The SYSCID map: a graphical and computational resource of molecular mechanisms across rheumatoid arthritis, systemic lupus erythematosus and inflammatory bowel disease.

Chronic inflammatory diseases (CIDs), including inflammatory bowel disease (IBD), rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) are thought to emerge from an impaired complex network of inter- and intracellular biochemical interactions among several proteins and small chemical compounds under strong influence of genetic and environmental factors. CIDs are characterised by shared and disease-specific processes, which is reflected by partially overlapping genetic risk maps and pathogenic cells (e.g., T cells). Their pathogenesis involves a plethora of intracellular pathways. The translation of the research findings on CIDs molecular mechanisms into effective treatments is challenging and may explain the low remission rates despite modern targeted therapies. Modelling CID-related causal interactions as networks allows us to tackle the complexity at a systems level and improve our understanding of the interplay of key pathways. Here we report the construction, description, and initial applications of the SYSCID map (https://syscid.elixir-luxembourg.org/), a mechanistic causal interaction network covering the molecular crosstalk between IBD, RA and SLE. We demonstrate that the map serves as an interactive, graphical review of IBD, RA and SLE molecular mechanisms, and helps to understand the complexity of omics data. Examples of such application are illustrated using transcriptome data from time-series gene expression profiles following anti-TNF treatment and data from genome-wide associations studies that enable us to suggest potential effects to altered pathways and propose possible mechanistic biomarkers of treatment response.

Comparative Computational Screening of Natural-based Partial Agonists for PPARγ Receptor.

INTRODUCTION: The nuclear transcription factor PPARγ, which can modulate cell growth via proliferation and apoptosis-related mechanisms, is a promising target in cancer therapy. This study aims to focus on PPARγ as the target and use virtual screening to find hits. METHODS: A set of 5,677 flavonoid compounds were filtered by subjecting them to descriptor-based drug-likeness and ADMET strategies to discover drug-like compounds. The candidates' modes of binding to PPARγ were then evaluated using docking and MD simulation. PharmMapper was used to identify the potential targets of selected hits. The pharmacological network was constructed based on the GO and KEGG pathway analysis. RESULTS: In primary screening, 3,057 compounds met various drug-likeness criteria and docked well as partial agonists in the PPARγ-LBD. Five compounds (euchrenone b1, kaempferol-7-Orhamnoside, vincetoxicoside B, morusin, and karanjin) were selected with the use of ADMET profiles for further MD simulation investigation. Based on the PharmMapper findings, 52 proteins were then submitted to GO and KEGG enrichment analysis. As expected by GO and KEGG pathway enrichment studies, core targets were enriched in the PI3K-Akt signaling pathway (p < 0.01), indicating that certain chemicals may be involved in cancer processes. CONCLUSION: Our results suggested that the selected compounds might have sufficient drug-likeness, pharmacokinetics, and in silico bioactivity by acting as PPARγ partial agonists. Although much work remains to illuminate extensive cancer therapeutic/ chemopreventive efficacy of flavonoids in vivo, in silico methodology of our cheminformatics research may be able to provide additional data regarding the efficacy and safety of potential candidates for therapeutic targets.

Long Non-coding RNAs: Mechanisms, Experimental, and Computational Approaches in Identification, Characterization, and Their Biomarker Potential in Cancer.

Long non-coding RNAs are diverse class of non-coding RNA molecules >200 base pairs of length having various functions like gene regulation, dosage compensation, epigenetic regulation. Dysregulation and genomic variations of several lncRNAs have been implicated in several diseases. Their tissue and developmental specific expression are contributing factors for them to be viable indicators of physiological states of the cells. Here we present an comprehensive review the molecular mechanisms and functions, state of the art experimental and computational pipelines and challenges involved in the identification and functional annotation of lncRNAs and their prospects as biomarkers. We also illustrate the application of co-expression networks on the TCGA-LIHC dataset for putative functional predictions of lncRNAs having a therapeutic potential in Hepatocellular carcinoma (HCC).

Arena3Dweb: interactive 3D visualization of multilayered networks.

Efficient integration and visualization of heterogeneous biomedical information in a single view is a key challenge. In this study, we present Arena3Dweb, the first, fully interactive and dependency-free, web application which allows the visualization of multilayered graphs in 3D space. With Arena3Dweb, users can integrate multiple networks in a single view along with their intra- and inter-layer connections. For clearer and more informative views, users can choose between a plethora of layout algorithms and apply them on a set of selected layers either individually or in combination. Users can align networks and highlight node topological features, whereas each layer as well as the whole scene can be translated, rotated and scaled in 3D space. User-selected edge colors can be used to highlight important paths, while node positioning, coloring and resizing can be adjusted on-the-fly. In its current version, Arena3Dweb supports weighted and unweighted undirected graphs and is written in R, Shiny and JavaScript. We demonstrate the functionality of Arena3Dweb using two different use-case scenarios; one regarding drug repurposing for SARS-CoV-2 and one related to GPCR signaling pathways implicated in melanoma. Arena3Dweb is available at http://bib.fleming.gr:3838/Arena3D or http://bib.fleming.gr/Arena3D.

Reusability and composability in process description maps: RAS-RAF-MEK-ERK signalling.

Detailed maps of the molecular basis of the disease are powerful tools for interpreting data and building predictive models. Modularity and composability are considered necessary network features for large-scale collaborative efforts to build comprehensive molecular descriptions of disease mechanisms. An effective way to create and manage large systems is to compose multiple subsystems. Composable network components could effectively harness the contributions of many individuals and enable teams to seamlessly assemble many individual components into comprehensive maps. We examine manually built versions of the RAS-RAF-MEK-ERK cascade from the Atlas of Cancer Signalling Network, PANTHER and Reactome databases and review them in terms of their reusability and composability for assembling new disease models. We identify design principles for managing complex systems that could make it easier for investigators to share and reuse network components. We demonstrate the main challenges including incompatible levels of detail and ambiguous representation of complexes and highlight the need to address these challenges.

Data and knowledge management in translational research: implementation of the eTRIKS platform for the IMI OncoTrack consortium.

BACKGROUND: For large international research consortia, such as those funded by the European Union's Horizon 2020 programme or the Innovative Medicines Initiative, good data coordination practices and tools are essential for the successful collection, organization and analysis of the resulting data. Research consortia are attempting ever more ambitious science to better understand disease, by leveraging technologies such as whole genome sequencing, proteomics, patient-derived biological models and computer-based systems biology simulations. RESULTS: The IMI eTRIKS consortium is charged with the task of developing an integrated knowledge management platform capable of supporting the complexity of the data generated by such research programmes. In this paper, using the example of the OncoTrack consortium, we describe a typical use case in translational medicine. The tranSMART knowledge management platform was implemented to support data from observational clinical cohorts, drug response data from cell culture models and drug response data from mouse xenograft tumour models. The high dimensional (omics) data from the molecular analyses of the corresponding biological materials were linked to these collections, so that users could browse and analyse these to derive candidate biomarkers. CONCLUSIONS: In all these steps, data mapping, linking and preparation are handled automatically by the tranSMART integration platform. Therefore, researchers without specialist data handling skills can focus directly on the scientific questions, without spending undue effort on processing the data and data integration, which are otherwise a burden and the most time-consuming part of translational research data analysis.

HO-1 concentrations 24 hours after cardiac surgery are associated with the incidence of acute kidney injury: a prospective cohort study.

BACKGROUND: Acute kidney injury (AKI) is a serious complication after cardiac surgery that is associated with increased mortality and morbidity. Heme oxygenase-1 (HO-1) is an enzyme synthesized in renal tubular cells as one of the most intense responses to oxidant stress linked with protective, anti-inflammatory properties. Yet, it is unknown if serum HO-1 induction following cardiac surgical procedure involving cardiopulmonary bypass (CPB) is associated with incidence and severity of AKI. PATIENTS AND METHODS: In the present study, we used data from a prospective cohort study of 150 adult cardiac surgical patients. HO-1 measurements were performed before, immediately after and 24 hours post-CPB. In univariate and multivariate analyses, the association between HO-1 and AKI was investigated. RESULTS: AKI with an incidence of 23.3% (35 patients) was not associated with an early elevation of HO-1 after CPB in all patients (P=0.88), whereas patients suffering from AKI developed a second burst of HO-1 24 hours after CBP. In patients without AKI, the HO-1 concentrations dropped to baseline values (P=0.031). Furthermore, early HO-1 induction was associated with CPB time (P=0.046), while the ones 24 hours later lost this association (P=0.219). CONCLUSION: The association of the second HO-1 burst 24 hours after CBP might help to distinguish between the causality of AKI in patients undergoing CBP, thus helping to adapt patient stratification and management.

Percutaneous coronary intervention of unprotected left main stenoses - Procedural data and outcome depending on SYNTAX I Score.

BACKGROUND: We hypothesized that SYNTAX I score is a predictor for procedure complexity in left main PCI. Procedure complexity, duration and contrast load may contribute to adverse outcome of complex left main percutaneous coronary intervention (PCI). METHODS: In 105 consecutive patients who underwent PCI of unprotected left main coronary artery stenoses between 2014 and 2016, clinical parameters as well as PCI characteristics and follow-up data were analyzed. RESULTS: The mean SYNTAX I score was 29 ±â€¯12, with 66 patients having a SYNTAX I score ≤ 32 and 39 patients a SYNTAX I score > 32. In patients with high SYNTAX I score vs. low-to-intermediate SYNTAX I score, single stent techniques were performed significantly less frequently (18% vs. 68%; p < 0.001), while Crush (44% vs. 5%; p < 0.001) and Culotte techniques (20% vs. 5%; p = 0.003) were performed significantly more frequently. Procedural success was achieved in all 105 cases without periprocedural mortality. During follow up, repeat PCI was necessary significantly more frequently in patients with high compared to patients with low-to-intermediate SYNTAX I score (34% vs.13%; p = 0.003). Nevertheless, overall mortality did not differ between patients with high vs. low-to-intermediate SYNTAX I score (20% vs. 18%). CONCLUSIONS: PCI strategies for the treatment of left main coronary artery stenoses get significantly more complex with increasing SYNTAX I scores. While this translates into a significantly longer procedure duration and contrast load, short-term outcome seems not to be influenced by the SYNTAX I score.

TIMP-2*IGFBP7 (Nephrocheck®) Measurements at Intensive Care Unit Admission After Cardiac Surgery are Predictive for Acute Kidney Injury Within 48 Hours.

BACKGROUND/AIMS: Acute kidney injury (AKI) is a postoperative complication after cardiac surgery with a high impact on mortality and morbidity. Nephrocheck® [TIMP-2*IGFBP7] determines markers of tubular stress, which occurs prior to tubular damage. It is unknown at which time-point [TIMP-2*IGFBP7] measurement should be performed to ideally predict AKI. We investigated the association of [TIMP-2*IGFBP7] at various time-points with the incidence of AKI in patients undergoing elective cardiac surgery including cardio-pulmonary bypass. METHODS: In a prospective cohort study, serial blood and urine samples were collected from 150 patients: pre-operative, at ICU-admission, 24h and 48h post-surgery. AKI was defined as Serum-Creatinine rise >0.3 mg/dl within 48hrs. Urinary [TIMP-2*IGFBP7] was measured at pre-operative, ICU-admission and 24h post-surgery; medical staff was kept blinded to these results. RESULTS: A total of 35 patients (23.5%) experienced AKI, with a higher incidence in those with high [TIMP-2*IGFBP7] values at ICU admission (57.1% vs. 10.1%, p<0.001). In logistic regression [TIMP-2*IGFBP7] at ICU admission was independently associated with the occurrence of AKI (Odds Ratio 11.83; p<0.001, C-statistic= 0.74) after adjustment for EuroSCORE II and CBP-time. CONCLUSIONS: Early detection of elevated [TIMP-2*IGFBP7] at ICU admission was strongly predictive for postoperative AKI and appeared to be more precise as compared to subsequent measurements.

SmartR: an open-source platform for interactive visual analytics for translational research data.

SUMMARY: In translational research, efficient knowledge exchange between the different fields of expertise is crucial. An open platform that is capable of storing a multitude of data types such as clinical, pre-clinical or OMICS data combined with strong visual analytical capabilities will significantly accelerate the scientific progress by making data more accessible and hypothesis generation easier. The open data warehouse tranSMART is capable of storing a variety of data types and has a growing user community including both academic institutions and pharmaceutical companies. tranSMART, however, currently lacks interactive and dynamic visual analytics and does not permit any post-processing interaction or exploration. For this reason, we developed SmartR , a plugin for tranSMART, that equips the platform not only with several dynamic visual analytical workflows, but also provides its own framework for the addition of new custom workflows. Modern web technologies such as D3.js or AngularJS were used to build a set of standard visualizations that were heavily improved with dynamic elements. AVAILABILITY AND IMPLEMENTATION: The source code is licensed under the Apache 2.0 License and is freely available on GitHub: https://github.com/transmart/SmartR . CONTACT: reinhard.schneider@uni.lu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Bioinformatics Mining and Modeling Methods for the Identification of Disease Mechanisms in Neurodegenerative Disorders.

Since the decoding of the Human Genome, techniques from bioinformatics, statistics, and machine learning have been instrumental in uncovering patterns in increasing amounts and types of different data produced by technical profiling technologies applied to clinical samples, animal models, and cellular systems. Yet, progress on unravelling biological mechanisms, causally driving diseases, has been limited, in part due to the inherent complexity of biological systems. Whereas we have witnessed progress in the areas of cancer, cardiovascular and metabolic diseases, the area of neurodegenerative diseases has proved to be very challenging. This is in part because the aetiology of neurodegenerative diseases such as Alzheimer´s disease or Parkinson´s disease is unknown, rendering it very difficult to discern early causal events. Here we describe a panel of bioinformatics and modeling approaches that have recently been developed to identify candidate mechanisms of neurodegenerative diseases based on publicly available data and knowledge. We identify two complementary strategies-data mining techniques using genetic data as a starting point to be further enriched using other data-types, or alternatively to encode prior knowledge about disease mechanisms in a model based framework supporting reasoning and enrichment analysis. Our review illustrates the challenges entailed in integrating heterogeneous, multiscale and multimodal information in the area of neurology in general and neurodegeneration in particular. We conclude, that progress would be accelerated by increasing efforts on performing systematic collection of multiple data-types over time from each individual suffering from neurodegenerative disease. The work presented here has been driven by project AETIONOMY; a project funded in the course of the Innovative Medicines Initiative (IMI); which is a public-private partnership of the European Federation of Pharmaceutical Industry Associations (EFPIA) and the European Commission (EC).

Balanced Hydroxyethylstarch (HES 130/0.4) Impairs Kidney Function In-Vivo without Inflammation.

Volume therapy is a standard procedure in daily perioperative care, and there is an ongoing discussion about the benefits of colloid resuscitation with hydroxyethylstarch (HES). In sepsis HES should be avoided due to a higher risk for acute kidney injury (AKI). Results of the usage of HES in patients without sepsis are controversial. Therefore we conducted an animal study to evaluate the impact of 6% HES 130/0.4 on kidney integrity with sepsis or under healthy conditions Sepsis was induced by standardized Colon Ascendens Stent Peritonitis (sCASP). sCASP-group as well as control group (C) remained untreated for 24 h. After 18 h sCASP+HES group (sCASP+VOL) and control+HES (C+VOL) received 50 ml/KG balanced 6% HES (VOL) 130/0.4 over 6 h. After 24 h kidney function was measured via Inulin- and PAH-Clearance in re-anesthetized rats, and serum urea, creatinine (crea), cystatin C and Neutrophil gelatinase-associated lipocalin (NGAL) as well as histopathology were analysed. In vitro human proximal tubule cells (PTC) were cultured +/- lipopolysaccharid (LPS) and with 0.1-4.0% VOL. Cell viability was measured with XTT-, cell toxicity with LDH-test. sCASP induced severe septic AKI demonstrated divergent results regarding renal function by clearance or creatinine measure focusing on VOL. Soleley HES (C+VOL) deteriorated renal function without sCASP. Histopathology revealed significantly derangements in all HES groups compared to control. In vitro LPS did not worsen the HES induced reduction of cell viability in PTC cells. For the first time, we demonstrated, that application of 50 ml/KG 6% HES 130/0.4 over 6 hours induced AKI without inflammation in vivo. Severity of sCASP induced septic AKI might be no longer susceptible to the way of volume expansion.

Regulation of Expression of Renal Organic Anion Transporters OAT1 and OAT3 in a Model of Ischemia/Reperfusion Injury.

BACKGROUND: Recently, we gained evidence that impairment of rOat1 and rOat3 expression induced by ischemic acute kidney injury (AKI) is mediated by COX metabolites and this suppression might be critically involved in renal damage. METHODS: (i) Basolateral organic anion uptake into proximal tubular cells after model ischemia and reperfusion (I/R) was investigated by fluorescein uptake. The putative promoter sequences from hOAT1 (SLC22A6) and hOAT3 (SCL22A8) were cloned into a reporter plasmid, transfected into HEK cells and (ii) transcriptional activity was determined after model ischemia and reperfusion as a SEAP reporter gen assay. Inhibitors or antagonists were applied with the beginning of reperfusion. RESULTS: By using inhibitors of PKA (H89) and PLC (U73122), antagonists of E prostanoid receptor type 2 (AH6809) and type 4 (L161,982), we gained evidence that I/R induced down regulation of organic anion transport is mediated by COX1 metabolites via E prostanoid receptor type 4. The latter signaling was confirmed by application of butaprost (EP2 agonist) or TCS2510 (EP4 agonist) to control cells. In brief, the latter signaling was verified for the transcriptional activity in the reporter gen assay established. Therein, selective inhibitors for COX1 (SC58125) and COX2 (SC560) were also applied. CONCLUSION: Our data show (a) that COX1 metabolites are involved in the regulation of renal organic anion transport(ers) after I/R via the EP4 receptor and (b) that this is due to transcriptional regulation of the respective transporters. As the promoter sequences cloned were of human origin and expressed in a human renal epithelial cell line we (c) hypothesize that the regulatory mechanisms described after I/R is meaningful for humans as well.

Systems genomics evaluation of the SH-SY5Y neuroblastoma cell line as a model for Parkinson's disease.

BACKGROUND: The human neuroblastoma cell line, SH-SY5Y, is a commonly used cell line in studies related to neurotoxicity, oxidative stress, and neurodegenerative diseases. Although this cell line is often used as a cellular model for Parkinson's disease, the relevance of this cellular model in the context of Parkinson's disease (PD) and other neurodegenerative diseases has not yet been systematically evaluated. RESULTS: We have used a systems genomics approach to characterize the SH-SY5Y cell line using whole-genome sequencing to determine the genetic content of the cell line and used transcriptomics and proteomics data to determine molecular correlations. Further, we integrated genomic variants using a network analysis approach to evaluate the suitability of the SH-SY5Y cell line for perturbation experiments in the context of neurodegenerative diseases, including PD. CONCLUSIONS: The systems genomics approach showed consistency across different biological levels (DNA, RNA and protein concentrations). Most of the genes belonging to the major Parkinson's disease pathways and modules were intact in the SH-SY5Y genome. Specifically, each analysed gene related to PD has at least one intact copy in SH-SY5Y. The disease-specific network analysis approach ranked the genetic integrity of SH-SY5Y as higher for PD than for Alzheimer's disease but lower than for Huntington's disease and Amyotrophic Lateral Sclerosis for loss of function perturbation experiments.

Sepsis-induced acute kidney injury by standardized colon ascendens stent peritonitis in rats - a simple, reproducible animal model.

BACKGROUND: Up to 50% of septic patients develop acute kidney injury (AKI). The pathomechanism of septic AKI is poorly understood. Therefore, we established an innovative rodent model to characterize sepsis-induced AKI by standardized colon ascendens stent peritonitis (sCASP). The model has a standardized focus of infection, an intensive care set up with monitoring of haemodynamics and oxygenation resulting in predictable impairment of renal function, AKI parameters as well as histopathology scoring. METHODS: Anaesthetized rats underwent the sCASP procedure, whereas sham animals were sham operated and control animals were just monitored invasively. Haemodynamic variables and blood gases were continuously measured. After 24 h, animals were reanesthetized; cardiac output (CO), inulin and PAH clearances were measured and later on kidneys were harvested; and creatinine, urea, cystatin C and neutrophil gelatinase-associated lipocalin (NGAL) were analysed. Additional sCASP-treated animals were investigated after 3 and 9 days. RESULTS: All sCASP-treated animals survived, whilst ubiquitous peritonitis and significantly deteriorated clinical and macrohaemodynamic sepsis signs after 24 h (MAP, CO, heart rate) were obvious. Blood analyses showed increased lactate and IL-6 levels as well as leucopenia. Urine output, inulin and PAH clearance were significantly decreased in sCASP compared to sham and control. Additionally, significant increase in cystatin C and NGAL was detected. Standard parameters like serum creatinine and urea were elevated and sCASP-induced sepsis increased significantly in a time-dependent manner. The renal histopathological score of sCASP-treated animals deteriorated after 3 and 9 days. CONCLUSIONS: The presented sCASP method is a standardized, reliable and reproducible method to induce septic AKI. The intensive care set up, continuous macrohaemodynamic and gas exchange monitoring, low mortality rate as well as the opportunity of detailed analyses of kidney function and impairments are advantages of this setup. Thus, our described method may serve as a new standard for experimental investigations of septic AKI.

Application of low-energy scanning transmission electron microscopy for the study of Pt-nanoparticle uptake in human colon carcinoma cells.

High-angle annular dark-field scanning transmission electron microscopy (HAADF STEM) in a scanning electron microscope facilitates the acquisition of images with high chemical sensitivity and high resolution. HAADF STEM at low electron energies is particularly suited to image nanoparticles (NPs) in thin cell sections which are not subjected to poststaining procedures as demonstrated by comparison with bright-field TEM. High membrane contrast is achieved and distinction of NPs with different chemical composition is possible at first sight. Low-energy HAADF STEM was applied to systematically study the uptake of Pt-NPs with a broad size distribution in HT29 colon carcinoma cells as a function of incubation time and incubation temperature. The cellular dose was quantified, that is, the amount and number density of NPs taken up by the cells, as well as the particle-size distribution. The results show a strong dependence of the amount of incubated NPs on the exposure time which can be understood by considering size-dependent diffusion and gravitational settling of the NPs in the cell culture medium.

Genome-wide identification and functional analyses of microRNA signatures associated with cancer pain.

Cancer pain remains a major challenge and there is an urgent demand for the development of specific mechanism-based therapies. Various diseases are associated with unique signatures of expression of microRNAs (miRNAs), which reveal deep insights into disease pathology. Using a comprehensive approach combining genome-wide miRNA screening, molecular and in silico analyses with behavioural approaches in a clinically relevant model of metastatic bone-cancer pain in mice, we now show that tumour-induced conditions are associated with a marked dysregulation of 57 miRNAs in sensory neurons corresponding to tumour-affected areas. By establishing protocols for interference with disease-induced miRNA dysregulation in peripheral sensory neurons in vivo, we functionally validate six dysregulated miRNAs as significant modulators of tumour-associated hypersensitivity. In silico analyses revealed that their predicted targets include key pain-related genes and we identified Clcn3, a gene encoding a chloride channel, as a key miRNA target in sensory neurons, which is functionally important in tumour-induced nociceptive hypersensitivity in vivo. Our results provide new insights into endogenous gene regulatory mechanisms in cancer pain and open up attractive and viable therapeutic options.

Transcriptional mechanisms underlying sensitization of peripheral sensory neurons by granulocyte-/granulocyte-macrophage colony stimulating factors.

BACKGROUND: Cancer-associated pain is a major cause of poor quality of life in cancer patients and is frequently resistant to conventional therapy. Recent studies indicate that some hematopoietic growth factors, namely granulocyte macrophage colony stimulating factor (GMCSF) and granulocyte colony stimulating factor (GCSF), are abundantly released in the tumor microenvironment and play a key role in regulating tumor-nerve interactions and tumor-associated pain by activating receptors on dorsal root ganglion (DRG) neurons. Moreover, these hematopoietic factors have been highly implicated in postsurgical pain, inflammatory pain and osteoarthritic pain. However, the molecular mechanisms via which G-/GMCSF bring about nociceptive sensitization and elicit pain are not known. RESULTS: In order to elucidate G-/GMCSF mediated transcriptional changes in the sensory neurons, we performed a comprehensive, genome-wide analysis of changes in the transcriptome of DRG neurons brought about by exposure to GMCSF or GCSF. We present complete information on regulated genes and validated profiling analyses and report novel regulatory networks and interaction maps revealed by detailed bioinformatics analyses. Amongst these, we validate calpain 2, matrix metalloproteinase 9 (MMP9) and a RhoGTPase Rac1 as well as Tumor necrosis factor alpha (TNFα) as transcriptional targets of G-/GMCSF and demonstrate the importance of MMP9 and Rac1 in GMCSF-induced nociceptor sensitization. CONCLUSION: With integrative approach of bioinformatics, in vivo pharmacology and behavioral analyses, our results not only indicate that transcriptional control by G-/GMCSF signaling regulates a variety of established pain modulators, but also uncover a large number of novel targets, paving the way for translational analyses in the context of pain disorders.

PhenoTimer: software for the visual mapping of time-resolved phenotypic landscapes.

Timing common and specific modulators of disease progression is crucial for treatment, but the understanding of the underlying complex system of interactions is limited. While attempts at elucidating this experimentally have produced enormous amounts of phenotypic data, tools that are able to visualize and analyze them are scarce and the insight obtained from the data is often unsatisfactory. Linking and visualizing processes from genes to phenotypes and back, in a temporal context, remains a challenge in systems biology. We introduce PhenoTimer, a 2D/3D visualization tool for the mapping of time-resolved phenotypic links in a genetic context. It uses a novel visualization approach for relations between morphological defects, pathways or diseases, to enable fast pattern discovery and hypothesis generation. We illustrate its capabilities of tracing dynamic motifs on cell cycle datasets that explore the phenotypic order of events upon perturbations of the system, transcriptional activity programs and their connection to disease. By using this tool we are able to fine-grain regulatory programs for individual time points of the cell cycle and better understand which patterns arise when these programs fail. We also illustrate a way to identify common mechanisms of misregulation in diseases and drug abuse.

EnrichNet: network-based gene set enrichment analysis.

MOTIVATION: Assessing functional associations between an experimentally derived gene or protein set of interest and a database of known gene/protein sets is a common task in the analysis of large-scale functional genomics data. For this purpose, a frequently used approach is to apply an over-representation-based enrichment analysis. However, this approach has four drawbacks: (i) it can only score functional associations of overlapping gene/proteins sets; (ii) it disregards genes with missing annotations; (iii) it does not take into account the network structure of physical interactions between the gene/protein sets of interest and (iv) tissue-specific gene/protein set associations cannot be recognized. RESULTS: To address these limitations, we introduce an integrative analysis approach and web-application called EnrichNet. It combines a novel graph-based statistic with an interactive sub-network visualization to accomplish two complementary goals: improving the prioritization of putative functional gene/protein set associations by exploiting information from molecular interaction networks and tissue-specific gene expression data and enabling a direct biological interpretation of the results. By using the approach to analyse sets of genes with known involvement in human diseases, new pathway associations are identified, reflecting a dense sub-network of interactions between their corresponding proteins. AVAILABILITY: EnrichNet is freely available at http://www.enrichnet.org. CONTACT: Natalio.Krasnogor@nottingham.ac.uk, reinhard.schneider@uni.lu or avalencia@cnio.es SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Online.