Organic acid profile of isovaleric acidemia: a comprehensive metabolomics approach

Isovaleric acidemia (IVA, MIM 248600) can be a severe and potentially life-threatening disease in affected neonates, but with a positive prognosis on treatment for some phenotypes. This study presents the first application of metabolomics to evaluate the metabolite profiles derived from urine samples of untreated and treated IVA patients as well as of obligate heterozygotes. All IVA patients carried the same homozygous c.367 G > A nucleotide change in exon 4 of the IVD gene but manifested phenotypic diversity. Concurrent class analysis (CONCA) was used to compare all the metabolites from the original complete data set obtained from the three case and two control groups used in this investigation. This application of CONCA has not been reported previously, and is used here to compare four different modes of scaling of all metabolites. The variables important in discrimination from the CONCA thus enabled the recognition of different metabolic patterns encapsulated within the data sets that would not have been revealed by using only one mode of scaling. Application of multivariate and univariate analyses disclosed 11 important metabolites that distinguished untreated IVA from controls. These included well-established diagnostic biomarkers of IVA, endogenous detoxification markers, and 3-hydroxycaproic acid, an indicator of ketosis, but not reported previously for this disease. Nine metabolites were identified that reflected the effect of treatment of IVA. They included detoxification products and indicators related to the high carbohydrate and low protein diet which formed the hallmark of the treatment. This investigation also provides the first comparative metabolite profile for heterozygotes of this inherited metabolic disorder. The detection of informative metabolites in even very low concentrations in all three experimental groups highlights the potential advantage of the holistic mode of analysis of inherited metabolic diseases in a metabolomics investigation.     

Reliable detection of cystic fibrosis in skin-derived fibroblast cultures

Summary The specific induction of alkaline phosphatase with Tamm-Horsfall glycoprotein, isoproterenol, and theophylline in skin-derived fibroblast cultures from patients with cystic fibrosis permits one to reliably discriminate between cystic fibrosis patients on the one hand, and heterozygotes and normals on the other.It was found that fibroblast-like and intermediary types of amniotic fluidderived cells behave essentially like skin-derived fibroblasts. These findings imply that if different amniotic fluid cell types can be reliably separated, prenatal diagnosis of cystic fibrosis should become feasable in the near future.     

Serum metabolomics profile of type 2 diabetes mellitus in a Brazilian rural population

Introduction

The development of common forms of diabetes comes from the interaction between environmental and genetic factors, and the consequences of poor glycemic control in these patients could result in several complications. Metabolomic studies for type 2 diabetes mellitus in serum/plasma have reported changes in numerous metabolites, which might be considered possible targets for future mechanistic research. However, the specific role of a particular metabolite as cause or consequence of diabetes derangements is difficult to establish.

Objectives

As type 2 diabetes is a disease that changes the metabolic profile in several levels, this work aimed to compare the metabolomic profiles of type 2 diabetes mellitus and non-diabetic participants. In addition, we exploited our family-based study design to bring a better understanding of the causal relationship of identified metabolites and diabetes.

Methods

In the current study, population based metabolomics was applied in 939 subjects from the Baependi Heart Study. Participants were separated into two groups: diabetic (77 individuals) and non-diabetic (862 individuals), and the metabolic profile was performed by GC/MS technique.

Results

We have identified differentially concentrated metabolites in serum of diabetic and non-diabetic individuals. We identified 72 metabolites up-regulated in type 2 diabetes mellitus compared to non-diabetics. It was possible to recapitulate the main pathways that the literature shows as changed in diabetes. Also, based on metabolomic profile, we separated pre-diabetic individuals (with glucose concentration between 100–125 mg/dL) from non-diabetics and diabetics. Finally, using heritability analysis, we were able to suggest metabolites in which altered levels may precede diabetic development.

Conclusion

Our data can be used to derive a better understanding of the causal relationship of the observed associations and help to prioritize diabetes-associated metabolites for further work.

     

A comparison of high-throughput plasma NMR protocols for comparative untargeted metabolomics

Metabolomics - Understanding the interaction between organisms and the environment is important for predicting and mitigating the effects of global phenomena such as climate change, and the fate,...     

基于比较代谢组学的理性优化方法提高阿维菌素产量

【目的】利用比较代谢组学的分析方法,研究不同发酵培养基中阿维链霉菌的胞内代谢差异,揭示合成阿维菌素的关键代谢物和代谢途径,再通过理性优化添加主要关键代谢物,提高阿维菌素产量。【方法】对M1和M2培养基中生长的菌体进行基于GC-MS的胞内代谢组学分析,通过理性添加强化前体代谢物,确定阿维菌素高产培养基。【结果】GC-MS共检测到232种物质,能够精确匹配70种胞内代谢物,通过PCA和PLS分析,最终确定了21种已知的胞内代谢物与阿维菌素的生物合成密切相关。其中乳酸、丙酮酸、琥珀酸、苏氨酸、异亮氨酸、缬氨酸和油脂类物质对阿维菌素的产量影响较为显著。通过单独或组合优化添加这些前体,阿维菌素的产量从5.36 g/L提高到了5.92 g/L,增加了10.4%。【结论】基于比较代谢组学分析的理性优化培养基的方法可有效提高阿维菌素的产量,并为提高当下生物基产品的产量提供了新思路。     

Urine metabolomics analysis for adrenal incidentaloma activity detection and biomarker discovery

This study describes the development of a method suitable for the analysis of nineteen major urinary steroid metabolites in human urine. The analytes of interest were isolated from urine using solid phase extraction, subjected to enzymatic hydrolysis and again extracted applying solid phase extraction. After derivatization, methyloxime-trimethylsilyl ether derivatives of steroid hormones were identified by gas chromatography-mass spectrometry (GC/MS) and quantified by gas chromatography with flame ionization detector (GC/FID). The quantification method was validated for linearity, trueness, precision and selectivity. The limits of detection were between 6.2 and 7.2 ng/mL and limits of quantification were between 12.3 and 14.8 ng/mL. The established method was applied to analyze 28 urine samples from patients diagnosed with non-functioning adrenal incidentalomas (AIs) and 30 healthy subjects. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) were employed to visualize the differences between metabolic profiles of patients and the control group and to determine possible markers of AIs activity. Both multivariate methods separated seven patients from the rest of the examined individuals. Five urinary metabolites including α-cortol, tetrahydrocorticosterone, tetrahydrocortisol, allo-tetrahydrocortisol and etiocholanolone were identified as potential biomarkers of pathological adrenal function. The altered metabolites reflected pathological metabolism mainly of cortisol and cortisone. This research proved that metabolomics is a suitable tool for disease research.     

Breast muscle and plasma metabolomics profile of broiler chickens exposed to chronic heat stress conditions

Understanding the variations of muscle and plasma metabolites in response to high environmental temperature can provide important information on the molecular mechanisms related to body energy homeostasis in heat-stressed broiler chickens. In this study, we investigated the effect of chronic heat stress conditions on the breast muscle (Pectoralis major) and plasma metabolomics profile of broiler chickens by means of an innovative, high-throughput analytical approach such as the proton nuclear magnetic resonance (¹H NMR) spectrometry. A total of 300 Ross 308 male chicks were split into two experimental groups and raised in either thermoneutral conditions for the entire rearing cycle (0–41 days) (TNT group; six replicates of 25 birds/each) or exposed to chronic heat stress conditions (30 °C for 24 h/day) from 35 to 41 days (CHS group; six replicates of 25 birds/each). At processing (41 days), plasma and breast muscle samples were obtained from 12 birds/experimental group and then subjected to ¹H NMR analysis. The reduction of BW and feed intake as well as the increase in rectal temperature and heterophil: lymphocyte ratio confirmed that our experimental model was able to stimulate a thermal stress response without significantly affecting mortality. The ¹H NMR analysis revealed that a total of 26 and 19 molecules, mostly related to energy and protein metabolism as well as antioxidant response, showed significantly different concentrations respectively in the breast muscle and plasma in response to the thermal challenge. In conclusion, the results obtained in this study indicated that chronic heat stress significantly modulates the breast muscle and plasma metabolome in fast-growing broiler chickens, allowing to delineate potential metabolic changes that can have important implications in terms of body energy homeostasis, growth performance and product quality.     

LC-MS-based metabolomics

Metabolomics aims at identification and quantitation of small molecules involved in metabolic reactions. LC-MS has enjoyed a growing popularity as the platform for metabolomic studies due to its high throughput, soft ionization, and good coverage of metabolites. The success of a LC-MS-based metabolomic study often depends on multiple experimental, analytical, and computational steps. This review presents a workflow of a typical LC-MS-based metabolomic analysis for identification and quantitation of metabolites indicative of biological/environmental perturbations. Challenges and current solutions in each step of the workflow are reviewed. The review intends to help investigators understand the challenges in metabolomic studies and to determine appropriate experimental, analytical, and computational methods to address these challenges.     

Microbial metabolomics

Microbial metabolomics constitutes an integrated component of systems biology. By studying the complete set of metabolites within a microorganism and monitoring the global outcome of interactions between its development processes and the environment, metabolomics can potentially provide a more accurate snap shot of the actual physiological state of the cell. Recent advancement of technologies and post-genomic developments enable the study and analysis of metabolome. This unique contribution resulted in many scientific disciplines incorporating metabolomics as one of their "omics" platforms. This review focuses on metabolomics in microorganisms and utilizes selected topics to illustrate its impact on the understanding of systems microbiology.     

Coding exon detection using comparative sequences.

We introduce a new system, called shortHMM, for predicting exons, which predicts individual exons using two related genomes. In this system, we build a hidden semi-Markov model to identify exons. In the hidden Markov model, we propose joint probability models of nucleotides in introns, splice sites, 5'UTR, 3'UTR, and intergenic regions by exploiting the homology between related genomes. In order to reduce the false positive rate of the hidden Markov model, we develop a screening process which is able to identify intergenic regions. We then build a classifier by combining the statistics from the hidden Markov model and the screening process. We implement shortHMM on human-mouse sequence alignments. The source codes are available at < www.stat.purdue.edu/ jingwu/hmm >. Compared to TWINSCAN and SLAM, shortHMM is substantially more powerful in identifying AT-rich RefSeq exons (8% more AT-rich RefSeq exons were predicted), as well as slightly more powerful in identifying RefSeq exons (3-10% more RefSeq exons were predicted), at a similar or lower false positive rate, with less computing time and with less memory usage. Last, shortHMM is also capable of finding new potential exons.     

Urine metabolomics analysis for biomarker discovery and detection of jaundice syndrome in patients with liver disease

Metabolomics is a powerful new technology that allows for the assessment of global metabolic profiles in easily accessible biofluids and biomarker discovery in order to distinguish between diseased and nondiseased status information. Deciphering the molecular networks that distinguish diseases may lead to the identification of critical biomarkers for disease aggressiveness. However, current diagnostic methods cannot predict typical Jaundice syndrome (JS) in patients with liver disease and little is known about the global metabolomic alterations that characterize JS progression. Emerging metabolomics provides a powerful platform for discovering novel biomarkers and biochemical pathways to improve diagnostic, prognostication, and therapy. Therefore, the aim of this study is to find the potential biomarkers from JS disease by using a nontarget metabolomics method, and test their usefulness in human JS diagnosis. Multivariate data analysis methods were utilized to identify the potential biomarkers. Interestingly, 44 marker metabolites contributing to the complete separation of JS from matched healthy controls were identified. Metabolic pathways (Impact-value≥0.10) including alanine, aspartate, and glutamate metabolism and synthesis and degradation of ketone bodies were found to be disturbed in JS patients. This study demonstrates the possibilities of metabolomics as a diagnostic tool in diseases and provides new insight into pathophysiologic mechanisms.     

Systems biology in unruptured intracranial aneurysm: a metabolomics study in serum for the detection of biomarkers

Intracranial aneurysm (IA) is a common devastating condition occurs in up to 6 % of the population. It is asymptomatic but potentially fatal because of the progressive enlargement and rupturing leads to subarachnoid hemorrhage. Early diagnosis of IA is more valuable before it ruptures and hemorrhage. The diagnosis of IA is usually carried out using computerized tomography or magnetic resonance imaging. However, there is no biochemical test or a marker available for diagnosis. Serum metabolites were analyzed from normal and unruptured intracranial aneurysms patients (UIA) by NMR spectroscopy to identify the presence of serum markers, which could provide a clue for diagnosis and altered metabolic pathways in UIA condition. Analysis of proton spectra revealed significant perturbations in 20 serum metabolites in UIA. Multivariate analysis showed a distinct separation of normal from UIA based on 17 most contributing metabolites, and the scoring algorithm determines the perturbed metabolic pathways in UIA (urea cycle, citric acid cycle and ammonia recycling). Also, the gene expression analysis shows the significant (p ≤ 0.05) change in ARG, CPS1 and OTC genes leading to dysregulation in the urea cycle. Further, estimation of urea showed a significant increase in serum urea, which provides the prospect of rapid diagnosis. Overall, this study demonstrates the promise of developing biomarkers for the diagnosis of UIA from serum. In addition, the implementation of systems biological approach in metabolomic context gained an understanding about UIA that reflects the numerous metabolic pathways identified to be affected in disease condition.     

Mass spectrometry strategies in metabolomics

MS has evolved as a critical component in metabolomics, which seeks to answer biological questions through large-scale qualitative and quantitative analyses of the metabolome. MS-based metabolomics techniques offer an excellent combination of sensitivity and selectivity, and they have become an indispensable platform in biology and metabolomics. In this minireview, various MS technologies used in metabolomics are briefly discussed, and future needs are suggested.     

Mass spectrometry methods in metabolomics

The review deals with metabolomics, a new and rapidly growing area directed to the comprehensive analysis of metabolites of biological objects. Metabolites are characterized by various physical and chemical properties, traditionally studied by methods of analytical chemistry focused on certain groups of chemical substances. However, current progress in mass spectrometry has led to formation of rather unified methods, such as metabolic fingerprinting and metabolomic profiling, which allow defining thousands of metabolites in one biological sample and therefore draw “a modern portrait of metabolomics.” This review describes basic characteristics of these methods, ways of metabolite separation, and analysis of metabolites by mass spectrometry. The examples shown in this review, allow to estimate these methods and to compare their advantages and disadvantages. Besides that, we consider the methods, which are of the most frequent use in metabolomics; these include the methods for data processing and the required resources, such as software for mass spectra processing and metabolite search database. In the conclusion, general suggestions for successful metabolomic experiments are given.     

Current progress in computational metabolomics

Being a relatively new addition to the 'omics' field, metabolomics is still evolving its own computational infrastructure and assessing its own computational needs. Due to its strong emphasis on chemical information and because of the importance of linking that chemical data to biological consequences, metabolomics must combine elements of traditional bioinformatics with traditional cheminformatics. This is a significant challenge as these two fields have evolved quite separately and require very different computational tools and skill sets. This review is intended to familiarize readers with the field of metabolomics and to outline the needs, the challenges and the recent progress being made in four areas of computational metabolomics: (i) metabolomics databases; (ii) metabolomics LIMS; (iii) spectral analysis tools for metabolomics and (iv) metabolic modeling.     

Single cell metabolomics

Recent discoveries suggest that cells of a clonal population often display multiple metabolic phenotypes at the same time. Motivated by the success of mass spectrometry (MS) in the investigation of population-level metabolomics, the analytical community has initiated efforts towards MS-based single cell metabolomics to investigate metabolic phenomena that are buried under the population average. Here, we review the current approaches and illustrate their advantages and disadvantages. Because of significant advances in the field, different technologies are now at the verge of generating data that are useful for exploring and investigating metabolic heterogeneity.     

Standardizing GC-MS metabolomics

Metabolomics being the most recently introduced "omic" analytical platform is currently at its development phase. For the metabolomics to be broadly deployed to biological and clinical research and practice, issues regarding data validation and reproducibility need to be resolved. Gas chromatography-mass spectrometry (GC-MS) will remain integral part of the metabolomics laboratory. In this paper, the sources of biases in GC-MS metabolomics are discussed and experimental evidence for their occurrence and impact on the final results is provided. When available, methods to correct or account for these biases are presented towards the standardization of a systematic methodology for quantitative GC-MS metabolomics.