Condition-specific series of metabolic sub-networks and its application for gene set enrichment analysis.

Genome-scale metabolic networks and transcriptomic data represent complementary sources of knowledge about an organism's metabolism, yet their integration to achieve biological insight remains challenging. We investigate here condition-specific series of metabolic sub-networks constructed by successively removing genes from a comprehensive network. The optimal order of gene removal is deduced from transcriptomic data. The sub-networks are evaluated via a fitness function, which estimates their degree of alteration. We then consider how a gene set, i.e. a group of genes contributing to a common biological function, is depleted in different series of sub-networks to detect the difference between experimental conditions. The method, named metaboGSE, is validated on public data for Yarrowia lipolytica and mouse. It is shown to produce GO terms of higher specificity compared to popular gene set enrichment methods like GSEA or topGO. The metaboGSE R package is available at https://CRAN.R-project.org/package=metaboGSE. Supplementary data are available at Bioinformatics online

Light-responsive expression atlas reveals the effects of light quality and intensity in Kalanchoë fedtschenkoi, a plant with crassulacean acid metabolism.

BackgroundCrassulacean acid metabolism (CAM), a specialized mode of photosynthesis, enables plant adaptation to water-limited environments and improves photosynthetic efficiency via an inorganic carbon-concentrating mechanism. Kalanchoë fedtschenkoi is an obligate CAM model featuring a relatively small genome and easy stable transformation. However, the molecular responses to light quality and intensity in CAM plants remain understudied.ResultsHere we present a genome-wide expression atlas of K. fedtschenkoi plants grown under 12 h/12 h photoperiod with different light quality (blue, red, far-red, white light) and intensity (0, 150, 440, and 1,000 μmol m-2 s-1) based on RNA sequencing performed for mature leaf samples collected at dawn (2 h before the light period) and dusk (2 h before the dark period). An eFP web browser was created for easy access of the gene expression data. Based on the expression atlas, we constructed a light-responsive co-expression network to reveal the potential regulatory relationships in K. fedtschenkoi. Measurements of leaf titratable acidity, soluble sugar, and starch turnover provided metabolic indicators of the magnitude of CAM under the different light treatments and were used to provide biological context for the expression dataset. Furthermore, CAM-related subnetworks were highlighted to showcase genes relevant to CAM pathway, circadian clock, and stomatal movement. In comparison with white light, monochrome blue/red/far-red light treatments repressed the expression of several CAM-related genes at dusk, along with a major reduction in acid accumulation. Increasing light intensity from an intermediate level (440 μmol m-2 s-1) of white light to a high light treatment (1,000 μmol m-2 s-1) increased expression of several genes involved in dark CO2 fixation and malate transport at dawn, along with an increase in organic acid accumulation.ConclusionsThis study provides a useful genomics resource for investigating the molecular mechanism underlying the light regulation of physiology and metabolism in CAM plants. Our results support the hypothesis that both light intensity and light quality can modulate the CAM pathway through regulation of CAM-related genes in K. fedtschenkoi

Gene Regulatory Network Analysis and Web-based Application Development

Microarray data is a valuable source for gene regulatory network analysis. Using earthworm microarray data analysis as an example, this dissertation demonstrates that a bioinformatics-guided reverse engineering approach can be applied to analyze time-series data to uncover the underlying molecular mechanism. My network reconstruction results reinforce previous findings that certain neurotransmitter pathways are the target of two chemicals - carbaryl and RDX. This study also concludes that perturbations to these pathways by sublethal concentrations of these two chemicals were temporary, and earthworms were capable of fully recovering. Moreover, differential networks (DNs) analysis indicates that many pathways other than those related to synaptic and neuronal activities were altered during the exposure phase. A novel differential networks (DNs) approach is developed in this dissertation to connect pathway perturbation with toxicity threshold setting from Live Cell Array (LCA) data. Findings from this proof-of-concept study suggest that this DNs approach has a great potential to provide a novel and sensitive tool for threshold setting in chemical risk assessment. In addition, a web-based tool “Web-BLOM” was developed for the reconstruction of gene regulatory networks from time-series gene expression profiles including microarray and LCA data. This tool consists of several modular components: a database, the gene network reconstruction model and a user interface. The Bayesian Learning and Optimization Model (BLOM), originally implemented in MATLAB, was adopted by Web-BLOM to provide an online reconstruction of large-scale gene regulation networks. Compared to other network reconstruction models, BLOM can infer larger networks with compatible accuracy, identify hub genes and is much more computationally efficient

A machine learning pipeline for discriminant pathways identification

Motivation: Identifying the molecular pathways more prone to disruption during a pathological process is a key task in network medicine and, more in general, in systems biology. Results: In this work we propose a pipeline that couples a machine learning solution for molecular profiling with a recent network comparison method. The pipeline can identify changes occurring between specific sub-modules of networks built in a case-control biomarker study, discriminating key groups of genes whose interactions are modified by an underlying condition. The proposal is independent from the classification algorithm used. Three applications on genomewide data are presented regarding children susceptibility to air pollution and two neurodegenerative diseases: Parkinson's and Alzheimer's. Availability: Details about the software used for the experiments discussed in this paper are provided in the Appendix

Novel methods for constructing, combining and comparing co-expression networks: Towards uncovering the molecular basis of human cognition

Network analyses, such as gene co-expression networks are an important approach for the systems-level study of biological data. For example, understanding patterns of \linebreak co-regulation in mental disorders can contribute to the development of new therapies and treatments. In a gene regulatory process a particular TF or ncRNA can up- or down-regulate other genes, therefore it is important to explicitly consider both positive and negative interactions. Although exists a variety of software and libraries for constructing and investigating such networks, none considers the sign of interaction. It is also required that the represented networks have high accuracy, where the interactions found have to be relevant and not found by chance or background noise. Another issue derived from building co-expression networks is the reproducibility of those. When constructing independent networks for the same phenotype, though, using different expression datasets, the output network can be remarkably distinct due to biological or technical noise in the data. However, most of the times the interest is not only to characterise a network but to compare its features to others. A series of questions arise from understanding phenotypes using co-expression networks: i) how to construct highly accurate networks; ii) how to combine multiple networks derived from different platforms; iii) how to compare multiple networks. For answering those questions, i) I improved the wTO method to construct highly accurate networks, where now each interaction in a network receives a probability. This method showed to be much more efficient in finding correct interactions than other well-known methods; ii) I developed a method that is able to combine multiple networks into one building a CN. This method enables the correction for background noise; iii) I developed a completely novel method for the comparison of multiple co-expression networks, CoDiNA. This method identifies genes specific to at least one network. It is natural that after associating genes to phenotypes, an inference whether those genes are enriched for a particular disorder is needed. I also present here a tool, RichR, that enables enrichment analysis and background correction. I applied the methods proposed here in two important studies. In the first one, the aim was to understand the neurogenesis process and how certain genes would affect it. The combination of the methods shown here pointed one particular TF, ZN787, as playing an important role in this process. Moreover, the application of this toolset to networks derived from brain samples of individuals with cognitive disorders identified genes and network connections that are specific to certain disorders, but also found an overlap between neurodegenerative disorders and brain development and between evolutionary changes and psychological disorders. CoDiNA also pointed out that there are genes involved in those disorders that are not only human-specific