Functional modules integrating essential cellular functions are predictive of the response of leukaemia cells to DNA damage

A Modular, Reconfigurable Microfabricated Assembly Platform for Microfluidic Transport and Multitype Cell Culture and Drug Testing

Modular microfluidics offer the opportunity to combine the precise fluid control, rapid sample processing, low sample and reagent volumes, and relatively lower cost of conventional microfluidics with the flexible reconfigurability needed to accommodate the requirements of target applications such as drug toxicity studies. However, combining the capabilities of fully adaptable modular microelectromechanical systems (MEMS) assembly with the simplicity of conventional microfluidic fabrication remains a challenge. A hybrid polydimethylsiloxane (PDMS)-molding/photolithographic process is demonstrated to rapidly fabricate LEGO®-like modular blocks. The blocks are created with different sizes that interlock via tongue-and-groove joints in the plane and stack via interference fits out of the plane. These miniature strong but reversible connections have a measured resistance to in-plane and out-of-plane forces of up to >6000× and >1000× the weight of the block itself, respectively. The LEGO®-like interference fits enable O-ring-free microfluidic connections that withstand internal fluid pressures of >120 kPa. A single layer of blocks is assembled into LEGO®-like cell culture plates, where the in vitro biocompatibility and drug toxicity to lung epithelial adenocarcinoma cells and hepatocellular carcinoma cells cultured in the modular microwells are measured. A double-layer block structure is then assembled so that a microchannel formed at the interface between layers connects two microwells. Breast tumor cells and hepatocytes cultured in the coupled wells demonstrate interwell migration as well as the simultaneous effects of a single drug on the two cell types.

Met-Controlled Allosteric Module of Neural Generation as A New Therapeutic Target in Rodent Brain Ischemia

OBJECTIVE

To investigate a Met-controlled allosteric module (AM) of neural generation as a potential therapeutic target for brain ischemia.

METHODS

We selected Markov clustering algorithm (MCL) to mine functional modules in the related target networks. According to the topological similarity, one functional module was predicted in the modules of baicalin (BA), jasminoidin (JA), cholic acid (CA), compared with I/R model modules. This functional module included three genes: Inppl1, Met and Dapk3 (IMD). By gene ontology enrichment analysis, biological process related to this functional module was obtained. This functional module participated in generation of neurons. Western blotting was applied to present the compound-dependent regulation of IMD. Co-immunoprecipitation was used to reveal the relationship among the three members. We used IF to determine the number of newborn neurons between compound treatment group and ischemia/reperfusion group. The expressions of vascular endothelial growth factor (VEGF) and matrix metalloproteinase 9 (MMP-9) were supposed to show the changing circumstances for neural generation under cerebral ischemia.

RESULTS

Significant reduction in infarction volume and pathological changes were shown in the compound treatment groups compared with the I/R model group (P<0.05). Three nodes in one novel module of IMD were found to exert diverse compound-dependent ischemic-specific excitatory regulatory activities. An anti-ischemic excitatory allosteric module (AM_E) of generation of neurons (AM_E-GN) was validated successfully in vivo. Newborn neurons increased in BJC treatment group (P<0.05). The expression of VEGF and MMP-9 decreased in the compound treatment groups compared with the I/R model group (P<0.05).

CONCLUSIONS

AM_E demonstrates effectiveness of our pioneering approach to the discovery of therapeutic target. The novel approach for AM discovery in an effort to identify therapeutic targets holds the promise of accelerating elucidation of underlying pharmacological mechanisms in cerebral ischemia.

Characterization of a d-Lactate Dehydrogenase from Lactobacillus fermentum JN248 with High Phenylpyruvate Reductive Activity

Phenyllactic acid (PLA) is a novel antimicrobial compound. A novel NADH-dependent d-lactate dehydrogenase (d-LDH), named as LF-d-LDH0653, with high phenylpyruvate (PPA) reducing activity was isolated from Lactobacillus fermentum JN248. Its optimum pH and temperature were 8.0 and 50 °C, respectively. The Michaelis-Menten constant (K_m ), turnover number (k_cat ), and catalytic efficiency (k_cat /K_m ) for NADH were 1.20 mmol/L, 67.39 s^-1 , and 56.16 (mmol/L)^-1 s^-1 , respectively. The (K_m ), (k_cat ), and (k_cat /K_m ) for phenylpyruvate were 1.68 mmol/L, 122.66 s^-1 , and 73.01 (mmol/L)^-1 s^-1 , respectively. This enzyme can catalyze phenylpyruvate and the product presented excellent optical purity (enantioselectivity >99%). The results suggest that LF-d-LDH0653 is a promising biocatalyst for the efficient synthesis of optically pure d-PLA.

PRACTICAL APPLICATION

A novel d-LDH with phenylpyruvate reducing activity has been isolated and identified. It could be used as a reference for improving the production of optically pure d-PLA. d-PLA has a potential for application as antimicrobial an agent in dairy industry and baking industry, pharmaceutical agent in medicine and cosmetics.

A systems biology approach reveals a link between systemic cytokines and skeletal muscle energy metabolism in a rodent smoking model and human COPD

Background

A relatively large percentage of patients with chronic obstructive pulmonary disease (COPD) develop systemic co-morbidities that affect prognosis, among which muscle wasting is particularly debilitating. Despite significant research effort, the pathophysiology of this important extrapulmonary manifestation is still unclear. A key question that remains unanswered is to what extent systemic inflammatory mediators might play a role in this pathology.

Cigarette smoke (CS) is the main risk factor for developing COPD and therefore animal models chronically exposed to CS have been proposed for mechanistic studies and biomarker discovery. Although mice have been successfully used as a pre-clinical in vivo model to study the pulmonary effects of acute and chronic CS exposure, data suggest that they may be inadequate models for studying the effects of CS on peripheral muscle function. In contrast, recent findings indicate that the guinea pig model (Cavia porcellus) may better mimic muscle wasting.

Methods

We have used a systems biology approach to compare the transcriptional profile of hindlimb skeletal muscles from a Guinea pig rodent model exposed to CS and/or chronic hypoxia to COPD patients with muscle wasting.

Results

We show that guinea pigs exposed to long-term CS accurately reflect most of the transcriptional changes observed in dysfunctional limb muscle of severe COPD patients when compared to matched controls. Using network inference, we could then show that the expression profile in whole lung of genes encoding for soluble inflammatory mediators is informative of the molecular state of skeletal muscles in the guinea pig smoking model. Finally, we show that CXCL10 and CXCL9, two of the candidate systemic cytokines identified using this pre-clinical model, are indeed detected at significantly higher levels in serum of COPD patients, and that their serum protein level is inversely correlated with the expression of aerobic energy metabolism genes in skeletal muscle.

Conclusions

We conclude that CXCL10 and CXCL9 are promising candidate inflammatory signals linked to the regulation of central metabolism genes in skeletal muscles. On a methodological level, our work also shows that a system level analysis of animal models of diseases can be very effective to generate clinically relevant hypothesis.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-014-0059-5) contains supplementary material, which is available to authorized users.

Spatiotemporal positioning of multipotent modules in diverse biological networks

A biological network exhibits a modular organization. The modular structure dependent on functional module is of great significance in understanding the organization and dynamics of network functions. A huge variety of module identification methods as well as approaches to analyze modularity and dynamics of the inter- and intra-module interactions have emerged recently, but they are facing unexpected challenges in further practical applications. Here, we discuss recent progress in understanding how such a modular network can be deconstructed spatiotemporally. We focus particularly on elucidating how various deciphering mechanisms operate to ensure precise module identification and assembly. In this case, a system-level understanding of the entire mechanism of module construction is within reach, with important implications for reasonable perspectives in both constructing a modular analysis framework and deconstructing different modular hierarchical structures.

Core modular blood and brain biomarkers in social defeat mouse model for post traumatic stress disorder

Background

Post-traumatic stress disorder (PTSD) is a severe anxiety disorder that affects a substantial portion of combat veterans and poses serious consequences to long-term health. Consequently, the identification of diagnostic and prognostic blood biomarkers for PTSD is of great interest. Previously, we assessed genome-wide gene expression of seven brain regions and whole blood in a social defeat mouse model subjected to various stress conditions.

Results

To extract biological insights from these data, we have applied a new computational framework for identifying gene modules that are activated in common across blood and various brain regions. Our results, in the form of modular gene networks that highlight spatial and temporal biological functions, provide a systems-level molecular description of response to social stress. Specifically, the common modules discovered between the brain and blood emphasizes molecular transporters in the blood-brain barrier, and the associated genes have significant overlaps with known blood signatures for PTSD, major depression, and bipolar disease. Similarly, the common modules specific to the brain highlight the components of the social defeat stress response (e.g., fear conditioning pathways) in each brain sub-region.

Conclusions

Many of the brain-specific genes discovered are consistent with previous independent studies of PTSD or other mental illnesses. The results from this study further our understanding of the mechanism of stress response and contribute to a growing list of diagnostic biomarkers for PTSD.

Knowledge management for systems biology a general and visually driven framework applied to translational medicine

BACKGROUND

To enhance our understanding of complex biological systems like diseases we need to put all of the available data into context and use this to detect relations, pattern and rules which allow predictive hypotheses to be defined. Life science has become a data rich science with information about the behaviour of millions of entities like genes, chemical compounds, diseases, cell types and organs, which are organised in many different databases and/or spread throughout the literature. Existing knowledge such as genotype-phenotype relations or signal transduction pathways must be semantically integrated and dynamically organised into structured networks that are connected with clinical and experimental data. Different approaches to this challenge exist but so far none has proven entirely satisfactory.

RESULTS

To address this challenge we previously developed a generic knowledge management framework, BioXM™, which allows the dynamic, graphic generation of domain specific knowledge representation models based on specific objects and their relations supporting annotations and ontologies. Here we demonstrate the utility of BioXM for knowledge management in systems biology as part of the EU FP6 BioBridge project on translational approaches to chronic diseases. From clinical and experimental data, text-mining results and public databases we generate a chronic obstructive pulmonary disease (COPD) knowledge base and demonstrate its use by mining specific molecular networks together with integrated clinical and experimental data.

CONCLUSIONS

We generate the first semantically integrated COPD specific public knowledge base and find that for the integration of clinical and experimental data with pre-existing knowledge the configuration based set-up enabled by BioXM reduced implementation time and effort for the knowledge base compared to similar systems implemented as classical software development projects. The knowledgebase enables the retrieval of sub-networks including protein-protein interaction, pathway, gene--disease and gene--compound data which are used for subsequent data analysis, modelling and simulation. Pre-structured queries and reports enhance usability; establishing their use in everyday clinical settings requires further simplification with a browser based interface which is currently under development.

Mapping drug physico-chemical features to pathway activity reveals molecular networks linked to toxicity outcome

The identification of predictive biomarkers is at the core of modern toxicology. So far, a number of approaches have been proposed. These rely on statistical inference of toxicity response from either compound features (i.e., QSAR), in vitro cell based assays or molecular profiling of target tissues (i.e., expression profiling). Although these approaches have already shown the potential of predictive toxicology, we still do not have a systematic approach to model the interaction between chemical features, molecular networks and toxicity outcome. Here, we describe a computational strategy designed to address this important need. Its application to a model of renal tubular degeneration has revealed a link between physico-chemical features and signalling components controlling cell communication pathways, which in turn are differentially modulated in response to toxic chemicals. Overall, our findings are consistent with the existence of a general toxicity mechanism operating in synergy with more specific single-target based mode of actions (MOAs) and provide a general framework for the development of an integrative approach to predictive toxicology.

Infection of bovine dendritic cells by rinderpest or measles viruses induces different changes in host transcription

The morbilliviruses are a closely related genus which are very similar in their sequences and share a common receptor, but nevertheless show significant restriction in the host species in which they cause disease. One contribution to this restriction might be the nature of the hosts' responses to infection. We have used microarrays to study the changes in the transcriptome of bovine dendritic cells after infection with wild-type (pathogenic) and vaccine (apathogenic) strains of rinderpest virus (RPV), a bovine pathogen, and a wild-type isolate of measles virus (MV), a morbillivirus that causes disease only in humans and some other primates. We found that, as previously observed in human cells, MV induces a rapid interferon response, while that induced by RPV was delayed and much reduced in magnitude. Pathogenic and apathogenic RPV also showed significant differences, with the latter inducing a slightly higher interferon response as well as significant effects on transcription of genes involved in cell cycle regulation.