Identification of direction in gene networks from expression and methylation

Construction of gene causal regulatory networks using microarray data with the coefficient of intrinsic dependence

BACKGROUND

In the past two decades, biologists have been able to identify the gene signatures associated with various phenotypes through the monitoring of gene expressions with high-throughput biotechnologies. These gene signatures have in turn been successfully applied to drug development, disease prevention, crop improvement, etc. However, ignoring the interactions among genes has weakened the predictive power of gene signatures in practical applications. Gene regulatory networks, in which genes are represented by nodes and the associations between genes are represented by edges, are typically constructed to analyze and visualize such gene interactions. More specifically, the present study sought to measure gene-gene associations by using the coefficient of intrinsic dependence (CID) to capture more nonlinear as well as cause-effect gene relationships.

RESULTS

A stepwise procedure using the CID along with the partial coefficient of intrinsic dependence (pCID) was demonstrated for the rebuilding of simulated networks and the well-known CBF-COR pathway under cold stress using Arabidopsis microarray data. The procedure was also applied to the construction of bHLH gene regulatory pathways under abiotic stresses using rice microarray data, in which OsbHLH104, a putative phytochrome-interacting factor (OsPIF14), and OsbHLH060, a positive regulator of iron homeostasis (OsPRI1) were inferred as the most affiliated genes. The inferred regulatory pathways were verified through literature reviews.

CONCLUSIONS

The proposed method can efficiently decipher gene regulatory pathways and may assist in achieving higher predictive power in practical applications. The lack of any mention in the literature of some of the regulatory event may have been due to the high complexity of the regulatory systems in the plant transcription, a possibility which could potentially be confirmed in the near future given ongoing rapid developments in bio-technology.

A Novel Data-Driven Boolean Model for Genetic Regulatory Networks

A Boolean model is a simple, discrete and dynamic model without the need to consider the effects at the intermediate levels. However, little effort has been made into constructing activation, inhibition, and protein decay networks, which could indicate the direct roles of a gene (or its synthesized protein) as an activator or inhibitor of a target gene. Therefore, we propose to focus on the general Boolean functions at the subfunction level taking into account the effectiveness of protein decay, and further split the subfunctions into the activation and inhibition domains. As a consequence, we developed a novel data-driven Boolean model; namely, the Fundamental Boolean Model (FBM), to draw insights into gene activation, inhibition, and protein decay. This novel Boolean model provides an intuitive definition of activation and inhibition pathways and includes mechanisms to handle protein decay issues. To prove the concept of the novel model, we implemented a platform using R language, called FBNNet. Our experimental results show that the proposed FBM could explicitly display the internal connections of the mammalian cell cycle between genes separated into the connection types of activation, inhibition and protein decay. Moreover, the method we proposed to infer the gene regulatory networks for the novel Boolean model can be run in parallel and; hence, the computation cost is affordable. Finally, the novel Boolean model and related Fundamental Boolean Networks (FBNs) could show significant trajectories in genes to reveal how genes regulated each other over a given period. This new feature could facilitate further research on drug interventions to detect the side effects of a newly-proposed drug.

Effects of DNA Methylation on Progression to Interstitial Fibrosis and Tubular Atrophy in Renal Allograft Biopsies: A Multi-Omics Approach

Progressive fibrosis of the interstitium is the dominant final pathway in renal destruction in native and transplanted kidneys. Over time, the continuum of molecular events following immunological and nonimmunological insults lead to interstitial fibrosis and tubular atrophy and culminate in kidney failure. We hypothesize that these insults trigger changes in DNA methylation (DNAm) patterns, which in turn could exacerbate injury and slow down the regeneration processes, leading to fibrosis development and graft dysfunction. Herein, we analyzed biopsy samples from kidney allografts collected 24 months posttransplantation and used an integrative multi-omics approach to understand the underlying molecular mechanisms. The role of DNAm and microRNAs on the graft gene expression was evaluated. Enrichment analyses of differentially methylated CpG sites were performed using GenomeRunner. CpGs were strongly enriched in regions that were variably methylated among tissues, implying high tissue specificity in their regulatory impact. Corresponding to this methylation pattern, gene expression data were related to immune response (activated state) and nephrogenesis (inhibited state). Preimplantation biopsies showed similar DNAm patterns to normal allograft biopsies at 2 years posttransplantation. Our findings demonstrate for the first time a relationship among epigenetic modifications and development of interstitial fibrosis, graft function, and inter-individual variation on long-term outcomes.

An argument for mechanism-based statistical inference in cancer

Cancer is perhaps the prototypical systems disease, and as such has been the focus of extensive study in quantitative systems biology. However, translating these programs into personalized clinical care remains elusive and incomplete. In this perspective, we argue that realizing this agenda—in particular, predicting disease phenotypes, progression and treatment response for individuals—requires going well beyond standard computational and bioinformatics tools and algorithms. It entails designing global mathematical models over network-scale configurations of genomic states and molecular concentrations, and learning the model parameters from limited available samples of high-dimensional and integrative omics data. As such, any plausible design should accommodate: biological mechanism, necessary for both feasible learning and interpretable decision making; stochasticity, to deal with uncertainty and observed variation at many scales; and a capacity for statistical inference at the patient level. This program, which requires a close, sustained collaboration between mathematicians and biologists, is illustrated in several contexts, including learning biomarkers, metabolism, cell signaling, network inference and tumorigenesis.