Biology subjects

Wolf, L.

Publications and source records attributed to Wolf, L..

6 recordsLinked to original sources

bioRxiv2025

Secretion and signaling properties of Wnt11 and Wnt3 are determined by their N-terminus

Wnt proteins are evolutionarily conserved signaling molecules that control cell-cell communication in health and disease. While different Wnt proteins share a requirement for palmitoleoylation and the cargo receptor Evi/Wls for their secretion, they interact with diverse signaling receptors and co-receptors to elicit distinct outcomes in signal-receiving cells. The molecular mechanisms underlying this contrast between conserved post-translational modification and secretion and diverse receptor interactions remain poorly understood. Here, we demonstrate that Wnt11 maintains partial secretion capability in the absence of Evi/Wls. Unexpectedly, the secreted protein is also non-palmitoleoylated, yet retains its ability to activate downstream signaling in receiving cells through the Frizzled 6 receptor. We identify the N-terminus as the mediator of this unusual behavior, as Wnt3, which strictly requires Evi/Wls, becomes Evi/Wls-independent when its N-terminus is replaced with that of Wnt11. In different cell lines and in Xenopus laevis embryos, the chimeric Wnt11Nterm-Wnt3 protein recapitulates known Wnt11 phenotypes, demonstrating that the N-terminal portion of Wnt11 is sufficient to determine signaling specificity. Our findings reveal novel determinants of Wnt protein specificity and provide mechanistic insights into the molecular basis of Wnt secretion and downstream signaling.

cell biology↗

bioRxiv2025

Non-Invasive Quantification of Viability in Spheroids Using Deep Learning

1In vitro viability assays are widely used in drug discovery, development, and pharmacovigilance. Traditional methods for evaluating cell viability often involve destructive processes, rendering the culture non-viable. As such, these methods are suitable only as endpoint solutions, providing a single measurement per cell culture and precluding further analyses of the cells. In this study, we introduce Neural Viability Regression (NViR), a real-time, deep learning-based method that enables non-invasive quantification of culture viability using microscopy images. The non-intrusive nature of NViR allows for frequent viability evaluations throughout experiments, capturing subtle changes while maintaining the structural integrity of the culture and significantly reducing both culture and labor costs. We demonstrate NViRs applicability by using it to predict Drug-Induced Liver Injury (DILI) in known drugs. By exposing human liver spheroids to 108 FDA-approved drugs and capturing microscopy images over time, NViRs viability assessments accurately predict whether a drug induces DILI in humans, playing a critical role in enhancing liver safety protocols. The cost-effectiveness and non-invasive characteristics of NViR enable high-frequency, high-throughput viability assessments. Consequently, NViR is poised to reduce both the costs and incidences of failures in drug discovery and development.

pharmacology and toxicology↗

bioRxiv2023

Harnessing the flexibility of neural networks to predict dynamic theoretical parameters underlying human choice behavior

Reinforcement learning (RL) models are used extensively to study human behavior. These rely on normative models of behavior and stress interpretability over predictive capabilities. More recently, neural network models have emerged as a descriptive modeling paradigm that is capable of high predictive power yet with limited interpretability. Here, we seek to augment the expressiveness of theoretical RL models with the high flexibility and predictive power of neural networks. We introduce a novel framework, which we term theoretical-RNN (t-RNN), whereby a recurrent neural network is trained to predict trial-by-trial behavior and to infer theoretical RL parameters using artificial data of RL agents performing a two-armed bandit task. In three studies, we then examined the use of our approach to dynamically predict unseen behavior along with time-varying theoretical RL parameters. We first validate our approach using synthetic data with known RL parameters. Next, as a proof-of-concept, we applied our framework to two independent datasets of humans performing the same task. In the first dataset, we describe differences in theoretical RL parameters dynamic among clinical psychiatric vs. healthy controls. In the second dataset, we show that the exploration strategies of humans varied dynamically in response to task phase and difficulty. For all analyses, we found better performance in the prediction of actions for t-RNN compared to the stationary maximum-likelihood RL method. We discuss the use of neural networks to facilitate the estimation of latent RL parameters underlying choice behavior. Author summaryCurrently, neural network models fitted directly to behavioral human data are thought to dramatically outperform theoretical computational models in terms of predictive accuracy. However, these networks do not provide a clear theoretical interpretation of the mechanisms underlying the observed behavior. Generating plausible theoretical explanations for observed human data is a major goal in computational neuroscience. Here, we provide a proof-of-concept for a novel method where a recurrent neural network (RNN) is trained on artificial data generated from a known theoretical model to predict both trial-by-trial actions and theoretical parameters. We then freeze the RNN weights and use it to predict both actions and theoretical parameters of empirical data. We first validate our approach using synthetic data where the theoretical parameters are known. We then show, using two empirical datasets, that our approach allows dynamic estimation of latent parameters while providing better action predictions compared to theoretical models fitted with a maximum-likelihood approach. This proof-of-concept suggests that neural networks can be trained to predict meaningful time-varying theoretical parameters.

animal behavior and cognition↗

bioRxiv2022

Robustness of the neuro-muscular octopaminergic system in the face of cold stress.

In recent decades, our planet has undergone dramatic environmental changes resulting in the loss of numerous species. This contrasts with species that can adapt quickly to rapidly changing ambient conditions, which require physiological plasticity and must occur rapidly. The Western honeybee (Apis mellifera) apparently meets this challenge with remarkable success, as this species is adapted to numerous climates, resulting in an almost worldwide distribution. Here, coordinated individual thermoregulatory activities ensure survival at the colony level and thus the transmission of genetic material. Recently, we showed that shivering thermogenesis, which is critical for honeybee thermoregulation, depends on octopamine signaling. In this study, we tested the hypothesis that the thoracic neuro-muscular octopaminergic system strives for a steady-state equilibrium under cold stress to maintain endogenous thermogenesis. We can show that this applies for both, octopamine provision by flight muscle innervating neurons and octopamine receptor expression in the flight muscles. Additionally, we discovered alternative splicing for AmOAR{beta}2 and our results suggest that at least one isoform is needed to survive cold stress conditions. We assume that the thoracic neuro-muscular octopaminergic system is finely tuned in order to contribute decisively to survival in a changing environment.

physiology↗

bioRxiv2022

The AI Neuropsychologist: Automatic scoring of memory deficits with deep learning

BackgroundMemory deficits are a hallmark of many different neurological and psychiatric conditions. The Rey-Osterrieth complex figure (ROCF) is the state-of-the-art assessment tool for neuropsychologists across the globe to assess the degree of non-verbal visual memory deterioration. To obtain a score, a trained clinician inspects a patients ROCF drawing and quantifies deviations from the original figure. This manual procedure is time-consuming, slow and scores vary depending on the clinicians experience, motivation and tiredness. MethodsHere, we leverage novel deep learning architectures to automatize the rating of memory deficits. For this, we collected more than 20k hand-drawn ROCF drawings from patients with various neurological and psychiatric disorders as well as healthy participants. Unbiased ground truth ROCF scores were obtained from crowdsourced human intelligence. This dataset was used to train and evaluate a multi-head convolutional neural network. ResultsThe model performs highly unbiased as it yielded predictions very close to the ground truth and the error was similarly distributed around zero. The neural network outperforms both online raters and clinicians. The scoring system can reliably identify and accurately score individual figure elements in previously unseen ROCF drawings, which facilitates explainability of the AI-scoring system. To ensure generalizability and clinical utility, the model performance was successfully replicated in a large independent prospective validation study that was pre-registered prior to data collection. ConclusionsOur AI-powered scoring system provides healthcare institutions worldwide with a digital tool to assess objectively, reliably and time-efficiently the performance in the ROCF test from hand-drawn images.

neuroscience↗

bioRxiv2020

EVI/WLS function is regulated by ubiquitination and linked to ER-associated degradation by ERLIN2

Wnt signalling is important for development in all metazoan animals and associated with various human diseases. The Ubiquitin-Proteasome System (UPS) and regulatory ER-associated degradation (ERAD) has been implicated in the secretion of WNT proteins. Here, we investigated how the WNT secretory factor EVI/WLS is ubiquitinated, recognised by ERAD components, and subsequently removed from the secretory pathway. We performed a focused, immunoblot-based RNAi screen for factors that influence EVI/WLS protein stability. We identified the VCP-binding proteins FAF2 and UBXN4 as novel interaction partners of EVI/WLS and showed that ERLIN2 links EVI/WLS to the ubiquitination machinery. Interestingly, we found in addition that EVI/WLS is ubiquitinated and degraded in cells irrespective of their level of WNT production. K11, K48, and K63-linked ubiquitination is mediated by the E2 ubiquitin conjugating enzymes UBE2J2, UBE2N, and UBE2K, but independent of the E3 ligases HRD1/SYVN. Taken together, our study identified factors that link UPS to the WNT secretory pathway and provides mechanistic details on the fate of an endogenous substrate of regulatory ERAD in mammalian cells.

cell biology↗