Combination therapy with ampicillin and azithromycin improved outcomes in a mouse model of group B streptococcal sepsis.

BACKGROUND: Evidence suggests that ß-lactam monotherapy of streptococcal infections may incite stronger inflammation and is inferior to combination therapy with macrolides. We hypothesized that use of macrolides alone or in combination with a ß-lactam for group B streptococcal (GBS) sepsis would improve outcomes by reducing inflammation. METHODS: TNF-α was measured from supernatants of RAW 264.7 cells stimulated with GBS isolates, in presence of four treatment regimens: ampicillin alone, azithromycin alone, or combination of azithromycin plus ampicillin. Mouse model of GBS sepsis was developed and treated with same four regimens. Clinical sepsis scores were monitored; serum cytokines (TNF-α, IL-6, IL-10) and chemokines (MIP-1α) were measured at the end. RESULTS: GBS isolates exposed to azithromycin or combination (compared to ampicillin alone) stimulated less TNF production in vitro. In the murine sepsis model, mortality was lower along with decreased sepsis scores in mice treated with combination therapy. Mean serum IL-6 was lower in mice treated with azithromycin alone (66±52 pg/ml) or combination of ampicillin plus azithromycin (52±22 pg/ml) compared to ampicillin alone (260±160 pg/ml) (p<0.005). CONCLUSIONS: Combination therapy of ampicillin+azithromycin improved outcomes in a murine GBS sepsis model; this therapeutic approach deserves additional study.

TrialWiz: an ontology-driven tool for authoring clinical trial protocols.

There has long been great interest in the clinical research community for automated support of clinical trials management. At the core of such efforts is formal specification of protocol knowledge. Building a clinical-trial knowledge base is a complex task involving software engineers and domain experts. As part of our Epoch ontological framework for clinical trials management, we have developed TrialWiz, an authoring tool for encoding a clinical-trial knowledge base. The main goals of TrialWiz are to manage the complexity of the protocol-encoding process and to improve efficiency in knowledge acquisition. TrialWiz provides intelligent guidance through the process of acquiring clinical-trial knowledge; graphical user interfaces intuitive to clinical trialists; a repository of reusable knowledge; and facilities to export the knowledge in different formats. We have successfully used TrialWiz to encode example clinical trials at the Immune Tolerance Network (ITN). In this presentation, we will demonstrate the intuitive authoring of clinical trial protocols using TrialWiz and how the protocol knowledge can be used by different clinical trial management applications at run time.

MIFlowCyt: the minimum information about a Flow Cytometry Experiment.

A fundamental tenet of scientific research is that published results are open to independent validation and refutation. Minimum data standards aid data providers, users, and publishers by providing a specification of what is required to unambiguously interpret experimental findings. Here, we present the Minimum Information about a Flow Cytometry Experiment (MIFlowCyt) standard, stating the minimum information required to report flow cytometry (FCM) experiments. We brought together a cross-disciplinary international collaborative group of bioinformaticians, computational statisticians, software developers, instrument manufacturers, and clinical and basic research scientists to develop the standard. The standard was subsequently vetted by the International Society for Advancement of Cytometry (ISAC) Data Standards Task Force, Standards Committee, membership, and Council. The MIFlowCyt standard includes recommendations about descriptions of the specimens and reagents included in the FCM experiment, the configuration of the instrument used to perform the assays, and the data processing approaches used to interpret the primary output data. MIFlowCyt has been adopted as a standard by ISAC, representing the FCM scientific community including scientists as well as software and hardware manufacturers. Adoptionof MIFlowCyt by the scientific and publishing communities will facilitate third-party understanding and reuse of FCM data.

CIDE-A is expressed in liver of old mice and in type 2 diabetic mouse liver exhibiting steatosis.

BACKGROUND: Increased levels of circulating fatty acids caused by insulin resistance and increased adipocyte lipolysis can accumulate within the liver resulting in steatosis. This steatosis sensitizes the liver to inflammation and further injury which can lead to liver dysfunction. We performed microarray analysis on normal mouse liver tissue at different ages and type 2 diabetic liver exhibiting steatosis to identify differentially expressed genes involved in lipid accumulation and liver dysfunction. RESULTS: Microarray analysis identified CIDE-A as the most differentially expressed gene as a function of age. Mice fed a high fat diet developed hyperinsulinemia, hyperglycemia and liver steatosis, all features of the human metabolic syndrome. Increased CIDE-A expression was observed in type 2 diabetic liver and the elevated CIDE-A expression could be reversed by weight loss and normalization of plasma insulin. Also, CIDE-A expression was found to be correlated with hepatic lipid accumulation. CONCLUSION: The corresponding increase in CIDE-A expression with hyperinsulinemia and liver steatosis suggests a novel pathway for lipid accumulation in the liver.

System-wide analysis of hepatotoxicological responses: tissomics is key.

BACKGROUND: Combining diverse data streams across different levels of biological observation, such as molecular, cellular, and clinical chemistry responses, support a system-wide diagnostic approach. Recent progress in slide-based cytometry contributes to the development of tissomics, a high-throughput and high-content phenotyping methodology that provides data-rich profiles of cellular heterogeneity in tissues enabling correlative statistical treatments over multiple scales of biological hierarchies. METHODS: Phenotypical data are covariants that can be used as biomarkers to identify relevant candidate genes by associating initiating molecular events with phenotypical changes and adverse outcomes. We introduce a procedure of combined statistical and analytical tools to identify and visualize such associations for nonpooled entities. The new utility is applied to a time-controlled, low-dose toxicological study including a control and two xenobiotic compounds. RESULTS: An integrated analysis identified specific molecular and phenotypical biomarkers, which support the classification of animals in the absence of any visual indicators from pathology readings. DISCUSSION: The introduction of controlled perturbations to tissues provides a prototypical setting to develop a sensitive, systems-based analysis methodology suitable for a broader range of biomedical applications.

Phenotypical enrichment strategies for microarray data analysis applied in a type II diabetes study.

Combining results from gene microarrays, clinical chemistry, and quantitative tissue histomorphology in an integrated bioinformatics setting enables prioritization of gene families as well as individual genes in a type II diabetes animal study. This new methodology takes advantage of a time-controlled mouse study as the animals progress from a normal phenotype to that of type II diabetes. Profiles from different levels of the biological hierarchy of unpooled entities provide an encompassing, system-wide view of biological changes. Here, phenotypic changes on the tissue-structural and physiological level are used as statistical covariants to enrich the gene expression analysis, suggesting correlative processes between gene expression and phenotype unlocked by multi-sample comparisons. We apply correlative and gene set enrichment procedures and compare the results to differential analysis to identify molecular markers. Evaluation based on ontological classifications proves changes in prioritization of disease-related genes that would have been overlooked by conventional gene expression analyses strategies.