Electrical stimulation (ES) is widely employed in both clinical therapies and research settings where it has shown promise in promoting tissue regeneration, wound healing, and inflammation control. Research has also highlighted ES as a regulator of DNA demethylation, which plays a critical role in nerve regeneration and cellular repair mechanisms. While the impact of ES on epigenetic processes is recognized, its broader effects on cellular functions, particularly in inflammation and wound healing, are less understood. We recently showed how ES impacts inflammatory states by modulating transcriptomic and metabolomic profiles in a 3Din vitromodel where human fibroblasts and keratinocytes are included in a collagen matrix, i.e., even in the absence of the nervous system. Here, we propose to deepen our exploration on the differential effects on DNA methylation, including an investigation of the correlation with age acceleration using a mitotic clock. These results confirm and caution on the differential effect of DC on inflamed and non-inflamed samples and suggest an involvement of direct current stimuli at 1 V ((Formula presented.)) in the control of senescent processes associated with mitosis and inflammation; the mechanistic details of these will have to be clarified with additional experiments.

Motivation: methyLImp, a method we recently introduced for the missing value estimation of DNA methylation data, has demonstrated competitive performance in data imputation compared to the existing, general-purpose, approaches. However, imputation running time was considerably long and unfeasible in case of large datasets with numerous missing values. Results: methyLImp2 made possible computations that were previously unfeasible. We achieved this by introducing two important modifications that have significantly reduced the original running time without sacrificing prediction performance. First, we implemented a chromosome-wise parallel version of methyLImp. This parallelization reduced the runtime by several 10-fold in our experiments. Then, to handle large datasets, we also introduced a mini-batch approach that uses only a subset of the samples for the imputation. Thus, it further reduces the running time from days to hours or even minutes in large datasets.

Electrostimulation is the object of the study of a variety of clinical approaches, ranging from bioelectronic medicine where the aim is to elicit the activity of the autonomic nervous system (ANS), to electroacupuncture with the general objective to restore homeostasis, to transcutaneous electrical nerve stimulation (TENS) to control pain and degeneration, to name a few. Among the numerous obstacles preventing from a clear adoption or rejection of these approaches in mainstream clinical practice, is the difficulty in standardizing experimental systems for testing and validation. Consequently, indications on the appropriate magnitude of an effective stimulus (duration, frequency, intensity) remain unclear. To approach this issue we present preliminary results on the differential molecular activity elicited in a 3D bioprinted construct containing fibroblasts and keratinocytes in a collagen matrix, by two diverse types of electrical stimulation (direct and alternate current). Two conditions, physiology and inflammation induced by TNF? perfusion were tested with anelectrobiomedical device. The system mimics a simplified model of skin, the largest and most accessible of our organs, in inflamed or physiological states, treated by electrostimulation. The bioprinted sample is constructed to yield an appropriate number of cell enabling high-throughput screens. We report here our preliminary results on RNA-seq differential expression comparing direct and alternate current stimuli, with a focus on wound healing and inflammation as part of the greater inflammatory pathway. Our construct offer reproducibility of the experience, and direct comparison among potentially numerous conditions and types of stimulation. Our preliminary results shows that electrostimulation offers differential elicitation of biological functions. In particular, direct and alternate current provoke differential activation of proliferation and development associated functions.

In this paper, we consider the problem of estimating the graphs of conditional dependencies between variables (i.e., graphical models) from multiple datasets under Gaussian settings. We present jewel 2.0, which improves our previous method jewel 1.0 by modeling commonality and class-specific differences in the graph structures and better estimating graphs with hubs, making this new approach more appealing for biological data applications. We introduce these two improvements by modifying the regression-based problem formulation and the corresponding minimization algorithm. We also present, for the first time in the multiple graphs setting, a stability selection procedure to reduce the number of false positives in the estimated graphs. Finally, we illustrate the performance of jewel 2.0 through simulated and real data examples. The method is implemented in the new version of the R package jewel

In this paper, we consider the problem of estimating multiple Gaussian Graphical Models from high-dimensional datasets. We assume that these datasets are sampled from different distributions with the same conditional independence structure, but not the same precision matrix. We propose jewel, a joint data estimation method that uses a node-wise penalized regression approach. In particular, jewel uses a group Lasso penalty to simultaneously guarantee the resulting adjacency matrix's symmetry and the graphs' joint learning. We solve the minimization problem using the group descend algorithm and propose two procedures for estimating the regularization parameter. Furthermore, we establish the estimator's consistency property. Finally, we illustrate our estimator's performance through simulated and real data examples on gene regulatory networks.