PubMed

Bioinformatics. 2024 May 24:btae321. doi: 10.1093/bioinformatics/btae321. Online ahead of print.ABSTRACTMOTIVATION: Although the human microbiome plays a key role in health and disease, the biological mechanisms underlying the interaction between the microbiome and its host are incompletely understood. Integration with other molecular profiling data offers an opportunity to characterize the role of the microbiome and elucidate therapeutic targets. However, this remains challenging to the high dimensionality, compositionality, and rare features found in microbiome profiling data. These challenges necessitate the use of methods that can achieve structured sparsity in learning cross-platform association patterns.RESULTS: We propose Tree-Aggregated factor RegressiOn (TARO) for the integration of microbiome and metabolomic data. We leverage information on the taxonomic tree structure to flexibly aggregate rare features. We demonstrate through simulation studies that TARO accurately recovers a low-rank coefficient matrix and identifies relevant features. We applied TARO to microbiome and metabolomic profiles gathered from subjects being screened for colorectal cancer to understand how gut microrganisms shape intestinal metabolite abundances.AVAILABILITY AND IMPLEMENTATION: The R package TARO implementing the proposed methods is available online at https://github.com/amishra-stats/taro-package.PMID:38788190 | DOI:10.1093/bioinformatics/btae321

Medicine (Baltimore). 2024 May 24;103(21):e38126. doi: 10.1097/MD.0000000000038126.ABSTRACTThe burgeoning field of metabolomics has piqued the interest of researchers in the context of benign gallbladder diseases, which include conditions such as gallbladder polyps, gallstones, and cholecystitis, which are common digestive system disorders. As metabolomics continues to advance, researchers have increasingly focused their attention on its applicability in the study of benign gallbladder diseases to provide new perspectives for diagnostic, therapeutic, and prognostic evaluation. This comprehensive review primarily describes the techniques of liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry, and nuclear magnetic resonance and their respective applications in the study of benign gallbladder disease. Metabolomics has made remarkable progress in various aspects of these diseases, ranging from early diagnosis, etiological research, assessment of disease progression and prognosis, and optimization of therapeutic strategies. However, challenges remain in the field of metabolomics in the study of benign gallbladder diseases. These include issues related to data processing and analysis, biomarker discovery and validation, interdisciplinary research integration, and the advancement of personalized medicine. This article attempts to summarize research findings to date, highlight future research directions, and provide a reference point for metabolomics research in benign gallbladder disease.PMID:38788004 | DOI:10.1097/MD.0000000000038126

Elife. 2024 May 24;13:RP95652. doi: 10.7554/eLife.95652.ABSTRACTThe tumor microenvironment is a determinant of cancer progression and therapeutic efficacy, with nutrient availability playing an important role. Although it is established that the local abundance of specific nutrients defines the metabolic parameters for tumor growth, the factors guiding nutrient availability in tumor compared to normal tissue and blood remain poorly understood. To define these factors in renal cell carcinoma (RCC), we performed quantitative metabolomic and comprehensive lipidomic analyses of tumor interstitial fluid (TIF), adjacent normal kidney interstitial fluid (KIF), and plasma samples collected from patients. TIF nutrient composition closely resembles KIF, suggesting that tissue-specific factors unrelated to the presence of cancer exert a stronger influence on nutrient levels than tumor-driven alterations. Notably, select metabolite changes consistent with known features of RCC metabolism are found in RCC TIF, while glucose levels in TIF are not depleted to levels that are lower than those found in KIF. These findings inform tissue nutrient dynamics in RCC, highlighting a dominant role of non-cancer-driven tissue factors in shaping nutrient availability in these tumors.PMID:38787918 | DOI:10.7554/eLife.95652

PLoS One. 2024 May 24;19(5):e0303856. doi: 10.1371/journal.pone.0303856. eCollection 2024.ABSTRACTThis study investigates the impact of casein hydrolysates on the poultry ceca inoculated with Campylobacter focusing on microbial molecular preferences for different protein sources in the presence of Campylobacter jejuni. Three casein sources (intact casein (IN), casein enzyme hydrolysate (EH), and casein acid hydrolysate (AH)) were introduced to cecal contents in combination with inoculated C. jejuni in an in vitro model system incubated for 48 h at 42°C under microaerophilic conditions. Samples were collected at 0, 24, and 48 h. Genomic DNA was extracted and amplified using custom dual-indexed primers, followed by sequencing on an Illumina MiSeq platform. The obtained sequencing data were then analyzed via QIIME2-2021.11. Metabolite extracts were analyzed with ultra-high-performance liquid orbitrap chromatography-mass spectrometry (UHPLC-MS). Statistical analysis of metabolites was conducted using MetaboAnalyst 5.0, while functional analysis was performed using Mummichog 2.0 with a significance threshold set at P < 0.00001. DNA sequencing and metabolomic analyses revealed that C. jejuni was most abundant in the EH group. Microbial diversity and richness improved in casein supplemented groups, with core microbial differences observed, compared to non-supplemented groups. Vitamin B-associated metabolites significantly increased in the supplemented groups, displaying distinct patterns in vitamin B6 and B9 metabolism between EH and AH groups (P < 0.05). Faecalibacterium and Phascolarctobacterium were associated with AH and EH groups, respectively. These findings suggest microbial interactions in the presence of C. jejuni and casein supplementation are influenced by microbial community preferences for casein hydrolysates impacting B vitamin production and shaping competitive dynamics within the cecal microbial community. These findings underscore the potential of nutritional interventions to modulate the poultry GIT microbiota for improved health outcomes.PMID:38787822 | DOI:10.1371/journal.pone.0303856

PLoS One. 2024 May 24;19(5):e0287877. doi: 10.1371/journal.pone.0287877. eCollection 2024.ABSTRACTType 1 diabetes (T1D) is characterized by HLA class I-mediated presentation of autoantigens on the surface of pancreatic β-cells. Recognition of these autoantigens by CD8+ T cells results in the destruction of pancreatic β-cells and, consequently, insulin deficiency. Most epitopes presented at the surface of β-cells derive from the insulin precursor molecule proinsulin. The intracellular processing pathway(s) involved in the generation of these peptides are poorly defined. In this study, we show that a proinsulin B-chain antigen (PPIB5-14) originates from proinsulin molecules that are processed by ER-associated protein degradation (ERAD) and thus originate from ER-resident proteins. Furthermore, screening genes encoding for E2 ubiquitin conjugating enzymes, we identified UBE2G2 to be involved in proinsulin degradation and subsequent presentation of the PPIB10-18 autoantigen. These insights into the pathway involved in the generation of insulin-derived peptides emphasize the importance of proinsulin processing in the ER to T1D pathogenesis and identify novel targets for future T1D therapies.PMID:38787820 | DOI:10.1371/journal.pone.0287877

Arch Dermatol Res. 2024 May 24;316(6):217. doi: 10.1007/s00403-024-03018-9.ABSTRACTWe aim to systemically review the genomics, transcriptomics, epigenetics, proteomics, metabonomics and microbiota of psoriatic arthritis and psoriasis, illustrating the differences of these two diseases, broadening our understanding of the pathogenesis of them and providing important clues for valuable biomarkers of earlier diagnosis and treatments. To our knowledge, this is the first study that combine all omics studies from genomics to microbiota and may serve as a reference for future studies to identify the key underlying pathways in psoriatic arthritis.PMID:38787526 | DOI:10.1007/s00403-024-03018-9

Metabolomics. 2024 May 24;20(3):61. doi: 10.1007/s11306-024-02107-0.ABSTRACTINTRODUCTION: IgA vasculitis diagnosis relies primarily on clinical features and is confirmed by pathological findings. To date, there is no reliable noninvasive diagnostic biomarker.OBJECTIVE: We aimed to explore the baseline serum metabolome of adult patients with IgA vasculitis to identify potential diagnostic biomarkers.METHODS: We performed a study comparing the serum metabolome of patients with IgA vasculitis to that of patients with inflammatory condition, namely spondyloarthritis. Serum analyses were performed by high-performance liquid chromatography-mass spectrometry.RESULTS: Fifty-five patients with IgA vasculitis and 77 controls with spondyloarthritis (age- and sex-matched) were included in this study. The median age of IgA vasculitis patients was 53 years. Two-thirds of patients were female (n = 32). At the time of vasculitis diagnosis, 100% of patients had skin involvement and 69% presented with glomerulonephritis (n = 38). Joint and digestive involvement were observed in 56% (n = 31) and 42% (n = 23) of patients. Four discriminative metabolites between the two groups were identified: 1-methyladenosine, L-glutamic acid, serotonin, and thymidine. The multivariate model built from the serum metabolomes of patients with IgA vasculitis and spondyloarthritis revealed an accuracy > 90%. As this model was significant according to the permutation test (p < 0.01), independent validation showed an excellent predictive value of the test set: sensitivity 98%; specificity 98%, positive predictive value 97% and negative predictive value 98%.CONCLUSION: To our knowledge, this study is the first to use the metabolomic approach for diagnostic purposes in adult IgA vasculitis, highlighting a specific diagnostic metabolome signature.PMID:38787468 | DOI:10.1007/s11306-024-02107-0

Toxics. 2024 May 7;12(5):339. doi: 10.3390/toxics12050339.ABSTRACTOridonin is the primary active component in the traditional Chinese medicine Rabdosia rubescens, displaying anti-inflammatory, anti-tumor, and antibacterial effects. It is widely employed in clinical therapy for acute and chronic pharyngitis, tonsillitis, as well as bronchitis. Nevertheless, the clinical application of oridonin is significantly restricted due to its reproductive toxicity, with the exact mechanism remaining unclear. The aim of this study was to investigate the mechanism of oridonin-induced damage to HTR-8/SVneo cells. Through the integration of epigenetics, proteomics, and metabolomics methodologies, the mechanisms of oridonin-induced reproductive toxicity were discovered and confirmed through fluorescence imaging, RT-qPCR, and Western blotting. Experimental findings indicated that oridonin altered m6A levels, gene and protein expression levels, along with metabolite levels within the cells. Additionally, oridonin triggered oxidative stress and mitochondrial damage, leading to a notable decrease in WNT6, β-catenin, CLDN1, CCND1, and ZO-1 protein levels. This implied that the inhibition of the Wnt/β-catenin signaling pathway and disruption of tight junction might be attributed to the cytotoxicity induced by oridonin and mitochondrial dysfunction, ultimately resulting in damage to HTR-8/SVneo cells.PMID:38787118 | DOI:10.3390/toxics12050339

Toxins (Basel). 2024 Apr 30;16(5):212. doi: 10.3390/toxins16050212.ABSTRACTMicrocystins (MCs), toxins generated by cyanobacteria, feature microcystin-LR (MC-LR) as one of the most prevalent and toxic variants in aquatic environments. MC-LR not only causes environmental problems but also presents a substantial risk to human health. This study aimed to investigate the impact of MC-LR on APCmin/+ mice, considered as an ideal animal model for intestinal tumors. We administered 40 µg/kg MC-LR to mice by gavage for 8 weeks, followed by histopathological examination, microbial diversity and metabolomics analysis. The mice exposed to MC-LR exhibited a significant promotion in colorectal cancer progression and impaired intestinal barrier function in the APCmin/+ mice compared with the control. Gut microbial dysbiosis was observed in the MC-LR-exposed mice, manifesting a notable alteration in the structure of the gut microbiota. This included the enrichment of Marvinbryantia, Gordonibacter and Family_XIII_AD3011_group and reductions in Faecalibaculum and Lachnoclostridium. Metabolomics analysis revealed increased bile acid (BA) metabolites in the intestinal contents of the mice exposed to MC-LR, particularly taurocholic acid (TCA), alpha-muricholic acid (α-MCA), 3-dehydrocholic acid (3-DHCA), 7-ketodeoxycholic acid (7-KDCA) and 12-ketodeoxycholic acid (12-KDCA). Moreover, we found that Marvinbryantia and Family_XIII_AD3011_group showed the strongest positive correlation with taurocholic acid (TCA) in the mice exposed to MC-LR. These findings provide new insights into the roles and mechanisms of MC-LR in susceptible populations, providing a basis for guiding values of MC-LR in drinking water.PMID:38787064 | DOI:10.3390/toxins16050212

Metabolites. 2024 May 19;14(5):290. doi: 10.3390/metabo14050290.ABSTRACTNMR is widely considered the gold standard for organic compound structure determination. As such, NMR is routinely used in organic compound identification, drug metabolite characterization, natural product discovery, and the deconvolution of metabolite mixtures in biofluids (metabolomics and exposomics). In many cases, compound identification by NMR is achieved by matching measured NMR spectra to experimentally collected NMR spectral reference libraries. Unfortunately, the number of available experimental NMR reference spectra, especially for metabolomics, medical diagnostics, or drug-related studies, is quite small. This experimental gap could be filled by predicting NMR chemical shifts for known compounds using computational methods such as machine learning (ML). Here, we describe how a deep learning algorithm that is trained on a high-quality, "solvent-aware" experimental dataset can be used to predict 1H chemical shifts more accurately than any other known method. The new program, called PROSPRE (PROton Shift PREdictor) can accurately (mean absolute error of <0.10 ppm) predict 1H chemical shifts in water (at neutral pH), chloroform, dimethyl sulfoxide, and methanol from a user-submitted chemical structure. PROSPRE (pronounced "prosper") has also been used to predict 1H chemical shifts for >600,000 molecules in many popular metabolomic, drug, and natural product databases.PMID:38786767 | DOI:10.3390/metabo14050290

Metabolites. 2024 May 19;14(5):289. doi: 10.3390/metabo14050289.ABSTRACTThe consumption of low-mineral water has been increasing worldwide. Drinking low-mineral water is associated with cardiovascular disease, osteopenia, and certain neurodegenerative diseases. However, the specific mechanism remains unclear. The liver metabolic alterations in rats induced by drinking purified water for 3 months were investigated with a metabolomics-based strategy. Compared with the tap water group, 74 metabolites were significantly changed in the purified water group (6 increased and 68 decreased), including 29 amino acids, 11 carbohydrates, 10 fatty acids, 7 short chain fatty acids (SCFAs), and 17 other biomolecules. Eight metabolic pathways were significantly changed, namely aminoacyl-tRNA biosynthesis; nitrogen metabolism; alanine, aspartate and glutamate metabolism; arginine and proline metabolism; histidine metabolism; biosynthesis of unsaturated fatty acids; butanoate metabolism; and glycine, serine and threonine metabolism. These changes suggested that consumption of purified water induced negative nitrogen balance, reduced expression of some polyunsaturated fatty acids and SCFAs, and disturbed energy metabolism in rats. These metabolic disturbances may contribute to low-mineral-water-associated health risks. The health risk of consuming low-mineral water requires attention.PMID:38786766 | DOI:10.3390/metabo14050289

Metabolites. 2024 May 15;14(5):285. doi: 10.3390/metabo14050285.ABSTRACTFermentation parameters, especially the duration, are important in imparting a peculiar taste and flavor to soy sauce. The main purpose of this research was to monitor metabolic changes occurring during the various time intervals of the fermentation process. NMR-based metabolomics was used to monitor the compositional changes in soy sauce during fermentation. The 1H-NMR spectra of the soy sauce samples taken from the fermentation tanks at 0 to 8 months were analyzed using 1H-NMR spectroscopy, and the obtained spectra were analyzed by multivariate statistical analysis. The Principal Component Analysis (PCA) and Partial Least Square Discriminate analysis (PLSDA) revealed the separation of samples fermented for various time durations under identical conditions. Key metabolites shown by corresponding loading plots exhibited variations in amino acids (lysine, threonine, isoleucine, etc.), acetate, glucose, fructose, sucrose, ethanol, glycerol, and others. The levels of ethanol in soy sauce increased with longer fermentation durations, which can be influenced by both natural fermentation and the intentional addition of ethanol as a preservative. The study shows that the variation in metabolite can be very efficiently monitored using 1H-NMR-based metabolomics, thus suggestion to optimize the time duration to get the soy sauce product with the desired taste and flavor.PMID:38786762 | DOI:10.3390/metabo14050285

Metabolites. 2024 May 15;14(5):284. doi: 10.3390/metabo14050284.ABSTRACTDepression is associated with pathological changes and metabolic abnormalities in multiple brain regions. The simultaneous comprehensive and in situ detection of endogenous molecules in all brain regions is essential for a comprehensive understanding of depression pathology, which is described in this paper. A method based on desorption electrospray ionization mass spectrometry imaging (DESI-MSI) technology was developed to classify mouse brain regions using characteristic lipid molecules and to detect the metabolites in mouse brain tissue samples simultaneously. The results showed that characteristic lipid molecules can be used to clearly distinguish each subdivision of the mouse brain, and the accuracy of this method is higher than that of the conventional staining method. The cerebellar cortex, medial prefrontal cortex, hippocampus, striatum, nucleus accumbens-core, and nucleus accumbens-shell exhibited the most significant differences in the chronic social defeat stress model. An analysis of metabolic pathways revealed that 13 kinds of molecules related to energy metabolism and purine metabolism exhibited significant changes. A DESI-MSI method was developed for the detection of pathological brain sections. We found, for the first time, that there are characteristic changes in the energy metabolism in the cortex and purine metabolism in the striatum, which is highly important for obtaining a deeper and more comprehensive understanding of the pathology of depression and discovering regulatory targets.PMID:38786761 | DOI:10.3390/metabo14050284

Metabolites. 2024 May 15;14(5):283. doi: 10.3390/metabo14050283.ABSTRACTThe tomato is a fruit vegetable rich in nutritional and medicinal value grown in greenhouses and fields worldwide. It is severely sensitive to heat stress, which frequently occurs with rising global warming. Predictions indicate a 0.2 °C increase in average surface temperatures per decade for the next three decades, which underlines the threat of austere heat stress in the future. Previous studies have reported that heat stress adversely affects tomato growth, limits nutrient availability, hammers photosynthesis, disrupts reproduction, denatures proteins, upsets signaling pathways, and damages cell membranes. The overproduction of reactive oxygen species in response to heat stress is toxic to tomato plants. The negative consequences of heat stress on the tomato have been the focus of much investigation, resulting in the emergence of several therapeutic interventions. However, a considerable distance remains to be covered to develop tomato varieties that are tolerant to current heat stress and durable in the perspective of increasing global warming. This current review provides a critical analysis of the heat stress consequences on the tomato in the context of global warming, its innate response to heat stress, and the elucidation of domains characterized by a scarcity of knowledge, along with potential avenues for enhancing sustainable tolerance against heat stress through the involvement of diverse advanced technologies. The particular mechanism underlying thermotolerance remains indeterminate and requires further elucidatory investigation. The precise roles and interplay of signaling pathways in response to heat stress remain unresolved. The etiology of tomato plants' physiological and molecular responses against heat stress remains unexplained. Utilizing modern functional genomics techniques, including transcriptomics, proteomics, and metabolomics, can assist in identifying potential candidate proteins, metabolites, genes, gene networks, and signaling pathways contributing to tomato stress tolerance. Improving tomato tolerance against heat stress urges a comprehensive and combined strategy including modern techniques, the latest apparatuses, speedy breeding, physiology, and molecular markers to regulate their physiological, molecular, and biochemical reactions.PMID:38786760 | DOI:10.3390/metabo14050283

Metabolites. 2024 May 11;14(5):280. doi: 10.3390/metabo14050280.ABSTRACTMass spectrometry (MS)-based clinical metabolomics is very promising for the discovery of new biomarkers and diagnostics. However, poor data accuracy and reproducibility limit its true potential, especially when performing data analysis across multiple sample sets. While high-resolution mass spectrometry has gained considerable popularity for discovery metabolomics, triple quadrupole (QqQ) instruments offer several benefits for the measurement of known metabolites in clinical samples. These benefits include high sensitivity and a wide dynamic range. Here, we present the Olaris Global Panel (OGP), a HILIC LC-QqQ MS method for the comprehensive analysis of ~250 metabolites from all major metabolic pathways in clinical samples. For the development of this method, multiple HILIC columns and mobile phase conditions were compared, the robustness of the leading LC method assessed, and MS acquisition settings optimized for optimal data quality. Next, the effect of U-13C metabolite yeast extract spike-ins was assessed based on data accuracy and precision. The use of these U-13C-metabolites as internal standards improved the goodness of fit to a linear calibration curve from r2 < 0.75 for raw data to >0.90 for most metabolites across the entire clinical concentration range of urine samples. Median within-batch CVs for all metabolite ratios to internal standards were consistently lower than 7% and less than 10% across batches that were acquired over a six-month period. Finally, the robustness of the OGP method, and its ability to identify biomarkers, was confirmed using a large sample set.PMID:38786757 | DOI:10.3390/metabo14050280

Metabolites. 2024 May 10;14(5):278. doi: 10.3390/metabo14050278.ABSTRACTMetabolomics has gained much attention due to its potential to reveal molecular disease mechanisms and present viable biomarkers. This work uses a panel of untargeted serum metabolomes from 602 children from the COPSAC2010 mother-child cohort. The annotated part of the metabolome consists of 517 chemical compounds curated using automated procedures. We created a filtering method for the quantified metabolites using predicted quantitative structure-bioactivity relationships for the Tox21 database on nuclear receptors and stress response in cell lines. The metabolites measured in the children's serums are predicted to affect specific targeted models, known for their significance in inflammation, immune function, and health outcomes. The targets from Tox21 have been used as targets with quantitative structure-activity relationships (QSARs). They were trained for ~7000 structures, saved as models, and then applied to the annotated metabolites to predict their potential bioactivities. The models were selected based on strict accuracy criteria surpassing random effects. After application, 52 metabolites showed potential bioactivity based on structural similarity with known active compounds from the Tox21 set. The filtered compounds were subsequently used and weighted by their bioactive potential to show an association with early childhood hs-CRP levels at six months in a linear model supporting a physiological adverse effect on systemic low-grade inflammation.PMID:38786755 | DOI:10.3390/metabo14050278

Metabolites. 2024 May 10;14(5):277. doi: 10.3390/metabo14050277.ABSTRACTIn recent years, salivary metabolome studies have provided new biological information and salivary biomarkers to diagnose different diseases at early stages. The saliva in the oral cavity is influenced by many factors that are reflected in the salivary metabolite profile. Oral microbes can alter the salivary metabolite profile and may express oral inflammation or oral diseases. The released microbial metabolites in the saliva represent the altered biochemical pathways in the oral cavity. This review highlights the oral microbial profile and microbial metabolites released in saliva and its use as a diagnostic biofluid for different oral diseases. The importance of salivary metabolites produced by oral microbes as risk factors for oral diseases and their possible relationship in oral carcinogenesis is discussed.PMID:38786754 | DOI:10.3390/metabo14050277

Metabolites. 2024 May 10;14(5):276. doi: 10.3390/metabo14050276.ABSTRACTMetabolomics profiles from blood, urine, or other body fluids have the potential to assess intakes of foods and nutrients objectively, thereby strengthening nutritional epidemiology research. Metabolomics platforms may include targeted components that estimate the relative concentrations for individual metabolites in a predetermined set, or global components, typically involving mass spectrometry, that estimate relative concentrations more broadly. While a specific metabolite concentration usually correlates with the intake of a single food or food group, multiple metabolites may be correlated with the intake of certain foods or with specific nutrient intakes, each of which may be expressed in absolute terms or relative to total energy intake. Here, I briefly review the progress over the past 20 years on the development and application intake biomarkers for foods/food groups, nutrients, and dietary patterns, primarily by drawing from several recent reviews. In doing so, I emphasize the criteria and study designs for candidate biomarker identification, biomarker validation, and intake biomarker application. The use of intake biomarkers for diet and chronic disease association studies is still infrequent in nutritional epidemiology research. My comments here will derive primarily from our research group's recent contributions to the Women's Health Initiative cohorts. I will complete the contribution by describing some opportunities to build on the collective 20 years of effort, including opportunities related to the metabolomics profiling of blood and urine specimens from human feeding studies that approximate habitual diets.PMID:38786753 | DOI:10.3390/metabo14050276

Metabolites. 2024 May 10;14(5):275. doi: 10.3390/metabo14050275.ABSTRACTMetabolomics, especially urine-based studies, offers incredible promise for the discovery and development of clinically impactful biomarkers. However, due to the unique challenges of urine, a highly precise and reproducible workflow for NMR-based urine metabolomics is lacking. Using 1D and 2D non-uniform sampled (NUS) 1H-13C NMR spectroscopy, we systematically explored how changes in hydration or specific gravity (SG) and pH can impact biomarker discovery. Further, we examined additional sources of error in metabolomics studies and identified Navigator molecules that could monitor for those biases. Adjustment of SG to 1.002-1.02 coupled with a dynamic sum-based peak thresholding eliminates false positives associated with urine hydration and reduces variation in chemical shift. We identified Navigator molecules that can effectively monitor for inconsistencies in sample processing, SG, protein contamination, and pH. The workflow described provides quality assurance and quality control tools to generate high-quality urine metabolomics data, which is the first step in biomarker discovery.PMID:38786752 | DOI:10.3390/metabo14050275

Metabolites. 2024 May 8;14(5):272. doi: 10.3390/metabo14050272.ABSTRACTTranscriptomics and metabolomics offer distinct advantages in investigating the differentially expressed genes and cellular entities that have the greatest influence on end-phenotype, making them crucial techniques for studying plant quality and environmental responses. While numerous relevant articles have been published, a comprehensive summary is currently lacking. This review aimed to understand the global and longitudinal research trends of transcriptomics and metabolomics in plant quality and environmental response (TMPQE). Utilizing bibliometric methods, we presented a comprehensive science mapping of the social structure, conceptual framework, and intellectual foundation of TMPQE. We uncovered that TMPQE research has been categorized into three distinct stages since 2020. A citation analysis of the 29 most cited articles, coupled with a content analysis of recent works (2020-2023), highlight five potential research streams in plant quality and environmental responses: (1) biosynthetic pathways, (2) abiotic stress, (3) biotic stress, (4) development and ripening, and (5) methodologies and tools. Current trends and future directions are shaped by technological advancements, species diversity, evolving research themes, and an environmental ecology focus. Overall, this review provides a novel and comprehensive perspective to understand the longitudinal trend on TMPQE.PMID:38786749 | DOI:10.3390/metabo14050272