Biology subjects

Mohr, H.

Publications and source records attributed to Mohr, H..

6 recordsLinked to original sources

bioRxiv2025

A genetic signature predicts aggressive paraganglioma sensitivity to dual PI3K-CDK4/6 inhibition therapy

Effective medical therapies for metastatic paraganglioma (mPPGL) are currently lacking, leading to dismal prognosis. Building on our knowledge of molecular mechanisms driving PPGL progression, we assessed the therapeutic potential of targeting two critical processes: PI3K signaling and cell cycle regulation. The efficacy of buparlisib (PI3Ki) and ribociclib (CDK4/6i), individually and combined, was assessed in vitro using PPGL cell lines, rat- and patient-derived primary cells, and in vivo using PPGL cells-derived mouse xenografts. The combination therapy demonstrated superior antitumor activity compared to single agents, particularly in vivo. Mechanistically, the efficacy of the combination therapy was associated to the downregulation of FOXM1-controlled genes implicated in mitotic spindle assembly and chromosomal segregation, leading to mitotic catastrophe. Data mining and qRT-PCR showed this genetic signature to be upregulated in human mPPGLs. This suggests that aggressive PPGLs exhibit heightened vulnerability to dual PI3K and CDK4/6 inhibition, offering a promising therapeutic avenue for these challenging cancers.

cancer biology↗

bioRxiv2024

Systematic mapping of MCU-mediated mitochondrial calcium signaling networks

The Mitochondrial Ca2+ Uniporter Channel (MCUC) allows calcium entry into the mitochondrial matrix to regulate energy metabolism but also cell death. Although, several MCUC components have been identified, the molecular basis of mitochondrial Ca2+ signaling networks and their remodeling upon changes in uniporter activity have not been systematically assessed. Using an unbiased and quantitative proteomic approach, we map the MCUC interactome in HEK293 cells under physiological conditions and upon chronic loss or gain of mitochondrial Ca2+ uptake. Besides all previously known subunits of the uniporter, we identify 89 high-confidence interactors linking MCUC to several mitochondrial complexes and pathways, half of which are currently linked to metabolic, neurological, and immunological diseases. As a proof-of-concept, we validate EFHD1 as a binding partner of MCU, EMRE and MCUB with a MICU1-dependent inhibitory effect on Ca2+ uptake. To investigate compensatory mechanisms and functional consequences of mitochondrial Ca2+ dyshomeostasis, we systematically survey the MCU interactome upon silencing of EMRE, MCUB, MICU1 or MICU2. We observe profound changes in the MCU interconnectivity, whereby downregulation of EMRE reduces the number of MCU interactors of over 10-fold, while silencing of MCUB leads to a wider functional network linking MCU to mitochondrial stress response pathways and cell death. Altogether our study provides a comprehensive map of MCUC protein-protein interactions and a rich, high-confidence resource that can be explored to gain insights into the players and mechanisms involved in calcium signal transduction cascades and their relevance in human diseases.

cell biology↗

bioRxiv2020

Fast estimation of L1-regularized linear models in the mass univariate setting

In certain modeling approaches, activation analyses of task-based fMRI data can involve a relatively large number of predictors. For example, in the encoding model approach, complex stimuli are represented in a high-dimensional feature space, resulting in design matrices with many predictors. Similarly, single-trial models and finite impulse response models may also encompass a large number of predictors. In settings where only few of those predictors are expected to be informative, a sparse model fit can be obtained via L1-regularization. However, estimating L1-regularized models requires an iterative fitting procedure, which considerably increases computation time compared to estimating unregularized or L2-regularized models, and complicates the application of L1-regularization on whole-brain data and large sample sizes. Here we provide several functions for estimating L1-regularized models that are optimized for the mass-univariate analysis approach. The package includes a parallel implementation of the coordinate descent algorithm for CPU-only systems and two implementations of the alternating direction method of multipliers algorithm requiring a GPU device. While the core algorithms are implemented in C++/CUDA, data input/output and parameter settings can be conveniently handled via Matlab. The CPU-based implementation is highly memory-efficient and provides considerable speed-up compared to the standard implementation not optimized for the mass-univariate approach. Further acceleration can be achieved on systems equipped with a CUDA-enabled GPU. Using the fastest GPU-based implementation, computation time for whole-brain estimates can be reduced from 9 hours to 5 minutes in an exemplary data setting. Overall, the provided package facilitates the use of L1-regularization for fMRI activation analyses and enables an efficient employment of L1-regularization on whole-brain data and large sample sizes.

neuroscience↗

bioRxiv2019

A Comparison of fMRI and Behavioral Models for Predicting Inter-Temporal Choices

In an inter-temporal choice (IteCh) task, subjects are offered a smaller amount of money immediately or a larger amount at a later time point. Here, we are using trial-by-trial fMRI data from 363 recording sessions and machine learning in an attempt to build a classifier that would ideally outperform established behavioral model given that it has access to brain activity specific to a single trial. Such methods could allow for future investigations of state-like factors that influence IteCh choices. To investigate this, coefficients of a GLM with one regressor per trial were used as features for a support vector machine (SVM) in combination with a searchlight approach for feature selection and cross-validation. We then compare the results to the performance of four different behavioral models. We found that the behavioral models reached mean accuracies of 90% and above, while the fMRI model only reached 54.84% at the best location in the brain with a spatial distribution similar to the well-known value-tracking network. This low, though significant, accuracy is in line with simulations showing that classifying based on signals with realistic correlations with subjective value produces comparable, low accuracies. These results emphasize the limitations of fMRI recordings from single events to predict human choices, especially when compared to conventional behavioral models. Better performance may be obtained with paradigms that allow the construction of miniblocks to improve the available signal-to-noise ratio.

neuroscience↗

bioRxiv2019

Initial neural representation of individual rules after first-time instruction

By following explicit instructions humans can instantaneously get the hang of tasks they have never performed before. Here, we used a specially calibrated multivariate analysis technique to uncover the elusive representational states following newly instructed arbitrary behavioural rules such as for coffee, press red button, while transitioning from knowing what to do to actually doing it. Subtle variation in distributed neural activity patterns reflected rule-specific representations within the ventrolateral prefrontal cortex (VLPFC), confined to instructed stimulus-response learning in contrast to incidental learning involving the same stimuli and responses. VLPFC representations were established right after first-time instruction and remained stable across early implementation trials. More and more fluent application of novel rule representations was channelled through increasing cooperation between VLPFC and anterior striatum. These findings inform representational theories on how the prefrontal cortex supports behavioural flexibility by enabling ad-hoc coding of novel task rules without recourse to familiar sub-routines

neuroscience↗

bioRxiv2018

Deterministic response strategies in trial-and-error learning

Trial-and-error learning is a universal strategy for establishing which actions are beneficial or harmful in new environments. However, learning stimulus-response associations solely via trial-and-error is often suboptimal, as in many settings dependencies among stimuli and responses can be exploited to increase learning efficiency. Previous studies have shown that in settings featuring such dependencies, humans typically engage high-level cognitive processes and employ advanced learning strategies to improve their learning efficiency. Here we analyze in detail the initial learning phase of a sample of human subjects (N = 85) performing a trial-and-error learning task with deterministic feedback and hidden stimulus-response dependencies. Using computational modeling, we find that the standard Q-learning model cannot sufficiently explain human learning strategies in this setting. Instead, newly introduced deterministic response models, which are theoretically optimal and transform stimulus sequences unambiguously into response sequences, provide the best explanation for 50.6% of the subjects. Most of the remaining subjects either show a tendency towards generic optimal learning (21.2%) or at least partially exploit stimulus-response dependencies (22.3%), while a few subjects (5.9%) show no clear preference for any of the employed models. After the initial learning phase, asymptotic learning performance during the subsequent practice phase is best explained by the standard Q-learning model. Our results show that human learning strategies in trial-and-error learning go beyond merely associating stimuli and responses via incremental reinforcement. Specifically during initial learning, high-level cognitive processes support sophisticated learning strategies that increase learning efficiency while keeping memory demands and computational efforts bounded. The good asymptotic fit of the Q-learning model indicates that these cognitive processes are successively replaced by the formation of stimulus-response associations over the course of learning.

animal behavior and cognition↗