strength of the immune response and localization of infection. Mathematical modeling is often used to describe respiratory viral infections and the immune response to them using ordinary differential equation (ODE) models. However, ODE models neglect spatially-resolved biophysical mechanisms like lesion shape and the details of viral transport, and so cannot model spatial effects of a viral infection and immune response. In this work, we develop a multiscale, multicellular spatiotemporal model of influenza infection and immune response by combining non-spatial ODE modeling and spatial, cell-based modeling. We employ cellularization, a recently developed method for generating spatial, cell-based, stochastic models from non-spatial ODE models, to generate much of our model from a calibrated ODE model that describes infection, death and recovery of susceptible cells and innate and adaptive responses during influenza infection, and develop models of cell migration and other mechanisms not explicitly described by the ODE model. We determine new model parameters to generate agreement between the spatial and original ODE models under certain conditions, where simulation replicas using our model serve as microconfigurations of the ODE model, and compare results between the models to investigate the nature of viral exposure and impact of heterogeneous infection on the time-evolution of the viral infection. We found that using spatially homogeneous initial exposure conditions consistently with those employed during calibration of the ODE model generates far less severe infection, and that local exposure to virus must be multiple orders of magnitude greater than a uniformly applied exposure to all available susceptible cells. This strongly suggests a prominent role of localization of exposure in influenza A infection. We propose that the particularities of the microenvironment to which a virus is introduced plays a dominant role in disease onset and progression, and that spatially resolved models like ours may be important to better understand and more reliably predict future health states based on susceptibility of potential lesion sites using spatially resolved patient data of the state of an infection. We can readily integrate the immune response components of our model into other modeling and simulation frameworks of viral infection dynamics that do detailed modeling of other mechanisms like viral internalization and intracellular viral replication dynamics, which are not explicitly represented in the ODE model. We can also combine our model with available experimental data and modeling of exposure scenarios and spatiotemporal aspects of mechanisms like mucociliary clearance that are only implicitly described by the ODE model, which would significantly improve the ability of our model to present spatially resolved predictions about the progression of influenza infection and immune response.

Multicellular spatial model of RNA virus replication and interferon responses reveals factors controlling plaque growth dynamics.

Respiratory viruses present major public health challenges, as evidenced by the 1918 Spanish Flu, the 1957 H2N2, 1968 H3N2, and 2009 H1N1 influenza pandemics, and the ongoing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. Severe RNA virus respiratory infections often correlate with high viral load and excessive inflammation. Understanding the dynamics of the innate immune response and its manifestations at the cell and tissue levels is vital to understanding the mechanisms of immunopathology and to developing strain-independent treatments. Here, we present a novel spatialized multicellular computational model of RNA virus infection and the type-I interferon-mediated antiviral response that it induces within lung epithelial cells. The model is built using the CompuCell3D multicellular simulation environment and is parameterized using data from influenza virus-infected cell cultures. Consistent with experimental observations, it exhibits either linear radial growth of viral plaques or arrested plaque growth depending on the local concentration of type I interferons. The model suggests that modifying the activity of signaling molecules in the JAK/STAT pathway or altering the ratio of the diffusion lengths of interferon and virus in the cell culture could lead to plaque growth arrest. The dependence of plaque growth arrest on diffusion lengths highlights the importance of developing validated spatial models of cytokine signaling and the need for in vitro measurement of these diffusion coefficients. Sensitivity analyses under conditions leading to continuous or arrested plaque growth found that plaque growth is more sensitive to variations of most parameters and more likely to have identifiable model parameters when conditions lead to plaque arrest. This result suggests that cytokine assay measurements may be most informative under conditions leading to arrested plaque growth. The model is easy to extend to include SARS-CoV-2-specific mechanisms or to use as a component in models linking epithelial cell signaling to systemic immune models.

Generation of multicellular spatiotemporal models of population dynamics from ordinary differential equations, with applications in viral infection.

BACKGROUND: The biophysics of an organism span multiple scales from subcellular to organismal and include processes characterized by spatial properties, such as the diffusion of molecules, cell migration, and flow of intravenous fluids. Mathematical biology seeks to explain biophysical processes in mathematical terms at, and across, all relevant spatial and temporal scales, through the generation of representative models. While non-spatial, ordinary differential equation (ODE) models are often used and readily calibrated to experimental data, they do not explicitly represent the spatial and stochastic features of a biological system, limiting their insights and applications. However, spatial models describing biological systems with spatial information are mathematically complex and computationally expensive, which limits the ability to calibrate and deploy them and highlights the need for simpler methods able to model the spatial features of biological systems. RESULTS: In this work, we develop a formal method for deriving cell-based, spatial, multicellular models from ODE models of population dynamics in biological systems, and vice versa. We provide examples of generating spatiotemporal, multicellular models from ODE models of viral infection and immune response. In these models, the determinants of agreement of spatial and non-spatial models are the degree of spatial heterogeneity in viral production and rates of extracellular viral diffusion and decay. We show how ODE model parameters can implicitly represent spatial parameters, and cell-based spatial models can generate uncertain predictions through sensitivity to stochastic cellular events, which is not a feature of ODE models. Using our method, we can test ODE models in a multicellular, spatial context and translate information to and from non-spatial and spatial models, which help to employ spatiotemporal multicellular models using calibrated ODE model parameters. We additionally investigate objects and processes implicitly represented by ODE model terms and parameters and improve the reproducibility of spatial, stochastic models. CONCLUSION: We developed and demonstrate a method for generating spatiotemporal, multicellular models from non-spatial population dynamics models of multicellular systems. We envision employing our method to generate new ODE model terms from spatiotemporal and multicellular models, recast popular ODE models on a cellular basis, and generate better models for critical applications where spatial and stochastic features affect outcomes.

Exact solution for the Anisotropic Ornstein-Uhlenbeck process.

Active-Matter models commonly consider particles with overdamped dynamics subject to a force (speed) with constant modulus and random direction. Some models also include random noise in particle displacement (a Wiener process), resulting in diffusive motion at short time scales. On the other hand, Ornstein-Uhlenbeck processes apply Langevin dynamics to the particles' velocity and predict motion that is not diffusive at short time scales. Experiments show that migrating cells have gradually varying speeds at intermediate and long time scales, with short-time diffusive behavior. While Ornstein-Uhlenbeck processes can describe the moderate-and long-time speed variation, Active-Matter models for over-damped particles can explain the short-time diffusive behavior. Isotropic models cannot explain both regimes, because short-time diffusion renders instantaneous velocity ill-defined, and prevents the use of dynamical equations that require velocity time-derivatives. On the other hand, both models correctly describe some of the different temporal regimes seen in migrating biological cells and must, in the appropriate limit, yield the same observable predictions. Here we propose and solve analytically an Anisotropic Ornstein-Uhlenbeck process for polarized particles, with Langevin dynamics governing the particle's movement in the polarization direction and a Wiener process governing displacement in the orthogonal direction. Our characterization provides a theoretically robust way to compare movement in dimensionless simulations to movement in experiments in which measurements have meaningful space and time units. We also propose an approach to deal with inevitable finite-precision effects in experiments and simulations.

A modular framework for multiscale, multicellular, spatiotemporal modeling of acute primary viral infection and immune response in epithelial tissues and its application to drug therapy timing and effectiveness.

Simulations of tissue-specific effects of primary acute viral infections like COVID-19 are essential for understanding disease outcomes and optimizing therapies. Such simulations need to support continuous updating in response to rapid advances in understanding of infection mechanisms, and parallel development of components by multiple groups. We present an open-source platform for multiscale spatiotemporal simulation of an epithelial tissue, viral infection, cellular immune response and tissue damage, specifically designed to be modular and extensible to support continuous updating and parallel development. The base simulation of a simplified patch of epithelial tissue and immune response exhibits distinct patterns of infection dynamics from widespread infection, to recurrence, to clearance. Slower viral internalization and faster immune-cell recruitment slow infection and promote containment. Because antiviral drugs can have side effects and show reduced clinical effectiveness when given later during infection, we studied the effects on progression of treatment potency and time-of-first treatment after infection. In simulations, even a low potency therapy with a drug which reduces the replication rate of viral RNA greatly decreases the total tissue damage and virus burden when given near the beginning of infection. Many combinations of dosage and treatment time lead to stochastic outcomes, with some simulation replicas showing clearance or control (treatment success), while others show rapid infection of all epithelial cells (treatment failure). Thus, while a high potency therapy usually is less effective when given later, treatments at late times are occasionally effective. We illustrate how to extend the platform to model specific virus types (e.g., hepatitis C) and add additional cellular mechanisms (tissue recovery and variable cell susceptibility to infection), using our software modules and publicly-available software repository.

CompuCell3D Simulations Reproduce Mesenchymal Cell Migration on Flat Substrates.

Mesenchymal cell crawling is a critical process in normal development, in tissue function, and in many diseases. Quantitatively predictive numerical simulations of cell crawling thus have multiple scientific, medical, and technological applications. However, we still lack a low-computational-cost approach to simulate mesenchymal three-dimensional (3D) cell crawling. Here, we develop a computationally tractable 3D model (implemented as a simulation in the CompuCell3D simulation environment) of mesenchymal cells crawling on a two-dimensional substrate. The Fürth equation, the usual characterization of mean-squared displacement (MSD) curves for migrating cells, describes a motion in which, for increasing time intervals, cell movement transitions from a ballistic to a diffusive regime. Recent experiments have shown that for very short time intervals, cells exhibit an additional fast diffusive regime. Our simulations' MSD curves reproduce the three experimentally observed temporal regimes, with fast diffusion for short time intervals, slow diffusion for long time intervals, and intermediate time -interval-ballistic motion. The resulting parameterization of the trajectories for both experiments and simulations allows the definition of time- and length scales that translate between computational and laboratory units. Rescaling by these scales allows direct quantitative comparisons among MSD curves and between velocity autocorrelation functions from experiments and simulations. Although our simulations replicate experimentally observed spontaneous symmetry breaking, short-timescale diffusive motion, and spontaneous cell-motion reorientation, their computational cost is low, allowing their use in multiscale virtual-tissue simulations. Comparisons between experimental and simulated cell motion support the hypothesis that short-time actomyosin dynamics affects longer-time cell motility. The success of the base cell-migration simulation model suggests its future application in more complex situations, including chemotaxis, migration through complex 3D matrices, and collective cell motion.

Sex-related difference in the use of percutaneous left ventricular assist device in patients undergoing complex high-risk percutaneous coronary intervention: Insight from the cVAD registry.

OBJECTIVE: To assess the in-hospital and short-term outcome differences between males and females who underwent high-risk PCI with mechanical circulatory support (MCS). BACKGROUND: Sex differences have been noted in several percutaneous coronary intervention (PCI) series with females less likely to be referred for PCI due increased risk of adverse events. However, data on sex differences in utilization and outcomes of high-risk PCI with MCS is scarce. METHODS: Using the cVAD Registry, we identified 1,053 high-risk patients who underwent PCI with MCS using Impella 2.5 or Impella CP. Patients with cardiogenic shock were excluded. A total of 792 (75.21%) males and 261 (24.79%) females were included in the analysis with median follow-up of 81.5 days. RESULTS: Females were more likely to be African American, older (72.05 ± 11.66 vs. 68.87 ± 11.17, p < .001), have a higher prevalence of diabetes (59.30 vs. 49.04%, p = .005), renal insufficiency (35.41 vs. 27.39%, p = .018), and peripheral vascular disease (31.89 vs. 25.39%, p of .05). Women had a higher mean STS score (8.21 ± 8.21 vs. 5.04 ± 5.97, p < .001) and lower cardiac output on presentation (3.64 ± 1.30 vs. 4.63 ± 1.49, p < .001). Although women had more comorbidities, there was no difference in in-hospital mortality, stroke, MI or need for recurrent revascularization compared to males. Females were more likely to have multivessel revascularization than males. Ejection fraction improved in both males and females at the time of discharge (26.59 to 31.40% and 30.75 to 36.05%, respectively, p < .0001). However, females had higher rate of bleeding requiring transfusion compared with males (9.58 vs. 5.30%, p = .019). CONCLUSION: Female patients undergoing high PCI were older and had more comorbidities but had similar outcomes compared to males.

The 2019 mathematical oncology roadmap.

Whether the nom de guerre is Mathematical Oncology, Computational or Systems Biology, Theoretical Biology, Evolutionary Oncology, Bioinformatics, or simply Basic Science, there is no denying that mathematics continues to play an increasingly prominent role in cancer research. Mathematical Oncology-defined here simply as the use of mathematics in cancer research-complements and overlaps with a number of other fields that rely on mathematics as a core methodology. As a result, Mathematical Oncology has a broad scope, ranging from theoretical studies to clinical trials designed with mathematical models. This Roadmap differentiates Mathematical Oncology from related fields and demonstrates specific areas of focus within this unique field of research. The dominant theme of this Roadmap is the personalization of medicine through mathematics, modelling, and simulation. This is achieved through the use of patient-specific clinical data to: develop individualized screening strategies to detect cancer earlier; make predictions of response to therapy; design adaptive, patient-specific treatment plans to overcome therapy resistance; and establish domain-specific standards to share model predictions and to make models and simulations reproducible. The cover art for this Roadmap was chosen as an apt metaphor for the beautiful, strange, and evolving relationship between mathematics and cancer.

A simple automated method for continuous fieldwise measurement of microvascular hemodynamics.

Microvascular perfusion dynamics are vital to physiological function and are frequently dysregulated in injury and disease. Typically studies measure microvascular flow in a few selected vascular segments over limited time, failing to capture spatial and temporal variability. To quantify microvascular flow in a more complete and unbiased way we developed STAFF (Spatial Temporal Analysis of Fieldwise Flow), a macro for FIJI open-source image analysis software. Using high-speed microvascular flow movies, STAFF generates kymographs for every time interval for every vascular segment, calculates flow velocities from red blood cell shadow angles, and outputs the data as color-coded velocity map movies and spreadsheets. In untreated mice, analyses demonstrated profound variation even between adjacent sinusoids over seconds. In acetaminophen-treated mice we detected flow reduction localized to pericentral regions. STAFF is a powerful new tool capable of providing novel insights by enabling measurement of the complex spatiotemporal dynamics of microvascular flow.

Large bore occlusive sheath management.

The field of interventional cardiology has evolved in its ability to carry out complex procedures. Procedures such as transcatheter aortic valve replacement (TAVR), endovascular aneurysm repair (EVAR), and mechanical circulatory support (MCS) devices require large bore access for successful deployment. With the use of large bore-access, comes with it an increased risk for vascular complications, such as thrombosis and limb ischemia. It is paramount for the interventional cardiologist to know how to manage such complications. In this manuscript, we describe our strategies for the management of occlusive sheaths increasingly encountered with large bore accesses in the upper and lower extremities. Strategies such as peeling away of the introducer sheath and the creation of internal and external bypass circuits are described. By using the described techniques, one can provide prolonged hemodynamic support and maintain large bore sheath access, without jeopardizing perfusion to the extremity.

A subarachnoid haemorrhage-specific outcome tool.

Functional outcome after subarachnoid haemorrhage has traditionally been assessed using scales developed for other neurological conditions. The modified Rankin score and Glasgow Outcome Scale are most commonly used. Employment of these scales in subarachnoid haemorrhage is hampered by well recognized limitations. We set out to develop and validate a new condition-specific subarachnoid haemorrhage outcome tool (SAHOT). Items addressing diverse aspects of the impact of subarachnoid haemorrhage were collected during focus groups involving patients, next-of-kin and multidisciplinary professionals involved in subarachnoid haemorrhage management. After a series of iterative revisions, the resultant questionnaire was applied to patients and their next-of-kin at 1, 3 and 6 months post-subarachnoid haemorrhage. Rasch methodology was used to finalize the structure of the questionnaire and explore the extent to which SAHOT scores met Rasch-based criteria of successful measurement. The SAHOT was further assessed using traditional scale evaluation techniques, and validated in a second separate subarachnoid haemorrhage patient cohort. The final SAHOT included 56 items dealing with cognitive, physical, and behavioural/psychological consequences of subarachnoid haemorrhage. Rasch analysis indicated the scale successfully measured functional outcome post-subarachnoid haemorrhage. Three item scoring categories produced the best scale performance. There was no evidence of differential item functioning between patients and next-of-kin. The SAHOT was found to be acceptable, have good convergent and divergent validity, good discrimination and excellent responsiveness. It was successfully validated in a second subarachnoid haemorrhage patient cohort. The SAHOT offers the first subarachnoid haemorrhage-specific scientifically robust outcome measure with potential utility in neurovascular clinical services and research studies.

A Notch positive feedback in the intestinal stem cell niche is essential for stem cell self-renewal.

The intestinal epithelium is the fastest regenerative tissue in the body, fueled by fast-cycling stem cells. The number and identity of these dividing and migrating stem cells are maintained by a mosaic pattern at the base of the crypt. How the underlying regulatory scheme manages this dynamic stem cell niche is not entirely clear. We stimulated intestinal organoids with Notch ligands and inhibitors and discovered that intestinal stem cells employ a positive feedback mechanism via direct Notch binding to the second intron of the Notch1 gene. Inactivation of the positive feedback by CRISPR/Cas9 mutation of the binding sequence alters the mosaic stem cell niche pattern and hinders regeneration in organoids. Dynamical system analysis and agent-based multiscale stochastic modeling suggest that the positive feedback enhances the robustness of Notch-mediated niche patterning. This study highlights the importance of feedback mechanisms in spatiotemporal control of the stem cell niche.

Molecular jenga: the percolation phase transition (collapse) in virus capsids.

Virus capsids are polymeric protein shells that protect the viral cargo. About half of known virus families have icosahedral capsids that self-assemble from tens to thousands of subunits. Capsid disassembly is critical to the lifecycles of many viruses yet is poorly understood. Here, we apply a graph and percolation theory to examine the effect of removing capsid subunits on capsid stability and fragmentation. Based on the structure of the icosahedral capsid of hepatitis B virus (HBV), we constructed a graph of rhombic subunits arranged with icosahedral symmetry. Though our approach neglects dependence on energetics, time, and molecular detail, it quantitatively predicts a percolation phase transition consistent with recent in vitro studies of HBV capsid dissociation. While the stability of the capsid graph followed a gradual quadratic decay, the rhombic tiling abruptly fragmented when we removed more than 25% of the 120 subunits, near the percolation threshold observed experimentally. This threshold may also affect results of capsid assembly, which also experimentally produces a preponderance of 90 mer intermediates, as the intermediate steps in these reactions are reversible and can thus resemble dissociation. Application of percolation theory to understanding capsid association and dissociation may prove a general approach to relating virus biology to the underlying biophysics of the virus particle.

Access and closure management of large bore femoral arterial access.

Femoral and radial artery access continue to be the standard of care for percutaneous coronary interventions. Cardiac catheterization has progressed to encompass a wide range of diagnostic and interventional procedures including coronary, peripheral, endovascular, and structural heart disease interventions. Despite advanced technology to make these procedures safe, bleeding, and vascular complications continue to be a substantial source of morbidity, especially in patients undergoing large-bore access procedures. New variations of percutaneous devices have reduced complications associated with these procedures. However, safe vascular access with effective hemostasis requires special techniques which have not been well described in the literature. Large-bore femoral artery access is feasible, safe, and associated with low complication rates when a protocol is implemented. Wayne State University, Detroit Medical Center Heart Hospital is a tertiary care, high-volume center for endovascular, structural heart and complex high risk indicated procedures with more 150 procedures involving mechanical circulatory support (MCS) devices per year. In this manuscript, we describe our approach to femoral artery large-bore sheath insertion and management. Our protocol includes proper identification of the puncture site, device selection, insertion, assessment of limb perfusion while on prolong MCS support, and hemostasis techniques after sheath removal.

Transcriptome analysis reveals manifold mechanisms of cyst development in ADPKD.

BACKGROUND: Autosomal dominant polycystic kidney disease (ADPKD) causes progressive loss of renal function in adults as a consequence of the accumulation of cysts. ADPKD is the most common genetic cause of end-stage renal disease. Mutations in polycystin-1 occur in 87% of cases of ADPKD and mutations in polycystin-2 are found in 12% of ADPKD patients. The complexity of ADPKD has hampered efforts to identify the mechanisms underlying its pathogenesis. No current FDA (Federal Drug Administration)-approved therapies ameliorate ADPKD progression. RESULTS: We used the de Almeida laboratory's sensitive new transcriptogram method for whole-genome gene expression data analysis to analyze microarray data from cell lines developed from cell isolates of normal kidney and of both non-cystic nephrons and cysts from the kidney of a patient with ADPKD. We compared results obtained using standard Ingenuity Volcano plot analysis, Gene Set Enrichment Analysis (GSEA) and transcriptogram analysis. Transcriptogram analysis confirmed the findings of Ingenuity, GSEA, and published analysis of ADPKD kidney data and also identified multiple new expression changes in KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways related to cell growth, cell death, genetic information processing, nucleotide metabolism, signal transduction, immune response, response to stimulus, cellular processes, ion homeostasis and transport and cofactors, vitamins, amino acids, energy, carbohydrates, drugs, lipids, and glycans. Transcriptogram analysis also provides significance metrics which allow us to prioritize further study of these pathways. CONCLUSIONS: Transcriptogram analysis identifies novel pathways altered in ADPKD, providing new avenues to identify both ADPKD's mechanisms of pathogenesis and pharmaceutical targets to ameliorate the progression of the disease.

Filopodial-Tension Model of Convergent-Extension of Tissues.

In convergent-extension (CE), a planar-polarized epithelial tissue elongates (extends) in-plane in one direction while shortening (converging) in the perpendicular in-plane direction, with the cells both elongating and intercalating along the converging axis. CE occurs during the development of most multicellular organisms. Current CE models assume cell or tissue asymmetry, but neglect the preferential filopodial activity along the convergent axis observed in many tissues. We propose a cell-based CE model based on asymmetric filopodial tension forces between cells and investigate how cell-level filopodial interactions drive tissue-level CE. The final tissue geometry depends on the balance between external rounding forces and cell-intercalation traction. Filopodial-tension CE is robust to relatively high levels of planar cell polarity misalignment and to the presence of non-active cells. Addition of a simple mechanical feedback between cells fully rescues and even improves CE of tissues with high levels of polarity misalignments. Our model extends easily to three dimensions, with either one converging and two extending axes, or two converging and one extending axes, producing distinct tissue morphologies, as observed in vivo.