Comparative analysis of targeted metabolomics: Dominance-based rough set approach versus orthogonal partial least square-discriminant analysis

BackgroundMetabolomics is an emerging field that includes ascertaining a metabolic profile from a combination of small molecules, and which has health applications. Metabolomic methods are currently applied to discover diagnostic biomarkers and to identify pathophysiological pathways involved in pathology. However, metabolomic data are complex and are usually analyzed by statistical methods. Although the methods have been widely described, most have not been either standardized or validated. Data analysis is the foundation of a robust methodology, so new mathematical methods need to be developed to assess and complement current methods. We therefore applied, for the first time, the dominance-based rough set approach (DRSA) to metabolomics data; we also assessed the complementarity of this method with standard statistical methods. Some attributes were transformed in a way allowing us to discover global and local monotonic relationships between condition and decision attributes. We used previously published metabolomics data (18 variables) for amyotrophic lateral sclerosis (ALS) and non-ALS patients.ResultsPrincipal Component Analysis (PCA) and Orthogonal Partial Least Square-Discriminant Analysis (OPLS-DA) allowed satisfactory discrimination (72.7%) between ALS and non-ALS patients. Some discriminant metabolites were identified: acetate, acetone, pyruvate and glutamine. The concentrations of acetate and pyruvate were also identified by univariate analysis as significantly different between ALS and non-ALS patients. DRSA correctly classified 68.7% of the cases and established rules involving some of the metabolites highlighted by OPLS-DA (acetate and acetone). Some rules identified potential biomarkers not revealed by OPLS-DA (beta-hydroxybutyrate). We also found a large number of common discriminating metabolites after Bayesian confirmation measures, particularly acetate, pyruvate, acetone and ascorbate, consistent with the pathophysiological pathways involved in ALS.ConclusionDRSA provides a complementary method for improving the predictive performance of the multivariate data analysis usually used in metabolomics. This method could help in the identification of metabolites involved in disease pathogenesis. Interestingly, these different strategies mostly identified the same metabolites as being discriminant. The selection of strong decision rules with high value of Bayesian confirmation provides useful information about relevant condition–decision relationships not otherwise revealed in metabolomics data.     

Untargeted metabolomic profiling reveals multiple pathway perturbations and new clinical biomarkers in urea cycle disorders

PurposeUntargeted metabolomic analysis is increasingly being used in the screening and management of individuals with inborn errors of metabolism (IEM). We aimed to test whether untargeted metabolomic analysis in plasma might be useful for monitoring the disease course and management of urea cycle disorders (UCDs).MethodsUntargeted mass spectrometry–based metabolomic analysis was used to generate z-scores for more than 900 metabolites in plasma from 48 individuals with various UCDs. Pathway analysis was used to identify common pathways that were perturbed in each UCD.ResultsOur metabolomic analysis in plasma identified multiple potentially neurotoxic metabolites of arginine in arginase deficiency and, thus, may have utility in monitoring the efficacy of treatment in arginase deficiency. In addition, we were also able to detect multiple biochemical perturbations in all UCDs that likely reflect clinical management, including metabolite alterations secondary to dietary and medication management.ConclusionIn addition to utility in screening for IEM, our results suggest that untargeted metabolomic analysis in plasma may be beneficial for monitoring efficacy of clinical management and off-target effects of medications in UCDs and potentially other IEM.     

Metabolic reprogramming of hydrogenosomal amino acids in Trichomonas vaginalis under glucose restriction

BackgroundGlucose is the major energy source that is converted to pyruvate for ATP generation in the trichomonad hydrogenosome. Under glucose restriction (GR), the regulation of amino acids metabolism is crucial for trichomonad growth and survival. RNA-sequencing (RNA-seq) analysis has been used to identify differentially expressed genes in Trichomonas vaginalis under GR, leading to significant advances in understanding adaptive responses of amino acid metabolism to GR. However, the levels of amino acid metabolites modulated by GR are unknown in T. vaginalis.MethodsHerein, we describe a comprehensive metabolomic analysis of amino acid metabolites in the hydrogenosome using liquid chromatography Fourier transform ion cyclotron resonance mass spectrometry (LC-FT MS). The relative abundance of 17 hydrogenosomal amino acids was analyzed under GR and high-glucose (HG) conditions.ResultsLevels of most amino acids were higher in GR culture. Arginine was not detectable in either HG or GR cultures; however, its metabolic end-product proline was slightly increased under GR, suggesting that the arginine dihydrolase pathway was more activated by GR. Additionally, methionine catabolism was less stimulated under GR because of greater methionine accumulation. Furthermore, branched chain amino acids (BCAA), including leucine, isoleucine and valine, as well as phenylalanine and alanine, markedly accumulated under GR, indicating that glutamate-related metabolic pathways were remarkably enhanced in this setting. Our metabolomic analysis combined with previous RNA-seq data confirm the existence of several amino acid metabolic pathways in the hydrogenosome and highlight their potentially important roles in T. vaginalis under glucose deprivation.     

A metabolomic map of Zellweger spectrum disorders reveals novel disease biomarkers

PurposePeroxisome biogenesis disorders–Zellweger spectrum disorders (PBD-ZSD) are metabolic diseases with multisystem manifestations. Individuals with PBD-ZSD exhibit impaired peroxisomal biochemical functions and have abnormal levels of peroxisomal metabolites, but the broader metabolic impact of peroxisomal dysfunction and the utility of metabolomic methods is unknown.MethodsWe studied 19 individuals with clinically and molecularly characterized PBD-ZSD. We performed both quantitative peroxisomal biochemical diagnostic studies in parallel with untargeted small molecule metabolomic profiling in plasma samples with detection of >650 named compounds.ResultsThe cohort represented intermediate to mild PBD-ZSD subjects with peroxisomal biochemical alterations on targeted analysis. Untargeted metabolomic profiling of these samples revealed elevations in pipecolic acid and long-chain lysophosphatidylcholines, as well as an unanticipated reduction in multiple sphingomyelin species. These sphingomyelin reductions observed were consistent across the PBD-ZSD samples and were rare in a population of >1,000 clinical samples. Interestingly, the pattern or “PBD-ZSD metabolome” was more pronounced in younger subjects suggesting studies earlier in life reveal larger biochemical changes.ConclusionUntargeted metabolomics is effective in detecting mild to intermediate cases of PBD-ZSD. Surprisingly, dramatic reductions in plasma sphingomyelin are a consistent feature of the PBD-ZSD metabolome. The use of metabolomics in PBD-ZSD can provide insight into novel biomarkers of disease.     

Dysregulation of glutamine/glutamate metabolism in COVID-19 patients: A metabolism study in African population and mini meta-analysis

Coronavirus disease 2019 (COVID-19) remains a serious global threat. The metabolic analysis had been successfully applied in the efforts to uncover the pathological mechanisms and biomarkers of disease severity. Here we performed a quasi-targeted metabolomic analysis on 56 COVID-19 patients from Sierra Leone in western Africa, revealing the metabolomic profiles and the association with disease severity, which was confirmed by the targeted metabolomic analysis of 19 pairs of COVID-19 patients. A meta-analysis was performed on published metabolic data of COVID-19 to verify our findings. Of the 596 identified metabolites, 58 showed significant differences between severe and nonsevere groups. The pathway enrichment of these differential metabolites revealed glutamine and glutamate metabolism as the most significant metabolic pathway (Impact = 0.5; −log10P = 1.959). Further targeted metabolic analysis revealed six metabolites with significant intergroup differences, with glutamine/glutamate ratio significantly associated with severe disease, negatively correlated with 10 clinical parameters and positively correlated with SPO₂ (r_s= 0.442, p = 0.005). Mini meta-analysis indicated elevated glutamate was related to increased risk of COVID-19 infection (pooled odd ratio [OR] = 2.02; 95% confidence interval [CI]: 1.17–3.50) and severe COVID-19 (pooled OR = 2.28; 95% CI: 1.14–4.56). In contrast, elevated glutamine related to decreased risk of infection and severe COVID-19, the pooled OR were 0.30 (95% CI: 0.20–0.44), and 0.44 (95% CI: 0.19–0.98), respectively. Glutamine and glutamate metabolism are associated with COVID-19 severity in multiple populations, which might confer potential therapeutic target of COVID-19, especially for severe patients.     

Dynamic metabolites profile of cerebral ischemia/reperfusion revealed by 1H NMR-based metabolomics contributes to potential biomarkers

Current metabolomic studies of ischemic brain mainly attach importance on a certain ischemic period, are lack of data about dynamic metabolites in ischemic stroke process, especially early period. Thus, in this study, ¹H NMR spectroscopy was used to investigate biochemical changes in the early stages of rats’ focal cerebral ischemia reperfusion (I/R) injury. Serum samples of 0, 0.5, 1, 3, 6, 12, 24 h of reperfusion, based on multivariate data analyses, were tested to analyze the changing of metabolites during the early disease process. Partial least squares-discriminant analysis scores plots of the ¹H NMR data revealed clear differences among the experiment groups. Combination the results of loading plot and t-test, we found that 13 metabolites were changed significantly. Among that, malonic acid and glycine are the most noticeable variable metabolites. Dramatic changed malonic acid and glycine most probably served as biomarkers in this study. These findings help us understand the biochemical metabolite changes in early ischemic stroke stages, especially different periods. That may be conducive to distinguish at-risk individuals, benefit early diagnosis and understand the dynamic pathogenesis of early cerebral ischemia.     

HIV-1 Tat induces biochemical changes in the serum of mice

The HIV-1 Tat protein is released by infected cells and has numerous biological activities which might contribute either to the impairment of the immune response or to viral dissemination and pathogenesis. To date, the effects of Tat protein on metabolites remain unclear. In this study, a metabolomic study on serum of HIV-1 Tat-induced ICR mice was performed to research the pathologic mechanism of Tat protein by using gas chromatography coupled to mass spectrometry (GC/MS). Clear separations among the HIV-1 Tat-induced mice and the inaTat-induced or control mice were observed by principal component analysis and partial least-squares discriminant analysis based on the GC/MS spectral data. Additionally, 16 significantly changed metabolites in HIV-1 Tat-induced mice were identified that are involved in multiple perturbed metabolic pathways, which contributed to the elucidation of the complex pathogenic mechanism of Tat protein and may shed new lights on future improvement of HIV-1 therapy.     

MetabolitePredict: A de novo human metabolomics prediction system and its applications in rheumatoid arthritis

Human metabolomics has great potential in disease mechanism understanding, early diagnosis, and therapy. Existing metabolomics studies are often based on profiling patient biofluids and tissue samples and are difficult owing to the challenges of sample collection and data processing. Here, we report an alternative approach and developed a computation-based prediction system, MetabolitePredict, for disease metabolomics biomarker prediction. We applied MetabolitePredict to identify metabolite biomarkers and metabolite targeting therapies for rheumatoid arthritis (RA), a last-lasting complex disease with multiple genetic and environmental factors involved.MetabolitePredict is a de novo prediction system. It first constructs a disease-specific genetic profile using genes and pathways data associated with an input disease. It then constructs genetic profiles for a total of 259,170 chemicals/metabolites using known chemical genetics and human metabolomic data. MetabolitePredict prioritizes metabolites for a given disease based on the genetic profile similarities between disease and metabolites. We evaluated MetabolitePredict using 63 known RA-associated metabolites. MetabolitePredict found 24 of the 63 metabolites (recall: 0.38) and ranked them highly (mean ranking: top 4.13%, median ranking: top 1.10%, P-value: 5.08E−19). MetabolitePredict performed better than an existing metabolite prediction system, PROFANCY, in predicting RA-associated metabolites (PROFANCY: recall: 0.31, mean ranking: 20.91%, median ranking: 16.47%, P-value: 3.78E−7). Short-chain fatty acids (SCFAs), the abundant metabolites of gut microbiota in the fermentation of fiber, ranked highly (butyrate, 0.03%; acetate, 0.05%; propionate, 0.38%). Finally, we established MetabolitePredict’s potential in novel metabolite targeting for disease treatment: MetabolitePredict ranked highly three known metabolite inhibitors for RA treatments (methotrexate:0.25%; leflunomide: 0.56%; sulfasalazine: 0.92%).MetabolitePredict is a generalizable disease metabolite prediction system. The only required input to the system is a disease name or a set of disease-associated genes. The web-based MetabolitePredict is available at:http://xulab.case.edu/MetabolitePredict.     

Serum metabolic profiling of human gastric cancer based on gas chromatography/mass spectrometry

Research on molecular mechanisms of carcinogenesis plays an important role in diagnosing and treating gastric cancer. Metabolic profiling may offer the opportunity to understand the molecular mechanism of carcinogenesis and help to non-invasively identify the potential biomarkers for the early diagnosis of human gastric cancer. The aims of this study were to explore the underlying metabolic mechanisms of gastric cancer and to identify biomarkers associated with morbidity. Gas chromatography/mass spectrometry (GC/MS) was used to analyze the serum metabolites of 30 Chinese gastric cancer patients and 30 healthy controls. Diagnostic models for gastric cancer were constructed using orthogonal partial least squares discriminant analysis (OPLS-DA). Acquired metabolomic data were analyzed by the nonparametric Wilcoxon test to find serum metabolic biomarkers for gastric cancer. The OPLS-DA model showed adequate discrimination between cancer and non-cancer cohorts while the model failed to discriminate different pathological stages (I-IV) of gastric cancer patients. A total of 44 endogenous metabolites such as amino acids, organic acids, carbohydrates, fatty acids, and steroids were detected, of which 18 differential metabolites were identified with significant differences. A total of 13 variables were obtained for their greatest contribution in the discriminating OPLS-DA model [variable importance in the projection (VIP) value >1.0], among which 11 metabolites were identified using both VIP values (VIP >1) and the Wilcoxon test. These metabolites potentially revealed perturbations of glycolysis and of amino acid, fatty acid, cholesterol, and nucleotide metabolism of gastric cancer patients. These results suggest that gastric cancer serum metabolic profiling has great potential in detecting this disease and helping to understand its metabolic mechanisms.     

Nuclear magnetic resonance metabolomic footprinting of human hepatic stem cells and hepatoblasts cultured in hyaluronan-matrix hydrogels

Human hepatoblasts (hHBs) and human hepatic stem cells (hHpSCs) were maintained in serum-free Kubota's medium, a defined medium tailored for hepatic progenitors, and on culture plastic versus hyaluronan hydrogels mixed with specific combinations of extracellular matrix components (e.g., type I collagen and laminin). Nuclear magnetic resonance spectroscopy was used to define metabolomic profiles for each substratum tested. The hHpSCs on culture plastic survived throughout the culture study, whereas hHBs on plastic died within 7-10 days. Both survived and expanded in all hydrogel-matrix combinations tested for more than 4 weeks. Profiles of hundreds of metabolites were narrowed to a detailed analysis of eight, such as glucose, lactate, and glutamine, shown to be significant components of cellular pathways, including the Krebs and urea cycles. The metabolomic profiles indicated that hHpSCs on plastic remained as stem cells expressing low levels of albumin but no alpha-fetoprotein (AFP); those in hydrogels were primarily hHBs, expressing AFP, albumin, and urea. Both hHpSCs and hHBs used energy provided by anaerobic metabolism. Variations in hyaluronan-matrix chemistry resulted in distinct profiles correlating with growth or with differentiative responses. Metabolomic footprinting offers noninvasive and nondestructive assessment of physiological states of stem/progenitor cells ex vivo. Disclosure of potential conflicts of interest is found at the end of this article.     

Anti-Inflammation Effects and Potential Mechanism of Saikosaponins by Regulating Nicotinate and Nicotinamide Metabolism and Arachidonic Acid Metabolism

Inflammation is an important immune response; however, excessive inflammation causes severe tissue damages and secondary inflammatory injuries. The long-term and ongoing uses of routinely used drugs such as non-steroidal anti-inflammatory drugs (NSAIDS) are associated with serious adverse reactions, and not all patients have a well response to them. Consequently, therapeutic products with more safer and less adverse reaction are constantly being sought. Radix Bupleuri, a well-known traditional Chinese medicine (TCM), has been reported to have anti-inflammatory effects. However, saikosaponins (SS) as the main pharmacodynamic active ingredient, their pharmacological effects and action mechanism in anti-inflammation have not been reported frequently. This study aimed to explore the anti-inflammatory activity of SS and clarify the potential mechanism in acute inflammatory mice induced by subcutaneous injection of formalin in hind paws. Paw edema was detected as an index to evaluate the anti-inflammatory efficacy of SS. Then, a metabolomic method was used to investigate the changed metabolites and potential mechanism of SS. Metabolite profiling was performed by high-performance liquid chromatography combined with quadrupole time-of-flight mass spectrometry (HPLC-Q-TOF-MS). The detection and identification of the changed metabolites were systematically analyzed by multivariate data and pathway analysis. As a result, 12 different potential biomarkers associated with SS in anti-inflammation were identified, including nicotinate, niacinamide, arachidonic acid (AA), and 20-carboxy-leukotriene B₄, which are associated with nicotinate and nicotinamide metabolism and arachidonic acid metabolism. The expression levels of biomarkers were effectively modulated towards the normal range by SS. It indicated that SS show their effective anti-inflammatory effects through regulating nicotinate and nicotinamide metabolism and arachidonic acid metabolism.     

Modeling of Tumor Growth with Input from Patient-Specific Metabolomic Data

Advances in omic technologies have provided insight into cancer progression and treatment response. However, the nonlinear characteristics of cancer growth present a challenge to bridge from the molecular- to the tissue-scale, as tumor behavior cannot be encapsulated by the sum of the individual molecular details gleaned experimentally. Mathematical modeling and computational simulation have been traditionally employed to facilitate analysis of nonlinear systems. In this study, for the first time tumor metabolomic data are linked via mathematical modeling to the tumor tissue-scale behavior, showing the capability to mechanistically simulate cancer progression personalized to omic information obtainable from patient tumor core biopsy analysis. Generally, a higher degree of metabolic dysregulation has been correlated with more aggressive tumor behavior. Accordingly, key parameters influenced by metabolomic data in this model include tumor proliferation, vascularization, aggressiveness, lactic acid production, monocyte infiltration and macrophage polarization, and drug effect. The model enables evaluating interactions of interest between these parameters which drive tumor growth based on the metabolomic data. The results show that the model can group patients consistently with the clinically observed outcomes of response/non-response to chemotherapy. This modeling approach provides a first step towards evaluation of tumor growth based on tumor-specific metabolomic data.

     

Random left censoring: a second look at bone lead concentration measurements

Bone lead concentrations measured in vivo by x-ray fluorescence (XRF) are subjected to left censoring due to limited precision of the technique at very low concentrations. In the analysis of bone lead measurements, inverse variance weighting (IVW) of measurements is commonly used to estimate the mean of a data set and its standard error. Student's t-test is used to compare the IVW means of two sets, testing the hypothesis that the two sets are from the same population. This analysis was undertaken to assess the adequacy of IVW in the analysis of bone lead measurements or to confirm the results of IVW using an independent approach. The rationale is provided for the use of methods of survival data analysis in the study of XRF bone lead measurements. The procedure is provided for bone lead data analysis using the Kaplan-Meier and Nelson-Aalen estimators. The methodology is also outlined for the rank tests that are used to determine whether two censored sets are from the same population. The methods are applied on six data sets acquired in epidemiological studies. The estimated parameters and test statistics were compared with the results of the IVW approach. It is concluded that the proposed methods of statistical analysis can provide valid inference about bone lead concentrations, but the computed parameters do not differ substantially from those derived by the more widely used method of IVW.     

Metabolic flux analysis: a powerful tool for monitoring tissue function.

In recent years, metabolic flux analysis has been widely used in bioprocess engineering to monitor cell viability and improve strain activity. Metabolic flux analysis refers to a methodology for investigating cellular metabolism whereby intracellular fluxes are calculated using a stoichiometric model for the major intracellular reactions and applying mass balances around intracellular metabolites. A powerful feature of this methodology is its ability to consider cellular biochemistry in terms of reaction networks. By considering the stoichiometry of biochemical reactions, it is possible to estimate the degree of engagement of each pathway participating in overall cellular activity, and hence obtain a comprehensive view of a cell s metabolic state. Given the potential impact of cellular energy metabolism on the function of engineered tissues, such comprehensive analysis of metabolic activity can be an extremely useful tool for tissue engineers. Estimates of intracellular fluxes under various environmental conditions could be used to optimize function in vivo as well as culture conditions in vitro. In this review, we provide a brief theoretical background of metabolic flux analysis and summarize the most widely used experimental approaches to obtain flux data. This review is intended as an overview of the field and as a starting point for tissue engineers wishing to learn about and eventually employ this methodology.     

基于机器学习的神经精神疾病辅助诊断研究进展

【摘要】神经影像技术被广泛应用于研究大脑结构和功能异常与神经精神疾病之间的相关性。与传统的统计学分析方法不同,机器学习模型能对神经影像学数据进行个体化预测,发掘潜在的生物学标记物。神经精神疾病辅助诊断包含数据预处理和机器学习算法。数据预处理是一种人为的特征工程,为机器学习算法提供量化特征;机器学习算法包含特征降维、模型训练和模型评估。鲁棒的机器学习算法可以实现对不同数据集的准确预测,并提供对预测结果贡献大的特征,作为潜在的生物学标记物。本文综述了近年来基于机器学习的神经精神疾病辅助诊断研究进展,从数据预处理、机器学习算法和生物学标记物3个角度进行介绍,并展望未来的研究方向。 【关键词】神经精神疾病;神经影像;机器学习;辅助诊断     

Mixed-effects models in psychophysiology

The current methodological policy in Psychophysiology stipulates that repeated-measures designs be analyzed using either multivariate analysis of variance (ANOVA) or repeated-measures ANOVA with the Greenhouse-Geisser or Huynh-Feldt correction. Both techniques lead to appropriate type I error probabilities under general assumptions about the variance-covariance matrix of the data. This report introduces mixed-effects models as an alternative procedure for the analysis of repeated-measures data in Psychophysiology. Mixed-effects models have many advantages over the traditional methods: They handle missing data more effectively and are more efficient, parsimonious, and flexible. We described mixed-effects modeling and illustrated its applicability with a simple example.     

Omics research project on prospective cohort studies from the Tohoku Medical Megabank Project

Population‐based prospective cohort studies are indispensable for modern medical research as they provide important knowledge on the influences of many kinds of genetic and environmental factors on the cause of disease. Although traditional cohort studies are mainly conducted using questionnaires and physical examinations, modern cohort studies incorporate omics and genomic approaches to obtain comprehensive physical information, including genetic information. Here, we report the design and midterm results of multi‐omics analysis on population‐based prospective cohort studies from the Tohoku Medical Megabank (TMM) Project. We have incorporated genomic and metabolomic studies in the TMM cohort study as both metabolome and genome analyses are suitable for high‐throughput analysis of large‐scale cohort samples. Moreover, an association study between the metabolome and genome show that metabolites are an important intermediate phenotype connecting genetic and lifestyle factors to physical and pathologic phenotypes. We apply our metabolome and genome analyses to large‐scale cohort samples in the following studies.     

医学图像识别中多分类器融合方法的研究进展

计算机辅助医学图像分析识别对多种疾病的临床诊断有着重要的意义。由于医学图像自身的复杂性,单一分类器的识别性能常常难以满足临床上的要求,因此近年来,作为一种能有效改进单一分类器识别性能的方法,多分类器融合技术被逐步应用到包括乳腺X光片识别、肿瘤细胞识别以及内窥镜图像分析等领域,并取得了更为满意的识别结果。在参阅大量文献的基础上,对多分类器融合识别技术的理论分析及其在医学领域的研究及应用现状进行了综述,进而对其存在的问题进行了分析以及前景展望。