Altering the Mitochondrial Fatty Acid Synthesis (mtFASII) Pathway Modulates Cellular Metabolic States and Bioactive Lipid Profiles as Revealed by Metabolomic Profiling

Despite the presence of a cytosolic fatty acid synthesis pathway, mitochondria have retained their own means of creating fatty acids via the mitochondrial fatty acid synthesis (mtFASII) pathway. The reason for its conservation has not yet been elucidated. Therefore, to better understand the role of mtFASII in the cell, we used thin layer chromatography to characterize the contribution of the mtFASII pathway to the fatty acid composition of selected mitochondrial lipids. Next, we performed metabolomic analysis on HeLa cells in which the mtFASII pathway was either hypofunctional (through knockdown of mitochondrial acyl carrier protein, ACP) or hyperfunctional (through overexpression of mitochondrial enoyl-CoA reductase, MECR). Our results indicate that the mtFASII pathway contributes little to the fatty acid composition of mitochondrial lipid species examined. Additionally, loss of mtFASII function results in changes in biochemical pathways suggesting alterations in glucose utilization and redox state. Interestingly, levels of bioactive lipids, including lysophospholipids and sphingolipids, directly correlate with mtFASII function, indicating that mtFASII may be involved in the regulation of bioactive lipid levels. Regulation of bioactive lipid levels by mtFASII implicates the pathway as a mediator of intracellular signaling.     

基因表达谱中信号通路基因集分析方法进展

微阵列技术是生物技术变革的核心,允许研究者同时监测成千上万个基的表达水平,已广泛应用医学研究.如何挖掘海量基表达信息中的有用信息并进行生物学专业解释,是基表达谱数据分析领所面临的一个重要挑战.生物信号通路研究已成为基芯片中不同表型差异表达研究的主要方法,其是以整个信号通路作为一个整体作为研究对象,此得出的研究结果更加科学和准确.在本文中我们简要描述了近10年来信号通路基集富集分析方法的发展情况,将其分为三个阶段,对每个阶段方法的基础和特点做了一些简单的总结和阐述.     

Metabolomic analysis using optimized NMR and statistical methods

NMR-based metabolomics requires robust automated methodologies, and the accuracy of NMR-based metabolomics data is greatly influenced by the reproducibility of data acquisition and processing methods. Effective water resonance signal suppression and reproducible spectral phasing and baseline traces across series of related samples are crucial for statistical analysis. We assess robustness, repeatability, sensitivity, selectivity, and practicality of commonly used solvent peak suppression methods in the NMR analysis of biofluids with respect to the automated processing of the NMR spectra and the impact of pulse sequence and data processing methods on the sensitivity of pattern recognition and statistical analysis of the metabolite profiles. We introduce two modifications to the excitation sculpting pulse sequence whereby the excitation solvent suppression pulse cascade is preceded by low-power water resonance presaturation pulses during the relaxation delay. Our analysis indicates that combining water presaturation with excitation sculpting water suppression delivers the most reproducible and information-rich NMR spectra of biofluids.     

Metabolomic Profiles of Body Mass Index in the Framingham Heart Study Reveal Distinct Cardiometabolic Phenotypes

Background

Although obesity and cardiometabolic traits commonly overlap, underlying pathways remain incompletely defined. The association of metabolite profiles across multiple cardiometabolic traits may lend insights into the interaction of obesity and metabolic health. We sought to investigate metabolic signatures of obesity and related cardiometabolic traits in the community using broad-based metabolomic profiling.

Methods and Results

We evaluated the association of 217 assayed metabolites and cross-sectional as well as longitudinal changes in cardiometabolic traits among 2,383 Framingham Offspring cohort participants. Body mass index (BMI) was associated with 69 of 217 metabolites (P<0.00023 for all), including aromatic (tyrosine, phenylalanine) and branched chain amino acids (valine, isoleucine, leucine). Additional metabolic pathways associated with BMI included the citric acid cycle (isocitrate, alpha-ketoglutarate, aconitate), the tryptophan pathway (kynurenine, kynurenic acid), and the urea cycle. There was considerable overlap in metabolite profiles between BMI, abdominal adiposity, insulin resistance [IR] and dyslipidemia, modest overlap of metabolite profiles between BMI and hyperglycemia, and little overlap with fasting glucose or elevated blood pressure. Metabolite profiles were associated with longitudinal changes in fasting glucose, but the involved metabolites (ornithine, 5-HIAA, aminoadipic acid, isoleucine, cotinine) were distinct from those associated with baseline glucose or other traits. Obesity status appeared to “modify” the association of 9 metabolites with IR. For example, bile acid metabolites were strongly associated with IR among obese but not lean individuals, whereas isoleucine had a stronger association with IR in lean individuals.

Conclusions

In this large-scale metabolite profiling study, body mass index was associated with a broad range of metabolic alterations. Metabolite profiling highlighted considerable overlap with abdominal adiposity, insulin resistance, and dyslipidemia, but not with fasting glucose or blood pressure traits.     

Metabolomic Profiling of Faecal Extracts from Cryptosporidium parvum Infection in Experimental Mouse Models

Cryptosporidiosis is a gastrointestinal disease in humans and animals caused by infection with the protozoan parasite Cryptosporidium. In healthy individuals, the disease manifests mainly as acute self-limiting diarrhoea, but may be chronic and life threatening for those with compromised immune systems. Control and treatment of the disease is challenged by the lack of sensitive diagnostic tools and broad-spectrum chemotherapy. Metabolomics, or metabolite profiling, is an emerging field of study, which enables characterisation of the end products of regulatory processes in a biological system. Analysis of changes in metabolite patterns reflects changes in biochemical regulation, production and control, and may contribute to understanding the effects of Cryptosporidium infection in the host environment. In the present study, metabolomic analysis of faecal samples from experimentally infected mice was carried out to assess metabolite profiles pertaining to the infection. Gas-chromatography mass spectrometry (GC-MS) carried out on faecal samples from a group of C. parvum infected mice and a group of uninfected control mice detected a mean total of 220 compounds. Multivariate analyses showed distinct differences between the profiles of C. parvum infected mice and uninfected control mice,identifying a total of 40 compounds, or metabolites that contributed most to the variance between the two groups. These metabolites consisted of amino acids (n = 17), carbohydrates (n = 8), lipids (n = 7), organic acids (n = 3) and other various metabolites (n = 5), which showed significant differences in levels of metabolite abundance between the infected and uninfected mice groups (p < 0.05). The metabolites detected in this study as well as the differences in abundance between the C. parvum infected and the uninfected control mice, highlights the effects of the infection on intestinal permeability and the fate of the metabolites as a result of nutrient scavenging by the parasite to supplement its streamlined metabolism.     

Plasma Metabolomic Profiles in Breast Cancer Patients and Healthy Controls: By Race and Tumor Receptor Subtypes

BACKGROUND: A few studies in the last several years have shown that metabolomics, the study of metabolites and small intermediate molecules, may help better understand the breast carcinogenesis. However, breast cancer is a heterogeneous disease with different subtypes. Additionally, there is a significant racial difference in terms of breast cancer incidence and mortality. Few, if any, metabolomics studies in breast cancer have considered race and tumor subtypes in the study design. METHODS: We performed a global metabolomic profiling using mass spectrometry and samples from 60 breast cancer cases and 60 matched controls. RESULTS: A total of 375 named metabolites were observed, with 117 metabolites whose levels were significantly different between African American and Caucasian American women (P < .05 and q < 0.10) and 78 that differed between breast cancer cases and healthy controls (P < .05 and q < 0.10). Most of those differentiated metabolites belong to amino acids, fatty acids, and lysolipids. In the pathway-based analysis, we found that plasma levels of many amino acids were statistically significantly lower in patients with breast cancer, especially those with triple-negative breast cancer, than healthy controls. However, plasma levels of many FAs related to β-oxidation were statistically significantly higher in patients with breast cancer than healthy controls, suggesting the possibility of altered FA β-oxidation in patients with breast cancer. CONCLUSIONS: Because of small sample size, the clinical usage of the metabolites from this study is unclear. Further validation of those significant metabolites is warranted, especially with the consideration of racial difference.     

Interpreting genotype-by-environment interaction using redundancy analysis

Summary Methods for the interpretation of genotype-by-environment interaction in the presense of explicitly measured environmental variables can be divided into two groups. Firstly, methods that extract environmental characterizations from the data itself, which are subsequently related to measured environmental variables, e.g., regression on the mean or singular value decomposition of the matrix of residuals from additivity, followed by correlation, or regression, methods. Secondly, methods that incorporate measured environmental variables directly into the model, e.g., multiple regression of individual genotypical responses on environmental variables, or factorial regression in which a genotype-by-environment matrix is modelled in terms of concomitant variables for the environmental factor. In this paper a redundancy analysis is presented, which can be derived from the singular-value decomposition of the residuals from additivity by imposing the restriction on the environmental scores of having to be linear combinations of environmental variables. At the same time, redundancy analysis is derivable from factorial regression by rotation of the axes in the space spanned by the fitted values of the factorial regression, followed by a reduction of dimensionality through discarding the least explanatory axes. Redundancy analysis is a member of the second group of methods, and can be an important tool in the interpretation of genotype-by-environment interaction, especially with reference to concomitant environmental information. A theoretical treatise of the method is given, followed by a practical example in which the results of the method are compared to the results of the other methods mentioned.     

Interpreting the protein language using proteomics

Post-translational modifications define the functional and structural plasticity of proteins in archaea, prokaryotes and eukaryotes. Multi-site protein modification modulates protein activity and macromolecular interactions and is involved in a range of fundamental molecular processes. Combining state-of-the-art technologies in molecular cell biology, protein mass spectrometry and bioinformatics, it is now feasible to discover and study the structural and functional roles of distinct protein post-translational modifications.     

Random Walk Models for Bayesian Clustering of Gene Expression Profiles

The analysis of gene expression temporal profiles is a topic of increasing interest in functional genomics. Model-based clustering methods are particularly interesting because they are able to capture the dynamic nature of these data and to identify the optimal number of clusters. We have defined a new Bayesian method that allows us to cope with some important issues that remain unsolved in the currently available approaches: the presence of time dislocations in gene expression, the non-stationarity of the processes generating the data, and the presence of data collected on an irregular temporal grid. Our method, which is based on random walk models, requires only mild a priori assumptions about the nature of the processes generating the data and explicitly models inter-gene variability within each cluster. It has first been validated on simulated datasets and then employed for the analysis of a dataset relative to serum-stimulated fibroblasts. In all cases, the results have been promising, showing that the method can be helpful in functional genomics research.     

Interpreting correlated motions using normal mode analysis

With the increased popularity of normal mode analyses in structural biology, it is important to carefully consider how to best utilize the results for gaining biological insights without over interpretation. The discussion in this article argues that for the purpose of identifying correlated motions in biomolecules, a case separate from concomitant conformational changes of structural motifs, it is generally important to use a large number of normal modes. This is illustrated through three increasingly complex examples. The simplest case includes two bilinearly coupled harmonic oscillators and serves as a straightforward problem where the important considerations are explicit and transparent. The argument is then generalized to include a system of N-coupled harmonic oscillators and finally to a realistic biomolecule. Although a small number of normal modes are useful for probing structural flexibility, it is clear that a much larger number of modes are required for properly investigating correlated motions in biomolecules.     

Metabolomic profiling of Drosophila using liquid chromatography Fourier transform mass spectrometry

Hydrophilic interaction chromatography (HILIC) interfaced with an Orbitrap Fourier transform mass spectrometer (FT-MS) was used to carry out metabolomic profiling of the classical Drosophila mutation, rosy (ry). This gene encodes a xanthine oxidase/dehydrogenase. In addition to validating the technology by detecting the same changes in xanthine, hypoxanthine, urate and allantoin that have been reported classically, completely unsuspected changes were detected in each of the tryptophan, arginine, pyrimidine and glycerophospholipid metabolism pathways. The rosy mutation thus ramifies far more widely than previously detected.     

Investigating Polycyclic Aromatic Hydrocarbons Profiles in Higher Plants Using Statistical Models

Thirty-six higher plants sampled from Olomoro, Irri, Uzere, and Oginni exploration sites in the Niger Delta region of Nigeria were subjected to GC/MS analysis to assess the occurrence, distribution and profiles of polycyclic aromatic hydrocarbons (PAHs) contained in them. The Σ₂₈PAHs ranged from 335 to 3094 ng/g. The results of the nonparametric regression models showed that PAHs concentration in a plant cannot be used in isolation to deduce the total PAHs concentration in soils hosting the plant since PAHs concentration in a plant is influenced by the presence (or absence) of other plants in that location. A combination of Factor analysis (FA) and principal component analysis (PCA) were used to recognize PAHs concentration patterns among the plants in the studied locations and individual PAHs compounds. Woody annuals and perennial plants formed similar patterns in Oginni and Irri locations. Three main clusters were formed by all the compounds with naphthalene and 2-methylnaphthalene standing as outliers in all the four locations.

Supplemental materials are available for this article. Go to the publisher's online edition of International Journal of Phytoremediation to view the supplemental file.     

Particle Deposition in Oral-tracheal Airway Models with Very Low Inhalation Profiles

Considerable progress has been made on modeling particle deposition in the oral-tracheal airway under some normal breathing conditions,i.e.,resting,light activity and moderate exercise.None of these standard breathing patterns correspond to very low inhalation profiles.It is known that particle deposition in the oral-tracheal airway is greatly influenced by flow and particle inlet conditions.In this work,very low inhalation flow rates are considered.Particle deposition is numerically investigated in different oral-tracheal airway models,i.e.,circular,elliptic and realistic oral-tracheal airway models.Both micro- and nano-particles that are normally present in cigarette smoke are considered.Results show that inhalation profiles greatly influence the particle deposition.Due to relatively low flow rate,for ultra-fine particles,the oral deposition is enhanced due to longer residence time in oral cavity and stronger Brownian motion.However,for larger particles,less particles deposit in the oral-tracheal airway due to the weaker impaction.The transition happens when particle size changes from 0.01 μm to 0.1 μm.The influence of the limited entrance area is shown and discussed.Under the low inhalation profiles,the highest deposition fraction could be in either circular or realistic models depending on the particle property and the geometric characteristic of oral cavity.The knowledge obtained in this study may be beneficial for the design of bionic inhaler and understanding of health effect from smoke particle on human being.     

NEMix: Single-cell Nested Effects Models for Probabilistic Pathway Stimulation

Nested effects models have been used successfully for learning subcellular networks from high-dimensional perturbation effects that result from RNA interference (RNAi) experiments. Here, we further develop the basic nested effects model using high-content single-cell imaging data from RNAi screens of cultured cells infected with human rhinovirus. RNAi screens with single-cell readouts are becoming increasingly common, and they often reveal high cell-to-cell variation. As a consequence of this cellular heterogeneity, knock-downs result in variable effects among cells and lead to weak average phenotypes on the cell population level. To address this confounding factor in network inference, we explicitly model the stimulation status of a signaling pathway in individual cells. We extend the framework of nested effects models to probabilistic combinatorial knock-downs and propose NEMix, a nested effects mixture model that accounts for unobserved pathway activation. We analyzed the identifiability of NEMix and developed a parameter inference scheme based on the Expectation Maximization algorithm. In an extensive simulation study, we show that NEMix improves learning of pathway structures over classical NEMs significantly in the presence of hidden pathway stimulation. We applied our model to single-cell imaging data from RNAi screens monitoring human rhinovirus infection, where limited infection efficiency of the assay results in uncertain pathway stimulation. Using a subset of genes with known interactions, we show that the inferred NEMix network has high accuracy and outperforms the classical nested effects model without hidden pathway activity. NEMix is implemented as part of the R/Bioconductor package ‘nem’ and available at www.cbg.ethz.ch/software/NEMix.     

Investigating Homology between Proteins using Energetic Profiles

Accumulated experimental observations demonstrate that protein stability is often preserved upon conservative point mutation. In contrast, less is known about the effects of large sequence or structure changes on the stability of a particular fold. Almost completely unknown is the degree to which stability of different regions of a protein is generally preserved throughout evolution. In this work, these questions are addressed through thermodynamic analysis of a large representative sample of protein fold space based on remote, yet accepted, homology. More than 3,000 proteins were computationally analyzed using the structural-thermodynamic algorithm COREX/BEST. Estimated position-specific stability (i.e., local Gibbs free energy of folding) and its component enthalpy and entropy were quantitatively compared between all proteins in the sample according to all-vs.-all pairwise structural alignment. It was discovered that the local stabilities of homologous pairs were significantly more correlated than those of non-homologous pairs, indicating that local stability was indeed generally conserved throughout evolution. However, the position-specific enthalpy and entropy underlying stability were less correlated, suggesting that the overall regional stability of a protein was more important than the thermodynamic mechanism utilized to achieve that stability. Finally, two different types of statistically exceptional evolutionary structure-thermodynamic relationships were noted. First, many homologous proteins contained regions of similar thermodynamics despite localized structure change, suggesting a thermodynamic mechanism enabling evolutionary fold change. Second, some homologous proteins with extremely similar structures nonetheless exhibited different local stabilities, a phenomenon previously observed experimentally in this laboratory. These two observations, in conjunction with the principal conclusion that homologous proteins generally conserved local stability, may provide guidance for a future thermodynamically informed classification of protein homology.     

Unbiased Rare Event Sampling in Spatial Stochastic Systems Biology Models Using a Weighted Ensemble of Trajectories

The long-term goal of connecting scales in biological simulation can be facilitated by scale-agnostic methods. We demonstrate that the weighted ensemble (WE) strategy, initially developed for molecular simulations, applies effectively to spatially resolved cell-scale simulations. The WE approach runs an ensemble of parallel trajectories with assigned weights and uses a statistical resampling strategy of replicating and pruning trajectories to focus computational effort on difficult-to-sample regions. The method can also generate unbiased estimates of non-equilibrium and equilibrium observables, sometimes with significantly less aggregate computing time than would be possible using standard parallelization. Here, we use WE to orchestrate particle-based kinetic Monte Carlo simulations, which include spatial geometry (e.g., of organelles, plasma membrane) and biochemical interactions among mobile molecular species. We study a series of models exhibiting spatial, temporal and biochemical complexity and show that although WE has important limitations, it can achieve performance significantly exceeding standard parallel simulation—by orders of magnitude for some observables.     

Features of Metabolomic Profiles in Different Stages of Ontogenesis in Prunella vulgaris (Lamiaceae) Grown in a Climate Chamber

Russian Journal of Bioorganic Chemistry - Metabolic analysis of methanol extracts of leaves of Prunella vulgaris L. (Lamiaceae) grown in a climate chamber has been carried out using gas...