Wellington-bootstrap: differential DNase-seq footprinting identifies cell-type determining transcription factors.

BACKGROUND: The analysis of differential gene expression is a fundamental tool to relate gene regulation with specific biological processes. Differential binding of transcription factors (TFs) can drive differential gene expression. While DNase-seq data can provide global snapshots of TF binding, tools for detecting differential binding from pairs of DNase-seq data sets are lacking. RESULTS: In order to link expression changes with changes in TF binding we introduce the concept of differential footprinting alongside a computational tool. We demonstrate that differential footprinting is associated with differential gene expression and can be used to define cell types by their specific TF occupancy patterns. CONCLUSIONS: Our new tool, Wellington-bootstrap, will enable the detection of differential TF binding facilitating the study of gene regulatory systems.

Interval estimation for concentration in the ELISA setting.

Enzyme-linked immunosorbent assays (ELISAs) are often used to quantify the concentration of biological substances. In a typical analysis only a point estimate of the concentration will be presented as interval estimation continues to present challenges for non-linear dose-response models. In this setting, interval estimates calculated using a Wald approach can suffer from poor coverage and have limits that fall outside parameter boundaries. Here we compare profile likelihood interval estimation procedures to Wald type intervals for the interval estimation of a concentration in the ELISA setting. Through a comprehensive simulation study, it is shown that profile likelihood methods result in interval estimates with superior coverage and that are more robust to differences in assay design when compared to Wald based approaches.

Toward Full-Stack In Silico Synthetic Biology: Integrating Model Specification, Simulation, Verification, and Biological Compilation.

We present the Infobiotics Workbench (IBW), a user-friendly, scalable, and integrated computational environment for the computer-aided design of synthetic biological systems. It supports an iterative workflow that begins with specification of the desired synthetic system, followed by simulation and verification of the system in high-performance environments and ending with the eventual compilation of the system specification into suitable genetic constructs. IBW integrates modeling, simulation, verification, and biocompilation features into a single software suite. This integration is achieved through a new domain-specific biological programming language, the Infobiotics Language (IBL), which tightly combines these different aspects of in silico synthetic biology into a full-stack integrated development environment. Unlike existing synthetic biology modeling or specification languages, IBL uniquely blends modeling, verification, and biocompilation statements into a single file. This allows biologists to incorporate design constraints within the specification file rather than using decoupled and independent formalisms for different in silico analyses. This novel approach offers seamless interoperability across different tools as well as compatibility with SBOL and SBML frameworks and removes the burden of doing manual translations for standalone applications. We demonstrate the features, usability, and effectiveness of IBW and IBL using well-established synthetic biological circuits.

Identifying interactions in the time and frequency domains in local and global networks - A Granger Causality Approach.

BACKGROUND: Reverse-engineering approaches such as Bayesian network inference, ordinary differential equations (ODEs) and information theory are widely applied to deriving causal relationships among different elements such as genes, proteins, metabolites, neurons, brain areas and so on, based upon multi-dimensional spatial and temporal data. There are several well-established reverse-engineering approaches to explore causal relationships in a dynamic network, such as ordinary differential equations (ODE), Bayesian networks, information theory and Granger Causality. RESULTS: Here we focused on Granger causality both in the time and frequency domain and in local and global networks, and applied our approach to experimental data (genes and proteins). For a small gene network, Granger causality outperformed all the other three approaches mentioned above. A global protein network of 812 proteins was reconstructed, using a novel approach. The obtained results fitted well with known experimental findings and predicted many experimentally testable results. In addition to interactions in the time domain, interactions in the frequency domain were also recovered. CONCLUSIONS: The results on the proteomic data and gene data confirm that Granger causality is a simple and accurate approach to recover the network structure. Our approach is general and can be easily applied to other types of temporal data.

Proteomic and metabolomic analysis of cardioprotection: Interplay between protein kinase C epsilon and delta in regulating glucose metabolism of murine hearts.

We applied a combined proteomic and metabolomic approach to obtain novel mechanistic insights in PKCvarepsilon-mediated cardioprotection. Mitochondrial and cytosolic proteins from control and transgenic hearts with constitutively active or dominant negative PKCvarepsilon were analyzed using difference in-gel electrophoresis (DIGE). Among the differentially expressed proteins were creatine kinase, pyruvate kinase, lactate dehydrogenase, and the cytosolic isoforms of aspartate amino transferase and malate dehydrogenase, the two enzymatic components of the malate aspartate shuttle, which are required for the import of reducing equivalents from glycolysis across the inner mitochondrial membrane. These enzymatic changes appeared to be dependent on PKCvarepsilon activity, as they were not observed in mice expressing inactive PKCvarepsilon. High-resolution proton nuclear magnetic resonance ((1)H-NMR) spectroscopy confirmed a pronounced effect of PKCvarepsilon activity on cardiac glucose and energy metabolism: normoxic hearts with constitutively active PKCvarepsilon had significantly lower concentrations of glucose, lactate, glutamine and creatine, but higher levels of choline, glutamate and total adenosine nucleotides. Moreover, the depletion of cardiac energy metabolites was slower during ischemia/reperfusion injury and glucose metabolism recovered faster upon reperfusion in transgenic hearts with active PKCvarepsilon. Notably, inhibition of PKCvarepsilon resulted in compensatory phosphorylation and mitochondrial translocation of PKCdelta. Taken together, our findings are the first evidence that PKCvarepsilon activity modulates cardiac glucose metabolism and provide a possible explanation for the synergistic effect of PKCdelta and PKCvarepsilon in cardioprotection.

Muscle metabolism and activation heterogeneity by combined 31P chemical shift and T2 imaging, and pulmonary O2 uptake during incremental knee-extensor exercise.

The integration of skeletal muscle substrate depletion, metabolite accumulation, and fatigue during large muscle-mass exercise is not well understood. Measurement of intramuscular energy store degradation and metabolite accumulation is confounded by muscle heterogeneity. Therefore, to characterize regional metabolic distribution in the locomotor muscles, we combined 31P magnetic resonance spectroscopy, chemical shift imaging, and T2-weighted imaging with pulmonary oxygen uptake during bilateral knee-extension exercise to intolerance. Six men completed incremental tests for the following: (1) unlocalized 31P magnetic resonance spectroscopy; and (2) spatial determination of 31P metabolism and activation. The relationship of pulmonary oxygen uptake to whole quadriceps phosphocreatine concentration ([PCr]) was inversely linear, and three of four knee-extensor muscles showed activation as assessed by change in T2. The largest changes in [PCr], [inorganic phosphate] ([Pi]) and pH occurred in rectus femoris, but no voxel (72 cm3) showed complete PCr depletion at exercise cessation. The most metabolically active voxel reached 11 ± 9 mM [PCr] (resting, 29 ± 1 mM), 23 ± 11 mM [Pi] (resting, 7 ± 1 mM), and a pH of 6.64 ± 0.29 (resting, 7.08 ± 0.03). However, the distribution of 31P metabolites and pH varied widely between voxels, and the intervoxel coefficient of variation increased between rest (∼10%) and exercise intolerance (∼30-60%). Therefore, the limit of tolerance was attained with wide heterogeneity in substrate depletion and fatigue-related metabolite accumulation, with extreme metabolic perturbation isolated to only a small volume of active muscle (<5%). Regional intramuscular disturbances are thus likely an important requisite for exercise intolerance. How these signals integrate to limit muscle power production, while regional "recruitable muscle" energy stores are presumably still available, remains uncertain.

Gene-centric association signals for haemostasis and thrombosis traits identified with the HumanCVD BeadChip.

Coagulation phenotypes show strong intercorrelations, affect cardiovascular disease risk and are influenced by genetic variants. The objective of this study was to search for novel genetic variants influencing the following coagulation phenotypes: factor VII levels, fibrinogen levels, plasma viscosity and platelet count. We genotyped the British Women's Heart and Health Study (n=3,445) and the Whitehall II study (n=5,059) using the Illumina HumanCVD BeadArray to investigate genetic associations and pleiotropy. In addition to previously reported associations (SH2B3, F7/F10, PROCR, GCKR, FGA/FGB/FGG, IL5), we identified novel associations at GRK5 (rs10128498, p=1.30x10(-6)), GCKR (rs1260326, p=1.63x10(-6)), ZNF259-APOA5 (rs651821, p=7.17x10(-6)) with plasma viscosity; and at CSF1 (rs333948, p=8.88x10(-6)) with platelet count. A pleiotropic effect was identified in GCKR which associated with factor VII (p=2.16x10(-7)) and plasma viscosity (p=1.63x10(-6)), and, to a lesser extent, ZNF259-APOA5 which also associated with factor VII and fibrinogen (p<1.00x10-²) and plasma viscosity (p<1.00x10(-5)). Triglyceride associated variants were overrepresented in factor VII and plasma viscosity associations. Adjusting for triglyceride levels resulted in attenuation of associations at the GCKR and ZNF259-APOA5 loci. In addition to confirming previously reported associations, we identified four single nucleotide polymorphisms (SNPs) associated with plasma viscosity and platelet count and found evidence of pleiotropic effects with SNPs in GCKR and ZNF259-APOA5. These triglyceride-associated, pleiotropic SNPs suggest a possible causal role for triglycerides in coagulation.

Can localised (19)F magnetic resonance spectroscopy pharmacokinetics of 5FU in colorectal metastases predict clinical response?

BACKGROUND: 5-Fluorouracil remains widely used in colorectal cancer treatment more than 40 years after its development. 19F magnetic resonance spectroscopy can be used in vivo to measure 5FU's half-life and metabolism to cytotoxic fluoronucleotides. Previous studies have shown better survival associated with longer 5FU tumour half-life. This work investigated 5FU pharmacokinetics in liver metastases of colorectal cancer. METHODS: A total of 32 subjects with colorectal cancer undergoing 5FU treatment, 15 of whom had liver metastases, were examined in a 1.5T MRI scanner, using a large coil positioned over the liver. Non-localised spectra were acquired in 1-min blocks for 32 min after injection of a 5FU bolus. The 5FU half-life was measured in each subject, and averaged spectra were examined for the presence of fluoronucleotides. Associations with progression-free survival were assessed. RESULTS: No association was observed between 5FU half-life, tumour burden and survival. Half-lives were all shorter than those associated with improved survival in the literature. Remarkably, in the group with liver metastases, high levels of fluoronucleotides were associated with poorer survival; this counterintuitive result may be due to the higher levels of fluoronucleotides (whose level is higher in tumour tissue than in normal liver) in patients with higher tumour burdens. CONCLUSIONS: It is recommended that future studies use chemical shift imaging at higher field strengths to better resolve tumour from normal liver. Non-localised spectroscopy retains prognostic potential by enabling straightforward detection of fluoronucleotides, which are present at very low concentrations distributed throughout the tissue.

Comparative lipidomics profiling of human atherosclerotic plaques.

BACKGROUND: We sought to perform a systematic lipid analysis of atherosclerotic plaques using emerging mass spectrometry techniques. METHODS AND RESULTS: A chip-based robotic nanoelectrospray platform interfaced to a triple quadrupole mass spectrometer was adapted to analyze lipids in tissue sections and extracts from human endarterectomy specimens by shotgun lipidomics. Eighteen scans for different lipid classes plus additional scans for fatty acids resulted in the detection of 150 lipid species from 9 different classes of which 24 were detected in endarterectomies only. Further analyses focused on plaques from symptomatic and asymptomatic patients and stable versus unstable regions within the same lesion. Polyunsaturated cholesteryl esters with long-chain fatty acids and certain sphingomyelin species showed the greatest relative enrichment in plaques compared to plasma and formed part of a lipid signature for vulnerable and stable plaque areas in a systems-wide network analysis. In principal component analyses, the combination of lipid species across different classes provided a better separation of stable and unstable areas than individual lipid classes. CONCLUSIONS: This comprehensive analysis of plaque lipids demonstrates the potential of lipidomics for unraveling the lipid heterogeneity within atherosclerotic lesions.

Beyond element-wise interactions: identifying complex interactions in biological processes.

BACKGROUND: Biological processes typically involve the interactions of a number of elements (genes, cells) acting on each others. Such processes are often modelled as networks whose nodes are the elements in question and edges pairwise relations between them (transcription, inhibition). But more often than not, elements actually work cooperatively or competitively to achieve a task. Or an element can act on the interaction between two others, as in the case of an enzyme controlling a reaction rate. We call "complex" these types of interaction and propose ways to identify them from time-series observations. METHODOLOGY: We use Granger Causality, a measure of the interaction between two signals, to characterize the influence of an enzyme on a reaction rate. We extend its traditional formulation to the case of multi-dimensional signals in order to capture group interactions, and not only element interactions. Our method is extensively tested on simulated data and applied to three biological datasets: microarray data of the Saccharomyces cerevisiae yeast, local field potential recordings of two brain areas and a metabolic reaction. CONCLUSIONS: Our results demonstrate that complex Granger causality can reveal new types of relation between signals and is particularly suited to biological data. Our approach raises some fundamental issues of the systems biology approach since finding all complex causalities (interactions) is an NP hard problem.

Combined metabolomic and proteomic analysis of human atrial fibrillation.

OBJECTIVES: We sought to decipher metabolic processes servicing the increased energy demand during persistent atrial fibrillation (AF) and to ascertain whether metabolic derangements might instigate this arrhythmia. BACKGROUND: Whereas electrical, structural, and contractile remodeling processes are well-recognized contributors to the self-perpetuating nature of AF, the impact of cardiac metabolism upon the persistence/initiation of this resilient arrhythmia has not been explored in detail. METHODS: Human atrial appendage tissues from matched cohorts in sinus rhythm (SR), from those who developed AF post-operatively, and from patients in persistent AF undergoing cardiac surgery were analyzed using a combined metabolomic and proteomic approach. RESULTS: High-resolution proton nuclear magnetic resonance (NMR) spectroscopy of cardiac tissue from patients in persistent AF revealed a rise in beta-hydroxybutyrate, the major substrate in ketone body metabolism, along with an increase in ketogenic amino acids and glycine. These metabolomic findings were substantiated by proteomic experiments demonstrating differential expression of 3-oxoacid transferase, the key enzyme for ketolytic energy production. Notably, compared with the SR cohort, the group susceptible to post-operative AF showed a discordant regulation of energy metabolites. Combined principal component and linear discriminant analyses of metabolic profiles from proton NMR spectroscopy correctly classified more than 80% of patients at risk of AF at the time of coronary artery bypass grafting. CONCLUSIONS: The present study characterized the metabolic adaptation to persistent AF, unraveling a potential role for ketone bodies, and demonstrated that discordant metabolic alterations are evident in individuals susceptible to post-operative AF.

Development of a decision support system for diagnosis and grading of brain tumours using in vivo magnetic resonance single voxel spectra.

A computer-based decision support system to assist radiologists in diagnosing and grading brain tumours has been developed by the multi-centre INTERPRET project. Spectra from a database of 1H single-voxel spectra of different types of brain tumours, acquired in vivo from 334 patients at four different centres, are clustered according to their pathology, using automated pattern recognition techniques and the results are presented as a two-dimensional scatterplot using an intuitive graphical user interface (GUI). Formal quality control procedures were performed to standardize the performance of the instruments and check each spectrum, and teams of expert neuroradiologists, neurosurgeons, neurologists and neuropathologists clinically validated each case. The prototype decision support system (DSS) successfully classified 89% of the cases in an independent test set of 91 cases of the most frequent tumour types (meningiomas, low-grade gliomas and high-grade malignant tumours--glioblastomas and metastases). It also helps to resolve diagnostic difficulty in borderline cases. When the prototype was tested by radiologists and other clinicians it was favourably received. Results of the preliminary clinical analysis of the added value of using the DSS for brain tumour diagnosis with MRS showed a small but significant improvement over MRI used alone. In the comparison of individual pathologies, PNETs were significantly better diagnosed with the DSS than with MRI alone.

Independent component analysis for automated decomposition of in vivo magnetic resonance spectra.

Fully automated methods for analyzing MR spectra would be of great benefit for clinical diagnosis, in particular for the extraction of relevant information from large databases for subsequent pattern recognition analysis. Independent component analysis (ICA) provides a means of decomposing signals into their constituent components. This work investigates the use of ICA for automatically extracting features from in vivo MR spectra. After its limits are assessed on artificial data, the method is applied to a set of brain tumor spectra. ICA automatically, and in an unsupervised fashion, decomposes the signals into interpretable components. Moreover, the spectral decomposition achieved by the ICA leads to the separation of some tissue types, which confirms the biochemical relevance of the components.