Design of Experiments in metabolomics-related studies: An overview

Chemometric-based drug discovery approaches from natural origins using hyphenated chromatographic techniques

INTRODUCTION

Isolation and characterization of bioactive components from complex matrices of marine or terrestrial biological origins are the most challenging issues for natural product chemists. Biochemometric is a new potential scope in natural product analytical science, and it is a methodology to find the compound's correlation to their bioactivity with the help of hyphenated chromatographic techniques and chemometric tools.

OBJECTIVES

The present review aims to evaluate the application of chemometric tools coupled to chromatographic techniques for drug discovery from natural resources.

METHODS

The searching keywords "biochemometric," "chemometric," "chromatography," "natural products bioassay," and "bioassay" were selected to search the published articles between 2010-2023 using different search engines including "Pubmed", "Web of Science," "ScienceDirect," and "Google scholar."

RESULTS

An initial stage in natural product analysis is applying the chromatographic hyphenated techniques in conjunction with biochemometric approaches. Among the applied chromatographic techniques, liquid chromatography (LC) techniques, have taken up more than half (53%) and also, mass spectroscopy (MS)-based chromatographic techniques such as LC-MS are the most widely used techniques applied in combination with chemometric methods for natural products bioassay. Considering the complexity of dataset achieved from chromatographic hyphenated techniques, chemometric tools have been increasingly employed for phytochemical studies in the context of determining botanicals geographical origin, quality control, and detection of bioactive compounds.

CONCLUSION

Biochemometric application is expected to be further improved with advancing in data acquisition methods, new efficient preprocessing, model validation and variable selection methods which would guarantee that the applied model to have good prediction ability in compound relation to its bioactivity.

Application of experimental design as a statistical approach to recover bioactive peptides from different food sources

Bioactive peptides (BAPs) derived from samples of animals and plants have been widely recommended and consumed for their beneficial properties to human health and to control several diseases. This work presents the applications of experimental designs (DoE) used to perform factor screening and/or optimization focused on finding the ideal hydrolysis condition to obtain BAPs with specific biological activities. The collection and discussion of articles revealed that Box Behnken Desing and Central Composite Design were the most used. The main parameters evaluated were pH, time, temperature and enzyme/substrate ratio. Among vegetable protein sources, soy was the most used in the generation of BAPs, and among animal proteins, milk and shrimp stood out as the most explored sources. The degree of hydrolysis and antioxidant activity were the most investigated responses in obtaining BAPs. This review brings new information that helps researchers apply these DoE to obtain high-quality BAPs with the desired biological activities.

Response surface methodology for process optimization in livestock wastewater treatment: A review

With increasing demand for meat and dairy products, the volume of wastewater generated from the livestock industry has become a significant environmental concern. The treatment of livestock wastewater (LWW) is a challenging process that involves removing nutrients, organic matter, pathogens, and other pollutants from livestock manure and urine. In response to this challenge, researchers have developed and investigated different biological, physical, and chemical treatment technologies that perform better upon optimization. Optimization of LWW handling processes can help improve the efficacy and sustainability of treatment systems as well as minimize environmental impacts and associated costs. Response surface methodology (RSM) as an optimization approach can effectively optimize operational parameters that affect process performance. This review article summarizes the main steps of RSM, recent applications of RSM in LWW treatment, highlights the advantages and limitations of this technique, and provides recommendations for future research and practice, including its cost-effectiveness, accuracy, and ability to improve treatment efficiency.

A green-footprint approach for parallel multiclass analysis of contaminants in roasted coffee via LC-HRMS

The development and validation of a simple, comprehensive, and environment-friendly procedure to determine pesticide residues, naturally occurring and processing contaminants in roasted coffee is presented. A solid-liquid extraction of pesticides and mycotoxins with ethyl acetate and the concurrent partition of acrylamide to an aqueous phase follows a parallel analytical strategy that requires a single analytical portion to determine contaminants that are typically analyzed by dedicated single residue methods. The partition rules the lipids out of the aqueous extract before an "in-tube" dispersive solid phase microextraction (dSPME) for acrylamide retention. This is followed by the elution with buffer prior to injection. This extract is independently introduced into the system front end followed by the injection of the compounds from the organic phase, yet all spotted in the same run. A novel liquid chromatography high-resolution mass spectrometry (LC-HRMS) method setup enables the quantification of 186 compounds at 10 µg/kg, 226 at 5 µg/kg, and the acrylamide at 200 µg/kg for a total of 414 molecules, with acceptable recoveries (70-120%) and precision (RSD < 20%) making this strategy significantly faster and cost-effective than the dedicated single residue methods. Even though the presence of chlorpyrifos, acrylamide, and ochratoxin A was confirmed on samples of different origins, the findings were below the limit of quantification. During the storage of raw coffee, no proof of masking of OTA was found; however, condensation with glucose was evidenced during thermal processing experiments with sucrose by using stable isotope labeling (SIL). No detected conjugates were found in roasted nor in commercial sugar-added torrefacto samples, an industrial processing usually carried out above the decomposition temperature of the disaccharide.

Is it Time to Migrate to Liquid Chromatography Automated Platforms in the Clinical Laboratory? A Brief Point of View

Liquid chromatography coupled to mass spectrometry already started to surpass the major drawbacks in terms of sensitivity, specificity and cross-reactivity that some analytical methods used in the clinical laboratory exhibit. This hyphenated technique is already preferred for specific applications while finding its own place in the clinical laboratory setting. However, large-scale usage, high-throughput analysis and lack of automation emerge as shortcomings that liquid chromatography coupled to mass spectrometry still has to overrun in order to be used on a larger scale in the clinical laboratory. The aim of this review article is to point out the present-day position of the liquid chromatography coupled to mass spectrometry technique while trying to understand how this analytical method relates to the basic working framework of the clinical laboratory. This paper offers insights about the main regulation and traceability criteria that this coupling method has to align and comply to, automation and standardization issues and finally the critical steps in sample preparation workflows all related to the high-throughput analysis framework. Further steps are to be made toward automation, speed and easy-to-use concept; however, the current technological and quality premises are favorable for chromatographic coupled to mass spectral methods.

Non-target influence of imidacloprid residues on grape global metabolome and berry quality with the identification of metabolite biomarkers

This paper illustrates the non-target impact of imidacloprid (IM) residues on the grape global metabolome and biomarker identification with high-resolution mass spectrometry. IM was applied at the recommended dose (SD), and ten times SD (10 RD). The global metabolome analysis revealed that 21 metabolites were up- and down-regulated with IM SD treatment. In 10 RD, 9 metabolites were upregulated, and 28 were downregulated. Pathway enrichment analysis revealed the primary and secondary pathway disruption in grapes. Berry quality was affected with decrease in flavonoids by 32.97% in 10 RD; phenols were reduced by 53.93 in SD, 50.8% in 10 RD. The non-target and target study revealed the degradation of IM in grapes to desnitro-IM and IM-urea which were identified as a potential biomarker for IM residues in grapes, which would benefit the authentication of organic product. Overall, imidacloprid showed a significant impact on the grape metabolome and quality.

Critical evaluation of MS acquisition conditions and identification process in LC-Q-Orbitrap-MS for non-targeted analysis: pesticide residues as case of study

In routine measurements, the length of analysis time and the number of samples analysed during a given time unit are crucial. Additionally, the analytical method used has to provide reliable results and be able to identify and quantify any compound present in the matrix. High-resolution equipment, including Orbitrap analysers, is commonly used for non-targeted determinations. However, researchers still rely on trial and error to achieve the best acquisition conditions on the mass spectrometer, which is a tedious and time-consuming process that can lead to errors. Moreover, tentative compound identification, particularly when using a non-targeted approach, heavily depends on commercial databases. All of these issues can ultimately result in incomplete identification of compounds in the study matrix. In this framework, the study presented here has a dual objective: to use the experimental design tool to optimise critical parameters in mass spectrometry using LC-Q-Orbitrap-MS equipment when working in a non-targeted approach and to compare the mzCloud™ and ChemSpider™ commercial databases included in Compound Discoverer software with TraceFinder home-made databases generated to evaluate the ability to identify compounds. The study's noteworthy findings reveal that employing an experimental design has facilitated rapid optimisation of the mass spectrometer's multiplexing and loop parameters. Furthermore, the study highlights that the lack of harmonisation in commercial databases poses a disadvantage in the identification of compounds, leading to superior results when using home-made databases. In the latter databases, around 80% of the compounds were identified, which is approximately twice the number identified in commercial databases (around 40% in the best case with ChemSpider™).

Enhancing 1,3-Propanediol Productivity in the Non-Model Chassis Clostridium beijerinckii through Genetic Manipulation

Biotechnological processes at biorefineries are considered one of the most attractive alternatives for valorizing biomasses by converting them into bioproducts, biofuels, and bioenergy. For example, biodiesel can be obtained from oils and grease but generates glycerol as a byproduct. Glycerol recycling has been studied in several bioprocesses, with one of them being its conversion to 1,3-propanediol (1,3-PDO) by Clostridium. Clostridium beijerinckii is particularly interesting because it can produce a range of industrially relevant chemicals, including solvents and organic acids, and it is non-pathogenic. However, while Clostridium species have many potential advantages as chassis for synthetic biology applications, there are significant limitations when considering their use, such as their limited genetic tools, slow growth rate, and oxygen sensitivity. In this work, we carried out the overexpression of the genes involved in the synthesis of 1,3-PDO in C. beijerinckii Br21, which allowed us to increase the 1,3-PDO productivity in this strain. Thus, this study contributed to a better understanding of the metabolic pathways of glycerol conversion to 1,3-PDO by a C. beijerinckii isolate. Also, it made it possible to establish a transformation method of a modular vector in this strain, therefore expanding the limited genetic tools available for this bacterium, which is highly relevant in biotechnological applications.

Augmented region of interest for untargeted metabolomics mass spectrometry (AriumMS) of multi-platform-based CE-MS and LC-MS data

In mass spectrometry (MS)-based metabolomics, there is a great need to combine different analytical separation techniques to cover metabolites of different polarities and apply appropriate multi-platform data processing. Here, we introduce AriumMS (augmented region of interest for untargeted metabolomics mass spectrometry) as a reliable toolbox for multi-platform metabolomics. AriumMS offers augmented data analysis of several separation techniques utilizing a region-of-interest algorithm. To demonstrate the capabilities of AriumMS, five datasets were combined. This includes three newly developed capillary electrophoresis (CE)-Orbitrap MS methods using the recently introduced nanoCEasy CE-MS interface and two hydrophilic interaction liquid chromatography (HILIC)-Orbitrap MS methods. AriumMS provides a novel mid-level data fusion approach for multi-platform data analysis to simplify and speed up multi-platform data processing and evaluation. The key feature of AriumMS lies in the optimized data processing strategy, including parallel processing of datasets and flexible parameterization for processing of individual separation methods with different peak characteristics. As a case study, Saccharomyces cerevisiae (yeast) was treated with a growth inhibitor, and AriumMS successfully differentiated the metabolome based on the augmented multi-platform CE-MS and HILIC-MS investigation. As a result, AriumMS is proposed as a powerful tool to improve the accuracy and selectivity of metabolome analysis through the integration of several HILIC-MS/CE-MS techniques.

Extraction of Isoflavones, Alpha-Hydroxy Acids, and Allantoin from Soybean Leaves-Optimization by a Mixture Design of the Experimental Method

Soybeans are commonly known as a valuable source of biologically active compounds including isoflavones as well as allantoin and alpha-hydroxy acids. Since these compounds exhibit skin therapeutic effects, they are widely used in the cosmetic and pharmaceutical industries. The presented paper shows the optimization of three solvent systems (ethanol, water, and 1,3-propanediol) to increase the extraction efficiency of isoflavones (daidzin, genistin, 6″-O-malonyldaidzin, 6″-O-malonylglycitin, 6″-O-malonylgenistin), allantoin, and alpha-hydroxy acids (citric acid, malic acid) from soybean leaves. A simplex centroid mixture design for three solvents with interior points was applied for the experimental plan creation. Based on the obtained results of metabolite extraction yield in relation to solvent composition, polynomial regression models were developed. All models were significant, with predicted R-squared values between 0.77 and 0.99, while in all cases the model's lack of fit was not significant. The optimal mixture composition enabling the maximization of extraction efficiency was as follows: 32.9% ethanol, 53.9% water, and 13.3% propanediol (v/v/v). Such a mixture composition provided the extraction of 99%, 91%, 100%, 92%, 99%, 70%, 92%, and 69% of daidzin, genistin, 6″-O-malonyldaidzin, 6″-O-malonylglycitin, 6″-O-malonylgenistin, allantoin, citric acid, and malic acid, respectively. The solvent mixture composition developed provides a good extraction efficiency of the metabolites from soybean leaves and high antioxidant properties.

Chromatographic method development using multivariate approaches for organic solvents optimized analysis in [18F]fluorocholine

A fast and simple method using Gas Chromatography combined with Flame Ionization Detection (GC-FID) was developed for the determination of ethanol, acetonitrile, dibromomethane, dimethylaminoethanol, and dimethyl sulfoxide in [¹⁸F]fluorocholine. The combination of fractional factorial design, Doehlert design, and Desirability function was used to evaluate the operational parameters and to establish the best working condition. The validation results revealed that the proposed method has good recovery (85.1-104.1%) and repeatability (RSD ≤8.1%). Correlation coefficients (R ≥ 0.983) indicated good linearity over a wide range. The limit of detection (≤2.5 ppm) and the limit of quantification (≤7.5 ppm) were satisfactory. The proposed method is based on minimum manual operation, sample preparation free, direct injection technique, and short chromatographic separation time. This method is useful for routine analysis of organic solvents in [¹⁸F]fluorocholine, feasible for the modernization of specific monograph, and was therefore successfully implemented to assess samples manufactured by Nuclear Technology Development Center (CDTN).

Optimization of extraction conditions for LC-ToF-MS analysis of mevalonate pathway metabolites in engineered E. coli strain via statistical experimental designs

Isoprenoids give rise to many functional products used today such as flavours, fragrances and even pharmaceutical compounds. Mevalonate pathway metabolites are the key intermediates that affect the production yield of isoprenoids. With increasing demand and benefit of isoprenoids, the present study adopts Analytical Quality-by-Design (AQbD) approach to establish an efficacious extraction protocol prior to the determination of mevalonate pathway metabolites in an engineered Escherichia coli model. The statistical experimental design approach, described in this work, has successfully validated an optimised sample preparation method i.e., using acetonitrile: 50 mM ammonium formate (pH 9.5) (7:3) (ACN73) at -20 °C for 10 min without solvent evaporation to retain the targeted mevalonate metabolites in engineered E. coli strain. The study also demonstrates the use of liquid chromatography paired with a Time-of-Flight Mass Spectrometer (LC-ToF-MS) for the quantitative analysis of the mevalonate pathway metabolites in E. coli. The analytical method was validated in accordance with guidelines in Metabolomics Standards Initiative and ICH Q2 (R1) with analyte spike recoveries at 80% and above. In short, the present study overcomes the one-variable-at-a-time (OVAT) limitations in analytical development, minimises metabolite losses and gives better cost and time efficiencies by eliminating the solvent evaporation and swapping process. This work highlights the importance of analytical methods development in microbial metabolomics studies.

Advances in experimental and computational methodologies for the study of microbial-surface interactions at different omics levels

The study of the biological response of microbial cells interacting with natural and synthetic interfaces has acquired a new dimension with the development and constant progress of advanced omics technologies. New methods allow the isolation and analysis of nucleic acids, proteins and metabolites from complex samples, of interest in diverse research areas, such as materials sciences, biomedical sciences, forensic sciences, biotechnology and archeology, among others. The study of the bacterial recognition and response to surface contact or the diagnosis and evolution of ancient pathogens contained in archeological tissues require, in many cases, the availability of specialized methods and tools. The current review describes advances in in vitro and in silico approaches to tackle existing challenges (e.g., low-quality sample, low amount, presence of inhibitors, chelators, etc.) in the isolation of high-quality samples and in the analysis of microbial cells at genomic, transcriptomic, proteomic and metabolomic levels, when present in complex interfaces. From the experimental point of view, tailored manual and automatized methodologies, commercial and in-house developed protocols, are described. The computational level focuses on the discussion of novel tools and approaches designed to solve associated issues, such as sample contamination, low quality reads, low coverage, etc. Finally, approaches to obtain a systems level understanding of these complex interactions by integrating multi omics datasets are presented.

Design of experiment techniques for the optimization of chromatographic analysis conditions: A review

Design of experiment (DoE) techniques have been widely used in the field of chromatographic parameters optimization as a valuable tool. A systematic literature review of the available DoE techniques applied to the development of a chromatographic analysis method is presented in this paper. First, the most common available designs and the implementation steps of DoE are comprehensively introduced. Then the studies in recent 10 years for the application of DoE techniques in various chromatographic techniques are discussed, such as capillary electrophoresis, liquid chromatography, gas chromatography, thin-layer chromatography, and high-speed countercurrent chromatography. Current problems and future outlooks are finally given to provide a certain inspiration of research in the application of DoE techniques to the different chromatographic techniques field. This review contributes to a better understanding of the DoE techniques for the efficient optimization of chromatographic analysis conditions, especially for the analysis of complex systems, such as multicomponent drugs and natural products.

Optimising process and product performance in complex systems: a study in the automotive industry

Purpose

This study responds to calls from industry and the literature to cope with the enormous challenges faced by companies operating in competitive business sectors. The main objective of this paper is to investigate how managers can optimise product quality and process efficiency of complex systems.

Design/methodology/approach

In this paper, a design of experiments (DoE) method was used to improve the development of complex products and manufacturing processes in the industry of automotive audio components. To identify the optimal combination that minimises quality problems occurring with subwoofer speakers in the marketing, this study proposed a full Factorial experiment 24 with three replications in a single block summarised in an analysis of the interaction among the factors.

Findings

The research findings revealed the factors and levels regarding both the product development and manufacturing processes that significantly impact the quality and reliability performance of the subwoofer speaker analysed. The findings from the article allowed the company to prioritise internal improvements to enhance product quality and process efficiency. Other automotive firms will benefit from the research findings obtained.

Practical implications

From a managerial perspective, this research presented the DoE methodology as a real opportunity to deal with the inherent complexity of the manufacturing process in the automotive audio components sector. This research assist managers with insights into how they can improve the quality performance in production systems and in the market.

Originality/value

This study is an original contribution to the advance of theory and empirical implementation of DoE in competitive industrial sectors.

Liquid Chromatography-Mass Spectrometry for Clinical Metabolomics: An Overview

Metabolomics is a discipline that offers a comprehensive analysis of metabolites in biological samples. In the last decades, the notable evolution in liquid chromatography and mass spectrometry technologies has driven an exponential progress in LC-MS-based metabolomics. Targeted and untargeted metabolomics strategies are important tools in health and medical science, especially in the study of disease-related biomarkers, drug discovery and development, toxicology, diet, physical exercise, and precision medicine. Clinical and biological problems can now be understood in terms of metabolic phenotyping. This overview highlights the current approaches to LC-MS-based metabolomics analysis and its applications in the clinical research.

DoE-Based Design of a Simple but Efficient Preparation Method for a Non-Effervescent Gastro-Retentive Floating Tablet Containing Metformin HCl

A sustained-release non-effervescent floating matrix tablet was prepared using a simple and efficient direct compression of spray-dried granules containing metformin hydrochloride and cetyl alcohol with hydroxypropyl methylcellulose K15M (HPMC K15M). The design of experiments was employed to explore the optimal composition of the tablet. The similarity factor was employed to evaluate the equivalence in dissolution profiles between the test tablets and Glucophage XR as a reference. Bootstrap analysis was used to eliminate the formulations for which the dissolution profile was potentially inequivalent to that of the reference. The optimized tablet consisting of 150 mg of cetyl alcohol and 17% HPMC K15M showed a dissolution profile comparable with that of the reference with a similarity factor of 52.41, exhibited a floating lag time of less than 3 s in buffer media, remained floating for 24 h, and reduced the tablet weight by about 20% compared to that of the reference. The current study sheds light on the potential use of non-effervescent gastro-retentive extended-release tablets for high-dose drugs using a simple and efficient direct compression method, and as a potential alternative treatment for Glucophage XR. This study also highlights the importance of a systematic approach to formulation optimization and the evaluation of the dissolution profile.

Computational Variation: An Underinvestigated Quantitative Variability Caused by Automated Data Processing in Untargeted Metabolomics

Computational tools are commonly used in untargeted metabolomics to automatically extract metabolic features from liquid chromatography-mass spectrometry (LC-MS) raw data. However, due to the incapability of software to accurately determine chromatographic peak heights/areas for features with poor chromatographic peak shape, automated data processing in untargeted metabolomics faces additional quantitative variation (i.e., computational variation) besides the well-recognized analytical and biological variations. In this work, using multiple biological samples, we investigated how experimental factors, including sample concentrations, LC separation columns, and data processing programs, contribute to computational variation. For example, we found that the peak height (PH)-based quantification is more precise when MS-DIAL was used for data processing. We further systematically compared the different patterns of computational variation between PH- and peak area (PA)-based quantitative measurements. Our results suggest that the magnitude of computational variation is highly consistent at a given concentration. Hence, we proposed a quality control (QC) sample-based correction workflow to minimize computational variation by automatically selecting PH or PA-based measurement for each intensity value. This bioinformatic solution was demonstrated in a metabolomic comparison of leukemia patients before and after chemotherapy. Our novel workflow can be effectively applied on 652 out of 915 metabolic features, and over 31% (206 out of 652) of corrected features showed distinctly changed statistical significance. Overall, this work highlights computational variation, a considerable but underinvestigated quantitative variability in omics-scale quantitative analyses. In addition, the proposed bioinformatic solution can minimize computational variation, thus providing a more confident statistical comparison among biological groups in quantitative metabolomics.

Comprehensive Lipidome and Metabolome Profiling Investigations of Panax quinquefolius and Application in Different Growing Regions Using Liquid Chromatography Coupled with Mass Spectrometry

Panax quinquefolius is one of the most recognized ginseng species. In this study, lipidome and metabolome extraction methods for P. quinquefolius were optimized with methanol/methyl-tert-butyl ether/water (0.3 mg/1 μL/6 μL/8 μL). A total of 497 metabolites were identified, including 365 lipids and 76 ginsenosides. Comprehensive lipidome profiling was first performed for P. quinquefolius, in which 32.6% glycerophospholipids, 39.5% glycerolipids, 9.3% sphingolipids, 3.3% sterol lipids, and 15.3% fatty acyls were identified. Orthogonal partial least squares discrimination analysis (OPLS-DA) showed obvious metabolomic differences in two growing regions of China. In the northern growing region, the ratio of bilayer- to nonbilayer-forming membrane lipids (PCs/PEs, DGDGs/MGDGs), the degree of unsaturation of acyl chains in galactolipids, and the content of membrane glycerophospholipids were increased. In the eastern growing region, the synthesis of storage lipids, ceramides, and fatty acyls was increased, and secondary metabolism was enhanced with 24 differential ginsenosides found. The investigation deepens the understanding of metabolic regulation mechanisms of P. quinquefolius.

An analysis of the effects of stakeholders management on IT project risks using Delphi and design of experiments methods

Purpose

This study aims to evaluate how stakeholders affect project risks by analyzing the connection between stakeholder management and risk management in the information technology (IT) project context.

Design/methodology/approach

Using the Delphi method, interviews with IT project experts were conducted to examine four factors concerning stakeholder management and define probabilities for three risk variables. The design of experiments (DoE) method was applied for the design of the research instrument and data analysis.

Findings

Stakeholder engagement and communication with stakeholders have significant influence on the occurrence of negative risks, opportunities and the development of contingency strategies. These results provide project leaders with relevant data that better guide actions toward stakeholders and enhance project success probability.

Originality/value

This study clarifies important aspects of two main project management areas – stakeholder and risk management – in the IT projects context. This research also presented an unusual application for the DoE method which allows the subjective variables analysis by statistical tools and enables to explore factor influences on real project situations.

HPLC-MS-based design of experiments approach on cocoa roasting

Modern statistical methods, such as the design of experiments and response surface methodology, are widely used to describe changes in multiparameter processes during the processing of food in both science and technology contexts. However, these approaches are described to a lesser degree in the case of cocoa roasting than other foods and processes. Our study aimed to use the design of experiments to establish a model of cocoa roasting for relevant flavor-related constituents. We have used HPLC-MS techniques to link standard process parameters with chemical compounds changing in concentration during cocoa roasting. Influence of time, temperature, the addition of water, acid, and base, on relative concentrations of procyanidin monomers, dimers, and trimers, an Amadori compound, and a peptide, was shown. High-quality models for each compound were established and validated, displaying good prediction accuracy. Such an approach could be used to optimize processing conditions for cocoa roasting in order to influence the concentration of certain chemical compounds, and in turn, improving the flavor of chocolate products.

Metabolomics, a Powerful Tool for Understanding Plant Abiotic Stress

Metabolomics is a technology that generates large amounts of data and contributes to obtaining wide and integral explanations of the biochemical state of a living organism. Plants are continuously affected by abiotic stresses such as water scarcity, high temperatures and high salinity, and metabolomics has the potential for elucidating the response-to-stress mechanisms and develop resistance strategies in affected cultivars. This review describes the characteristics of each of the stages of metabolomic studies in plants and the role of metabolomics in the characterization of the response of various plant species to abiotic stresses.

Screening of Electrospray-operating Parameters in the Production of Alginate-Royal Jelly Microbeads Using Factorial Design

Introduction:

Royal jelly (RJ) has been consumed as food or as a supplement because of its high nutritional and medicinal values. A fresh harvested RJ is yellowish to whitish in color and contains proteins, free amino acids, lipids, vitamins, and sugar. Without proper storage conditions, such as at 4°C, the color of RJ changes to much darker yellow and produces a rancid smell. To prolong its shelf life, RJ is usually mixed with honey. Alginate, a natural and edible polymer derived from seaweed, is commonly used to encapsulate drugs and food due to its ability to form gels by reacting with divalent cations. However, there is a lack of research on the microencapsulation of RJ in alginate using electrospray. The electrospray technique has the advantage in producing consistent size and shape of alginate microbeads under optimum parameters.

Aim:

This research aimed to optimize electrospray-operating parameters in producing alginate–RJ microbeads.

Materials and Methods:

Optimization of alginate–RJ microbeads electrospray parameters was carried out using 2⁴ factorial design with three center points (19 runs). The studied parameters were flow rate, high voltage, nozzle size, and tip-to-collector distance, whereas the responses were particle size, particle size distribution, and sphericity factor. The responses of each run were analyzed using Design-Expert software.

Results:

Nozzle size is a significant parameter that influences the particle size. Flow rate is a significant parameter influencing the sphericity factor.

Conclusion:

Screening of the electrospray-operating parameters paves the way in determining the significant parameters and their design space to produce consistent alginate–RJ microbeads.

Design of experiments for development and optimization of a liquid chromatography coupled to tandem mass spectrometry bioanalytical assay

Design of experiments (DoE) is a valuable tool for the optimization of quantitative bioanalytical methods utilizing liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). Liquid chromatography mass spectrometry (LC-MS) is composed of several processes, including, liquid introduction and analyte ionization. The goal is to transfer analytes from atmospheric pressure to vacuum and maintain conditions that are compatible for both LC and MS. These processes involve many experimental factors which need to be simultaneously optimized to obtain maximum sensitivity and resolution at minimum retention time. In this tutorial, the basic concepts of DoE will be explained with focus on practical use of DoE. Three case studies optimized with DoE for liquid chromatography tandem mass spectrometry (LC-MS/MS) quantitative assays will then be presented.

Approaches in metabolomics for regulatory toxicology applications

Innovative methodological approaches are needed to conduct human health and environmental risk assessments on a growing number of marketed chemicals. Metabolomics is progressively proving its value as an efficient strategy to perform toxicological evaluations of new and existing substances, and it will likely become a key tool to accelerate chemical risk assessments. However, additional guidance with widely accepted and harmonized procedures is needed before metabolomics can be routinely incorporated in decision-making for regulatory purposes. The aim of this review is to provide an overview of metabolomic strategies that have been successfully employed in toxicity assessment as well as the most promising workflows in a regulatory context. First, we provide a general view of the different steps of regulatory toxicology-oriented metabolomics. Emphasis is put on three key elements: robustness of experimental design, choice of analytical platform, and use of adapted data treatment tools. Then, examples in which metabolomics supported regulatory toxicology outputs in different scenarios are reviewed, including chemical grouping, elucidation of mechanisms of toxicity, and determination of points of departure. The overall intention is to provide insights into why and how to plan and conduct metabolomic studies for regulatory toxicology purposes.

Identification of novel diagnostic biomarkers in endometrial cancer using targeted metabolomic profiling

PURPOSE

Endometrial cancer (EC) is the most common gynecological malignancy with high disease burden especially in advanced stages of the disease. Our study investigated the metabolomic profile of EC patient's serum with the aim of identifying novel diagnostic biomarkers that could be used especially in early disease detection.

MATERIAL AND METHODS

Using targeted metabolomic serum profiling based on HPLC-TQ/MS, women with EC (n ​= ​15) and controls (n ​= ​21) were examined for 232 endogenous metabolites.

RESULTS

Top performing biomarkers included ceramides, acylcarnitines and 1-methyl adenosine. Top 4 biomarkers combined achieved 94% sensitivity with 75% specificity with AUC 92.5% (CI 90.5-94.5%). Individual markers also provided significant predictive values: C16-ceramide achieved sensitivity 73%, specificity 81%, AUC 0.83, C22-ceramide sensitivity 67%, specificity 81%, AUC 0.77, hydroxyhexadecenoylcarnitine sensitivity 60%, specificity 96%, AUC 0.76 and 1-methyladenosine sensitivity 67%, specificity 81%, AUC 0.75. The individual markers, however, did not reach the high sensitivity and specificity of the 4-biomarker combination.

CONCLUSIONS

Using mass spectrometry targeted metabolomic profiling, ceramides, acylcarnitines and 1-methyladenosine were identified as potential diagnostic biomarkers for EC. Additionally, these identified metabolites may provide additional insight into cancer cell metabolism.

An appraisal of experimental designs: Application to enantioselective capillary electromigration techniques

Enantioresolution processes are vital tools for investigating the enantioselectivities of chiral compounds. An analyst resolves to optimize enantioresolution conditions once they are determined. Generally, optimization is conducted by a one-factor-at-a-time (OFAT) approach. Although this approach may determine an adequate condition for the method, it does not often allow the estimation of the real optimum condition. Experimental designs are conducive for the optimization of enantioresolution methods via capillary electromigration techniques (CETs). They can efficiently extract information from the behavior of a method and enable the estimation of the real optimum condition. Furthermore, the application of the analytical quality by design (AQbD) approach to the development of CET-based enantioselective methods is a trend. This article (i) offers an overview of the application of experimental designs to the development of enantioselective methods from 2015 to mid-2020, (ii) reveals the experimental designs that are presently employed in CET-based enantioresolutions, and (iii) offers a critical point of view on how the different experimental designs can aid the optimization of enantioresolution processes by considering the method parameters.

New insights into the biodegradation of chlorpyrifos by a novel bacterial consortium: Process optimization using general factorial experimental design

Himalayan mountains are subjected to the intensive and unjudicial application of chlorpyrifos (CP) in agricultural practices; hence it has spurred concerns over food safety and environmental consequences. These low-temperature mountainous regions are foremost ecosystems, representing the large-scale distribution of cold trapped CP residues. A bacterial consortium ECO-M was formed by isolating the CP degrading bacterial strains viz Agrobacterium tumefaciens strain ECO1, Cellulosimicrobium funkei strain ECO2, Shinella zoogloeoides strain ECO3 and Bacillus aryabhattai strain ECO4. At an initial concentration of 50 mg L^-1, consortium ECO-M degraded 100% of CP within 6 days. Emergence and subsequent degradation of the two metabolites, 3, 5, 6-trichloro-2-pyridinol (TCP) and 2-hydroxypyridine were confirmed by GC-MS analysis. A degradation pathway of CP by isolated strains has been proposed. A general factorial experimental design was effectuated to prognosticate the optimum biodegradation by manifesting the optimal biological and physicochemical factors. Fitness of the experimental design was affirmed experimentally by employing optimized factors i.e., temperature 30 °C, CP concentration 50 mg L^-1 and an inoculum size of 10% (v/v). The model appropriacy and the rationality of the optimization procedure were appraised by installing an in-situ microcosms experiment using the real contaminated soil collected from the Himalayan mountain ecosystem. The augmentation culture seems to be effectively conspicuous in stimulating maximum degradation up to 94.3% in the CP contaminated soil.

Statistical Design of Experiments for Synthetic Biology

The design and optimization of biological systems is an inherently complex undertaking that requires careful balancing of myriad synergistic and antagonistic variables. However, despite this complexity, much synthetic biology research is predicated on One Factor at A Time (OFAT) experimentation; the genetic and environmental variables affecting the activity of a system of interest are sequentially altered while all other variables are held constant. Beyond being time and resource intensive, OFAT experimentation crucially ignores the effect of interactions between factors. Given the ubiquity of interacting genetic and environmental factors in biology this failure to account for interaction effects in OFAT experimentation can result in the development of suboptimal systems. To address these limitations, an increasing number of studies have turned to Design of Experiments (DoE), a suite of methods that enable efficient, systematic exploration and exploitation of complex design spaces. This review provides an overview of DoE for synthetic biologists. Key concepts and commonly used experimental designs are introduced, and we discuss the advantages of DoE as compared to OFAT experimentation. We dissect the applicability of DoE in the context of synthetic biology and review studies which have successfully employed these methods, illustrating the potential of statistical experimental design to guide the design, characterization, and optimization of biological protocols, pathways, and processes.

Identification of Novel Diagnostic Biomarkers in Breast Cancer Using Targeted Metabolomic Profiling

INTRODUCTION

Breast cancer (BC) is the most common cancer in women, with a high disease burden, especially in the advanced disease stages. Our study investigated the metabolomic profile of breast cancer patients' serum with the aim of identifying novel diagnostic biomarkers that could be used, especially for early disease detection.

MATERIALS AND METHODS

Using targeted metabolomic serum profiling based on high-performance liquid chromatography mass spectrometry, women with BC (n = 39) and a control group (n = 21) were examined for 232 endogenous metabolites.

RESULTS

The top performing biomarkers included acylcarnitines (ACs) and 9,12-linoleic acid. A combined panel of the top 4 biomarkers achieved 83% sensitivity and 81% specificity, with an area under the curve (AUC) of 0.839 (95% confidence interval, 0.811-0.867). Individual markers also provided significant predictive values: AC 12:0, sensitivity of 72%, specificity of 67%, and AUC of 0.71; AC 14:2, sensitivity of 74%, specificity of 71%, and AUC of 0.73; AC 14:0: sensitivity of 67%, specificity of 81%, and AUC of 0.73; and 9,12-linoleic acid, sensitivity of 69%, specificity of 67%, and AUC of 0.71. The individual markers, however, did not reach the high sensitivity and specificity of the 4-biomarker combination.

CONCLUSION

Using mass spectrometry-targeted metabolomic profiling, ACs and 9,12-linoleic acid were identified as potential diagnostic biomarkers for breast cancer. Additionally, these identified metabolites could provide additional insight into cancer cell metabolism.

Comparative investigation of phytochemicals among ten citrus herbs by ultra high performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry and evaluation of their antioxidant properties

The citrus herbs have proved their important medicinal and nutritional values as medicine-food dual-purpose herbs, functional foods, or medical herbs in China. In this study, phytochemicals and antioxidant activity among ten typical citrus herbs (ethanol extracts) were investigated comprehensively. The major ingredients and their contents were analyzed by high-resolution mass spectrometry, and the differences of typical fragment ions between flavanone-7-O-rutinoside(s) and flavanone-7-O-neohesperidoside(s) were discriminated properly in negative electrospray ionization mode. Total polyphenols, total flavonoids, 1,1-diphenyl-2-picrylhydrazyl, 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid), and ferric reducing antioxidant power tests were performed, which indicated their beneficial values and antioxidant effects. The medicine-food dual-purpose herbs including Chenpi, Juluo, Daidaihua, Huajuhong, Xiangyuan, and Foshou exhibited antioxidant capacities significantly by decreasing intracellular reactive oxygen species intensity (P < 0.01), enhancing superoxide dismutase, catalase, and glutathione peroxidase activities (P < 0.01) in H₂ O₂ -induced RIN-m5F cells. Moreover, the functional foods Zhishi, Zhiqiao, and Qingpi showed moderate antioxidant bioactivity, while the medical herb Juhe showed weak antioxidant bioactivity, which were consistent with the multivariate analysis of their major flavonoids. The study provided a new sight for the chemical differentiation and practical application of citrus herbs as medicine-food dual-purpose herbs, functional foods, or medical herbs.

Tutorial: multivariate classification for vibrational spectroscopy in biological samples

Vibrational spectroscopy techniques, such as Fourier-transform infrared (FTIR) and Raman spectroscopy, have been successful methods for studying the interaction of light with biological materials and facilitating novel cell biology analysis. Spectrochemical analysis is very attractive in disease screening and diagnosis, microbiological studies and forensic and environmental investigations because of its low cost, minimal sample preparation, non-destructive nature and substantially accurate results. However, there is now an urgent need for multivariate classification protocols allowing one to analyze biologically derived spectrochemical data to obtain accurate and reliable results. Multivariate classification comprises discriminant analysis and class-modeling techniques where multiple spectral variables are analyzed in conjunction to distinguish and assign unknown samples to pre-defined groups. The requirement for such protocols is demonstrated by the fact that applications of deep-learning algorithms of complex datasets are being increasingly recognized as critical for extracting important information and visualizing it in a readily interpretable form. Hereby, we have provided a tutorial for multivariate classification analysis of vibrational spectroscopy data (FTIR, Raman and near-IR) highlighting a series of critical steps, such as preprocessing, data selection, feature extraction, classification and model validation. This is an essential aspect toward the construction of a practical spectrochemical analysis model for biological analysis in real-world applications, where fast, accurate and reliable classification models are fundamental.

Implementation of liquid chromatography-high resolution mass spectrometry methods for untargeted metabolomic analyses of biological samples: A tutorial

Untargeted metabolomics is now widely recognized as a useful tool for exploring metabolic changes taking place in biological systems under different conditions. By its nature, this is a highly interdisciplinary field of research, and mastering all of the steps comprised in the pipeline can be a challenging task, especially for those researchers new to the topic. In this tutorial, we aim to provide an overview of the most widely adopted methods of performing LC-HRMS-based untargeted metabolomics of biological samples. A detailed protocol is provided in the Supplementary Information for rapidly implementing a basic screening workflow in a laboratory setting. This tutorial covers experimental design, sample preparation and analysis, signal processing and data treatment, and, finally, data analysis and its biological interpretation. Each section is accompanied by up-to-date literature to guide readers through the preparation and optimization of such a workflow, as well as practical information for avoiding or fixing some of the most frequently encountered pitfalls.

From Samples to Insights into Metabolism: Uncovering Biologically Relevant Information in LC-HRMS Metabolomics Data

Untargeted metabolomics (including lipidomics) is a holistic approach to biomarker discovery and mechanistic insights into disease onset and progression, and response to intervention. Each step of the analytical and statistical pipeline is crucial for the generation of high-quality, robust data. Metabolite identification remains the bottleneck in these studies; therefore, confidence in the data produced is paramount in order to maximize the biological output. Here, we outline the key steps of the metabolomics workflow and provide details on important parameters and considerations. Studies should be designed carefully to ensure appropriate statistical power and adequate controls. Subsequent sample handling and preparation should avoid the introduction of bias, which can significantly affect downstream data interpretation. It is not possible to cover the entire metabolome with a single platform; therefore, the analytical platform should reflect the biological sample under investigation and the question(s) under consideration. The large, complex datasets produced need to be pre-processed in order to extract meaningful information. Finally, the most time-consuming steps are metabolite identification, as well as metabolic pathway and network analysis. Here we discuss some widely used tools and the pitfalls of each step of the workflow, with the ultimate aim of guiding the reader towards the most efficient pipeline for their metabolomics studies.

Metabolomics analysis of Pulsatilla decoction on treatment of wetness-heat-induced diarrhea in rats based on UPLC-Q/TOF-MS/MS

Pulsatilla decoction (PD) is a classical prescription in traditional Chinese medicine that has therapeutic effects on wetness-heat-induced diarrhea (WHD). To investigate the therapeutic effects of PD in the treatment of WHD and elucidate the potential mechanism, we used a metabolomics strategy on the base of ultraperformance liquid chromatography coupled with quadrupole time-of-flight/mass spectrometry (UPLC-Q/TOF-MS/MS) and analyzed the serum samples of 32 rats to identify differential metabolites and pathways associated with the PD treatment of WHD. With variable importance for projection >1.0 in the Orthogonal partial least-squares discriminant analysis (OPLS-DA ) models and FC ≥1.2 or ≤0.8, 67 differential metabolites in the model and control groups and 33 differential metabolites in the model and PD groups were screened. A total of 23 differential metabolites were selected based on Venny analysis. Functional analysis showed that the differential metabolites identified were primarily involved in pentose and glucuronate interconversions, glycerophospholipid metabolism, tryptophan metabolism, starch and sucrose metabolism, and glycerolipid metabolism. This study suggested that PD exerts inhibitory effects on WHD. In particular, the significant roles of PD for treating WHD lie in regulating perturbed energy metabolism, glycerophospholipid metabolism and glycerolipid metabolism, and promoting lysoPC production restoring the function of intestinal tract.

Chromium(VI) Removal by Polyvinyl Chloride (PVC)/Aliquat-336 Polymeric Inclusion Membranes in a Multiframe Flat Sheet Membrane Module

A new multiframe flat sheet membrane contactor module containing several flat membranes was designed and implemented. Each frame contains a chamber (central hole) in which the feed and the receiving phases are put in contact with polyvinyl chloride (PVC)/Aliquat-336 polymeric flat sheet membranes for Cr(VI) removal from aqueous solutions (feed phase). To evaluate the efficiency of the system, the experimental design methodology was used to analyze the effect of temperature (T, °C), PVC/Aliquat-336 ratio, and Cr (VI) concentration in the feed phase and the concentration of sodium chloride (NaOH-NaCl) in the receiving phase. Two representative mathematical models of the two responses (extraction and back-extraction) were respectively obtained. A good correlation between the experimental results and those predicted (RS2 = 97.77 and RR2 = 97.87) was achieved, allowing the optimization of the different factors selected for each response, separately. The proposed system showed a good separation performance, leading to Cr(VI) extractions up to 93% when working at the optimized operating conditions.

Application of Artificial Neural Networks for Noise Barrier Optimization

In the modern world, noise pollution continues to be a major problem that impairs people’s health, and road traffic is a primary contributor to noise emissions. This article describes an environmental impact study of the noise generated by the reconstruction of an urban section of a highway. Noise maps were calculated, and an environmental impact matrix was generated to determine the environmental impact of this reconstruction. The implementation of noise barriers was simulated based on these noise maps, and the effectiveness of the barriers was evaluated using Artificial Neural Networks (ANNs) combined with Design of Experiments (DoE). A functional variable significance analysis was then made for two parameters, namely, the coefficient of absorption of the barrier material and the barrier height. The aim was to determine the influence of these parameters on sound attenuation and on the formation of acoustic shadows. The results obtained from the ANNs and DoE were consistent in demonstrating that the absorption coefficient strongly influences the noise attenuation provided by noise barriers, while barrier height is correlated with the formation of larger areas of acoustic shadow. The environmental impact matrix also indicates that the existence of noise pollution has a negative effect on the environment, but that this impact can be reversed or minimized. The application of simulated noise barriers demonstrated that noise levels can be reduced to legally acceptable levels.