**2. Enabling Technologies for Metabolomics Research and Engineering**

Microbiome-derived metabolomics or metabolomics profiling refers to the detection of metabolites or small molecules in gut microbial communities that are related to alcoholic liver disease (ALD) and non-alcoholic fatty liver disease (NAFLD) [11,12]. Metabolomics science, a targeted and untargeted profiling method, involves the large-scale study of the metabolic complements of the cells and has the ability to provide adequate coverage of the metabolome. Accurate quantitative information can be provided with wide-spanning technical care for its use in the analysis of metabolic oscillations in the gut microbial environment [13,14]. Metabolomics is a promising platform for the identification of potential responses to stimuli, molecular signatures, and organic compounds that are closely related to metabolic phenotype and therapeutic biomarker discoveries [15,16]. The isotopes of 1H-, 13C-, 14N-, 19F-, 31P-, and 43Ca-rich metabolites in liver cells have led to the development of therapeutic screening applications [11,17].

The metabolomics profiling of microbial metabolites and their computational technologies act as a high-throughput global analytical platform. Metabolomics can illustrate small molecules (molecular weight < 1 kDa) [18–20]. Figure 1A shows the long history of metabolomics. Metabolites, or small molecules, are the fundamental output of combined microbiome and host interactions that may provide signatures of gut-microbiome-mediated ALD conditions.

In Figure 1B, and Figure 1C, the guiding principles of genomics, transcriptomics, proteomics, and metabolomics are listed with targeted and untargeted profiling methods, each with their own benefits and limitations [21–23]. Untargeted metabolomics is focused on the examination of recognizable metabolites and/or metabolomes in biological mixtures,

including unknown chemicals. The metabolome is the set of metabolites within a given cell. Metabolome concentrations are widely connected with phenotypic expression [17,24].

**Figure 1.** (**A**) The long history of metabolomics; timeline of major research and development milestones related to metabolomics and their medical applications. (**B**) Normal liver and liver cancer cells. (**C**) A schematic representation showing the multiomics cascade of systems biology. The multiomics analysis is influenced by epigenetics, toxicity, disease, and other environmental exposures. Here, metabolic communication within cells is carried out by DNA (metagenomics), RNA (metatranscriptomics), protein (metaproteomics), and metabolites (metabolomics).

In omics sciences, gut-microbiota-based liver therapeutic candidate metabolome screening and metabolomic profiling are significant. Technically, metabolomics has already entered the clinic, with applications in various liver disease screenings. Many metabolomicsignature-based clinical tests can be used to quantitatively analyze low-molecular-weight metabolites in cells, tissue, and/or biofluids [11,25]. Metabolomic signatures have been connected to phenotype expression, which acts as a functional endpoint of a biochemical reaction. The metabolome is the quantification of metabolites that result from the interplay between many domains [26–28]. Microbial metabolomic signature-based liver cancer represents the most 'cutting-edge' example of metabolomics, enabling precision medicine.

Mass spectrometry (MS)-based data analysis and peak identification have been used to explore the regulation of the biological actions of the gut microbiota and host–microbiome relationships by utilizing metabolomics methods [29,30]. The quantitative analysis of microbiome-derived small molecules delivers a functional read-out of cells. MS is a more prominent platform in metabolomics than nuclear magnetic resonance (NMR). MS has become more extensive in host-microbiota analysis because of its high sensitivity, highthroughput discovery, and wide variety of metabolome analyses [31–34]. While NMR can evaluate metabolites in the micromolar range, the utilization of MS licenses the discovery of up to nanomolar concentrations. MS is likewise effectively connected with chromatographic partition, decreasing the impacts of biological samples as well as restricting the complexity of analytes at the time of identification [35–38].

Gas/liquid chromatography (GC/LC) has become the most applied chromatography-MS device for the investigation of both polar and nonpolar metabolites [39,40]. GC/LC-MS has been applied for the examination of various volatile and nonvolatile compounds and for important metabolites after derivatization. Capillary electrophoresis (CE)-MS (CE-MS) is also used for the examination of polar, charged metabolites, as explored in the previous literature [41,42]. LC/GC-MS focuses on changes in mass-to-charge (*m*/*z*), with NMR

spectroscopy providing the spectral intensities. The analytical characteristics of NMR, MS, Raman micro spectroscopy, immunochemistry, and enzymatic assays are briefly discussed in Table 1. Currently, the NMR and LC/GC-MS methods offer high-quality metabolomic datasets.

In metabolomics investigations, NMR operates with a lower sensitivity than MS-based methods. NMR spectroscopy can quantify and target the metabolites in biofluids, with rapid sample preparation. When the sample complexity can be mitigated, NMR delivers valuable structural information. NMR is valuable for identifying gut microbiota-derived compounds (i.e., amino acids, lipids, fatty acids, organic and inorganic metabolites) [43–46]. However, both MS and NMR allow for small-molecule profiling and the identification of the high diversity of microbial products.

**Table 1.** Application, analytes, detection, and comparison of top analytical devices for metabolomics scientific technology.


Abbreviations: DESI-MS, desorption electrospray ionization mass spectrometry; GC-MS, gas chromatography– mass spectrometry; LC-MS, liquid chromatography–mass spectrometry; Raman MS, Raman micro spectroscopy; UHPLC, Ultra-High-Performance Liquid Chromatography; MALDI, matrix-assisted laser desorption; nano SIMS, nanoscale secondary ion mass spectrometry; SCFA, short-chain fatty acid; Ref, References.

Both NMR and MS-based metabolomics have been applied to study the gut microbiota via isotope tracing in nutrient accommodation. Metabolic alterations using isotope labeling remain challenging because of their structural exchange in hosts and various microorganisms and the difficulty of identifying the paths of small molecules and/or metabolites [43,44]. The topographies of MS, nanoscale secondary ion MS, and Raman spectroscopy deliver high-throughput three-dimensional data, which are shared with fluorescent probes and stable isotope tracing to achieve a single-cell resolution within host and gut microbial cells [43,44].

Targeted metabolomics profiling and lipidomic profiling have been used to measure defined groups of metabolites. The methodologies can be defined by the quantity of notable metabolites and the reliability of the quantification of a specific approach. Here, reliability is introduced either as the exactness of entire quantifications, normally transferred in micromolar units, or as accuracy, given by semiquantitative judgments in normalized units [11,24,25]. The best-accuracy approach could be hypothetically accomplished when an isotopically considered internal standard of a specific metabolite is spiked in biofluids during extraction at different concentrations (isotope dilution mass spectrometry). A slightly less reliable technique utilizes an alignment curve of a specific standard spiked at various concentrations, standardized to a spiked constant concentration of an internal standard [26,53]. Table 2 shows the computational tools used for NMR- and MSbased metabolomics analysis in biological samples, focusing on the main breakthroughs in this field.

**Table 2.** Computational tools used for metabolomic technologies in biological samples.


Notes and abbreviations: SIMCA, Soft Independent Modeling of Class Analogy; HMDB, Human Metabolome Database; KEGG, Kyoto Encyclopedia of Genes and Genomics; ChemRICH, Chemical Similarity Enrichment Analysis; GEM-Vis, Genome-Scale Metabolic Model Visualization; FEMTO, Functional Evaluation of Metabolic Time Series Observations; Ref, References.
