Predictive integration of gene functional similarity and co-expression defines treatment response of endothelial progenitor cells

Background  

Endothelial progenitor cells (EPCs) have been implicated in different processes crucial to vasculature repair, which may offer the basis for new therapeutic strategies in cardiovascular disease. Despite advances facilitated by functional genomics, there is a lack of systems-level understanding of treatment response mechanisms of EPCs. In this research we aimed to characterize the EPCs response to adenosine (Ado), a cardioprotective factor, based on the systems-level integration of gene expression data and prior functional knowledge. Specifically, we set out to identify novel biosignatures of Ado-treatment response in EPCs.     

A genomewide functional network for the laboratory mouse

Establishing a functional network is invaluable to our understanding of gene function, pathways, and systems-level properties of an organism and can be a powerful resource in directing targeted experiments. In this study, we present a functional network for the laboratory mouse based on a Bayesian integration of diverse genetic and functional genomic data. The resulting network includes probabilistic functional linkages among 20,581 protein-coding genes. We show that this network can accurately predict novel functional assignments and network components and present experimental evidence for predictions related to Nanog homeobox (Nanog), a critical gene in mouse embryonic stem cell pluripotency. An analysis of the global topology of the mouse functional network reveals multiple biologically relevant systems-level features of the mouse proteome. Specifically, we identify the clustering coefficient as a critical characteristic of central modulators that affect diverse pathways as well as genes associated with different phenotype traits and diseases. In addition, a cross-species comparison of functional interactomes on a genomic scale revealed distinct functional characteristics of conserved neighborhoods as compared to subnetworks specific to higher organisms. Thus, our global functional network for the laboratory mouse provides the community with a key resource for discovering protein functions and novel pathway components as well as a tool for exploring systems-level topological and evolutionary features of cellular interactomes. To facilitate exploration of this network by the biomedical research community, we illustrate its application in function and disease gene discovery through an interactive, Web-based, publicly available interface at http://mouseNET.princeton.edu.     

Irreversible cell-cycle transitions are due to systems-level feedback

The irreversibility of cell-cycle transitions is commonly thought to derive from the irreversible degradation of certain regulatory proteins. We argue that irreversible transitions in the cell cycle (or in any other molecular control system) cannot be attributed to a single molecule or reaction, but that they derive from feedback signals in reaction networks. This systems-level view of irreversibility is supported by many experimental observations.     

When the human viral infectome and diseasome networks collide: towards a systems biology platform for the aetiology of human diseases

Background  

Comprehensive understanding of molecular mechanisms underlying viral infection is a major challenge towards the discovery of new antiviral drugs and susceptibility factors of human diseases. New advances in the field are expected from systems-level modelling and integration of the incessant torrent of high-throughput "-omics" data.     

Systems biology approaches in cell signaling research

The use of methods for global and quantitative analysis of cells is providing new systems-level insights into signal transduction processes. Recent studies reveal important information about the rates of signal transmission and propagation, help establish some general regulatory characteristics of multi-tiered signaling cascades, and illuminate the combinatorial nature of signaling specificity in cell differentiation.     

Making space for rats: from synapse to place code

The formation of spatial memories is believed to depend on long-term potentiation (LTP) of synapses within the hippocampal network. In this issue of Neuron, Dragoi et al. demonstrate that LTP can cause changes in the hippocampal representation of space without disrupting network dynamics. These results help define the elusive relationship between cellular-level synaptic plasticity and systems-level neural coding.     

EGF-ERBB signalling: towards the systems level

Signalling through the ERBB/HER receptors is intricately involved in human cancer and already serves as a target for several cancer drugs. Because of its inherent complexity, it is useful to envision ERBB signalling as a bow-tie-configured, evolvable network, which shares modularity, redundancy and control circuits with robust biological and engineered systems. Because network fragility is an inevitable trade-off of robustness, systems-level understanding is expected to generate therapeutic opportunities to intercept aberrant network activation.     

Elements of a neurobiological theory of the hippocampus: the role of activity-dependent synaptic plasticity in memory

The hypothesis that synaptic plasticity is a critical component of the neural mechanisms underlying learning and memory is now widely accepted. In this article, we begin by outlining four criteria for evaluating the 'synaptic plasticity and memory (SPM)' hypothesis. We then attempt to lay the foundations for a specific neurobiological theory of hippocampal (HPC) function in which activity-dependent synaptic plasticity, such as long-term potentiation (LTP), plays a key part in the forms of memory mediated by this brain structure. HPC memory can, like other forms of memory, be divided into four processes: encoding, storage, consolidation and retrieval. We argue that synaptic plasticity is critical for the encoding and intermediate storage of memory traces that are automatically recorded in the hippocampus. These traces decay, but are sometimes retained by a process of cellular consolidation. However, we also argue that HPC synaptic plasticity is not involved in memory retrieval, and is unlikely to be involved in systems-level consolidation that depends on HPC-neocortical interactions, although neocortical synaptic plasticity does play a part. The information that has emerged from the worldwide focus on the mechanisms of induction and expression of plasticity at individual synapses has been very valuable in functional studies. Progress towards a comprehensive understanding of memory processing will also depend on the analysis of these synaptic changes within the context of a wider range of systems-level and cellular mechanisms of neuronal transmission and plasticity.     

脂质组学研究进展

综述了脂质组学的研究现状和发展趋势.脂质组学是对生物体、组织或细胞中的脂质以及与其相互作用的分子进行系统分析的一门新兴学科.脂质具有多种重要的生物功能,脂质代谢异常可引发诸多人类疾病,包括糖尿病、肥胖症、癌症以及神经退行性疾病等.目前,脂质组学研究已成为一个前景广阔的热门领域,并广泛地应用到包括药物研发、分子生理学、分子病理学、功能基因组学、营养学以及环境与健康等重要领域.     

Computational identification of obligatorily autocatalytic replicators embedded in metabolic networks

Background  

If chemical A is necessary for the synthesis of more chemical A, then A has the power of replication (such systems are known as autocatalytic systems). We provide the first systems-level analysis searching for small-molecular autocatalytic components in the metabolisms of diverse organisms, including an inferred minimal metabolism.     

The status of cognitive neuroscience.

Cognitive neuroscience rests on findings, methods, and theory from three fields: experimental psychology, systems-level neuroscience, and computer science. The strong trend over the past few years has been for a greater integration across these fields. The influence of this interdisciplinary approach on current research on memory, perception, and language will be illustrated.     

An analysis of normalization methods for Drosophila RNAi genomic screens and development of a robust validation scheme

Genome-wide RNA interference (RNAi) screening allows investigation of the role of individual genes in a process of choice. Most RNAi screens identify a large number of genes with a continuous gradient in the assessed phenotype. Screeners must decide whether to examine genes with the most robust phenotype or the full gradient of genes that cause an effect and how to identify candidate genes. The authors have used RNAi in Drosophila cells to examine viability in a 384-well plate format and compare 2 screens, untreated control and treatment. They compare multiple normalization methods, which take advantage of different features within the data, including quantile normalization, background subtraction, scaling, cellHTS2 (Boutros et al. 2006), and interquartile range measurement. Considering the false-positive potential that arises from RNAi technology, a robust validation method was designed for the purpose of gene selection for future investigations. In a retrospective analysis, the authors describe the use of validation data to evaluate each normalization method. Although no method worked ideally, a combination of 2 methods, background subtraction followed by quantile normalization and cellHTS2, at different thresholds, captures the most dependable and diverse candidate genes. Thresholds are suggested depending on whether a few candidate genes are desired or a more extensive systems-level analysis is sought. The normalization approaches and experimental design to perform validation experiments are likely to apply to those high-throughput screening systems attempting to identify genes for systems-level analysis.