ATF4 and mTOR regulate metabolic reprogramming in TGF-ß-treated lung fibroblasts.

Idiopathic pulmonary fibrosis is a fatal disease characterized by the TGF-ß-dependent activation of lung fibroblasts, leading to excessive deposition of collagen proteins and progressive replacement of healthy lung with scar tissue. We and others have shown that fibroblast activation is supported by metabolic reprogramming, including the upregulation of the de novo synthesis of glycine, the most abundant amino acid found in collagen protein. How fibroblast metabolic reprogramming is regulated downstream of TGF-ß is incompletely understood. We and others have shown that TGF-ß-mediated activation of the Mechanistic Target of Rapamycin Complex 1 (mTORC1) and downstream upregulation of Activating Transcription Factor 4 (ATF4) promote increased expression of the enzymes required for de novo glycine synthesis; however, whether mTOR and ATF4 regulate other metabolic pathways in lung fibroblasts has not been explored. Here, we used RNA sequencing to determine how both ATF4 and mTOR regulate gene expression in human lung fibroblasts following TGF-ß. We found that ATF4 primarily regulates enzymes and transporters involved in amino acid homeostasis as well as aminoacyl-tRNA synthetases. mTOR inhibition resulted not only in the loss of ATF4 target gene expression, but also in the reduced expression of glycolytic enzymes and mitochondrial electron transport chain subunits. Analysis of TGF-ß-induced changes in cellular metabolite levels confirmed that ATF4 regulates amino acid homeostasis in lung fibroblasts while mTOR also regulates glycolytic and TCA cycle metabolites. We further analyzed publicly available single cell RNAseq data sets and found increased expression of ATF4 and mTOR metabolic targets in pathologic fibroblast populations from the lungs of IPF patients. Our results provide insight into the mechanisms of metabolic reprogramming in lung fibroblasts and highlight novel ATF4 and mTOR-dependent pathways that may be targeted to inhibit fibrotic processes.

ProFAB-open protein functional annotation benchmark.

As the number of protein sequences increases in biological databases, computational methods are required to provide accurate functional annotation with high coverage. Although several machine learning methods have been proposed for this purpose, there are still two main issues: (i) construction of reliable positive and negative training and validation datasets, and (ii) fair evaluation of their performances based on predefined experimental settings. To address these issues, we have developed ProFAB: Open Protein Functional Annotation Benchmark, which is a platform providing an infrastructure for a fair comparison of protein function prediction methods. ProFAB provides filtered and preprocessed protein annotation datasets and enables the training and evaluation of function prediction methods via several options. We believe that ProFAB will be useful for both computational and experimental researchers by enabling the utilization of ready-to-use datasets and machine learning algorithms for protein function prediction based on Gene Ontology terms and Enzyme Commission numbers. ProFAB is available at https://github.com/kansil/ProFAB and https://profab.kansil.org.

Data Centric Molecular Analysis and Evaluation of Hepatocellular Carcinoma Therapeutics Using Machine Intelligence-Based Tools.

PURPOSE: Computational approaches have been used at different stages of drug development with the purpose of decreasing the time and cost of conventional experimental procedures. Lately, techniques mainly developed and applied in the field of artificial intelligence (AI), have been transferred to different application domains such as biomedicine. METHODS: In this study, we conducted an investigative analysis via data-driven evaluation of potential hepatocellular carcinoma (HCC) therapeutics in the context of AI-assisted drug discovery/repurposing. First, we discussed basic concepts, computational approaches, databases, modeling approaches, and featurization techniques in drug discovery/repurposing. In the analysis part, we automatically integrated HCC-related biological entities such as genes/proteins, pathways, phenotypes, drugs/compounds, and other diseases with similar implications, and represented these heterogeneous relationships via a knowledge graph using the CROssBAR system. RESULTS: Following the system-level evaluation and selection of critical genes/proteins and pathways to target, our deep learning-based drug/compound-target protein interaction predictors DEEPScreen and MDeePred have been employed for predicting new bioactive drugs and compounds for these critical targets. Finally, we embedded ligands of selected HCC-associated proteins which had a significant enrichment with the CROssBAR system into a 2-D space to identify and repurpose small molecule inhibitors as potential drug candidates based on their molecular similarities to known HCC drugs. CONCLUSIONS: We expect that these series of data-driven analyses can be used as a roadmap to propose early-stage potential inhibitors (from database-scale sets of compounds) to both HCC and other complex diseases, which may subsequently be analyzed with more targeted in silico and experimental approaches.