Biology subjects

Mendes, E.

Publications and source records attributed to Mendes, E..

4 recordsLinked to original sources

bioRxiv2025

CDK8 Inhibition Releases the Muscle Differentiation Block in Fusion-driven Alveolar Rhabdomyosarcoma

Alveolar rhabdomyosarcoma (aRMS) is a fusion-driven pediatric cancer with poor survival and limited therapeutic options. To uncover novel vulnerabilities, we employed complex-based analysis of the DepMap functional genomic data, identifying CDK8 as a dependency in aRMS. Both CDK8 knockout and pharmacologic inhibition impaired tumor cell growth and induced myogenic differentiation in vitro and in vivo. Compared to genetic loss, CDK8 inhibition induced more dynamic transcriptional changes. With a genome-scale CRISPR-Cas9 drug modifier screen, we determined that the maximal anti-tumor activity of the CDK8 inhibitor requires the presence of the Mediator kinase module and transcriptional cooperation with the SAGA complex. We further identified SIX4 as a key transcription factor mediating CDK8 inhibitor-induced transcriptional activation of myogenic differentiation genes and tumor cell proliferation. These findings suggest a distinct gain-of-function mechanism of the CDK8 inhibitor and establish a strong rationale for CDK8 inhibition as a differentiation-inducing therapeutic strategy in aRMS. STATEMENT OF SIGNIFICANCEWe provide a framework for uncovering therapeutic targets by network-based analysis of functional genomic screens. We identify CDK8 as a druggable target in aRMS and determine that CDK8 inhibition drives myogenic differentiation and impairs tumor progression via a collaborative mechanism involving the Mediator kinase module, SAGA complex, and SIX4.

cancer biology↗

bioRxiv2025

Engineered micropillars to unveil oligodendrocyte responses to physical cues

Destruction of myelin internodes, oligodendrocyte (OL) apoptosis, and axonal degeneration characterize diseased or aged central nervous systems. While OLs can partially regenerate myelin sheaths, the remyelination process ultimately fails. Tissue mechanical and physical properties, such as stiffness and axonal curvature, play a role in this process. However, the complexity of existing models has hindered studies of OL mechanobiology. Here, a tissue-engineered model is presented to investigate the impact of stiffness and axonal diameter on OL myelination. The model consists of poly(dimethylsiloxane) micropillars with biologically relevant diameters (1-5 {micro}m), tunable rigidity, and amenable for surface functionalization. The optimized method enables the production of high-aspect-ratio, transparent micropillar arrays, in a reproducible and scalable system, serving as surrogate axons. Additionally, new protocols for quantifying myelin formation are introduced, which can be adapted to any myelination studies. Softer micropillars accelerate OL differentiation, while rigid ones promote the maintenance of mature OL states. Wrapping of OLs increased with micropillar diameter on rigid substrates, but not on softer ones, suggesting a complex interplay between curvature and rigidity. These processes involve calcium-sensitive channels, histone deacetylases, and microtubules dynamics. The proposed platform constitutes a versatile and user-friendly system, with applications from fundamental myelin research to drug discovery.

bioengineering↗

bioRxiv2021

Exploratory methods for high-performance EEG speech decoding

State-of-the-art technologies in neural speech decoding utilize data collected from microwires or microarrays implanted directly into the cerebral cortex. Yet as a tool accessible only to individuals with implanted electrodes, speech decoding from devices of this nature is severely limited in its implementation, and cannot be considered a viable solution for widespread application. Speech decoding from non-invasive EEG signals can achieve relatively high accuracy (70-80%), but only from very small classification tasks, with more complex tasks typically yielding a limited (20-50%) classification accuracy. We propose a novel combination of technologies in which transcranial magnetic stimulation (TMS) is first applied to augment the neural signals of interest, producing a greater signal-to-noise ratio in the EEG data. Next, delay differential analysis (DDA) - a cutting-edge computational method based on nonlinear dynamics - is implemented to capture the widest range of information available in the neural signal, by incorporating both linear and nonlinear dynamics.

neuroscience↗

bioRxiv2020

Active probing to highlight approaching transitions to ictal states in coupled neural mass models

The extraction of electrophysiological features that reliably forecast the occurrence of seizures is one of the most challenging goals in epilepsy research. Among possible approaches to tackle this problem is the use of active probing paradigms in which responses to stimuli are used to detect underlying system changes leading up to seizures. This work evaluates the theoretical and mechanistic underpinnings of this strategy using a well-studied seizure-generating neural mass model with two coupled populations. Different simulations are done by shifting parameters (excitability, slow inhibition, and coupling gains) towards ictal states while low-frequency probing stimuli are applied to either one or both populations. The correlation between the extracted features and the shifted parameter indicates if the impending transition to the ictal state may be identified in advance. Results show that not only can the response to the probing stimuli forecast seizures but this is true regardless of the altered ictogenic parameter. That is, similar feature changes are highlighted by probing stimuli responses in advance of the seizure including: increased response variance and lag-1 autocorrelation, decreased skewness, and increased mutual information between the outputs of both model subsets. These changes were restricted to the stimulated population, showing a local effect of this perturbational approach. The transition latencies from normal activity to sustained discharges of spikes were not affected, suggesting that stimuli had no pro-ictal effects. However, stimuli were found to elicit interictal-like spikes just before the transition to the ictal state. Furthermore, the observed feature changes highlighted by probing the neuronal populations may reflect the phenomenon of critical slowing down, where increased recovery times from perturbations may signal the loss of a systems resilience and are common hallmarks of an impending critical transition. These results provide more evidence that active probing approaches highlight information about underlying system changes involved in ictogenesis and may be able to play a role in assisting seizure forecasting methods which can be incorporated into early-warning systems that ultimately enable closing the loop for targeted seizure-controlling interventions. Author summaryEpilepsy is characterized by spontaneous and recurrent seizures. Developing a method to forecast the occurrence of these events would ease part of the burden to patients by providing warning systems and identifying critical periods for closed-loop seizure suppressing methods. However, this task is far from trivial and doing this in a clinical setting has been a challenge that is not yet solved. A possible way to achieve this is by using active probing paradigms, in which stimuli are used to highlight otherwise hidden changes in brain dynamics that may lead to seizures. In this work, this approach is tested in a computational model with two coupled neuronal populations. Active probing responses highlighted underlying model parameter changes that led neuronal activity closer to seizures, something that was not detected with the passive observation in most evaluated model settings. Observed feature changes revealed higher excitability, longer recovery times from stimuli, and increased synchrony between populations as the threshold to seizure-like activity approached. This indicates that low frequency stimuli may be used to reveal early-warning signs and assist in seizure forecasting methods.

neuroscience↗