The metabolomics of airway diseases,including COPD,asthma and cystic fibrosis

Abstract

Chronic obstructive pulmonary disease (COPD), asthma and cystic fibrosis (CF) are characterized by airway obstruction and an inflammatory process. Reaching early diagnosis and discrimination of subtypes of these respiratory diseases are quite a challenging task than other chronic illnesses. Metabolomics is the study of metabolic pathways and the measurement of unique biochemical molecules generated in a living system. In the last decade, metabolomics has already proved to be useful for the characterization of several pathological conditions and offers promises as a clinical tool. In this article, we review the current state of the metabolomics of COPD, asthma and CF with a focus on the different methods and instrumentation being used for the discovery of biomarkers in research and translation into clinic as diagnostic aids for the choice of patient-specific therapies.     

The value of urine osmolality as an index of stress in the ovine fetus

In ovine fetuses, during 100-130 days of gestation, urine osmolalities less than 175 mosmol/kg water were associated with plasma immunoreactive adrenocorticotrophin (ACTH) concentrations below 40 pg/ml in 40/41 samples. In 18/29 fetuses with urine osmolalities greater than 175 mosmol/kg water plasma ACTH was significantly elevated. In 38 samples of fetal blood there was a significant correlation between plasma ADH and ACTH concentrations. By least squares regression the equation to the line was [ACTH] = 5.06 + 3.70 [ADH] (r = 0.62, P less than 0.001). In 50 samples from fetuses of gestational ages 100-140 days, with urine osmolalities of 302 +/- 86 mosmol/kg (mean +/- SD) the blood pH, pO2 and pCO2 values were not significantly different from those in 50 samples from fetuses with urine osmolalities of 125 +/- 22 mosmol/kg. It is proposed that the measurement of fetal urine osmolality provides a good index of fetal stress. A fetus with a urine osmolality less than 175 mosmol/kg is almost invariably in the optimum, unstressed condition.     

Normalization strategies for metabonomic analysis of urine samples

Unlike plasma and most biological fluids which have solute concentrations that are tightly controlled, urine volume can vary widely based upon water consumption and other physiological factors. As a result, the concentrations of endogenous metabolites in urine vary widely and normalizing for these effects is necessary. Normalization approaches that utilized urine volume, osmolality, creatinine concentration, and components that are common to all samples (“total useful MS signal”) were compared in order to determine which strategies could be successfully used to differentiate between dose groups based upon the complete endogenous metabolite profile. Variability observed in LC/MS results obtained from targeted and non-targeted metabonomic analyses was highly dependent on the strategy used for normalization. We therefore recommend the use of two different normalization techniques in order to facilitate detection of statistically significant changes in the endogenous metabolite profile when working with urine samples.     

A novel structural specific creatinine sensing scheme for the determination of the urine creatinine

In this work, a highly structural dependent amperometric scheme was proposed for the determination of creatinine without enzymatic assistance. The principle of this novel method is based upon the formation of a soluble copper–creatinine complex on the copper electrode surface. Subsequently, an oxidative current from the regeneration of the surface oxide layer is monitored and it is proportional to the concentration of the creatinine. This scheme can be conducted at potential of ?0.1 V (vs. Ag/AgCl, 3 M) in phosphate buffer (pH 7). A typical calibration plot from 25 μg/dL to 1.5 mg/dL (R² = 0.997) with a detection limit of 6.8 μg/dL (S/N = 3) is achieved. The relative standard deviation of 21 successive injections of 0.2 mg/dL creatinine is 0.018. Under the optimal conditions, the frequently encountered biological interferences at physiological or higher concentration were investigated. Only uric acid revealed an obvious interference (298.1%). However, a Nafion^® coated copper plating electrode shows a successful decrement of the interference of the uric acid with slightly decreased sensitivity of creatinine. The feasibility of this scheme for further clinical application is demonstrated by both HPLC and FIA to evaluate the creatinine concentration in a urine sample.     

High-throughput extraction and quantification method for targeted metabolomics in murine tissues

Introduction

Global metabolomics analyses using body fluids provide valuable results for the understanding and prediction of diseases. However, the mechanism of a disease is often tissue-based and it is advantageous to analyze metabolomic changes directly in the tissue. Metabolomics from tissue samples faces many challenges like tissue collection, homogenization, and metabolite extraction.

Objectives

We aimed to establish a metabolite extraction protocol optimized for tissue metabolite quantification by the targeted metabolomics AbsoluteIDQ? p180 Kit (Biocrates). The extraction method should be non-selective, applicable to different kinds and amounts of tissues, monophasic, reproducible, and amenable to high throughput.

Methods

We quantified metabolites in samples of eleven murine tissues after extraction with three solvents (methanol, phosphate buffer, ethanol/phosphate buffer mixture) in two tissue to solvent ratios and analyzed the extraction yield, ionization efficiency, and reproducibility.

Results

We found methanol and ethanol/phosphate buffer to be superior to phosphate buffer in regard to extraction yield, reproducibility, and ionization efficiency for all metabolites measured. Phosphate buffer, however, outperformed both organic solvents for amino acids and biogenic amines but yielded unsatisfactory results for lipids. The observed matrix effects of tissue extracts were smaller or in a similar range compared to those of human plasma.

Conclusion

We provide for each murine tissue type an optimized high-throughput metabolite extraction protocol, which yields the best results for extraction, reproducibility, and quantification of metabolites in the p180 kit. Although the performance of the extraction protocol was monitored by the p180 kit, the protocol can be applicable to other targeted metabolomics assays.

     

NanoStriDE: normalization and differential expression analysis of NanoString nCounter data

Background

Relationships between species, genes and genomes have been printed as trees for over a century. Whilst this may have been the best format for exchanging and sharing phylogenetic hypotheses during the 20^th century, the worldwide web now provides faster and automated ways of transferring and sharing phylogenetic knowledge. However, novel software is needed to defrost these published phylogenies for the 21^st century.

Results

TreeRipper is a simple website for the fully-automated recognition of multifurcating phylogenetic trees (http://linnaeus.zoology.gla.ac.uk/~jhughes/treeripper/). The program accepts a range of input image formats (PNG, JPG/JPEG or GIF). The underlying command line c++ program follows a number of cleaning steps to detect lines, remove node labels, patch-up broken lines and corners and detect line edges. The edge contour is then determined to detect the branch length, tip label positions and the topology of the tree. Optical Character Recognition (OCR) is used to convert the tip labels into text with the freely available tesseract-ocr software. 32% of images meeting the prerequisites for TreeRipper were successfully recognised, the largest tree had 115 leaves.

Conclusions

Despite the diversity of ways phylogenies have been illustrated making the design of a fully automated tree recognition software difficult, TreeRipper is a step towards automating the digitization of past phylogenies. We also provide a dataset of 100 tree images and associated tree files for training and/or benchmarking future software. TreeRipper is an open source project licensed under the GNU General Public Licence v3.     

Protein: creatinine and trypsin inhibitor: creatinine ratios in the urine of marathon runners

We measured changes in the protein: creatinine and trypsin inhibitor: creatinine ratios in the urine of six male marathon runners. Samples of urine were collected before the run, immediately after the run and in 6-h collections for 2 days. We found the greatest increase in the protein: creatinine ratio (2.6 times greater) in urine collected immediately after the run and the greatest increase in the trypsin inhibitor: creatinine ratio in urine samples collected 6-12 and 12-18 h after the run (2 and 3 times greater, respectively). This suggests the existence of different mechanisms for these two physiological processes. The later increase in the trypsin inhibitor: creatinine ratio was perhaps, due to a state of short-lived inflamation and shock after severe physical effort.     

基于LC-MS代谢组学技术的丽豆-大豆差异代谢物比较研究

丽豆(Calophaca sinica)是我国华北地区特有的一种珍稀野生植物。为探明丽豆的营养价值，该文以大豆(Glycine max)为参照组，利用液相色谱-质谱联用(LC-MS)技术对其种子进行了比较代谢组学研究。结果表明：(1)丽豆和大豆中共检测到1 857种代谢产物，二者成分相同且含量相似的代谢物有1 698种(>90%),差异代谢物有159种(<10%)。(2)在差异代谢物中，成分差异的有9种，其中有5种为丽豆特有，剩余150种均为含量差异，其中48种(约30%)在丽豆中的含量高于大豆。(3) KEGG注释到8条差异代谢物显著富集(P<0.1)的通路，主要包括初生代谢物的各类氨基酸生物合成途径和次生代谢物的罗汉松脂素、花生四烯酸以及二萜类等生物合成途径。(4)丽豆比大豆含量低的化学组分主要是初生代谢产物，比大豆含量高的化学组分主要是次生代谢物，而这些次生代谢物在调节血糖、骨坏损修复、增强免疫以及消炎抗癌等生理过程中有着积极的作用。综上所述，该研究认为丽豆与大豆具有相近的营养价值，并且对改善人类亚健康状况有积极的影响；此外，该文使我们对丽豆的营养价值和代谢组成...     

Serum metabolomics for the diagnosis and classification of myasthenia gravis

Myasthenia gravis (MG) is a chronic autoimmune neuromuscular disease with few reliable diagnostic measures. Therefore, it is great important to explore novel tools for the diagnosis of MG. In this study, a serum metabolomic approach based on LC?CMS in combination with multivariate statistical analyses was used to identify and classify patients with various grades of MG. Serum samples from 42 MG patients and 16 healthy volunteers were analyzed by liquid chromatography Fourier transform mass spectrometry (LC-FTMS). MG patients were clearly distinguished from healthy subjects based on their global serum metabolic profiles by using orthogonal partial least squares (OPLS) analysis. Moreover, different changes in metabolic profiles were observed between early- and late-stages MG patients. Nine biomarkers, including gamma-aminobutyric acid and sphingosine 1-phosphate were identified. In addition, 92.8% sensitivity, 83.3% specificity and 90% accuracy were obtained from the OPLS discriminant analysis (OPLS-DA) class prediction model in detecting MG. The results presented here illustrate that serum metabolomics exhibits great potential in the detecting and grading of MG, and it is potentially applicable as a new diagnostic approach for MG.     

The use of inference strategies in the differential diagnosis of microcytic anemia

A new expert system developed on a Macintosh personal computer using a commercially available artificial intelligence shell was compared with four different discriminant functions (DFs) for the differentiation of microcytic anemia into etiologic categories. Several databases were used with a different composition but all contained at least some samples from thalassemic individuals and from patients with iron deficiency anemia. The DFs analyzed were those proposed by England and Fraser, Green and colleagues, Mentzer, and by Shine and Lal. None of the databases performed satisfactorily when used singly, whereas very high false-positive rates were obtained by one of them. The diagnostic efficiency was somewhat improved by combining several DFs. An expert system using an artificial intelligence "shell" with an "interference engine" was developed using cluster analysis and a set of learning examples. The input necessary for the system to achieve a conclusion consists of MCV, RBC, and RDW as well as a statement as to whether the patient has anemia. Based upon the values of these parameters, the expert system will give an "advice" regarding the probabilities for thalassemia, iron deficiency, and/or other probabilities such as previous transfusions, anemia of chronic disease, laboratory error, etc. In a prospective trial, the system functioned with an accuracy of better than 85%.     

Statistical strategies for avoiding false discoveries in metabolomics and related experiments

Many metabolomics, and other high-content or high-throughput, experiments are set up such that the primary aim is the discovery of biomarker metabolites that can discriminate, with a certain level of certainty, between nominally matched ‘case’ and ‘control’ samples. However, it is unfortunately very easy to find markers that are apparently persuasive but that are in fact entirely spurious, and there are well-known examples in the proteomics literature. The main types of danger are not entirely independent of each other, but include bias, inadequate sample size (especially relative to the number of metabolite variables and to the required statistical power to prove that a biomarker is discriminant), excessive false discovery rate due to multiple hypothesis testing, inappropriate choice of particular numerical methods, and overfitting (generally caused by the failure to perform adequate validation and cross-validation). Many studies fail to take these into account, and thereby fail to discover anything of true significance (despite their claims). We summarise these problems, and provide pointers to a substantial existing literature that should assist in the improved design and evaluation of metabolomics experiments, thereby allowing robust scientific conclusions to be drawn from the available data. We provide a list of some of the simpler checks that might improve one’s confidence that a candidate biomarker is not simply a statistical artefact, and suggest a series of preferred tests and visualisation tools that can assist readers and authors in assessing papers. These tools can be applied to individual metabolites by using multiple univariate tests performed in parallel across all metabolite peaks. They may also be applied to the validation of multivariate models. We stress in particular that classical p-values such as “p < 0.05”, that are often used in biomedicine, are far too optimistic when multiple tests are done simultaneously (as in metabolomics). Ultimately it is desirable that all data and metadata are available electronically, as this allows the entire community to assess conclusions drawn from them. These analyses apply to all high-dimensional ‘omics’ datasets.     

Complexity and pitfalls of mass spectrometry-based targeted metabolomics in brain research

Current quantitative metabolomic research in brain tissue is challenged by several analytical issues. To compare data of metabolite pattern, ratios of individual metabolite concentrations and composed classifiers characterizing a distinct state, standardized workup conditions, and extraction medium are crucial. Differences in physicochemical properties of individual compounds and compound classes such as polarity determine extraction yields and, thus, ratios of compounds with varying properties. Also, variations in suppressive effects related to coextracted matrix components affect standards or references and their concentration-dependent responses.The selection of a common tissue extraction protocol is an ill-posed problem because it can be regarded as a multiple objective decision depending on factors such as sample handling practicability, measurement precision, control of matrix effects, and relevance of the chemical assay. This study systematically evaluates the impact of extraction solvents and the impact of the complex brain tissue on measured metabolite levels, taking into account ionization efficiency as well as challenges encountered in the trace-level quantification of the analytes in brain matrices. In comparison with previous studies that relied on nontargeted platforms, consequently emphasizing the global behavior of the metabolomic fingerprint, here we focus on several series of metabolites spanning over extensive polarity, concentration, and molecular mass ranges.     

Specific gravity and creatinine as corrections for variation in urine concentration in humans,gorillas, and woolly monkeys

Hormones excreted in the urine are widely used to assess the physiological and psychological condition of unrestrained animals. In order to control for variation in the water concentration of urine samples, the hormone concentration is often indexed to the concentration of creatinine. Because there are several problems with using creatinine, we have investigated the efficacy of specific gravity as an alternative basis for adjusting the hormone concentration in humans, gorillas, and woolly monkeys. In an experimental manipulation of human urine hydration, ten volunteers drank a water load proportional to body weight, and provided complete urine collection and saliva samples for four consecutive 20 min intervals. From the urine, we measured cortisol (radioimmunoassay), creatinine (colorimetric assay), and specific gravity (refractometer). Only cortisol was assayed from saliva. During 80 min following water ingestion, cortisol, creatinine, and specific gravity declined as urine became diluted; however, total cortisol excretion remained constant. Only cortisol concentration indexed to specific gravity accurately reflected the consistent cortisol excretion. Specific gravity and creatinine‐corrected cortisol values were highly correlated but were significantly different. Salivary cortisol provided evidence for the relative stability of serum cortisol. To determine the utility of these corrections in other primates, we compared specific gravity‐ and creatinine‐corrected cortisol in urine samples from captive gorillas (N=16) and woolly monkeys (N=8). As with the human study, the two corrections were strongly correlated in each species, but the means were different. Specific gravity correction was superior in revealing the circadian variation in cortisol. Am. J. Primatol. 72:1082–1091, 2010. © 2010 Wiley‐Liss, Inc.