AtGGM2014, an Arabidopsis gene co-expression network for functional studies

Gene co-expression networks provide an important tool for systems biology studies. Using microarray data from the Array Express database, we constructed an Arabidopsis gene co-expression network, termed At GGM2014, based on the graphical Gaussian model, which contains 102,644 co-expression gene pairs among 18,068 genes. The network was grouped into 622 gene co-expression modules. These modules function in diverse house-keeping, cell cycle, development, hormone response, metabolism, and stress response pathways. We developed a tool to facilitate easy visualization of the expression patterns of these modules either in a tissue context or their regulation under different treatment conditions. The results indicate that at least six modules with tissue-specific expression pattern failed to record modular regulation under various stress conditions. This discrepancy could be best explained by the fact that experiments to study plant stress responses focused mainly on leaves and less on roots, and thus failed to recover specific regulation pattern in other tissues. Overall, the modular structures revealed by our network provide extensive information to generate testable hypotheses about diverse plant signaling pathways. At GGM2014 offers a constructive tool for plant systems biology studies.     

Protein complexes are central in the yeast genetic landscape

If perturbing two genes together has a stronger or weaker effect than expected, they are said to genetically interact. Genetic interactions are important because they help map gene function, and functionally related genes have similar genetic interaction patterns. Mapping quantitative (positive and negative) genetic interactions on a global scale has recently become possible. This data clearly shows groups of genes connected by predominantly positive or negative interactions, termed monochromatic groups. These groups often correspond to functional modules, like biological processes or complexes, or connections between modules. However it is not yet known how these patterns globally relate to known functional modules. Here we systematically study the monochromatic nature of known biological processes using the largest quantitative genetic interaction data set available, which includes fitness measurements for ～5.4 million gene pairs in the yeast Saccharomyces cerevisiae. We find that only 10% of biological processes, as defined by Gene Ontology annotations, and less than 1% of inter-process connections are monochromatic. Further, we show that protein complexes are responsible for a surprisingly large fraction of these patterns. This suggests that complexes play a central role in shaping the monochromatic landscape of biological processes. Altogether this work shows that both positive and negative monochromatic patterns are found in known biological processes and in their connections and that protein complexes play an important role in these patterns. The monochromatic processes, complexes and connections we find chart a hierarchical and modular map of sensitive and redundant biological systems in the yeast cell that will be useful for gene function prediction and comparison across phenotypes and organisms. Furthermore the analysis methods we develop are applicable to other species for which genetic interactions will progressively become more available.     

Modularity: a new perspective in biology

Several decades of research in biochemistry and molecular biology have been devoted for studies on isolated enzymes and proteins. Recent high throughput technologies in genomics and proteomics have resulted in avalanche of information about several genes, proteins and enzymes in variety of living systems. Though these efforts have greatly contributed to the detailed understanding of a large number of individual genes and proteins, this explosion of information has simultaneously brought out the limitations of reductionism in understanding complex biological processes. The genes or gene products do not function in isolation in vivo. A delicate and dynamic molecular architecture is required for precision of the chemical reactions associated with "life". In future, a paradigm shift is, therefore, envisaged, in biology leading to exploration of molecular organizations in physical and genomic context, a subtle transition from conventional molecular biology to modular biology. A module can be defined as an organization of macromolecules performing a synchronous function in a given metabolic pathway. In modular biology, the biological processes of interest are explored as complex systems of functionally interacting macromolecules. The present article describes the perceptions of the concept of modularity, in terms of associations among genes and proteins, presenting a link between reductionist approach and system biology.     

A gene co-expression network in whole blood of schizophrenia patients is independent of antipsychotic-use and enriched for brain-expressed genes

Despite large-scale genome-wide association studies (GWAS), the underlying genes for schizophrenia are largely unknown. Additional approaches are therefore required to identify the genetic background of this disorder. Here we report findings from a large gene expression study in peripheral blood of schizophrenia patients and controls. We applied a systems biology approach to genome-wide expression data from whole blood of 92 medicated and 29 antipsychotic-free schizophrenia patients and 118 healthy controls. We show that gene expression profiling in whole blood can identify twelve large gene co-expression modules associated with schizophrenia. Several of these disease related modules are likely to reflect expression changes due to antipsychotic medication. However, two of the disease modules could be replicated in an independent second data set involving antipsychotic-free patients and controls. One of these robustly defined disease modules is significantly enriched with brain-expressed genes and with genetic variants that were implicated in a GWAS study, which could imply a causal role in schizophrenia etiology. The most highly connected intramodular hub gene in this module (ABCF1), is located in, and regulated by the major histocompatibility (MHC) complex, which is intriguing in light of the fact that common allelic variants from the MHC region have been implicated in schizophrenia. This suggests that the MHC increases schizophrenia susceptibility via altered gene expression of regulatory genes in this network.     

Why modules matter

The serendipitous discovery of the SH2 domain unleashed a sea-change in our conceptual molecular understanding of protein function. The reductionist approaches that followed from the recognition of modular protein interaction domains transformed our understanding of cellular signal transduction systems, how they evolve and how they may be manipulated. We now recognize thousands of conserved protein modules - many of which have been described in structure and function, implicated in disease, or underlie targeted therapeutics. The reductionist study of isolated protein modules has enabled the reconstruction of the protein interaction networks that underlie cellular signalling. Protein modules themselves are becoming tools to probe cellular activation states and identify key interactions hubs in both normal and diseased cells and the concept of protein modularity is central to the field of synthetic biology. This brief word of introduction serves to highlight the historical impact of the very powerful idea of protein modules and sets the stage for the exciting on-going discoveries discussed in this issue.     

The road to modularity

A network of interactions is called modular if it is subdivided into relatively autonomous, internally highly connected components. Modularity has emerged as a rallying point for research in developmental and evolutionary biology (and specifically evo-devo), as well as in molecular systems biology. Here we review the evidence for modularity and models about its origin. Although there is an emerging agreement that organisms have a modular organization, the main open problem is the question of whether modules arise through the action of natural selection or because of biased mutational mechanisms.     

Synthetic biology of minimal systems

“Synthetic biology” is a concept that has developed together with, or slightly after, “systems biology”. But while systems biology aims at the full understanding of large systems by integrating more and more details into their models, synthetic biology phrases different questions, namely: what particular biological function could be obtained with a certain known subsystem of reduced complexity; can this function be manipulated or engineered in artificial environments or genetically modified organisms; and if so, how? The most prominent representation of synthetic biology has so far been microbial engineering by recombinant DNA technology, employing modular concepts known from information technology. However, there are an increasing number of biophysical groups who follow similar strategies of dissecting cellular processes and networks, trying to identify functional minimal modules that could then be combined in a bottom-up approach towards biology. These modules are so far not as particularly defined by their impact on DNA processing, but rather influenced by core fields of biophysics, such as cell mechanics and membrane dynamics. This review will give an overview of some classical and some quite new biophysical strategies for constructing minimal systems of certain cellular modules. We will show that with recent advances in understanding of cytoskeletal and membrane elements, the time might have come to experimentally challenge the concept of a minimal cell.     

Beyond modules and hubs: the potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders

In a research environment dominated by reductionist approaches to brain disease mechanisms, gene network analysis provides a complementary framework in which to tackle the complex dysregulations that occur in neuropsychiatric and other neurological disorders. Gene–gene expression correlations are a common source of molecular networks because they can be extracted from high‐dimensional disease data and encapsulate the activity of multiple regulatory systems. However, the analysis of gene coexpression patterns is often treated as a mechanistic black box, in which looming ‘hub genes’ direct cellular networks, and where other features are obscured. By examining the biophysical bases of coexpression and gene regulatory changes that occur in disease, recent studies suggest it is possible to use coexpression networks as a multi‐omic screening procedure to generate novel hypotheses for disease mechanisms. Because technical processing steps can affect the outcome and interpretation of coexpression networks, we examine the assumptions and alternatives to common patterns of coexpression analysis and discuss additional topics such as acceptable datasets for coexpression analysis, the robust identification of modules, disease‐related prioritization of genes and molecular systems and network meta‐analysis. To accelerate coexpression research beyond modules and hubs, we highlight some emerging directions for coexpression network research that are especially relevant to complex brain disease, including the centrality–lethality relationship, integration with machine learning approaches and network pharmacology .     

Gene module level analysis: identification to networks and dynamics

Nature exhibits modular design in biological systems. Gene module level analysis is based on this module concept, aiming to understand biological network design and systems behavior in disease and development by emphasizing on modules of genes rather than individual genes. Module level analysis has been extensively applied in genome wide level analysis, exploring the organization of biological systems from identifying modules to reconstructing module networks and analyzing module dynamics. Such module level perspective provides a high level representation of the regulatory scenario and design of biological systems, promising to revolutionize our view of systems biology, genetic engineering as well as disease mechanisms and molecular medicine.     

Darwin to DNA, molecules to morphology: the end of classical population genetics and the road ahead.

Molecular reductionism has permeated all of biology and because of successive new technical breakthroughs it has succeeded in unraveling the structural details of genes and genomes. The molecular revolution has reached its reductionist limit, i.e., the study of component parts in isolation, and is ready to come full circle through genomics, proteomics, and gene expression studies back to the phenotype and bring evolutionary biology to confront the Darwinian paradigm, the relationship between gene, organism, and environment. Classical experimental population genetics, dealing with genetic polymorphism and estimation of selection coefficients on a gene-by-gene basis, is coming to an end and a new era of interdisciplinary and interactive biology focusing on dynamic relationships among gene, organism, and environment has begun. In the new population genetics, there will be a shift in focus from single genes to gene networks, from gene-structure to gene-regulation, from additivity to epistasis, and from simple phenotypes to gene-interaction networks and the evolution of complex and modular systems.     

Plug and play modular strategies for synthetic retrotransposons

Recent progress in L1 biology highlights its role as a major driving force in the evolution of mammalian genome structure and function. This coincides with direct confirmation of the preponderance of long interspersed elements in mammalian genomes at the nucleotide level by large scale sequencing efforts. Two assay systems have been prominently featured in L1 studies over the past decade, which are used to assess L1 activities in cultured cells and transgenic mice respectively. However, constructing retrotransposon assay vectors and subsequent mapping of integration sites remain technically challenging aspects of the field. Synthetic biology approaches have changed the playing field with regard to the strategic design of retrotransposons. To streamline the construction and optimization of synthetic retrotransposons, we have implemented a highly efficient modular design for L1 vectors allowing “plug and play” swapping of individual modules as new knowledge is gained and optimization of constructs proceeds. Seven functional modules are divided by strategically placed unique restriction sites. These are utilized to facilitate module exchange and construction of L1 vectors for gene targeting, transgenesis and cell culture assays. A “double SfiI” strategy utilizing two non-complementary overhangs allows insert swapping to be carried out with a single, robust restriction/ligation cycle. The double-SfiI strategy is generic and can be applied to many other problems in synthetic biology or genetic engineering. To facilitate genomic mapping of L1 insertions, we have developed an optimized inverse PCR protocol using 4-base cutters and step-down cycling conditions. Using this protocol, de novo L1 insertions can be efficiently recovered after a single round of PCR. The proposed modular design also incorporates features allowing streamlined insertion mapping without repeated optimization. Furthermore, we have presented evidence that efficient L1 retrotransposition is not dependent on pCEP4 conferred autonomous replication capabilities when a shortened puromycin selection protocol is used, providing a great opportunity for further optimization of L1 cell culture assay vectors by using alternative vector backbones.     

Hormone signaling in evolution and development: a non-model system approach

Cooption and modularity are informative concepts in evolutionary developmental biology. Genes function within complex networks that act as modules in development. These modules can then be coopted in various functional and evolutionary contexts. Hormonal signaling, the main focus of this review, has a modular character. By regulating the activities of genes, proteins and other cellular molecules, a hormonal signal can have major effects on physiological and ontogenetic processes within and across tissues over a wide spatial and temporal scale. Because of this property, we argue that hormones are frequently involved in the coordination of life history transitions (LHTs) and their evolution (LHE). Finally, we promote the usefulness of a comparative, non-model system approach towards understanding how hormones function and guide development and evolution, highlighting thyroid hormone function in echinoids as an example.     

LinkNMF: Identification of histone modification modules in the human genome using nonnegative matrix factorization

Histone modifications are ubiquitous processes involved in various cellular mechanisms. Systemic analysis of multiple chromatin modifications has been used to characterize various chromatin states associated with functional DNA elements, gene expression, and specific biological functions. However, identification of modular modification patterns is still required to understand the functional associations between histone modification patterns and specific chromatin/DNA binding factors. To recognize modular modification patterns, we developed a novel algorithm that combines nonnegative matrix factorization (NMF) and a clique-detection algorithm. We applied it, called LinkNMF, to generate a comprehensive modification map in human CD4 + T cell promoter regions. Initially, we identified 11 modules not recognized by conventional approaches. The modules were grouped into two major classes: gene activation and repression. We found that genes targeted by each module were enriched with distinguishable biological functions, suggesting that each modular pattern plays a unique functional role. To explain the formation of modular patterns, we investigated the module-specific binding patterns of chromatin regulators. Application of LinkNMF to histone modification maps of diverse cells and developmental stages will be helpful for understanding how histone modifications regulate gene expression. The algorithm is available on our website at biodb.kaist.ac.kr/LinkNMF.     

A spectrum of modularity in multi‐functional gene circuits

A major challenge in systems biology is to understand the relationship between a circuit's structure and its function, but how is this relationship affected if the circuit must perform multiple distinct functions within the same organism? In particular, to what extent do multi‐functional circuits contain modules which reflect the different functions? Here, we computationally survey a range of bi‐functional circuits which show no simple structural modularity: They can switch between two qualitatively distinct functions, while both functions depend on all genes of the circuit. Our analysis reveals two distinct classes: hybrid circuits which overlay two simpler mono‐functional sub‐circuits within their circuitry, and emergent circuits, which do not. In this second class, the bi‐functionality emerges from more complex designs which are not fully decomposable into distinct modules and are consequently less intuitive to predict or understand. These non‐intuitive emergent circuits are just as robust as their hybrid counterparts, and we therefore suggest that the common bias toward studying modular systems may hinder our understanding of real biological circuits.     

Inference of disease-related molecular logic from systems-based microarray analysis

Computational analysis of gene expression data from microarrays has been useful for medical diagnosis and prognosis. The ability to analyze such data at the level of biological modules, rather than individual genes, has been recognized as important for improving our understanding of disease-related pathways. It has proved difficult, however, to infer pathways from microarray data by deriving modules of multiple synergistically interrelated genes, rather than individual genes. Here we propose a systems-based approach called Entropy Minimization and Boolean Parsimony (EMBP) that identifies, directly from gene expression data, modules of genes that are jointly associated with disease. Furthermore, the technique provides insight into the underlying biomolecular logic by inferring a logic function connecting the joint expression levels in a gene module with the outcome of disease. Coupled with biological knowledge, this information can be useful for identifying disease-related pathways, suggesting potential therapeutic approaches for interfering with the functions of such pathways. We present an example providing such gene modules associated with prostate cancer from publicly available gene expression data, and we successfully validate the results on additional independently derived data. Our results indicate a link between prostate cancer and cellular damage from oxidative stress combined with inhibition of apoptotic mechanisms normally triggered by such damage.     

Synthetic biology of polyketide synthases

Complex reduced polyketides represent the largest class of natural products that have applications in medicine, agriculture, and animal health. This structurally diverse class of compounds shares a common methodology of biosynthesis employing modular enzyme systems called polyketide synthases (PKSs). The modules are composed of enzymatic domains that share sequence and functional similarity across all known PKSs. We have used the nomenclature of synthetic biology to classify the enzymatic domains and modules as parts and devices, respectively, and have generated detailed lists of both. In addition, we describe the chassis (hosts) that are used to assemble, express, and engineer the parts and devices to produce polyketides. We describe a recently developed software tool to design PKS system and provide an example of its use. Finally, we provide perspectives of what needs to be accomplished to fully realize the potential that synthetic biology approaches bring to this class of molecules.     

Detecting disease associated modules and prioritizing active genes based on high throughput data

Background  

The accumulation of high-throughput data greatly promotes computational investigation of gene function in the context of complex biological systems. However, a biological function is not simply controlled by an individual gene since genes function in a cooperative manner to achieve biological processes. In the study of human diseases, rather than to discover disease related genes, identifying disease associated pathways and modules becomes an essential problem in the field of systems biology.