Systems Biology Level Research Articles

532 NG2 expression identifies a tumour competent population in glioblastoma with distinct molecular signature

In order to identify genes involved in complex diseases, it is crucial to study the genetic interactions at the systems biology level. By utilizing modern high throughput microarray technology, it has become feasible to obtain gene expressions data and turn it into knowledge that explains the regulatory behavior of genes. In this study, an unsupervised nonlinear model was proposed to infer gene regulatory networks on a genome-wide scale. The proposed model consists of two components, a robust correlation estimator and a nonlinear recurrent model. The robust correlation estimator was used to initialize the parameters of the nonlinear recurrent curve-fitting model. Then the initialized model was used to fit the microarray data. The model was used to simulate the underlying nonlinear regulatory mechanisms in biological organisms. The proposed algorithm was applied to infer the regulatory mechanisms of the general network in Saccharomyces cerevisiae and the pulmonary disease pathways in Homo sapiens. The proposed algorithm requires no prior biological knowledge to predict linkages between genes. The prediction results were checked against true positive links obtained from the YEASTRACT database, the TRANSFAC database, and the KEGG database. By checking the results with known interactions, we showed that the proposed algorithm could determine some meaningful pathways, many of which are supported by the existing literature.

Analysis of connectivity map: Control to glutamate injured and phenobarbital treated neuronal network

We study the responses of a cultured neural network when it is exposed to epileptogenesis glutamate injury causing epilepsy and subsequent treatment with phenobarbital by constructing connectivity map of neurons using correlation matrix. This study is particularly useful in understanding the pharmaceutical drug induced changes in the neuronal network properties with insights into changes at the systems biology level.

A comparative study of cancer proteins in the human protein-protein interaction network

BackgroundCancer is a complex disease. So far, many genes have been reported to involve in the development of cancer. Rather than the traditional approach to studying individual genes or loci, a systematic investigation of cancer proteins in the human protein-protein interaction network may provide important biological information for uncovering the molecular mechanisms of cancer and, potentially, other complex diseases.ResultsWe explored global and local network characteristics of the proteins encoded by cancer genes (cancer proteins) in the human interactome. We found that the network topology of the cancer proteins was much different from that of the proteins encoded by essential genes (essential proteins) or control genes (control proteins). Relative to the essential proteins or control proteins, cancer proteins tended to have higher degree, higher betweenness, shorter shortest-path distance, and weaker clustering coefficient in the human interactome. We further separated the cancer proteins into two groups (recessive and dominant cancer proteins) and compared their topological features. Recessive cancer proteins had higher betweenness than dominant cancer proteins, while their degree distribution and characteristic shortest path distance were also significantly different. Finally, we found that cancer proteins were not randomly distributed in the human interactome and they connected strongly with each other.ConclusionOur study revealed much stronger protein-protein interaction characteristics of cancer proteins relative to the essential proteins or control proteins in the whole human interactome. We also found stronger network characteristics of recessive than dominant cancer proteins. The results are helpful for cancer candidate gene prioritization and verification, biomarker discovery, and, ultimately, understanding the etiology of cancer at the systems biological level.

A Systems Biology Consideration of the Vasculopathy of Sickle Cell Anemia: The Need for Multi-Modality Chemo-Prophylaxis

Much of the morbidity and mortality of sickle cell anemia is accounted for by a chronic vasculopathy syndrome. There is currently no identified therapy, interventional or prophylactic, for this problem. For two reasons, development of an effective therapeutic approach will require a systems biology level perspective on the vascular pathobiology of sickle disease. In the first place, multiple biological processes contribute to the pathogenesis of vasculopathy: red cell sickling, inflammation and adhesion biology, coagulation activation, stasis, deficient bioavailability and excessive consumption of NO, excessive oxidation, and reperfusion injury physiology. The probable hierarchy of involvement of these disparate sub-biologies places inflammation caused by reperfusion injury physiology as the likely, proximate, linking pathophysiological factor. In the second place, most of these sub-biologies overlap with each other and, in any case, have multiple points of potential interaction and transactivation. Consequently, an approach modeled upon chemotherapy for cancer is needed. This would be a truly multi-modality approach that hopefully could be achieved via employment of relatively few drugs. It is proposed here that the specific combination of a statin with suberoylanilide hydroxamic acid would provide a suitable, broad, multi-modality approach to chemo-prophylaxis for sickle vasculopathy.

Lipidomic Analysis of Dynamic Eicosanoid Responses during the Induction and Resolution of Lyme Arthritis

Eicosanoids and other bioactive lipid mediators are indispensable regulators of biological processes, as demonstrated by the numerous inflammatory diseases resulting from their dysregulation, including cancer, hyperalgesia, atherosclerosis, and arthritis. Despite their importance, a robust strategy comparable with gene or protein array technology for comprehensively analyzing the eicosanoid metabolome has not been forthcoming. We have developed liquid chromatography-tandem mass spectrometry methodology that quantitatively and comprehensively analyzes the eicosanoid metabolome and utilized this approach to characterize eicosanoid production during experimental Lyme arthritis in mice infected with the bacterium Borrelia burgdorferi. Eicosanoids were extracted throughout infection from the joints of Lyme arthritis-resistant and -susceptible mice and subjected to lipidomic profiling. We identified temporal and quantitative differences between these mouse strains in the production of eicosanoids, which correlated with differences in arthritis development. The eicosanoid biosynthetic enzyme cyclooxygenase (COX)-2 has been implicated in the regulation of Lyme arthritis pathology, and subsequent lipidomic profiling of B. burgdorferi-infected COX-2(-/-) mice identified reductions not only in COX-2 products but, surprisingly, also significant off-target reductions in 5-lipoxygenase metabolites. Our results demonstrate the utility of a comprehensive lipidomic approach for identifying potential contributors to disease pathology and may facilitate the development of more precisely targeted treatment strategies.

A robust correlation estimator and nonlinear recurrent model to infer genetic interactions in Saccharomyces cerevisiae and pathways of pulmonary disease in Homo sapiens

In order to identify genes involved in complex diseases, it is crucial to study the genetic interactions at the systems biology level. By utilizing modern high throughput microarray technology, it has become feasible to obtain gene expressions data and turn it into knowledge that explains the regulatory behavior of genes. In this study, an unsupervised nonlinear model was proposed to infer gene regulatory networks on a genome-wide scale. The proposed model consists of two components, a robust correlation estimator and a nonlinear recurrent model. The robust correlation estimator was used to initialize the parameters of the nonlinear recurrent curve-fitting model. Then the initialized model was used to fit the microarray data. The model was used to simulate the underlying nonlinear regulatory mechanisms in biological organisms. The proposed algorithm was applied to infer the regulatory mechanisms of the general network in Saccharomyces cerevisiae and the pulmonary disease pathways in Homo sapiens. The proposed algorithm requires no prior biological knowledge to predict linkages between genes. The prediction results were checked against true positive links obtained from the YEASTRACT database, the TRANSFAC database, and the KEGG database. By checking the results with known interactions, we showed that the proposed algorithm could determine some meaningful pathways, many of which are supported by the existing literature.

Proteomic Biomarkers for Blast Neurotrauma: Targeting Cerebral Edema, Inflammation, and Neuronal Death Cascades

Proteomics for blast traumatic brain injury (bTBI) research represents an exciting new approach that can greatly help to address the complex pathology of this condition. Antibody-based platforms, antibody microarrays (AbMA), and reverse capture protein microarrays (RCPM) can complement the classical methods based on 2D gel electrophoresis and mass spectrometry (2DGE/MS). These new technologies can address problematic issues, such as sample complexity, sensitivity, quantitation, reproducibility, and analysis time, which are typically associated with 2DGE/MS. Combined with bioinformatics analysis and interpretation of primary microarray data, these methods will generate a new level of understanding about bTBI at the level of systems biology. As biological and clinical knowledge and the availability of these systems become more widely established, we expect that AbMA and RCPM will be used routinely in clinical diagnostics, and also for following therapeutic progress. At the technical level, we anticipate that these platforms will evolve to accommodate comprehensive, high-speed, label-free analysis on a human proteome-wide scale.

Biological Redox Switches

Research over the last decade has substantially advanced our understanding of cellular redox chemistry and introduced new terms to redox signaling and biological redox switches. It emerges that redox switching affects a plethora of biological processes and seems to be necessary to counterbalance oxidative stress and guarantee cellular survival in oxidative conditions. Despite intensive studies, the mechanisms of redox switching and its consequences are poorly understood, which points towards an extremely complex nature of these phenomena. Indeed, in contrast to classical signalling cascades, cellular redox signaling seems to affect the whole cellular redox environment and a large number of different redox switches. A majority of biological redox switches rely on the oxidation of thiol group(s) of cysteine residue(s); however, thiolates also bind transition metal ions like Zn(II), Cu(I), and iron, and these metal-thiolate motifs also function as redox switches. It follows that a deeper understanding of redox signalling and redox switches could be achieved by a multidisciplinary approach combining advances in the redox chemistry of sulphur, the chemistry of reactive oxygen and nitrogen species, as well as the bioinorganic chemistry of metal complexes. Many of these aspects are reviewed in the current forum issue on biological redox switches with the aim to promote the understanding of cellular redox phenomena at system biology level.

Integrated Analysis of Transcript, Protein and Metabolite Data To Study Lignin Biosynthesis in Hybrid Aspen

Tree biotechnology will soon reach a mature state where it will influence the overall supply of fiber, energy and wood products. We are now ready to make the transition from identifying candidate genes, controlling important biological processes, to discovering the detailed molecular function of these genes on a broader, more holistic, systems biology level. In this paper, a strategy is outlined for informative data generation and integrated modeling of systematic changes in transcript, protein and metabolite profiles measured from hybrid aspen samples. The aim is to study characteristics of common changes in relation to genotype-specific perturbations affecting the lignin biosynthesis and growth. We show that a considerable part of the systematic effects in the system can be tracked across all platforms and that the approach has a high potential value in functional characterization of candidate genes.

The interactome: Predicting the protein-protein interactions in cells

The term Interactome describes the set of all molecular interactions in cells, especially in the context of protein-protein interactions. These interactions are crucial for most cellular processes, so the full representation of the interaction repertoire is needed to understand the cell molecular machinery at the system biology level. In this short review, we compare various methods for predicting protein-protein interactions using sequence and structure information. The ultimate goal of those approaches is to present the complete methodology for the automatic selection of interaction partners using their amino acid sequences and/or three dimensional structures, if known. Apart from a description of each method, details of the software or web interface needed for high throughput prediction on the whole genome scale are also provided. The proposed validation of the theoretical methods using experimental data would be a better assessment of their accuracy.

The challenges for molecular nutrition research 4: the “nutritional systems biology level”

Nutritional systems biology may be defined as the ultimate goal of molecular nutrition research, where all relevant aspects of regulation of metabolism in health and disease states at all levels of its complexity are taken into account to describe the molecular physiology of nutritional processes. The complexity spans from intracellular to inter-organ dynamics, and involves iterations between mathematical modelling and analysis employing all profiling methods and other biological read-outs. On the basis of such dynamic models we should be enabled to better understand how the nutritional status and nutritional challenges affect human metabolism and health. Although the achievement of this proposition may currently sound unrealistic, many initiatives in theoretical biology and biomedical sciences work on parts of the solution. This review provides examples and some recommendations for the molecular nutrition research arena to move onto the systems level.

Construction of a chloroplast protein interaction network and functional mining of photosynthetic proteins in Arabidopsis thaliana

Chloroplast is a typical plant cell organelle where photosynthesis takes place. In this study, a total of 1,808 chloroplast core proteins in Arabidopsis thaliana were reliably identified by combining the results of previously published studies and our own predictions. We then constructed a chloroplast protein interaction network primarily based on these core protein interactions. The network had 22,925 protein interaction pairs which involved 2,214 proteins. A total of 160 previously uncharacterized proteins were annotated in this network. The subunits of the photosynthetic complexes were modularized, and the functional relationships among photosystem I (PSI), photosystem II (PSII), light harvesting complex of photosystem I (LHC I) and light harvesting complex of photosystem I (LHC II) could be deduced from the predicted protein interactions in this network. We further confirmed an interaction between an unknown protein AT1G52220 and a photosynthetic subunit PSI-D2 by yeast two-hybrid analysis. Our chloroplast protein interaction network should be useful for functional mining of photosynthetic proteins and investigation of chloroplast-related functions at the systems biology level in Arabidopsis.

Metabotyping of Caenorhabditis elegans reveals latent phenotypes.

Assigning functions to every gene in a living organism is the next challenge for functional genomics. In fact, 85-90% of the 19,000 genes of the nematode Caenorhabditis elegans genome do not produce any visible phenotype when inactivated, which hampers determining their function, especially when they do not belong to previously characterized gene families. We used (1)H high-resolution magic angle spinning NMR spectroscopy ((1)H HRMAS-NMR) to reveal the latent phenotype associated to superoxide dismutase (sod-1) and catalase (ctl-1) C. elegans mutations, both involved in the elimination of radical oxidative species. These two silent mutations are significantly discriminated from the wild-type strain and from each other. We identify a metabotype significantly associated with these mutations involving a general reduction of fatty acyl resonances from triglycerides, unsaturated lipids being known targets of free radicals. This work opens up perspectives for the use of (1)H HRMAS-NMR as a molecular phenotyping device for model organisms. Because it is amenable to high throughput and is shown to be highly informative, this approach may rapidly lead to a functional and integrated metabonomic mapping of the C. elegans genome at the systems biology level.

Prefrontal Dopamine Signaling in Schizophrenia - The Corticocentric Model

Diminished prefrontal dopamine (DA) signaling apparently contributes to schizophrenia illness. In the proposed corticocentric model, diminished DA is changing the D2/D1 receptor activation ratio in favor of D2 activation which - through a cascade of intracellular molecular events - ultimately decreases the neuronal signal-to-noise ratio (SNR) thereby destabilizing cortical microcircuits resulting in cognitive deficits and other clinical symptoms. The present overview will outline cortical DA effects both on the neural network and systems biology level and their relationship to schizophrenia illness. The proposed model will be finally discussed within the framework of the currently still dominant hypothesis of the striatal hyperdopaminergic state in schizophrenia.

ESF-EMBO Symposium on Molecular Bioenergetics of Cyanobacteria: Towards Systems Biology Level of Understanding

FEBS LettersVolume 581, Issue 26 p. 5165-5165 AnnouncementFree Access ESF-EMBO Symposium on Molecular Bioenergetics of Cyanobacteria: Towards Systems Biology Level of Understanding First published: 22 October 2007 https://doi.org/10.1016/S0014-5793(07)01058-7AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume581, Issue26October 30, 2007Pages 5165-5165 RelatedInformation

Ultrahigh resolution mass spectrometry

The integration of soft ionization technologies and ultrahigh resolution FTICR mass spectrometry (Fourier transform ion-cyclotron MS) has enabled the examination of molecules, directly from mixtures, with ultrahigh mass resolution and sub-ppm mass accuracy. This ability to observe structure and reactivity aspects of intact (macro)molecules has, in particular, contributed to the molecular-level characterization of complex matrices, for example novel materials and biological and biogeochemical samples. Here, thousands of molecular formulae can be determined in a single measurement directly from mixtures. The exceptional performance of ultrahigh-resolution FTICR mass spectroscopy in the characterization of complex systems suffers much less from the detrimental effects of intrinsic averaging, which is a characteristic of most other methods of organic structural spectroscopy. This novel information-rich and direct perception has the potential to entirely change the way we address the molecular-level analysis of complex materials, systems, and processes in general, thereby opening novel perspectives in data-driven systems biology and “omics” approaches. The inauguration of Europe’s first 12 Tesla FTICR mass spectrometer at the GSF National Research Center for Environment and Health provided the opportunity to hear expert views on novel research dedicated to the molecular-level characterization of complex systems. A dozen presentations organized in four sessions were delivered during the “First International Symposium on Ultrahigh-Resolution Mass Spectrometry for the Molecular Level Analysis of Complex (BioGeo)Systems”, held at the GSF on November 6 and 7, 2006 (www.gsf.de/FTMS2006). In session one, General Foundations of FTICR Mass Spectrometry, Alan G. Marshall (National High Magnetic Field Laboratory, Tallahassee, USA) provided a wide-ranging overview about the currently available opportunities of FTMS in molecular-resolution analysis: High-field Fourier transform ion cyclotron resonance mass spectrometry: a platform for “omics”. Then, Eugene N. Nikolaev (Institute of Biochemical Physics, Moscow, Russian Federation) investigated the complex trajectories of ions in FTMS cells: The new possibilities in ion clouds dynamic simulation using supercomputers. Application to FTICR and quadrupole devices. Arnd Ingendoh (Bruker Daltonics, Bremen, Germany) provided an overview about current and foreseeable trends in modern mass spectrometry: Mass spectrometry and beyond—new areas of applications. Session two, From Biological Chemistry to Chemical Biology, focused on life sciences applications. Jeremy K. Nicholson (Division of Surgery, Oncology, Reproductive Biology and Anaesthetics, Imperial College, London, UK) produced a comprehensive assessment about integrated views in “omics” sciences (Metabonomics and top-down systems biology: from personalized healthcare to molecular epidemiology) and Hannelore Daniel (Technical University Munich, Weihenstephan, Germany) complemented this interesting and versatile perspective with The bottom-up approach: from individual genes to the systems biology level of nutrition. Michael Sattler (European Molecular Biology Laboratory, Heidelberg, Germany and GSF Research Center for Environment and Health) illustrated the power of modern NMR methods capable of producing well resolved three-dimensional structures of large biomolecules including description of dynamics and reactivity: NMR to study molecular recognition and dynamics of biomacromolecules. Session three, Novel MS Approaches in Biochemistry and Medicine, illustrated novel MS approaches by Gary Siuzdak (Scripps Center for Mass Spectrometry, La Jolla, USA): Novel mass-based approaches to proteomics and metabolomics and advanced carbohydrate analysis by Jasna Peter-Katalinic (Institute fur Medical Physics and Biophysics, Munster, Germany): Requirements on FTICR mass spectrometry for glycomics in structural biology and medicine. Session four, Repetitive and Non-Repetitive Complex Systems, focused on proteome and complex biogeochemical mixture analysis. Michael Przybylski (Department of Chemistry, University of Konstanz, Germany) elucidated aspects of advanced protein analysis High resolution FTICR mass spectrometry and affinity-proteomics: powerful tool for elucidation of antigen-antibody recognition structures and vaccine development in molecular immunology and complex biogeochemical materials and William T. Cooper (Department of Chemistry and Biochemistry, FSU, Tallahassee, USA) provided insights into the structure, synthesis and degradation of natural organic matter: Insights into organic chemistry of the natural environment; FTICR mass spectrometry of really complex mixtures. Norbert Hertkorn (GSF Institute of Ecological Chemistry, Neuherberg, Germany) described the specific utility and complementarity of NMR and mass spectrometry in the analysis of complex biogeochemical systems: Mapping the molecular compositional space with mass spectrometry, and Philippe Schmitt-Kopplin (GSF Institute of Ecological Chemistry, Neuherberg, Germany) provided a comprehensive assessment of biogeochemical analysis with a variety of current examples: Molecular-level analysis of non-repetitive structures in biogeosystems. To recognize the importance of the technique and provide the reader of the journal Analytical and Bioanalytical Chemistry with a perspective of the current trends in FTICR mass spectrometry, this special issue on “Ultrahigh Resolution Mass Spectrometry” was prepared to include research, perspective, and review articles from experts in the field. This special issue reflects the wide scope of current FTICR mass spectrometry utilization in the analytical characterization of natural products, biomolecules and biogeochemical mixtures, hyphenation of separation and mass spectrometry, and integration of spectroscopy and separation in the molecular-level characterization of complex systems. At this point, we would like to thank all the authors for their high-quality articles, all referees for the rapid evaluations.

Consensus recommendations to promote and advance predictive systems toxicology and toxicogenomics

The number of high throughput -omics technologies continues to grow. Toxicogenomic application of these technologies is poised to greatly influence current regulatory toxicology. However, many changes are needed before a systems biology level approach can be effectively incorporated into the regulatory toxicology framework. A workshop was held at the Annual Environmental Mutagen Society meeting in Vancouver, British Columbia, on advances in -omics applications. A number of recommendations emerged from the workshop discussion (beyond what activities are currently ongoing) aimed at advancing the ultimate goal of predictive systems toxicology from the present formative state of toxicogenomics. Recommendations include: (1) encouraging investigators to embrace open-access data sharing, (2) increasing current database and curation capacity, (3) establishment of large collaborative projects investigating multiple -omics endpoints within the same groups of animals, (4) mechanisms to encourage collaborative science including increasing the value of junior authorship on multi-authored papers and changes in the promotion process, (5) further development of standardized protocols, and (6) investment from the funding agencies and toxicology community to build the required infrastructure.

Unravelling the world of cis-regulatory elements

Genome-wide comparisons indicate that only studying the coding regions will not be enough for explaining the biological complexity of an organism, while the genetic variants and the epigenetic differences of cis-regulatory elements are crucial to elucidate many complicated biological phenomena. Their various regulatory functions also play indispensable roles in forming organismal polymorphism. Recent studies showed that the cis-regulatory elements can regulate gene expression as nuclear organizers, and involve in functional noncoding transcription and produce regulatory noncoding RNA molecules. Novel high-throughput strategies and in silico analysis make a great amount data of cis-regulatory elements available. Particularly, the computational methods could help to combine reductionist studies with network biomedical investigations, and begin the era to understand organismal regulatory events at systems biology level.

Genome sequences of the honey bee pathogens Paenibacillus larvae and Ascosphaera apis.

Genome sequences offer a broad view of host–pathogen interactions at the systems biology level. With the completion of the sequence of the honey bee, interest in the relevant pathogens is heightened. Here we report the genome sequences of two of the major pathogens of honey bees, the bacterium Paenibacillus larvae (causative agent for American foulbrood disease) and the fungus Ascosphaera apis. (causative agent for chalkbrood disease). Ongoing efforts to characterize the genomes of these species can be used to understand and mitigate the effects of two important pathogens, and will provide a contrast with pathogenic, benign and freeliving relatives.

An Integrated Study of Acute Effects of Valproic Acid in the Liver Using Metabonomics, Proteomics, and Transcriptomics Platforms

An integrated omics approach was undertaken in order to elucidate a systems biology level understanding of the acute hepatotoxcity of valproic acid (VPA). Metabonomics, proteomics and gene expression microarray platforms were employed in this systems biology study. CD-1 female pregnant mice were injected subcutaneously with 600 mg/kg VPA or vehicle control. Urine, serum, and liver tissue were collected at 6, 12, and 24 h after dosing. Principal component analysis (PCA) of the metabonomics data showed clustering of the dosed groups away from the controls for the urine samples. Looser clustering was seen in the other sample sets investigated. However, VPA administration resulted in altered glucose concentrations in urine samples at 12 and 24 h and in aqueous liver tissue extracts at 12 h after VPA administration. Proteomics studies identified two proteins, glycogen phosphorylase and amylo-1,6-glucosidase, which were increased in dosed animals relative to control. Both of these proteins are involved in converting glycogen to glucose. Examination of the expression of 20,000 liver genes did not reveal significantly altered expression at 6, 12, or 24 h after VPA exposure. The combined studies indicated a perturbation in the glycogenolysis pathway following administration of VPA.