Consecutive Queries to Assess Biological Correlation in NMR Metabolomics: Performance of Comprehensive Search of Multiplets over Typical 1D 1H NMR Database Search

Multiplet-Assisted Peak Alignment for 1H NMR-Based Metabolomics

NMR-based metabolomics aims at recovering biological information by comparing spectral data from samples of biological interest and appropriate controls. Any statistical analysis performed on the data matrix relies on the proper peak alignment to produce meaningful results. Through the last decades, several peak alignment algorithms have been proposed, as well as alternatives like spectral binning or strategies for annotation and quantification, the latter depending on reference databases. Most of the alignment algorithms, mainly based on segmentation of the spectra, present limitations for regions with peak overlap or cases of frequency order exchange. Here, we present our multiplet-assisted peak alignment algorithm, a new methodology that consists of aligning peaks by matching multiplet profiles of f1 traces from J-resolved spectra. A correspondence matrix with the linked f1 traces is built, and multivariate data analysis can be performed on it to obtain useful information from the data, overcoming the issues of peak overlap and frequency crossovers. Statistical total correlation spectroscopy can be applied on the matrix as well, toward a better identification of molecules of interest. The results can be queried on one-dimensional (1D) 1H databases or can be directly coupled to our previously published Chemical Shift Multiplet Database.

Problems, principles and progress in computational annotation of NMR metabolomics data

BACKGROUND

Compound identification remains a critical bottleneck in the process of exploiting Nuclear Magnetic Resonance (NMR) metabolomics data, especially for ¹H 1-dimensional (¹H 1D) data. As databases of reference compound spectra have grown, workflows have evolved to rely heavily on their search functions to facilitate this process by generating lists of potential metabolites found in complex mixture data, facilitating annotation and identification. However, approaches for validating and communicating annotations are most often guided by expert knowledge, and therefore are highly variable despite repeated efforts to align practices and define community standards.

AIM OF REVIEW

This review is aimed at broadening the application of automated annotation tools by discussing the key ideas of spectral matching and beginning to describe a set of terms to classify this information, thus advancing standards for communicating annotation confidence. Additionally, we hope that this review will facilitate the growing collaboration between chemical data scientists, software developers and the NMR metabolomics community aiding development of long-term software solutions.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We begin with a brief discussion of the typical untargeted NMR identification workflow. We differentiate between annotation (hypothesis generation, filtering), and identification (hypothesis testing, verification), and note the utility of different NMR data features for annotation. We then touch on three parts of annotation: (1) generation of queries, (2) matching queries to reference data, and (3) scoring and confidence estimation of potential matches for verification. In doing so, we highlight existing approaches to automated and semi-automated annotation from the perspective of the structural information they utilize, as well as how this information can be represented computationally.

Cutting-edge strategies and critical advancements in characterization and quantification of metabolites concerning translational metabolomics

Despite remarkable progress in drug discovery strategies, significant challenges are still remaining in translating new insights into clinical applications. Scientists are devising creative approaches to bridge the gap between scientific and translational research. Metabolomics is a unique field among other omics techniques for identifying novel metabolites and biomarkers. Fortunately, characterization and quantification of metabolites are becoming faster due to the progress in the field of orthogonal analytical techniques. This review detailed the advancement in the progress of sample preparation, and data processing techniques including data mining tools, database, and their quality control (QC). Advances in data processing tools make it easier to acquire unbiased data that includes a diverse set of metabolites. In addition, novel breakthroughs including, miniaturization as well as their integration with other devices, metabolite array technology, and crystalline sponge-based method have led to faster, more efficient, cost-effective, and holistic metabolomic analysis. The use of cutting-edge techniques to identify the human metabolite, including biomarkers has proven to be advantageous in terms of early disease identification, tracking the progression of illness, and possibility of personalized treatments. This review addressed the constraints of current metabolomics research, which are impeding the facilitation of translation of research from bench to bedside. Nevertheless, the possible way out from such constraints and future direction of translational metabolomics has been conferred.

Effects of Dufulin on Oxidative Stress and Metabolomic Profile of Tubifex

Dufulin is a highly effective antiviral pesticide used in plants. In this study, a seven-day experiment was conducted to evaluate the effects of Dufulin at five different concentrations (1 × 10⁻⁴, 1 × 10⁻³, 1 × 10⁻², 0.1, and 1 mg/L) on Tubifex. LC-MS-based metabolome analysis detected a total of 5356 features in positive and 9110 features in negative, of which 41 showed significant changes and were identified as differential metabolites. Four metabolic pathways were selected for further study. Detailed analysis revealed that Dufulin exposure affected the urea cycle of Tubifex, probably via argininosuccinate lyase (ASL) inhibition. It also affected the fatty acid metabolism, leading to changes in the concentration of free fatty acids in Tubifex. Furthermore, the changes in metabolites after exposure to Dufulin at 1 × 10⁻² mg/L were different from those at the other concentrations.

New software tools, databases, and resources in metabolomics: updates from 2020

BACKGROUND

Precision medicine, space exploration, drug discovery to characterization of dark chemical space of habitats and organisms, metabolomics takes a centre stage in providing answers to diverse biological, biomedical, and environmental questions. With technological advances in mass-spectrometry and spectroscopy platforms that aid in generation of information rich datasets that are complex big-data, data analytics tend to co-evolve to match the pace of analytical instrumentation. Software tools, resources, databases, and solutions help in harnessing the concealed information in the generated data for eventual translational success.

AIM OF THE REVIEW

In this review, ~ 85 metabolomics software resources, packages, tools, databases, and other utilities that appeared in 2020 are introduced to the research community.

KEY SCIENTIFIC CONCEPTS OF REVIEW

In Table 1 the computational dependencies and downloadable links of the tools are provided, and the resources are categorized based on their utility. The review aims to keep the community of metabolomics researchers updated with all the resources developed in 2020 at a collated avenue, in line with efforts form 2015 onwards to help them find these at one place for further referencing and use.