MicroRNA expression profiles in non­epithelial ovarian tumors.

Ovarian germ cell tumors (OGCTs) and sex cord stromal tumors (SCSTs) are rare gynecologic tumors that are derived from germ and stromal cells, respectively. Unlike their epithelial counterparts, molecular pathogenesis of these tumor types is still poorly understood. Here, we characterized microRNA (miRNA) expression profiles of 9 OGCTs (2 malignant and 7 benign) and 3 SCSTs using small RNA sequencing. We observed significant miRNA expression variations among the three tumor groups. To further demonstrate the biological relevance of our findings, we selected 12 miRNAs for validation in an extended cohort of 16 OGCTs (9 benign and 7 malignant) and 7 SCSTs by reverse transcription-quantitative polymerase chain reaction. Higher expression of miR­373­3p, miR­372­3p and miR­302c­3p and lower expression of miR­199a­5p, miR­214­5p and miR­202­3p were reproducibly observed in malignant OGCTs as compared to benign OGCTs or SCSTs. Comparing with benign OGCTs, miR­202c­3p and miR­513c­5p were more abundant in SCSTs. Additionally, we examined Beclin 1 (BECN1), a target of miR­199a­5p, in the clinical samples using western blot analysis. Our results show that BECN1 expression was higher in malignant OGCTs than benign OGCTs, which is concordant with their lower miR­199a­5p expression. This study suggests that these miRNAs may have potential value as tumor markers and implications for further understanding the molecular basis of these tumor types.

Mass Spectrometry Based Metabolomics Comparison of Liver Grafts from Donors after Circulatory Death (DCD) and Donors after Brain Death (DBD) Used in Human Orthotopic Liver Transplantation.

Use of marginal liver grafts, especially those from donors after circulatory death (DCD), has been considered as a solution to organ shortage. Inferior outcomes have been attributed to donor warm ischaemic damage in these DCD organs. Here we sought to profile the metabolic mechanisms underpinning donor warm ischaemia. Non-targeted Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometry metabolomics was applied to biopsies of liver grafts from donors after brain death (DBD; n = 27) and DCD (n = 10), both during static cold storage (T1) as well as post-reperfusion (T2). Furthermore 6 biopsies from DBD donors prior to the organ donation (T0) were also profiled. Considering DBD and DCD together, significant metabolic differences were discovered between T1 and T2 (688 peaks) that were primarily related to amino acid metabolism, meanwhile T0 biopsies grouped together with T2, denoting the distinctively different metabolic activity of the perfused state. Major metabolic differences were discovered between DCD and DBD during cold-phase (T1) primarily related to glucose, tryptophan and kynurenine metabolism, and in the post-reperfusion phase (T2) related to amino acid and glutathione metabolism. We propose tryptophan/kynurenine and S-adenosylmethionine as possible biomarkers for the previously established higher graft failure of DCD livers, and conclude that the associated pathways should be targeted in more exhaustive and quantitative investigations.

The Adipose Transcriptional Response to Insulin Is Determined by Obesity, Not Insulin Sensitivity.

Metabolically healthy obese subjects display preserved insulin sensitivity and a beneficial white adipose tissue gene expression pattern. However, this observation stems from fasting studies when insulin levels are low. We investigated adipose gene expression by 5'Cap-mRNA sequencing in 17 healthy non-obese (NO), 21 insulin-sensitive severely obese (ISO), and 30 insulin-resistant severely obese (IRO) subjects, before and 2 hr into a hyperinsulinemic euglycemic clamp. ISO and IRO subjects displayed a clear but globally similar transcriptional response to insulin, which differed from the small effects observed in NO subjects. In the obese, 231 genes were altered; 71 were enriched in ISO subjects (e.g., phosphorylation processes), and 52 were enriched in IRO subjects (e.g., cellular stimuli). Common cardio-metabolic risk factors and gender do not influence these findings. This study demonstrates that differences in the acute transcriptional response to insulin are primarily driven by obesity per se, challenging the notion of healthy obese adipose tissue, at least in severe obesity.

Two-compartment model as a teaching tool for cholesterol homeostasis.

Cholesterol is a vital structural and functional molecule in the human body that is only slightly soluble in water and therefore does not easily travels by itself in the bloodstream. To enable cholesterol's targeted delivery to cells and tissues, it is encapsulated by different fractions of lipoproteins, complex particles containing both proteins and lipids. Maintaining cholesterol homeostasis is a highly regulated process with multiple factors acting at both molecular and tissue levels. Furthermore, to regulate the circulatory transport of cholesterol in lipoproteins, the amount of cholesterol present depends on and is controlled by cholesterol dietary intake, de novo synthesis, usage, and excretion; abnormal and/or unbalanced cholesterol levels have been shown to lead to severe outcomes, e.g., cardiovascular diseases. To investigate cholesterol transport in the circulatory system, we have previously developed a two-compartment mathematical model. Here, we show how this model can be used as a teaching tool for cholesterol homeostasis. Using the model and a hands-on approach, students can familiarize themselves with the basic components and mechanisms behind balanced cholesterol circulatory transport as well as investigate the consequences of and countermeasures to abnormal cholesterol levels. Among others, various treatments of high blood cholesterol levels can be simulated, e.g., with commonly prescribed de novo cholesterol synthesis inhibitors.

Integration of somatic mutation, expression and functional data reveals potential driver genes predictive of breast cancer survival.

MOTIVATION: Genome and transcriptome analyses can be used to explore cancers comprehensively, and it is increasingly common to have multiple omics data measured from each individual. Furthermore, there are rich functional data such as predicted impact of mutations on protein coding and gene/protein networks. However, integration of the complex information across the different omics and functional data is still challenging. Clinical validation, particularly based on patient outcomes such as survival, is important for assessing the relevance of the integrated information and for comparing different procedures. RESULTS: An analysis pipeline is built for integrating genomic and transcriptomic alterations from whole-exome and RNA sequence data and functional data from protein function prediction and gene interaction networks. The method accumulates evidence for the functional implications of mutated potential driver genes found within and across patients. A driver-gene score (DGscore) is developed to capture the cumulative effect of such genes. To contribute to the score, a gene has to be frequently mutated, with high or moderate mutational impact at protein level, exhibiting an extreme expression and functionally linked to many differentially expressed neighbors in the functional gene network. The pipeline is applied to 60 matched tumor and normal samples of the same patient from The Cancer Genome Atlas breast-cancer project. In clinical validation, patients with high DGscores have worse survival than those with low scores (P = 0.001). Furthermore, the DGscore outperforms the established expression-based signatures MammaPrint and PAM50 in predicting patient survival. In conclusion, integration of mutation, expression and functional data allows identification of clinically relevant potential driver genes in cancer. AVAILABILITY AND IMPLEMENTATION: The documented pipeline including annotated sample scripts can be found in http://fafner.meb.ki.se/biostatwiki/driver-genes/. CONTACT: yudi.pawitan@ki.se SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Transcribed enhancers lead waves of coordinated transcription in transitioning mammalian cells.

Although it is generally accepted that cellular differentiation requires changes to transcriptional networks, dynamic regulation of promoters and enhancers at specific sets of genes has not been previously studied en masse. Exploiting the fact that active promoters and enhancers are transcribed, we simultaneously measured their activity in 19 human and 14 mouse time courses covering a wide range of cell types and biological stimuli. Enhancer RNAs, then messenger RNAs encoding transcription factors, dominated the earliest responses. Binding sites for key lineage transcription factors were simultaneously overrepresented in enhancers and promoters active in each cellular system. Our data support a highly generalizable model in which enhancer transcription is the earliest event in successive waves of transcriptional change during cellular differentiation or activation.

Towards a system level understanding of non-model organisms sampled from the environment: a network biology approach.

The acquisition and analysis of datasets including multi-level omics and physiology from non-model species, sampled from field populations, is a formidable challenge, which so far has prevented the application of systems biology approaches. If successful, these could contribute enormously to improving our understanding of how populations of living organisms adapt to environmental stressors relating to, for example, pollution and climate. Here we describe the first application of a network inference approach integrating transcriptional, metabolic and phenotypic information representative of wild populations of the European flounder fish, sampled at seven estuarine locations in northern Europe with different degrees and profiles of chemical contaminants. We identified network modules, whose activity was predictive of environmental exposure and represented a link between molecular and morphometric indices. These sub-networks represented both known and candidate novel adverse outcome pathways representative of several aspects of human liver pathophysiology such as liver hyperplasia, fibrosis, and hepatocellular carcinoma. At the molecular level these pathways were linked to TNF alpha, TGF beta, PDGF, AGT and VEGF signalling. More generally, this pioneering study has important implications as it can be applied to model molecular mechanisms of compensatory adaptation to a wide range of scenarios in wild populations.

Application of metabolomics to investigate the process of human orthotopic liver transplantation: a proof-of-principle study.

To improve the outcome of orthotopic liver transplantation (OLT), knowledge of early molecular events occurring upon ischemia/reperfusion is essential. Powerful approaches for profiling metabolic changes in tissues and biofluids are now available. Our objective was to investigate the applicability of two technologies to a small but well-defined cohort of patients undergoing OLT: consecutive liver biopsies by Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) and microdialysates of extracellular fluid by coulometric electrochemical array detection (CEAD). FT-ICR MS detected reproducibly more than 4,000 peaks, revealing hundreds of significant metabolic differences between pre- and postreperfusion grafts. These included increased urea production, bile acid synthesis and clearance of preservation solution upon reperfusion, indicating a rapid resumption of biochemical function within the graft. FT-ICR MS also identified successfully the only graft obtained by donation-after-cardiac-death as a "metabolic outlier." CEAD time-profile analysis showed that there was considerable change in redox-active metabolites (up to 18 h postreperfusion), followed by their stabilization. Collectively these results verify the applicability of FT-ICR MS and CEAD for characterizing multiple metabolic pathways during OLT. The success of this proof-of-principle application of these technologies to a clinical setting, considering the potential metabolic heterogeneity across only eight donor livers, is encouraging.