Metabolomics-based biomarkers of probiotic efficacy in toxicological settings

Abstract

Background

Metabolomics is the study of metabolites in cells, tissues, live organisms, and biological fluids to elucidate their composition and possible roles. Metabolomics and its biomarkers have emerged as a powerful tool for evaluating the efficacy of probiotics in various pathological conditions, including toxicological settings. This review explores the use of metabolomics-based markers to assess the safety and efficacy of probiotics in toxicological settings.

Result

This review aims to determine biomarkers for measuring the effectiveness of probiotic therapies in toxicological contexts based on exposure, effects, susceptibility, prognostic, and therapeutic biomarkers. In this study, 1979 articles were systematically searched in PubMed (PM), Scopus (Sc), Google Scholar (GS), and Web of Science (WOS) between the years 2013 and 2023 inclusive using keywords, inclusion, and exclusion criteria. The result showed that 1439 human samples were used from 2013 to 2023 publications with the UK having the highest number of publications, data, and sample types in 2023. Again, the result showed most of the publications are on susceptibility-based biomarkers.

Conclusion

Metabolomics-based biomarkers may provide insight into metabolism-related alterations related to probiotic therapies while clarifying their biological mechanisms, especially in toxicology enabling specific probiotic therapy.

1 Background

The metabolomic technique has gained recent attention due to its ability to identify early signs of exposure (biomarkers), uncover the mechanisms of toxicity, and assess the impact of these biomarkers on cellular metabolism [1]. Metabolomics biomarkers (MB) are not distinct molecules, but instead collections of interconnected metabolites. They present an improved awareness of biological well-being and how the body responds to hazardous substances [2]. Metabolomics is the analysis of metabolites from diverse anatomical features to identify their compositions and their respective functions to gain a comprehensive understanding of the functioning of MB, and it is pertinent we explore the significant roles metabolic receptors (MR) play within cells. Metabolic receptors (metabotropic receptors) are membrane-bound proteins, i.e., proteins found on the outermost surfaces of cells or inside the cells where they bind to certain compounds, triggering diverse physiological reactions. They are responsible for homeostatic maintenance as well as regulating metabolic processes and have an impact on the toxicants and environments. MR is usually activated by a ligand, particularly a hormone which triggers a series of molecular events intracellularly. Also, they can modify the process of genetic transcription, control the activity of proteins, the release of calcium within cells, or influence the functioning of ion channels. MR includes the following: glutamate receptors, responsible for promoting excitatory neurotransmission in the central nervous system (CNS), aiding in knowledge acquisition, retention, and modification of neuronal connections [3]. The muscarinic acetylcholine receptors (MAR) are activated by acetylcholine and can be seen in several tissues like the heart [4] while gamma-aminobutyric acid (GABA) B receptors play an inhibitory neurotransmission role and above all control the level of s of activities in neurons. Other examples of MR are serotine and neuromodulator receptors. Serotonin is responsible for the mediation of diverse physiological processes such as mood, sleep, and control of appetite whereas neuromodulators have diverse functions in the body; ranging from pain perception to cardiovascular mediation [5]. Considering these multiple roles, in toxicological settings, metabolomics has been increasingly significant because of its ability to identify on set of exposures, popularly known as exposure (biomarkers) [6]. It also can identify the mechanism of individual toxicity [7], and its impact on metabolic processes, [1]. MB can be utilized to assess the intensity and precision of metabolic changes caused by toxic substances [2], indicators for prospective adverse effects, and therapies such as probiotic treatment [8]. Additionally, MB is used in the diagnostic processes of diseases that enhance therapeutic purposes [9]. On top of that, toxicologically, MB detects initial indications of exposure and its mechanism which helps in the identification of new biomarkers. Meanwhile, in the context of this study, exposure, effect, susceptibility, and prognostic-based biomarkers are the different types of metabolomic-based approaches in toxicology. Exposure metabolomics biomarkers are responsible for alterations in molecular structure in biological samples that might reveal details about chemical or environmental exposure such as toxins [10], pollutants, medications, or nutrients from food. They provide detailed molecular effects of toxicants and reveal the mechanisms by which toxins cause disease. Also, effect biomarkers offer valuable insights into the impact of toxicants' specific metabolic pathways or cellular processes [1]. Majorly, they uncover the molecular mechanisms behind the effects induced by toxicants, like changes in cellular metabolism, energy production, and oxidative stress by generating reactive oxygen species (ROS), and inflammation through immune response activation, and modulating apoptosis that regulates cell death [11]. Besides, effect biomarkers are used mostly in risk assessment, environmental exposure monitoring, and the development of targeted interventions to mitigate the adverse effects of toxicants on human health and the environment [12]. Meanwhile, susceptibility biomarkers have variable characteristics in terms of an individual’s response to toxicant exposure. Similarly, susceptibility biomarkers, encompassing a wide range of molecular changes such as genetic polymorphisms [13], epigenetic modifications, and protein expression alterations [14], offer insights into the underlying mechanisms of inter-individual variability in response to toxicants. However, further studies and validation are often required to improve our understanding of susceptibility biomarkers and their potential to guide interventions for vulnerable populations. For the prognostic biomarkers, they offer valuable predictive insights into disease outcomes, treatment response, and patient prognosis [15]. Most times, they inform treatment decisions and clinical trials, which influences the decisions in patient care and resource allocations. However, their validation requires thorough scientific investigation, which addresses the challenges such as standardization and compliance from regulatory bodies. On the other hand, probiotics are live microorganisms that have the capability of providing healthy beneficial effects to the host when sufficient quantities are administered [16]. It is a varied complex mechanism: altering the composition of the gut microbiota [17], improving the functioning of the intestinal barrier, regulating the immune system [18], and providing antioxidant benefits.

Meanwhile, the possibility of probiotics reducing a range of toxicological disorders has been the subject of substantial research in toxicology in recent years [19]. The studies compare probiotics to synthetic and environmentally harmful alternatives as well as their beneficial effects. Also, Bist and Choudhary [20] study reviewed the effect heavy metals have on the gut microbiota when exposed which can result in diverse health conditions including perturbations. As a result, probiotics provide an avenue to reduce the negative impacts of toxicants by influencing the gut microbiota, while metabolomics allows for the discovery of biomarkers in toxicology. Therefore, in this study, we present a concise review of the literature on metabolomics-based biomarkers and their application to the toxicological evaluation of probiotic efficacy.

2 Methods

There is growing evidence that metabolomics is key to understanding the effects of probiotics. Our goal in performing this literature review was to add to the expanding body of knowledge regarding the use of metabolomics-based indicators to assess the safety and effectiveness of probiotics in toxicological settings. Firstly, we searched extensively in PubMed (PM), Scopus (Sc), Google Scholar (G), and Web of Science (WOS) for studies during the years 2013 and 2023 inclusive using a combination of these keywords: metabolomics, biomarkers, probiotic and toxicology. A total of 1979 articles were recorded. Some of the displayed results were out of scope, duplicated, and peer-reviewed in other languages other than English. They were further screened resulting in GS with 93 records, PM with 98 records, Sc, and WOS with 73 and 81 records, respectively. Then we applied, Boolean operators, and enhanced search filters for relevant published articles, the exclusion criteria which resulted in a total of 43 records used for this review. In this study, the flowchart of the systematic search on the various databases and their corresponding outcomes are shown in Fig. 1.

Fig. 1

Flowchart of the systematic search on the various databases

3 Results

Meanwhile, this review was conducted using 43 records. The 43 records showed 951 distinct keywords indicating the relationship between metabolomics, biomarkers, probiotics, and toxicology. The keywords were downloaded as comma-separated values (CSV) files and then imported into VOSviewer, an application used for the viewing and analysis of bibliometric networks as can be seen in Fig. 2.

Fig. 2

Bibliographic scan showing 951 keywords on metabolomics and toxicology (Scopus 2023)

A search using the keyword displayed a total of 688, 393, 563, and 335 articles on GS, PM, Sc, and WOS, respectively, as can be seen in Fig. 3. Some of the displayed results were excluded because they were out of scope, duplicated, and peer-reviewed in other languages other than English. The displayed articles were further screened to remove unwanted resulting in 354, 298, 307, and 290 articles for GS, PM, Sc, and WOS. Furthermore, we applied inclusion criteria: articles that analyze the metabolic effects of probiotics, articles that identified exposure, prognostic, susceptibility, effective as well as therapeutic biomarkers, and articles with metabolites being tested in toxicological conditions. A total of 93, 98, 73, and 81 articles, respectively, for GS, PM, Sc, and WOS were recorded. Finally, using document type, we filtered the records to 17, 8, 10, and 8 for GS, PM, Sc, and WOS which form the 43 articles reviewed.

Fig. 3

Sequence of search during screening and exclusion criteria

Also, the 43 records used for this review have shown recently published (2019 to 2023) studies in metabolomics-based biomarkers of probiotic efficacy in toxicological settings including trends in metabolomics, probiotics, gut health, and various clinical conditions. For example, the impact of Lactobacillus plantarum HY7715 on riboflavin production and gut microbial communities [21], the protective effects of Lactobacillus rhamnosus GG against oxidative stress induced by nanoparticles in the livers of young rats [22], and the modulation of inflammatory indicators and oxidative-nitrosamine levels in rheumatoid arthritis patients through probiotic mixtures are reigning trends in toxicology. The recently published Metabolomics is rapidly becoming a valuable diagnostic tool for diseases like Non-inflammatory bowel disease (IBD), and damp-heat syndrome (DHS) [23]. Studies have identified specific characteristics for these diseases, and the connection between short-chain fatty acids, body mass index (BMI), and metabolic health has provided effective weight management methods. Innovative methods like high-resolution 2D gas chromatography and time-of-flight mass spectrometry help understand metabolic dynamics. These studies provide an in-depth understanding of disease mechanisms, emphasizing the interconnected nature of food, gut microbiota, and metabolomics in disorders like IBD. Combining data from various studies can help develop personalized medicine for future periods, utilizing metabolomics-based biomarkers and probiotics in toxicological contexts as recorded in Table 1

Table 1 Summary of the recent studies used in this review

4 Discussion

This study focuses on using metabolomics-based biomarkers in the evaluation of the effectiveness of probiotics used in toxicological environments. The study's emphasis lies on the synergistic relationship that exists between metabolomics and probiotics, which both were previously independently researched based on toxicity. This study demonstrates a novel approach that integrates metabolomics which shows biomarkers that have an exposure effect. This effect was then mitigated by the probiotics, which have the potential to alleviate these effects. Moreover, this study's uniqueness comes in introducing a new approach that incorporates both metabolomics with probiotics to prevent and reduce toxicological consequences.

Meanwhile, the data in Table 1 have promising results. In the future, biochemical biomarkers for diseases like lung cancer and inflammatory bowel disease may be identified using metabolomics, allowing for more precise and accurate diagnosis. Studies have indicated that probiotics can protect the liver from oxidative stress, reduce inflammation, increase riboflavin production, and alter the composition of gut microbes. Additionally, the recent studies summarize the probiotic potential of Lactobacilli thus responsible for the protection of liver damage, gut macrobiotic modulation, and alleviation of obesity. Also, the studies have demonstrated the role mycobiome plays in diseases like inflammatory diseases. Again, the biomarkers show the nutritional relationship that exists between age, diet, and body mass index. Furthermore, this review contains four groups of biomarkers, each showing different levels of exposure, assessing hazards and susceptibility. They also predict future health outcomes, aiding early intervention and personalized healthcare plans, highlighting the complex relationship between environmental exposures and biological responses as can be seen in Table 2.

Table 2 Data from literary studies between 2013 and 2023

According to the values in Table 2, the UK ranks first with 1439 datasets, followed by France with 792 and China and Japan with 720. This is because these countries have strong research funding infrastructure and resources available to scientists to have higher publication rates. Also, these countries have research priorities on metabolomics and collaborate with international scientists. Other countries had comparatively few publications based on the number of datasets used; for example, Canada, Dublin, and Denmark as can be seen in Fig. 4.

Fig. 4

The number of datasets used by different countries

According to this study, the limited number could be a result of limited resources, funding, and research focus on other studies. Similarly, the distribution of the exposure, effect, susceptibility, and prognostic biomarkers were as recorded in Fig. 5 with publication in susceptibility biomarker achieving the highest score.

Fig. 5

Distribution of exposure, effect, susceptibility, and prognostic biomarkers

4.1 Challenges and future directions in metabolomics

Metabolomics is a rapidly growing field that involves the examination of small metabolites in biological samples, with numerous applications in toxicology, including the identification of biomarkers. However, the use of metabolomics-based biomarkers in toxicology faces several challenges. The significant variation in metabolite concentrations among individuals, influenced by genetics, environmental exposures, diet, and lifestyle, makes it difficult to establish normal levels and identify slight fluctuations that may signal toxicity. Standardization in sample preparation and analysis is lacking, and differences in analytical methods can lead to differences in metabolite detection and quantification, making it difficult to compare results across studies or establish reference ranges.

Metabolomic data are complex, multifaceted, highly dimensional, and with a network of biological data because of numerous metabolites detected during the metabolomic assay. As a result, statistical approaches often are used in the analysis. However, this is characterized by errors that could not identify the relevant metabolites. It also results in overfitting giving a false-positive result. Relatedly, there is no adequate validation of results as a result most of the findings are governed by experimental factors. Also, data on the toxicity mechanisms are not adequate or in some cases are confounded with other factors. These pose a big threat to the future of metabolism and its future applications.

5 Conclusion

In conclusion, we reviewed the potential of using metabolomics-based biomarkers to assess the effectiveness of probiotics in toxicological settings while concurrently highlighting numerous challenges as well as limitations of this study. In this review, 1979 articles were reviewed between 2013 and 2023 inclusive. Interestingly, what stands out in this review is that researchers from the UK had the highest number of datasets (above 14,500) used in different studies reviewed. This was followed by France, Netherlands, Norway (above 800 datasets), Pakistan, China, and Japan (88 datasets). Again, the majority of the studies reviewed have blood and urine are the major sample types utilized by many scientists in their studies because these samples are easily accessible, noninvasive, and above all provide comprehensive systemic metabolic information. Nevertheless, gaps such as technical inconsistencies, inadequate protocols, and difficulties associated with the integration of data. Understanding the precise processes of probiotics' effects, including metabolic pathways and dosage-response correlations, is crucial. Current studies are limited, primarily conducted in specific geographic areas and using blood and urine samples. Further research is needed to assess lasting impacts and bridge the gap between findings and clinical use. Standardized methodologies should be prioritized, including different populations, sample types, and relating results to clinical use.