GC-MS-based urine metabolic profiling of autism spectrum disorders

Post hoc analysis of plasma amino acid profiles: towards a specific pattern in autism spectrum disorder and intellectual disability

Objectives Autism spectrum disorders and intellectual disability present a challenge for therapeutic and dietary management. We performed a re-analysis of plasma amino acid chromatography of children with autism spectrum disorders ( n = 22) or intellectual disability ( n = 29) to search for a metabolic signature that can distinguish individuals with these disorders from controls ( n = 30). Methods We performed univariate and multivariate analyses using different machine learning strategies, from the raw data of the amino acid chromatography. Finally, we analysed the metabolic pathways associated with discriminant biomarkers. Results Multivariate analysis revealed models to discriminate patients with autism spectrum disorders or intellectual disability and controls from plasma amino acid profiles ( P < 0.0003). Univariate analysis showed that autism spectrum disorder and intellectual disability patients shared similar differences relative to controls, including lower glutamate ( P < 0.0001 and P = 0.0002, respectively) and serine ( P = 0.002 for both) concentrations. The multivariate model ( P < 6.12.10^-7) to discriminate between autism spectrum disorders and intellectual disability revealed the involvement of urea, 3-methyl-histidine and histidine metabolism. Biosigner analysis and univariate analysis confirmed the role of 3-methylhistidine ( P = 0.004), histidine ( P = 0.003), urea ( P = 0.0006) and lysine ( P = 0.002). Conclusions We revealed discriminant metabolic patterns between autism spectrum disorders, intellectual disability and controls. Amino acids known to play a role in neurotransmission were discriminant in the models comparing autism spectrum disorders or intellectual disability to controls, and histidine and b-alanine metabolism was specifically highlighted in the model.

Serum Metabolic Profiling of Oocyst-Induced Toxoplasma gondii Acute and Chronic Infections in Mice Using Mass-Spectrometry

Toxoplasma gondii is an obligate intracellular parasite causing severe diseases in immunocompromised individuals and congenitally infected neonates, such as encephalitis and chorioretinitis. This study aimed to determine whether serum metabolic profiling can (i) identify metabolites associated with oocyst-induced T. gondii infection and (ii) detect systemic metabolic differences between T. gondii-infected mice and controls. We performed the first global metabolomics analysis of mice serum challenged with 100 sporulated T. gondii Pru oocysts (Genotype II). Sera from acutely infected mice (11 days post-infection, dpi), chronically infected mice (33 dpi) and control mice were collected and analyzed using LC-MS/MS platform. Following False Discovery Rate filtering, we identified 3871 and 2825 ions in ESI+ or ESI- mode, respectively. Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) identified metabolomic profiles that clearly differentiated T. gondii-infected and -uninfected serum samples. Acute infection significantly influenced the serum metabolome. Our results identified common and uniquely perturbed metabolites and pathways. Acutely infected mice showed perturbations in metabolites associated with glycerophospholipid metabolism, biosynthesis of amino acid, and tyrosine metabolism. These findings demonstrated that acute T. gondii infection induces a global perturbation of mice serum metabolome, providing new insights into the mechanisms underlying systemic metabolic changes during early stage of T. gondii infection.

Health Effects of Dietary Oxidized Tyrosine and Dityrosine Administration in Mice with Nutrimetabolomic Strategies

This study aims to investigate the health effects of long-term dietary oxidized tyrosine (O-Tyr) and its main product (dityrosine) administration on mice metabolism. Mice received daily intragastric administration of either O-Tyr (320 μg/kg body weight), dityrosine (Dityr, 320 μg/kg body weight), or saline for consecutive 6 weeks. Urine and plasma samples were analyzed by NMR-based metabolomics strategies. Body weight, clinical chemistry, oxidative damage indexes, and histopathological data were obtained as complementary information. O-Tyr and Dityr exposure changed many systemic metabolic processes, including reduced choline bioavailability, led to fat accumulation in liver, induced hepatic injury, and renal dysfunction, resulted in changes in gut microbiota functions, elevated risk factor for cardiovascular disease, altered amino acid metabolism, induced oxidative stress responses, and inhibited energy metabolism. These findings implied that it is absolutely essential to reduce the generation of oxidation protein products in food system through improving modern food processing methods.

Targeting aggression in severe mental illness: The predictive role of genetic, epigenetic, and metabolomic markers

Human aggression is a complex and widespread social behavior that is overrepresented in individuals affected by severe mental illness (SMI), such as schizophrenia (SCZ), bipolar disorder (BD), autism spectrum disorder (ASD), and attention-deficit/hyperactivity disorder (ADHD). A substantial proportion of the liability threshold for aggressive behavior is determined by genetic factors, and environmental moderators might precipitate the manifestation of this behavioral phenotype through modification of gene expression via the epigenetic machinery. These specific alterations in the genetic and epigenetic make-up of aggressive individuals might determine distinct biochemical signatures detectable through metabolomics. An additional pathophysiological component playing a role in aggressive behavior might be determined by alterations of gut microbiota. Here, we present a selective review of human data on genetic, epigenetic, and metabolomic markers of aggressive behavior in SMI, discussing also the available evidence on the role of microbiome alterations. Clinical implication of these evidences, as well as future perspectives, will be discussed.

"Gestaltomics": Systems Biology Schemes for the Study of Neuropsychiatric Diseases

The integration of different sources of biological information about what defines a behavioral phenotype is difficult to unify in an entity that reflects the arithmetic sum of its individual parts. In this sense, the challenge of Systems Biology for understanding the “psychiatric phenotype” is to provide an improved vision of the shape of the phenotype as it is visualized by “Gestalt” psychology, whose fundamental axiom is that the observed phenotype (behavior or mental disorder) will be the result of the integrative composition of every part. Therefore, we propose the term “Gestaltomics” as a term from Systems Biology to integrate data coming from different sources of information (such as the genome, transcriptome, proteome, epigenome, metabolome, phenome, and microbiome). In addition to this biological complexity, the mind is integrated through multiple brain functions that receive and process complex information through channels and perception networks (i.e., sight, ear, smell, memory, and attention) that in turn are programmed by genes and influenced by environmental processes (epigenetic). Today, the approach of medical research in human diseases is to isolate one disease for study; however, the presence of an additional disease (co-morbidity) or more than one disease (multimorbidity) adds complexity to the study of these conditions. This review will present the challenge of integrating psychiatric disorders at different levels of information (Gestaltomics). The implications of increasing the level of complexity, for example, studying the co-morbidity with another disease such as cancer, will also be discussed.

Metabolic profiling of tyrosine, tryptophan, and glutamate in human urine using gas chromatography-tandem mass spectrometry combined with single SPE cleanup

The tyrosine, tryptophan, and glutamate metabolic pathways play key roles on pathological state of neuronal functions and the change of their levels in biological systems reflects the progress degree of neuronal diseases. Comprehensive profiling of these metabolites is important to find new biomarkers for diagnosis or prognosis of various neuronal diseases. However, the overall profiling analysis of various neurochemicals in biological sample is confronted with several limitations due to their low concentration and physicochemical properties and the coexistence of matrices. We developed an efficient and feasible method using gas chromatography-tandem mass spectrometry (GC-MS/MS). Wide-bore mixed cation exchange (MCX) SPE process enables a rapid and effective cleanup of 20 neurochemicals even including acidic and basic neurochemicals in a single SPE cartridge by using different composition of eluents. Selective derivatization of various types of metabolites was applied to achieve highly chromatographic separation and sensitive mass detection. Appropriate selection of precursor and product transition ions used in multiple reaction-monitoring (MRM) mode based on the MS/MS fragmentations of the derivatized neurochemicals could be significantly minimized the matrix effects and enhanced the reliability of quantification results. The developed method was validated in terms of linearity, limits of detection, precision, accuracy, and matrix effects. The intra- and inter-assay analytical variations were less than 10%. The overall linearity for all of the targets was excellent (R²≥0.996). The detection limits ranged between 0.38 and 8.13ng/mL for the acidic neurochemicals and between 0.02 and 11.1ng/mL for the basic neurochemicals. The developed protocol will be expected to be a promising tool for the understanding of the pathological state and diagnosis of various neuronal diseases.

An untargeted metabolomics approach to determine component differences and variation in their in vivo distribution between Kuqin and Ziqin, two commercial specifications of Scutellaria Radix

Kuqin (KQ) and Ziqin (ZQ), derived from the roots of Scutellaria baicalensis Georgi, are two important commercial specifications of Scutellariae Radix (SR, termed Huang qin in Chinese).

Urinary metabolomics of young Italian autistic children supports abnormal tryptophan and purine metabolism

Background

Autism spectrum disorder (ASD) is still diagnosed through behavioral observation, due to a lack of laboratory biomarkers, which could greatly aid clinicians in providing earlier and more reliable diagnoses. Metabolomics on human biofluids provides a sensitive tool to identify metabolite profiles potentially usable as biomarkers for ASD. Initial metabolomic studies, analyzing urines and plasma of ASD and control individuals, suggested that autistic patients may share some metabolic abnormalities, despite several inconsistencies stemming from differences in technology, ethnicity, age range, and definition of “control” status.

Methods

ASD-specific urinary metabolomic patterns were explored at an early age in 30 ASD children and 30 matched controls (age range 2–7, M:F = 22:8) using hydrophilic interaction chromatography (HILIC)-UHPLC and mass spectrometry, a highly sensitive, accurate, and unbiased approach. Metabolites were then subjected to multivariate statistical analysis and grouped by metabolic pathway.

Results

Urinary metabolites displaying the largest differences between young ASD and control children belonged to the tryptophan and purine metabolic pathways. Also, vitamin B₆, riboflavin, phenylalanine-tyrosine-tryptophan biosynthesis, pantothenate and CoA, and pyrimidine metabolism differed significantly. ASD children preferentially transform tryptophan into xanthurenic acid and quinolinic acid (two catabolites of the kynurenine pathway), at the expense of kynurenic acid and especially of melatonin. Also, the gut microbiome contributes to altered tryptophan metabolism, yielding increased levels of indolyl 3-acetic acid and indolyl lactate.

Conclusions

The metabolic pathways most distinctive of young Italian autistic children largely overlap with those found in rodent models of ASD following maternal immune activation or genetic manipulations. These results are consistent with the proposal of a purine-driven cell danger response, accompanied by overproduction of epileptogenic and excitotoxic quinolinic acid, large reductions in melatonin synthesis, and gut dysbiosis. These metabolic abnormalities could underlie several comorbidities frequently associated to ASD, such as seizures, sleep disorders, and gastrointestinal symptoms, and could contribute to autism severity. Their diagnostic sensitivity, disease-specificity, and interethnic variability will merit further investigation.

Electronic supplementary material

The online version of this article (doi:10.1186/s13229-016-0109-5) contains supplementary material, which is available to authorized users.

Metabolomics of autism spectrum disorders: early insights regarding mammalian-microbial cometabolites

INTRODUCTION

Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders consisting of delayed or impaired language development and difficulties in social interactions. The very high degree of phenotypic heterogeneity in ASD originates from the interaction between environmental risk factors and susceptible genetic loci, leading to epigenetic DNA methylation. Advances in system biology are becoming strategic for implementing knowledge on the ASD aetiology and for the early diagnosis of the disease after birth.

AREAS COVERED

We overhauled the value of either targeted or untargeted metabolomics studies in autism for identifying the most relevant metabolic pathways and key metabolites implicated in the disease, with special emphasis to mammalian-microbial metabolites. The most discriminant metabolites in ASD belong to amino acid metabolism, antioxidant status, nicotinic acid metabolism, and mitochondrial metabolism. Expert commentary: Most published studies point out the role of metabolites derived from the gut microbiota: they can modulate the behavioral phenotype of the autistic children, greatly influencing host metabolic pathways and the immune system, shaping the individual susceptibility to the disease. Pitfalls and caveats in metabolomics results across studies have been additionally recognized and discussed leading to the conclusion that metabolomics studies in ASD are far to be definitive and univocal.

Modulatory Effects of Gut Microbiota on the Central Nervous System: How Gut Could Play a Role in Neuropsychiatric Health and Diseases

Gut microbiome is an integral part of the Gut-Brain axis. It is becoming increasingly recognized that the presence of a healthy and diverse gut microbiota is important to normal cognitive and emotional processing. It was known that altered emotional state and chronic stress can change the composition of gut microbiome, but it is becoming more evident that interaction between gut microbiome and central nervous system is bidirectional. Alteration in the composition of the gut microbiome can potentially lead to increased intestinal permeability and impair the function of the intestinal barrier. Subsequently, neuro-active compounds and metabolites can gain access to the areas within the central nervous system that regulate cognition and emotional responses. Deregulated inflammatory response, promoted by harmful microbiota, can activate the vagal system and impact neuropsychological functions. Some bacteria can produce peptides or short chain fatty acids that can affect gene expression and inflammation within the central nervous system. In this review, we summarize the evidence supporting the role of gut microbiota in modulating neuropsychological functions of the central nervous system and exploring the potential underlying mechanisms.

The Brain-Gut-Microbiome Axis: What Role Does It Play in Autism Spectrum Disorder?

The brain-gut-microbiome axis refers to the interactions between the central nervous system, gastrointestinal system, and microorganisms that live in the gastrointestinal tract. Exploring these interactions provides a rationale for why gastrointestinal disorders commonly occur in children with Autism Spectrum Disorders (ASD). Signs of altered brain-gut interactions that are closely associated with functional GI disorders (FGIDs) commonly occur in children with ASD. Studies of microbiome in ASD suggest that changes in the gut microbiome may be associated with ASD and with GI disorders in children with ASD. Further studies into the brain-gut-microbiome axis could lead to new techniques for identifying GI disorders in children with ASD and novel therapies for treating ASD behaviors.

Potential serum biomarkers from a metabolomics study of autism

BACKGROUND

Early detection and diagnosis are very important for autism. Current diagnosis of autism relies mainly on some observational questionnaires and interview tools that may involve a great variability. We performed a metabolomics analysis of serum to identify potential biomarkers for the early diagnosis and clinical evaluation of autism.

METHODS

We analyzed a discovery cohort of patients with autism and participants without autism in the Chinese Han population using ultra-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry (UPLC/Q-TOF MS/MS) to detect metabolic changes in serum associated with autism. The potential metabolite candidates for biomarkers were individually validated in an additional independent cohort of cases and controls. We built a multiple logistic regression model to evaluate the validated biomarkers.

RESULTS

We included 73 patients and 63 controls in the discovery cohort and 100 cases and 100 controls in the validation cohort. Metabolomic analysis of serum in the discovery stage identified 17 metabolites, 11 of which were validated in an independent cohort. A multiple logistic regression model built on the 11 validated metabolites fit well in both cohorts. The model consistently showed that autism was associated with 2 particular metabolites: sphingosine 1-phosphate and docosahexaenoic acid.

LIMITATIONS

While autism is diagnosed predominantly in boys, we were unable to perform the analysis by sex owing to difficulty recruiting enough female patients. Other limitations include the need to perform test-retest assessment within the same individual and the relatively small sample size.

CONCLUSION

Two metabolites have potential as biomarkers for the clinical diagnosis and evaluation of autism.

A GC-MS urinary quantitative metabolomics analysis in depressed patients treated with TCM formula of Xiaoyaosan

Xiaoyaosan, one of the best-known traditional Chinese medicine prescriptions, has been widely used in China for the treatment of mental disorders such as depression. Although both clinical application and animal experiments indicate that Xiaoyaosan has an obvious antidepressant effect, the mechanism still remains unclarified, and there are few studies quantitatively measured the biomarkers of Xiaoyaosan treatment by metabolomics to determination. In this study, 25 depressed patients and 33 healthy volunteers were recruited. A GC-MS based metabolomics approach and the multivariate statistical methods were used for analyzing the urine metabolites of depressed patients before and after treatment compared with healthy controls. Then the biomakers through metabolomics determination were carried out the quantitative analysis. In total, 5 metabolites were identified as the potential diseased and therapeutic biomarkers of depression and Xiaoyaosan. Alanine, citrate and hippurate levels were significantly increased in the urine samples from depressed patients compared with healthy controls, while phenylalanie and tyrosine levels were significantly decreased. However, after Xiaoyaosan treatment for 6 weeks, phenylalanie and tyrosine levels were significantly increased (p<0.05) and alanine, citrate and hippurate levels significantly decreased (p<0.05). Xiaoyaosan has a good priority on the treatment of depression and the ability to adjust the neurotransmitters to obtain the best treated response and also could regulate the metabolism of amino acids and promote to produce energy meet the needs of the body.

GC-MS based metabolomics identification of possible novel biomarkers for schizophrenia in peripheral blood mononuclear cells

Schizophrenia is a debilitating mental disorder. Currently, the lack of disease biomarkers to support objective laboratory tests constitutes a bottleneck in the clinical diagnosis of schizophrenia. Here, a gas chromatography-mass spectrometry (GC-MS) based metabolomic approach was applied to characterize the metabolic profile of schizophrenia subjects (n = 69) and healthy controls (n = 85) in peripheral blood mononuclear cells (PBMCs) to identify and validate biomarkers for schizophrenia. Multivariate statistical analysis was used to visualize group discrimination and to identify differentially expressed metabolites in schizophrenia subjects relative to healthy controls. The multivariate statistical analysis demonstrated that the schizophrenia group was significantly distinguishable from the control group. In total, 18 metabolites responsible for the discrimination between the two groups were identified. These differential metabolites were mainly involved in energy metabolism, oxidative stress and neurotransmitter metabolism. A simplified panel of PBMC metabolites consisting of pyroglutamic acid, sorbitol and tocopherol-α was identified as an effective diagnostic tool, yielding an area under the receiver operating characteristic curve (AUC) of 0.82 in the training samples (45 schizophrenia subjects and 50 healthy controls) and 0.71 in the test samples (24 schizophrenic patients and 35 healthy controls). Taken together, these findings help to develop diagnostic tools for schizophrenia.

Analytical methodology for metabolomics study of adherent mammalian cells using NMR, GC-MS and LC-HRMS

We developed a methodology for the analysis of intracellular metabolites using nuclear magnetic resonance spectrometry (NMR), gas-chromatography coupled with mass spectrometry (GC-MS), and liquid chromatography coupled with high resolution mass spectrometry (LC-HRMS). The main steps for analysis of adherent cells in order to recover the widest possible range of intracellular compounds are blocking metabolic activity by quenching and extraction of intracellular metabolites. We explored three protocols to quench NSC-34 cell metabolism and four different extraction methods, analyzed by NMR. On the basis of the number of metabolites extracted and their relative standard deviation (RSD) analyzed by NMR, the most reproducible protocol [quenching by MeOH at -40 °C and extraction with CH2Cl2/MeOH/H2O (3:3:2)] was used to obtain intracellular media to be analyzed by GC-MS and LC-HRMS. GC-MS analysis was optimized by three oximation procedures followed by silylation derivatization and these were compared to silylation alone. Using reversed-phase liquid chromatography (C18), four different gradients for LC-MS were compared. The analytical protocols were determined to establish the reliability and suitability of sample treatments required to achieve the correct biological analysis of untargeted mammalian cell metabolomics.

Combined 1H-NMR and 1H-13C HSQC-NMR to improve urinary screening in autism spectrum disorders

Autism spectrum disorders (ASD) are neurodevelopmental diseases with complex genetic and environmental etiological factors. Although genetic causes play a significant part in the etiology of ASD, metabolic disturbances may also play a causal role or modulate the clinical features of ASD. The number of ASD studies involving metabolomics is increasing, and sometime with conflicting findings. We assessed the metabolomics profiling of urine samples to determine a comprehensive biochemical signature of ASD. Furthermore, to date no study has combined metabolic profiles obtained from different analytical techniques to distinguish patient with ASD from healthy individuals. We obtained (1)H-NMR spectra and 2D (1)H-(13)C HSQC NMR spectra from urine samples of patients with ASD or healthy controls. We analyzed these spectra by multivariate statistical data analysis. The OPLS-DA model obtained from (1)H NMR spectra showed a good discrimination between ASD samples and non-ASD samples (R(2)Y(cum) = 0.70 and Q(2) = 0.51). Combining the (1)H NMR spectra and the 2D (1)H-(13)C HSQC NMR spectra increased the overall quality and predictive value of the OPLS-DA model (R(2)Y(cum) = 0.84 and Q(2) = 0.71), leading to a better sensitivity and specificity. Urinary excretion of succinate, glutamate and 3-methyl-histidine differed significantly between ASD and non-ASD samples. Urinary screening of children with neurodevelopmental disorders by combining NMR spectroscopies (1D and 2D) in multivariate analysis is a better sensitive and a straightforward method that could help the diagnosis ASD.

Urinary proteomics and metabolomics in the diagnosis of pediatric disorders

Proteomics is the study of structures and functions of proteins, while metabolomics is the study of small-molecule metabolites in the cells, tissues, and organs of the organism. Proteomic technologies have wide applications in medical field. The current revolution in proteomics has led to the discovery of several new protein markers for various disorders. Urinary proteomics and metabolomics have also evolved in the recent years, for the diagnosis of both renal and nonrenal disorders. The urinary proteome varies in normal and abnormal conditions. Different techniques are employed for the analysis of pediatric urinary proteome, the commonest being MS. Before introduction into clinical use, there is the need for careful standardization. Available data suggest that there are differences in urinary proteome between adult and pediatric populations. It is noted that infant urine contains proteins involved in translation and transcription, cellular growth, and metabolic processes, which are not predominant in adult urine. Available data on urinary proteomic and metabolomic profile in common pediatric disorders are also reviewed here.

Metabolic Profiling and Phenotyping of Central Nervous System Diseases: Metabolites Bring Insights into Brain Dysfunctions

Metabolic phenotyping corresponds to the large-scale quantitative and qualitative analysis of the metabolome i.e., the low-molecular weight <1 KDa fraction in biological samples, and provides a key opportunity to advance neurosciences. Proton nuclear magnetic resonance and mass spectrometry are the main analytical platforms used for metabolic profiling, enabling detection and quantitation of a wide range of compounds of particular neuro-pharmacological and physiological relevance, including neurotransmitters, secondary messengers, structural lipids, as well as their precursors, intermediates and degradation products. Metabolic profiling is therefore particularly indicated for the study of central nervous system by probing metabolic and neurochemical profiles of the healthy or diseased brain, in preclinical models or in human samples. In this review, we introduce the analytical and statistical requirements for metabolic profiling. Then, we focus on key studies in the field of metabolic profiling applied to the characterization of animal models and human samples of central nervous system disorders. We highlight the potential of metabolic profiling for pharmacological and physiological evaluation, diagnosis and drug therapy monitoring of patients affected by brain disorders. Finally, we discuss the current challenges in the field, including the development of systems biology and pharmacology strategies improving our understanding of metabolic signatures and mechanisms of central nervous system diseases.

Gas chromatography-mass spectrometry of biofluids and extracts

Gas chromatography-mass spectrometry (GC-MS) has been widely used in metabonomics analyses of biofluid samples. Biofluids provide a wealth of information about the metabolism of the whole body and from multiple regions of the body that can be used to study general health status and organ function. Blood serum and blood plasma, for example, can provide a comprehensive picture of the whole body, while urine can be used to monitor the function of the kidneys, and cerebrospinal fluid (CSF) will provide information about the status of the brain and central nervous system (CNS). Different methods have been developed for the extraction of metabolites from biofluids, these ranging from solvent extracts, acids, heat denaturation, and filtration. These methods vary widely in terms of efficiency of protein removal and in the number of metabolites extracted. Consequently, for all biofluid-based metabonomics studies, it is vital to optimize and standardize all steps of sample preparation, including initial extraction of metabolites. In this chapter, recommendations are made of the optimum experimental conditions for biofluid samples for GC-MS, with a particular focus on blood serum and plasma samples.

Autism spectrum disorders and intestinal microbiota

Through extensive microbial-mammalian co-metabolism, the intestinal microbiota have evolved to exert a marked influence on health and disease via gut-brain-microbiota interactions. In this addendum, we summarize the findings of our recent study on the fecal microbiota and metabolomes of children with pervasive developmental disorder-not otherwise specified (PDD-NOS) or autism (AD) compared with healthy children (HC). Children with PDD-NOS or AD have altered fecal microbiota and metabolomes (including neurotransmitter molecules). We hypothesize that the degree of microbial alteration correlates with the severity of the disease since fecal microbiota and metabolomes alterations were higher in children with PDD-NOS and, especially, AD compared to HC. Our study indicates that the levels of free amino acids (FAA) and volatile organic compounds (VOC) differ in AD subjects compared to children with PDD-NOS, who are more similar to HC. Finally, we propose a new perspective on the implications for the interaction between intestinal microbiota and AD.

Sample collection and preparation of biofluids and extracts for gas chromatography-mass spectrometry

To maximize the utility of gas chromatography-mass spectrometry (GC-MS) in metabonomics research, all stages of the experimental design should be standardized, including sample collection, storage, preparation, and sample separation. Moreover, the prerequisite for any GC-MS analysis is that a compound must be volatile and thermally stable if it is to be analyzed using this technique. Since many metabolites are nonvolatile and polar in nature, they are not readily amenable to analysis by GC-MS and require initial chemical derivatization of the polar functional groups in order to reduce the polarity and to increase the thermal stability and volatility of the analytes. In this chapter, an overview is presented of the optimum approach to sample collection, storage, and preparation for gas chromatography-mass spectrometry-based metabonomics with particular focus on urine samples as example of biofluids.

Urinary p-cresol is elevated in young French children with autism spectrum disorder: a replication study

The aromatic compound p-cresol (4-methylphenol) has been found elevated in the urines of Italian autistic children up to 8 years of age. The present study aims at replicating these initial findings in an ethnically distinct sample and at extending them by measuring also the three components of urinary p-cresol, namely p-cresylsulfate, p-cresylglucuronate and free p-cresol. Total urinary p-cresol, p-cresylsulfate and p-cresylglucuronate were significantly elevated in 33 French autism spectrum disorder (ASD) cases compared with 33 sex- and age-matched controls (p < 0.05). This increase was limited to ASD children aged ≤8 years (p < 0.01), and not older (p = 0.17). Urinary levels of p-cresol and p-cresylsulfate were associated with stereotypic, compulsive/repetitive behaviors (p < 0.05), although not with overall autism severity. These results confirm the elevation of urinary p-cresol in a sizable set of small autistic children and spur interest into biomarker roles for p-cresol and p-cresylsulfate in autism.

MIRA: mutual information-based reporter algorithm for metabolic networks

MOTIVATION

Discovering the transcriptional regulatory architecture of the metabolism has been an important topic to understand the implications of transcriptional fluctuations on metabolism. The reporter algorithm (RA) was proposed to determine the hot spots in metabolic networks, around which transcriptional regulation is focused owing to a disease or a genetic perturbation. Using a z-score-based scoring scheme, RA calculates the average statistical change in the expression levels of genes that are neighbors to a target metabolite in the metabolic network. The RA approach has been used in numerous studies to analyze cellular responses to the downstream genetic changes. In this article, we propose a mutual information-based multivariate reporter algorithm (MIRA) with the goal of eliminating the following problems in detecting reporter metabolites: (i) conventional statistical methods suffer from small sample sizes, (ii) as z-score ranges from minus to plus infinity, calculating average scores can lead to canceling out opposite effects and (iii) analyzing genes one by one, then aggregating results can lead to information loss. MIRA is a multivariate and combinatorial algorithm that calculates the aggregate transcriptional response around a metabolite using mutual information. We show that MIRA's results are biologically sound, empirically significant and more reliable than RA.

RESULTS

We apply MIRA to gene expression analysis of six knockout strains of Escherichia coli and show that MIRA captures the underlying metabolic dynamics of the switch from aerobic to anaerobic respiration. We also apply MIRA to an Autism Spectrum Disorder gene expression dataset. Results indicate that MIRA reports metabolites that highly overlap with recently found metabolic biomarkers in the autism literature. Overall, MIRA is a promising algorithm for detecting metabolic drug targets and understanding the relation between gene expression and metabolic activity.

AVAILABILITY AND IMPLEMENTATION

The code is implemented in C# language using .NET framework. Project is available upon request.

Biomarkers in autism spectrum disorder: the old and the new

RATIONALE

Autism spectrum disorder (ASD) is a complex heterogeneous neurodevelopmental disorder with onset during early childhood and typically a life-long course. The majority of ASD cases stems from complex, 'multiple-hit', oligogenic/polygenic underpinnings involving several loci and possibly gene-environment interactions. These multiple layers of complexity spur interest into the identification of biomarkers able to define biologically homogeneous subgroups, predict autism risk prior to the onset of behavioural abnormalities, aid early diagnoses, predict the developmental trajectory of ASD children, predict response to treatment and identify children at risk for severe adverse reactions to psychoactive drugs.

OBJECTIVES

The present paper reviews (a) similarities and differences between the concepts of 'biomarker' and 'endophenotype', (b) established biomarkers and endophenotypes in autism research (biochemical, morphological, hormonal, immunological, neurophysiological and neuroanatomical, neuropsychological, behavioural), (c) -omics approaches towards the discovery of novel biomarker panels for ASD, (d) bioresource infrastructures and (e) data management for biomarker research in autism.

RESULTS

Known biomarkers, such as abnormal blood levels of serotonin, oxytocin, melatonin, immune cytokines and lymphocyte subtypes, multiple neuropsychological, electrophysiological and brain imaging parameters, will eventually merge with novel biomarkers identified using unbiased genomic, epigenomic, transcriptomic, proteomic and metabolomic methods, to generate multimarker panels. Bioresource infrastructures, data management and data analysis using artificial intelligence networks will be instrumental in supporting efforts to identify these biomarker panels.

CONCLUSIONS

Biomarker research has great heuristic potential in targeting autism diagnosis and treatment.

Evaluating an alternative method for rapid urinary creatinine determination

Creatinine (CR) is an endogenously produced chemical that is routinely assayed in urine specimens to assess kidney function and sample dilution. The industry-standard method for CR determination, known as the kinetic Jaffé (KJ) method, relies on an exponential rate of a colorimetric change, and can therefore require automated processing equipment for moderate- to high-throughput analysis (hundreds to thousands of samples per day). This study evaluates an alternative colorimetric method, the "plateau Jaffé" (PJ) method, which utilizes the chemistry of the KJ method, a commercially available kit, and a multipoint calibration curve. This method is amenable to moderate-throughput sample analysis and does not require automated processing equipment. Thirty-two spot urine samples from healthy adult volunteers were analyzed for creatinine concentration (CRc) using the KJ and PJ methods. Samples were also analyzed using a liquid chromatography time-of-flight mass spectrometry (LC-TOF/MS) method, which acted as an analytical control. Replicate measurements of spot samples (natural log-transformed values) were used to estimate method precision, and linear regression models were used to evaluate method accuracy (LC-TOF/MS measurements were considered the analytical benchmark). Measurement precision was comparable across all three methods, with coefficent of variation estimates ranging from 3 to 6%. Regression models generally showed good agreement across methods with R(2) estimates ranging from .996 to .998, slope estimates ranging from .944 to .986, and y-intercept estimates ranging from 0.111 to 0.303. Minor bias (between 2 and 16%) was observed across methods at the tails of the measurement distributions. The provided regression equations can be used to adjust for this bias and to improve CR measurement comparisons across studies employing different methods. Considering these results, the PJ method is a suitable alternative to the industry standard KJ method for urinary CRc determination. It can be implemented for moderate-throughput sample analysis using modest and commonly available lab instrumentation and manual sample preparation techniques.

Altered metabolites in the plasma of autism spectrum disorder: a capillary electrophoresis time-of-flight mass spectroscopy study

Clinical diagnosis and severity of autism spectrum disorders (ASD) are determined by trained clinicians based on clinical evaluations of observed behaviors. As such, this approach is inevitably dependent on the expertise and subjective assessment of those administering the clinical evaluations. There is a need to identify objective biological markers associated with diagnosis or clinical severity of the disorder. To identify novel candidate metabolites as potential biomarkers for ASD, the current study applied capillary electrophoresis time-of-flight mass spectroscopy (CE-TOFMS) for high-throughput profiling of metabolite levels in the plasma of 25 psychotropic-naïve adult males with high-functioning ASD and 28 age-matched typically-developed control subjects. Ten ASD participants and ten age-matched controls were assigned in the first exploration set, while 15 ASD participants and 18 controls were included in the second replication set. By CE-TOFMS analysis, a total of 143 metabolites were detected in the plasma of the first set. Of these, 17 metabolites showed significantly different relative areas between the ASD participants and the controls (p<0.05). Of the 17 metabolites, we consistently found that the ASD participants had significantly high plasma levels of arginine (p = 0.024) and taurine (p = 0.018), and significantly low levels of 5-oxoproline (p<0.001) and lactic acid (p = 0.031) compared with the controls in the second sample set. Further confirmatory analysis using quantification of absolute metabolite concentrations supported the robustness of high arginine (p = 0.001) and low lactic acid (p = 0.003) in the combined sample (n = 53). The present study identified deviated plasma metabolite levels associated with oxidative stress and mitochondrial dysfunction in individuals with ASD.