Metabolic profiling strategy of Caenorhabditis elegans by whole-organism nuclear magnetic resonance

NMR spectroscopy of a single mammalian early stage embryo

The resolving power, chemical sensitivity and non-invasive nature of NMR have made it an established technique for in vivo studies of large organisms both for research and clinical applications. NMR would clearly be beneficial for analysis of entities at the microscopic scale of about 1 nL (the nanoliter scale), typical of early development of mammalian embryos, microtissues and organoids: the scale where the building blocks of complex organisms could be observed. However, the handling of such small samples (about 100 µm) and sensitivity issues have prevented a widespread adoption of NMR. In this article we show how these limitations can be overcome to obtain NMR spectra of a mammalian embryo in its early stage. To achieve this we employ ultra-compact micro-chip technologies in combination with 3D-printed micro-structures. Such device is packaged for use as plug & play sensor and it shows sufficient sensitivity to resolve NMR signals from individual bovine pre-implantation embryos. The embryos in this study are obtained through In Vitro Fertilization (IVF) techniques, transported cryopreserved to the NMR laboratory, and measured shortly after thawing. In less than 1 h these spherical samples of just 130-190 µm produce distinct spectral peaks, largely originating from lipids contained inside them. We further observe how the spectra vary from one sample to another despite their optical and morphological similarities, suggesting that the method can further develop into a non-invasive embryo assay for selection prior to embryo transfer.

Statistical analysis in metabolic phenotyping

Metabolic phenotyping is an important tool in translational biomedical research. The advanced analytical technologies commonly used for phenotyping, including mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy, generate complex data requiring tailored statistical analysis methods. Detailed protocols have been published for data acquisition by liquid NMR, solid-state NMR, ultra-performance liquid chromatography (LC-)MS and gas chromatography (GC-)MS on biofluids or tissues and their preprocessing. Here we propose an efficient protocol (guidelines and software) for statistical analysis of metabolic data generated by these methods. Code for all steps is provided, and no prior coding skill is necessary. We offer efficient solutions for the different steps required within the complete phenotyping data analytics workflow: scaling, normalization, outlier detection, multivariate analysis to explore and model study-related effects, selection of candidate biomarkers, validation, multiple testing correction and performance evaluation of statistical models. We also provide a statistical power calculation algorithm and safeguards to ensure robust and meaningful experimental designs that deliver reliable results. We exemplify the protocol with a two-group classification study and data from an epidemiological cohort; however, the protocol can be easily modified to cover a wider range of experimental designs or incorporate different modeling approaches. This protocol describes a minimal set of analyses needed to rigorously investigate typical datasets encountered in metabolic phenotyping.

Real-Time In-Organism NMR Metabolomics Reveals Different Roles of AMP-Activated Protein Kinase Catalytic Subunits

AMP-activated protein kinase (AMPK in human and AAK in C. elegans) is a master regulator of metabolism. It has many isotypes, but its isotype-dependent functions are largely unknown. By developing real-time in-organism NMR metabolomics for C. elegans, we were able to study different roles of the isotypic catalytic subunits of AAK/AMPK, AAK-1, and AAK-2 in live worms at the whole organism level. The aak-1 knockout animals exhibited enhanced glucose production under starvation, strikingly opposite to aak-2 knockout animals. Unusually high compensatory expression of the reciprocal isotypes in each KO strain and the results for the double KO animals suggested an unconventional phenotype-genotype relationship and the dominance of aak-2 in glucose production. The gene expression patterns showed that the differential phenotypes of aak-1 KO strain are due to reduced TCA and glycolysis and enhanced gluconeogenesis compared to the aak-2 KO strain. Subsequent ¹³C-isotope incorporation experiment showed that the glucose production in aak-1 KO occurs through the activation of fatty acid oxidation and glyoxylate shunt. Revealing differential roles of the isotypes of AAK/AMPK, our convenient approach is readily applicable to many C. elegans models for human metabolic diseases.

General Guidelines for Sample Preparation Strategies in HR-µMAS NMR-based Metabolomics of Microscopic Specimens

The study of the metabolome within tissues, organisms, cells or biofluids can be carried out by several bioanalytical techniques. Among them, nuclear magnetic resonance (NMR) is one of the principal spectroscopic methods. This is due to a sample rotation technique, high-resolution magic angle spinning (HR-MAS), which targets the analysis of heterogeneous specimens with a bulk sample mass from 5 to 10 mg. Recently, a new approach, high-resolution micro-magic angle spinning (HR-μMAS), has been introduced. It opens, for the first time, the possibility of investigating microscopic specimens (<500 μg) with NMR spectroscopy, strengthening the concept of homogeneous sampling in a heterogeneous specimen. As in all bioanalytical approaches, a clean and reliable sample preparation strategy is a significant component in designing metabolomics (or -omics, in general) studies. The sample preparation for HR-μMAS is consequentially complicated by the μg-scale specimen and has yet to be addressed. This report details the strategies for three specimen types: biofluids, fluid matrices and tissues. It also provides the basis for designing future μMAS NMR studies of microscopic specimens.

Impact of nuclear distribution element genes in the typical and atypical antipsychotics effects on nematode Caenorhabditis elegans: Putative animal model for studying the pathways correlated to schizophrenia

The nuclear distribution element genes are conserved from fungus to humans. The nematode Caenorhabditis elegans expresses two isoforms of nuclear distribution element genes, namely nud-1 and nud-2. While nud-1 was functionally demonstrated to be the worm nudC ortholog, bioinformatic analysis revealed that the nud-2 gene encodes the worm ortholog of the mammalian NDE1 (Nuclear Distribution Element 1 or NudE) and NDEL1 (NDE-Like 1 or NudEL) genes, which share overlapping roles in brain development in mammals and also mediate the axon guidance in mammalian and C. elegans neurons. A significantly higher NDEL1 enzyme activity was shown in treatment non-resistant compared to treatment resistant SCZ patients, who essentially present response to the therapy with atypical clozapine but not with typical antipsychotics. Using C. elegans as a model, we tested the consequence of nud genes suppression in the effects of typical and atypical antipsychotics. To assess the role of nud genes and antipsychotic drugs over C. elegans behavior, we measured body bend frequency, egg laying and pharyngeal pumping, which traits are controlled by specific neurons and neurotransmitters known to be involved in SCZ, as dopamine and serotonin. Evaluation of metabolic and behavioral response to the pharmacotherapy with these antipsychotics demonstrates an important unbalance in serotonin pathway in both nud-1 and nud-2 knockout worms, with more significant effects for nud-2 knockout. The present data also show an interesting trend of mutant knockout worm strains to present a metabolic profile closer to that observed for the wild-type animals after the treatment with the typical antipsychotic haloperidol, but which was not observed for the treatment with the atypical antipsychotic clozapine. Paradoxically, behavioral assays showed more evident effects for clozapine than for haloperidol, which is in line with previous studies with rodent animal models and clinical evaluations with SCZ patients. In addition, the validity and reliability of using this experimental animal model to further explore the convergence between the dopamine/serotonin pathways and neurodevelopmental processes was demonstrated here, and the potential usefulness of this model for evaluating the metabolic consequences of treatments with antipsychotics is also suggested.

3D printed microchannels for sub-nL NMR spectroscopy

Nuclear magnetic resonance (NMR) experiments on subnanoliter (sub-nL) volumes are hindered by the limited sensitivity of the detector and the difficulties in positioning and holding such small samples in proximity of the detector. In this work, we report on NMR experiments on liquid and biological entities immersed in liquids having volumes down to 100 pL. These measurements are enabled by the fabrication of high spatial resolution 3D printed microfluidic structures, specifically conceived to guide and confine sub-nL samples in the sub-nL most sensitive volume of a single-chip integrated NMR probe. The microfluidic structures are fabricated using a two-photon polymerization 3D printing technique having a resolution better than 1 μm3. The high spatial resolution 3D printing approach adopted here allows to rapidly fabricate complex microfluidic structures tailored to position, hold, and feed biological samples, with a design that maximizes the NMR signals amplitude and minimizes the static magnetic field inhomogeneities. The layer separating the sample from the microcoil, crucial to exploit the volume of maximum sensitivity of the detector, has a thickness of 10 μm. To demonstrate the potential of this approach, we report NMR experiments on sub-nL intact biological entities in liquid media, specifically ova of the tardigrade Richtersius coronifer and sections of Caenorhabditis elegans nematodes. We show a sensitivity of 2.5x1013 spins/Hz1/2 on 1H nuclei at 7 T, sufficient to detect 6 pmol of 1H nuclei of endogenous compounds in active volumes down to 100 pL and in a measurement time of 3 hours. Spectral resolutions of 0.01 ppm in liquid samples and of 0.1 ppm in the investigated biological entities are also demonstrated. The obtained results may indicate a route for NMR studies at the single unit level of important biological entities having sub-nL volumes, such as living microscopic organisms and eggs of several mammalians, humans included.

High-resolution Magic-angle Spinning (HR-MAS) NMR Spectroscopy

Since the beginning of high-resolution magic-angle spinning (HR-MAS) NMR spectroscopy in 1990s, we have witnessed tremendous instrumentation and methodological advancements in the HR-MAS NMR technique for semisolids. With HR-MAS, it is now possible to acquire reliable high-quality spectra in a routine and high-throughput fashion, and it has become a well-integrated metabolic screening tool for ex vivo biospecimens such as tissue biopsies, cells and organisms for NMR-based metabolomics research. This chapter provides the basic principles of HR-MAS and describes a few recent noteworthy developments that could strengthen the role of HR-MAS as a frontline NMR technique for metabolomics.

A sensitive mass spectrometry platform identifies metabolic changes of life history traits in C. elegans

Abnormal nutrient metabolism is a hallmark of aging, and the underlying genetic and nutritional framework is rapidly being uncovered, particularly using C. elegans as a model. However, the direct metabolic consequences of perturbations in life history of C. elegans remain to be clarified. Based on recent advances in the metabolomics field, we optimized and validated a sensitive mass spectrometry (MS) platform for identification of major metabolite classes in worms and applied it to study age and diet related changes. Using this platform that allowed detection of over 600 metabolites in a sample of 2500 worms, we observed marked changes in fatty acids, amino acids and phospholipids during worm life history, which were independent from the germ-line. Worms underwent a striking shift in lipid metabolism after early adulthood that was at least partly controlled by the metabolic regulator AAK-2/AMPK. Most amino acids peaked during development, except aspartic acid and glycine, which accumulated in aged worms. Dietary intervention also influenced worm metabolite profiles and the regulation was highly specific depending on the metabolite class. Altogether, these MS-based methods are powerful tools to perform worm metabolomics for aging and metabolism-oriented studies.

In-Cell Solid-State NMR: An Emerging Technique for the Study of Biological Membranes

Biological molecular processes are often studied in model systems, which simplifies their inherent complexity but may cause investigators to lose sight of the effects of the molecular environment. Information obtained in this way must therefore be validated by experiments in the cell. NMR has been used to study biological cells since the early days of its development. The first NMR structural studies of a protein inside a cell (by solution-state NMR) and of a membrane protein (by solid-state NMR) were published in 2001 and 2011, respectively. More recently, dynamic nuclear polarization, which has been used to enhance the signal in solid-state NMR, has also been applied to the study of frozen cells. Much progress has been made in the past 5 years, and in this review we take stock of this new technique, which is particularly appropriate for the study of biological membranes.

High-resolution NMR-based metabolic detection of microgram biopsies using a 1 mm HRμMAS probe

A prototype 1 mm High-Resolution micro-Magic Angle Spinning (HRμMAS) probe is described. High quality (1)H NMR spectra were obtained from 490 μg of heterogeneous biospecimens, offering a rich-metabolite profiling. The results demonstrate the potential of HRμMAS as a new NMR analytical tool in metabolomics.

Power Analysis and Sample Size Determination in Metabolic Phenotyping

Estimation of statistical power and sample size is a key aspect of experimental design. However, in metabolic phenotyping, there is currently no accepted approach for these tasks, in large part due to the unknown nature of the expected effect. In such hypothesis free science, neither the number or class of important analytes nor the effect size are known a priori. We introduce a new approach, based on multivariate simulation, which deals effectively with the highly correlated structure and high-dimensionality of metabolic phenotyping data. First, a large data set is simulated based on the characteristics of a pilot study investigating a given biomedical issue. An effect of a given size, corresponding either to a discrete (classification) or continuous (regression) outcome is then added. Different sample sizes are modeled by randomly selecting data sets of various sizes from the simulated data. We investigate different methods for effect detection, including univariate and multivariate techniques. Our framework allows us to investigate the complex relationship between sample size, power, and effect size for real multivariate data sets. For instance, we demonstrate for an example pilot data set that certain features achieve a power of 0.8 for a sample size of 20 samples or that a cross-validated predictivity QY(2) of 0.8 is reached with an effect size of 0.2 and 200 samples. We exemplify the approach for both nuclear magnetic resonance and liquid chromatography-mass spectrometry data from humans and the model organism C. elegans.

Applications of NMR spectroscopy to systems biochemistry

The past decades of advancements in NMR have made it a very powerful tool for metabolic research. Despite its limitations in sensitivity relative to mass spectrometric techniques, NMR has a number of unparalleled advantages for metabolic studies, most notably the rigor and versatility in structure elucidation, isotope-filtered selection of molecules, and analysis of positional isotopomer distributions in complex mixtures afforded by multinuclear and multidimensional experiments. In addition, NMR has the capacity for spatially selective in vivo imaging and dynamical analysis of metabolism in tissues of living organisms. In conjunction with the use of stable isotope tracers, NMR is a method of choice for exploring the dynamics and compartmentation of metabolic pathways and networks, for which our current understanding is grossly insufficient. In this review, we describe how various direct and isotope-edited 1D and 2D NMR methods can be employed to profile metabolites and their isotopomer distributions by stable isotope-resolved metabolomic (SIRM) analysis. We also highlight the importance of sample preparation methods including rapid cryoquenching, efficient extraction, and chemoselective derivatization to facilitate robust and reproducible NMR-based metabolomic analysis. We further illustrate how NMR has been applied in vitro, ex vivo, or in vivo in various stable isotope tracer-based metabolic studies, to gain systematic and novel metabolic insights in different biological systems, including human subjects. The pathway and network knowledge generated from NMR- and MS-based tracing of isotopically enriched substrates will be invaluable for directing functional analysis of other 'omics data to achieve understanding of regulation of biochemical systems, as demonstrated in a case study. Future developments in NMR technologies and reagents to enhance both detection sensitivity and resolution should further empower NMR in systems biochemical research.

Developmental Changes for the Hemolymph Metabolome of Silkworm (Bombyx mori L.)

Silkworm (Bombyx mori) is a lepidopteran-holometabolic model organism. To understand its developmental biochemistry, we characterized the larval hemolymph metabonome from the third instar to prepupa stage using (1)H NMR spectroscopy whilst hemolymph fatty acid composition using GC-FID/MS. We unambiguously assigned more than 60 metabolites, among which tyrosine-o-β-glucuronide, mesaconate, homocarnosine, and picolinate were reported for the first time from the silkworm hemolymph. Phosphorylcholine was the most abundant metabolite in all developmental stages with exception for the periods before the third and fourth molting. We also found obvious developmental dependence for the hemolymph metabonome involving multiple pathways including protein biosyntheses, glycolysis, TCA cycle, the metabolisms of choline amino acids, fatty acids, purines, and pyrimidines. Most hemolymph amino acids had two elevations during the feeding period of the fourth instar and prepupa stage. Trehalose was the major blood sugar before day 8 of the fifth instar, whereas glucose became the major blood sugar after spinning. C16:0, C18:0 and its unsaturated forms were dominant fatty acids in hemolymph. The developmental changes of hemolymph metabonome were associated with dietary nutrient intakes, biosyntheses of cell membrane, pigments, proteins, and energy metabolism. These findings offered essential biochemistry information in terms of the dynamic metabolic changes during silkworm development.

Sample size calculation in metabolic phenotyping studies

The number of samples needed to identify significant effects is a key question in biomedical studies, with consequences on experimental designs, costs and potential discoveries. In metabolic phenotyping studies, sample size determination remains a complex step. This is due particularly to the multiple hypothesis-testing framework and the top-down hypothesis-free approach, with no a priori known metabolic target. Until now, there was no standard procedure available to address this purpose. In this review, we discuss sample size estimation procedures for metabolic phenotyping studies. We release an automated implementation of the Data-driven Sample size Determination (DSD) algorithm for MATLAB and GNU Octave. Original research concerning DSD was published elsewhere. DSD allows the determination of an optimized sample size in metabolic phenotyping studies. The procedure uses analytical data only from a small pilot cohort to generate an expanded data set. The statistical recoupling of variables procedure is used to identify metabolic variables, and their intensity distributions are estimated by Kernel smoothing or log-normal density fitting. Statistically significant metabolic variations are evaluated using the Benjamini-Yekutieli correction and processed for data sets of various sizes. Optimal sample size determination is achieved in a context of biomarker discovery (at least one statistically significant variation) or metabolic exploration (a maximum of statistically significant variations). DSD toolbox is encoded in MATLAB R2008A (Mathworks, Natick, MA) for Kernel and log-normal estimates, and in GNU Octave for log-normal estimates (Kernel density estimates are not robust enough in GNU octave). It is available at http://www.prabi.fr/redmine/projects/dsd/repository, with a tutorial at http://www.prabi.fr/redmine/projects/dsd/wiki.

μHigh resolution-magic-angle spinning NMR spectroscopy for metabolic phenotyping of Caenorhabditis elegans

Analysis of model organisms, such as the submillimeter-size Caenorhabditis elegans, plays a central role in understanding biological functions across species and in characterizing phenotypes associated with genetic mutations. In recent years, metabolic phenotyping studies of C. elegans based on (1)H high-resolution magic-angle spinning (HR-MAS) nuclear magnetic resonance (NMR) spectroscopy have relied on the observation of large populations of nematodes, requiring labor-intensive sample preparation that considerably limits high-throughput characterization of C. elegans. In this work, we open new platforms for metabolic phenotyping of C. elegans mutants. We determine rich metabolic profiles (31 metabolites identified) from samples of 12 individuals using a (1)H NMR microprobe featuring high-resolution magic-angle coil spinning (HR-MACS), a simple conversion of a standard HR-MAS probe to μHR-MAS. In addition, we characterize the metabolic variations between two different strains of C. elegans (wild-type vs slcf-1 mutant). We also acquire a NMR spectrum of a single C. elegans worm at 23.5 T. This study represents the first example of a metabolomic investigation carried out on a small number of submillimeter-size organisms, demonstrating the potential of NMR microtechnologies for metabolomics screening of small model organisms.

Metabolomics analysis uncovers that dietary restriction buffers metabolic changes associated with aging in Caenorhabditis elegans

Dietary restriction (DR) is one of the most universal means of extending lifespan. Yet, whether and how DR specifically affects the metabolic changes associated with aging is essentially unknown. Here, we present a comprehensive and unbiased picture of the metabolic variations that take place with age at the whole organism level in Caenorhabditis elegans by using ¹H high-resolution magic-angle spinning (HR-MAS) nuclear magnetic resonance (NMR) analysis of intact worms. We investigate metabolic variations potentially important for lifespan regulation by comparing the metabolic fingerprint of two previously described genetic models of DR, the long-lived eat-2(ad465) and slcf-1(tm2258) worms, as single mutants or in combination with a genetic suppressor of their lifespan phenotype. Our analysis shows that significant changes in metabolite profiles precede the major physiological decline that accompanies aging and that DR protects from some of those metabolic changes. More specifically, low phosphocholine (PCho) correlates with high life expectancy. A mutation in the tumor suppressor gene PTEN/DAF-18, which suppresses the beneficial effects of DR in both C. elegans and mammals, increases both PCho level and choline kinase expression. Furthermore, we show that choline kinase function in the intestine can regulate lifespan. This study highlights the relevance of NMR metabolomic approaches for identifying potential biomarkers of aging.

Data-driven sample size determination for metabolic phenotyping studies

Sample size determination is a key question in the experimental design of medical studies. The number of patients to include in a clinical study is actually critical to evaluate costs and inclusion requirements to achieve a sufficient statistical power of test and the identification of significant variations among the factors under study. Metabolic phenotyping is an expanding field of translational research in medicine, focusing on the identification of metabolism rearrangements due to various pathophysiological conditions. This top-down hypothesis-free approach uses analytical chemistry methods, coupled to statistical analysis, to quantify subtle and coordinated metabolite concentration variations and eventually identify candidate biomarkers. The sample size determination in metabolic phenotyping studies is difficult considering the absence of a priori metabolic target. This technical note introduces a data-driven sample size determination for metabolic phenotyping studies. Starting from nuclear magnetic resonance (NMR) spectra belonging to a small cohort, metabolic NMR variables are identified by the statistical recoupling of variables (SRV) procedure. A larger data set is then generated on the basis of Kernel density estimation of SRV variable distributions. Statistically significant variations of metabolic NMR signals identified by SRV are assessed by the Benjamini-Yekutieli correction for simulated data sets of variable sizes. Simulated model robustness is evaluated by receiver operating characteristic analysis (sensitivity and specificity) on an independent cohort and cross-validation. Sample size determination is obtained by identifying the optimal data set size, depending on the purpose of the study: at least one statistically significant variation (biomarker discovery) or a maximum of statistically significant variations (metabolic exploration).

Batch profiling calibration for robust NMR metabonomic data analysis

Metabonomic studies involve the analysis of large numbers of samples to identify significant changes in the metabolic fingerprints of biological systems, possibly with sufficient statistical power for analysis. While procedures related to sample preparation and spectral data acquisition generally include the use of independent sample batches, these might be sources of systematic variation whose effects should be removed to focus on phenotyping the relevant biological variability. In this work, we describe a grouped-batch profile (GBP) calibration strategy to adjust nuclear magnetic resonance (NMR) metabolomic data-sets for batch effects either introduced during NMR experiments or samples work-up. We show how this method can be applied to data calibration in the context of a large-scale NMR epidemiological study where quality control samples are available. We also illustrate the efficiency of a batch profile correction for NMR metabonomic investigation of cell extracts, where GBP can significantly improve the predictive power of multivariate statistical models for discriminant analysis of the cell infection status. The method is applicable to a broad range of NMR metabolomic/metabonomic cohort studies.

Metabolomics-on-a-chip and predictive systems toxicology in microfluidic bioartificial organs

The world faces complex challenges for chemical hazard assessment. Microfluidic bioartificial organs enable the spatial and temporal control of cell growth and biochemistry, critical for organ-specific metabolic functions and particularly relevant to testing the metabolic dose-response signatures associated with both pharmaceutical and environmental toxicity. Here we present an approach combining a microfluidic system with (1)H NMR-based metabolomic footprinting, as a high-throughput small-molecule screening approach. We characterized the toxicity of several molecules: ammonia (NH(3)), an environmental pollutant leading to metabolic acidosis and liver and kidney toxicity; dimethylsulfoxide (DMSO), a free radical-scavenging solvent; and N-acetyl-para-aminophenol (APAP, or paracetamol), a hepatotoxic analgesic drug. We report organ-specific NH(3) dose-dependent metabolic responses in several microfluidic bioartificial organs (liver, kidney, and cocultures), as well as predictive (99% accuracy for NH(3) and 94% for APAP) compound-specific signatures. Our integration of microtechnology, cell culture in microfluidic biochips, and metabolic profiling opens the development of so-called "metabolomics-on-a-chip" assays in pharmaceutical and environmental toxicology.

Potential new method of mixture effects testing using metabolomics and Caenorhabditis elegans

The development of superior tools for molecular and computational biology in recent years has provided an opportunity for the creation of faster toxicological screens that are relevant for, but do not rely on, mammalian systems. In this study, NMR spectroscopy and GC-MS based metabolomics have been used in conjunction with multivariate statistics to examine the metabolic changes in the nematode Caenorhabditis elegans following exposure to different concentrations of the heavy metal nickel, the pesticide chlorpyrifos, and their mixture. Novel metabolic profiles were associated with both exposure and dose level. The biochemical responses were more closely matched when exposure was at the same effect level, even for different chemicals, than when exposure was for different levels of the same chemical (e.g., low versus high dose). Responses to the mixture reflected the contribution of the chemicals to the overall exposure. In common with the metabolic responses of several other species exposed to the same chemicals, we observed changes in branch chain amino acids and tricarboxylic acid cycle intermediates. These results form the basis for a rapid and economically viable toxicity test that defines the molecular effects of pollution/toxicant exposure in a manner that is relevant to higher vertebrates.

From cancer metabolism to new biomarkers and drug targets

Great interest is presently given to the analysis of metabolic changes that take place specifically in cancer cells. In this review we summarize the alterations in glycolysis, glutamine utilization, fatty acid synthesis and mitochondrial function that have been reported to occur in cancer cells and in human tumors. We then propose considering cancer as a system-level disease and argue how two hallmarks of cancer, enhanced cell proliferation and evasion from apoptosis, may be evaluated as system-level properties, and how this perspective is going to modify drug discovery. Given the relevance of the analysis of metabolism both for studies on the molecular basis of cancer cell phenotype and for clinical applications, the more relevant technologies for this purpose, from metabolome and metabolic flux analysis in cells by Nuclear Magnetic Resonance and Mass Spectrometry technologies to positron emission tomography on patients, are analyzed. The perspectives offered by specific changes in metabolism for a new drug discovery strategy for cancer are discussed and a survey of the industrial activity already going on in the field is reported.

Hierarchical alignment and full resolution pattern recognition of 2D NMR spectra: application to nematode chemical ecology

Nuclear magnetic resonance (NMR) is the most widely used nondestructive technique in analytical chemistry. In recent years, it has been applied to metabolic profiling due to its high reproducibility, capacity for relative and absolute quantification, atomic resolution, and ability to detect a broad range of compounds in an untargeted manner. While one-dimensional (1D) (1)H NMR experiments are popular in metabolic profiling due to their simplicity and fast acquisition times, two-dimensional (2D) NMR spectra offer increased spectral resolution as well as atomic correlations, which aid in the assignment of known small molecules and the structural elucidation of novel compounds. Given the small number of statistical analysis methods for 2D NMR spectra, we developed a new approach for the analysis, information recovery, and display of 2D NMR spectral data. We present a native 2D peak alignment algorithm we term HATS, for hierarchical alignment of two-dimensional spectra, enabling pattern recognition (PR) using full-resolution spectra. Principle component analysis (PCA) and partial least squares (PLS) regression of full resolution total correlation spectroscopy (TOCSY) spectra greatly aid the assignment and interpretation of statistical pattern recognition results by producing back-scaled loading plots that look like traditional TOCSY spectra but incorporate qualitative and quantitative biological information of the resonances. The HATS-PR methodology is demonstrated here using multiple 2D TOCSY spectra of the exudates from two nematode species: Pristionchus pacificus and Panagrellus redivivus. We show the utility of this integrated approach with the rapid, semiautomated assignment of small molecules differentiating the two species and the identification of spectral regions suggesting the presence of species-specific compounds. These results demonstrate that the combination of 2D NMR spectra with full-resolution statistical analysis provides a platform for chemical and biological studies in cellular biochemistry, metabolomics, and chemical ecology.

Pyruvate imbalance mediates metabolic reprogramming and mimics lifespan extension by dietary restriction in Caenorhabditis elegans

Dietary restriction (DR) is the most universal intervention known to extend animal lifespan. DR also prevents tumor development in mammals, and this effect requires the tumor suppressor PTEN. However, the metabolic and cellular processes that underly the beneficial effects of DR are poorly understood. We identified slcf-1 in an RNAi screen for genes that extend Caenorhabditis elegans lifespan in a PTEN/daf-18-dependent manner. We showed that slcf-1 mutation, which increases average lifespan by 40%, mimics DR in worms fed ad libitum. An NMR-based metabolomic characterization of slcf-1 mutants revealed lower lipid levels compared to wild-type animals, as expected for dietary-restricted animals, but also higher pyruvate content. Epistasis experiments and metabolic measurements support a model in which the long lifespan of slcf-1 mutants relies on increased mitochondrial pyruvate metabolism coupled to an adaptive response to oxidative stress. This response requires DAF-18/PTEN and the previously identified DR effectors PHA-4/FOXA, HSF-1/HSF1, SIR-2.1/SIRT-1, and AMPK/AAK-2. Overall, our data show that pyruvate homeostasis plays a central role in lifespan control in C. elegans and that the beneficial effects of DR results from a hormetic mechanism involving the mitochondria. Analysis of the SLCF-1 protein sequence predicts that slcf-1 encodes a plasma membrane transporter belonging to the conserved monocarboxylate transporter family. These findings suggest that inhibition of this transporter homolog in mammals might also promote a DR response.

Metabotyping of Caenorhabditis elegans and their culture media revealed unique metabolic phenotypes associated to amino acid deficiency and insulin-like signaling

Insulin/IGF-like signaling (IIS) and nutrient sensing are among the most potent regulators of health status and aging. Here, a global view of the metabolic changes in C. elegans with impaired function of IIS represented by daf-2 and daf-16 and the intestinal di- and tripeptide transport pept-1 was generated using (1)H nuclear magnetic resonance spectroscopic analysis of worm extracts and spent culture media. We showed that specific metabolic profiles were significantly associated with each type of mutant. On the basis of the metabonomics data, selected underlying processes were further investigated using proteomic and transcriptomic approaches. The observed changes suggest a decreased activity of the one carbon metabolism in pept-1(lg601) mutants. Higher concentration of branched-chain amino acids (BCAA) and altered transcript levels of genes involved in BCAA metabolism were observed in long-living strains daf-2(e1370) and daf-2(e1370);pept-1(lg601) when compared to wild types and daf-16(m26);daf-2(e1370);pept-1(lg601) C. elegans, suggesting a DAF-16-dependent mechanism.

High-throughput screening and small animal models, where are we?

Current high-throughput screening methods for drug discovery rely on the existence of targets. Moreover, most of the hits generated during screenings turn out to be invalid after further testing in animal models. To by-pass these limitations, efforts are now being made to screen chemical libraries on whole animals. One of the most commonly used animal model in biology is the murine model Mus musculus. However, its cost limit its use in large-scale therapeutic screening. In contrast, the nematode Caenorhabditis elegans, the fruit fly Drosophila melanogaster, and the fish Danio rerio are gaining momentum as screening tools. These organisms combine genetic amenability, low cost and culture conditions that are compatible with large-scale screens. Their main advantage is to allow high-throughput screening in a whole-animal context. Moreover, their use is not dependent on the prior identification of a target and permits the selection of compounds with an improved safety profile. This review surveys the versatility of these animal models for drug discovery and discuss the options available at this day.

Applications of cold temperature stress to age fractionate Caenorhabditis elegans: a simple inexpensive technique

The nematode Caenorhabditis elegans’s (CE) successful use in studies of aging is well documented. Cold temperature stress of mixed populations of CE provides a rapid inexpensive means of obtaining three life stage–specific cohorts. Cohorts are obtained in quantities that allow acquisition of replicate metabolite profiles of changes associated with development, aging, and senescence. The fractionation technique is effective with monoxenic and axenic CE cultures. Cohort Y contains 100% young worms, and Cohort A contains 75% adult worms. Cohort M, prereproductive and reproductive, contains some A and Y due to continuous egg laying and hatch. Principal component analysis of normalized data from metabolite profiles obtained using high-performance liquid chromatography electrochemical analysis clearly separates Cohort Y from Cohort A and monoxenic from axenic cultured worms. Access to replicate quantities of age-defined worms will aid studies of alterations in homeostatic controls associated with aging and senescence.

Label-free quantitative analysis of lipid metabolism in living Caenorhabditis elegans

The ubiquity of lipids in biological structures and functions suggests that lipid metabolisms are highly regulated. However, current invasive techniques for lipid studies prevent characterization of the dynamic interactions between various lipid metabolism pathways. Here, we describe a noninvasive approach to study lipid metabolisms using a multifunctional coherent anti-Stokes Raman scattering (CARS) microscope. Using living Caenorhabditis elegans as a model organism, we report label-free visualization of coexisting neutral and autofluorescent lipid species. We find that the relative expression level of neutral and autofluorescent lipid species can be used to assay the genotype-phenotype relationship of mutant C. elegans with deletions in the genes encoding lipid synthesis transcription factors, LDL receptors, transforming growth factor beta receptors, lipid desaturation enzymes, and antioxidant enzymes. Furthermore, by coupling CARS with fingerprint confocal Raman analysis, we analyze the unsaturation level of lipids in wild-type and mutant C. elegans. Our study shows that complex genotype-phenotype relationships between lipid storage, peroxidation, and desaturation can be rapidly and quantitatively analyzed in a single living C. elegans.