PubMed

Biomed Chromatogr. 2024 Apr 29:e5862. doi: 10.1002/bmc.5862. Online ahead of print.ABSTRACTAntibiotic-associated diarrhea is a common adverse reaction caused by the widespread use of antibiotics. The decrease in probiotics is one of the reasons why antibiotics cause drug-induced diarrhea. However, few studies have addressed the intrinsic mechanism of antibiotics inhibiting probiotics. To investigate the underlying mechanism of levofloxacin against Bifidobacterium adolescentis, we used a metabolomics mass spectrometry-based approach and molecular docking analysis for a levofloxacin-induced B. adolescentis injury model. The results showed that levofloxacin reduced the survival rate of B. adolescentis and decreased the number of B. adolescentis. The untargeted metabolomics analysis identified 27 potential biomarkers, and many of these metabolites are involved in energy metabolism, amino acid metabolism and the lipid metabolism pathway. Molecular docking showed that levofloxacin can bind with aminoacyl-tRNA synthetase and lactic acid dehydrogenase. This result provides a novel insight into the mechanism of the adverse reactions of levofloxacin.PMID:38684194 | DOI:10.1002/bmc.5862

Proteomics. 2024 Apr 29:e2200335. doi: 10.1002/pmic.202200335. Online ahead of print.ABSTRACTRecent advancements in omics techniques have revolutionised the study of biological systems, enabling the generation of high-throughput biomolecular data. These innovations have found diverse applications, ranging from personalised medicine to forensic sciences. While the investigation of multiple aspects of cells, tissues or entire organisms through the integration of various omics approaches (such as genomics, epigenomics, metagenomics, transcriptomics, proteomics and metabolomics) has already been established in fields like biomedicine and cancer biology, its full potential in forensic sciences remains only partially explored. In this review, we have presented a comprehensive overview of state-of-the-art analytical platforms employed in omics research, with specific emphasis on their application in the forensic field for the identification of the cadaver and the cause of death. Moreover, we have conducted a critical analysis of the computational integration of omics approaches, and highlighted the latest advancements in employing multi-omics techniques for forensic investigations.PMID:38683823 | DOI:10.1002/pmic.202200335

J Am Soc Mass Spectrom. 2024 Apr 29. doi: 10.1021/jasms.4c00036. Online ahead of print.ABSTRACTBiological amines and amino acids play essential roles in many biochemical processes. The chemical complexity of biological samples is challenging, and the selective identification and quantification of amines and amino acid stereoisomers would be very useful for amine-focused "amino-omics" studies. Many amines and amino acids are chiral, and their stereoisomers cannot be resolved on achiral media without chiral derivatization. In prior studies, we demonstrated the use of Marfey's reagent─a chiral derivatization reagent for amines and phenolic OH groups─for the LC-MS/MS resolution and quantification of amines and amino acid stereoisomers. In this study, a heavy atom isotope labeled Marfey's reagent approach for the stereoselective detection and quantification of amines and amino acids was developed. Heavy (13C2) l-Marfey's (Hl-Mar) and heavy (2H3) d-Marfey's (Hd-Mar) were synthesized from 13C2-l-Ala and 2H3-d-Ala, respectively. Both light and heavy Marfey's reagents were used to derivatize standard amine mixtures, which were analyzed by LC-QToF-HRMS. Aligned peak lists were comparatively analyzed by light vs heavy Mar mass differences to identify mono-, di-, and tri-Marfey's adducts and then by the retention time difference between l- and d-Mar derivatives to identify stereoisomers. This approach was then applied to identify achiral and chiral amine and amino acid components in a methicillin-resistant Staphylococcus aureus (MRSA) extract. This approach shows high analytical selectivity and reproducibility.PMID:38683793 | DOI:10.1021/jasms.4c00036

Metabolomics. 2024 Apr 29;20(3):48. doi: 10.1007/s11306-024-02120-3.ABSTRACTINTRODUCTION: Changes in skin phenotypic characteristics are based on skin tissue. The study of the metabolic changes in skin tissue can help understand the causes of skin diseases and identify effective therapeutic interventions.OBJECTIVES: We aimed to establish and optimize a non-targeted skin metabolome extraction system for skin tissue metabolomics with high metabolite coverage, recovery, and reproducibility using gas chromatography/mass spectrometry.METHODS: The metabolites in skin tissues were extracted using eleven different extraction systems, which were designed using reagents with different polarities based on sequential solid-liquid extraction employing a two-step strategy and analyzed using gas chromatograph/mass spectrometry. The extraction efficiency of diverse solvents was evaluated by coefficient of variation (CV), multivariate analysis, metabolites coverage, and relative peak area analysis.RESULTS: We identified 119 metabolites and the metabolite profiles differed significantly between the eleven extraction systems. Metabolites with high abundances in the organic extraction systems, followed by aqueous extraction, were involved in the biosynthesis of unsaturated fatty acids, while metabolites with high abundances in the aqueous extraction systems, followed by organic extraction, were involved in amino sugar and nucleotide sugar metabolism, and glycerolipid metabolism. MeOH/chloroform-H2O and MeOH/H2O-chloroform were the extraction systems that yielded the highest number of metabolites, while MeOH/acetonitrile (ACN)-H2O and ACN/H2O-IPA exhibited superior metabolite recoveries.CONCLUSION: Our results demonstrated that our research facilitates the selection of an appropriate metabolite extraction approach based on the experimental purpose for the metabolomics study of skin tissue.PMID:38683208 | DOI:10.1007/s11306-024-02120-3

Aging (Albany NY). 2024 Apr 26;16. doi: 10.18632/aging.205779. Online ahead of print.ABSTRACTMetabolomics is a rapidly expanding field in systems biology used to measure alterations of metabolites and identify metabolic biomarkers in response to disease processes. The discovery of metabolic biomarkers can improve early diagnosis, prognostic prediction, and therapeutic intervention for cancers. However, there are currently no databases that provide a comprehensive evaluation of the relationship between metabolites and cancer processes. In this review, we summarize reported metabolites in body fluids across pan-cancers and characterize their clinical applications in liquid biopsy. We conducted a search for metabolic biomarkers using the keywords ("metabolomics" OR "metabolite") AND "cancer" in PubMed. Of the 22,254 articles retrieved, 792 were deemed potentially relevant for further review. Ultimately, we included data from 573,300 samples and 17,083 metabolic biomarkers. We collected information on cancer types, sample size, the human metabolome database (HMDB) ID, metabolic pathway, area under the curve (AUC), sensitivity and specificity of metabolites, sample source, detection method, and clinical features were collected. Finally, we developed a user-friendly online database, the Human Cancer Metabolic Markers Database (HCMMD), which allows users to query, browse, and download metabolite information. In conclusion, HCMMD provides an important resource to assist researchers in reviewing metabolic biomarkers for diagnosis and progression of cancers.PMID:38683118 | DOI:10.18632/aging.205779

Ann Bot. 2024 Apr 29:mcae061. doi: 10.1093/aob/mcae061. Online ahead of print.ABSTRACTBACKGROUND AND AIMS: Chinese pistachio (Pistacia chinensis Bunge), an important horticultural plant species, holds great ornamental value with beautiful leaves and fruits. Seedling propagation of this tree species is restricted by its erratic seed germination, however, the germination mechanism is ambiguous yet. The aim of this study was to figure out the germination mechanism from the novel perspective based on the multi-omics data.METHODS: The multi-omics technique combined with hormone content measurement was first applied in seed germination of Chinese pistachio.KEY RESULTS: Due to the great accumulation during seed germination, catechin stood out from the identified metabolites by broadly targeted metabolomic analysis. Exogenous catechin of 10 mg/L significantly improved the germination of Chinese pistachio seeds. An interesting result of hormone analysis showed that the improving effect of catechin could be attributed to increase of the gibberellic acid 3 (GA3) content rather than decrease of the abscisic acid (ABA) content before germination. The paclobutrazol (PAC, a GA biosynthesis inhibitor) and PAC + catechin treatments also showed that the promoting effect of catechin on seed germination depends on GA biosynthesis. Transcriptome analysis and qRT‒PCR further revealed that catechin induced the expression of PcGA20ox5 to activate GA biosynthesis. Several transcription factors were induced by catechin and GA treatments, such as TCP, bZIP and C3H, which may play an important regulatory role in GA biosynthesis in a catechin-mediated way.CONCLUSIONS: Catechin promotes seed germination via GA biosynthesis in Chinese pistachios. This study proposes a novel mechanism by which catechin promotes seed germination via the GA pathway, which provides new insight into a comprehensive understanding of seed dormancy and germination.PMID:38682952 | DOI:10.1093/aob/mcae061

mSystems. 2024 Apr 29:e0130523. doi: 10.1128/msystems.01305-23. Online ahead of print.ABSTRACTMicrobial communities in nature are dynamically evolving as member species change their interactions subject to environmental variations. Accounting for such context-dependent dynamic variations in interspecies interactions is critical for predictive ecological modeling. In the absence of generalizable theoretical foundations, we lack a fundamental understanding of how microbial interactions are driven by environmental factors, significantly limiting our capability to predict and engineer community dynamics and function. To address this issue, we propose a novel theoretical framework that allows us to represent interspecies interactions as an explicit function of environmental variables (such as substrate concentrations) by combining growth kinetics and a generalized Lotka-Volterra model. A synergistic integration of these two complementary models leads to the prediction of alterations in interspecies interactions as the outcome of dynamic balances between positive and negative influences of microbial species in mixed relationships. The effectiveness of our method was experimentally demonstrated using a synthetic consortium of two Escherichia coli mutants that are metabolically dependent (due to an inability to synthesize essential amino acids) but competitively grow on a shared substrate. The analysis of the E. coli binary consortium using our model not only showed how interactions between the two amino acid auxotrophic mutants are controlled by the dynamic shifts in limiting substrates but also enabled quantifying previously uncharacterizable complex aspects of microbial interactions, such as asymmetry in interactions. Our approach can be extended to other ecological systems to model their environment-dependent interspecies interactions from growth kinetics.IMPORTANCEModeling environment-controlled interspecies interactions through separate identification of positive and negative influences of microbes in mixed relationships is a new capability that can significantly improve our ability to understand, predict, and engineer the complex dynamics of microbial communities. Moreover, the prediction of microbial interactions as a function of environmental variables can serve as valuable benchmark data to validate modeling and network inference tools in microbial ecology, the development of which has often been impeded due to the lack of ground truth information on interactions. While demonstrated against microbial data, the theory developed in this work is readily applicable to general community ecology to predict interactions among macroorganisms, such as plants and animals, as well as microorganisms.PMID:38682902 | DOI:10.1128/msystems.01305-23

Compr Rev Food Sci Food Saf. 2024 May;23(3):e13351. doi: 10.1111/1541-4337.13351.ABSTRACTConsumer priorities in healthy diets and lifestyle boosted the demand for nutritious and functional foods as well as plant-based ingredients. Avocado has become a food trend due to its nutritional and functional values, which in turn is increasing its consumption and production worldwide. Avocado edible portion has a high content of lipids, with the pulp and its oil being rich in monounsaturated fatty acids and essential omega - 3 and omega - 6 polyunsaturated fatty acids (PUFA). These fatty acids are mainly esterified in triacylglycerides, the major lipids in pulp, but also in minor components such as polar lipids (phospholipids and glycolipids). Polar lipids of avocado have been overlooked despite being recently highlighted with functional properties as well. The growth in the industry of avocado products is generating an increased amount of their byproducts, such as seed and peels (nonedible portions), still undervalued. The few studies on avocado byproducts pointed out that they also contain interesting lipids, with seeds particularly rich in polar lipids bearing PUFA, and thus can be reused as a source of add-value phytochemical. Mass spectrometry-based lipidomics approaches appear as an essential tool to unveil the complex lipid signature of avocado and its byproducts, contributing to the recognition of value-added lipids and opening new avenues for their use in novel biotechnological applications. The present review provides an up-to-date overview of the lipid signature from avocado pulp, peel, seed, and its oils.PMID:38682674 | DOI:10.1111/1541-4337.13351

Small Methods. 2024 Apr 29:e2400305. doi: 10.1002/smtd.202400305. Online ahead of print.ABSTRACTMetabolomics, leveraging techniques like NMR and MS, is crucial for understanding biochemical processes in pathophysiological states. This field, however, faces challenges in metabolite sensitivity, data complexity, and omics data integration. Recent machine learning advancements have enhanced data analysis and disease classification in metabolomics. This study explores machine learning integration with metabolomics to improve metabolite identification, data efficiency, and diagnostic methods. Using deep learning and traditional machine learning, it presents advancements in metabolic data analysis, including novel algorithms for accurate peak identification, robust disease classification from metabolic profiles, and improved metabolite annotation. It also highlights multiomics integration, demonstrating machine learning's potential in elucidating biological phenomena and advancing disease diagnostics. This work contributes significantly to metabolomics by merging it with machine learning, offering innovative solutions to analytical challenges and setting new standards for omics data analysis.PMID:38682615 | DOI:10.1002/smtd.202400305

Zool Res. 2024 May 18;45(3):520-534. doi: 10.24272/j.issn.2095-8137.2024.028.ABSTRACTIridovirus poses a substantial threat to global aquaculture due to its high mortality rate; however, the molecular mechanisms underpinning its pathogenesis are not well elucidated. Here, a multi-omics approach was applied to groupers infected with Singapore grouper iridovirus (SGIV), focusing on the roles of key metabolites. Results showed that SGIV induced obvious histopathological damage and changes in metabolic enzymes within the liver. Furthermore, SGIV significantly reduced the contents of lipid droplets, triglycerides, cholesterol, and lipoproteins. Metabolomic analysis indicated that the altered metabolites were enriched in 19 pathways, with a notable down-regulation of lipid metabolites such as glycerophosphates and alpha-linolenic acid (ALA), consistent with disturbed lipid homeostasis in the liver. Integration of transcriptomic and metabolomic data revealed that the top enriched pathways were related to cell growth and death and nucleotide, carbohydrate, amino acid, and lipid metabolism, supporting the conclusion that SGIV infection induced liver metabolic reprogramming. Further integrative transcriptomic and proteomic analysis indicated that SGIV infection activated crucial molecular events in a phagosome-immune depression-metabolism dysregulation-necrosis signaling cascade. Of note, integrative multi-omics analysis demonstrated the consumption of ALA and linoleic acid (LA) metabolites, and the accumulation of L-glutamic acid (GA), accompanied by alterations in immune, inflammation, and cell death-related genes. Further experimental data showed that ALA, but not GA, suppressed SGIV replication by activating antioxidant and anti-inflammatory responses in the host. Collectively, these findings provide a comprehensive resource for understanding host response dynamics during fish iridovirus infection and highlight the antiviral potential of ALA in the prevention and treatment of iridoviral diseases.PMID:38682434 | DOI:10.24272/j.issn.2095-8137.2024.028

Rapid Commun Mass Spectrom. 2024 Jul 15;38(13):e9743. doi: 10.1002/rcm.9743.ABSTRACTINTRODUCTION: Distinguishing and categorizing the origin of garlic are highly significant, considering its widespread use as a flavoring agent. With billions of dollars annually in global trade, garlic is frequently susceptible to fraudulent practices.METHODOLOGY: Paper spray ionization mass spectrometry (PS-MS) was employed to quickly analyze garlic samples from distinct geographic origins: China and Brazil. The so-generated PS-MS data were treated with metabolomic multivariate approaches, and the garlic samples from these different geographic regions were easily discriminated.RESULTS: Brazilian garlic was characterized to contain higher levels of amino acids, such as arginine, proline, and valine, and organosulfur compounds, such as allicin, alliin, and l-γ-glutamil-S-allyl-l-cysteine, compared to Chinese garlic. The PS-MS data were treated employing multivariate approaches, typically used in the metabolomics field, and this protocol was promptly able to discern among both types of samples.CONCLUSION: Hence, this combined strategy holds promise not only as an effective tool for the authentication of the geographical origin of garlic but also as a powerful means for biomarker discovery.PMID:38682308 | DOI:10.1002/rcm.9743

Heliyon. 2024 Apr 17;10(8):e29794. doi: 10.1016/j.heliyon.2024.e29794. eCollection 2024 Apr 30.ABSTRACTBACKGROUND: Psoriasis is a chronic, inflammatory skin disease with autoimmune characteristics. Recent research has made significant progress in the field of psoriasis metabolomics. However, there is a lack of bibliometric analysis on metabolomics of psoriasis. The objective of this study is to utilize bibliometrics to present a comprehensive understanding of the knowledge structure and research hotspots in psoriasis within the field of metabolomics.METHODS: We conducted a bibliometric analysis by searching the Web of Science Core Collection database for publications on metabolomics in psoriasis from 2011 to 2024. To perform this analysis, we utilized tools such as VOSviewers, CiteSpace, and the R package "bibliometrix".RESULTS: A total of 307 articles from 47 countries, with the United States and China leading the way, were included in the analysis. The publications focusing on metabolomics in psoriasis have shown a steady year-on-year growth. The Medical University of Bialystok is the main research institution. The International Journal of Molecular Sciences emerges as the prominent journal in the field, while the Journal of Investigative Dermatology stands out as the highly co-cited publication. A total of 2029 authors contributed to these publications, with Skrzydlewska Elzbieta, Baran Anna, Flisiak Iwona, Murakami Makoto being the most prolific contributors. Notably, Armstrong April W. received the highest co-citation. Investigating the mechanisms of metabolomics in the onset and progression of psoriasis, as well as exploring therapeutic strategies, represents the primary focus of this research area. Emerging research hotspots encompass inflammation, lipid metabolism, biomarker, metabolic syndrome, obesity, and arthritis.CONCLUSION: The results of this study indicate that metabolism-related research is thriving in psoriasis, with a focus on the investigation of metabolic targets and interventions within the metabolic processes. Metabolism is expected to be a hot topic in future psoriasis research.PMID:38681652 | PMC:PMC11053280 | DOI:10.1016/j.heliyon.2024.e29794

Heliyon. 2024 Apr 18;10(8):e29791. doi: 10.1016/j.heliyon.2024.e29791. eCollection 2024 Apr 30.ABSTRACTOBJECTIVES: Chronic loneliness is a widespread issue, and the gut-brain axis is known to be crucial in facilitating communication between the gut and brain. However, the precise mechanism by which chronic loneliness affects the gut-brain axis remains uncertain.METHODS: Fourteen 55-week-old Balb/c mice were used in the experiment, with seven mice being randomly assigned to the chronic social isolation (CSI) group. The CSI group mice underwent 12 weeks of isolation to simulate the psychiatric state of a population in prolonged social isolation. The mental state of the CSI mice was assessed through animal behavior analysis, while plasma cytokines were measured using ELISA. Additionally, the composition of the gut microbiota was analyzed using 16S rRNA sequencing, and the metabolite composition of the intestinal contents was examined using nontargeted metabolomics. The Student-T test was used to determine significant mean differences.RESULTS: Mice that were exposed to the CSI exhibited increased immobility time lengths in forced swimming and hanging tail experiments, and decreased movement lengths and number of times traversing the intermediate region, compared to control mice. Additionally, CSI decreased the abundance of the probiotics Ruminococcaceae, Akkermansiaceae, and Christensenellaceae. Additionally, CSI reduced the production of the metabolites oleamide and tryptophan. Furthermore, IL-1β, IL-4, and IL-6 were significantly increased, while TNF-α was significantly decreased.CONCLUSION: CSI induces a dysbiotic gut microbiota and the production of neurorelated metabolites, which in turn increase inflammatory responses and result in depressive behaviors in CSI mice. Therefore, these findings suggest that the gut microbiota may serve as a target for the treatment of long-term social isolation-induced mental disorders.PMID:38681644 | PMC:PMC11046198 | DOI:10.1016/j.heliyon.2024.e29791

World J Gastroenterol. 2024 Apr 14;30(14):1982-1989. doi: 10.3748/wjg.v30.i14.1982.ABSTRACTUnmet needs exist in metabolic dysfunction-associated steatotic liver disease (MASLD) risk stratification. Our ability to identify patients with MASLD with advanced fibrosis and at higher risk for adverse outcomes is still limited. Incorporating novel biomarkers could represent a meaningful improvement to current risk predictors. With this aim, omics technologies have revolutionized the process of MASLD biomarker discovery over the past decades. While the research in this field is thriving, much of the publication has been haphazard, often using single-omics data and specimen sets of convenience, with many identified candidate biomarkers but lacking clinical validation and utility. If we incorporate these biomarkers to direct patients' management, it should be considered that the roadmap for translating a newly discovered omics-based signature to an actual, analytically valid test useful in MASLD clinical practice is rigorous and, therefore, not easily accomplished. This article presents an overview of this area's current state, the conceivable opportunities and challenges of omics-based laboratory diagnostics, and a roadmap for improving MASLD biomarker research.PMID:38681130 | PMC:PMC11045490 | DOI:10.3748/wjg.v30.i14.1982

Front Microbiol. 2024 Apr 12;15:1376653. doi: 10.3389/fmicb.2024.1376653. eCollection 2024.ABSTRACTThe exchange of small molecules between the cell and the environment happens through transporter proteins. Besides nutrients and native metabolic products, xenobiotic molecules are also transported, however it is not well understood which transporters are involved. In this study, by combining exo-metabolome screening in yeast with transporter characterization in Xenopus oocytes, we mapped the activity of 30 yeast transporters toward six small non-toxic substrates. Firstly, using LC-MS, we determined 385 compounds from a chemical library that were imported and exported by S. cerevisiae. Of the 385 compounds transported by yeast, we selected six compounds (viz. sn-glycero-3-phosphocholine, 2,5-furandicarboxylic acid, 2-methylpyrazine, cefadroxil, acrylic acid, 2-benzoxazolol) for characterization against 30 S. cerevisiae xenobiotic transport proteins expressed in Xenopus oocytes. The compounds were selected to represent a diverse set of chemicals with a broad interest in applied microbiology. Twenty transporters showed activity toward one or more of the compounds. The tested transporter proteins were mostly promiscuous in equilibrative transport (i.e., facilitated diffusion). The compounds 2,5-furandicarboxylic acid, 2-methylpyrazine, cefadroxil, and sn-glycero-3-phosphocholine were transported equilibratively by transporters that could transport up to three of the compounds. In contrast, the compounds acrylic acid and 2-benzoxazolol, were strictly transported by dedicated transporters. The prevalence of promiscuous equilibrative transporters of non-native substrates has significant implications for strain development in biotechnology and offers an explanation as to why transporter engineering has been a challenge in metabolic engineering. The method described here can be generally applied to study the transport of other small non-toxic molecules. The yeast transporter library is available at AddGene (ID 79999).PMID:38680917 | PMC:PMC11045925 | DOI:10.3389/fmicb.2024.1376653

Comput Struct Biotechnol J. 2024 Apr 12;23:1608-1618. doi: 10.1016/j.csbj.2024.04.018. eCollection 2024 Dec.ABSTRACTAntlers are hallmark organ of deer, exhibiting a relatively high growth rate among mammals, and requiring large amounts of nutrients to meet its development. The rumen microbiota plays key roles in nutrient metabolism. However, changes in the microbiota and metabolome in the rumen during antler growth are largely unknown. We investigated rumen microbiota (liquid, solid, ventral epithelium, and dorsal epithelium) and metabolic profiles of sika deer at the early (EG), metaphase (MG) and fast growth (FG) stages. Our data showed greater concentrations of acetate and propionate in the rumens of sika deer from the MG and FG groups than in those of the EG group. However, microbial diversity decreased during antler growth, and was negatively correlated with short-chain fatty acid (SCFA) levels. Prevotella, Ruminococcus, Schaedlerella and Stenotrophomonas were the dominant bacteria in the liquid, solid, ventral epithelium, and dorsal epithelium fractions. The proportions of Stomatobaculum, Succiniclasticum, Comamonas and Anaerotruncus increased significantly in the liquid or dorsal epithelium fractions. Untargeted metabolomics analysis revealed that the metabolites also changed significantly, revealing 237 significantly different metabolites, among which the concentrations of γ-aminobutyrate and creatine increased during antler growth. Arginine and proline metabolism and alanine, aspartate and glutamate metabolism were enhanced. The co-occurrence network results showed that the associations between the rumen microbiota and metabolites different among the three groups. Our results revealed that the different rumen ecological niches were characterized by distinct microbiota compositions, and the production of SCFAs and the metabolism of specific amino acids were significantly changed during antler growth.PMID:38680874 | PMC:PMC11047195 | DOI:10.1016/j.csbj.2024.04.018

Eco Environ Health. 2024 Mar 20;3(2):227-237. doi: 10.1016/j.eehl.2024.03.001. eCollection 2024 Jun.ABSTRACTSoil metabolomics is an emerging approach for profiling diverse small molecule metabolites, i.e., metabolomes, in the soil. Soil metabolites, including fatty acids, amino acids, lipids, organic acids, sugars, and volatile organic compounds, often contain essential nutrients such as nitrogen, phosphorus, and sulfur and are directly linked to soil biogeochemical cycles driven by soil microorganisms. This paper presents an overview of methods for analyzing soil metabolites and the state-of-the-art of soil metabolomics in relation to soil nutrient cycling. We describe important applications of metabolomics in studying soil carbon cycling and sequestration, and the response of soil organic pools to changing environmental conditions. This includes using metabolomics to provide new insights into the close relationships between soil microbiome and metabolome, as well as responses of soil metabolome to plant and environmental stresses such as soil contamination. We also highlight the advantage of using soil metabolomics to study the biogeochemical cycles of elements and suggest that future research needs to better understand factors driving soil function and health.PMID:38680731 | PMC:PMC11047296 | DOI:10.1016/j.eehl.2024.03.001

iScience. 2024 Mar 28;27(5):109640. doi: 10.1016/j.isci.2024.109640. eCollection 2024 May 17.ABSTRACTThe tumor suppressor p53 has been described to control various aspects of metabolic reprogramming in solid tumors, but in B cell malignancies that role is as yet unknown. We generated pairs of p53 functional and knockout (KO) clones from distinct B cell malignancies (acute lymphoblastic leukemia, chronic lymphocytic leukemia, diffuse large B cell lymphoma, and multiple myeloma). Metabolomics and isotope tracing showed that p53 loss did not drive a common metabolic signature. Instead, cell lines segregated according to cell of origin. Next, we focused on glutamine as a crucial energy source in the B cell tumor microenvironment. In both TP53 wild-type and KO cells, glutamine deprivation induced cell death through the integrated stress response, via CHOP/ATF4. Lastly, combining BH3 mimetic drugs with glutamine starvation emerged as a possibility to target resistant clones. In conclusion, our analyses do not support a common metabolic signature of p53 deficiency in B cell malignancies and suggest therapeutic options for exploration based on glutamine dependency.PMID:38680661 | PMC:PMC11053310 | DOI:10.1016/j.isci.2024.109640

Asian J Urol. 2024 Apr;11(2):221-241. doi: 10.1016/j.ajur.2022.11.005. Epub 2023 Sep 12.ABSTRACTOBJECTIVE: Metabolomics has been extensively utilized in bladder cancer (BCa) research, employing mass spectrometry and nuclear magnetic resonance spectroscopy to compare various variables (tissues, serum, blood, and urine). This study aimed to identify potential biomarkers for early BCa diagnosis.METHODS: A search strategy was designed to identify clinical trials, descriptive and analytical observational studies from databases such as Medline, Embase, Cochrane Central Register of Controlled Trials, and Latin American and Caribbean Literature in Health Sciences. Inclusion criteria comprised studies involving BCa tissue, serum, blood, or urine profiling using widely adopted metabolomics techniques like mass spectrometry and nuclear magnetic resonance. Primary outcomes included description of metabolites and metabolomics profiling in BCa patients and the association of metabolites and metabolomics profiling with BCa diagnosis compared to control patients. The risk of bias was assessed using the Quality Assessment of Studies of Diagnostic Accuracy.RESULTS: The search strategy yielded 2832 studies, of which 30 case-control studies were included. Urine was predominantly used as the primary sample for metabolite identification. Risk of bias was often unclear inpatient selection, blinding of the index test, and reference standard assessment, but no applicability concerns were observed. Metabolites and metabolomics profiles associated with BCa diagnosis were identified in glucose, amino acids, nucleotides, lipids, and aldehydes metabolism.CONCLUSION: The identified metabolites in urine included citric acid, valine, tryptophan, taurine, aspartic acid, uridine, ribose, phosphocholine, and carnitine. Tissue samples exhibited elevated levels of lactic acid, amino acids, and lipids. Consistent findings across tissue, urine, and serum samples revealed downregulation of citric acid and upregulation of lactic acid, valine, tryptophan, taurine, glutamine, aspartic acid, uridine, ribose, and phosphocholine.PMID:38680576 | PMC:PMC11053311 | DOI:10.1016/j.ajur.2022.11.005

ACS Cent Sci. 2024 Mar 21;10(4):758-774. doi: 10.1021/acscentsci.3c01438. eCollection 2024 Apr 24.ABSTRACTThis outlook explores how two different molecular imaging approaches might be combined to gain insight into dynamic, subcellular metabolic processes. Specifically, we discuss how matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) and stimulated Raman scattering (SRS) microscopy, which have significantly pushed the boundaries of imaging metabolic and metabolomic analyses in their own right, could be combined to create comprehensive molecular images. We first briefly summarize the recent advances for each technique. We then explore how one might overcome the inherent limitations of each individual method, by envisioning orthogonal and interchangeable workflows. Additionally, we delve into the potential benefits of adopting a complementary approach that combines both MSI and SRS spectro-microscopy for informing on specific chemical structures through functional-group-specific targets. Ultimately, by integrating the strengths of both imaging modalities, researchers can achieve a more comprehensive understanding of biological and chemical systems, enabling precise metabolic investigations. This synergistic approach holds substantial promise to expand our toolkit for studying metabolites in complex environments.PMID:38680555 | PMC:PMC11046475 | DOI:10.1021/acscentsci.3c01438