The aim of this study was to compare the neuromuscular function of the plantar flexors following caffeine or placebo administration. Thirteen subjects (25 ± 3 years) ingested caffeine or placebo in a randomized, controlled, counterbalanced, double-blind crossover design. Neuromuscular tests were performed before and 1 h after caffeine or placebo intake. During neuromuscular testing, rate of torque development, isometric maximum voluntary torque, and neural drive to the muscles were measured. Triceps surae muscle activation was assessed by normalized root mean square of the EMG signal during the initial phase of contraction (0-100 ms, 100-200 ms) and maximal voluntary contraction (MVC). Furthermore, evoked spinal reflex responses of the soleus muscle (H-reflex evoked at rest and during MVC, V-wave) and peak twitch torques were evaluated. The isometric maximum voluntary torque and evoked potentials were not different. However, we found a significant difference between groups for rate of torque development in the time intervals 0-100 ms [41.1 N · m/s (95% CI: 8.3-73.9 N · m/s, P = 0.016)] and 100-200 ms [32.8 N · m/s (95% CI: 2.8-62.8 N · m/s, P = 0.034)]. These changes were accompanied by enhanced neural drive to the plantar flexors. Data suggest that caffeine solely increased explosive voluntary strength of the triceps surae because of enhanced neural activation at the onset of contraction whereas MVC strength was not affected.
This study analyzed the relationships between isometric as well as concentric maximum voluntary contraction (MVC) strength of the leg muscles and the times as well as speeds over different distances in 17 young short track speed skaters. Isometric as well as concentric single-joint MVC strength and multi-joint MVC strength in a stable (without skates) and unstable (with skates) condition were tested. Furthermore, time during maximum skating performances on ice was measured. Results indicate that maximum torques during eversion and dorsal flexion have a significant influence on skating speed. Concentric MVC strength of the knee extensors was higher correlated with times as well as speeds over the different distances than isometric MVC strength. Multi-joint MVC testing revealed that the force loss between measurements without and with skates amounts to 25%, while biceps femoris and soleus showed decreased muscle activity and peroneus longus, tibialis anterior, as well as rectus femoris exhibited increased muscle activity. The results of this study depict evidence that the skating times and speeds are primarily influenced by concentric MVC strength of the leg extensors. To be able to transfer the strength onto ice in an optimal way, it is necessary to stabilize the knee and ankle joints.
BackgroundThe data-driven inference of intracellular networks is one of the key challenges of computational and systems biology. As suggested by recent works, a simple yet effective approach for reconstructing regulatory networks comprises the following two steps. First, the observed effects induced by directed perturbations are collected in a signed and directed perturbation graph (PG). In a second step, Transitive Reduction (TR) is used to identify and eliminate those edges in the PG that can be explained by paths and are therefore likely to reflect indirect effects.ResultsIn this work we introduce novel variants for PG generation and TR, leading to significantly improved performances. The key modifications concern: (i) use of novel statistical criteria for deriving a high-quality PG from experimental data; (ii) the application of local TR which allows only short paths to explain (and remove) a given edge; and (iii) a novel strategy to rank the edges with respect to their confidence. To compare the new methods with existing ones we not only apply them to a recent DREAM network inference challenge but also to a novel and unprecedented synthetic compendium consisting of 30 5000-gene networks simulated with varying biological and measurement error variances resulting in a total of 270 datasets. The benchmarks clearly demonstrate the superior reconstruction performance of the novel PG and TR variants compared to existing approaches. Moreover, the benchmark enabled us to draw some general conclusions. For example, it turns out that local TR restricted to paths with a length of only two is often sufficient or even favorable. We also demonstrate that considering edge weights is highly beneficial for TR whereas consideration of edge signs is of minor importance. We explain these observations from a graph-theoretical perspective and discuss the consequences with respect to a greatly reduced computational demand to conduct TR. Finally, as a realistic application scenario, we use our framework for inferring gene interactions in yeast based on a library of gene expression data measured in mutants with single knockouts of transcription factors. The reconstructed network shows a significant enrichment of known interactions, especially within the 100 most confident (and for experimental validation most relevant) edges.ConclusionsThis paper presents several major achievements. The novel methods introduced herein can be seen as state of the art for inference techniques relying on perturbation graphs and transitive reduction. Another key result of the study is the generation of a new and unprecedented large-scale in silico benchmark dataset accounting for different noise levels and providing a solid basis for unbiased testing of network inference methodologies. Finally, applying our approach to Saccharomyces cerevisiae suggested several new gene interactions with high confidence awaiting experimental validation.
BackgroundThe inference of gene regulatory networks (GRNs) from transcriptional expression profiles is challenging, predominantly due to its underdetermined nature. One important consequence of underdetermination is the existence of many possible solutions to this inference. Our previously proposed ensemble inference algorithm TRaCE addressed this issue by inferring an ensemble of network directed graphs (digraphs) using differential gene expressions from gene knock-out (KO) experiments. However, TRaCE could not deal with the mode of the transcriptional regulations (activation or repression), an important feature of GRNs.ResultsIn this work, we developed a new algorithm called TRaCE+ for the inference of an ensemble of signed GRN digraphs from transcriptional expression data of gene KO experiments. The sign of the edges indicates whether the regulation is an activation (positive) or a repression (negative). TRaCE+ generates the upper and lower bounds of the ensemble, which define uncertain regulatory interactions that could not be verified by the data. As demonstrated in the case studies using Escherichia coli GRN and 100-gene gold-standard GRNs from DREAM 4 network inference challenge, by accounting for regulatory signs, TRaCE+ could extract more information from the KO data than TRaCE, leading to fewer uncertain edges. Importantly, iterating TRaCE+ with an optimal design of gene KOs could resolve the underdetermined issue of GRN inference in much fewer KO experiments than using TRaCE.ConclusionsTRaCE+ expands the applications of ensemble GRN inference strategy by accounting for the mode of the gene regulatory interactions. In comparison to TRaCE, TRaCE+ enables a better utilization of gene KO data, thereby reducing the cost of tackling underdetermined GRN inference. TRaCE+ subroutines for MATLAB are freely available at the following website: http://www.cabsel.ethz.ch/tools/trace.html.
Modern methods for the inference of cellular networks from experimental data often express nondeterminism through an ensemble of candidate models. To discriminate among these candidates new experiments need to be carried out. Theoretically, the number of possible experiments is exponential in the number of possible perturbations. In praxis, experiments are expensive and there exist several limiting constraints. Limiting factors exist on the combinations of perturbations that are technically possible, which components can be measured, and on the number of affordable experiments. Further, not all experiments are equally well suited to discriminate model candidates. The goal of optimal experiment design is to determine those experiments that discriminate most of the candidates while minimizing the costs. We present an approach for experiment planning with interaction graph models and sign consistency methods. This new approach can be used in combination with methods for network inference and consistency checking. We applied our method to study the Erythropoietin signal transduction in human kidney cells HEK293. We first used simulated experiment data from an ODE model to demonstrate in silico that our experimental design results in the inference of the gold standard model. Finally, we used the approach to plan in vivo experiments that discriminate model candidates for the Erythropoietin signal transduction in this cell line.
Introduction: Chronic myeloproliferative neoplasms (MPNs), including Polycythemia vera (PV), Essential Thrombocythemia (ET) and Primary Myelofibrosis (PMF) are a spectrum of clonal hematological disorders. An acquired somatic mutation of the JAK2-gene (JAK2V617F) drives clonal expansion of hematopoietic progenitor cells in 90-95% in cases of PV and in approximately 40-50% in ET and PMF patients. This mutation induces an aberrant cellular signaling affecting the activation of several downstream protein cascades of cytokine receptors and substrates of the JAK2 protein. This constitutive activation leads to deregulated proliferation and differentiation of one or several myeloid lineages. Studies have shown that progenitor cells derived from JAK2V617F-positive patients are characterized by hypersensitivity to cytokines, e.g. erythropoietin (EPO), leading to an increased proliferation and disturbed differentiation under low –dose cytokine treatment. Methods: Murine 32D progenitor cells and I-11 proerythroblasts were transduced with retroviral vectors expressing cDNAs of the erythropoietin receptor and JAK2WT/JAK2V617F. Stably transduced cell lines were treated with increasing concentrations of EPO. To study the kinetics within the EPO signaling network, cell lines were starved for 4h and then treated with increasing doses of EPO (1 – 10 IU EPO for 32D, 0.5 – 3 IU EPO for I-11) for 1h (32D cells) and for 30min (I-11 cells). Phosphorylation of STAT3/STAT5, JAK2, Erk1/2, Akt and PLCg1, respectively were detected by immunoblot analysis. Densitometric analysis of immunoblot-signals (phospho-signals adjusted to total protein signals) was performed to quantitatively determine differences in phosphorylation of the main molecular pathways of EPO signaling. Results: Treatment with low physiologic doses of EPO resulted in an enhanced cell proliferation in JAK2V617F expressing cells compared to JAK2WT. However, this effect leveled off upon EPO concentrations >0.75 IU/ml. To investigate the molecular mechanisms of this hypersensitive status, we quantitatively monitored dose- and time-dependent phosphorylation of signaling proteins, which play a major role in the EPO signaling network, including STAT3/STAT5, Erk1/2, Akt and PLCg1. In unstimulated JAK2V617F mutated 32D and I-11 cells, we identified constitutive activation of these key signaling regulators. Moreover, we could demonstrate that in JAK2V617F expressing cells EPO dependent activation of these key signaling molecules is significantly more sensitive to low EPO concentrations as compared to the situation in JAK2WT expressing cells. Thus, we detected a higher peak of phosphorylation/activation of STAT3/STAT4, Erk1/2, Akt and PLCg1 in low-dose treated JAK2V617F cells. In addition, in JAK2V617F positive cells, three patterns of signaling kinetics were observed: 1. Left shift of activation curve and higher maximum of activation which is overcome by high EPO concentrations (e.g. phospho-JAK2); 2. Left shift of activation curve and higher maximum of activation which cannot be overcome by high EPO concentrations (e.g. phospho-STAT5); 3. Pattern exhibiting minor differences only (e.g. phospho-ERK1/2). We hypothesize that this is due to differential activation of feedback and feedforward loops. Quantitative and qualitative modelling is currently being performed to identify the molecular mechanisms involved. Along this line, we identified the docking protein Grb2-associated-binding protein1 (Gab1) as a member of EPO-dependent proteins. Gab1 plays a major role in co-activation of MAPK- and PI3K-pathway. Thus, for the first time we demonstrate constitutive activation of Gab1 in JAK2V617F mutated cells. Conclusions: We here demonstrate that hypersensitivity in proliferation of JAK2V617F mutated progenitor cells and proerythroblasts is molecularly corroborated by differential sensitivity of the pro-proliferative signaling network. We identified at least four different signaling pathways, which show higher sensitivity for activation in JAK2V617F mutated cells compared wild type cells. Hypersensitivity to low-dose EPO treatment on a molecular level may explain some of the biological features observed in JAK2V617F positive MPNs and may offer novel targets for therapeutic intervention. Disclosures No relevant conflicts of interest to declare.
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