RESUMO
Gene regulatory network inference is essential to uncover complex relationships among gene pathways and inform downstream experiments, ultimately enabling regulatory network re-engineering. Network inference from transcriptional time-series data requires accurate, interpretable, and efficient determination of causal relationships among thousands of genes. Here, we develop Bootstrap Elastic net regression from Time Series (BETS), a statistical framework based on Granger causality for the recovery of a directed gene network from transcriptional time-series data. BETS uses elastic net regression and stability selection from bootstrapped samples to infer causal relationships among genes. BETS is highly parallelized, enabling efficient analysis of large transcriptional data sets. We show competitive accuracy on a community benchmark, the DREAM4 100-gene network inference challenge, where BETS is one of the fastest among methods of similar performance and additionally infers whether causal effects are activating or inhibitory. We apply BETS to transcriptional time-series data of differentially-expressed genes from A549 cells exposed to glucocorticoids over a period of 12 hours. We identify a network of 2768 genes and 31,945 directed edges (FDR ≤ 0.2). We validate inferred causal network edges using two external data sources: Overexpression experiments on the same glucocorticoid system, and genetic variants associated with inferred edges in primary lung tissue in the Genotype-Tissue Expression (GTEx) v6 project. BETS is available as an open source software package at https://github.com/lujonathanh/BETS.
Assuntos
Glucocorticoides/farmacologia , Modelos Estatísticos , Transcriptoma/efeitos dos fármacos , Células A549 , Algoritmos , Biologia Computacional , Humanos , Pulmão/química , Pulmão/metabolismo , Aprendizado de Máquina , Software , Transcriptoma/genéticaRESUMO
Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single tissue or small sets of tissues. Here, we built networks that additionally capture the regulation of relative isoform abundance and splicing, along with tissue-specific connections unique to each of a diverse set of tissues. We used the Genotype-Tissue Expression (GTEx) project v6 RNA sequencing data across 50 tissues and 449 individuals. First, we developed a framework called Transcriptome-Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the interplay between the regulation of splicing and transcription. We built TWNs for 16 tissues and found that hubs in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome. Next, we used a Bayesian biclustering model that identifies network edges unique to a single tissue to reconstruct Tissue-Specific Networks (TSNs) for 26 distinct tissues and 10 groups of related tissues. Finally, we found genetic variants associated with pairs of adjacent nodes in our networks, supporting the estimated network structures and identifying 20 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships of the human transcriptome across tissues.
Assuntos
Perfilação da Expressão Gênica/métodos , Redes Reguladoras de Genes , Splicing de RNA , Análise de Sequência de RNA/métodos , Teorema de Bayes , Bases de Dados Genéticas , Regulação da Expressão Gênica , Técnicas de Genotipagem , Humanos , Especificidade de Órgãos , Polimorfismo de Nucleotídeo ÚnicoRESUMO
BACKGROUND: Chronic disease management is a major health issue worldwide. With the paradigm shift to preventive medicine, disease prediction modeling using machine learning is gaining importance for precise and accurate medical judgement. OBJECTIVE: This study aimed to develop high-performance prediction models for 4 chronic diseases using the common data model (CDM) and machine learning and to confirm the possibility for the extension of the proposed models. METHODS: In this study, 4 major chronic diseases-namely, diabetes, hypertension, hyperlipidemia, and cardiovascular disease-were selected, and a model for predicting their occurrence within 10 years was developed. For model development, the Atlas analysis tool was used to define the chronic disease to be predicted, and data were extracted from the CDM according to the defined conditions. A model for predicting each disease was built with 4 algorithms verified in previous studies, and the performance was compared after applying a grid search. RESULTS: For the prediction of each disease, we applied 4 algorithms (logistic regression, gradient boosting, random forest, and extreme gradient boosting), and all models show greater than 80% accuracy. As compared to the optimized model's performance, extreme gradient boosting presented the highest predictive performance for the 4 diseases (diabetes, hypertension, hyperlipidemia, and cardiovascular disease) with 80% or greater and from 0.84 to 0.93 in area under the curve standards. CONCLUSIONS: This study demonstrates the possibility for the preemptive management of chronic diseases by predicting the occurrence of chronic diseases using the CDM and machine learning. With these models, the risk of developing major chronic diseases within 10 years can be demonstrated by identifying health risk factors using our chronic disease prediction machine learning model developed with the real-world data-based CDM and National Health Insurance Corporation examination data that individuals can easily obtain.
RESUMO
Angiotensin-converting enzyme 2 (ACE2) and serine protease TMPRSS2 have been implicated in cell entry for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for coronavirus disease 2019 (COVID-19). The expression of ACE2 and TMPRSS2 in the lung epithelium might have implications for the risk of SARS-CoV-2 infection and severity of COVID-19. We use human genetic variants that proxy angiotensin-converting enzyme (ACE) inhibitor drug effects and cardiovascular risk factors to investigate whether these exposures affect lung ACE2 and TMPRSS2 gene expression and circulating ACE2 levels. We observed no consistent evidence of an association of genetically predicted serum ACE levels with any of our outcomes. There was weak evidence for an association of genetically predicted serum ACE levels with ACE2 gene expression in the Lung eQTL Consortium (p = 0.014), but this finding did not replicate. There was evidence of a positive association of genetic liability to type 2 diabetes mellitus with lung ACE2 gene expression in the Gene-Tissue Expression (GTEx) study (p = 4 × 10-4) and with circulating plasma ACE2 levels in the INTERVAL study (p = 0.03), but not with lung ACE2 expression in the Lung eQTL Consortium study (p = 0.68). There were no associations of genetically proxied liability to the other cardiometabolic traits with any outcome. This study does not provide consistent evidence to support an effect of serum ACE levels (as a proxy for ACE inhibitors) or cardiometabolic risk factors on lung ACE2 and TMPRSS2 expression or plasma ACE2 levels.
RESUMO
Analysis of gene expression patterns in normal tissues and their perturbations in tumours can help to identify the functional roles of oncogenes or tumour suppressors and identify potential new therapeutic targets. Here, gene expression correlation networks were derived from 92 normal human lung samples and patient-matched adenocarcinomas. The networks from normal lung show that NKX2-1 is linked to the alveolar type 2 lineage, and identify PEBP4 as a novel marker expressed in alveolar type 2 cells. Differential correlation analysis shows that the NKX2-1 network in tumours includes pathways associated with glutamate metabolism, and identifies Vaccinia-related kinase (VRK1) as a potential drug target in a tumour-specific mitotic network. We show that VRK1 inhibition cooperates with inhibition of poly (ADP-ribose) polymerase signalling to inhibit growth of lung tumour cells. Targeting of genes that are recruited into tumour mitotic networks may provide a wider therapeutic window than that seen by inhibition of known mitotic genes.
Assuntos
Adenocarcinoma/patologia , Linhagem da Célula , Neoplasias Pulmonares/patologia , Pulmão/patologia , Mitose , Adenocarcinoma/genética , Perfilação da Expressão Gênica , Humanos , Neoplasias Pulmonares/genéticaRESUMO
An accurate representation of solute-water interactions is necessary for molecular dynamics simulations of biomolecules that reside in aqueous environments. Modern force fields and advanced water models describe solute-solute and water-water interactions reasonably accurately but have known shortcomings in describing solute-water interactions, demonstrated by the large differences between calculated and experimental solvation free energies across a range of peptide and drug chemistries. In this work, we introduce a method for optimizing solute-water van der Waals interactions to reproduce experimental solvation free energy data and apply it to the optimization of a fixed charge force field (AMBER ff99SB/GAFF) and advanced water model (TIP4P-Ew). We show that, with these optimizations, the combination of AMBER ff99SB/GAFF and TIP4P-Ew is able to reproduce the solvation free energies of a variety of biologically relevant small molecules to within 1.0 k(B)T. We further validate these optimizations by examining the aggregation propensities of dipeptide-water solutions, the conformational preferences of short disordered peptides, and the native state stability and dynamics of a folded protein.