Biology subjects

Moulin, P.

Publications and source records attributed to Moulin, P..

3 recordsLinked to original sources

bioRxiv2024

The transcription factor ZNF469 regulates collagen production in liver fibrosis

Non-alcoholic fatty liver disease (NAFLD) - characterized by excess accumulation of fat in the liver - now affects one third of the worlds population. As NAFLD progresses, extracellular matrix components including collagen accumulate in the liver causing tissue fibrosis, a major determinant of disease severity and mortality. To identify transcriptional regulators of fibrosis, we computationally inferred the activity of transcription factors (TFs) relevant to fibrosis by profiling the matched transcriptomes and epigenomes of 108 human liver biopsies from a deeply-characterized cohort of patients spanning the full histopathologic spectrum of NAFLD. CRISPR-based genetic knockout of the top 100 TFs identified ZNF469 as a regulator of collagen expression in primary human hepatic stellate cells (HSCs). Gain- and loss-of-function studies established that ZNF469 regulates collagen genes and genes involved in matrix homeostasis through direct binding to gene bodies and regulatory elements. By integrating multiomic large-scale profiling of human biopsies with extensive experimental validation we demonstrate that ZNF469 is a transcriptional regulator of collagen in HSCs. Overall, these data nominate ZNF469 as a previously unrecognized determinant of NAFLD-associated liver fibrosis.

molecular biology↗

bioRxiv2022

Plant-herbivore interactions: experimental demonstration of genetic variability in plant-plant signaling

O_LIPlant-herbivore interactions mediated by plant-plant signaling were documented in different species. Here, we tested if herbivore foraging activity on plants was influenced by plants prior contact with a damaged plant and if the effect of such plant-plant signaling was variable across genotypes. C_LIO_LIWe filmed snails during one hour on two plants differing only in a prior contact with a damaged plant or not. We replicated eight times the experiment on 113 natural genotypes of Arabidopsis thaliana. We recorded snails first choice, and measured its first duration on a plant, the proportion of time spent on both plants and leaf consumption. C_LIO_LIOn average, snails spent more time on plants that experienced a prior contact with a damaged plant, and consumed them more. However, plant-plant signaling effect on snail behavior was variable across genotypes. Genome-wide association studies revealed that a small number of genetic polymorphisms related to stress coping ability and jasmonate pathway explained this variation. C_LIO_LIPlant-plant signaling modified the foraging activity of herbivores in A. thaliana. Depending on the plant genotype, plant-plant signaling made undamaged plants more repulsive or attractive to snails. This finding questions the theoretical basement of the evolution of plant-herbivore interactions mediated by plant-plant signaling. C_LI HighlightPlant-plant signaling differently affects snail foraging activity depending on genetic variations in A.thaliana. These findings question the theoretical basement of the evolution of plant-herbivore interactions mediated by plant-plant signaling.

plant biology↗

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↗