Kiwi: a tool for integration and visualization of network topology and gene-set analysis

Evolution from adherent to suspension: systems biology of HEK293 cell line development

The need for new safe and efficacious therapies has led to an increased focus on biologics produced in mammalian cells. The human cell line HEK293 has bio-synthetic potential for human-like production attributes and is currently used for manufacturing of several therapeutic proteins and viral vectors. Despite the increased popularity of this strain we still have limited knowledge on the genetic composition of its derivatives. Here we present a genomic, transcriptomic and metabolic gene analysis of six of the most widely used HEK293 cell lines. Changes in gene copy and expression between industrial progeny cell lines and the original HEK293 were associated with cellular component organization, cell motility and cell adhesion. Changes in gene expression between adherent and suspension derivatives highlighted switching in cholesterol biosynthesis and expression of five key genes (RARG, ID1, ZIC1, LOX and DHRS3), a pattern validated in 63 human adherent or suspension cell lines of other origin.

Interactive and coordinated visualization approaches for biological data analysis

The field of computational biology has become largely dependent on data visualization tools to analyze the increasing quantities of data gathered through the use of new and growing technologies. Aside from the volume, which often results in large amounts of noise and complex relationships with no clear structure, the visualization of biological data sets is hindered by their heterogeneity, as data are obtained from different sources and contain a wide variety of attributes, including spatial and temporal information. This requires visualization approaches that are able to not only represent various data structures simultaneously but also provide exploratory methods that allow the identification of meaningful relationships that would not be perceptible through data analysis algorithms alone. In this article, we present a survey of visualization approaches applied to the analysis of biological data. We focus on graph-based visualizations and tools that use coordinated multiple views to represent high-dimensional multivariate data, in particular time series gene expression, protein-protein interaction networks and biological pathways. We then discuss how these methods can be used to help solve the current challenges surrounding the visualization of complex biological data sets.

Type 2 diabetes and obesity induce similar transcriptional reprogramming in human myocytes

BACKGROUND

Skeletal muscle is one of the primary tissues involved in the development of type 2 diabetes (T2D). The close association between obesity and T2D makes it difficult to isolate specific effects attributed to the disease alone. Therefore, here we set out to identify and characterize intrinsic properties of myocytes, associated independently with T2D or obesity.

METHODS

We generated and analyzed RNA-seq data from primary differentiated myotubes from 24 human subjects, using a factorial design (healthy/T2D and non-obese/obese), to determine the influence of each specific factor on genome-wide transcription. This setup enabled us to identify intrinsic properties, originating from muscle precursor cells and retained in the corresponding myocytes. Bioinformatic and statistical methods, including differential expression analysis, gene-set analysis, and metabolic network analysis, were used to characterize the different myocytes.

RESULTS

We found that the transcriptional program associated with obesity alone was strikingly similar to that induced specifically by T2D. We identified a candidate epigenetic mechanism, H3K27me3 histone methylation, mediating these transcriptional signatures. T2D and obesity were independently associated with dysregulated myogenesis, down-regulated muscle function, and up-regulation of inflammation and extracellular matrix components. Metabolic network analysis identified that in T2D but not obesity a specific metabolite subnetwork involved in sphingolipid metabolism was transcriptionally regulated.

CONCLUSIONS

Our findings identify inherent characteristics in myocytes, as a memory of the in vivo phenotype, without the influence from a diabetic or obese extracellular environment, highlighting their importance in the development of T2D.

The yeast osmostress response is carbon source dependent

Adaptation to altered osmotic conditions is a fundamental property of living cells and has been studied in detail in the yeast Saccharomyces cerevisiae. Yeast cells accumulate glycerol as compatible solute, controlled at different levels by the High Osmolarity Glycerol (HOG) response pathway. Up to now, essentially all osmostress studies in yeast have been performed with glucose as carbon and energy source, which is metabolised by glycolysis with glycerol as a by-product. Here we investigated the response of yeast to osmotic stress when yeast is respiring ethanol as carbon and energy source. Remarkably, yeast cells do not accumulate glycerol under these conditions and it appears that trehalose may partly take over the role as compatible solute. The HOG pathway is activated in very much the same way as during growth on glucose and is also required for osmotic adaptation. Slower volume recovery was observed in ethanol-grown cells as compared to glucose-grown cells. Dependence on key regulators as well as the global gene expression profile were similar in many ways to those previously observed in glucose-grown cells. However, there are indications that cells re-arrange redox-metabolism when respiration is hampered under osmostress, a feature that could not be observed in glucose-grown cells.

Genome scale metabolic modeling of cancer

Cancer cells reprogram metabolism to support rapid proliferation and survival. Energy metabolism is particularly important for growth and genes encoding enzymes involved in energy metabolism are frequently altered in cancer cells. A genome scale metabolic model (GEM) is a mathematical formalization of metabolism which allows simulation and hypotheses testing of metabolic strategies. It has successfully been applied to many microorganisms and is now used to study cancer metabolism. Generic models of human metabolism have been reconstructed based on the existence of metabolic genes in the human genome. Cancer specific models of metabolism have also been generated by reducing the number of reactions in the generic model based on high throughput expression data, e.g. transcriptomics and proteomics. Targets for drugs and bio markers for diagnostics have been identified using these models. They have also been used as scaffolds for analysis of high throughput data to allow mechanistic interpretation of changes in expression. Finally, GEMs allow quantitative flux predictions using flux balance analysis (FBA). Here we critically review the requirements for successful FBA simulations of cancer cells and discuss the symmetry between the methods used for modeling of microbial and cancer metabolism. GEMs have great potential for translational research on cancer and will therefore become of increasing importance in the future.

A Metabolomics Study of Retrospective Forensic Data from Whole Blood Samples of Humans Exposed to 3,4-Methylenedioxymethamphetamine: A New Approach for Identifying Drug Metabolites and Changes in Metabolism Related to Drug Consumption

The illicit drug 3,4-methylenedioxymethamphetamine (MDMA) has profound physiological cerebral, cardiac, and hepatic effects that are reflected in the blood. Screening of blood for MDMA and other narcotics are routinely performed in forensics analysis using ultra-performance liquid chromatography with high-resolution time-of-flight mass spectrometry (UPLC-HR-TOFMS). The aim of this study was to investigate whether such UPLC-HR-TOFMS data collected over a two-year period could be used for untargeted metabolomics to determine MDMA metabolites as well as endogenous changes related to drug response and toxicology. Whole blood samples from living Danish drivers' positive for MDMA in different concentrations were compared to negative control samples using various statistical methods. The untargeted identification of known MDMA metabolites was used to validate the methods. The results further revealed changes of several acylcarnitines, adenosine monophosphate, adenosine, inosine, thiomorpholine 3-carboxylate, tryptophan, S-adenosyl-l-homocysteine (SAH), and lysophospatidylcholine (lysoPC) species in response to MDMA. These endogenous metabolites could be implicated in an increased energy demand and mechanisms related to the serotonergic syndrome as well as drug induced neurotoxicity. The findings showed that it was possible to extract meaningful results from retrospective UPLC-HR-TOFMS screening data for metabolic profiling in relation to drug metabolism, endogenous physiological effects, and toxicology.

Proteome- and transcriptome-driven reconstruction of the human myocyte metabolic network and its use for identification of markers for diabetes

Skeletal myocytes are metabolically active and susceptible to insulin resistance and are thus implicated in type 2 diabetes (T2D). This complex disease involves systemic metabolic changes, and their elucidation at the systems level requires genome-wide data and biological networks. Genome-scale metabolic models (GEMs) provide a network context for the integration of high-throughput data. We generated myocyte-specific RNA-sequencing data and investigated their correlation with proteome data. These data were then used to reconstruct a comprehensive myocyte GEM. Next, we performed a meta-analysis of six studies comparing muscle transcription in T2D versus healthy subjects. Transcriptional changes were mapped on the myocyte GEM, revealing extensive transcriptional regulation in T2D, particularly around pyruvate oxidation, branched-chain amino acid catabolism, and tetrahydrofolate metabolism, connected through the downregulated dihydrolipoamide dehydrogenase. Strikingly, the gene signature underlying this metabolic regulation successfully classifies the disease state of individual samples, suggesting that regulation of these pathways is a ubiquitous feature of myocytes in response to T2D.