The analytical process to search for metabolomics biomarkers

Exploring novel protein-based biomarkers for advancing breast cancer diagnosis: A review

This review provides a contemporary examination of the evolving landscape of breast cancer (BC) diagnosis, focusing on the pivotal role of novel protein-based biomarkers. The overview begins by elucidating the multifaceted nature of BC, exploring its prevalence, subtypes, and clinical complexities. A critical emphasis is placed on the transformative impact of proteomics, dissecting the proteome to unravel the molecular intricacies of BC. Navigating through various sources of samples crucial for biomarker investigations, the review underscores the significance of robust sample processing methods and their validation in ensuring reliable outcomes. The central theme of the review revolves around the identification and evaluation of novel protein-based biomarkers. Cutting-edge discoveries are summarised, shedding light on emerging biomarkers poised for clinical application. Nevertheless, the review candidly addresses the challenges inherent in biomarker discovery, including issues of standardisation, reproducibility, and the complex heterogeneity of BC. The future direction section envisions innovative strategies and technologies to overcome existing challenges. In conclusion, the review summarises the current state of BC biomarker research, offering insights into the intricacies of proteomic investigations. As precision medicine gains momentum, the integration of novel protein-based biomarkers emerges as a promising avenue for enhancing the accuracy and efficacy of BC diagnosis. This review serves as a compass for researchers and clinicians navigating the evolving landscape of BC biomarker discovery, guiding them toward transformative advancements in diagnostic precision and personalised patient care.

Metabolomic Study of Urine from Workers Exposed to Low Concentrations of Benzene by UHPLC-ESI-QToF-MS Reveals Potential Biomarkers Associated with Oxidative Stress and Genotoxicity

Benzene is a human carcinogen whose exposure to concentrations below 1 ppm (3.19 mg·m^-3) is associated with myelotoxic effects. The determination of biomarkers such as trans-trans muconic acid (AttM) and S-phenylmercapturic acid (SPMA) show exposure without reflecting the toxic effects of benzene. For this reason, in this study, the urinary metabolome of individuals exposed to low concentrations of benzene was investigated, with the aim of understanding the biological response to exposure to this xenobiotic and identifying metabolites correlated with the toxic effects induced by it. Ultra-efficient liquid chromatography coupled to a quadrupole-time-of-flight mass spectrometer (UHPLC-ESI-Q-ToF-MS) was used to identify metabolites in the urine of environmentally (n = 28) and occupationally exposed (n = 32) to benzene (mean of 22.1 μg·m^-3 and 31.8 μg·m^-3, respectively). Non-targeted metabolomics analysis by PLS-DA revealed nine urinary metabolites discriminating between groups and statistically correlated with oxidative damage (MDA, thiol) and genetic material (chromosomal aberrations) induced by the hydrocarbon. The analysis of metabolic pathways revealed important alterations in lipid metabolism. These results point to the involvement of alterations in lipid metabolism in the mechanisms of cytotoxic and genotoxic action of benzene. Furthermore, this study proves the potential of metabolomics to provide relevant information to understand the biological response to exposure to xenobiotics and identify early effect biomarkers.

Wearable Sensor for Continuous Sweat Biomarker Monitoring

In recent years, wearable sensors have enabled the unique mode of real-time and noninvasive monitoring to develop rapidly in medical care, sports, and other fields. Sweat contains a wide range of biomarkers such as metabolites, electrolytes, and various hormones. Combined with wearable technology, sweat can reflect human fatigue, disease, mental stress, dehydration, and so on. This paper comprehensively describes the analysis of sweat components such as glucose, lactic acid, electrolytes, pH, cortisol, vitamins, ethanol, and drugs by wearable sensing technology, and the application of sweat wearable devices in glasses, patches, fabrics, tattoos, and paper. The development trend of sweat wearable devices is prospected. It is believed that if the sweat collection, air permeability, biocompatibility, sensing array construction, continuous monitoring, self-healing technology, power consumption, real-time data transmission, specific recognition, and other problems of the wearable sweat sensor are solved, we can provide the wearer with important information about their health level in the true sense.

Food Metabolites as Tools for Authentication, Processing, and Nutritive Value Assessment

Secondary metabolites are molecules with unlimited applications that have been gaining importance in various industries and studied from many angles. They are mainly used for their bioactive capabilities, but due to the improvement of sensibility in analytical chemistry, they are also used for authentication and as a quality control parameter for foods, further allowing to help avoid food adulteration and food fraud, as well as helping understand the nutritional value of foods. This manuscript covers the examples of secondary metabolites that have been used as qualitative and authentication molecules in foods, from production, through processing and along their shelf-life. Furthermore, perspectives of analytical chemistry and their contribution to metabolite detection and general perspectives of metabolomics are also discussed.

Sweat metabolome and proteome: Recent trends in analytical advances and potential biological functions

Metabolome and proteome profiling of biofluids, e.g., urine, plasma, has generated vast and ever-increasing amounts of knowledge over the last few decades. Paradoxically, omics analyses of sweat, one of the most readily available human biofluids, have lagged behind. This review capitalizes on the current knowledge and state of the art analytical advances of sweat metabolomics and proteomics. Moreover, current applications of sweat omics such as the discovery of disease biomarkers and monitoring athletic performance are also presented in this review. Another area of emerging knowledge that has been highlighted herein lies in the role of skin host-microbiome interactions in shaping the sweat metabolite-protein profiles. Discussion of future research directions describes the need to have a better grasp of sweat chemicals and to better understand how they function as aided by advances in omics tools. Overall, the role of sweat as an information-rich biofluid that could complement the exploration of the skin metabolome/proteome is emphasized.

Dietary patterns related to zinc and polyunsaturated fatty acids intake are associated with serum linoleic/dihomo-γ-linolenic ratio in NHANES males and females

Identifying dietary patterns that contribute to zinc (Zn) and fatty acids intake and their biomarkers that may have an impact on health of males and females. The present study was designed to (a) extract dietary patterns with foods that explain the variation of Zn and PUFAs intake in adult men and women; and (b) evaluate the association between the extracted dietary patterns with circulating levels of serum dihomo-γ-linolenic fatty acid (DGLA) or serum linoleic/dihomo-γ-linolenic (LA/DGLA) ratio in males and females. We used reduced rank regression (RRR) to extract the dietary patterns separated by sex in the NHANES 2011-2012 data. A dietary pattern with foods rich in Zn (1st quintile = 8.67 mg/day; 5th quintile = 11.11 mg/day) and poor in PUFAs (5th quintile = 15.28 g/day; 1st quintile = 18.03 g/day) was found in females (S-FDP2) and the same pattern, with foods poor in PUFAs (5th quintile = 17.6 g/day; 1st quintile = 20.7 g/day) and rich in Zn (1st quintile = 10.4 mg/day; 5th quintile = 12.9 mg/day) (S-MDP2), was found in males. The dietary patterns with foods rich in Zn and poor in PUFAs were negatively associated with serum LA/DGLA ratio. This is the first study to associate the LA/DGLA ratio with Zn and PUFAs related dietary patterns in males and females.

The bioinformatics and metabolomics research on anti-hypoxic molecular mechanisms of Salidroside via regulating the PTEN mediated PI3K/Akt/NF-κB signaling pathway

Salidroside (SAL), a major bioactive compound of Rhodiola crenulata, has significant anti-hypoxia effect, however, its underlying molecular mechanism has not been elucidated. In order to explore the protective mechanism of SAL, the lactate dehydrogenase (LDH), reactive oxygen species (ROS), superoxide dismutase (SOD) and hypoxia-induced factor 1α (HIF-1α) were measured to establish the PC12 cell hypoxic model. Cell staining and cell viability analyses were performed to evaluate the protective effects of SAL. The metabolomics and bioinformatics methods were used to explore the protective effects of salidroside under hypoxia condition. The metabolite-protein interaction networks were further established and the protein expression level was examined by Western blotting. The results showed that 59 endogenous metabolites changed and the expression of the hub proteins of CK2, p-PTEN/PTEN, PI3K, p-Akt/Akt, NF-κB p65 and Bcl-2 were increased, suggesting that SAL could increase the expression of CK2, which induced the phosphorylation and inactivation of PTEN, reduced the inhibitory effect on PI3K signaling pathways and activated the PI3K/Akt/NF-κB survival signaling pathway. Our study provided an important insight to reveal the protective molecular mechanism of SAL as a novel drug candidate.

Electrospray ionization and heterogeneous matrix effects in liquid chromatography/mass spectrometry based meta-metabolomics: A biomarker or a suppressed ion?

RATIONALE

Correct biomarker determination in metabolomics is crucial for unbiased conclusions and reliable applications. However, this determination is subject to several drifts, e.g. matrix effects and ion suppression in Liquid Chromatography/Mass Spectrometry (LC/MS)-based approaches. This phenomenon provokes critical issues for biomarker determination, particularly during comparative studies dealing with samples exhibiting heterogeneous complexities.

METHODS

Occurrence of the issue was coincidentally noticed when studying the environmental impact of a complex bioinsecticide: Bacillus thuringiensis israelensis. The studied samples comprised insecticide-spiked sediments and untreated control sediments. QuEChERS extractions followed by LC/ESI-Q/ToF analyses were performed on sediments after 15 days of incubation. Meta-metabolomes containing pesticide xenometabolites and sediment endometabolites were analyzed in depth using XCMS-based computational data preprocessing. Multivariate statistical analyses (PCA, OPLS-DA) and raw data crosschecks were performed to search for environmental biomarkers.

RESULTS

Multivariate analyses and raw data crosschecks led to the selection of nine metabolites as biomarker candidates. However, when exploring the mass spectra, co-elutions were noticed between seven of these metabolites and multi-charged macromolecules originating from the pesticide. Provoked false positives were thus suspected due to a potential ion suppression exclusively occurring in the spiked samples. A dilution-based approach was then applied. It confirmed five metabolites as suppressed ions.

CONCLUSIONS

Ion suppression should be considered as a critical issue for biomarker determination when comparing heterogeneous metabolic profiles. Raw chromatograms and mass spectra crosschecks are mandatory to reveal potential ion suppressions in such cases. Dilution is a suitable approach to filter reliable biomarker candidates before their identification and absolute quantification.

Machine learning for metabolic engineering: A review

Machine learning provides researchers a unique opportunity to make metabolic engineering more predictable. In this review, we offer an introduction to this discipline in terms that are relatable to metabolic engineers, as well as providing in-depth illustrative examples leveraging omics data and improving production. We also include practical advice for the practitioner in terms of data management, algorithm libraries, computational resources, and important non-technical issues. A variety of applications ranging from pathway construction and optimization, to genetic editing optimization, cell factory testing, and production scale-up are discussed. Moreover, the promising relationship between machine learning and mechanistic models is thoroughly reviewed. Finally, the future perspectives and most promising directions for this combination of disciplines are examined.

Identifying novel treeline biomarkers in lake sediments using an untargeted screening approach

Paleolimnology uses sedimentary biomarkers as proxies to reconstruct long-term changes in environmental conditions from lake sediment cores. This work describes an untargeted metabolomics-based approach and uniquely applies it to the field of paleolimnology to identify novel sediment biomarkers to track long-term patterns in treeline dynamics. We identified new potential biomarkers across the Canadian northern Arctic, non-alpine, treeline using high-resolution accurate mass spectrometry, and pattern recognition analysis. This method was applied to 120 sediment core extracts from 14 boreal, 25 forest-tundra, and 21 tundra lakes to assess long-term fluctuations in treeline position. High resolution accurate mass spectrometry resolved many compounds from complex mixtures with low mass accuracy errors. This generated a large dataset that required metabolomics styled statistical analyses to identify potential biomarkers. In total, 29 potential biomarkers discriminated between boreal and tundra lakes. Tetrapyrrole-type phorbides and squalene derivatives dominated in boreal regions, while biohopane-type lipids were in the tundra regions. Tetrapyrroles were in both surface and subsurface sediments of boreal lakes indicating these compounds can survive long-term burial in sediments. At the ecozone level, tetrapyrroles were more abundant in boreal Taiga Shield, and Taiga Plains. Boreal plant extracts belonging to Pinaceae and Ericaceae also contained tetrapyrroles. Squalene derivatives demonstrated long-term preservation, but wider distribution than tetrapyrroles. Hopanoids were present in tundra and forest-tundra lake regions, specifically the Low Arctic and Taiga Shield, and were absent in all boreal lake sediments. Herein, we describe a method that can systematically identify new paleolimnological biomarkers. Novel biomarkers would facilitate multi-proxy paleolimnological studies and potentially lead to more accurate paleoenvironmental reconstructions.

Tools in the Investigation of Volatile Semiochemicals on Insects: From Sampling to Statistical Analysis

The recognition of volatile organic compounds (VOCs) involved in insect interactions with plants or other organisms is essential for constructing a holistic comprehension of their role in ecology, from which the implementation of new strategies for pest and disease vector control as well as the systematic exploitation of pollinators and natural enemies can be developed. In the present paper, some of the general methods employed in this field are examined, focusing on their available technologies. An important part of the investigations conducted in this context begin with VOC collection directly from host organisms, using classical extraction methods, by the employment of adsorption materials used in solid-phase micro extraction (SPME) and direct-contact sorptive extraction (DCSE) and, subsequently, analysis through instrumental analysis techniques such as gas chromatography (GC), nuclear magnetic resonance (NMR) and mass spectrometry (MS), which provide crucial information for determining the chemical identity of volatile metabolites. Behavioral experiments, electroantennography (EAG), and biosensors are then carried out to define the semiochemicals with the best potential for performing relevant functions in ecological relationships. Chemical synthesis of biologically-active VOCs is alternatively performed to scale up the amount to be used in different purposes such as laboratory or field evaluations. Finally, the application of statistical analysis provides tools for drawing conclusions about the type of correlations existing between the diverse experimental variables and data matrices, thus generating models that simplify the interpretation of the biological roles of VOCs.

Metabolomics analysis of the therapeutic mechanism of Semen Descurainiae Oil on hyperlipidemia rats using 1 H-NMR and LC-MS

Semen descurainiae oil (SDO) is an important traditional Chinese medicine that was recently discovered to have the function of reducing blood lipids. Metabolomics analyses of plasma, liver and kidney in rats were performed using ¹ H-NMR and LC-MS to illuminate the lower blood lipid concentration effect of SDO, and niacin was considered as the active control. The measure of total cholesterol (TC) and low-density-lipoprotein cholesterol (LDL-C) in plasma showed that SDO treatment decreased significantly the content of TC and LDL-C. An orthogonal partial least squares-discriminant analysis approach was applied to identify the different metabolic profiles of plasma, liver and kidney in rats and to detect related potential biomarkers. The results suggested that the metabolic profiles of the control group and hyperlipidemia group showed significant difference and the SDO and niacin group had effective anti-hyperlipidemia function. The biomarkers primarily concern lipid metabolism, amino acid metabolism and glycometabolism, and the change in biomarkers indicated that hyperlipidemia could cause the unbalance of these metabolic pathways in vivo. SDO reduced blood lipids by repairing amino acid and lipid metabolism.

Systems Metabolic Engineering Meets Machine Learning: A New Era for Data-Driven Metabolic Engineering

The recent increase in high-throughput capacity of 'omics datasets combined with advances and interest in machine learning (ML) have created great opportunities for systems metabolic engineering. In this regard, data-driven modeling methods have become increasingly valuable to metabolic strain design. In this review, the nature of 'omics is discussed and a broad introduction to the ML algorithms combining these datasets into predictive models of metabolism and metabolic rewiring is provided. Next, this review highlights recent work in the literature that utilizes such data-driven methods to inform various metabolic engineering efforts for different classes of application including product maximization, understanding and profiling phenotypes, de novo metabolic pathway design, and creation of robust system-scale models for biotechnology. Overall, this review aims to highlight the potential and promise of using ML algorithms with metabolic engineering and systems biology related datasets.