Simultaneous detection of multiple inherited metabolic diseases using GC-MS urinary metabolomics by chemometrics multi-class classification strategies

MultiClassMetabo: A Superior Classification Model Constructed Using Metabolic Markers in Multiclass Metabolomics

Multiclass metabolomics has become a popular technique for revealing the mechanisms underlying certain physiological processes, different tumor types, or different therapeutic responses. In multiclass metabolomics, it is highly important to uncover the underlying biological information on biosamples by identifying the metabolic markers with the most associations and classifying the different sample classes. The classification problem of multiclass metabolomics is more difficult than that of the binary problem. To date, various methods exist for constructing classification models and identifying metabolic markers consisting of well-established techniques and newly emerging machine learning algorithms. However, how to construct a superior classification model using these methods remains unclear for a given multiclass metabolomic data set. Herein, MultiClassMetabo has been developed for constructing a superior classification model using metabolic markers identified in multiclass metabolomics. MultiClassMetabo can enable online services, including (a) identifying metabolic markers by marker identification methods, (b) constructing classification models by classification methods, and (c) performing a comprehensive assessment from multiple perspectives to construct a superior classification model for multiclass metabolomics. In summary, MultiClassMetabo is distinguished for its capability to construct a superior classification model using the most appropriate method through a comprehensive assessment, which makes it an important complement to other available tools in multiclass metabolomics. MultiClassMetabo can be accessed at http://idrblab.cn/multiclassmetabo/.

Mass Spectrometry Analysis for Clinical Applications: A Review

Mass spectrometry (MS) has become an attractive analytical method in clinical analysis due to its comprehensive advantages of high sensitivity, high specificity and high throughput. Separation techniques coupled MS detection (e.g., LC-MS/MS) have shown unique advantages over immunoassay and have developed as golden criterion for many clinical applications. This review summarizes the characteristics and applications of MS, and emphasizes the high efficiency of MS in clinical research. In addition, this review also put forward further prospects for the future of mass spectrometry technology, including the introduction of miniature MS instruments, point-of-care detection and high-throughput analysis, to achieve better development of MS technology in various fields of clinical application. Moreover, as ambient ionization mass spectrometry (AIMS) requires little or no sample pretreatment and improves the flux of MS, this review also summarizes its potential applications in clinic.

Critical Assessment of the Biomarker Discovery and Classification Methods for Multiclass Metabolomics

Multiclass metabolomics has been widely applied in clinical practice to understand pathophysiological processes involved in disease progression and diagnostic biomarkers of various disorders. In contrast to the binary problem, the multiclass classification problem is more difficult in terms of obtaining reliable and stable results due to the increase in the complexity of determining exact class decision boundaries. In particular, methods of biomarker discovery and classification have a significant effect on the multiclass model because different methods with significantly varied theories produce conflicting results even for the same dataset. However, a systematic assessment for selecting the most appropriate methods of biomarker discovery and classification for multiclass metabolomics is still lacking. Therefore, a comprehensive assessment is essential to measure the suitability of methods in multiclass classification models from multiple perspectives. In this study, five biomarker discovery methods and nine classification methods were assessed based on four benchmark datasets of multiclass metabolomics. The performance assessment of the biomarker discovery and classification methods was performed using three evaluation criteria: assessment a (cluster analysis of sample grouping), assessment b (biomarker consistency in multiple subgroups), and assessment c (accuracy in the classification model). As a result, 13 combining strategies with superior performance were selected under multiple criteria based on these benchmark datasets. In conclusion, superior strategies that performed consistently well are suggested for the discovery of biomarkers and the construction of a classification model for multiclass metabolomics.

Using random forest to detect multiple inherited metabolic diseases simultaneously based on GC-MS urinary metabolomics

Inborn errors of metabolism, also known as inherited metabolic diseases (IMDs), are related to genetic mutations and cause corresponding biochemical metabolic disorder of newborns and even sudden infant death. Timely detection and diagnosis of IMDs are of great significance for improving survival of newborns. Here we propose a strategy for simultaneously detecting six types of IMDs via combining GC-MS technique with the random forest algorithm (RF). Clinical urine samples from IMD and healthy patients are analyzed using GC-MS for acquiring metabolomics data. Then, the RF model is established as a multi-classification tool for the GC-MS data. Compared with the models built by artificial neural network and support vector machine, the results demonstrated the RF model has superior performance of high specificity, sensitivity, precision, accuracy, and matthews correlation coefficients on identifying all six types of IMDs and normal samples. The proposed strategy can afford a useful method for reliable and effective identification of multiple IMDs in clinical diagnosis.

Data Analytics for Environmental Science and Engineering Research

The advent of new data acquisition and handling techniques has opened the door to alternative and more comprehensive approaches to environmental monitoring that will improve our capacity to understand and manage environmental systems. Researchers have recently begun using machine learning (ML) techniques to analyze complex environmental systems and their associated data. Herein, we provide an overview of data analytics frameworks suitable for various Environmental Science and Engineering (ESE) research applications. We present current applications of ML algorithms within the ESE domain using three representative case studies: (1) Metagenomic data analysis for characterizing and tracking antimicrobial resistance in the environment; (2) Nontarget analysis for environmental pollutant profiling; and (3) Detection of anomalies in continuous data generated by engineered water systems. We conclude by proposing a path to advance incorporation of data analytics approaches in ESE research and application.

NOREVA: enhanced normalization and evaluation of time-course and multi-class metabolomic data

Biological processes (like microbial growth & physiological response) are usually dynamic and require the monitoring of metabolic variation at different time-points. Moreover, there is clear shift from case-control (N=2) study to multi-class (N>2) problem in current metabolomics, which is crucial for revealing the mechanisms underlying certain physiological process, disease metastasis, etc. These time-course and multi-class metabolomics have attracted great attention, and data normalization is essential for removing unwanted biological/experimental variations in these studies. However, no tool (including NOREVA 1.0 focusing only on case-control studies) is available for effectively assessing the performance of normalization method on time-course/multi-class metabolomic data. Thus, NOREVA was updated to version 2.0 by (i) realizing normalization and evaluation of both time-course and multi-class metabolomic data, (ii) integrating 144 normalization methods of a recently proposed combination strategy and (iii) identifying the well-performing methods by comprehensively assessing the largest set of normalizations (168 in total, significantly larger than those 24 in NOREVA 1.0). The significance of this update was extensively validated by case studies on benchmark datasets. All in all, NOREVA 2.0 is distinguished for its capability in identifying well-performing normalization method(s) for time-course and multi-class metabolomics, which makes it an indispensable complement to other available tools. NOREVA can be accessed at https://idrblab.org/noreva/.

Psychopharmacological effects of riparin III from Aniba riparia (Nees) Mez. (Lauraceae) supported by metabolic approach and multivariate data analysis

Background

Currently there is a high prevalence of humor disorders such as anxiety and depression throughout the world, especially concerning advanced age patients. Aniba riparia (Nees) Mez. (Lauraceae), popular known as “louro”, can be found from the Amazon through Guianas until the Andes. Previous studies have already reported the isolation of alkamide-type alkaloids such as riparin III (O-methyl-N-2,6-dyhydroxy-benzoyl tyramine) which has demonstrated anxiolytic and antidepressant-like effects in high doses by intraperitoneal administration.

Methods

Experimental protocol was conducted in order to analyze the anxiolytic-like effect of riparin III at lower doses by intravenous administration to Wistar rats (Rattus norvegicus) (n = 5). The experimental approach was designed to last 15 days, divided in 3 distinct periods of five days: control, anxiogenic and treatment periods. The anxiolytic-like effect was evaluated by experimental behavior tests such as open field and elevated plus-maze test, combined with urine metabolic footprint analysis. The urine was collected daily and analyzed by ¹H NMR. Generated data were statistically treated by Principal Component Analysis in order to detect patterns among the distinct periods evaluated as well as biomarkers responsible for its distinction.

Results

It was observed on treatment group that cortisol, biomarker related to physiological stress was reduced, indicating anxiolytic-like effect of riparin III, probably through activation of 5-HT_2A receptors, which was corroborated by behavioral tests.

Conclusion

¹H NMR urine metabolic footprint combined with multivariate data analysis have demonstrated to be an important diagnostic tool to prove the anxiolytic-like effect of riparin III in a more efficient and pragmatic way.

Investigation of the mechanism of incompatible herb pair gansui-gancao-induced hepatotoxicity and nephrotoxicity and the attenuated effect of gansuibanxia decoction by UHPLC-FT-ICR-MS-based plasma metabonomic analysis

Gansui-Gancao is one of the "eighteen incompatible herb pairs" which was recorded 2000 years ago according to TCM (Traditional Chinese Medicine) theory for their toxicity when using together. Nevertheless, Gansuibanxia decoction contained the herb pair have satisfactory effect on the treatment of cancerous ascites, pericardial effusion, etc. The present study aimed to investigate the mechanism of the incompatibility of Gansui-Gancao and the compatibility of Gansuibanxia decoction using UHPLC-FT-ICR-MS in a metabonomic perspective. Rats were divided into four groups administrated with different herb combination extracts for successive 14 days. Orthogonal partial least squares-discriminant analysis (OPLS-DA) was used to plot the metabolic state and screen the potential biomarkers in plasma. A total of 20 biomarkers contributed to the separation of Gansui-Gancao group and control group were tentatively identified mainly involved in 7 metabolic pathways related to hepatotoxicity and nephrotoxicity. The contents of these biomarkers were adjusted to normal levels in Gansuibanxia decoction group. Thus, the results of our study reveled the mechanism of the incompatibility of Gansui-Gancao and the compatibility of Gansuibanxia decoction in a metabonomic perspective and it's valuable for better understanding the "eighteen incompatible madicaments" of TCM theory.

Discovering significantly different metabolites between Han and Uygur two racial groups using urinary metabolomics in Xinjiang, China

The main object of the study was to discover the associated significantly different metabolites between Han and Uygur, two main racial groups in Xinjiang, China with urinary metabolomics. Urine samples from 96 Han and 96 Uygur were analyzed using gas chromatography coupled to mass spectrometry (GCMS). Multivariate analysis was used to investigate the effect of race, age and gender on the urinary metabolomic profiles. Totally eight metabolites are identified contributed to the discrimination between Han and Uygur, including phenylacetylglutamine, myoinositol, d-galactose, ribonolactone, octadecanoic acid, galactitol, threonic acid and succinic acid. The metabolic pathways of them are mainly involved in carbohydrate, TCA cycle, fatty acid and mammalian gut microbial-related metabolism. Importantly, three metabolites, being used as biomarkers in clinic, are also differentially expressed in urine samples of two races. It suggests that the race effect should be critically considered prior to make diagnostic result in multi-race coexisted areas to decrease the false positive rate caused by above biomarkers. Moreover, the results show that the age-period and the gender also affect the urinary metabolomics profiles, but with different levels compared to race. We hope that the work can provide some help for developing novel diagnostic tests, understanding the mechanism of disease, designing clinical trials and refining precision medicine in multi-race coexisted areas.

Automated supervised learning pipeline for non-targeted GC-MS data analysis

Non-targeted analysis is nowadays applied in many different domains of analytical chemistry such as metabolomics, environmental and food analysis. Conventional processing strategies for GC-MS data include baseline correction, feature detection, and retention time alignment before multivariate modeling. These techniques can be prone to errors and therefore time-consuming manual corrections are generally necessary. We introduce here a novel fully automated approach to non-targeted GC-MS data processing. This new approach avoids feature extraction and retention time alignment. Supervised machine learning on decomposed tensors of segmented chromatographic raw data signal is used to rank regions in the chromatograms contributing to differentiation between sample classes. The performance of this novel data analysis approach is demonstrated on three published datasets.