Aging is accompanied by a general decline in the function of many cellular pathways. However, whether these are causally or functionally interconnected remains elusive. Here, we study the effect of mitochondrial–nuclear communication on stem cell aging. We show that aged mesenchymal stem cells exhibit reduced chromatin accessibility and lower histone acetylation, particularly on promoters and enhancers of osteogenic genes. The reduced histone acetylation is due to impaired export of mitochondrial acetyl-CoA, owing to the lower levels of citrate carrier (CiC). We demonstrate that aged cells showed enhanced lysosomal degradation of CiC, which is mediated via mitochondrial-derived vesicles. Strikingly, restoring cytosolic acetyl-CoA levels either by exogenous CiC expression or via acetate supplementation, remodels the chromatin landscape and rescues the osteogenesis defects of aged mesenchymal stem cells. Collectively, our results establish a tight, age-dependent connection between mitochondrial quality control, chromatin and stem cell fate, which are linked together by CiC.
Knowledge-based biomedical data science (KBDS) involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey the progress in the last year in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing, and the expansion of knowledge-based approaches to novel domains, such as Chinese Traditional Medicine and biodiversity.TBoxes (for terminology), and assertions composed of them are ABoxes (for assertion) (12). Knowledge-base vs. Knowledge GraphKnowledge-bases that can be represented as graphs are often called knowledge graphs.While not all knowledge-bases are implemented as graphs (e.g. some are databases where table structure makes implicit assertions), in recent years, it has become very common to represent knowledge-bases using the Semantic Web standard or, at least be able to produce and consume Semantic Web compatible versions. For that reason, the terms knowledge-base and knowledge graph are often used interchangeably. In 2012, Google announced its proprietary Knowledge Graph, which also popularized the use of the term (13). The literature sometimes contains terminological imprecision about what the differences are between knowledge-bases, knowledge graphs and ontologies; there is a review and analysis of various published definitions (14). In this review, we use the term knowledge graph (or KG) and say a KG is grounded in the set of primitives from which it is constructed. Some KGs also include a set of logical rules that relate assertions to each other (e.g. Human TP53 is the subclass of TP53 proteins that is found in the organism human) called axioms. Biomedical ApplicationsKBDS does computation over KGs (and perhaps other inputs) to make inferences about biomedicine. While each of the publications surveyed below addresses different problems using different techniques, there are some common themes in the computational approaches to using KGs.
Motivation: Although knowledge graphs (KGs) are used extensively in biomedical research to model complex phenomena, many KG construction methods remain largely unable to account for the use of different standardized terminologies or vocabularies, are often difficult to use, and perform poorly as the size of the KG increases in scale. We introduce PheKnowLator (Phenotype Knowledge Translator), a novel KG framework and fully automated Python 3 library explicitly designed for optimized construction of semantically-rich, large-scale biomedical KGs. To demonstrate the functionality of the framework, we built and evaluated eight different parameterizations of a large semantic KG of human disease mechanisms.
Transcription factors are managers of the cellular factory, and key components to many diseases. Many non-coding single nucleotide polymorphisms affect transcription factors, either by directly altering the protein or its functional activity at individual binding sites. Here we first briefly summarize high-throughput approaches to studying transcription factor activity. We then demonstrate, using published chromatin accessibility data (specifically ATAC-seq), that the genome-wide profile of TF recognition motifs relative to regions of open chromatin can determine the key transcription factor altered by a perturbation. Our method of determining which TFs are altered by a perturbation is simple, is quick to implement, and can be used when biological samples are limited. In the future, we envision that this method could be applied to determine which TFs show altered activity in response to a wide variety of drugs and diseases.
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