HIHISIV: a database of gene expression in HIV and SIV host immune response.

In the battle of the host against lentiviral pathogenesis, the immune response is crucial. However, several questions remain unanswered about the interaction with different viruses and their influence on disease progression. The simian immunodeficiency virus (SIV) infecting nonhuman primates (NHP) is widely used as a model for the study of the human immunodeficiency virus (HIV) both because they are evolutionarily linked and because they share physiological and anatomical similarities that are largely explored to understand the disease progression. The HIHISIV database was developed to support researchers to integrate and evaluate the large number of transcriptional data associated with the presence/absence of the pathogen (SIV or HIV) and the host response (NHP and human). The datasets are composed of microarray and RNA-Seq gene expression data that were selected, curated, analyzed, enriched, and stored in a relational database. Six query templates comprise the main data analysis functions and the resulting information can be downloaded. The HIHISIV database, available at  https://hihisiv.github.io , provides accurate resources for browsing and visualizing results and for more robust analyses of pre-existing data in transcriptome repositories.

SubcellulaRVis: a web-based tool to simplify and visualise subcellular compartment enrichment.

Cells contain intracellular compartments, including membrane-bound organelles and the nucleus, and are surrounded by a plasma membrane. Proteins are localised to one or more of these cellular compartments; the correct localisation of proteins is crucial for their correct processing and function. Moreover, proteins and the cellular processes they partake in are regulated by relocalisation in response to various cellular stimuli. High-throughput 'omics experiments result in a list of proteins or genes of interest; one way in which their functional role can be understood is through the knowledge of their subcellular localisation, as deduced through statistical enrichment for Gene Ontology Cellular Component (GOCC) annotations or similar. We have designed a bioinformatics tool, named SubcellulaRVis, that compellingly visualises the results of GOCC enrichment for quick interpretation of the localisation of a group of proteins (rather than single proteins). We demonstrate that SubcellulaRVis precisely describes the subcellular localisation of gene lists whose locations have been previously ascertained. SubcellulaRVis can be accessed via the web (http://phenome.manchester.ac.uk/subcellular/) or as a stand-alone app (https://github.com/JoWatson2011/subcellularvis). SubcellulaRVis will be useful for experimental biologists with limited bioinformatics expertise who want to analyse data related to protein (re)localisation and location-specific modules within the intracellular protein network.

Transcriptional Profile of the Industrial Hybrid Saccharomyces pastorianus Reveals Temperature-Dependent Allele Expression Bias and Preferential Orthologous Protein Assemblies.

Saccharomyces pastorianus is a natural yeast evolved from different hybridization events between the mesophilic S. cerevisiae and the cold-tolerant S. eubayanus. This complex aneuploid hybrid carries multiple copies of the parental alleles alongside specific hybrid genes and encodes for multiple protein isoforms which impart novel phenotypes, such as the strong ability to ferment at low temperature. These characteristics lead to agonistic competition for substrates and a plethora of biochemical activities, resulting in a unique cellular metabolism. Here, we investigated the transcriptional signature of the different orthologous alleles in S. pastorianus during temperature shifts. We identified temperature-dependent media-independent genes and showed that 35% has their regulation dependent on extracellular leucine uptake, suggesting an interplay between leucine metabolism and temperature response. The analysis of the expression of ortholog parental alleles unveiled that the majority of the genes expresses preferentially one parental allele over the other and that S. eubayanus-like alleles are significantly over-represented among the genes involved in the cold acclimatization. The presence of functionally redundant parental alleles may impact on the nature of protein complexes established in the hybrid, where both parental alleles are competing. Our expression data indicate that the majority of the protein complexes investigated in the hybrid are likely to be either exclusively chimeric or unispecific and that the redundancy is discouraged, a scenario that fits well with the gene balance hypothesis. This study offers the first overview of the transcriptional pattern of S. pastorianus and provides a rationalization for its unique industrial traits at the expression level.

Cytosolic fumarase acts as a metabolic fail-safe for both high and low temperature acclimation of Arabidopsis thaliana.

Plants acclimate their photosynthetic capacity (Pmax) in response to changing environmental conditions. In Arabidopsis thaliana, photosynthetic acclimation to cold requires the accumulation of the organic acid fumarate, catalysed by a cytosolically localized fumarase, FUM2. However, the role of this accumulation is currently unknown. Here, we use an integrated experimental and modelling approach to examine the role of FUM2 and fumarate across the physiological temperature range. We have studied three genotypes: Col-0; a fum2 mutant in a Col-0 background; and C24, an accession with reduced FUM2 expression. While low temperature causes an increase in Pmax in the Col-0 plants, this parameter decreases following exposure of plants to 30 °C for 7 d. Plants in which fumarate accumulation is partially (C24) or completely (fum2) abolished show a reduced acclimation of Pmax across the physiological temperature range (i.e. Pmax changes less in response to changing temperature). To understand the role of fumarate accumulation, we have adapted a reliability engineering technique, Failure Mode and Effect Analysis (FMEA), to formalize a rigorous approach for ranking metabolites according to the potential risk that they pose to the metabolic system. FMEA identifies fumarate as a low-risk metabolite, while its precursor, malate, is shown to be high risk and liable to cause system instability. We propose that the role of FUM2 is to provide a fail-safe in order to control malate concentration, maintaining system stability in a changing environment. We suggest that FMEA is a technique that is not only useful in understanding plant metabolism but can also be used to study reliability in other systems and synthetic pathways.

Combinatorial mathematical modelling approaches to interrogate rear retraction dynamics in 3D cell migration.

Cell migration in 3D microenvironments is a complex process which depends on the coordinated activity of leading edge protrusive force and rear retraction in a push-pull mechanism. While the potentiation of protrusions has been widely studied, the precise signalling and mechanical events that lead to retraction of the cell rear are much less well understood, particularly in physiological 3D extra-cellular matrix (ECM). We previously discovered that rear retraction in fast moving cells is a highly dynamic process involving the precise spatiotemporal interplay of mechanosensing by caveolae and signalling through RhoA. To further interrogate the dynamics of rear retraction, we have adopted three distinct mathematical modelling approaches here based on (i) Boolean logic, (ii) deterministic kinetic ordinary differential equations (ODEs) and (iii) stochastic simulations. The aims of this multi-faceted approach are twofold: firstly to derive new biological insight into cell rear dynamics via generation of testable hypotheses and predictions; and secondly to compare and contrast the distinct modelling approaches when used to describe the same, relatively under-studied system. Overall, our modelling approaches complement each other, suggesting that such a multi-faceted approach is more informative than methods based on a single modelling technique to interrogate biological systems. Whilst Boolean logic was not able to fully recapitulate the complexity of rear retraction signalling, an ODE model could make plausible population level predictions. Stochastic simulations added a further level of complexity by accurately mimicking previous experimental findings and acting as a single cell simulator. Our approach highlighted the unanticipated role for CDK1 in rear retraction, a prediction we confirmed experimentally. Moreover, our models led to a novel prediction regarding the potential existence of a 'set point' in local stiffness gradients that promotes polarisation and rapid rear retraction.

XBP1 signalling is essential for alleviating mutant protein aggregation in ER-stress related skeletal disease.

The unfolded protein response (UPR) is a conserved cellular response to the accumulation of proteinaceous material in endoplasmic reticulum (ER), active both in health and disease to alleviate cellular stress and improve protein folding. Multiple epiphyseal dysplasia (EDM5) is a genetic skeletal condition and a classic example of an intracellular protein aggregation disease, whereby mutant matrilin-3 forms large insoluble aggregates in the ER lumen, resulting in a specific 'disease signature' of increased expression of chaperones and foldases, and alternative splicing of the UPR effector XBP1. Matrilin-3 is expressed exclusively by chondrocytes thereby making EDM5 a perfect model system to study the role of protein aggregation in disease. In order to dissect the role of XBP1 signalling in aggregation-related conditions we crossed a p.V194D Matn3 knock-in mouse model of EDM5 with a mouse line carrying a cartilage specific deletion of XBP1 and analysed the resulting phenotype. Interestingly, the growth of mice carrying the Matn3 p.V194D mutation compounded with the cartilage specific deletion of XBP1 was severely retarded. Further phenotyping revealed increased intracellular retention of amyloid-like aggregates of mutant matrilin-3 coupled with dramatically decreased cell proliferation and increased apoptosis, suggesting a role of XBP1 signalling in protein accumulation and/or degradation. Transcriptomic analysis of chondrocytes extracted from wild type, EDM5, Xbp1-null and compound mutant lines revealed that the alternative splicing of Xbp1 is crucial in modulating levels of protein aggregation. Moreover, through detailed transcriptomic comparison with a model of metaphyseal chondrodysplasia type Schmid (MCDS), an UPR-related skeletal condition in which XBP1 was removed without overt consequences, we show for the first time that the differentiation-state of cells within the cartilage growth plate influences the UPR resulting from retention of a misfolded mutant protein and postulate that modulation of XBP1 signalling pathway presents a therapeutic target for aggregation related conditions in cells undergoing proliferation.

Using Multilayer Heterogeneous Networks to Infer Functions of Phosphorylated Sites.

Mass spectrometry-based quantitative phosphoproteomics has become an essential approach in the study of cellular processes such as signaling. Commonly used methods to analyze phosphoproteomics datasets depend on generic, gene-centric annotations such as Gene Ontology terms, which do not account for the function of a protein in a particular phosphorylation state. Analysis of phosphoproteomics data is hampered by a lack of phosphorylated site-specific annotations. We propose a method that combines shotgun phosphoproteomics data, protein-protein interactions, and functional annotations into a heterogeneous multilayer network. Phosphorylation sites are associated to potential functions using a random walk on the heterogeneous network (RWHN) algorithm. We validated our approach against a model of the MAPK/ERK pathway and functional annotations from PhosphoSitePlus and were able to associate differentially regulated sites on the same proteins to their previously described specific functions. We further tested the algorithm on three previously published datasets and were able to reproduce their experimentally validated conclusions and to associate phosphorylation sites with known functions based on their regulatory patterns. Our approach provides a refinement of commonly used analysis methods and accurately predicts context-specific functions for sites with similar phosphorylation profiles.

Mechanical loading activates the YAP/TAZ pathway and chemokine expression in the MLO-Y4 osteocyte-like cell line.

Osteocytes are mechanosensitive cells that control bone remodeling in response to mechanical loading. To date, specific signaling pathways modulated by mechanical loading in osteocytes are not well understood. Yes associated protein (YAP) and transcriptional coactivator with PDZ-binding motif (TAZ), the main effectors of the Hippo pathway, are reported to play a role in mechanotransduction and during osteoblastogenesis. Here, we hypothesized that YAP/TAZ signaling mediates osteocyte mechanosensing to target genes of the bone remodeling process. We aimed to investigate the contribution of YAP/TAZ in modulating the gene expression in an osteocyte-like cell line MLO-Y4. We developed a 3D osteocyte compression culture model from an MLO-Y4 osteocyte cell line embedded in concentrated collagen hydrogel. 3D-mechanical loading led to the increased expression of mechanosensitive genes and a subset of chemokines, including M-csf, Cxcl1, Cxcl2, Cxcl3, Cxcl9, and Cxcl10. The transcription regulators YAP and TAZ translocated to the nucleus and upregulated their target genes and proteins. RNAseq analysis revealed that YAP/TAZ knockdown mediated the regulation of several genes including osteocyte dendrite formation. Use of YAP/TAZ knockdown partially blunted the increase in M-csf and Cxcl3 levels in response to MLO-Y4 compression. These findings demonstrate that YAP/TAZ signaling is required for osteocyte-like cell mechano-transduction, regulates the gene expression profiles and controls chemokine expression.

Metabolic flux from the chloroplast provides signals controlling photosynthetic acclimation to cold in Arabidopsis thaliana.

Photosynthesis is especially sensitive to environmental conditions, and the composition of the photosynthetic apparatus can be modulated in response to environmental change, a process termed photosynthetic acclimation. Previously, we identified a role for a cytosolic fumarase, FUM2 in acclimation to low temperature in Arabidopsis thaliana. Mutant lines lacking FUM2 were unable to acclimate their photosynthetic apparatus to cold. Here, using gas exchange measurements and metabolite assays of acclimating and non-acclimating plants, we show that acclimation to low temperature results in a change in the distribution of photosynthetically fixed carbon to different storage pools during the day. Proteomic analysis of wild-type Col-0 Arabidopsis and of a fum2 mutant, which was unable to acclimate to cold, indicates that extensive changes occurring in response to cold are affected in the mutant. Metabolic and proteomic data were used to parameterize metabolic models. Using an approach called flux sampling, we show how the relative export of triose phosphate and 3-phosphoglycerate provides a signal of the chloroplast redox state that could underlie photosynthetic acclimation to cold.

SkeletalVis: an exploration and meta-analysis data portal of cross-species skeletal transcriptomics data.

MOTIVATION: Skeletal diseases are prevalent in society, but improved molecular understanding is required to formulate new therapeutic strategies. Large and increasing quantities of available skeletal transcriptomics experiments give the potential for mechanistic insight of both fundamental skeletal biology and skeletal disease. However, no current repository provides access to processed, readily interpretable analysis of this data. To address this, we have developed SkeletalVis, an exploration portal for skeletal gene expression experiments. RESULTS: The SkeletalVis data portal provides an exploration and comparison platform for analysed skeletal transcriptomics data. It currently hosts 287 analysed experiments with 739 perturbation responses with comprehensive downstream analysis. We demonstrate its utility in identifying both known and novel relationships between skeletal expression signatures. SkeletalVis provides users with a platform to explore the wealth of available expression data, develop consensus signatures and the ability to compare gene signatures from new experiments to the analysed data to facilitate meta-analysis. AVAILABILITY AND IMPLEMENTATION: The SkeletalVis data portal is freely accessible at http://phenome.manchester.ac.uk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

From empirical to theoretical models of light response curves - linking photosynthetic and metabolic acclimation.

Light response curves (LRCs) describe how the rate of photosynthesis varies as a function of light. They provide information on the maximum photosynthetic capacity, quantum yield, light compensation point and leaf radiation use efficiency of leaves. Light response curves are widely used to capture photosynthetic phenotypes in response to changing environmental conditions. However, models describing these are predominantly empirical and do not attempt to explain behaviour at a mechanistic level. Here, we use modelling to understand the metabolic changes required for photosynthetic acclimation to changing environmental conditions. Using a simple kinetic model, we predicted LRCs across the physiological temperature range of Arabidopsis thaliana and confirm these using experimental data. We use our validated metabolic model to make novel predictions about the metabolic changes of temperature acclimation. We demonstrate that NADPH utilization are enhanced in warm-acclimated plants, whereas both NADPH and CO2 utilization is enhanced in cold-acclimated plants. We demonstrate how different metabolic acclimation strategies may lead to the same photosynthetic response across environmental change. We further identify that certain metabolic acclimation strategies, such as NADPH utilization, are only triggered when plants are moved beyond a threshold high or low temperature.

Why COVID-19 models should incorporate the network of social interactions.

The global spread of coronavirus disease 2019 (COVID-19) is overwhelming many health-care systems. As a result, epidemiological models are being used to inform policy on how to effectively deal with this pandemic. The majority of existing models assume random diffusion but do not take into account differences in the amount of interactions between individuals, i.e. the underlying human interaction network, whose structure is known to be scale-free. Here, we demonstrate how this network of interactions can be used to predict the spread of the virus and to inform policy on the most successful mitigation and suppression strategies. Using stochastic simulations in a scale-free network, we show that the epidemic can propagate for a long time at a low level before the number of infected individuals suddenly increases markedly, and that this increase occurs shortly after the first hub is infected. We further demonstrate that mitigation strategies that target hubs are far more effective than strategies that randomly decrease the number of connections between individuals. Although applicable to infectious disease modelling in general, our results emphasize how network science can improve the predictive power of current COVID-19 epidemiological models.

The chaperone activity of 4PBA ameliorates the skeletal phenotype of Chihuahua, a zebrafish model for dominant osteogenesis imperfecta.

Classical osteogenesis imperfecta (OI) is a bone disease caused by type I collagen mutations and characterized by bone fragility, frequent fractures in absence of trauma and growth deficiency. No definitive cure is available for OI and to develop novel drug therapies, taking advantage of a repositioning strategy, the small teleost zebrafish (Danio rerio) is a particularly appealing model. Its small size, high proliferative rate, embryo transparency and small amount of drug required make zebrafish the model of choice for drug screening studies, when a valid disease model is available. We performed a deep characterization of the zebrafish mutant Chihuahua, that carries a G574D (p.G736D) substitution in the α1 chain of type I collagen. We successfully validated it as a model for classical OI. Growth of mutants was delayed compared with WT. X-ray, µCT, alizarin red/alcian blue and calcein staining revealed severe skeletal deformity, presence of fractures and delayed mineralization. Type I collagen extracted from different tissues showed abnormal electrophoretic migration and low melting temperature. The presence of endoplasmic reticulum (ER) enlargement due to mutant collagen retention in osteoblasts and fibroblasts of mutant fish was shown by electron and confocal microscopy. Two chemical chaperones, 4PBA and TUDCA, were used to ameliorate the cellular stress and indeed 4PBA ameliorated bone mineralization in larvae and skeletal deformities in adult, mainly acting on reducing ER cisternae size and favoring collagen secretion. In conclusion, our data demonstrated that ER stress is a novel target to ameliorate OI phenotype; chemical chaperones such as 4PBA may be, alone or in combination, a new class of molecules to be further investigated for OI treatment.

A quantitative brain map of experimental cerebral malaria pathology.

The murine model of experimental cerebral malaria (ECM) has been utilised extensively in recent years to study the pathogenesis of human cerebral malaria (HCM). However, it has been proposed that the aetiologies of ECM and HCM are distinct, and, consequently, no useful mechanistic insights into the pathogenesis of HCM can be obtained from studying the ECM model. Therefore, in order to determine the similarities and differences in the pathology of ECM and HCM, we have performed the first spatial and quantitative histopathological assessment of the ECM syndrome. We demonstrate that the accumulation of parasitised red blood cells (pRBCs) in brain capillaries is a specific feature of ECM that is not observed during mild murine malaria infections. Critically, we show that individual pRBCs appear to occlude murine brain capillaries during ECM. As pRBC-mediated congestion of brain microvessels is a hallmark of HCM, this suggests that the impact of parasite accumulation on cerebral blood flow may ultimately be similar in mice and humans during ECM and HCM, respectively. Additionally, we demonstrate that cerebrovascular CD8+ T-cells appear to co-localise with accumulated pRBCs, an event that corresponds with development of widespread vascular leakage. As in HCM, we show that vascular leakage is not dependent on extensive vascular destruction. Instead, we show that vascular leakage is associated with alterations in transcellular and paracellular transport mechanisms. Finally, as in HCM, we observed axonal injury and demyelination in ECM adjacent to diverse vasculopathies. Collectively, our data therefore shows that, despite very different presentation, and apparently distinct mechanisms, of parasite accumulation, there appear to be a number of comparable features of cerebral pathology in mice and in humans during ECM and HCM, respectively. Thus, when used appropriately, the ECM model may be useful for studying specific pathological features of HCM.

Metabolic acclimation-a key to enhancing photosynthesis in changing environments?

Plants adjust their photosynthetic capacity in response to their environment in a way that optimizes their yield and fitness. There is growing evidence that this acclimation is a response to changes in the leaf metabolome, but the extent to which these are linked and how this is optimized remain poorly understood. Using as an example the metabolic perturbations occurring in response to cold, we define the different stages required for acclimation, discuss the evidence for a metabolic temperature sensor, and suggest further work towards designing climate-smart crops. In particular, we discuss how constraint-based and kinetic metabolic modelling approaches can be used to generate targeted hypotheses about relevant pathways, and argue that a stronger integration of experimental and in silico studies will help us to understand the tightly regulated interplay of carbon partitioning and resource allocation required for photosynthetic acclimation to different environmental conditions.

Using set theory to reduce redundancy in pathway sets.

BACKGROUND: The consolidation of pathway databases, such as KEGG, Reactome and ConsensusPathDB, has generated widespread biological interest, however the issue of pathway redundancy impedes the use of these consolidated datasets. Attempts to reduce this redundancy have focused on visualizing pathway overlap or merging pathways, but the resulting pathways may be of heterogeneous sizes and cover multiple biological functions. Efforts have also been made to deal with redundancy in pathway data by consolidating enriched pathways into a number of clusters or concepts. We present an alternative approach, which generates pathway subsets capable of covering all of genes presented within either pathway databases or enrichment results, generating substantial reductions in redundancy. RESULTS: We propose a method that uses set cover to reduce pathway redundancy, without merging pathways. The proposed approach considers three objectives: removal of pathway redundancy, controlling pathway size and coverage of the gene set. By applying set cover to the ConsensusPathDB dataset we were able to produce a reduced set of pathways, representing 100% of the genes in the original data set with 74% less redundancy, or 95% of the genes with 88% less redundancy. We also developed an algorithm to simplify enrichment data and applied it to a set of enriched osteoarthritis pathways, revealing that within the top ten pathways, five were redundant subsets of more enriched pathways. Applying set cover to the enrichment results removed these redundant pathways allowing more informative pathways to take their place. CONCLUSION: Our method provides an alternative approach for handling pathway redundancy, while ensuring that the pathways are of homogeneous size and gene coverage is maximised. Pathways are not altered from their original form, allowing biological knowledge regarding the data set to be directly applicable. We demonstrate the ability of the algorithms to prioritise redundancy reduction, pathway size control or gene set coverage. The application of set cover to pathway enrichment results produces an optimised summary of the pathways that best represent the differentially regulated gene set.

Stratification of knee osteoarthritis: two major patient subgroups identified by genome-wide expression analysis of articular cartilage.

INTRODUCTION: Osteoarthritis (OA) is a heterogeneous and complex disease. We have used a network biology approach based on genome-wide analysis of gene expression in OA knee cartilage to seek evidence for pathogenic mechanisms that may distinguish different patient subgroups. METHODS: Results from RNA-Sequencing (RNA-Seq) were collected from intact knee cartilage at total knee replacement from 44 patients with OA, from 16 additional patients with OA and 10 control patients with non-OA. Results were analysed to identify patient subsets and compare major active pathways. RESULTS: The RNA-Seq results showed 2692 differentially expressed genes between OA and non-OA. Analysis by unsupervised clustering identified two distinct OA groups: Group A with 24 patients (55%) and Group B with 18 patients (41%). A 10 gene subgroup classifier was validated by RT-qPCR in 16 further patients with OA. Pathway analysis showed increased protein expression in both groups. PhenomeExpress analysis revealed group differences in complement activation, innate immune responses and altered Wnt and TGFß signalling, but no activation of inflammatory cytokine expression. Both groups showed suppressed circadian regulators and whereas matrix changes in Group A were chondrogenic, in Group B they were non-chondrogenic with changes in mechanoreceptors, calcium signalling, ion channels and in cytoskeletal organisers. The gene expression changes predicted 478 potential biomarkers for detection in synovial fluid to distinguish patients from the two groups. CONCLUSIONS: Two subgroups of knee OA were identified by network analysis of RNA-Seq data with evidence for the presence of two major pathogenic pathways. This has potential importance as a new basis for the stratification of patients with OA for drug trials and for the development of new targeted treatments.

p53 modeling as a route to mesothelioma patients stratification and novel therapeutic identification.

BACKGROUND: Malignant pleural mesothelioma (MPM) is an orphan disease that is difficult to treat using traditional chemotherapy, an approach which has been effective in other types of cancer. Most chemotherapeutics cause DNA damage leading to cell death. Recent discoveries have highlighted a potential role for the p53 tumor suppressor in this disease. Given the pivotal role of p53 in the DNA damage response, here we investigated the predictive power of the p53 interactome model for MPM patients' stratification. METHODS: We used bioinformatics approaches including omics type analysis of data from MPM cells and from MPM patients in order to predict which pathways are crucial for patients' survival. Analysis of the PKT206 model of the p53 network was validated by microarrays from the Mero-14 MPM cell line and RNA-seq data from 71 MPM patients, whilst statistical analysis was used to identify the deregulated pathways and predict therapeutic schemes by linking the affected pathway with the patients' clinical state. RESULTS: In silico simulations demonstrated successful predictions ranging from 52 to 85% depending on the drug, algorithm or sample used for validation. Clinical outcomes of individual patients stratified in three groups and simulation comparisons identified 30 genes that correlated with survival. In patients carrying wild-type p53 either treated or not treated with chemotherapy, FEN1 and MMP2 exhibited the highest inverse correlation, whereas in untreated patients bearing mutated p53, SIAH1 negatively correlated with survival. Numerous repositioned and experimental drugs targeting FEN1 and MMP2 were identified and selected drugs tested. Epinephrine and myricetin, which target FEN1, have shown cytotoxic effect on Mero-14 cells whereas marimastat and batimastat, which target MMP2 demonstrated a modest but significant inhibitory effect on MPM cell migration. Finally, 8 genes displayed correlation with disease stage, which may have diagnostic implications. CONCLUSIONS: Clinical decisions related to MPM personalized therapy based on individual patients' genetic profile and previous chemotherapeutic treatment could be reached using computational tools and the predictions reported in this study upon further testing in animal models.

Effect of IAPP on the proteome of cultured Rin-5F cells.

BACKGROUND: Islet amyloid polypeptide (IAPP) or amylin deposits can be found in the islets of type 2 diabetes patients. The peptide is suggested to be involved in the etiology of the disease through formation of amyloid deposits and destruction of ß islet cells, though the underlying molecular events leading from IAPP deposition to ß cell death are still largely unknown. RESULTS: We used OFFGEL™ proteomics to study how IAPP exposure affects the proteome of rat pancreatic insulinoma Rin-5F cells. The OFFGEL™ methodology is highly effective at generating quantitative data on hundreds of proteins affected by IAPP, with its accuracy confirmed by In Cell Western and Quantitative Real Time PCR results. Combining data on individual proteins identifies pathways and protein complexes affected by IAPP. IAPP disrupts protein synthesis and degradation, and induces oxidative stress. It causes decreases in protein transport and localization. IAPP disrupts the regulation of ubiquitin-dependent protein degradation and increases catabolic processes. IAPP causes decreases in protein transport and localization, and affects the cytoskeleton, DNA repair and oxidative stress. CONCLUSIONS: Results are consistent with a model where IAPP aggregates overwhelm the ability of a cell to degrade proteins via the ubiquitin system. Ultimately this leads to apoptosis. IAPP aggregates may be also toxic to the cell by causing oxidative stress, leading to DNA damage or by decreasing protein transport. The reversal of any of these effects, perhaps by targeting proteins which alter in response to IAPP, may be beneficial for type II diabetes.

Insight into glucocorticoid receptor signalling through interactome model analysis.

Glucocorticoid hormones (GCs) are used to treat a variety of diseases because of their potent anti-inflammatory effect and their ability to induce apoptosis in lymphoid malignancies through the glucocorticoid receptor (GR). Despite ongoing research, high glucocorticoid efficacy and widespread usage in medicine, resistance, disease relapse and toxicity remain factors that need addressing. Understanding the mechanisms of glucocorticoid signalling and how resistance may arise is highly important towards improving therapy. To gain insight into this we undertook a systems biology approach, aiming to generate a Boolean model of the glucocorticoid receptor protein interaction network that encapsulates functional relationships between the GR, its target genes or genes that target GR, and the interactions between the genes that interact with the GR. This model named GEB052 consists of 52 nodes representing genes or proteins, the model input (GC) and model outputs (cell death and inflammation), connected by 241 logical interactions of activation or inhibition. 323 changes in the relationships between model constituents following in silico knockouts were uncovered, and steady-state analysis followed by cell-based microarray genome-wide model validation led to an average of 57% correct predictions, which was taken further by assessment of model predictions against patient microarray data. Lastly, semi-quantitative model analysis via microarray data superimposed onto the model with a score flow algorithm has also been performed, which demonstrated significantly higher correct prediction ratios (average of 80%), and the model has been assessed as a predictive clinical tool using published patient microarray data. In summary we present an in silico simulation of the glucocorticoid receptor interaction network, linked to downstream biological processes that can be analysed to uncover relationships between GR and its interactants. Ultimately the model provides a platform for future development both by directing laboratory research and allowing for incorporation of further components, encapsulating more interactions/genes involved in glucocorticoid receptor signalling.