Validation-based model selection for 13C metabolic flux analysis with uncertain measurement errors.

Accurate measurements of metabolic fluxes in living cells are central to metabolism research and metabolic engineering. The gold standard method is model-based metabolic flux analysis (MFA), where fluxes are estimated indirectly from mass isotopomer data with the use of a mathematical model of the metabolic network. A critical step in MFA is model selection: choosing what compartments, metabolites, and reactions to include in the metabolic network model. Model selection is often done informally during the modelling process, based on the same data that is used for model fitting (estimation data). This can lead to either overly complex models (overfitting) or too simple ones (underfitting), in both cases resulting in poor flux estimates. Here, we propose a method for model selection based on independent validation data. We demonstrate in simulation studies that this method consistently chooses the correct model in a way that is independent on errors in measurement uncertainty. This independence is beneficial, since estimating the true magnitude of these errors can be difficult. In contrast, commonly used model selection methods based on the χ2-test choose different model structures depending on the believed measurement uncertainty; this can lead to errors in flux estimates, especially when the magnitude of the error is substantially off. We present a new approach for quantification of prediction uncertainty of mass isotopomer distributions in other labelling experiments, to check for problems with too much or too little novelty in the validation data. Finally, in an isotope tracing study on human mammary epithelial cells, the validation-based model selection method identified pyruvate carboxylase as a key model component. Our results argue that validation-based model selection should be an integral part of MFA model development.

Functional Analysis of Immune Signature Genes in Th1* Memory Cells Links ISOC1 and Pyrimidine Metabolism to IFN-γ and IL-17 Production.

CCR6+CXCR3+CCR4-CD4+ memory T cells, termed Th1*, are important for long-term immunity to Mycobacterium tuberculosis and the pathogenesis of autoimmune diseases. Th1* cells express a unique set of lineage-specific transcription factors characteristic of both Th1 and Th17 cells and display distinct gene expression profiles compared with other CD4+ T cell subsets. To examine molecules and signaling pathways important for the effector function of Th1* cells, we performed loss-of-function screening of genes selectively enriched in the Th1* subset. The genetic screen yielded candidates whose depletion significantly impaired TCR-induced IFN-γ production. These included genes previously linked to IFN-γ or M. tuberculosis susceptibility and novel candidates, such as ISOC1, encoding a metabolic enzyme of unknown function in mammalian cells. ISOC1-depleted T cells, which produced less IFN-γ and IL-17, displayed defects in oxidative phosphorylation and glycolysis and impairment of pyrimidine metabolic pathway. Supplementation with extracellular pyrimidines rescued both bioenergetics and IFN-γ production in ISOC1-deficient T cells, indicating that pyrimidine metabolism is a key driver of effector functions in CD4+ T cells and Th1* cells. Results provide new insights into the immune-stimulatory function of ISOC1 as well as the particular metabolic requirements of human memory T cells, providing a novel resource for understanding long-term T cell-driven responses.

Mapping choline metabolites in normal and transformed cells.

INTRODUCTION: Choline is an essential human nutrient that is particular important for proliferating cells, and altered choline metabolism has been associated with cancer transformation. Yet, the various metabolic fates of choline in proliferating cells have not been investigated systematically. OBJECTIVES: This study aims to map the metabolic products of choline in normal and cancerous proliferating cells. METHODS: We performed 13C-choline tracing followed by liquid chromatography-high resolution mass spectrometry (LC-HRMS) analysis of metabolic products in normal and in vitro-transformed (tumor-forming) epithelial cells, and also in tumor-derived cancer cell lines. Selected metabolites were quantified by internal standards. RESULTS: Untargeted analysis revealed 121 LCMS peaks that were 13C-labeled from choline, including various phospholipid species, but also previously unknown products such as monomethyl- and dimethyl-ethanolamines. Interestingly, we observed formation of betaine from choline specifically in tumor-derived cells. Expression of choline dehydrogenase (CHDH), which catalyzes the first step of betaine synthesis, correlated with betaine synthesis across the cell lines studied. RNAi silencing of CHDH did not affect cell proliferation, although we observed an increased fraction of G2M phase cells with some RNAi sequences, suggesting that CHDH and its product betaine may play a role in cell cycle progression. Betaine cell concentration was around 10 µM, arguing against an osmotic function, and was not used as a methyl donor. The function of betaine in these tumor-derived cells is presently unknown. CONCLUSION: This study identifies novel metabolites of choline in cancer and normal cell lines, and reveals altered choline metabolism in cancer cells.

An siRNA screen for NFAT activation identifies septins as coordinators of store-operated Ca2+ entry.

The STIM1-ORAI1 pathway of store-operated Ca(2+) entry is an essential component of cellular Ca(2+) signalling. STIM1 senses depletion of intracellular Ca(2+) stores in response to physiological stimuli, and relocalizes within the endoplasmic reticulum to plasma-membrane-apposed junctions, where it recruits and gates open plasma membrane ORAI1 Ca(2+) channels. Here we use a genome-wide RNA interference screen in HeLa cells to identify filamentous septin proteins as crucial regulators of store-operated Ca(2+) entry. Septin filaments and phosphatidylinositol-4,5-bisphosphate (also known as PtdIns(4,5)P2) rearrange locally at endoplasmic reticulum-plasma membrane junctions before and during formation of STIM1-ORAI1 clusters, facilitating STIM1 targeting to these junctions and promoting the stable recruitment of ORAI1. Septin rearrangement at junctions is required for PtdIns(4,5)P2 reorganization and efficient STIM1-ORAI1 communication. Septins are known to demarcate specialized membrane regions such as dendritic spines, the yeast bud and the primary cilium, and to serve as membrane diffusion barriers and/or signalling hubs in cellular processes such as vesicle trafficking, cell polarity and cytokinesis. Our data show that septins also organize the highly localized plasma membrane domains that are important in STIM1-ORAI1 signalling, and indicate that septins may organize membrane microdomains relevant to other signalling processes.

Gabapentin Can Suppress Cell Proliferation Independent of the Cytosolic Branched-Chain Amino Acid Transferase 1 (BCAT1).

The metabolism of branched-chain amino acids (BCAA) has recently been implicated in the growth of several cancer cell types. Gabapentin, a synthetic amino acid, is commonly used in high concentrations in this context to inhibit the cytosolic branched-chain amino acid transferase (BCAT1) enzyme. Here, we report that 10 mM gabapentin reduces the growth of HCT116 cells, which have an active branched-chain amino acid transferase but express very low levels of BCAT1, and presumably rely on the mitochondrial BCAT2 enzyme. Gabapentin did not affect transamination of BCAA to branched-chain keto acids (BCKA) in HCT116 cells, nor the reverse formation of BCAA from BCKA, indicating that the branched-chain amino acid transaminase is not inhibited. Moreover, the growth-inhibitory effect of gabapentin could not be rescued by supplementation with BCKA, and this was not due to the lack of uptake of BCKA, indicating that other effects of gabapentin are important. An untargeted LC-MS analysis of gabapentin-treated cells revealed a marked depletion of branched-chain carnitines. These results demonstrate that gabapentin at high concentrations can inhibit cell proliferation without affecting BCAT1 and may affect mitochondrial BCKA catabolism.

A Web Service Framework for Interactive Analysis of Metabolomics Data.

Analyzing mass spectrometry-based metabolomics data presents a major challenge to metabolism researchers, as it requires downloading and processing large data volumes through complex "pipelines", even in cases where only a single metabolite or peak is of interest. This presents a significant hurdle for data sharing, reanalysis, or meta-analysis of existing data sets, whether locally stored or available from public repositories. Here we introduce mzAccess, a software system that provides interactive, online access to primary mass spectrometry data in real-time via a Web service protocol, circumventing the need for bulk data processing. mzAccess allows querying instrument data for spectra, chromatograms, or two-dimensional MZ-RT areas in either profile or centroid modes through a simple, uniform interface that is independent of vendor or instrument type. Using a cache mechanism, mzAccess achieves response times in the millisecond range for typical liquid chromatography-mass spectrometry (LC-MS) peaks, enabling real-time browsing of large data sets with hundreds or even thousands of samples. By simplifying access to metabolite data, we hope that this system will help enable data sharing and reanalysis in the metabolomics field.

Estimation of flux ratios without uptake or release data: Application to serine and methionine metabolism.

Model-based metabolic flux analysis (MFA) using isotope-labeled substrates has provided great insight into intracellular metabolic activities across a host of organisms. One challenge with applying MFA in mammalian systems, however, is the need for absolute quantification of nutrient uptake, biomass composition, and byproduct release fluxes. Such measurements are often not feasible in complex culture systems or in vivo. One way to address this issue is to estimate flux ratios, the fractional contribution of a flux to a metabolite pool, which are independent of absolute measurements and yet informative for cellular metabolism. Prior work has focused on "local" estimation of a handful of flux ratios for specific metabolites and reactions. Here, we perform systematic, model-based estimation of all flux ratios in a metabolic network using isotope labeling data, in the absence of uptake/release data. In a series of examples, we investigate what flux ratios can be well estimated with reasonably tight confidence intervals, and contrast this with confidence intervals on normalized fluxes. We find that flux ratios can provide useful information on the metabolic state, and is complementary to normalized fluxes: for certain metabolic reactions, only flux ratios can be well estimated, while for others normalized fluxes can be obtained. Simulation studies of a large human metabolic network model suggest that estimation of flux ratios is technically feasible for complex networks, but additional studies on data from actual isotopomer labeling experiments are needed to validate these results. Finally, we experimentally study serine and methionine metabolism in cancer cells using flux ratios. We find that, in these cells, the methionine cycle is truncated with little remethylation from homocysteine, and polyamine synthesis in the absence of methionine salvage leads to loss of 5-methylthioadenosine, suggesting a new mode of overflow metabolism in cancer cells. This work highlights the potential for flux ratio analysis in the absence of absolute quantification, which we anticipate will be important for both in vitro and in vivo studies of cancer metabolism.

Metabolite Profiling and Stable Isotope Tracing in Sorted Subpopulations of Mammalian Cells.

Biological samples such as tissues, blood, or tumors are often complex and harbor heterogeneous populations of cells. Separating out specific cell types or subpopulations from such complex mixtures to study their metabolic phenotypes is challenging because experimental procedures for separation may disturb the metabolic state of cells. To address this issue, we developed a method for analysis of cell subpopulations using stable isotope tracing and fluorescence-activated cell sorting followed by liquid chromatography-high-resolution mass spectrometry. To ensure a faithful representation of cellular metabolism after cell sorting, we benchmarked sorted extraction against direct extraction. While peak areas differed markedly with lower signal for amino acids but higher signal for nucleotides, mass isotopomer distributions from sorted cells were generally in good agreement with those obtained from direct extractions, indicating that they reflect the true metabolic state of cells prior to sorting. In proof-of-principle studies, our method revealed metabolic phenotypes specific to T cell subtypes, and also metabolic features of cells in the committed phase of the cell division cycle. Our approach enables studies of a wide range of adherent and suspension cell subpopulations, which we anticipate will be of broad importance in cell biology and biomedicine.

A systematic survey of lipids across mouse tissues.

Lipids are a diverse collection of macromolecules essential for normal physiology, but the tissue distribution and function for many individual lipid species remain unclear. Here, we report a mass spectrometry survey of lipid abundance across 18 mouse tissues, detecting ~1,000 mass spectrometry features, of which we identify 179 lipids from the glycerolipids, glycerophospholipids, lysophospholipids, acylcarnitines, sphingolipids, and cholesteryl ester classes. Our data reveal tissue-specific organization of lipids and can be used to generate testable hypotheses. For example, our data indicate that circulating triglycerides positively and negatively associated with future diabetes in humans are enriched in mouse adipose tissue and liver, respectively, raising hypotheses regarding the tissue origins of these diabetes-associated lipids. We also integrate our tissue lipid data with gene expression profiles to predict a number of substrates of lipid-metabolizing enzymes, highlighting choline phosphotransferases and sterol O-acyltransferases. Finally, we identify several tissue-specific lipids not present in plasma under normal conditions that may be of interest as biomarkers of tissue injury, and we show that two of these lipids are released into blood following ischemic brain injury in mice. This resource complements existing compendia of tissue gene expression and may be useful for integrative physiology and lipid biology.

A computational screen for regulators of oxidative phosphorylation implicates SLIRP in mitochondrial RNA homeostasis.

The human oxidative phosphorylation (OxPhos) system consists of approximately 90 proteins encoded by nuclear and mitochondrial genomes and serves as the primary cellular pathway for ATP biosynthesis. While the core protein machinery for OxPhos is well characterized, many of its assembly, maturation, and regulatory factors remain unknown. We exploited the tight transcriptional control of the genes encoding the core OxPhos machinery to identify novel regulators. We developed a computational procedure, which we call expression screening, which integrates information from thousands of microarray data sets in a principled manner to identify genes that are consistently co-expressed with a target pathway across biological contexts. We applied expression screening to predict dozens of novel regulators of OxPhos. For two candidate genes, CHCHD2 and SLIRP, we show that silencing with RNAi results in destabilization of OxPhos complexes and a marked loss of OxPhos enzymatic activity. Moreover, we show that SLIRP plays an essential role in maintaining mitochondrial-localized mRNA transcripts that encode OxPhos protein subunits. Our findings provide a catalogue of potential novel OxPhos regulators that advance our understanding of the coordination between nuclear and mitochondrial genomes for the regulation of cellular energy metabolism.

Multi-organ expression profiling uncovers a gene module in coronary artery disease involving transendothelial migration of leukocytes and LIM domain binding 2: the Stockholm Atherosclerosis Gene Expression (STAGE) study.

Environmental exposures filtered through the genetic make-up of each individual alter the transcriptional repertoire in organs central to metabolic homeostasis, thereby affecting arterial lipid accumulation, inflammation, and the development of coronary artery disease (CAD). The primary aim of the Stockholm Atherosclerosis Gene Expression (STAGE) study was to determine whether there are functionally associated genes (rather than individual genes) important for CAD development. To this end, two-way clustering was used on 278 transcriptional profiles of liver, skeletal muscle, and visceral fat (n = 66/tissue) and atherosclerotic and unaffected arterial wall (n = 40/tissue) isolated from CAD patients during coronary artery bypass surgery. The first step, across all mRNA signals (n = 15,042/12,621 RefSeqs/genes) in each tissue, resulted in a total of 60 tissue clusters (n = 3958 genes). In the second step (performed within tissue clusters), one atherosclerotic lesion (n = 49/48) and one visceral fat (n = 59) cluster segregated the patients into two groups that differed in the extent of coronary stenosis (P = 0.008 and P = 0.00015). The associations of these clusters with coronary atherosclerosis were validated by analyzing carotid atherosclerosis expression profiles. Remarkably, in one cluster (n = 55/54) relating to carotid stenosis (P = 0.04), 27 genes in the two clusters relating to coronary stenosis were confirmed (n = 16/17, P<10(-27 and-30)). Genes in the transendothelial migration of leukocytes (TEML) pathway were overrepresented in all three clusters, referred to as the atherosclerosis module (A-module). In a second validation step, using three independent cohorts, the A-module was found to be genetically enriched with CAD risk by 1.8-fold (P<0.004). The transcription co-factor LIM domain binding 2 (LDB2) was identified as a potential high-hierarchy regulator of the A-module, a notion supported by subnetwork analysis, by cellular and lesion expression of LDB2, and by the expression of 13 TEML genes in Ldb2-deficient arterial wall. Thus, the A-module appears to be important for atherosclerosis development and, together with LDB2, merits further attention in CAD research.

Mitochondrial and nuclear genomic responses to loss of LRPPRC expression.

Rapid advances in genotyping and sequencing technology have dramatically accelerated the discovery of genes underlying human disease. Elucidating the function of such genes and understanding their role in pathogenesis, however, remain challenging. Here, we introduce a genomic strategy to characterize such genes functionally, and we apply it to LRPPRC, a poorly studied gene that is mutated in Leigh syndrome, French-Canadian type (LSFC). We utilize RNA interference to engineer an allelic series of cellular models in which LRPPRC has been stably silenced to different levels of knockdown efficiency. We then combine genome-wide expression profiling with gene set enrichment analysis to identify cellular responses that correlate with the loss of LRPPRC. Using this strategy, we discovered a specific role for LRPPRC in the expression of all mitochondrial DNA-encoded mRNAs, but not the rRNAs, providing mechanistic insights into the enzymatic defects observed in the disease. Our analysis shows that nuclear genes encoding mitochondrial proteins are not collectively affected by the loss of LRPPRC. We do observe altered expression of genes related to hexose metabolism, prostaglandin synthesis, and glycosphingolipid biology that may either play an adaptive role in cell survival or contribute to pathogenesis. The combination of genetic perturbation, genomic profiling, and pathway analysis represents a generic strategy for understanding disease pathogenesis.

Transcriptional profiling uncovers a network of cholesterol-responsive atherosclerosis target genes.

Despite the well-documented effects of plasma lipid lowering regimes halting atherosclerosis lesion development and reducing morbidity and mortality of coronary artery disease and stroke, the transcriptional response in the atherosclerotic lesion mediating these beneficial effects has not yet been carefully investigated. We performed transcriptional profiling at 10-week intervals in atherosclerosis-prone mice with human-like hypercholesterolemia and a genetic switch to lower plasma lipoproteins (Ldlr(-/-)Apo(100/100)Mttp(flox/flox) Mx1-Cre). Atherosclerotic lesions progressed slowly at first, then expanded rapidly, and plateaued after advanced lesions formed. Analysis of lesion expression profiles indicated that accumulation of lipid-poor macrophages reached a point that led to the rapid expansion phase with accelerated foam-cell formation and inflammation, an interpretation supported by lesion histology. Genetic lowering of plasma cholesterol (e.g., lipoproteins) at this point all together prevented the formation of advanced plaques and parallel transcriptional profiling of the atherosclerotic arterial wall identified 37 cholesterol-responsive genes mediating this effect. Validation by siRNA-inhibition in macrophages incubated with acetylated-LDL revealed a network of eight cholesterol-responsive atherosclerosis genes regulating cholesterol-ester accumulation. Taken together, we have identified a network of atherosclerosis genes that in response to plasma cholesterol-lowering prevents the formation of advanced plaques. This network should be of interest for the development of novel atherosclerosis therapies.

Promoting the implementation of evidence-based practice: a literature review focusing on the role of nursing leadership.

OBJECTIVES: Despite a growing interest in evidence-based practice (EBP), the implementation into clinical practice of knowledge derived from research has proved to be a cumbersome process. Additionally, the literature seems to present a fragmented picture with research mainly focusing on a few factors of possible importance, among which leadership appears to be one of the more important. Thus, this study aimed to systematically review the literature regarding leadership and its possible influence on the process of implementing EBP. APPROACH: A literature review was conducted. Electronic database searches were conducted to identify studies on leadership, administrators, managers, implementation, evidence-based and nursing. The search identified 43 potentially relevant papers, of which 36 were excluded after an appraisal was performed by two independent reviewers. Results were extracted and synthesised into a narrative text. FINDINGS: Seven papers were included in the literature review. The findings can be divided into three major areas: (1) characteristics of the leader, (2) characteristics of the organisation and (3) characteristics of the culture. Our findings indicate that leadership is vital for the process of implementing EBP in nursing and also highlights the possible importance of the organisation and the culture in which the leader operates. These factors together with their characteristics were interpreted to be intrinsic in the creation of a nursing milieu that is open and responsive to the implementation of EBP. CONCLUSIONS: Although there seems to be scholarly agreement that leadership is a vital part of the process of implementing EBP, more rigorous research is needed concerning the possible role of the leader. Our findings also indicate that leadership cannot be studied in isolation or without being clearly defined.

Inhibition of fatty acid synthesis induces differentiation and reduces tumor burden in childhood neuroblastoma.

Many metabolic pathways, including lipid metabolism, are rewired in tumors to support energy and biomass production and to allow adaptation to stressful environments. Neuroblastoma is the second deadliest solid tumor in children. Genetic aberrations, as the amplification of the MYCN-oncogene, correlate strongly with disease progression. Yet, there are only a few molecular targets successfully exploited in the clinic. Here we show that inhibition of fatty acid synthesis led to increased neural differentiation and reduced tumor burden in neuroblastoma xenograft experiments independently of MYCN-status. This was accompanied by reduced levels of the MYCN or c-MYC oncoproteins and activation of ERK signaling. Importantly, the expression levels of genes involved in de novo fatty acid synthesis showed prognostic value for neuroblastoma patients. Our findings demonstrate that inhibition of de novo fatty acid synthesis is a promising pharmacological intervention strategy for the treatment of neuroblastoma independently of MYCN-status.

The one-carbon pool controls mitochondrial energy metabolism via complex I and iron-sulfur clusters.

Induction of the one-carbon cycle is an early hallmark of mitochondrial dysfunction and cancer metabolism. Vital intermediary steps are localized to mitochondria, but it remains unclear how one-carbon availability connects to mitochondrial function. Here, we show that the one-carbon metabolite and methyl group donor S-adenosylmethionine (SAM) is pivotal for energy metabolism. A gradual decline in mitochondrial SAM (mitoSAM) causes hierarchical defects in fly and mouse, comprising loss of mitoSAM-dependent metabolites and impaired assembly of the oxidative phosphorylation system. Complex I stability and iron-sulfur cluster biosynthesis are directly controlled by mitoSAM levels, while other protein targets are predominantly methylated outside of the organelle before import. The mitoSAM pool follows its cytosolic production, establishing mitochondria as responsive receivers of one-carbon units. Thus, we demonstrate that cellular methylation potential is required for energy metabolism, with direct relevance for pathophysiology, aging, and cancer.

Validity of natural isotope abundance correction for metabolic flux analysis.

A pervasive issue in stable isotope tracing and metabolic flux analysis is the presence of naturally occurring isotopes such as 13C. For mass isotopomer distributions (MIDs) measured by mass spectrometry, it is common practice to correct for natural occurrence of isotopes within metabolites of interest using a linear transform based on binomial distributions. The resulting corrected MIDs are often used to fit metabolic network models and infer metabolic fluxes, which implicitly assumes that corrected MIDs will yield the same flux solution as the actual observed MIDs. Although this assumption can be empirically verified in special cases by simulation studies, there seems to be no published proof of this important property for the general case. In this paper, we prove that this property holds for the case of noise-free MID data obtained at steady state. On the other hand, for noisy MID data, the flux solution will generally differ between the two representations. These results provide a theoretical foundation for the common practice of MID correction in metabolic flux analysis.

Large-Scale Profiling of Cellular Metabolic Activities Using Deep 13C Labeling Medium.

The recently developed deep labeling method allows for large-scale profiling of metabolic activities in human cells or tissues using isotope tracing with a highly 13C enriched culture medium in combination with liquid chromatography-high resolution mass spectrometry. This method generates mass spectrometry data sets where endogenous cellular products can be identified, and active pathways can be determined from observed 13C mass isotopomers of the various metabolites measured. Here we describe in detail the experimental procedures for deep labeling experiments in cultured mammalian cells, including synthesis of the deep labeling medium, experimental considerations for cell culture, metabolite extractions and sample preparation, and liquid chromatography-mass spectrometry. We also outline a workflow for the downstream data analysis using publicly available software.

Mapping metabolic oscillations during cell cycle progression.

Proliferating cells must synthesize a wide variety of macromolecules while progressing through the cell cycle, but the coordination between cell cycle progression and cellular metabolism is still poorly understood. To identify metabolic processes that oscillate over the cell cycle, we performed comprehensive, non-targeted liquid chromatography-high resolution mass spectrometry (LC-HRMS) based metabolomics of HeLa cells isolated in the G1 and SG2M cell cycle phases, capturing thousands of diverse metabolite ions. When accounting for increased total metabolite abundance due to cell growth throughout the cell cycle, 18% of the observed LC-HRMS peaks were at least twofold different between the stages, consistent with broad metabolic remodeling throughout the cell cycle. While most amino acids, phospholipids, and total ribonucleotides were constant across cell cycle phases, consistent with the view that total macromolecule synthesis does not vary across the cell cycle, certain metabolites were oscillating. For example, ribonucleotides were highly phosphorylated in SG2M, indicating an increase in energy charge, and several phosphatidylinositols were more abundant in G1, possibly indicating altered membrane lipid signaling. Within carbohydrate metabolism, pentose phosphates and methylglyoxal metabolites were associated with the cycle. Interestingly, hundreds of yet uncharacterized metabolites similarly oscillated between cell cycle phases, suggesting previously unknown metabolic activities that may be synchronized with cell cycle progression, providing an important resource for future studies.