Biology subjects

Doelemeyer, A.

Publications and source records attributed to Doelemeyer, A..

3 recordsLinked to original sources

bioRxiv2025

Exercise-Induced Myostimulin Enhances Muscle Function in Health and Disease

Musculoskeletal diseases are a leading contributor to years lived with disability worldwide1,2. While exercise offers significant benefits for people with these conditions, many individuals do not engage in adequate physical activity3. Consequently, there is growing interest in pharmacological interventions that can emulate essential health-promoting effects of exercise4,5. By integrating transcriptomics data of exercised skeletal muscle, we identified C1orf54/C1ORF54 as a novel exercise-responsive gene in mice and humans. We demonstrate that removal of the first sixteen N-terminal amino acids of C1ORF54 gives rise to a previously uncharacterized protein that stimulates the proliferation of muscle precursor cells and which we named myostimulin. Intriguingly, repeated intermittent treatment of mice with recombinant myostimulin boosts maximal isometric strength in mice within a week. Moreover, we have engineered a variant with improved biophysical properties, increased biological activity in vitro and enhanced efficacy in vivo. This variant even accelerates the recovery of muscle strength from axonotmesis, a condition associated with pronounced muscle weakness. Our data ascribe to myostimulin a role for enhancing the regenerative capacity of skeletal muscle and mediating functional adaptations characteristic of sustained resistance training. Therefore, myostimulin could be an innovative, fast acting therapeutic for certain human musculoskeletal diseases, injuries and other disorders that improve with exercise.

physiology↗

bioRxiv2022

StandardRat: A multi-center consensus protocol to enhance functional connectivity specificity in the rat brain

Task-free functional connectivity in animal models provides an experimental framework to examine connectivity phenomena under controlled conditions and allows comparison with invasive or terminal procedures. To date, animal acquisitions are performed with varying protocols and analyses that hamper result comparison and integration. We introduce StandardRat, a consensus rat functional MRI acquisition protocol tested across 20 centers. To develop this protocol with optimized acquisition and processing parameters, we initially aggregated 65 functional imaging datasets acquired in rats from 46 centers. We developed a reproducible pipeline for the analysis of rat data acquired with diverse protocols and determined experimental and processing parameters associated with a more robust functional connectivity detection. We show that the standardized protocol enhances biologically plausible functional connectivity patterns, relative to pre-existing acquisitions. The protocol and processing pipeline described here are openly shared with the neuroimaging community to promote interoperability and cooperation towards tackling the most important challenges in neuroscience.

neuroscience↗

bioRxiv2019

A deep learning-based model of normal histology

Deep learning models have been applied on various tissues in order to recognize malignancies. However, these models focus on relatively narrow tissue context or well-defined pathologies. Here, instead of focusing on pathologies, we introduce models characterizing the diversity of normal tissues. We obtained 1,690 slides with rat tissue samples from the control groups of six preclinical toxicology studies, on which tissue regions were outlined and annotated by pathologists into 46 different tissue classes. From these annotated regions, we sampled small patches of 224 x 224 pixels at six different levels of magnification. Using four studies as training set and two studies as test set, we trained VGG-16, ResNet-50, and Inception-v3 networks separately at each of these magnification levels. Among these models, Inception-v3 consistently outperformed the other networks and attained accuracies up to 83.4% (top-3 accuracy: 96.3%). Further analysis showed that most tissue confusions occurred within clusters of histologically similar tissues. Investigation of the embedding layer using the UMAP method revealed not only pronounced clusters corresponding to the individual tissues, but also subclusters corresponding to histologically meaningful structures that had neither been annotated nor trained for. This suggests that the histological representation learned by the normal histology network could also be used to flag abnormal tissue as outliers in the embedding space without a need to explicitly train for specific types of abnormalities. Finally, we found that models trained on rat tissues can be used on non-human primate and minipig tissues with minimal retraining. Author contributionT.S. and H.H. contributed equally to this work. Significance statementLike many other scientific disciplines, histopathology has been profoundly impacted by recent advances in machine learning with deep neural networks. In this field, most deep learning models reported in the literature are trained on pathologies in specific tissues/contexts. Here, we aim to establish a model of normal tissues as a foundation for future models of histopathology. We build models that are specific to histopathology images and we show that their embeddings are better feature vectors for describing the underlying images than those of off-the shelf CNN models. Therefore, our models could be useful for transfer learning to improve the accuracy of other histopathology models.

pathology↗