Michael Pargett

Brat promotes stem cell differentiation via control of a bistable switch that restricts BMP signaling.

Summary Drosophila ovarian germline stem cells (GSCs) are maintained by Dpp signaling and the Pumilio (Pum) and Nanos (Nos) translational repressors. Upon division, Dpp signaling is extinguished, and Nos is downregulated in one daughter cell, causing it to switch to a differentiating cystoblast (CB). However, downstream effectors of Pum-Nos remain unknown, and how CBs lose their responsiveness to Dpp is unclear. Here, we identify Brain Tumor (Brat) as a potent differentiation factor and target of Pum-Nos regulation. Brat is excluded from GSCs by Pum-Nos but functions with Pum in CBs to translationally repress distinct targets, including the Mad and dMyc mRNAs. Regulation of both targets simultaneously lowers cellular responsiveness to Dpp signaling, forcing the cell to become refractory to the self-renewal signal. Mathematical modeling elucidates bistability of cell fate in the Brat-mediated system, revealing how autoregulation of GSC number can arise from Brat coupling extracellular Dpp regulation to intracellular interpretation.

Rhythmic abdominal compression CPR ventilates without supplemental breaths and provides effective blood circulation

OBJECTIVES Standard chest-compression CPR has an out-of-hospital resuscitation rate of less than 10% and can result in rib fractures or mouth-to-mouth transfer of infection. Recently, we introduced a new CPR method that utilizes only rhythmic abdominal compressions (OAC-CPR). The present study compares ventilation and hemodynamics produced by chest and abdominal compression CPR. METHODS Twelve swine (29-34kg) were anesthetized, intubated and allowed to breathe spontaneously. Physiologic dead space, resting tidal volume, compression-induced lung air flow, and blood pressures were recorded. Ventricular fibrillation (VF) was electrically induced and subjects were treated with either standard CPR or OAC-CPR at various force and rate settings. Minute alveolar ventilation (MAV) and mean coronary perfusion pressure (CPP) were compared. RESULTS For OAC-CPR, ventilation per compression tended to increase with increasing force and decreasing rate. Chest only compressions produced no MAV, while OAC-CPR at 80cycles/min or less, matched the MAV for spontaneous respiration. For all rates, abdominal compressions met, or exceeded, the CPP of chest compressions performed at 100lbs. CONCLUSIONS OAC-CPR generated ventilatory volumes significantly greater than the dead space and produced equivalent, or larger, CPP than with chest compressions. Thus, OAC-CPR ventilates a subject, eliminating the need for mouth-to-mouth breathing, and effectively circulates blood during VF without breaking ribs. Furthermore, this technique is simple to perform, can be administered by a single rescuer, and should reduce bystander reluctance to administer CPR.

Comparison of CPR outcome predictors between rhythmic abdominal compression and continuous chest compression CPR techniques

Objective Bystander cardiopulmonary resuscitation (CPR) provides treatment for out-of-hospital cardiac arrest since perfusion of vital organs is critical to resuscitation. Alternatives to standard CPR are evaluated for effectiveness based upon outcome predictive metrics and survival studies. This study focuses on evaluating the performance of rhythmic-only abdominal compression CPR (OAC-CPR) relative to chest compression (CC-CPR) using a complementary suite of mechanistically based CPR outcome predictors. Combined, these predictors provide insight on the transduction of compression-induced pressures into flow perfusing vital organs. Methods Intrasubject comparisons between the CPR techniques were made during multiple 2-min intervals of induced fibrillation in 17 porcine subjects. Arterial pO2, cardiac output, carotid blood flow, coronary perfusion pressure (CPP), minute alveolar ventilation (MAV), end-tidal CO2, and time from defibrillation to the return of spontaneous circulation (ROSC) were recorded. Organ damage was assessed by necropsy. Results Compared with CC-CPR, OAC-CPR had higher pressure and ventilation metrics with increased relative CPP (+16 mm Hg), MAV (+75/ml/min/kg) and a lower reduction in arterial pO2(−22% baseline), but suffered from lower carotid flows (−9.3 ml/min). No significant difference was found comparing cardiac outputs. Furthermore, resuscitation was qualitatively more difficult after OAC-CPR, with a longer time to ROSC (+70 s). No abdominal damage was observed over short periods of OAC-CPR. Conclusions Although OAC-CPR appeared superior to CC-CPR by pressure and ventilation metrics, lower carotid flow and longer delay until ROSC raise concerns about overall performance. These paradoxical observations suggest that the evaluation of efficacious alternative CPR techniques may require more direct measurements of vital organ perfusion.

Model-Based Analysis for Qualitative Data: An Application in Drosophila Germline Stem Cell Regulation

Discovery in developmental biology is often driven by intuition that relies on the integration of multiple types of data such as fluorescent images, phenotypes, and the outcomes of biochemical assays. Mathematical modeling helps elucidate the biological mechanisms at play as the networks become increasingly large and complex. However, the available data is frequently under-utilized due to incompatibility with quantitative model tuning techniques. This is the case for stem cell regulation mechanisms explored in the Drosophila germarium through fluorescent immunohistochemistry. To enable better integration of biological data with modeling in this and similar situations, we have developed a general parameter estimation process to quantitatively optimize models with qualitative data. The process employs a modified version of the Optimal Scaling method from social and behavioral sciences, and multi-objective optimization to evaluate the trade-off between fitting different datasets (e.g. wild type vs. mutant). Using only published imaging data in the germarium, we first evaluated support for a published intracellular regulatory network by considering alternative connections of the same regulatory players. Simply screening networks against wild type data identified hundreds of feasible alternatives. Of these, five parsimonious variants were found and compared by multi-objective analysis including mutant data and dynamic constraints. With these data, the current model is supported over the alternatives, but support for a biochemically observed feedback element is weak (i.e. these data do not measure the feedback effect well). When also comparing new hypothetical models, the available data do not discriminate. To begin addressing the limitations in data, we performed a model-based experiment design and provide recommendations for experiments to refine model parameters and discriminate increasingly complex hypotheses.

BMP signaling in wing development: A critical perspective on quantitative image analysis

Bone Morphogenetic Proteins (BMPs) are critical for pattern formation in many animals. In numerous tissues, BMPs become distributed in spatially non‐uniform profiles. The gradients of signaling activity can be detected by a number of biological assays involving fluorescence microscopy. Quantitative analyses of BMP gradients are powerful tools to investigate the regulation of BMP signaling pathways during development. These approaches rely heavily on images as spatial representations of BMP activity levels, using them to infer signaling distributions that inform on regulatory mechanisms. In this perspective, we discuss current imaging assays and normalization methods used to quantify BMP activity profiles with a focus on the Drosophila wing primordium. We find that normalization tends to lower the number of samples required to establish statistical significance between profiles in controls and experiments, but the increased resolvability comes with a cost. Each normalization strategy makes implicit assumptions about the biology that impacts our interpretation of the data. We examine the tradeoffs for normalizing versus not normalizing, and discuss their impacts on experimental design and the interpretation of resultant data.

Specifying informative experiment stimulation conditions for resolving dynamical uncertainty in biological systems

A computationally efficient model-based design of experiments (MBDOE) strategy is developed to plan an optimal experiment by specifying the experimental stimulation magnitudes and measurement points. The strategy is extended from previous work which optimized the experimental design over a space of measurable species and time points. We include system inputs (stimulation conditions) in the experiment design search to investigate if the addition of perturbations enhances the ability of the MBDOE method to resolve uncertainties in system dynamics. The MBDOE problem is made computationally tractable by using a sparse-grid approximation of the model output dynamics, pre-specifying the time points at which the input or experimental perturbations can be applied, and creating scenario trees to explore the endogenous uncertainty. Consecutive scenario trees are used to determine the best input magnitudes and select the optimal associated measurement species and time points. We demonstrate the effectiveness of this strategy on a T-Cell Receptor (TCR) signaling pathway model.