Heterotopic ossification is a disorder caused by abnormal mineralization of soft tissues in which signaling pathways such as BMP, TGFβ and WNT are known key players in driving ectopic bone formation. Identifying novel genes and pathways related to the mineralization process are important steps for future gene therapy in bone disorders. In this study, we detect an inter-chromosomal insertional duplication in a female proband disrupting a topologically associating domain and causing an ultra-rare progressive form of heterotopic ossification. This structural variant lead to enhancer hijacking and misexpression of ARHGAP36 in fibroblasts, validated here by orthogonal in vitro studies. In addition, ARHGAP36 overexpression inhibits TGFβ, and activates hedgehog signaling and genes/proteins related to extracellular matrix production. Our work on the genetic cause of this heterotopic ossification case has revealed that ARHGAP36 plays a role in bone formation and metabolism, outlining first details of this gene contributing to bone-formation and -disease.
Abstract-Proteomics is currently driven by mass spectrometry. For the analysis of tandem mass spectra many computational algorithms have been proposed. There are two approaches, one which assigns a peptide sequence to a tandem mass spectrum directly and one which employs a sequence database for looking up possible solutions. The former method needs high quality spectra while the latter can tolerate lower quality spectra. Since both methods are computationally expensive, it is sensible to establish spectral quality using an independent fast algorithm. In this study, we first establish proper settings for database search algorithms for the analysis of spectra in our gold benchmark dataset and then analyze the performance of ScanRanker, an algorithm for quality assessment of tandem MS spectra, on this ground truth data. We found that OMSSA and MSGFDB have limitations in their scoring functions but were able to form a proper consensus prediction using majority vote for our benchmark data. Unfortunately, ScanRanker's results do not correlate well with the consensus and ScanRanker is also too slow to be used in the capacity it is supposed to be used.
Modeling biological mechanisms is a key for disease understanding and drug-target identification. However, formulating quantitative models in the field of Alzheimer’s Disease is challenged by a lack of detailed knowledge of relevant biochemical processes. Additionally, fitting differential equation systems usually requires time resolved data and the possibility to perform intervention experiments, which is difficult in neurological disorders. This work addresses these challenges by employing the recently published Variational Autoencoder Modular Bayesian Networks (VAMBN) method, which we here trained on combined clinical and patient level gene expression data while incorporating a disease focused knowledge graph. Our approach, called iVAMBN, resulted in a quantitative model that allowed us to simulate a down-expression of the putative drug target CD33, including potential impact on cognitive impairment and brain pathophysiology. Experimental validation demonstrated a high overlap of molecular mechanism predicted to be altered by CD33 perturbation with cell line data. Altogether, our modeling approach may help to select promising drug targets.
Modeling biological mechanisms is a key for disease understanding and drug-target identification. However, formulating quantitative models in the field of Alzheimer’s Disease is challenged by a lack of detailed knowledge of relevant biochemical processes. Additionally, fitting differential equation systems usually requires time resolved data and the possibility to perform intervention experiments, which is difficult in neurological disorders. This work addresses these challenges by employing the recently published Variational Autoencoder Modular Bayesian Networks (VAMBN) method, which we here trained on combined clinical and patient level gene expression data while incorporating a disease specific knowledge graph. Our approach, called iVAMBN, resulted in a quantitative model that allowed us to simulate a down-expression of the putative drug target CD33, including potential impact on cognitive impairment and brain pathophysiology. Experimental validation demonstrated a high overlap of molecular mechanism predicted to be altered by CD33 perturbation with cell line data. Altogether, our modeling approach may help to select promising drug targets.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.