Christophe Ladroue

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.

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.

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.

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. Magn Reson Med 50:697–703, 2003.

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.

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.

Granger Causality: Theory and Applications

A question of great interest in systems biology is how to uncover complex network structures from experimental data[1, 3, 18, 38, 55]. With the rapid progress of experimental techniques, a crucial task is to develop methodologies that are both statistically sound and computationally feasible for analysing increasingly large datasets and reliably inferring biological interactions from them [16, 17, 22, 37, 40, 42]. The building block of such enterprise is to being able to detect relations (causal, statistical or functional) between nodes of the network. Over the past two decades, a number of approaches have been developed: information theory ([4]), control theory ([17]) or Bayesian statistics ([35]). Here we will be focusing on another successful alternative approach: Granger causality. In recent Cell papers [7, 12], the authors have come to the conclusion that the ordinary differential equation approach outperforms the other reverse engineering approaches (Bayesian network and information theory) in building causal networks. We have demonstrated that the Granger causality achieves better results than the ordinary differential approach [34]. The basic idea of Granger causality can be traced back to Wiener[47] who conceived the notion that, if the prediction of one time series is improved by incorporating the knowledge of a second time series, then the latter is said to have a causal influence on the first. Granger[23, 24] later formalized Wiener’s idea in the context of linear regression models. Specifically, two auto-regressive models are fitted to the first time series – with and without including the second time series – and the improvement of the prediction is measured by the ratio of the variance of the error terms. A ratio larger than one signifies an improvement, hence a causal connection. At worst, the ratio is 1 and signifies causal independence from the second time series to the first. Geweke’s decomposition of a vector autoregressive process ([20, 21]) led to a set of causality measures which have a spectral representation and make the interpretation more informative and useful by extending Granger causality to the frequency domain. In this chapter, we aim to present Granger causality and how its original formalism has been extended to address biological and computational issues, as summarized in Fig. 5.1.

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

BackgroundReverse-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.ResultsHere 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.ConclusionsThe 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.

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

BackgroundThe 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.ResultsIn 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.ConclusionsOur new tool, Wellington-bootstrap, will enable the detection of differential TF binding facilitating the study of gene regulatory systems.

Modelling and Stochastic Simulation of Synthetic Biological Boolean Gates

Synthetic Biology aspires to design, compose and engineer biological systems that implement specified behaviour. When designing such systems, hypothesis testing via computational modelling and simulation is vital in order to reduce the need of costly wet lab experiments. As a case study, we discuss the use of computational modelling and stochastic simulation for engineered genetic circuits that implement Boolean AND and OR gates that have been reported in the literature. We present performance analysis results for nine different state-of-the-art stochastic simulation algorithms and analyse the dynamic behaviour of the proposed gates. Stochastic simulations verify the desired functioning of the proposed gate designs.