Advances in synthetic biology and genetic engineering are bringing into the spotlight a wide range of bio-based applications that demand better sensing and control of biological behaviours. Transcription factor (TF)-based biosensors are promising tools that can be used to detect several types of chemical compounds and elicit a response according to the desired application. However, the wider use of this type of device is still hindered by several challenges, which can be addressed by increasing the current metabolite-activated transcription factor knowledge base, developing better methods to identify new transcription factors, and improving the overall workflow for the design of novel biosensor circuits. These improvements are particularly important in the bioproduction field, where researchers need better biosensor-based approaches for screening production-strains and precise dynamic regulation strategies. In this work, we summarize what is currently known about transcription factor-based biosensors, discuss recent experimental and computational approaches targeted at their modification and improvement, and suggest possible future research directions based on two applications: bioproduction screening and dynamic regulation of genetic circuits. Biosensors are biological devices combining two essential components: a sensing component that detects a particular input—typically the presence of a chemical—and a reporter that produces a measurable output after receiving the signal transduced by the sensing component. Whole-cell biosensors use biochemical transformations inside living cells to detect and react to different inputs .One important class of whole-cell biosensors are those based on transcription factors (TF). TFs are proteins that can control the expression of genes by binding to specific DNA sequences. Some TFs are triggered after binding to a metabolite or external compound . Once activated, a conformational change in the TF makes itself release from or attach to the DNA sequence upstream of the target gene, thereby activating or repressing its expression. TFs can be assembled together with other DNA parts commonly used in synthetic biology, such as promoters, ribosome binding sites (RBSs), terminators and reporter genes, to create TF-based biosensor circuits. These genetic devices can thus be used to sense and react to a range of intracellular or environmental ligand concentrations . Even thaTFs can present several architectures. The relationship between the effector molecule and the aTF defines their mode of action: repression of activator aTF, activation of repressor aTF, repression of repressor aTF, or activation of activator aTF . A sizabHere, we provide a roadmap for the design of new biosensor circuits based on aTFs that leads from gathering data and theoretical prediction to experimental validation. We also provide guidelines for the rapid prototyping of biosensor circuits with improved features using computational tools and discuss experimental validation methods best suited for this task. Additionally, we focus on two crucial applications of biosensors for current synthetic biology targets, namely production screening and dynamic regulation.For years, researchers have studied the regulatory networks of different cells and organisms to understand, among other things, how they react to environmental changes by controlling essential cellular activities through the expression or repression of their genes. With the advent of genetic engineering and synthetic biology, this information can nowadays be used to re-engineer and create fine-tuned genetic circuits for various purposes, notably biosensors. However, the data is often scattered and incomplete, and gathering efforts have to be made to organize and make easily available the current knowledge on the topic. Through literature and database mining, it is possible to build a dataset of transcription factors triggered by the binding of molecules as well as other types of inputs . This valuable dataset can be used to determine the known detectable input space , i.e., tThe databases in etc.)   as a refetc.) . Similaretc.)  to find Protein structure information can also be used to discover and engineer new biosensors. However, despite the rather large number of protein structures available for certain aTFs , many candidates still await structural characterization, a problem that negatively impacts the development of biosensors tailored to recognize arbitrary compounds of interest. Although many aTFs do show sufficient homology with existing structures and might therefore be targeted with homology modeling, the quality of these models can at times be lacking—especially in the twilight zone of sequence homology. This, in turn, limits the accuracy with which residues that are crucial for the binding of effector candidates can be predicted by docking calculations. Based on the current knowledge of aTF-ligands , we calcEscherichia coli K-12 genome, including TFs not previously reported in databases, and which the authors were able to validate using TF and non-TF protein sequences as training data. This tool can be used to identify new TF sequences from known and new genomic and metagenomic information, in combination with sequence-based annotation tools. Other AI-based methods that identify DNA binding protein domains   is the next step. Sequence homology among TFBSs of the same aTF can be used together with AI  to prediOnce novel TFs and their TFBSs have been found, it is necessary to determine their most likely effector molecules. To do this, Hanko and others  describeAllosteric sites can be good candidates for AI-mediated site-directed mutagenesis experiments  Figure  trying tin silico docking algorithms. Recent ML-based advances, such as DiffDock  is commonly used to assess the binding sites of the aTF anywhere in the genome, effectively assessing the regulated genes . To thisPseudomonas aeruginosa. To do this, they purified 371 putative TFs, mixed them with randomised DNA sequences and ran high-throughput systematic evolution of ligands by exponential enrichment (HT-SELEX)  is a standard procedure that can be used to confirm the DNA binding site of newly discovered TFs  Figure . BasicalT-SELEX)  for fourT-SELEX) . In a nuIn the cases where no aTFs are known to be triggered by the target compound, alternative approaches may be considered. One possibility is to enlarge the biosensor space is through retrosynthesis approaches . The appThis approach has been used for instance to analyse the biosensing of the production of naringenin that is used for dynamic regulation through its conversion into kaempferol . The stuThe approach has also been used systematically in order to develop a protocol for the development of cell-free biosensors through metabolic and genetic layers . The autOther engineering efforts have been showcased which focus on different areas. Biocomputing, for instance, would also benefit from a wider range of TF-based biosensors. To that end, Rondon and others  created in vitro and in vivo validation experiments provide essential information to biosensor designers. An intermediate step between prediction and experimental validation and actual whole-cell biosensor construction can be achieved by cell-free assays. The technique relies on cell extracts containing all the necessary cellular components for protein expression. It removes cellular maintenance, growth, other native processes and all cellular unknowns from the equation, allowing the experiment to be essentially focused on the synthetic circuit and behavior designed by the researchers  such as GFP and RFP are simple to assemble in genetic circuits, easy to measure and do not rely on any metabolic substrate (other than oxygen) to work. Most of the experimental references detailed in this work use FPs as reporter systems and this type of biosensor is predominantly used in the field. Different FPs offer different characteristics to the biosensor designer. One should consider, among other factors, the excitation and emission wavelength, the maturation time  and the via a synthetic electron transport chain  isolates cells based on fluorescence emission detected by a flow cytometer. This technique allows researchers to select, from a cell population, the individuals that perform best. FACS has previously been used to reduce the affinity space of an aTF library, thereby creating a sensor for L-histidine and L-arginine which is unable to detect L-lysine . Machadobla gene) using the aTF binding site. The new variant managed to respond to new ligands, but no longer responded to the original effector.The promoter under control of the effector molecule can be put in front of other types of markers. In via coupling the allosteric effector activity to the expression of Taq polymerase in vivo. The variants that manage to produce larger concentrations of Taq will then be amplified using emulsion PCR.Less common techniques have also been used to evolve aTFs to recognise new ligands. One example is a method called compartmentalised partnered replication (CPR) . In 2018E. coli. pIII protein is required for phage infection. After mutagenesis, only the gene variants able to induce enough pIII production will propagate and enter the next cycle. A modification of this system was applied to the evolution of a TF , described in 2011 , can be  of a TF  that regAs we have described, building a new biosensor circuit using a new aTF requires many experimental steps from ligand and DNA affinity validation, to parametric characterisation, to fine tuning inside the heterologous host. All these steps can quickly become a bottleneck, especially if several ligands and aTFs are tested at the same time for different purposes. For this reason, high-throughput automated construction must be considered by the biosensor circuit designer. An application is showcased in  where reThe discussion above focuses on general principles related to biosensor design. In the following section we will explore instructive examples from the literature that highlight practical aspects and possible limitations in current biosensor projects.Exemplarily, we will discuss here three biosensors that were successfully developed by employing current experimental and computational approaches and which also underline the relevance of structural insights into the aTF.etc.) directed-evolution approaches have proved to be indispensable  expressed PcaV repressor protein. In its initial form, the biosensor was responsive to a narrow range of hydroxyl-substituted benzoate derivatives and displayed only a modest dynamic range for these effector molecules, which is commonly observed with aTFs. By applying directed evolution, This example, in which the specificity of an aTF was successfully engineered, demonstrates the effectiveness of structure-based, directed-evolution approaches. But, as noted above, the properties of a complete aTF-based biosensor are affected by multiple variables, such as the sequences of operator and ribosome binding sites, which can turn the optimization of the complete system into a daunting task. This particular problem was addressed by via statistical modeling, which even revealed non-linear effects among the parameters, and derived general design rules for repression-based aTF biosensor systems. According to the authors, the first step should be to identify the strongest combination of promoter-operator and RBS; Then, the regulator (aTF) expression should be fine-tuned by testing a wide range of expression levels. As a last step, if the dynamic range of the sensor is still not satisfactory, the RBS, which drives signal output, should be weakened.With the DoE approach employed, the authors assessed the impact of each variable  in a semi-quantitative way The authors further demonstrated the generalizability of the DoE approach by optimizing a biosensor for ferulic acid that they had previously developed. With just twelve experiments, they were able to improve the maximal output signal of this biosensor by a factor of 32 and the dynamic range by a factor of five.As discussed, structural or homology information about the aTF can be crucial for limiting the experimental effort in biosensor-design, which is also highlighted by the recent work of For improving the properties of CcdR, the authors applied a semi-rational design strategy, made possible by the high homology of CcdR with other, structurally resolved, members of the FFRP-like (feast/famine regulatory protein) aTF family.Because cysteine is already the cognate ligand for CcdR, the authors focused not on the effector binding but rather on the dimerization domain of the aTF, arguing that improving the dimerization properties of the repressor would increase its biological activity. Based on homology, the researchers selected eight sites in the putative dimerization interface of CcdR for saturation mutagenesis, followed by FACS-based selection, similar to the strategy described above for the PcaV-based sensor. This approach eventually led to the identification of a single-point mutant with significantly increased responsiveness to L-cysteine and a higher signal-to-noise ratio than the wild type protein. With the help of an AlphaFold-generated model , the autNext, the authors tested the ideal combination from two promoter and two RBS sequence variants in order to further improve the switching dynamics and the sensitivity of the biosensor, and thus faced much less combinatorial complexity than was encountered with the approach discussed above for the PCA biosensor.via error-prone PCR, they succeeded in identifying a CysE double mutant with a 7-fold increased activity, and a single mutant, whose L-cysteine producing capability was 2.7-fold higher than wild-type levels.In order to facilitate the screening for L-cysteine-overproducing strains in directed evolution experiments, After demonstrating that the sensor system can in principle be applied for the screening of strains, the authors assessed its ability to handle large mutant libraries. To this end, they subjected the biosensor-containing strain to ARTP (atmospheric and room temperature plasma) mutagenesis and applied the HTS protocol, which reduced an initial amount of ten million cells to ten strains that all featured higher production levels for L-cysteine than the control.The previous examples relied on FACS for the selection of biosensor candidates. However, screening for strongly repressing variants by negative selection can be challenging. Sh Ble in a way that depends on the full repression capabilities of the biosensor-candidate. To this end, the expression of Sh Ble is repressed by λ cI, which, in turn, is regulated by the same repressor-sensitive operator sequence as the reporter gene. If the repressor is fully active in the absence of an effector (or in the presence of a non-target ligand), λ cI cannot be formed and Sh Ble will be expressed, which ensures zeocin resistance and cell survival. Surviving cells can then be positively selected by plating them on zeocin-free agar plates in the presence of the effector and screening for strongly fluorescent colonies.The idea behind SELIS is to add to the regular construct—consisting of the consecutive expression system for the repressor and the repressor-regulated reporter gene see —One addiS. typhimurium.The authors demonstrated the effectiveness of their SELIS approach by designing sensitive and selective biosensors for a group of pharmacologically relevant benzylisoquinoline alkaloids (BIAs), namely tetrahydropapaverine, papaverine, glaucine, rotundine, and noscapine. As a starting point for biosensor development, the approach focussed on multidrug-resistance regulators that control the expression of multidrug-efflux pumps, specifically RamR from The authors were able to devise an efficient mutagenesis strategy due to the availability of a crystal structure of RamR bound to the alkaloid berberine, which is structurally similar to the BIAs selected for the study. Based on this structural information, they targeted five helices surrounding the effector-binding region by creating five distinct libraries. In each library, three residues were chosen for site-saturated mutagenesis. In independent experiments, the authors also applied error-prone mutagenesis to the entire RamR gene, resulting in libraries with two mutations per gene, on average.Starting with wild-type RamR, which displays an inherently high promiscuity for structurally diverse compounds, the authors were able with just four rounds of directed evolution, to produce highly specific biosensors that each showed >100-fold preference in binding for their cognate ligands. At the same time, each biosensor also displayed high sensitivity (<30 mM) for its target compound.Having demonstrated the advantages of the SELIS procedure over the more traditional and purely fluorescence-based selection approach to biosensor engineering, the researchers showed how their newly evolved biosensors can be applied to the engineering of metabolic pathways. They chose the biosynthesis of tetrahydropapaverine (THP) as an example, which, in plants, involves a complicated multistep process catalysed by an oxidase and four O-methyltransferases.Glaucium flavum (GfOMT1) into an enzyme that is capable of methylating all four phenolic hydroxy groups of the substrate norlaudanosoline (NOR) in a single step to directly yield THP, thereby circumventing its complex biosynthesis and allowing for the efficient production of this pharmacologically relevant compound. To this end, they used error-prone PCR to generate mutagenesis libraries of the GfOMT1 gene, which resulted in enzyme variants with two mutations on average. After cotransforming plasmids with the THP-specific biosensor and GfOMT1 into E. coli cells, the researchers then selected strains based on high fluorescence in the presence of the substrate NOR, indicating the production of THP. Strikingly, after only three rounds of directed evolution, variants could be identified that completely converted the precursor NOR to THP, thus demonstrating the effectiveness of the biosensor for metabolic engineering projects.Using the previously evolved THP-specific biosensor to screen for THP-producing strain variants, the authors aimed to evolve a methyltransferase from These examples highlight the significant progress that the combination of directed evolution and high-throughput fluorescence-based selection methods have created in the development of biosensors, but also show, as a recurring theme, the importance of reliable structural information about the aTF needed to accelerate the development process or even render the experimental effort feasible. This is especially true if an aTF needs to be engineered to recognize a non-cognate ligand for which there is no natural counterpart.For the foreseeable future, experimental structure elucidation will progress at a much slower pace than the discovery of new sequence information, which emphasizes the need for structure-prediction methods that are reliable even in the absence of sufficient homology with solved structures.Ideally, these efforts will lead to an automated protocol that hides the details of the modeling process from the synthetic biologist and automatically proposes mutagenesis libraries for a given objective.aTF-based biosensors have a range of applications  . ArguablOne challenge preventing the broader adoption of current bioproduction strains is that they often perform poorly  in industrial settings. Screening through hundreds of constructs and selecting the best performers is one of the most important bottlenecks. Thanks to advances in genetic engineering and automation, researchers can nowadays create hundreds of production strain variants in a short period of time. However, the next step involves screening those strains for production and yield. This can become a barrier since traditional methods like HPLC or LC-MS do not scale up easily. Biosensors can provide a simple, fast and affordable solution to the screening process of production strains by linking the biosensor output to the synthesis of the desired molecule . Biosensvia the modification of the promoter driving the expression of the reporter gene regulated by the aTF has also been proven possible , such as pressure, acidity changes, accumulation of toxic metabolites, agitation, nutrient availability and heat transfer, among others . The strDifferent approaches have been proposed to overcome those issues and increase the viability of large-scale cell-factory projects. Dynamic regulation is one of these strategies for controlling the production of key molecules, often found in nature and finely optimised through evolution. Similarly, biosensor-based dynamic regulation of microbial production pathways is a strategy that can be used to control genetic circuits based on a feedback loop that regulates the production of a target metabolite to keep its concentration at desired levels . The biop-coumaric acid, as feedback regulators of the (2S)-naringenin pathway. Initial low concentrations of (2S)-naringenin allow malonyl-CoA to be used in fatty acid (FA) biosynthesis pathways, favouring cell growth. Higher concentrations of naringenin, represses these FA synthesis routes, slowing cell growth and increasing the availability of malonyl-CoA for the production of more (2S)-naringenin. Through several rounds of optimization, including directed evolution of biosensors and optimization of fermentation conditions, titers above 500 mg/L from glucose were obtained in 5-L bioreactors using an E. coli chassis. Other strategies decouple the production phase from the growth phase of the microbial culture. For example, to improve the production of glucaric acid (GA), the glycolysis pathway can be repressed using the accumulation of N-Acyl homoserine lactone (AHL) in E. coli (Bacillus subtilis for the regulation of glucaric acid production (vice versa.Growth-coupled production ties the target molecule (or one of its intermediate compounds) to the production/consumption of some essential cellular metabolite . An exam E. coli . Pyruvatoduction . Pyruvatoduction , Xu et aEven though the aforementioned examples show that successful dynamic regulation is possible, true industrial utilisation of dynamic regulation circuits and scale-up is still rare in bioproduction projects. Tested under homogeneous laboratory conditions, dynamic regulation bioproduction circuits are not normally designed to react to environmental changes and, therefore, the resulting strains adapt poorly to the different conditions during the scale-up process . Among oIn this work we reviewed recent advances in the prediction, design and validation of biosensor circuits focused on aTFs and whole-cell implementations. This type of biosensor can be used to detect a wide range of molecules  for different uses, including environmental, medical and industrial applications. We showcased the role of biosensors in the development of new-generation bioproduction devices, driving forward important steps for bioproduction advancement such as screening and dynamic regulation of producer strains. aTF biosensors, however, suffer from certain shortcomings that will need addressing in future research endeavours. To that end, we detail some of the possible routes towards better biosensors.in silico models at almost atomic resolution might be within reach—It might be possible to take the computational design of bespoke binding sites from merely instructing mutagenesis experiments on an, at best, semi-quantitative basis, to predictive reliability.As described in this article, several methodologies can be used to reveal as-yet-undiscovered aTFs that may behave closely to what a biosensor designer might expect. However, associated tools and databases only offer fragmented information and it is up to the designer to gather and trust the results from different sources. Therefore, a single computational tool that takes this burden from the researcher and provides confidence metrics is needed. Even though directed mutagenesis has shown great potential to improve the performance of a given aTF, it is still not possible to accurately predict the impact of a mutagenesis experiment on the biosensor characteristics . Structural approaches are probably the key to this issue and the recent development of structural prediction and docking programs may soon provide researchers with this type of tool . Until rAs shown for several applications in this review, biosensor circuits can be used to screen for the best performers in a bioproduction experiment by linking the reporter expression to the production level. High-throughput FACS or the less-scalable but more affordable plate-reader-based approach can be used to select the cells with higher reporter expression levels. However, this can become a problem if so-called cheaters come into play , giving


Metabolites are closely intertwined genotypes that can provide clear information about the final phenotype. The high-throughput analysis platform used to identify candidate metabolites and describe their contributions can help to quickly detect metabolic characteristics from large spectral data, which may lead to peak data preprocessing, statistical analysis and functional interpretation. Developing a comprehensive strategy for discovering and verifying bioactive metabolites can provide a large number of new functional biomarkers, and then more closely reveal their functional changes, which has relevant biological significance for disease diagnosis and prognosis treatment. In 2022, five studies on high-throughput mass spectrometry metabolomics analytical platforms and softwares for identifying metabolites and delineating their contribution were published in Nature Protocols . These sMetabolites are the final downstream products of protein translation and gene transcription or cellular perturbations to the proteome, genome or transcriptome, which serve as potential key links between genotype and environment and can provide a clear image of the final phenotype. Small-molecule metabolites can provide insights into unique metabolic mechanisms and therapeutic targets of diseases, and lead to personalized applications of metabolic phenotypes. Furthermore, metabolite identification or metabolic pathway changes will help to understand the pathophysiology of the disease and assist in determining therapeutic targets. Metabolomes are comprehensively characterized by measuring the small metabolites in cells, biofluids, organs, or other biological systems, and represents the upstream input from the environment and the downstream output from the genome. In particular, due to the huge complexity of metabolome, there is urgency to developed various powerful tools to facilitate spectral data processing and interpretation that help to identify bioactive metabolites. Identification of small molecule metabolite is one of the most important processes in the discovery stage and can be performed by a range of established innovative technologies . TherefoNature Protocols by Horvath et al. developed a LC/MS quantitative analytical platform for liquid chromatography-tandem mass spectrometry metabolomics method for targeting analysis and monitoring the content change of microbially derived short-chain fatty acids and neurotransmitters from the bacterial cultures, organoid cultures and in vivo samples (in vivo and in vitro (media and organoids) models, reveal the contributions of microbes to host neurotransmitters, and dissect the important bidirectional connections, between commensal microbes and host. In this study, the authors demonstrated a systematic approach to reveal how the bacteria regulate the CNS and gut, provide valuable information by determining the specific pathways into the functional mechanisms that specific microbes influence host metabolism -based approaches  to conduct high-resolution mass spectrometry spectra processing, statistical analysis of complex metabolic datasets, and joint-pathway analysis of multi-omics integration (NOREVA to facilitate optimizing the multiclass metabolomic data processing (Kirkwood and colleagues have analyzed lipidome data by the open-source software proaches . Importaegration . It can ocessing . This prMetaboAnalyst includes numerous modules for metabolite enrichment and relevant metabolic pathway analysis with pathophysiological mechanism by establishing the network interaction and visualization map (Taken together, these helpful studies provide the technical advances for identifying active metabolites and greatly broaden the coverage level of metabolites for exploring metabolic altered dimensions of diseases. The described analytical platforms and software tools provide some advantages in the sensitivity, dynamic range, reproducibility, time-saving and throughput for the combination of qualitative and quantitative. Rapid analysis of complex metabolome requires the specialized tools for the preprocessing raw data followed by multivariate statistical analysis, omics data mining and bioinformatics integration and functional interpretation. In this setting, tion map .Despite these tremendous advances, at present, no single analytical method can fulfill the mission of identification of entire metabolome in the large sample sizes. Future works aimed at developing the integrated strategy for enhancing the accuracy of discovering a list of biologically active metabolites with pathological basis of diseases, could provide a large number of the relatively abundant ions and acquire more reliable identifications, and then shed light on the functional changes of small molecule metabolites more closely. Moreover, it should establish multi-cycle training optimization models of experimental and clinical studies to validate the overall prediction performance of particular metabolite bioactivity and relevant biological significance for disease diagnosis and prognostic treatment that are worthy of further exploration. In the future, the integrated use of computational algorithms, artificial intelligence, and big data mining method will improve the precise identification ability of metabolites for resulting optimization and verification.


Drosophila as an aging model organism, we probe the in vivo tracing kinetics with quantitative information on labeling patterns, extents and rates on a metabolome-wide scale. We curate a system-wide metabolic network to characterize metabolic homeostasis and disclose a system-wide loss of metabolic coordinations that impacts both intra- and inter-tissue metabolic homeostasis significantly during Drosophila aging. Importantly, we reveal an unappreciated metabolic diversion from glycolysis to serine metabolism and purine metabolism as Drosophila aging. The developed technology facilitates a system-level understanding of metabolic regulation in living organisms.System-wide metabolic homeostasis is crucial for maintaining physiological functions of living organisms. Stable-isotope tracing metabolomics allows to unravel metabolic activity quantitatively by measuring the isotopically labeled metabolites, but has been largely restricted by coverage. Delineating system-wide metabolic homeostasis at the whole-organism level remains challenging. Here, we develop a global isotope tracing metabolomics technology to measure labeled metabolites with a metabolome-wide coverage. Using  Drosophila.Stable-isotope tracing allows quantifying metabolic activity by measuring isotopically labeled metabolites, but its metabolome coverage has been limited. Here, the authors develop a global isotope tracing approach with metabolome-wide coverage and use it to characterize metabolic activities in aging  Disturbance to metabolic homeostasis causes cellular malfunctions and several major human diseases4. In the past two decades, untargeted metabolomics has been developed to measure differences in metabolite concentrations between biological conditions, aiming to provide system-wide characterizations of metabolic homeostasis in living systems7. However, changes in metabolite concentrations do not readily imply alterations in metabolic pathway activities, since metabolite levels measured are the converged results of both production and consumption from multiple metabolic reactions12. Alternatively, stable-isotope tracing-based metabolomics administers isotopic tracers such as 13C-glucose and 13C-glutamine into living systems, and tracks the isotopically labeled metabolite products generated by cellular metabolism11. By quantifying labeling patterns and fractions of the labeled metabolites, this technology enables unraveling metabolic activity at both the cellular and organismal levels18. Yet, many isotope tracing studies were largely restricted by targeting only a limited number of metabolites in specific pathways, and it is difficult to delineate the system-wide metabolic homeostasis with a high metabolite coverage19. Recently, combination of untargeted metabolomics and isotope tracing analysis, such as X13CMS21, geoRge22, and others29, has been developed to comprehensively characterize isotope labeled metabolites in an untargeted manner, and provides possibilities to discover new metabolic transformations and pathways. Owing to the complexity of isotope labeling metabolomics data, however, untargeted isotope tracing metabolomics usually has relatively low coverages in the detection of labeled metabolites and lacks quantitative information such as labeling rates on a large scale. Global tracking of the isotopically labeled metabolites remains the bottleneck to delineate metabolic activities with a metabolome-wide coverage. More challenging is to calculate absolute fluxes for hundreds of metabolites in a pathways-intertwined metabolic network, wherein complicated mathematical frameworks and algorithms are usually required. These challenges are particularly prominent in mammalian systems,which impedes a large-scale quantitative investigation of system-wide metabolic homeostasis19.System-wide metabolic homeostasis and coordination are of central importance to maintaining physiological functions for living organisms32. Increasing studies have demonstrated the age-dependent and system-wide dysregulation in metabolic pathways in different organisms. Various metabolic interventions have been proven to be beneficial to extend lifespans in different model organisms36. Among them, Drosophila melanogaster (hereafter Drosophila) is an important model organism for metabolism research38, and has emerged as a model system to investigate metabolic alternations and organismal homeostasis during aging40. For instance, in Drosophila, levels of intermediate metabolites in the glycolysis pathway progressively declined with aging, and boosting glycolysis was demonstrated to extend lifespan41. Instead, S-adenosylmethionine (SAM) levels are increased during Drosophila aging, while enhancing SAM catabolism extends the lifespan35. Despite increasing aging-related metabolites and metabolic pathways are uncovered, these studies were independently investigated, and the underlying mechanism by which coordination of metabolic activities impacts aging is not elucidated due to the lack of appropriate technologies. In addition, quantitative investigations of metabolic activities during aging on a metabolome-wide coverage has not been achieved yet, which hinders the delineation of the complex milieu of metabolic coordination and the system-level understanding of metabolic regulation of aging and longevity.Disruption of metabolic homeostasis has been recognized as a hallmark of agingDrosophila as a model organism for untargeted in vivo isotope tracing metabolomics and quantified the in vivo labeling rates and extents of hundreds of labeled metabolites. We demonstrated that MetTracer supported quantitative comparisons of metabolite labeling extents across different conditions and discovered a system-wide loss of metabolic coordination that impacted both intra- and inter-tissue metabolic homeostasis significantly in aging Drosophila. In particular, we discovered a metabolic rewiring model during aging, wherein glucose was metabolically channeled to serine metabolism and purine metabolism from glycolysis. In summary, global in vivo isotope tracing metabolomics enabled delineation of metabolic activities with a metabolome-wide coverage and unraveled the system-wide loss of metabolic homeostasis during aging.In this study, we first developed an untargeted isotope tracing metabolomics technology, namely MetTracer, to trace the stable-isotope labeled metabolites globally. This technology leveraged the advantages of untargeted metabolomics and targeted extraction to track the isotopically labeled metabolites in living organisms with a metabolome-wide coverage. MetTracer enabled to simultaneously quantify the labeling patterns and extents of several hundreds of metabolites in one experiment. Performances of MetTracer were benchmarked and validated with other existing tools. We further employed Stable-isotope tracing technology is significantly restricted by the analysis coverage of labeled metabolites. In this work, we developed a global stable-isotope tracing metabolomics workflow, namely MetTracer, to trace the isotopically labeled metabolites in a metabolome-wide coverage Fig. . MetTrac13C]-glucose, [U-13C]-glutamine and [U-13C]-acetate) using a time-of-flight (TOF) mass spectrometer. Specifically, 1347 metabolites were putatively annotated . Then, the theoretical m/z values for 12,020 possible 13C-isotopologues (M0-Mn) were calculated from the formulas of 1347 metabolites. MetTracer performed targeted extraction of all possible isotopologues and successfully extracted a total of 10,663 isotopologues (88.7%) from 1203 metabolites (89.3%) , and 14,132 corresponding isotopologues were generated in 293T cell samples. With a total of 896 metabolites (86.6%) and 11,844 isotopologues (83.8%) being extracted, MetTracer identified 635 Drosophila as a model organism to demonstrate the in vivo application of global isotope tracing metabolomics, and applied MetTracer to quantify the labeling extents and rates of metabolites in Drosophila. We conducted the continuous stable-isotope labeling in Drosophila using [U-13C]-glucose and collected head tissues at six time intervals for global isotope tracing metabolomics. In head tissues, a total of 745 metabolites were annotated, and 390 labeled metabolites from 59 metabolic pathways were tracked by MetTracer . Labeled metabolites in cluster 2 exhibited slower labeling rates, which required 12–24 h to reach the isotopic steady state. These metabolites were enriched in lysine degradation. In contrast, metabolites grouped as cluster 3 wherein purine metabolism and fatty acid biosynthesis were enriched, could not reach the isotopic steady state after the 24-h labeling.Next, we used Drosophila using the general first-order exponential function, and enabled large-scale quantitation of labeling rates from the [U-13C]-glucose tracer to the downstream metabolites (see Methods). Labeling extents for each metabolite at all time points were used to fit the exponential function. Thus, the labeling rate k, which is the apparent first-order constant in the exponential function, was calculated to quantify the incorporation rate of the tracer to metabolite targets in the continuous stable-isotope labeling. Taking aspartate as an example, the labeling extent was gradually increased to 0.84 within 24 h labeling. The fitted labeling rate k of aspartate was calculated as 0.12 h−1 with an R-value of 0.99  and cluster 3 being the smallest k-value (0.08 h−1) calculated  was found as the pathway with the highest labeling rate, and fatty acid biosynthesis (dme00061) was the lowest one. Collectively, we demonstrated that the application of MetTracer in the continuous stable-isotope labeling of Drosophila provided in vivo and quantitative characterizations of labeling kinetics from the tracer with a metabolome-wide coverage.We then fitted the in vivo tracing kinetics of labeled metabolites in .99 Fig. . In totated Fig. . Other mel. Fig. . GeneralDrosophila using global isotope tracing metabolomics. In the same continuous labeling experiment, MetTracer tracked 597 labeled metabolites from 64 metabolic pathways in Drosophila muscle tissue , we next examined the relationship between pyruvate and glycine in the two age groups. Results showed that pyruvate was also positively correlated with glycine in young Drosophila head, while this correlative relationship was not present in the aged ones  was decreased after 24-h isotope labeling in the old Drosophila, while that of serine (M + 3) was increased. In 30d Drosophila, glycine was isotopically labeled rapidly after 1 h feeding of [U-13C]-glucose, and its labeled fraction (M + 2) was significantly higher than that in 3d Drosophila , inosine monophosphate , adenosine monophosphate , adenosine (M + 10), and adenine (M + 5) in old Drosophila head tissues than those in young ones , was significantly reduced in 30d Drosophila. Glucose metabolism provides the precursor 3-phosphoglycerate (3-PG), thereby supporting serine biosynthesis. We found that two genes involved in serine biosynthesis, CG11899 and CG6287, were significantly increased in 30d Drosophila. Higher levels of shmt in old Drosophila heads were also observed. In addition, genes responsible for purine metabolism, including Pfas, Gart, Paics, and AdSL showed increased levels in 30d Drosophila. Altogether, these data revealed that declined glycolysis activity during aging was coupled with enhanced purine metabolism that was fueled by serine and glycine supported one-carbon metabolism. Finally, we propose a metabolic rewiring model during Drosophila aging, wherein glucose is metabolically channeled to one-carbon metabolism and purine metabolism from glycolysis  and aged (30d) wild-type (WT) Drosophila, and aged (30d) PRC2 mutants for 24 h. The results demonstrated that the labeled fraction of pyruvate (M + 3) was decreased with aging in wild-type Drosophila but rescued in the age-matched PRC2 mutants  and inosine (M + 10) were also significantly reversed by PRC2 mutation. The results suggested that reversing the metabolic shift from glycolysis to one-carbon metabolism and purine metabolism may contribute to the lifespan extension in Drosophila.To verify the metabolic remodeling, we investigate whether the metabolic shift from glycolysis to serine metabolism and purine metabolism in aged nts Fig. . In addiUntargeted metabolomics measures metabolite concentrations on a large scale and provides system-wide characterizations of metabolic status in living systems. However, changes in metabolite concentrations do not readily imply metabolic activities. Isotope tracing technology enables unraveling metabolic activity, but is largely restricted by the metabolite coverage. In this work, we developed MetTracer, an untargeted isotope tracing metabolomics technology, to trace labeled metabolites with a metabolome-wide coverage. We benchmarked that MetTracer has made pivotal improvements in terms of coverage, quantification accuracy, reproducibility and false-positive rate. Although we have tried our best to perform fair comparisons between different software tools, and provided the detailed parameters for each tool, such comparisons are still difficult to judge, because output of each tool being compared may depend significantly on the choice of tool-specific parameters. The main advancement of MetTracer comes from the integration of untargeted metabolite annotation and targeted isotopologue extraction. First of all, MetTracer benefited from the input of metabolite annotations of unlabeled samples from different bioinformatic tools, which ensures the high coverage of labeled metabolite extraction. Second, MetTracer has also made several data processing innovations to targeted isotopologue extraction, which ultimately contributes to the high coverage of labeled metabolites and high accuracy of isotopologue quantitation. For example, MetTracer performed targeted extraction of all possible isotopologues and employed the most intensive isotopologue peak instead of the M0 base peak for peak detection. For highly labeled metabolites wherein M0 peak has a relatively low intensity, the use of the most intensive isotopologue peak achieved a higer rate of successful peak detection. Then, the employment of targeted extracted ion chromatogram  had the in vivo tracing kinetics successfully fitted with the first-order exponential equation. Similarly, 73% metabolites (437 out of 597) in the muscle tissue had successful fittings. Since the k-value indicates the incorporation rate of the tracer to a metabolite target, it is reasonable that the isotopically labeled metabolites nearer to the [U-13C]-glucose tracer in the metabolic network have higher k-values being calculated. Indeed, we showed that metabolites in glycolysis such as pyruvate exhibited larger k-values than those in fatty acid synthesis such as hexadecenoic acid  and flux balance analysis (FBA). However, it requires comprehensive prior knowledge of biochemical reactions in specific pathways, and is very time and computationally intensive. More challenging is to calculate fluxes for hundreds of metabolites in a pathways-intertwined metabolic network. These challenges are particularly prominent in mammalian systems for fluxes analyses, and most of existed quantitative flux studies were limited to specific pathways and simple model organisms (such as yeast and bacteria). Previously, Yuan et al. developed the kinetic flux profiling (KFP) analysis for metabolic flux calculation, which required a fast switch of labeling substratesDrosophila is a widely used model organism to study aging. Previous studies using traditional metabolomics methods have disclosed metabolic changes associated with aging in varied genotypes of Drosophila53  cycle was downregulated in flies under diet restricted condition compared to the age-matched flies with normal diet, suggesting that the long-lived flies have lower level of SAM46. Similarly, enhancement of SAM catabolism by overexpressing glycine N-methyltransferase (Gnmt) has also been demonstrated to extend lifespan of Drosophila, as reported by Obata et al.35 Elevated purine metabolism was also found to be associated with short lifespan of Drosophila50.Drosophila, in which carbohydrate related metabolic pathways were enriched with metabolites of declined metabolic activities in aging, while amino acid related metabolic pathways and purine metabolism were enriched with metabolites of increased metabolic activities in aging, which were consistent with the observations in previous studies  that have enhanced glycolysis activity41, we revealed that the PRC2 mutation had decreased metabolic activities for intermediate metabolites in one-carbon metabolism and purine metabolism compared with the age-matched wild-type Drosophila. This result, combined with our findings of the system-wide loss of metabolic coordination observed in naturally aging Drosophila, highlights the importance of metabolic homeostasis and coordination among glycolysis, one-carbon metabolism, and purine metabolism as a critical regulation of aging and longevity.Despite the accumulative aging-related metabolites and metabolic pathways being uncovered by measuring the changes of metabolite concentrations associated with aging, these were independently studied, and the underlying mechanism by which coordination of metabolic activities impacts aging is not elucidated due to the lack of appropriate technologies. Here, in addition to metabolite concentrations, we demonstrated that the global isotope tracing metabolomics can provide an orthogonal perspective to the changed metabolome by monitoring the labeling parameters at different levels, thereby supporting quantitative comparison of metabolic activities during aging. We discerned the age-dependent changes of metabolic activities in w1118 fly model to demonstrate the utility of MetTracer in studying aging, and we considered that, the metabolic changes between 3d flies and 30d flies were caused by aging here. Although the w1118 flies were widely used as wild-type controls in many fly aging studies51, it is worth noting that, previous studies have discovered that the w mutant of w1118 flies have an impact on fly metabolism. The Drosophila w gene encodes an ATP binding cassette transporter, which contributes to transportation of metabolites such as guanine, tryptophan and kynurenine58. The w mutant flies were also observed with low levels of the biogenic amines, such as serotonin, dopamine, and histamine60. The Drosophila w gene is expressed principally in eyes and excretory organs and testes, but has very low levels observed in the glia and neurons of the brain and various other tissues62. Although whether the metabolome of w1118 will be affected by aging is unclear, we should still be cautious that this mutation alters the metabolism of Drosophila.In this study, we used 2O) was purchased from Honeywell . LC–MS grade acetonitrile (ACN) was purchased from Merck . Ammonium hydroxide (NH4OH) and ammonium acetate (NH4OAc) were purchased from Sigma . The [U-13C]-glucose, [U-13C]-glutamine and [U-13C]-acetate was purchased from Cambridge Isotopes laboratories .LC–MS grade water  solvent mixture. The plates were incubated at −80 °C for 40 min. Cells were scraped from the plate and transferred to a 1.5-mL centrifuge tube. The samples were vortexed for 1 min, and followed by 15 min centrifugation using 16,200 × g at 4 °C. The supernatant was taken and evaporated to dryness in a vacuum concentrator. Dry extracts were reconstituted in 100 μL of ACN:H2O , followed by 10 min sonication  and 15 min centrifugation using 16, 200 × g at 4 °C to remove insoluble material. Supernatants were finally transferred to HPLC glass vials and stored at −20 °C prior to LC/MS analysis.The 293T cell line was bought from ATCC with Product No. CRL-2925. 293T cells were seeded in 6-cm cell culture plates with Dulbecco Modified Eagle’s Medium (DMEM) containing with 10% dialyzed fetal bovine serum (dFBS) and 1% penicillin/streptomycin. When cells were grown to 80% confluence, unlabeled DMEM was removed. Cells were washed using ~3 mL of PBS. The culture medium was replaced as glucose-free and glutamine-free DMEM containing 25 mM -glucose. When the desired time points  were reached, the flies were dissected under CO2 anesthesia. It took about 15–20 s to dissect a fly. The whole muscle tissues and whole head tissues were collected from flies. Head or muscle tissues from fifteen flies were pooled into one microcentrifuge tube as one biological replicate. After dissection, the tissue samples were quickly frozen using liquid nitrogen, and stored at −80 °C. The pooled sample was used as one biologically independent replicate. For each condition, 10 biologically independent replicates were prepared. The sample heterogeneity was confirmed similar between 3d and 30d flies 12c253/+. Three groups of flies  were included in data re-analysis and there were eight biologically independent replicates in each group.The fly culture and stable-isotope labeling experiments followed our previous publications2O and 20 ceramic beads (0.1 mm) using the homogenizer. Samples were extracted with 800 μL of ACN:MeOH , and followed by 10 min sonication . To precipitate protein, samples were incubated for 2 h at −20 °C, followed by 15 min centrifugation using 16,200 × g at 4 °C. The supernatant was taken and evaporated to dryness in a vacuum concentrator. Dry extracts were reconstituted in 100 μL of ACN:H2O , followed by 10 min sonication , and 15 min centrifugation using 16,200 × g at 4 °C to remove insoluble material. Supernatants were transferred to HPLC glass vials and stored at −20˚C prior to LC/MS analysis.For metabolite extraction, dissected fly tissues were homogenized with 200 μL of HA = 25 mM ammonium acetate and 25 mM ammonium hydroxide in 100% water, and B = 100% acetonitrile, were used for both ESI positive and negative modes. The linear gradient eluted from 95% B (0.0–0.5 min), 95% B to 65% B (0.5–7.0 min), 65% B to 40% B (7.0–8.0 min), 40% B (8.0–9.0 min), 40% B to 95% B (9.0–9.1 min), then stayed at 95% B for 2.9 min. The flow rate was 0.5 mL/min. The sample injection volume was 2 μL. For RP mode, Mobile phases A = 0.01% acetic acid in 100% water, and B = acetonitrile/isopropanol , were used for both ESI positive and negative ionization modes. The linear gradient eluted from 1% B (0.0–1.0 min), 1% B to 99% B (1.0–8.0 min), 99% B (8.0–9.0 min), 99% B to 1% B (9.0–9.1 min), then stayed at 95% B for 2.9 min. The flow rate was 0.3 mL/min. The sample injection volume was 2 μL. ESI source parameters were set as follows: spray voltage, 3500 V or −2800 V, in positive or negative modes, respectively; aux gas heater temperature, 350 °C; sheath gas, 50 arb; aux gas, 15 arb; capillary temperature, 400 °C. LC–MS data acquisition was operated under full scan polarity switching mode for all samples. A ddMS2 scan was appied for QC samples to acquire MS/MS spectra. The full scan was set as: orbitrap resolution, 60,000; AGC target, 1e6; maximum injection time, 100 ms; scan range, 70–1200 Da. The ddMS2 scan was set as: orbitrap resolution, 30,000; AGC target, 1e5; maximum injection time, 60 ms; scan range, 50–1200 Da; top N setting, 6; isolation width, 1.0 m/z; collision energy mode, stepped; collision energy type, normalized; HCD collision energies (%), 20,30,40; Dynamic exclusion duration was set as 4 s for excluding after 1 time.Metabolomics data of 293T cell samples were acquired using a UHPLC system  coupled to an orbitrap mass spectrometer . Waters ACQUITY UPLC BEH Amide column  × 2.1 mm (i.d.)) and Phenomenex Kinetex C18 column  × 2.1 mm (i.d.)) were used for LC separation for HILIC mode and RP mode, respectively. Column temperature were both kept at 25 °C. For HILIC mode, Mobile phases m/z range 60–1200 Da for TOF MS scan and the m/z range 25–1200 Da for product ion scan. Collision energy for product ion scan was 30 eV or –30 eV in positive or negative modes, respectively, while collision energy spread (CES) was 0 eV. The accumulation time for TOF MS scan was set at 150 ms per spectrum and product ion scan at 30 ms per spectrum. The unit resolution was selected for precursor ion selection. IDA settings were set as followings: charge state 1 to 1, intensity 100 cps, exclude isotopes within 4 Da, mass tolerance 10 ppm, and maximum number of candidate ions 12. The “exclude former target ions” was set as 4 s after two occurrences. In IDA advanced tab, “dynamic background subtraction” was chosen.Metabolomics data of 293T cell samples and fly tissue samples were also acquired using a UHPLC system  coupled to a quadruple time-of-flight mass spectrometer . Waters ACQUITY UPLC BEH Amide column  × 2.1 mm (i.d.)) was used for the LC separation and LC condition was the same as that described above. ESI source parameters were set as followings: ion source gas 1 (GS1), 60 psi; ion source gas 2 (GS2), 60 psi; curtain gas (CUR), 35 psi; temperature (TEM), 600 °C; ion spray voltage floating (ISVF), 5000 V or −4000 V, in positive or negative modes, respectively; declustering potential (DP), 60 V or −60 V in positive or negative modes, respectively. LC–MS data acquisition was operated under information-dependent acquisition (IDA) mode. The instrument was set to acquire over the https://bioconductor.org/packages/release/bioc/html/xcms.html). Key parameters were set as follows: method, “centWave”; ppm, 10; snthr, 10; peakwidth, c; minfrac, 0.5. For data acquired using TOF mass spectrometer, the raw MS data files (.wiff) were converted to.mzXML format using ProteoWizard (Version 3.0.6150). Then, mzXML data files of unlabeled samples were grouped for peak detection and alignment using the R package “xcms” . The key parameters were set as follows: method, “centWave”; ppm, 25; snthr, 10; peakwidth, c; minfrac, 0.5. The intermediate xcmsSet object from “xcms” after peak detection was exported as a xcmsSet file (.Rda). The generated MS1 peak table and MS2 files were uploaded to MetDNA64  for metabolite annotation. The metabolite annotation parameters were set as “HILIC” or “RP” according to liquid chromatography mode, “Sciex TripleTOF” or “Thermo Exploris” according to instrument platform, and “30” or “SNCE_20_30_40%” for collision energy. We performed the metabolite annotation separately on both positive and negative modes. Before MetTracer analysis, MetDNA annotation results were further filtered, including removal of isotope annotations, removal of grade 4 annotations, removal of redundant peaks, while annotation types such as seed and metAnnotation, and annotations with reliable adducts  were kept. Finally, MS-Finder  was used for formula prediction (top 5 candidates) and annotation filter. The final annotation table for 293T cells and fly samples were provided in Supplementary Data 65, SIRIUS CSI-FingerID66, and GNPS67.The raw data was acquired from orbitrap mass spectrometer using Xcalibur  and TOF mass spectrometer using Analyst TF Software . For data acquired from the orbitrap mass spectrometer, the raw MS data files (.raw) were converted to.mzXML (for full scan mode) and.mgf (for ddMS2 mode) format using ProteoWizard version 3.0.20360). Then, mzXML data files of unlabeled samples were grouped for peak detection and alignment using the R package “xcms”  generation of a targeted list for isotopologues; (2) extraction of isotopologue peaks, and (3) correction and quantification.m/z values of all possible 13C-isotopologues (M0-Mn) for each metabolite with the R package “enviPat”68 (version 2.4). For each monoisotopic peak (M0), the corresponding extracted ion chromatogram (EIC) peaks were extracted from each unlabeled sample according to the feature information recorded in the xcmsSet file. The EIC peak with highest peak height was selected as the “targeted EIC” to aid the extraction of labeled metabolites in labeled samples. The retention time and peak shape of the targeted EIC were also recorded in the isotopologue targeted list. Finally, for each annotated metabolite, the isotopologue targeted list included m/z values of all possible 13C-isotopologues, retention time, and peak shape of the target EIC.(1) Generation of isotopologue targeted list. The metabolite formulas in the annotation table were used to calculate the theoretical left boundary – 15 s, RTright boundary + 15 s]). The m/z tolerance of extraction was set as 25 ppm (or 0.01 Da for ion <400 Da). For each EIC of isotopologue, the noise level and baseline were determined. The EIC with maximum signal-to-noise (S/N) ratio lower than 3 were removed. Then the EICs were smoothed using “Gaussian” method and subjected to peak detection with the “centWave” algorithm. The peak-peak correlation (PPC) was also calculated to characterize the similarity between the detected isotopologue EIC peaks and the targeted EIC in the isotopologue targeted list using a modified Pearson correlation coefficient. All isotopologue peaks with PPC ≥ 0.6 were kept. For the remaining isotopologue peaks, hierarchical-clustering analysis was applied for clustering the peak apexes with a cutoff threshold of 3 s, which indicated that the RT differences among peak apexes should be <3 s in one isotopologue peak group. The isotopologue peak group with the closest RT to the targeted EIC was selected. The RT range and peak apex of isotopologue peaks in the selected isotopologue peak group was re-adjusted according to the peak with highest peak height in the group.(2) Extraction of isotopologue peaks. The isotopologue targeted list was used for targeted extraction of isotopologue peaks in the labeled samples (.mzXML). For each metabolite, the ion chromatograms of all isotopologues were extracted within the extended retention time (RT) range  to obtain the corrected peak intensities and mass isotopemer distribution (MID) table. The MID describes the labeled fraction of each isotopologue of metabolites. Then the isotope contamination was estimated in unlabeled samples. For each metabolite, if monoisotopic peak (M0) intensities were 0 in >50% of unlabeled samples, we considered that the metabolite did not exist in unlabeled samples. In labeled samples, the labeled fractions of all the isotopologues of this metabolite were set as 0. In unlabeled samples, if the labeled fraction of one isotopologue (except M0) is larger than 0.02 in >50% of samples, we consider the isotopologue was isotopically contaminated. We assumed that the contamination levels in both labeled and unlabeled samples were the same. Thus, the labeled fractions of contaminated isotopologues in labeled samples were corrected by subtracting the contaminate level estimated in unlabeled samples. Finally, the MID table that describes the labeled fractions of isotopologues of each metabolite was outputted as the final result.(3) Correction and quantification. For each isotopologue peak of one metabolite, the sum intensity of three scans around the peak apex was used as a proxy to the intensity of the peak. If one isotopologue in isotopologue targeted list was not detected, this isotopologue peak was extracted mandatorily according to the theoretical 69. It is calculated by Eq. (Labeling extent (LE) represents the labeling enrichment of one metabolited by Eq. :1\documeHere, in global isotope tracing analysis, we fitted the in vivo tracing kinetics of the isotopically labeled metabolites using the general first-order exponential equation for large-scale quantitation of the labeling rates of metabolties, which correspond to the incorptation rate of the tracer to the downstream metabolite targets. Labeling extents (LE) for each metabolite at all time points were used to fit the exponential function.13CMS20, geoRge22 and El-MAVEN29 were evaluated as follows. For X13CMS, the R package “X13CMS” was downloaded from the website . For geoRge, the R package “geoRge” was downloaded from GitHub . Both X13CMS and geoRge provided the result table of 13C-labeled isotopologues for each peak group. Then, the unlabeled peak in the peak group was matched with the peak in MetDNA annotation table according to the m/z and RT. For El-MAVEN, the GUI software was used. The list of 1347 annotated metabolites including metabolite name, formula, and retention time and the raw data files (.mzXML) were imported into El-MAVEN for targeted extraction of labeled metabolites. In El-MAVEN, the labeled metabolite was defined if any isotopologue except M0 had the labeled fraction >0.02 in >50% of samples. The detailed parameters of these software tools were provided in Supplementary Table The extractions of labeled metabolites using Xhttps://skyline.ms/project/home/software/Skyline/begin.view) was used for manual analysis of labeled metabolites. The raw data files (.mzXML) and the isotopologue target list were imported into Skyline. For each isotopologue, the integration range was manually checked and adjusted for accurate quantification of each isotopologue peak. The quantification result was also corrected with “Accucor” for natural isotope correction, and then labeled fractions and labeling extents were calculated. The relative errors for labeled metabolites were calculated by Eq. , the labeled fraction of isotopologues were calculated. We defined the isotopologue with the labeled fraction >2% in any one sample as a false positive. False-positive rate at isotopologue level referred to the ratio of the number of false-positive labeled isotopologues to the number of all isotopologues for all metabolites. False-positive rate at metabolite level referred to the ratio of the number of labeled metabolites to the number of all extracted metabolites. Detailed numbers were listed in Supplementary Tables 70. For the enrichment analysis of the labeled metabolites in cluster 1, 2, and 3 in the head and muscle tissues of Drosophila, the background database was all of the labeled metabolites in the head and muscle tissues, respectively. For the enrichment analysis of the shared labeled metabolites in both head and muscle tissues, the background database was the metabolites in KEGG.Metabolic pathway-enrichment analysis was performed via hypergeometric testHierarchical-clustering was performed using an R package “pheatmap” (version 1.0.12) with the default parameters. The mean labeling extent values of metabolites in each labeling time point were used. The clustering method was “ward.D” for head and “complete” for muscle. The correlation values were used as the clustering distance in heat map plot.p-value <0.05 after FDR correction were retained to construct the correlation network. The correlation network was visualized by R package “circlize” (version 0.2.10).The labeling extent values after 24 h labeling were used for Pearson correlation calculation between metabolites from each cluster. Only metabolite correlations with For RNA-seq experiments, dissected fly head tissues (3d and 30d) were used. Tissues were homogenized in a 1.5 mL tube containing 1 mL of Trizol Reagent . RNA isolation was followed in accordance with manufacturer’s instruction. RNA was resuspended in DEPC-treated RNase-free water (Thermo Fisher Scientific). TURBO DNA free kit was used to remove residual DNA contamination according to manufacturer’s instruction (Thermo Fisher Scientific). One microgram of total RNA was used for sequencing library preparation. PolyA-tailed RNAs were selected by NEBNext Poly(A) mRNA Magnetic Isolation Module , followed by the library preparation using NEBNext Ultra RNA library Prep Kit for Illumina according to manufacturer’s instruction . Libraries were pooled and sequenced on the Illumina Miseq platform with single end 100 bps . Sequencing reads were mapped to the reference genome dm6 with STAR2.3.0e by default parameter. The read counts for each gene were calculated by HTSeq-0.5.4e htseq-count with parameters “-m intersection-strict -s no” with STAR generated SAM files. The count files were used as input to R package “DESeq” (version 1.8.3) for normalization.Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Reporting Summary


Metabolomics is an essential method to study the dynamic changes of metabolic networks and products using modern analytical techniques, as well as reveal the life phenomena and their inherent laws. Currently, more and more attention has been paid to the development of metabolic histochemistry in the fungus field. This paper reviews the application of metabolomics in fungal research from five aspects: identification, response to stress, metabolite discovery, metabolism engineering, and fungal interactions with plants. Metabolomics is an emerging omics technology following genomics, proteomics, and transcriptomics. The concept of metabolomics first originated from metabolomic profiling proposed in 1971 by Devaux et al. . NicholsMetabolomics, through modern instrumental analytical methods with high throughput, sensitivity, and resolution, combined with chemometric methods, analyzes the change law of metabolites after stimulation or interference in biological systems. Its focus is more on small-molecule metabolites with relative molecular weights of less than 1000 in biological tissues or cells and is often used to study plant and microbial systems ,8,9. TheThe technical route for metabolomic research in fungi mainly involves three main processes: sample preparation, data collection and processing, and analysis . Sample Rapid sampling from fermentation tanks or shake flasks is the first step in performing fungal sample preparation, since 1969 when Harrison et al. first attempted rapid sampling from small-scale laboratory bioreactors , to Iverv/w) methanol quenching at −40 °C is one of the standard methods for microbial metabolomics research. However, this method causes the leakage of cellular metabolites [v/v) ratio to 1:5 or less for quenching in pure methanol at ≤−40 °C effectively prevents metabolite leakage [The purpose of quenching is to rapidly stop various metabolic activities within the cells, inactivate enzymes, keep the metabolite content stable, and reduce the degradation of metabolites. The cold methanol quenching method is a standard method used in the past, and the research on cell quenching mainly focused on different methanol content and quenching temperature. Specifically, 60%  [Mortierella alpina. Compared with biphasic extraction at different pH with methanol extraction, which is easy and fast and suitable for the extraction of metabolites from Phanerochaete chrysosporium, biphasic extraction at different pH is more suitable for target analysis [How to extract the quenching sample is a critical step in the sample preparation stage. Furthermore, the ideal extraction should not change the metabolites’ physical properties and chemical structure and should maximize the sequestration of the metabolite content. Current extraction methods can presumably be grouped into physical and chemical categories. Physical methods often use homogenizers, ultrasonic, microwave, and other tools. Chemical methods mostly use acid, base, water, methanol, ethanol, and different ratios of water and organic solvents, mixed solvents, etc. Researchers can choose different solvents depending on different samples. As mentioned above, strong acids and bases can cause severe metabolite leakage resulting in a low recovery rate of final metabolites. Although this is a classical extraction method, using strong acids and bases such as perchloric acid is still not recommended. Solvents with the highest extraction efficiency can be selected experimentally. Three sample preparation methods and five solvent mixtures of  (GC–MS) . The resanalysis . Of noteanalysis .Commonly used metabolomics analysis methods require collecting raw data after sample quenching and extraction. Currently, several analytical methods exist for qualitative and quantitative analysis of metabonomic extracts in metabonomic research. The commonly used analytical methods for fungal metabolomics include gas chromatography/mass spectrometry (GC–MS), liquid chromatography/mass spectrometry (LC–MS), nuclear magnetic resonance spectroscopy/mass spectrometry (NMR–MS), capillary electrophoresis/mass spectrometry (CE–MS), and matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS). GC–MS is often used to analyze substances with excellent thermal stability and volatility, allowing simultaneous analysis of sugars, amino acids, phosphorylated metabolites, organic acids, lipids, amines, and other compounds. It exhibits extraordinary robustness, excellent separation ability, high selectivity, effective sensitivity, and reproducibility . HoweverThe original data obtained by the analytical instrument cannot provide a clean and comparable metabolite spectrum. Therefore, the original data must be preprocessed and generally completed in the experimental system. This mainly includes noise reduction and baseline correction, peak detection and deconvolution, normalization, and data standardization ,93. The Trichoderma (33 strains) using its sequence and metabolome-based chemotaxonomic comparison. They found that the chemical taxonomy based on secondary metabolites was more accurate than its sequence and identified an unknown group of Trichoderma [Ganodermalucidum fruiting bodies and screened four marker components as discriminative variables to distinguish Ganoderma lucidum [Ganoderma lucidum with the NMR metabolomics method was less time-consuming and faster, which was in line with the quality control of large-scale production. Through NMR metabolomics, labeling choline and propionic amino acids as discriminating variables not only successfully differentiated between Chinese and Korean Ganoderma lucidum but also differentiated the cultivated origin of Chinese Ganoderma lucidum [Morphological methods, as the traditional fungal classification method , have sochoderma . Chen us lucidum . However lucidum . This meNevertheless, metabolome-based chemotaxonomy also has drawbacks. First, how to overcome the problem of finding specific markers from a plethora of metabolites for optimal biological interpretation is still unanswered. Second, changes in various environmentally relevant regulatory genes may not affect the expression of taxonomic-related genes. Lastly, while it is desirable to analyze metabolites of specific organelles, how organelles can be isolated while maintaining a structural, metabolic state remains unattainable.Fungi can survive only under various stress reactions such as ionizing radiation, hydration activity, acid–base environment, hypoxic stress, solar ultraviolet radiation, agricultural and industrial pollutants, biological stress, nutrient stress, oxidative stress, heat stress, and cold stress to survive. Ecological metabolomics studies changes in endogenous metabolites produced by biological systems that are affected by environmental factors . Using mPhanerochaete chrysosporium under air and 100% oxygen revealed that three metabolites associated with the oxygen stress response were veratryl alcohol (VA), threonate, and erythronate. High concentrations of ROS can directly activate the VA synthesis pathway, and the intracellular oxygen concentration is significantly elevated. In contrast, threonate and erythronate resistance to hyperoxia is a process of progressive gradient accumulation [Alternaria sp. MG1 grown on grape interiors was subjected to starvation treatment, the shikimate pathway and the phenylpropanoid (PPPN) pathway were strongly activated, and relevant metabolites such as resveratrol were significantly upregulated [Cryptococcus neoformans changed under Cu stress, and the differential metabolites were mainly related to the metabolism of amino acids and carbohydrates. Replacing the carbon source with glycerol and ethanol can counteract the toxic effect of copper on Cryptococcus neoformans and improve urea clearance [Pleurotus ostreatus under different heat stress times  for dynamic metabolite changes. They found that the contents of metabolites such as amino acids, nucleotides, and lipids showed an increasing trend with increased heat stress time [Volvariella volvacea showed little resistance to low temperatures. Nevertheless, under chilling stress, the relative levels of compounds such as amino acids and organic acids inside Volvariella volvacea increase significantly, and soluble sugars such as sorbitol are induced to be produced, improving its osmoregulatory capacity [Aspergillus aculeatus, under drought and heat stress, increased the accumulation of amino acids and sugars and enhanced the total photosynthesis of tall fescue, resulting in a vastly improved ability of tall fescue inoculated with Aspergillus aculeatus to resist cold and heat stress [Environmental stress, i.e., abiotic stress, is usually the most severe situation faced by fungi. Oliveira et al. found that the nutrient content of low-molecular-weight metabolites of wild mushrooms is higher than that of indoor cultivated ones , perhapsmulation . When thegulated . The metlearance . Yan et ess time . Zhao ett stress .Aspergillus flavus showed significant changes in carbohydrates, sulfur-containing amino acids and their derivatives, fatty acids, etc. under drought stress [Aspergillus flavus and affect mitochondrial function, which induces apoptosis in Aspergillus flavus [Aspergillus niger resisted copper stress by converting sorbitol from glucose to produce a large amount of mannitol [Aspergillus niger was exposed to 5% ethanol stress, its growth amount was about 70% less than that under normal growth conditions [Aspergillus niger strain Es4 to ethanol stress can be used as a new response of the fungus to ethanol stress still needs further confirmation. Hammerl et al. further established a differential offline LC–NMR (DOLC–NMR) method to qualitatively and quantitatively analyze metabolic changes in Penicillium roqueforti when L-tyrosine levels are perturbed. Twenty-three metabolites were affected by the amino-acid perturbation method, among which the amino-acid degradation products 2-(4-hydroxyphenyl) acetic acid and 2- acetic acid were significantly upregulated [Ganoderma lucidum with methyl jasmonate (MeJA) for 24 h was the optimal condition to induce the biosynthesis of Ganoderma lucidum. MeJA induction can lead to metabolic rearrangements in Ganoderma lucidum, inhibit its normal glucose metabolism, energy supply, and protein synthesis, and promote cellular secondary metabolic production [Cunninghamella echinulata changed, the metabolic activity was inhibited, and glycolysis and the TCA cycle were dysregulated. This fungus can eliminate the hazard of tributyltin compounds to the organisms by accumulating amino acids with antioxidant functions, such as betaine, proline, and GABA, to recover from the toxic TBT environment [t stress . 1-Nonans flavus . Aspergimannitol . When Asnditions . Using unditions ,126. Wheegulated . Jiang eoduction . After tironment .Penicillium sr21. Furthermore, 200 mg/L p-aminobenzoic acid even promoted glucose catabolism during glycolysis, increased the mevalonate pathway, weakened the tricarboxylic acid cycle, and promoted the production of tetrahydrofolate and NADPH [yeast by promoting cell membrane-associated fatty-acid synthesis before freeze–thaw and promoting alglucan biosynthesis and glycerophospholipid metabolism after freeze–thaw [In contrast, benzoic acid derivatives such as sodium benzoate, p-aminobenzoic acid, and p-methylbenzoic acid all promote lipid synthesis in nd NADPH . The anteze–thaw . From thA large number of metabolites such as primary and secondary metabolites exist in fungi. Primary metabolites are monomers synthesized by primary metabolisms, such as monosaccharides or monosaccharide derivatives, nucleotides, vitamins, amino acids, fatty acids, and various macromolecular polymers composed of them, including proteins, nucleic acids, polysaccharides, lipids, and other essential substances. Secondary metabolites refer to substances synthesized by fungi in which primary metabolites serve as precursors with no clear function in their life activities, such as gibberellins, penicillins, aflatoxins, and cordycepin . Fungi cCordyceps sinensis. While nucleosides such as thymine, uracil, adenine, and guanosine are also the main substances used by researchers to identify and analyze Cordyceps sinensis. Mishra reidentified the nucleobases in samples of Ganoderma lucidum and Cordyceps sinensis by HPLC–MS and found that both had abundant nucleosides. Furthermore, the water extract of Ganoderma lucidum and the ethanolic extract of Cordyceps sinensis had the highest nucleobase content [Cordyceps militaris and Cordyceps sinensis belong to the same family as Clavicipitaceae, and Cordyceps militaris is even called “northern Cordyceps sinensis” in China, they have drastically different nutritional contents, which Chen et al. confirmed from the metabolite level. The results of utilizing LC–MS technology to analyze natural Cordyceps sinensis and artificial cultured Cordyceps militaris showed significant metabolomic differences between them [Cordyceps sinensis and Cordyceps militaris cultured with tussah pupae was compared, and 25 differential metabolites were found, involving 16 metabolic pathways such as histidine metabolism. Cordyceps sinensis has many healthy nutrients, especially amino acids, unsaturated fatty acids, peptides, and mannitol. Moreover, the superior hemostatic activity and the antioxidant capacity of Cordyceps sinensis cultured with tussah pupae suggest its extreme clinical value as an affordable alternative to oridonin [Cordyceps militaris strains were inoculated on germinated soybean (GSC), and the yield and biological activity of GSCs reached the highest after 1 week. Compounds 1–4, which were highly abundant in GSCs, were identified as four novel isoflavone methyl glycosides  [Cordyceps militaris. Coculture of fungi is often beneficial to induce purposeful fungal differentiation, affect the content of metabolites, and produce multiple metabolic pathways. Coculture of Coriolus versicolor and Ganoderma lucidum with 62 newly synthesized or high-yielding features compared to monoculture. Two new xylosides (compounds 2 and 3) were included. Compound 2 was further identified as N-(4-methoxyphenyl) carboxamide 2-O-β-D-xyloside, which increases the viability of the BEAS-2B human immortalized bronchial epithelial cell line. 3-Phenyllactic acid and orsellinic acid were first detected in malate bacilli [Aspergillus oryzae and Zygosaccharomyces rouxii reduced the amounts of imidazoleacetic acid, phenylpyruvic acid, and n-formyl-l-aspartic acid, taurine, and glycolic acid [Zygosaccharomyces rouxii.Cordycepin is considered to be an important marker for the identification of  content . Joshi e content . The gro content . Furthereen them . Similaroridonin . Cordycemethide) . Apparen bacilli . HoweverAgaricus bisporus is a worldwide edible mushroom. The surface browning of mushrooms is one of the major factors affecting consumers’ purchase. The nutritional value of Agaricus bisporus changed after UV irradiation, with 47 compounds increasing in concentration and 72 compounds decreasing in concentration [Agaricus bisporus cultivars at the metabolic level, genes such as AbPPo were found to be involved in the regulation of mushroom browning, and higher levels of organic acids, such as butyric acid, were found in brown tolerant Agaricus bisporus cultivars [Flammulina velutipes [ntration . Lookingultivars . In addielutipes .Thra terreus, Aspergillide B1, as well as 3a-hydroxy-3,5-dihydromonacolin L [Aspergillus flocculus from Markhamia Platycalyx, and the secondary metabolites cis-4-hydroxymellein, 5-hydroxymellein, diorcinol, bo-tryoisocoumarin A, and mellein had anticancer activity and inhibited the growth of the chronic leukemia cell line K562 3-hydroxymellein. Moreover, diorcinol can suppress sleeping sickness caused by the parasite Trypanosoma brucei [Lasiodiplodia theobromae [Endophytic fungi are ubiquitous in the plants body and should be actively exploited to utilize these resources, whether harmful to the plant or not. Hawary et al. isolated a butenolide derivative from the endophytic soybean fungus acolin L . Using ca brucei . Kamal eeobromae .Aspergillus sydowii H-1 on the second and eighth days of fermentation and found significant differences in the production of five anthocyanins, the chalcone synthase gene, and cinnamic acid-4-hydroxylase gene, which may be associated with the synthesis of anthocyanins [The metabolites of fungi would change under different fermentation times. When the fermentation time is too short, the content of the target metabolites might not yet have peaked, whereas, when the fermentation time is too long, the target metabolites might have undergone decomposition. For the first time, Bu et al. showed that anthocyanins could be produced from fungi as a metabolite often thought to exist only in natural plants. They performed a comparative analysis of the metabolome of ocyanins .Pyr enochaetopsis sp. They identified proteins A, B, and C in FVE-001 and protein D in FVE-087, four novel decaprenylspirotetraenoic acid derivatives with anti-melanoma activity [Morchella sp. Le et al. used this approach to investigate the metabolomics of a strain of Penicillium mms417 isolated from blue mussel Mytilus edulis and obtained five new derivatives of natural fungus pyran-2-one derivatives: 5,6-dihydro-6S-hydroxymethyl-4-methoxy-2H-pyran-2-one, -LL-P880β, 5,6-dihydro-4-methoxy-6S--enyl)-2H-pyran-2-one, 4-methoxy-6--enyl)-2H-pyran-2-one, and 4-methoxy-2H-pyran-2-one [Fan et al. applied HRMS/MS feature-based molecular networking technology (FBMN) to determine activity . FBMN haactivity  from wilan-2-one . CombiniBailey defined metabolic engineering as “improving cellular activity by manipulating the enzymatic, transport, and regulatory functions of cells through the use of recombinant DNA technology” . Fungi eAspergillus nidulans, Albright et al. analyzed the changes in more than 1000 small molecules secreted by Aspergillus nidulans. They found that almost the same number of compounds were upregulated and downregulated more than 100-fold after genetic or chemical reduction of HDACi [Aspergillus nidulans, were first detected as a proteasome inhibitor that can be expressed about 100-fold or more upon HDACi induction. When using ionic liquids to stimulate Aspergillus nidulans, choline upregulated the primary metabolism of Aspergillus nidulans, while 1-ethyl-3-methylimidazolium chloride downregulated the primary metabolism, both of which stimulated the production of acetyl CoA and nonproteinogenic amino acids. Twenty-one of 66 known skeleton genes were upregulated [Huang et al. measured six major metabolites in the isoprene biosynthetic pathway using GC–SIM-MS and detected the changes after gene modification . The rolof HDACi . Fellutaegulated .Fusarium verticillioides were much greater than those of Streptomyces sp. Compared with monoculture, many metabolites of Fusarium verticillioides were overproduced under resistant conditions compared to Streptomyces sp., especially 16 proteinogenic amino acids, inosine, and uridine, which means that the corresponding rate of protein synthesis would be slowed down, resulting in slower growth and less toxigenesis of Fusarium verticillioides [Fusarium verticillioides. After targeted disruption of Fusarium verticillioides by the pH-responsive transcription factor PAC1, pH and PAC1 interference were found to affect the biosynthesis of arabitol, mannitol, and trehalose. Trehalose biosynthesis is reduced in PAC1-impaired plants. All three genes are downregulated when PAC1 is perturbed [Fusarium graminearum strain deficient for the H3K27 methyltransferase kmt6 to assign metabolites to genes, Ampressa et al. isolated large amounts of fusaristatin A, gibepyrone A, and fusarpyrones A and B from kmt6 mutants by activating silent metabolic pathways through mutations in repressive chromatin modifying enzymes. Triterpenones and trioctanoic acid were found in kmt6fus1 double mutants [Fusarium graminearum strains producing low toxins using a metabolome approach based on NMR and GC–MS and found new possible bactericidal targets [Aspergillus nidulans tolerance to Boscalid were validated by kalampokis et al. through metabolomic analysis of various biosynthetic pathways and metabolites [Interactions between fungi and bacteria cause metabolic modifications in fungi. After undergoing in vitro confrontation culture, the metabolome changes of llioides . Both therturbed . Using a mutants . GC–EI-M targets . The pheabolites .Relative to other microorganisms, fungi are more classified. Furthermore, filamentous fungi are quite different from fungi such as yeasts in terms of growth mode and genetic characteristics. Multinucleated filamentous fungi are prone to heterokaryotic transformant phenomena. Thus, gene editing on filamentous fungi requires rapid and efficient manipulation techniques. Metabolomics and the construction of metabolic networks benefit the optimization and improvement of fungal metabolic pathways. Compared with transcriptomics and proteomics, metabolomics is able to more keenly analyze the effects of environmental perturbations or stresses on cells. Because there are cases where environmental alterations do not affect changes at the cellular transcriptional or protein level but can be manifested by metabolites.As mentioned above, endophytic fungi are ubiquitous in plants in nature. Some endophytic fungi have developed a mutually beneficial symbiosis with the hosts during a long period of evolution. They can regulate the hormone levels of plants, produce secondary metabolites similar to their hosts, assist the host plants in resisting environmental stresses, etc. Others are harmful fungi, and plant diseases caused by harmful fungi pose a significant threat to global food security. Understanding the interactions between fungi and plants is essential for preventing and controlling plant diseases. In the last decade, metabolomics technologies have been widely applied in various research fields on fungal plant interactions, such as identifying fungi, determining the mechanism of infection, and detecting the interaction between fungi and the host. The applications of metabolomics can help us to understand the pathogenesis and plant defense mechanisms of pathogenic fungi and develop effective prevention and treatment strategies for fungal diseases.Diaporthe phaseolorum (DP) and Trichoderma spirale (TS) during their symbiosis with Combretum lanceolatum. DP promotes the biosynthesis of primary metabolites such as threonine, malate, and N-acetylmannosamine of Combretum lanceolatum, which are metabolite precursors that have been shown to be bioprotective [Pisolithus tinctorius was parasitized on cork oak roots, the contents of root exudates such as carbohydrates, organic acids, tannins, long-chain fatty acids, and monoacylglycerols were significantly decreased. In contrast, root defense substances such as γ-aminobutyric acid (GABA), a terpenoid, guarantee that the cork oak roots can control the proliferative range of Pisolithus tinctorius while symbiosing with Pisolithus tinctorius [Fusarium Verticillium compared to normal soybean. Flavonoid contents are significantly higher in soybean inoculated with molds, and Ja induces the synthesis of biomacromolecules such as glycine to enhance soybean resistance [Armillaria luteobuablina-treated roots [Armillaria luteobuablina in E. grandis roots and trigger hormonal responses in root cells, a phenomenon that was not detected in previous studies.The rate of plant primary and secondary metabolite production is limited by its growth cycle, but endophytic fungi can promote the formation of metabolites from parasitic plants, as seen in otective . In the nctorius . Phytohonctorius ,136. Metsistance . Moreoveed roots . ExogenoTilletia controverta and normal grains  isolated from  disease . Metabolno acids . These aAs can be seen from the above analysis, metabolomics is a potent and effective tool. Through instrument analysis and data processing, we can gain insight into the changes of small-molecule metabolic components in test samples caused by biotic or abiotic factors. Furthermore, they can be associated with related metabolic pathways, metabolic networks, and metabolically related enzyme gene sets, transcriptomes, and proteomes. Metabolomics is widely used in fungal research and can provide a comprehensive and systematic analytical approach for fungal research. With the continuous development and improvement of sample preparation methods and analytical techniques, fungal metabolomics has made great progress in recent years. However, there are still some urgent issues to be solved in fungal metabolomics. For example, there is no standard method to quench and extract fungal metabolites, the data processing is complicated, and the automatic data processing platform technology is imperfect. The fungal metabolomics database is rare and incomplete, which is a crucial factor constraining the further development of fungal metabolomics.On the other hand, although more than a few thousand metabolites have been identified, this is still only the tip of the iceberg for fungal metabolites. More importantly, many researchers still have an insufficient understanding of the metabolic pathways for fungal metabolomics and are only limited to primary and secondary metabolite studies in fungi. Compared with the application of metabolomics in disease diagnosis and drug research development, fungal metabolomics is still at an early stage of development. It is believed that metabolomics technology will be continuously improved with the continuous development of science and technology. Greater progress will also be made in fungal metabolomics studies. In conclusion, metabolomics has provided new insights into fungal research from different perspectives, which can be tightly integrated with other studies so that metabolic pathways, regulatory responses, and homeostatic mechanisms can be deeply investigated. This contributes to a better and deeper understanding of fungi’s complex interactions and their responses to environmental and genetic changes.