Development and validation of a portable membrane inlet mass spectrometry method for the measurement of monoaromatic hydrocarbons in water from a river canal

This work reports the validation of an analytical method for the determination of monoaromatic hydrocarbons such as benzene, toluene, and xylene (BTX) using a portable membrane inlet mass spectrometer (MIMS) with a quadrupole mass analyser. In this study of BTX in river canal water that is used for irrigation, we present a detailed analytical method for rapid, self-contained, field-transportable screening and quantitative analysis for environmental monitoring. The validation study showed that in the analytical range of 10-250 μg L-1, the correlation coefficient for all the analytes was greater than 0.99, the accuracy was in the range of 95.32-104.30%, the precision was less than 10%, and the selectivity was satisfactory. The LOD and LOQ values for benzene, toluene, and xylene were 4.88, 7.43, and 7.46 μg L-1 and 16.27, 24.77, and 27.85 μg L-1, respectively. The method was benchmarked against a lab-based GC-MS method, which confirmed its accuracy for the target compounds.

Plasma metabolomic and lipidomic profiles accurately classify mothers of children with congenital heart disease: an observational study

INTRODUCTION

Congenital heart disease (CHD) is the most common congenital anomaly, representing a significant global disease burden. Limitations exist in our understanding of aetiology, diagnostic methodology and screening, with metabolomics offering promise in addressing these.

OBJECTIVE

To evaluate maternal metabolomics and lipidomics in prediction and risk factor identification for childhood CHD.

METHODS

We performed an observational study in mothers of children with CHD following pregnancy, using untargeted plasma metabolomics and lipidomics by ultrahigh performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS). 190 cases (157 mothers of children with structural CHD (sCHD); 33 mothers of children with genetic CHD (gCHD)) from the children OMACp cohort and 162 controls from the ALSPAC cohort were analysed. CHD diagnoses were stratified by severity and clinical classifications. Univariate, exploratory and supervised chemometric methods were used to identify metabolites and lipids distinguishing cases and controls, alongside predictive modelling.

RESULTS

499 metabolites and lipids were annotated and used to build PLS-DA and SO-CovSel-LDA predictive models to accurately distinguish sCHD and control groups. The best performing model had an sCHD test set mean accuracy of 94.74% (sCHD test group sensitivity 93.33%; specificity 96.00%) utilising only 11 analytes. Similar test performances were seen for gCHD. Across best performing models, 37 analytes contributed to performance including amino acids, lipids, and nucleotides.

CONCLUSIONS

Here, maternal metabolomic and lipidomic analysis has facilitated the development of sensitive risk prediction models classifying mothers of children with CHD. Metabolites and lipids identified offer promise for maternal risk factor profiling, and understanding of CHD pathogenesis in the future.

Linking Clinical Blood Metabogram and Gut Microbiota

Recently, a clinical blood metabogram was developed as a fast, low-cost and reproducible test that allows the implementation of metabolomics in clinical practice. The components of the metabogram are functionally related groups of blood metabolites associated with humoral regulation, the metabolism of lipids, carbohydrates and amines, lipid intake into the organism, and liver function, thereby providing clinically relevant information. It is known that the gut microbiota affects the blood metabolome, and the components of the blood metabolome may affect the composition of the gut microbiota. Therefore, before using the metabogram in the clinic, the link between the metabogram components and the level of gut microorganisms should be established. For this purpose, the metabogram and microbiota data were obtained in this work for the same individuals. Metabograms of blood plasma were obtained by direct mass spectrometry of blood plasma, and the gut microbiome was determined by a culture-based method and real-time polymerase chain reaction (PCR). This study involved healthy volunteers and individuals with varying degrees of deviation in body weight (n = 44). A correlation analysis determined which metabogram components are linked to which gut microorganisms and the strength of this link. Moreover, diagnostic parameters (sensitivity, specificity and accuracy) confirmed the capacity of metabogram components to be used for diagnosing gut microbiota alterations. Therefore, the obtained results allow the use of the metabogram in a clinical setting, taking into account its relationship with gut microbiota.

An end-to-end deep learning framework for translating mass spectra to de-novo molecules

Elucidating the structure of a chemical compound is a fundamental task in chemistry with applications in multiple domains including drug discovery, precision medicine, and biomarker discovery. The common practice for elucidating the structure of a compound is to obtain a mass spectrum and subsequently retrieve its structure from spectral databases. However, these methods fail for novel molecules that are not present in the reference database. We propose Spec2Mol, a deep learning architecture for molecular structure recommendation given mass spectra alone. Spec2Mol is inspired by the Speech2Text deep learning architectures for translating audio signals into text. Our approach is based on an encoder-decoder architecture. The encoder learns the spectra embeddings, while the decoder, pre-trained on a massive dataset of chemical structures for translating between different molecular representations, reconstructs SMILES sequences of the recommended chemical structures. We have evaluated Spec2Mol by assessing the molecular similarity between the recommended structures and the original structure. Our analysis showed that Spec2Mol is able to identify the presence of key molecular substructures from its mass spectrum, and shows on par performance, when compared to existing fragmentation tree methods particularly when test structure information is not available during training or present in the reference database.

Statistical methods and resources for biomarker discovery using metabolomics

Metabolomics is a dynamic tool for elucidating biochemical changes in human health and disease. Metabolic profiles provide a close insight into physiological states and are highly volatile to genetic and environmental perturbations. Variation in metabolic profiles can inform mechanisms of pathology, providing potential biomarkers for diagnosis and assessment of the risk of contracting a disease. With the advancement of high-throughput technologies, large-scale metabolomics data sources have become abundant. As such, careful statistical analysis of intricate metabolomics data is essential for deriving relevant and robust results that can be deployed in real-life clinical settings. Multiple tools have been developed for both data analysis and interpretations. In this review, we survey statistical approaches and corresponding statistical tools that are available for discovery of biomarkers using metabolomics.

Potential of in vitro nuclear magnetic resonance of biofluids and tissues in clinical research

Body fluids, cells, and tissues contain a wide variety of metabolites that consist of a mixture of various low-molecular-weight compounds, including amino acids, peptides, lipids, nucleic acids, and organic acids, which makes comprehensive analysis more difficult. Quantitative nuclear magnetic resonance (NMR) spectroscopy is a well-established analytical technique for analyzing the metabolic profiles of body fluids, cells, and tissues. It enables fast and comprehensive detection, characterization, a high level of experimental reproducibility, minimal sample preparation, and quantification of various endogenous metabolites. In recent times, NMR-based metabolomics has been appreciably utilized in diverse branches of medicine, including microbiology, toxicology, pathophysiology, pharmacology, nutritional intervention, and disease diagnosis/prognosis. In this review, the utility of NMR-based metabolomics in clinical studies is discussed. The significance of in vitro NMR-based metabolomics as an effective tool for detecting metabolites and their variations in different diseases are discussed, together with the possibility of identifying specific biomarkers that can contribute to early detection and diagnosis of disease.

Metabolomics as a Tool to Understand Nano-Plant Interactions: The Case Study of Metal-Based Nanoparticles

Metabolomics is a powerful tool in diverse research areas, enabling an understanding of the response of organisms, such as plants, to external factors, their resistance and tolerance mechanisms against stressors, the biochemical changes and signals during plant development, and the role of specialized metabolites. Despite its advantages, metabolomics is still underused in areas such as nano-plant interactions. Nanoparticles (NPs) are all around us and have a great potential to improve and revolutionize the agri-food sector and modernize agriculture. They can drive precision and sustainability in agriculture as they can act as fertilizers, improve plant performance, protect or defend, mitigate environmental stresses, and/or remediate soil contaminants. Given their high applicability, an in-depth understanding of NPs' impact on plants and their mechanistic action is crucial. Being aware that, in nano-plant interaction work, metabolomics is much less addressed than physiology, and that it is lacking a comprehensive review focusing on metabolomics, this review gathers the information available concerning the metabolomic tools used in studies focused on NP-plant interactions, highlighting the impact of metal-based NPs on plant metabolome, metabolite reconfiguration, and the reprogramming of metabolic pathways.

Camel (Camelus spp.) Urine Bioactivity and Metabolome: A Systematic Review of Knowledge Gaps, Advances, and Directions for Future Research

Up to the present day, studies on the therapeutic properties of camel (Camelus spp.) urine and the detailed characterization of its metabolomic profile are scarce and often unrelated. Information on inter individual variability is noticeably limited, and there is a wide divergence across studies regarding the methods for sample storage, pre-processing, and extract derivatization for metabolomic analysis. Additionally, medium osmolarity is not experimentally adjusted prior to bioactivity assays. In this scenario, the methodological standardization and interdisciplinary approach of such processes will strengthen the interpretation, repeatability, and replicability of the empirical results on the compounds with bioactive properties present in camel urine. Furthermore, sample enlargement would also permit the evaluation of camel urine's intra- and interindividual variability in terms of chemical composition, bioactive effects, and efficacy, while it may also permit researchers to discriminate potential animal-intrinsic and extrinsic conditioning factors. Altogether, the results would help to evaluate the role of camel urine as a natural source for the identification and extraction of specific novel bioactive substances that may deserve isolated chemical and pharmacognostic investigations through preclinical tests to determine their biological activity and the suitability of their safety profile for their potential inclusion in therapeutic formulas for improving human and animal health.

How to employ metabolomic analysis to research on functions of prebiotics and probiotics in poultry gut health?

Gut health can be considered one of the major, manageable constituents of the animal immunity and performance. The fast spread of intestinal diseases, and increase of antimicrobial resistance have been observed, therefore the intestinal health has become not only economically relevant, but also highly important subject addressing the interest of public health. It is expected, that the strategies to control infections should be based on development of natural immunity in animals and producing resilient flocks using natural solutions, whilst eliminating antibiotics and veterinary medicinal products from action. Probiotics and prebiotics have been favored, because they have potential to directly or indirectly optimize intestinal health by manipulating the metabolism of the intestinal tract, including the microbiota. Studying the metabolome of probiotics and gut environment, both in vivo, or using the in vitro models, is required to attain the scientific understanding about the functions of bioactive compounds in development of gut health and life lasting immunity. There is a practical need to identify new metabolites being the key bioactive agents regulating biochemical pathways of systems associated with gut (gut-associated axes). Technological advancement in metabolomics studies, and increasing access to the powerful analytical platforms have paved a way to implement metabolomics in exploration of the effects of prebiotics and probiotics on the intestinal health of poultry. In this article, the basic principles of metabolomics in research involving probiotics and probiotics are introduced, together with the overview of existing strategies and suggestions of their use to study metabolome in poultry.

Identification of Candidate Salivary, Urinary and Serum Metabolic Biomarkers for High Litter Size Potential in Sows (Sus scrofa)

The selection of sows that are reproductively fit and produce large litters of piglets is imperative for success in the pork industry. Currently, low heritability of reproductive and litter-related traits and unfavourable genetic correlations are slowing the improvement of pig selection efficiency. The integration of biomarkers as a supplement or alternative to the use of genetic markers may permit the optimization and increase of selection protocol efficiency. Metabolite biomarkers are an advantageous class of biomarkers that can facilitate the identification of cellular processes implicated in reproductive condition. Metabolism and metabolic biomarkers have been previously implicated in studies of female mammalian fertility, however a systematic analysis across multiple biofluids in infertile and high reproductive potential phenotypes has not been explored. In the current study, the serum, urinary and salivary metabolomes of infertile (INF) sows and high reproductive potential (HRP) sows with a live litter size ≥ 13 piglets were examined using LC-MS/MS techniques, and a data pipeline was used to highlight possible metabolite reproductive biomarkers discriminating the reproductive groups. The metabolomes of HRP and INF sows were distinct, including significant alterations in amino acid, fatty acid, membrane lipid and steroid hormone metabolism. Carnitines and fatty acid related metabolites were most discriminatory in separating and classifying the HRP and INF sows based on their biofluid metabolome. It appears that urine is a superior biofluid than saliva and serum for potentially predicting the reproductive potential level of a given female pig based on the performance of the resultant biomarker models. This study lays the groundwork for improving gilt and sow selection protocols using metabolomics as a tool for the prediction of reproductive potential.

Exogenous metabolite feeding on altering antibiotic susceptibility in Gram-negative bacteria through metabolic modulation: a review

BACKGROUND

The rise of antimicrobial resistance at an alarming rate is outpacing the development of new antibiotics. The worrisome trends of multidrug-resistant Gram-negative bacteria have enormously diminished existing antibiotic activity. Antibiotic treatments may inhibit bacterial growth or lead to induce bacterial cell death through disruption of bacterial metabolism directly or indirectly. In light of this, it is imperative to have a thorough understanding of the relationship of bacterial metabolism with antimicrobial activity and leverage the underlying principle towards development of novel and effective antimicrobial therapies.

OBJECTIVE

Herein, we explore studies on metabolic analyses of Gram-negative pathogens upon antibiotic treatment. Metabolomic studies revealed that antibiotic therapy caused changes of metabolites abundance and perturbed the bacterial metabolism. Following this line of thought, addition of exogenous metabolite has been employed in in vitro, in vivo and in silico studies to activate the bacterial metabolism and thus potentiate the antibiotic activity.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Exogenous metabolites were discovered to cause metabolic modulation through activation of central carbon metabolism and cellular respiration, stimulation of proton motive force, increase of membrane potential, improvement of host immune protection, alteration of gut microbiome, and eventually facilitating antibiotic killing. The use of metabolites as antimicrobial adjuvants may be a promising approach in the fight against multidrug-resistant pathogens.

Metabolomic Insights Into Endophyte-Derived Bioactive Compounds

Among the various plant-associated microbiota, endophytes (the microbial communities inhabiting plant endosphere without causing disease symptoms) exhibit the most intimate and specific association with host plants. Endophytic microbes influence various aspects of plant responses (such as increasing availability of nutrients, tolerance against biotic and abiotic stresses, etc.) by modulating the primary and secondary metabolism of the host. Besides, endophytic microbes produce a diverse array of bioactive compounds, which have potential applications in the pharmaceutical, food, and cosmetic industries. Further, there is sufficient evidence for endophyte-derived plant metabolites, which could be pursued as alternative sources of commercially important plant metabolites. The field of bioprospecting, the discovery of novel chemistries, and endophyte-mediated production of plant metabolites have witnessed a boom with the advent of omics technologies (especially metabolomics) in endophyte research. The high throughput study of small metabolites at a particular timepoint or tissue forms the core of metabolomics. Being downstream to transcriptome and proteome, the metabolome provides the most direct reflection of the phenotype of an organism. The contribution of plant and microbial metabolomics for answering fundamental questions of plant-endophyte interaction, such as the effect of endophyte inoculation on plant metabolome, composition of metabolites on the impact of environmental stressors (biotic and abiotic), etc., have also been discussed.

A Checklist for Reproducible Computational Analysis in Clinical Metabolomics Research

Clinical metabolomics emerged as a novel approach for biomarker discovery with the translational potential to guide next-generation therapeutics and precision health interventions. However, reproducibility in clinical research employing metabolomics data is challenging. Checklists are a helpful tool for promoting reproducible research. Existing checklists that promote reproducible metabolomics research primarily focused on metadata and may not be sufficient to ensure reproducible metabolomics data processing. This paper provides a checklist including actions that need to be taken by researchers to make computational steps reproducible for clinical metabolomics studies. We developed an eight-item checklist that includes criteria related to reusable data sharing and reproducible computational workflow development. We also provided recommended tools and resources to complete each item, as well as a GitHub project template to guide the process. The checklist is concise and easy to follow. Studies that follow this checklist and use recommended resources may facilitate other researchers to reproduce metabolomics results easily and efficiently.

Review on in vivo profiling of drug metabolites with LC-MS/MS in the past decade

Metabolite profiling is an indispensable part of drug discovery and development, enabling a comprehensive understanding of the drug's metabolic behavior. Liquid chromatography-mass spectrometry facilitates metabolite profiling by reducing sample complexity and providing high sensitivity. This review discusses the in vivo metabolite profiling involving LC-MS/MS and the utilization of QTOF, QQQ mass analyzers with a particular emphasis on a mass filter. Further, a summary of sample extraction procedures in biological matrices such as plasma, urine, feces, serum and hair as in vivo samples are outlined. toward the end, we present 15 case studies in biological matrices and their LC-MS/MS conditions to understand the metabolic disposition.

Nanotechnology Approaches for Enhanced CNS Drug Delivery in the Management of Schizophrenia

Schizophrenia is a neuropsychiatric disorder mainly affecting the central nervous system, presented with auditory and visual hallucinations, delusion and withdrawal from society. Abnormal dopamine levels mainly characterise the disease; various theories of neurotransmitters explain the pathophysiology of the disease. The current therapeutic approach deals with the systemic administration of drugs other than the enteral route, altering the neurotransmitter levels within the brain and providing symptomatic relief. Fluid biomarkers help in the early detection of the disease, which would improve the therapeutic efficacy. However, the major challenge faced in CNS drug delivery is the blood-brain barrier. Nanotherapeutic approaches may overcome these limitations, which will improve safety, efficacy, and targeted drug delivery. This review article addresses the main challenges faced in CNS drug delivery and the significance of current therapeutic strategies and nanotherapeutic approaches for a better understanding and enhanced drug delivery to the brain, which improve the quality of life of schizophrenia patients.

Identification of potential serum metabolic biomarkers for patient with keratoconus using untargeted metabolomics approach

This study aimed to investigate the metabolite differences between patients with keratoconus and control subjects and identify potential serum biomarkers for keratoconus using a non-targeted metabolomics approach. Venous blood samples were obtained from patients with keratoconus (n = 20) as well as from age-, gender- and race-matched control subjects (n = 20). Metabolites extracted from serum were separated and analyzed by liquid chromatography/quadrupole time-of-flight mass spectrometer. Processing of raw data and analysis of the data files was performed using Agilent Mass Hunter Qualitative software. The identified metabolites were subjected to a principal component and hierarchical cluster analysis. Appropriate statistical tests were used to analyze the metabolomic profiling data. Together, the analysis revealed that the dehydroepiandrosterone sulfate from the steroidal hormone synthesis pathway was significantly upregulated in patients with keratoconus (p < 0.05). Also, a combination of eicosanoids from the arachidonic acid pathway, mainly prostaglandin F2α, prostaglandin A2, 16,16-dimethyl prostaglandin E2, and 5-hydroxyeicosatetraenoic acid were collectively up-regulated as a group in keratoconus patients (p < 0.05). On the other hand, glycerophospholipid PS(17:2(9Z,12Z)/20:4(5Z,8Z,11Z,14Z)) was found to be significantly upregulated in the metabolomics profiles of control subjects (p < 0.05). The differently regulated metabolites provide insights into the pathophysiology of keratoconus and could potentially be used as biomarkers for keratoconus to aid in screening for individuals at risk hence, enabling early diagnosis and timely monitoring of disease.

Dengue and metabolomics in humans

BACKGROUND

Dengue virus causes dengue fever (DF)disease, transmitted by the mosquito Aedes aegypti. The symptoms could be severe and disable the affected individuals for weeks. The severe form, dengue hemorrhagic fever (DHF), can lead to death if not adequately attended to. Due to global warming, the vector mosquito will advance over new areas and expose more people to this disease over the next decades. Despite the severity, there are no treatments nor efficient vaccines available. Metabolomic studies have shown a new perspective to understand this disease better at a new molecular level.

AIM OF REVIEW

Many published works rely on samples obtained from animal studies. This review will mainly focus on human samples and cell culture experiments to view how the dengue virus affects the metabolomic profile.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The review compiles the sample sources, metabolomic techniques used, the detected compounds, and how they behave in different DF stages. This disease causes a significant change in many metabolites, but some results are still conflicting between studies. The results gathered here show that metabolomic approaches prove to be an excellent and viable way to expand knowledge about DF.

M. tuberculosis curli pili (MTP) is associated with alterations in carbon, fatty acid and amino acid metabolism in a THP-1 macrophage infection model

The initial host-pathogen interaction is crucial for the establishment of infection. An improved understanding of the pathophysiology of Mycobacterium tuberculosis (M. tuberculosis) during macrophage infection can aid the development of intervention therapeutics against tuberculosis. M. tuberculosis curli pili (MTP) is a surface located adhesin, involved in the first point-of-contact between pathogen and host. This study aimed to better understand the role of MTP in modulating the intertwined metabolic pathways of M. tuberculosis and its THP-1 macrophage host. Metabolites were extracted from pelleted wet cell mass of THP-1 macrophages infected with M. tuberculosis wild-type V9124 (WT), Δmtp-deletion mutant and the mtp-complemented strains, respectively, via a whole metabolome extraction method using a 1:3:1 ratio of chloroform:methanol:water. Metabolites were detected by two-dimensional gas chromatography time-of-flight mass spectrometry. Significant metabolites were determined through univariate and multivariate statistical tests and online pathway databases. Relative to the WT, a total of nine and ten metabolites were significantly different in the Δmtp and complement strains, respectively. All nine significant metabolites were found in elevated levels in the Δmtp relative to the WT. Additionally, of the ten significant metabolites, eight were detected in lower levels and two were detected in higher levels in the complement relative to the WT. The absence of the MTP adhesin resulted in reduced virulence of M. tuberculosis leading to alterations in metabolites involved in carbon, fatty acid and amino acid metabolism during macrophage infection, suggesting that MTP plays an important role in the modulation of host metabolic activity. These findings support the prominent role of the MTP adhesin as a virulence factor as well as a promising biomarker for possible diagnostic and therapeutic intervention.

Role of Multiomics Data to Understand Host-Pathogen Interactions in COVID-19 Pathogenesis

Human infectious diseases are contributed equally by the host immune system's efficiency and any pathogens' infectivity. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the coronavirus strain causing the respiratory pandemic coronavirus disease 2019 (COVID-19). To understand the pathobiology of SARS-CoV-2, one needs to unravel the intricacies of host immune response to the virus, the viral pathogen's mode of transmission, and alterations in specific biological pathways in the host allowing viral survival. This review critically analyzes recent research using high-throughput "omics" technologies (including proteomics and metabolomics) on various biospecimens that allow an increased understanding of the pathobiology of SARS-CoV-2 in humans. The altered biomolecule profile facilitates an understanding of altered biological pathways. Further, we have performed a meta-analysis of significantly altered biomolecular profiles in COVID-19 patients using bioinformatics tools. Our analysis deciphered alterations in the immune response, fatty acid, and amino acid metabolism and other pathways that cumulatively result in COVID-19 disease, including symptoms such as hyperglycemic and hypoxic sequelae.

An emerging potential of metabolomics in multiple sclerosis: a comprehensive overview

Multiple sclerosis (MS) is an inflammatory demyelinating disease of the nervous system that primarily affects young adults. Although the exact etiology of the disease remains obscure, it is clear that alterations in the metabolome contribute to this process. As such, defining a reliable and disease-specific metabolome has tremendous potential as a diagnostic and therapeutic strategy for MS. Here, we provide an overview of studies aimed at identifying the role of metabolomics in MS. These offer new insights into disease pathophysiology and the contributions of metabolic pathways to this process, identify unique markers indicative of treatment responses, and demonstrate the therapeutic effects of drug-like metabolites in cellular and animal models of MS. By and large, the commonly perturbed pathways in MS and its preclinical model include lipid metabolism involving alpha-linoleic acid pathway, nucleotide metabolism, amino acid metabolism, tricarboxylic acid cycle, d-ornithine and d-arginine pathways with collective role in signaling and energy supply. The metabolomics studies suggest that metabolic profiling of MS patient samples may uncover biomarkers that will advance our understanding of disease pathogenesis and progression, reduce delays and mistakes in diagnosis, monitor the course of disease, and detect better drug targets, all of which will improve early therapeutic interventions and improve evaluation of response to these treatments.

Multi-omics tools for studying microbial biofilms: current perspectives and future directions

The advent of omics technologies has greatly improved our understanding of microbial biology, particularly in the last two decades. The field of microbial biofilms is, however, relatively new, consolidated in the 1980s. The morphogenic switching by microbes from planktonic to biofilm phenotype confers numerous survival advantages such as resistance to desiccation, antibiotics, biocides, ultraviolet radiation, and host immune responses, thereby complicating treatment strategies for pathogenic microorganisms. Hence, understanding the mechanisms governing the biofilm phenotype can result in efficient treatment strategies directed specifically against molecular markers mediating this process. The application of omics technologies for studying microbial biofilms is relatively less explored and holds great promise in furthering our understanding of biofilm biology. In this review, we provide an overview of the application of omics tools such as transcriptomics, proteomics, and metabolomics as well as multi-omics approaches for studying microbial biofilms in the current literature. We also highlight how the use of omics tools directed at various stages of the biological information flow, from genes to metabolites, can be integrated via multi-omics platforms to provide a holistic view of biofilm biology. Following this, we propose a future artificial intelligence-based multi-omics platform that can predict the pathways associated with different biofilm phenotypes.

Metabolomic analysis in ophthalmology

Modern science takes into account phenotype complexity and establishes approaches to track changes on every possible level. Many "omics" studies have been developed over the last decade. Metabolomic analysis enables dynamic measurement of the metabolic response of a living system to a variety of stimuli or genetic modifications. Important targets of metabolomics is biomarker development and translation to the clinic for personalized diagnosis and a greater understanding of disease pathogenesis. The current review highlights the major aspects of metabolomic analysis and its applications for the identification of relevant predictive, diagnostic and prognostic biomarkers for some ocular diseases including dry eye, keratoconus, retinal diseases, macular degeneration, and glaucoma. To date, possible biomarker candidates for dry eye disease are lipid metabolites and androgens, for keratoconus cytokeratins, urea, citrate cycle, and oxidative stress metabolites. Palmitoylcarnitine, sphingolipids, vitamin D related metabolites, and steroid precursors may be used for distinguishing glaucoma patients from healthy controls. Dysregulation of amino acid and carnitine metabolism is critical in the development and progression of diabetic retinopathy. Further work is needed to discover and validate metabolic biomarkers as a powerful tool for understanding the molecular mechanisms of ocular diseases, to provide knowledge on their etiology and pathophysiology and opportunities for personalized clinical intervention at an early stage.

Novel biomarkers for lysosomal storage disorders: Metabolomic and proteomic approaches

Lysosomal storage disorders (LSDs) are characterized by the accumulation of specific disease substrates inside the lysosomes of various cells, eventually leading to the deterioration of cellular function and multisystem organ damage. With the continuous discovery and validation of novel and advanced therapies for most LSDs, there is an urgent need to discover more versatile and clinically relevant biomarkers. The utility of these biomarkers should ideally extend beyond the screening and diagnosis of LSDs to the evaluation of disease severity and monitoring of therapy. Metabolomic and proteomic approaches provide the means to the discovery and validation of such novel biomarkers. This is achieved mainly through the application of various mass spectrometric techniques to common and easily accessible biological samples, such as plasma, urine and dried blood spots. In this review, we tried to summarize the complexity of the lysosomal disorders phenotypes, their current diagnostic and therapeutic approaches, the various techniques supporting metabolomic and proteomic studies and finally we tried to explore the newly discovered biomarkers for most LSDs and their reported clinical values.

Salivary metabolites to detect patients with cancer: a systematic review

Novel adjunctive screening aids are needed to reduce the morbidity and mortality related to cancer, and every effort should be made for early diagnosis. This systematic review aimed to evaluate salivary metabolites and their diagnostic value in patients with cancer.The systematic review was performed in two phases and included studies that focused on the diagnostic value of salivary metabolites in humans with solid malignant neoplasms. Five electronic databases were searched, and the risk of bias in individual studies was evaluated using the revised Quality Assessment of Diagnostic Accuracy Studies criteria (QUADAS-2). All procedures were performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.Of the 1151 studies retrieved, 25 were included; 13 studies used targeted and 12 untargeted metabolomics approaches. Most studies included patients with breast and oral cancer. Except for one, all studies had case-control designs, and none fulfilled all quality assessments. Overall, 140 salivary metabolites were described. The most frequently reported metabolites were alanine, valine, and leucine. Among the 11 studies that reported diagnostic test accuracy (DTA) values, proline, threonine, and histidine in combination and monoacylglycerol alone demonstrated the highest DTA for breast cancer. Combined choline, betaine, pipecolinic acid, and L-carnitine showed better discriminatory performance for early oral cancer.This systematic review highlights the current evidence on salivary metabolites that may be used as a future strategy to diagnose cancer. Further studies including larger sample sizes with confirmation of the results by untargeted analysis are warranted.