A densely interconnected genome-wide network of microRNAs and oncogenic pathways revealed using gene expression signatures

MicroRNAs (miRNAs) are important components of cellular signaling pathways, acting either as pathway regulators or pathway targets. Currently, only a limited number of miRNAs have been functionally linked to specific signaling pathways. Here, we explored if gene expression signatures could be used to represent miRNA activities and integrated with genomic signatures of oncogenic pathway activity to identify connections between miRNAs and oncogenic pathways on a high-throughput, genome-wide scale. Mapping >300 gene expression signatures to >700 primary tumor profiles, we constructed a genome-wide miRNA-pathway network predicting the associations of 276 human miRNAs to 26 oncogenic pathways. The miRNA-pathway network confirmed a host of previously reported miRNA/pathway associations and uncovered several novel associations that were subsequently experimentally validated. Globally, the miRNA-pathway network demonstrates a small-world, but not scale-free, organization characterized by multiple distinct, tightly knit modules each exhibiting a high density of connections. However, unlike genetic or metabolic networks typified by only a few highly connected nodes ("hubs"), most nodes in the miRNA-pathway network are highly connected. Sequence-based computational analysis confirmed that highly-interconnected miRNAs are likely to be regulated by common pathways to target similar sets of downstream genes, suggesting a pervasive and high level of functional redundancy among coexpressed miRNAs. We conclude that gene expression signatures can be used as surrogates of miRNA activity. Our strategy facilitates the task of discovering novel miRNA-pathway connections, since gene expression data for multiple normal and disease conditions are abundantly available.     

microRNAs in stroke pathogenesis

Stroke is one of the leading causes of death and disability worldwide. There are two major types of stroke: cerebral ischemia caused by obstruction of blood vessels in the brain and haemorrhagic stroke that is triggered by the disruption of blood vessels. Thrombolytic therapy involving recombinant tissue plasminogen activator (rtPA) has been shown to be beneficial only when used within 4.5 hours of onset of acute ischemic stroke. rtPA treatment beyond this time window has been found to be unsuitable and usually resulting in haemorrhagic transformation. Stroke is a multifactorial disease that forms a possible end state for majority of patients suffering from diabetes, atherosclerosis and hypertension which are known risk factors. Although the biochemistry of stroke and related diseases is quite well understood, the knowledge on the molecular mechanisms underlying these diseases is still at its infancy. microRNAs that form a unique class of endogenous riboregulators of gene function, offer tremendous potential in unraveling the mechanisms underlying stroke pathogenesis. microRNA expression also reflects the response of individuals to drugs and therapy. Several microRNAs and their target genes, known to be involved in endothelial dysfunction, dysregulation of neurovascular integrity, edema formation, pro-apoptosis, inflammation and extra-cellular matrix remodeling contribute to the critical processes in the pathogenesis of stroke. In this review, we will also be discussing the role of microRNAs as possible diagnostic and prognostic biomarkers as well as potential therapeutic targets in stroke pathogenesis.     

A method for handling metabonomics data from liquid chromatography/mass spectrometry: combinational use of support vector machine recursive feature elimination, genetic algorithm and random forest for feature selection

Metabolic markers are the core of metabonomic surveys. Hence selection of differential metabolites is of great importance for either biological or clinical purpose. Here, a feature selection method was developed for complex metabonomic data set. As an effective tool for metabonomics data analysis, support vector machine (SVM) was employed as the basic classifier. To find out meaningful features effectively, support vector machine recursive feature elimination (SVM-RFE) was firstly applied. Then, genetic algorithm (GA) and random forest (RF) which consider the interaction among the metabolites and independent performance of each metabolite in all samples, respectively, were used to obtain more informative metabolic difference and avoid the risk of false positive. A data set from plasma metabonomics study of rat liver diseases developed from hepatitis, cirrhosis to hepatocellular carcinoma was applied for the validation of the method. Besides the good classification results for 3 kinds of liver diseases, 31 important metabolites including lysophosphatidylethanolamine (LPE) C16:0, palmitoylcarnitine, lysophosphatidylethanolamine (LPC) C18:0 were also selected for further studies. A better complementary effect of the three feature selection methods could be seen from the current results. The combinational method also represented more differential metabolites and provided more metabolic information for a “global” understanding of diseases than any single method. Further more, this method is also suitable for other complex biological data sets.     

The role of CXCR7 on the adhesion, proliferation and angiogenesis of endothelial progenitor cells

Previous studies confirmed that stromal cell-derived factor 1 (SDF-1) was a principal regulator of retention, migration and mobilization of haematopoietic stem cells and endothelial progenitor cells (EPCs) during steady-state homeostasis and injury. CXC chemokine receptor 4 (CXCR4) has been considered as the unique receptor of SDF-1 and as the only mediator of SDF-1-induced biological effects for many years. However, recent studies found that SDF-1 could bind to not only CXCR4 but also CXC chemokine receptor 7 (CXCR7). The evidence that SDF-1 binds to the CXCR7 raises a concern how to distinguish the potential contribution of the SDF-1/CXCR7 pathway from SDF-1/CXCR4 pathway in all the processes that were previously attributed to SDF-1/CXCR4. In this study, the role of CXCR7 in EPCs was investigated in vitro. RT-PCR, Western blot and flow cytometry assay demonstrate that both CXCR4 and CXCR7 were expressed highly in EPCs. The adhesion of EPCs induced by SDF-1 was inhibited by blocking either CXCR4 or CXCR7 with their antibodies or antagonists. SDF-1 regulated the migration of EPCs via CXCR4 but not CXCR7. However, the transendothelial migration of EPCs was inhibited by either blocking of CXCR4 or CXCR7. Both CXCR7 and CXCR4 are essential for the tube formation of EPCs induced by SDF-1. These results suggested that both CXCR7 and CXCR4 are important for EPCs in response to SDF-1, indicating that CXCR7 may be another potential target molecule for angiogenesis-dependent diseases.     

Micropilot: automation of fluorescence microscopy-based imaging for systems biology

Quantitative microscopy relies on imaging of large cell numbers but is often hampered by time-consuming manual selection of specific cells. The 'Micropilot' software automatically detects cells of interest and launches complex imaging experiments including three-dimensional multicolor time-lapse or fluorescence recovery after photobleaching in live cells. In three independent experimental setups this allowed us to statistically analyze biological processes in detail and is thus a powerful tool for systems biology.     

Computation of steady-state probability distributions in stochastic models of cellular networks

Cellular processes are "noisy". In each cell, concentrations of molecules are subject to random fluctuations due to the small numbers of these molecules and to environmental perturbations. While noise varies with time, it is often measured at steady state, for example by flow cytometry. When interrogating aspects of a cellular network by such steady-state measurements of network components, a key need is to develop efficient methods to simulate and compute these distributions. We describe innovations in stochastic modeling coupled with approaches to this computational challenge: first, an approach to modeling intrinsic noise via solution of the chemical master equation, and second, a convolution technique to account for contributions of extrinsic noise. We show how these techniques can be combined in a streamlined procedure for evaluation of different sources of variability in a biochemical network. Evaluation and illustrations are given in analysis of two well-characterized synthetic gene circuits, as well as a signaling network underlying the mammalian cell cycle entry.     

GC (Guanine‐Cytosine)‐Selective DNA‐Binding and Antitumor Activity of a Quercetin?Manganese(II) Complex

The DNA binding and cleavage properties of quercetin? manganese(II) complexes have been studied, but little attention has been devoted to the relationship between the antitumor activity of these complexes and the DNA‐binding properties. Here, the DNA binding properties of the quercetin? manganese(II) complex [Mn(Que)₂(H₂O)₂] were studied using UV/VIS and fluorescence spectroscopy and viscosity measurements. The results indicate that the complex was preferentially bound to DNA in the GC (guanine? cytosine)‐rich regions via an intercalative mode. Furthermore, the cytotoxicity experiments confirmed its apoptosis‐inducing activity. We also demonstrated that the levels of survivin protein expression in HepG2 cells decreased and that the relative activity of caspase‐3 significantly increased after treatment with the complex. Hence, our results suggest that the antitumor activity of the [Mn(Que)₂(H₂O)₂] complex might be related to its intercalation into DNA and its DNA‐binding selectivity.     

mTOR drives its own activation via SCF(βTrCP)-dependent degradation of the mTOR inhibitor DEPTOR

The activities of both mTORC1 and mTORC2 are negatively regulated by their endogenous inhibitor, DEPTOR. As such, the abundance of DEPTOR is a critical determinant in the activity status of the mTOR network. DEPTOR stability is governed by the 26S-proteasome through a largely unknown mechanism. Here we describe an mTOR-dependent phosphorylation-driven pathway for DEPTOR destruction via SCF(βTrCP). DEPTOR phosphorylation by mTOR in response to growth signals, and in collaboration with casein kinase I (CKI), generates a phosphodegron that binds βTrCP. Failure to degrade DEPTOR through either degron mutation or βTrCP depletion leads to reduced mTOR activity, reduced S6 kinase activity, and activation of autophagy to reduce cell growth. This work expands the current understanding of mTOR regulation by revealing a positive feedback loop involving mTOR and CKI-dependent turnover of its inhibitor, DEPTOR, suggesting that misregulation of the DEPTOR destruction pathway might contribute to aberrant activation of mTOR in disease.     

First report of the benthic dinoflagellate,Gambierdiscus belizeanus (Gonyaulacales: Dinophyceae) for the east coast of Sabah,Malaysian Borneo

Species of the genus Gambierdiscus Adachi & Fukuyo, in particular G. toxicus Adachi & Fukuyo are known producers of neurotoxins associated with ciguatera fish poisoning (CFP). In this study live samples were collected from seaweed beds of the east coast of Sabah, Malaysian Borneo and a strain of Gambierdiscus was isolated and cultured. Examination of the thecal fine morphology was undertaken using light, epifluorescence, and scanning electron microscopy. Observed morphological features and their associated morphometric information enabled identification to Gambierdiscus belizeanus Faust. This represents the first report for the occurrence of G. belizeanus in the Asia Pacific region.