Systems Biology Level Research Articles

Towards Systems Biology of Mycotoxin Regulation

Systems biology is a scientific approach that integrates many scientific disciplines to develop a comprehensive understanding of biological phenomena, thus allowing the prediction and accurate simulation of complex biological behaviors. It may be presumptuous to write about toxin regulation at the level of systems biology, but the last decade of research is leading us closer than ever to this approach. Past research has delineated multiple levels of regulation in the pathways leading to the biosynthesis of secondary metabolites, including mycotoxins. At the top of this hierarchy, the global or master transcriptional regulators perceive various environmental cues such as climatic conditions, the availability of nutrients, and the developmental stages of the organism. Information accumulated from various inputs is integrated through a complex web of signalling networks to generate the eventual outcome. This review will focus on adapting techniques such as chemical and other genetic tools available in the model system Saccharomyces cerevisiae, to disentangle the various biological networks involved in the biosynthesis of mycotoxins in the Fusarium spp.

Impact of Iron Precipitant on Toxicity of Arsenic in Water: A Combined in Vivo and in Vitro Study

Removing arsenic (As) from drinking water is widely dependent on iron (Fe)-based coagulation/flocculation techniques. However, little is known about the influence of Fe precipitant on As toxicity. In this present study, the influence of Fe on As toxicity was determined at systems biology level by in vitro and in vivo experiments. In vitro study based on HepG2 cell line found that Fe increased the As toxicity on cell viability and DNA damage, indicating the synergetic toxic effects. However, when the Fe and As were simultaneously exposed to mice by drinking water for 90 days, the results showed that Fe reduced the changes of hepatic transcriptomic profiles and serum and urine metabolic profiles caused by As exposure, showing the antagonistic toxic effects. The antagonistic effects might be because Fe reduced As bioavailability and accumulation, which was verified by As and Fe levels in feces and liver. The results of this study indicate that Fe precipitant can influence the As toxicity. The interactions between As and Fe and their bioavailability might play important roles in the As toxicity. When assessing the safety of As in drinking water, it is necessary to fully consider the combined effects of As and Fe.

Identifying breast cancer subtype related miRNAs from two constructed miRNAs interaction networks in silico method.

Background. It has been known that microRNAs (miRNAs) regulate the expression of multiple proteins and therefore are likely to emerge as more effective targets of selective therapeutic modalities for breast cancer. Although recent lines of evidence have approved that miRNAs are associated with the most common molecular breast cancer subtypes, the studies to breast cancer subtypes have not been well characterized. Objectives. In this study, we propose a silico method to identify breast cancer subtype related miRNAs based on two constructed miRNAs interaction networks using miRNA-mRNA dual expression profiling data arising from the same samples. Methods. Firstly, we used a new mutual information estimation method to construct two miRNAs interaction networks based on miRNA-mRNA dual expression profiling data. Secondly, we compared and analyzed the topological properties of these two networks. Finally, miRNAs showing the outstanding topological properties in both of the two networks were identified. Results. Further functional analysis and literature evidence confirm that the identified potential breast cancer subtype related miRNAs are essential to unraveling their biological function. Conclusions. This study provides a new silico method to predict candidate miRNAs of breast cancer subtype from a system biology level and can help exploit for functional studies of important breast cancer subtype related miRNAs.

A Systems Level Comparison of Mycobacterium tuberculosis, Mycobacterium leprae and Mycobacterium smegmatis Based on Functional Interaction Network Analysis

Mycobacterium leprae is a pathogenic bacteria that causes leprosy, a disease which affects mainly the skin, peripherical nerves, eyes and mucosa of the upper respiratory tract. Despite significant progress recorded in the last few years to stop this disease through a Multi-Drug Therapy (MDT) strategy, every year there are new reported cases of the disease. According to the World Health Organization, there were 192,242 new cases at the beginning of 2011. Mycobacterium leprae cannot be cultured in the laboratory but can be grown in mouse foot pads and more recently in nine banded armadillos because of its susceptibility to leprosy. Its highly reduced genome makes it an interesting species as a model for reductive evolution within the mycobacterial genus; it shares the same ancestor with Mycobacterium tuberculosis (MTB). A functional network for MTB was generated previously and extensive computational analyses were conducted to reveal the biological organization of the organism on the basis of the network’s topological properties. Here, we use genomic sequences and functional data from public databases to build protein functional networks for another slow grower, Mycobacterium leprae (MLP) and the fast growing non-pathogenic Mycobacterium smegmatis (MSM). Together with the MTB network, this provides an opportunity for comparison of three mycobacteria with different sized genomes. In this paper, we use network centrality measures to systematically compare MTB, MLP and MSM to quantify differences between these organisms at the systems biology level and to study network biology and evolution.

Identifying novel glioma associated pathways based on systems biology level meta-analysis

BackgroundWith recent advances in microarray technology, including genomics, proteomics, and metabolomics, it brings a great challenge for integrating this "-omics" data to analysis complex disease. Glioma is an extremely aggressive and lethal form of brain tumor, and thus the study of the molecule mechanism underlying glioma remains very important. To date, most studies focus on detecting the differentially expressed genes in glioma. However, the meta-analysis for pathway analysis based on multiple microarray datasets has not been systematically pursued.ResultsIn this study, we therefore developed a systems biology based approach by integrating three types of omics data to identify common pathways in glioma. Firstly, the meta-analysis has been performed to study the overlapping of signatures at different levels based on the microarray gene expression data of glioma. Among these gene expression datasets, 12 pathways were found in GeneGO database that shared by four stages. Then, microRNA expression profiles and ChIP-seq data were integrated for the further pathway enrichment analysis. As a result, we suggest 5 of these pathways could be served as putative pathways in glioma. Among them, the pathway of TGF-beta-dependent induction of EMT via SMAD is of particular importance.ConclusionsOur results demonstrate that the meta-analysis based on systems biology level provide a more useful approach to study the molecule mechanism of complex disease. The integration of different types of omics data, including gene expression microarrays, microRNA and ChIP-seq data, suggest some common pathways correlated with glioma. These findings will offer useful potential candidates for targeted therapeutic intervention of glioma.

Signal Propagation in Protein Interaction Network during Colorectal Cancer Progression

Colorectal cancer is generally categorized into the following four stages according to its development or serious degree: Dukes A, B, C, and D. Since different stage of colorectal cancer actually corresponds to different activated region of the network, the transition of different network states may reflect its pathological changes. In view of this, we compared the gene expressions among the colorectal cancer patients in the aforementioned four stages and obtained the early and late stage biomarkers, respectively. Subsequently, the two kinds of biomarkers were both mapped onto the protein interaction network. If an early biomarker and a late biomarker were close in the network and also if their expression levels were correlated in the Dukes B and C patients, then a signal propagation path from the early stage biomarker to the late one was identified. Many transition genes in the signal propagation paths were involved with the signal transduction, cell communication, and cellular process regulation. Some transition hubs were known as colorectal cancer genes. The findings reported here may provide useful insights for revealing the mechanism of colorectal cancer progression at the cellular systems biology level.

Application of Systems Biology and Bioinformatics Methods in Biochemistry and Biomedicine

Application of Systems Biology and Bioinformatics Methods in Biochemistry and Biomedicine

The comparative analysis of in vivo and in vitro transcriptome data based on systems biology

We investigated whether an in vitro cellbased system can represent toxicity of an in vivo organ using gene expression profiles. We performed an analysis of differentially expressed transcripts, pathway analysis, and Gene Ontology (GO) grouping with the toxicity-induced data following treatments with diclofenac, sulindac, itraconazole, and ketoconazole. The number of genes regulated in vitro and in vivo were much more than the randomly sampled number, but no significant correlation was observed between the in vitro and in vivo experiments. We performed a pathway analysis and GO grouping to expand the approach. In the pathway analysis, we narrowed down the hepatotoxic pathways to focus on toxicity. Then, the percentages of overlapping pathways increased. We found pathways associated with liver function in all experiments, such as the adipocyte signaling pathway, JAKSTAT signaling pathway, and peroxisome proliferatoractivated receptor (PPAR) signaling pathway. In the GO grouping analysis, the clusters obtained were primarily related to lipid metabolic and synthetic processes. The percentage of common GO terms between in vitro and in vivo experiments also increased. Therefore, we identified than an analysis performed at the systems biology level was more correlated for in vitro and in vivo systems.

A Computational Analysis Framework for Molecular Cell Dynamics: Case-Study of Exocytosis

One difficulty in conducting biologically meaningful dynamic analysis at the systems biology level is that in vivo system regulation is complex. Meanwhile, many kinetic rates are unknown, making global system analysis intractable in practice. In this article, we demonstrate a computational pipeline to help solve this problem, using the exocytotic process as an example. Exocytosis is an essential process in all eukaryotic cells that allows communication in cells through vesicles that contain a wide range of intracellular molecules. During this process a set of proteins called SNAREs acts as an engine in this vesicle-membrane fusion, by forming four-helical bundle complex between (membrane) target-specific and vesicle-specific SNAREs. As expected, the regulatory network for exocytosis is very complex. Based on the current understanding of the protein-protein interaction network related to exocytosis, we mathematically formulated the whole system, by the ordinary differential equations (ODE). We then applied a mathematical approach (called inverse problem) to estimating the kinetic parameters in the fundamental subsystem (without regulation) from limited in vitro experimental data, which fit well with the reports by the conventional assay. These estimates allowed us to conduct an efficient stability analysis under a specified parameter space for the exocytotic process with or without regulation. Finally, we discuss the potential of this approach to explain experimental observations and to make testable hypotheses for further experimentation.

Structural and functional consequences of phosphate-arsenate substitutions in selected nucleotides: DNA, RNA, and ATP.

A recent finding of a bacterial strain (GFAJ-1) that can rely on arsenic instead of phosphorus raised the questions of if and how arsenate can replace phosphate in biomolecules that are essential to sustain cell life. Apart from questions related to chemical stability, there are those of the structural and functional consequences of phosphate-arsenate substitutions in vital nucleotides in GFAJ1-like cells. In this study we selected three types of molecules (ATP/ADP as energy source and replication regulation; DNA–protein complexes for DNA replication and transcription initiation; and a tRNA–protein complex and ribosome for protein synthesis) to computationally probe if arsenate nucleotides can retain the structural and functional features of phosphate nucleotides. Hydrolysis of adenosine triarsenate provides 2–3 kcal/mol less energy than ATP hydrolysis. Arsenate DNA/RNA interacts with proteins slightly less strongly than phosphate DNA/RNA, mainly due to the weaker electrostatic interactions of arsenate. We observed that the weaker arsenate RNA–protein interactions may hamper rRNA assembly into a functional ribosome. We further compared the experimental EXAFS spectra of the arsenic bacteria with theoretical EXAFS spectra for arsenate DNA and rRNA. Our results demonstrate that while it is possible that dried GFAJ-1 cells contain linear arsenate DNA, the arsenate 70S ribosome does not contribute to the main arsenate depository in the GFAJ-1 cell. Our study indicates that evolution has optimized the inter-relationship between proteins and DNA/RNA, which requires overall changes at the molecular and systems biology levels when replacing phosphate by arsenate.

Structural and Functional Consequences of Phosphate-Arsenate Substitutions in Selected Nucleotides: DNA, RNA, and ATP

A recent finding of a bacterial strain (GFAJ-1) that can rely on arsenic instead of phosphorus raised the questions of if and how arsenate can replace phosphate in biomolecules that are essential to sustain cell life. Apart from questions related to chemical stability, there are those of the structural and functional consequences of phosphate-arsenate substitutions in vital nucleotides in GFAJ1-like cells. In this study we selected three types of molecules (ATP/ADP as energy source and replication regulation; DNA-protein complexes for DNA replication and transcription initiation; and a tRNA-protein complex and ribosome for protein synthesis) to computationally probe if arsenate nucleotides can retain the structural and functional features of phosphate nucleotides. We further compared the experimental EXAFS spectra of the arsenic bacteria with theoretical EXAFS spectra for arsenate DNA and rRNA. Our results demonstrate that while it is possible that dried GFAJ-1 cells contain linear arsenate DNA, the arsenate 70S ribosome does not contribute to the main arsenate depository in the GFAJ-1 cell. Our study indicates that evolution has optimized the inter-relationship between proteins and DNA/RNA, which requires overall changes at the molecular and systems biology levels when replacing phosphate by arsenate.

Metabolomic response of human skin tissue to low dose ionizing radiation

Understanding how human organs respond to ionizing radiation (IR) at a systems biology level and identifying biomarkers for IR exposure at low doses can help provide a scientific basis for establishing radiation protection standards. Little is known regarding the physiological responses to low dose IR at the metabolite level, which represents the end-point of biochemical processes inside cells. Using a full thickness human skin tissue model and GC-MS-based metabolomic analysis, we examined the metabolic perturbations at three time points (3, 24 and 48 h) after exposure to 3, 10 and 200 cGy of X-rays. PLS-DA score plots revealed dose- and time-dependent clustering between sham and irradiated groups. Importantly, delayed metabolic responses were observed at low dose IR. When compared with the high dose at 200 cGy, a comparable number of significantly changed metabolites were detected 48 h after exposure to low doses (3 and 10 cGy) of irradiation. Biochemical pathway analysis showed perturbations to DNA/RNA damage and repair, lipid and energy metabolisms, even at low doses of IR.

Large-Scale Protein-Protein Interaction Analysis in Arabidopsis Mesophyll Protoplasts by Split Firefly Luciferase Complementation

Protein-protein interactions (PPIs) constitute the regulatory network that coordinates diverse cellular functions. There are growing needs in plant research for creating protein interaction maps behind complex cellular processes and at a systems biology level. However, only a few approaches have been successfully used for large-scale surveys of PPIs in plants, each having advantages and disadvantages. Here we present split firefly luciferase complementation (SFLC) as a highly sensitive and noninvasive technique for in planta PPI investigation. In this assay, the separate halves of a firefly luciferase can come into close proximity and transiently restore its catalytic activity only when their fusion partners, namely the two proteins of interest, interact with each other. This assay was conferred with quantitativeness and high throughput potential when the Arabidopsis mesophyll protoplast system and a microplate luminometer were employed for protein expression and luciferase measurement, respectively. Using the SFLC assay, we could monitor the dynamics of rapamycin-induced and ascomycin-disrupted interaction between Arabidopsis FRB and human FKBP proteins in a near real-time manner. As a proof of concept for large-scale PPI survey, we further applied the SFLC assay to testing 132 binary PPIs among 8 auxin response factors (ARFs) and 12 Aux/IAA proteins from Arabidopsis. Our results demonstrated that the SFLC assay is ideal for in vivo quantitative PPI analysis in plant cells and is particularly powerful for large-scale binary PPI screens.

Phosphoproteomic Analysis: An Emerging Role in Deciphering Cellular Signaling in Human Embryonic Stem Cells and Their Differentiated Derivatives

Cellular signaling is largely controlled by protein phosphorylation. This post-translational modification (PTM) has been extensively analyzed when examining one or a few protein phosphorylation events that effect cell signaling. However, protein kinase-driven signaling networks, comprising total (phospho)proteomes, largely control cell fate. Therefore, large-scale analysis of differentially regulated protein phosphorylation is central to elucidating complex cellular events, including maintenance of pluripotency and differentiation of embryonic stem cells (ESCs). The current technology of choice for total phosphoproteome and combined total proteome plus total phosphoproteome (termed (phospho)proteome) analyses is multidimensional liquid chromatography-(MDLC) tandem mass spectrometry (MS/MS). Advances in the use of MDLC for separation of peptides comprising total (phospho)proteomes, phosphopeptide enrichment, separation of enriched fractions, and quantitative peptide identification by MS/MS have been rapid in recent years, as have improvements in the sensitivity, speed, and accuracy of mass spectrometers. Increasingly deep coverage of (phospho)proteomes is allowing an improved understanding of changes in protein phosphorylation networks as cells respond to stimuli and progress from one undifferentiated or differentiated state to another. Although MDLC-MS/MS studies are powerful, understanding the interpretation of the data is important, and targeted experimental pursuit of biological predictions provided by total (phospho)proteome analyses is needed. (Phospho)proteomic analyses of pluripotent stem cells are in their infancy at this time. However, such studies have already begun to contribute to an improved and accelerated understanding of basic pluripotent stem cell signaling and fate control, especially at the systems-biology level.

Lipid classification, structures and tools

The study of lipids has developed into a research field of increasing importance as their multiple biological roles in cell biology, physiology and pathology are becoming better understood. The Lipid Metabolites and Pathways Strategy (LIPID MAPS) consortium is actively involved in an integrated approach for the detection, quantitation and pathway reconstruction of lipids and related genes and proteins at a systems-biology level. A key component of this approach is a bioinformatics infrastructure involving a clearly defined classification of lipids, a state-of-the-art database system for molecular species and experimental data and a suite of user-friendly tools to assist lipidomics researchers. Herein, we discuss a number of recent developments by the LIPID MAPS bioinformatics core in pursuit of these objectives. This article is part of a Special Issue entitled Lipodomics and Imaging Mass Spectrometry.

BiologicalNetworks - tools enabling the integration of multi-scale data for the host-pathogen studies

BackgroundUnderstanding of immune response mechanisms of pathogen-infected host requires multi-scale analysis of genome-wide data. Data integration methods have proved useful to the study of biological processes in model organisms, but their systematic application to the study of host immune system response to a pathogen and human disease is still in the initial stage.ResultsTo study host-pathogen interaction on the systems biology level, an extension to the previously described BiologicalNetworks system is proposed. The developed methods and data integration and querying tools allow simplifying and streamlining the process of integration of diverse experimental data types, including molecular interactions and phylogenetic classifications, genomic sequences and protein structure information, gene expression and virulence data for pathogen-related studies. The data can be integrated from the databases and user's files for both public and private use.ConclusionsThe developed system can be used for the systems-level analysis of host-pathogen interactions, including host molecular pathways that are induced/repressed during the infections, co-expressed genes, and conserved transcription factor binding sites. Previously unknown to be associated with the influenza infection genes were identified and suggested for further investigation as potential drug targets. Developed methods and data are available through the Java application (from BiologicalNetworks program at http://www.biologicalnetworks.org) and web interface (at http://flu.sdsc.edu).

RNA polymerase mutants found through adaptive evolution reprogram Escherichia coli for optimal growth in minimal media

Specific small deletions within the rpoC gene encoding the β'-subunit of RNA polymerase (RNAP) are found repeatedly after adaptation of Escherichia coli K-12 MG1655 to growth in minimal media. Here we present a multiscale analysis of these mutations. At the physiological level, the mutants grow 60% faster than the parent strain and convert the carbon source 15-35% more efficiently to biomass, but grow about 30% slower than the parent strain in rich medium. At the molecular level, the kinetic parameters of the mutated RNAP were found to be altered, resulting in a 4- to 30-fold decrease in open complex longevity at an rRNA promoter and a ∼10-fold decrease in transcriptional pausing, with consequent increase in transcript elongation rate. At a genome-scale, systems biology level, gene expression changes between the parent strain and adapted RNAP mutants reveal large-scale systematic transcriptional changes that influence specific cellular processes, including strong down-regulation of motility, acid resistance, fimbria, and curlin genes. RNAP genome-binding maps reveal redistribution of RNAP that may facilitate relief of a metabolic bottleneck to growth. These findings suggest that reprogramming the kinetic parameters of RNAP through specific mutations allows regulatory adaptation for optimal growth in new environments.

Discovering conditional co-regulated protein complexes by integrating diverse data sources

BackgroundProteins interacting with each other as a complex play an important role in many molecular processes and functions. Directly detecting protein complexes is still costly, whereas many protein-protein interaction (PPI) maps for model organisms are available owing to the fast development of high-throughput PPI detecting techniques. These binary PPI data provides fundamental and abundant information for inferring new protein complexes. However, PPI data from different experiments do not overlap very much usually. The main reason is that the functions of proteins can activate only on certain environment or stimulus. In a short, PPI is condition-specific. Therefore specifying the conditions on when complexes are present is necessary for a deep understanding of their behaviours. Meanwhile, proteins have various interaction ways and control mechanisms to form different kinds of complexes. Thus the discovery of a certain type of complexes should depend on their own distinct biological or topological characteristics. We do not attempt to find all kinds of complexes by using certain features. Here, we integrate transcription regulation data (TR), gene expression data (GE) and protein-protein interaction data at the systems biology level to discover a special kind of protein complex called conditional co-regulated protein complexes. A conditional co-regulated protein complex has three remarkable features: the coding genes of the member proteins share the same transcription factor (TF), under a certain condition the coding genes express co-ordinately and the member proteins interact mutually as a complex to implement a common biological function.ResultsA framework of discovering the conditional co-regulated protein complexes is proposed. Testing on the Yeast data sets under the Cell Cycle, DNA Damage and Dauxic Shift conditions, we identified a total of 29 conditional co-regulated complexes, among which the coding genes in 14 complexes show a strong association with their TFs activity. Based on the close relationship among co-regulation, co-expression and protein-protein interactions in the conditional co-regulated protein complexes, 39 novel TRs were predicted and explained. ConclusionsThis paper was initiated to study conditional co-regulated protein complexes by integrating multiple data sources. Taking into consideration the influence of TFs activity on the protein interactions, we found that the expression coherence of the protein complexes’ coding genes changed in accordance to their TFs’ activity, which implied that the proteins’ interactions also changed in response to the environments. Based on the three features of conditional co-regulated protein complexes, new transcriptional regulation interactions were predicted.

Reverse phase protein microarray technology in traumatic brain injury

Antibody based, high throughput proteomics technology represents an exciting new approach in understanding the pathobiologies of complex disorders such as cancer, stroke and traumatic brain injury. Reverse phase protein microarray (RPPA) can complement the classical methods based on mass spectrometry as a high throughput validation and quantification method. RPPA technology can address problematic issues, such as sample complexity, sensitivity, quantification, reproducibility and throughput, which are currently associated with mass spectrometry-based approaches. However, there are technical challenges, predominantly associated with the selection and use of antibodies, preparation and representation of samples and with analyzing and quantifying primary RPPA data. Here we present ways to identify and overcome some of the current issues associated with RPPA. We believe that using stringent quality controls, improved bioinformatics analysis and interpretation of primary RPPA data, this method will significantly contribute in generating new level of understanding about complex disorders at the level of systems biology.

Creating and improving detoxification pathways for interpretation of toxicogenomics data

AbstractCurrent high-throughput technology in genomics creates a large amount of biological data. Bioinformatics approaches are directed towards understanding such data on a systems biology level. Advanced mathematical methods like principal component analysis, clustering, neural networks, support vector machine (SVM) approaches and neural networks can help to find patterns in the data. However, to really understand the data the patterns must be combined with existing knowledge. One of the approaches to do so is to associate these data to functional classifications such as can be found in the Gene Ontology. Other methods focus on using biological pathways coming from both public and private pathway databases like KEGG, WikiPathways, Reactome, and MetaCore. Some of these pathway databases contain rather aspecific information about which genes are involved in reactions. A single identifier, like an Enzyme Code, may correspond to a full family of enzymes, whereas only one family member is responsible for the reaction in the given biological context. Another problem arises in the availability of relevant biological pathways for both rodents and humans. This is especially true in the field of toxicology, where many pathways are still lacking. The focus of this project was to create phase I and phase II detoxification pathways using PathVisio, the pathway editor of WikiPathways. New pathway content was generated using information obtained from several trusted resources: a) handbooks, b) manual/automated PubMed literature searches; c) online pathway resources; d) UniProt Knowledgebase; e) EnsEMBL; f) GeneCards. In addition, the content of some existing toxicology pathways was improved. To illustrate the usefulness of the new pathways data from experiments with carcinogenic compounds were retrieved from the main online microarray data repositories Gene Expression Omnibus (GEO) and ArrayExpress. The resulting data were visualized on the new pathways using PathVisio. All these pathways are made available to the community at WikiPathways, where they can be further used for statistical pathway analysis and visualization in the (toxico)genomics field.