Bayesian Network Inference Enables Unbiased Phenotypic Anchoring of Transcriptomic Responses to Cigarette Smoke in Humans

The application of omics-based human liver platforms for investigating the mechanism of drug-induced hepatotoxicity in vitro

Drug-induced liver injury (DILI) complicates safety assessment for new drugs and poses major threats to both patient health and drug development in the pharmaceutical industry. A number of human liver cell-based in vitro models combined with toxicogenomics methods have been developed as an alternative to animal testing for studying human DILI mechanisms. In this review, we discuss the in vitro human liver systems and their applications in omics-based drug-induced hepatotoxicity studies. We furthermore present bioinformatic approaches that are useful for analyzing toxicogenomic data generated from these models and discuss their current and potential contributions to the understanding of mechanisms of DILI. Human pluripotent stem cells, carrying donor-specific genetic information, hold great potential for advancing the study of individual-specific toxicological responses. When co-cultured with other liver-derived non-parenchymal cells in a microfluidic device, the resulting dynamic platform enables us to study immune-mediated drug hypersensitivity and accelerates personalized drug toxicology studies. A flexible microfluidic platform would also support the assembly of a more advanced organs-on-a-chip device, further bridging gap between in vitro and in vivo conditions. The standard transcriptomic analysis of these cell systems can be complemented with causality-inferring approaches to improve the understanding of DILI mechanisms. These approaches involve statistical techniques capable of elucidating regulatory interactions in parts of these mechanisms. The use of more elaborated human liver models, in harmony with causality-inferring bioinformatic approaches will pave the way for establishing a powerful methodology to systematically assess DILI mechanisms across a wide range of conditions.

Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data

In the past decade, significant progress has been made in complex disease research across multiple omics layers from genome, transcriptome and proteome to metabolome. There is an increasing awareness of the importance of biological interconnections, and much success has been achieved using systems biology approaches. However, because of the typical focus on one single omics layer at a time, existing systems biology findings explain only a modest portion of complex disease. Recent advances in multi-omics data collection and sharing present us new opportunities for studying complex diseases in a more comprehensive fashion, and yet simultaneously create new challenges considering the unprecedented data dimensionality and diversity. Here, our goal is to review extant and emerging network approaches that can be applied across multiple biological layers to facilitate a more comprehensive and integrative multilayered omics analysis of complex diseases.

Identification of essential transcription factors for adequate DNA damage response after benzo(a)pyrene and aflatoxin B1 exposure by combining transcriptomics with functional genomics

DNA damage mediates widespread changes in transcription through activation or repression of transcription factors (TFs). However, the consequences of regulating specific TFs for the outcome of the DNA repair process remain incompletely understood. Here, we combined transcriptomics and TF binding prediction with functional genomics to identify TFs essential for adequate DNA repair in HepG2 liver cells after a non-cytotoxic dose of carcinogens benzo(a)pyrene (BaP) (2μM) and aflatoxin B1 (AFB1) (5μM). BaP and AFB1 induced a largely common transcriptional response, mediated by similar TFs. A lentiviral shRNA screen knocking down the top31 identified TFs, was performed to determine their effect on DNA repair by assessing phosphorylation of H2AX (γ-H2AX). In addition to the top candidate p53, we identified several other interesting TFs that modulated γ-H2AX after BaP and AFB1 treatment. Validation studies confirmed the role of p53 in reducing γ-H2AX formation and DNA breaks measured by COMET assay after BaP and AFB1 exposure. Expression of the cell cycle inhibitor p21 was profoundly impaired upon p53 knock-down. In addition, the expression of 2 genes involved in nucleotide exchange repair, DDB2 and XPC was significantly reduced in p53 knock-down cells. Although p63 knock-down affected DNA damage upon BaP treatment this was not associated with altered expression of DDB2 or XPC. Finally, knock-down of ARNT reduced γ-H2AX in response to BaP, which was associated with reduced CYP1A1 expression. Importantly, our results suggest a new role for ARNT and its dimerization partner AHR in the occurrence of H2AX phosphorylation after AFB1 treatment. These data show that modulation of TF activity impacts on the repair of BaP- and AFB1-induced DNA damage. Our study also demonstrates the potential of combining functional genomics with genome-wide expression analysis to identify yet unknown causal relationships, thereby aiding in the interpretation of complex biological systems.

Verification of Three-Phase Dependency Analysis Bayesian Network Learning Method for Maize Carotenoid Gene Mining

Background and Objective

Mining the genes related to maize carotenoid components is important to improve the carotenoid content and the quality of maize.

Methods

On the basis of using the entropy estimation method with Gaussian kernel probability density estimator, we use the three-phase dependency analysis (TPDA) Bayesian network structure learning method to construct the network of maize gene and carotenoid components traits.

Results

In the case of using two discretization methods and setting different discretization values, we compare the learning effect and efficiency of 10 kinds of Bayesian network structure learning methods. The method is verified and analyzed on the maize dataset of global germplasm collection with 527 elite inbred lines.

Conclusions

The result confirmed the effectiveness of the TPDA method, which outperforms significantly another 9 kinds of Bayesian network learning methods. It is an efficient method of mining genes for maize carotenoid components traits. The parameters obtained by experiments will help carry out practical gene mining effectively in the future.