Decision support system and outcome prediction in a cohort of patients with necrotizing soft-tissue infections.

INTRODUCTION: Necrotizing Soft Tissue Infections (NSTI) are severe infections with high mortality affecting a heterogeneous patient population. There is a need for a clinical decision support system which predicts outcomes and provides treatment recommendations early in the disease course. METHODS: To identify relevant clinical needs, interviews with eight medical professionals (surgeons, intensivists, general practitioner, emergency department physician) were conducted. This resulted in 24 unique questions. Mortality was selected as first endpoint to develop a machine learning (Random Forest) based prediction model. For this purpose, data from the prospective, international INFECT cohort (N = 409) was used. RESULTS: Applying a feature selection procedure based on an unsupervised algorithm (Boruta) to the  > 1000 variables available in INFECT, including baseline, and both NSTI specific and NSTI non-specific clinical data yielded sixteen predictive parameters available on or prior to the first day on the intensive care unit (ICU). Using these sixteen variables 30-day mortality could be accurately predicted (AUC = 0.91, 95% CI 0.88-0.96). Except for age, all variables were related to sepsis (e.g. lactate, urine production, systole). No NSTI-specific variables were identified. Predictions significantly outperformed the SOFA score(p < 0.001, AUC = 0.77, 95% CI 0.69-0.84) and exceeded but did not significantly differ from the SAPS II score (p = 0.07, AUC = 0.88, 95% CI 0.83-0.92). The developed model proved to be stable with AUC  > 0.8 in case of high rates of missing data (50% missing) or when only using very early (<1 h) available variables. CONCLUSIONS: This study shows that mortality can be accurately predicted using a machine learning model. It lays the foundation for a more extensive, multi-endpoint clinical decision support system in which ultimately other outcomes and clinical questions (risk for septic shock, AKI, causative microbe) will be included.

Systems and Precision Medicine in Necrotizing Soft Tissue Infections.

Necrotizing soft tissue infections (NSTI) are multifactorial and characterized by dysfunctional, time dependent, highly varying hyper- to hypo-inflammatory host responses contributing to disease severity. Furthermore, host-pathogen interactions are diverse and difficult to identify and characterize, due to the many different disease endotypes. There is a need for both refined bedside diagnostics as well as novel targeted treatment options to improve outcome in NSTI. In order to achieve clinically relevant results and to guide preclinical and clinical research the vast amount of fragmented clinical and experimental datasets, which often include omics data at different levels (transcriptomics, proteomics, metabolomics, etc.), need to be organized, harmonized, integrated, and analyzed taking into account the Big Data nature of these datasets. In this chapter, we address these matters from a systems perspective and yet personalized approach. The chapter provides an overview on the increasingly more frequent use of Big Data and Artificial Intelligence (AI) to aggregate and generate knowledge from burgeoning clinical and biochemical information, addresses the challenges to manage this information, and summarizes current efforts to develop robust computer-aided clinical decision support systems so to tackle the serious challenges in NSTI diagnosis, stratification, and optimized tailored therapy.

Histone deacetylase 5 regulates interleukin 6 secretion and insulin action in skeletal muscle.

OBJECTIVE: Physical exercise training is associated with increased glucose uptake in skeletal muscle and improved glycemic control. HDAC5, a class IIa histone deacetylase, has been shown to regulate transcription of the insulin-responsive glucose transporter GLUT4 in cultured muscle cells. In this study, we analyzed the contribution of HDAC5 to the transcriptional network in muscle and the beneficial effect of muscle contraction and regular exercise on glucose metabolism. METHODS: HDAC5 knockout mice (KO) and wild-type (WT) littermates were trained for 8 weeks on treadmills, metabolically phenotyped, and compared to sedentary controls. Hdac5-deficient skeletal muscle and cultured Hdac5-knockdown (KD) C2C12 myotubes were utilized for studies of gene expression and glucose metabolism. Chromatin immunoprecipitation (ChIP) studies were conducted to analyze Il6 promoter activity using H3K9ac and HDAC5 antibodies. RESULTS: Global transcriptome analysis of Hdac5 KO gastrocnemius muscle demonstrated activation of the IL-6 signaling pathway. Accordingly, knockdown of Hdac5 in C2C12 myotubes led to higher expression and secretion of IL-6 with enhanced insulin-stimulated activation of AKT that was reversed by Il6 knockdown. Moreover, Hdac5-deficient myotubes exhibited enhanced glucose uptake, glycogen synthesis, and elevated expression levels of the glucose transporter GLUT4. Transcription of Il6 was further enhanced by electrical pulse stimulation in Hdac5-deficient C2C12 myotubes. ChIP identified a ∼1 kb fragment of the Il6 promoter that interacts with HDAC5 and demonstrated increased activation-associated histone marker AcH3K9 in Hdac5-deficient muscle cells. Exercise intervention of HDAC5 KO mice resulted in improved systemic glucose tolerance as compared to WT controls. CONCLUSIONS: We identified HDAC5 as a negative epigenetic regulator of IL-6 synthesis and release in skeletal muscle. HDAC5 may exert beneficial effects through two different mechanisms, transcriptional control of genes required for glucose disposal and utilization, and HDAC5-dependent IL-6 signaling cross-talk to improve glucose uptake in muscle in response to exercise.

Analyzing and interpreting genome data at the network level with ConsensusPathDB.

ConsensusPathDB consists of a comprehensive collection of human (as well as mouse and yeast) molecular interaction data integrated from 32 different public repositories and a web interface featuring a set of computational methods and visualization tools to explore these data. This protocol describes the use of ConsensusPathDB (http://consensuspathdb.org) with respect to the functional and network-based characterization of biomolecules (genes, proteins and metabolites) that are submitted to the system either as a priority list or together with associated experimental data such as RNA-seq. The tool reports interaction network modules, biochemical pathways and functional information that are significantly enriched by the user's input, applying computational methods for statistical over-representation, enrichment and graph analysis. The results of this protocol can be observed within a few minutes, even with genome-wide data. The resulting network associations can be used to interpret high-throughput data mechanistically, to characterize and prioritize biomarkers, to integrate different omics levels, to design follow-up functional assay experiments and to generate topology for kinetic models at different scales.

Transcriptional signature induced by a metastasis-promoting c-Src mutant in a human breast cell line.

UNLABELLED: Deletions at the C-terminus of the proto-oncogene protein c-Src kinase are found in the viral oncogene protein v-Src as well as in some advanced human colon cancers. They are associated with increased kinase activity and cellular invasiveness. Here, we analyzed the mRNA expression signature of a constitutively active C-terminal mutant of c-Src, c-Src(mt), in comparison with its wild-type protein, c-Src(wt), in the human non-transformed breast epithelial cell line MCF-10A. We demonstrated previously that the mutant altered migratory and metastatic properties. Genome-wide transcriptome analysis revealed that c-Src(mt) de-regulated the expression levels of approximately 430 mRNAs whose gene products are mainly involved in the cellular processes of migration and adhesion, apoptosis and protein synthesis. 82.9% of these genes have previously been linked to cellular migration, while the others play roles in RNA transport and splicing processes, for instance. Consistent with the transcriptome data, cells expressing c-Src(mt), but not those expressing c-Src(wt), showed the capacity to metastasize into the lungs of mice in vivo. The mRNA expression profile of c-Src(mt)-expressing cells shows significant overlap with that of various primary human tumor samples, possibly reflecting elevated Src activity in some cancerous cells. Expression of c-Src(mt) led to elevated migratory potential. We used this model system to analyze the transcriptional changes associated with an invasive cellular phenotype. These genes and pathways de-regulated by c-Src(mt) may provide suitable biomarkers or targets of therapeutic approaches for metastatic cells. DATABASE: This project was submitted to the National Center for Biotechnology Information BioProject under ID PRJNA288540. The Illumina RNA-Seq reads are available in the National Center for Biotechnology Information Sequence Read Archive under study ID SRP060008 with accession numbers SRS977414 for MCF-10A cells, SRS977717 for mock cells, SRS978053 for c-Src(wt) cells and SRS978046 for c-Src(mt) cells.

An Impaired Respiratory Electron Chain Triggers Down-regulation of the Energy Metabolism and De-ubiquitination of Solute Carrier Amino Acid Transporters.

Hundreds of genes have been associated with respiratory chain disease (RCD), the most common inborn error of metabolism so far. Elimination of the respiratory electron chain by depleting the entire mitochondrial DNA (mtDNA, ρ(0) cells) has therefore one of the most severe impacts on the energy metabolism in eukaryotic cells. In this study, proteomic data sets including the post-translational modifications (PTMs) phosphorylation and ubiquitination were integrated with metabolomic data sets and selected enzyme activities in the osteosarcoma cell line 143B.TK(-) A shotgun based SILAC LC-MS proteomics and a targeted metabolomics approach was applied to elucidate the consequences of the ρ(0) state. Pathway and protein-protein interaction (PPI) network analyses revealed a nonuniform down-regulation of the respiratory electron chain, the tricarboxylic acid (TCA) cycle, and the pyruvate metabolism in ρ(0) cells. Metabolites of the TCA cycle were dysregulated, such as a reduction of citric acid and cis-aconitic acid (six and 2.5-fold), and an increase of lactic acid, oxalacetic acid (both twofold), and succinic acid (fivefold) in ρ(0) cells. Signaling pathways such as GPCR, EGFR, G12/13 alpha, and Rho GTPases were up-regulated in ρ(0) cells, which could be indicative for the mitochondrial retrograde response, a pathway of communication from mitochondria to the nucleus. This was supported by our phosphoproteome data, which revealed two main processes, GTPase-related signal transduction and cytoskeleton organization. Furthermore, a general de-ubiquitination in ρ(0) cells was observed, for example, 80S ribosomal proteins were in average threefold and SLC amino acid transporters fivefold de-ubiquitinated. The latter might cause the observed significant increase of amino acid levels in ρ(0) cells. We conclude that an elimination of the respiratory electron chain, e.g. mtDNA depletion, not only leads to an uneven down-regulation of mitochondrial energy pathways, but also triggers the retrograde response.

DIPSBC--data integration platform for systems biology collaborations.

BACKGROUND: Modern biomedical research is often organized in collaborations involving labs worldwide. In particular in systems biology, complex molecular systems are analyzed that require the generation and interpretation of heterogeneous data for their explanation, for example ranging from gene expression studies and mass spectrometry measurements to experimental techniques for detecting molecular interactions and functional assays. XML has become the most prominent format for representing and exchanging these data. However, besides the development of standards there is still a fundamental lack of data integration systems that are able to utilize these exchange formats, organize the data in an integrative way and link it with applications for data interpretation and analysis. RESULTS: We have developed DIPSBC, an interactive data integration platform supporting collaborative research projects, based on Foswiki, Solr/Lucene, and specific helper applications. We describe the main features of the implementation and highlight the performance of the system with several use cases. All components of the system are platform independent and open-source developments and thus can be easily adopted by researchers. An exemplary installation of the platform which also provides several helper applications and detailed instructions for system usage and setup is available at http://dipsbc.molgen.mpg.de. CONCLUSIONS: DIPSBC is a data integration platform for medium-scale collaboration projects that has been tested already within several research collaborations. Because of its modular design and the incorporation of XML data formats it is highly flexible and easy to use.