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The niche of aphids is largely defined by their consumption of plant phloem sap and its composition, including nutrients and specialized metabolites. Niche construction is the change of the environment by organisms, which may influence the fitness of these organisms and their offspring. To better understand interactions between plants and aphids, it is necessary to investigate whether aphids modify the chemical composition of the phloem sap of their host plants and whether conspecifics are affected by previous infestation. In the current study, ears of wheat (Triticum aestivum) plants were infested with clonal lineages of the English grain aphid (Sitobion avenae) or were left uninfested. The metabolic composition of ear phloem sap exudates was analyzed through amino acid profiling and metabolic fingerprinting. Aphids of the clonal lineages were either put on previously aphid-infested or on uninfested ears and their colony sizes followed over time. Furthermore, it was investigated whether aphids choose one treatment group over another. Sitobion avenae infestation affected the relative concentrations of some metabolites in the phloem exudates of the ears. Compared to uninfested plants, the relative concentration of asparagine was higher after aphid infestation. Colonies grew significantly larger on previously aphid-infested ears, which the aphids also clearly chose in the choice experiment. The pronounced positive effect of previous infestation on aphid colonies indicates niche construction, while the choice of these constructed niches reveals niche choice by S. avenae on wheat. The interplay between these different niche realization processes highlights the complexity of interactions between aphids and their hosts. [ABSTRACT FROM AUTHOR]

Prostate cancer (PCa) is the second most common cancer in males worldwide. The Gleason scoring system, which classifies the pathological growth pattern of cancer, is considered one of the most important prognostic factors for PCa. Compared to indolent PCa, PCa with high Gleason score (h‐GS PCa, GS ≥ 8) has greater clinical significance due to its high aggressiveness and poor prognosis. It is crucial to establish a rapid, non‐invasive diagnostic modality to decipher patients with h‐GS PCa as early as possible. In this study, ferric nanoparticle‐assisted laser desorption/ionization mass spectrometry (FeNPALDI‐MS) to extract prostate fluid metabolic fingerprint (PSF‐MF) is employed and combined with the clinical features of patients, such as prostate‐specific antigen (PSA), to establish a multi‐modal diagnosis assisted by machine learning. This approach yields an impressive area under the curve (AUC) of 0.87 to diagnose patients with h‐GS, surpassing the results of single‐modal diagnosis using only PSF‐MF or PSA, respectively. Additionally, using various screening methods, six key metabolites that exhibit greater diagnostic efficacy (AUC = 0.96) are identified. These findings also provide insights into related metabolic pathways, which may provide valuable information for further elucidation of the pathological mechanisms underlying h‐GS PCa. [ABSTRACT FROM AUTHOR]

Developing a standardized screening tool for the detection of early and small hepatocellular carcinoma (HCC) through urinary metabolic analysis poses a challenging yet intriguing research endeavor. In this study, a range of intricately interlaced 2D rough nanosheets featuring well‐defined sharp edges is fabricated, with the aim of constructing diverse trimetal oxide heterojunctions exhibiting multiscale structures. By carefully engineering synergistic effects in composition and structure, including improved adsorption, diffusion, and other surface‐driven processes, the optimized heterojunctions demonstrate a substantial enhancement in signal intensity compared to monometallic or bimetallic oxides, as well as fragmented trimetallic oxides. Additionally, optimal heterojunctions enable the extraction of high‐quality urinary metabolic fingerprints using high‐throughput mass spectrometry. Leveraging machine learning, discrimination of HCC patients from high‐risk and healthy populations achieves impressive performance, with area under the curve values of 0.940 and 0.916 for receiver operating characteristic and precision‐recall curves, respectively. Six crucial metabolites are identified, enabling accurate detection of early, small‐tumor, alpha‐fetoprotein‐negative HCC (93.3%–97.3%). A comprehensive screening strategy tailored to clinical reality yields precision metrics (accuracy, precision, recall, and F1 score) exceeding 95.0%. This study advances the application of cutting‐edge matrices‐based metabolic phenotyping in practical clinical diagnostics. [ABSTRACT FROM AUTHOR]

The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. The nutritional components and quality of milk are influenced by the rumen microbiota and its metabolites at different lactation stages. Hence, rumen fluid and milk samples from 6 dairy cows fed the same diet were collected during peak lactation, early mid-lactation, and later mid-lactation. Untargeted metabolomics and 16S rRNA sequencing were applied for analyzing milk and rumen metabolites, as well as rumen microbial composition, respectively. The levels of lipid-related metabolites, l-glutamate, glucose-1-phosphate, and acetylphosphate in milk exhibited lactation-dependent attenuation. Maltol, N -acetyl-d-glucosamine, and choline, which are associated with milk flavor or coagulation properties, as well as l-valine, lansioside A, clitocine, and ginsenoside La, increased significantly in early mid-lactation and later mid-lactation, especially in later mid-lactation. The obvious increase in rumen microbial diversities (ACE and Shannon indices) were observed in early mid-lactation compared with peak lactation. Twenty-one differential bacterial genera of the rumen were identified, with Succinivibrionaceae_UCG-001 , Candidatus Saccharimonas , Fibrobacter , and SP3-e08 being significantly enriched in peak lactation. Rikenellaceae_RC9_gut_group , Eubacterium_ruminantium_group , Lachnospira , Butyrivibrio , Eubacterium_hallii_group , and Schwartzia were most significantly enriched in early mid-lactation. In comparison, only 2 bacteria (unclassified_f__Prevotellaceae and Prevotellaceae_UCG-001) were enriched in later mid-lactation. For rumen metabolites, LysoPE(16:0), l-glutamate, and l-tyrosine had higher levels in peak lactation, whereas PE(17:0/0:0), PE(16:0/0:0), PS(18:1(9Z)/0:0), l-phenylalanine, dulcitol, 2-(methoxymethyl)furan, and 3-phenylpropyl acetate showed higher levels in early mid-lactation and later mid-lactation. Multiomics-integrated analysis revealed that a greater abundance of Fibrobacter contributed to phospholipid content in milk by increasing ruminal acetate, l-glutamate, and LysoPE(16:0). Prevotellaceae_UCG-001 and unclassified_f_Prevotellaceae provide substrates for milk metabolites of the same category by increasing ruminal l-phenylalanine and dulcitol contents. These results demonstrated that milk metabolomic fingerprints and critical functional metabolites during lactation, and the key bacteria in rumen related to them. These findings provide new insights into the development of functional dairy products. [ABSTRACT FROM AUTHOR]

Mitoxantrone (MTX) is a therapeutic agent used in the treatment of solid tumors and multiple sclerosis, recognized for its cardiotoxicity, with underlying molecular mechanisms not fully disclosed. The cardiotoxicity is influenced by risk factors, including age. Our study intended to assess the molecular effect of MTX on the cardiac muscle of old male CD-1 mice. Mice aged 19 months received a total cumulative dose of 4.5 mg/kg of MTX (MTX group) or saline solution (CTRL group). Two months post treatment, blood was collected, animals sacrificed, and the heart removed. MTX caused structural cardiac changes, which were accompanied by extracellular matrix remodeling, as indicated by the increased ratio between matrix metallopeptidase 2 and metalloproteinase inhibitor 2. At the metabolic level, decreased glycerol levels were found, together with a trend towards increased content of the electron transfer flavoprotein dehydrogenase. In contrast, lower glycolysis, given by the decreased content of glucose transporter GLUT4 and phosphofructokinase, seemed to occur. The findings suggest higher reliance on fatty acids oxidation, despite no major remodeling occurring at the mitochondrial level. Furthermore, the levels of glutamine and other amino acids (although to a lesser extent) were decreased, which aligns with decreased content of the E3 ubiquitin-protein ligase Atrogin-1, suggesting a decrease in proteolysis. As far as we know, this was the first study made in old mice with a clinically relevant dose of MTX, evaluating its long-term cardiac effects. Even two months after MTX exposure, changes in metabolic fingerprint occurred, highlighting enduring cardiac effects that may require clinical vigilance. [Display omitted] • First study regarding late-onset chronic cardiotoxicity in aged MTX-treated mice. • Cardiac metabolic remodeling remained in aged heart two months post-MTX treatment. • Molecular outcomes include increased FAO and oxidation of amino acids after MTX. • Decreased glycolysis and UPP-mediated proteolysis were also seen in these aged mice. [ABSTRACT FROM AUTHOR]

Supplemental oxygen (hyperoxia) improves physical performance during hypoxic exercise. Based on the analysis of metabolome and iron homeostasis from human athlete blood samples, we show that hyperoxia during recovery periods interferes with metabolic alterations following hypoxic exercise. This may impair beneficial adaptations to exercise and/or hypoxia and highlights risks of oxygen supplementation in hypoxia. [Display omitted] • Hyperoxic recovery does not improve training efficiency of hypoxic exercise in healthy men. • Hyperoxic recovery prevents inter-session improvements of hypoxic exercise. • Hyperoxic recovery abolishes the metabolic fingerprint of hypoxic exercise after one week. • Hyperoxic recovery during hypoxic exercise may reduce hypoxia acclimatization, potentially increasing the risks of hypoxia. [ABSTRACT FROM AUTHOR]

Renal cell carcinoma (RCC) is a substantial pathology of the urinary system with a growing prevalence rate. However, current clinical methods have limitations for managing RCC due to the heterogeneity manifestations of the disease. Metabolic analyses are regarded as a preferred noninvasive approach in clinics, which can substantially benefit the characterization of RCC. This study constructs a nanoparticle‐enhanced laser desorption ionization mass spectrometry (NELDI MS) to analyze metabolic fingerprints of renal tumors (n = 456) and healthy controls (n = 200). The classification models yielded the areas under curves (AUC) of 0.938 (95% confidence interval (CI), 0.884–0.967) for distinguishing renal tumors from healthy controls, 0.850 for differentiating malignant from benign tumors (95% CI, 0.821–0.915), and 0.925–0.932 for classifying subtypes of RCC (95% CI, 0.821–0.915). For the early stage of RCC subtypes, the averaged diagnostic sensitivity of 90.5% and specificity of 91.3% in the test set is achieved. Metabolic biomarkers are identified as the potential indicator for subtype diagnosis (p < 0.05). To validate the prognostic performance, a predictive model for RCC participants and achieve the prediction of disease (p = 0.003) is constructed. The study provides a promising prospect for applying metabolic analytical tools for RCC characterization. [ABSTRACT FROM AUTHOR]

Pancreatic cancer is highly lethal, and survival chances improve only with early detection at a precancerous stage. However, there remains a significant gap in developing tools for large‐scale, rapid screening. To this end, a high‐throughput On‐Target Array Extraction Platform (OTAEP) by direct sintering of a series of metal–organic frameworks (MOFs) for dual in situ extraction, encompassing both exosomes and their metabolic profiles, is developed. Based on the principle of geometry‐dependent photothermal conversion efficiency and standard testing, the appropriate MOF functional unit is identified. This unit enables exosome enrichment within 10 min and metabolic fingerprint extraction in under 1 s of laser irradiation, with over five reuse. To further accelerate and enhance the quality of metabolic profile analysis, the application of Surrogate Variable Analysis to eliminate hidden confounding factors within the profiles is proposed, and five biomarkers demonstrated by MS/MS experiments are identified. These biomarkers enable early diagnosis, risk stratification, and staging of pancreatic cancer simultaneously, with sensitivity of 94.1%, specificity of 98.8%, and precision of 94.9%. This work represents a breakthrough for overcoming throughput challenges in large‐scale testing and for addressing confounding factors in big data analysis. [ABSTRACT FROM AUTHOR]

Using a new red blood cell (RBC) metabolite extraction protocol, we performed a metabolomic analysis on RBCs in rheumatoid arthritis (RA) patients treated or not with methotrexate (MTX), with the two following objectives: to compare the RBC metabolic profiles of MTX-naïve RA patients and healthy controls (HC), and to investigate whether RBC profiles before and after MTX treatment in RA differed between responders and non-responders. Plasma analysis was performed in parallel. Metabolites were extracted and identified in RBCs and plasma by liquid chromatography-mass spectrometry. We compared the metabolomic fingerprints of 31 DMARD-naïve RA patients and 39 HCs. We also compared the RBC and plasma metabolomes of 25 RA patients who responded or not to MTX therapy before (M0) and after a 3-month treatment period (M3). Significance was determined by Storey's false discovery rate (FDR) q-values to correct for multiple testing. RA patients and HCs differed in the metabolomic signature of RBCs. The signature mainly contained amino acids (AA). Eleven metabolites, including 4 metabolites belonging to the carbohydrate subclass and 2 amino acids (creatine and valine) showed accumulation in RBCs from RA patients. Conversely, citrulline (fold change = 0.83; q = 0.025), histidine (fold change = 0.86; q = 0.014) and ergothioneine (EGT) (fold change = 0.66; q = 0.024), were lower in RBC of RA patients. Five plasma metabolites, including succinic acid and hydroxyproline, were higher in RA patients, and 7 metabolites, including DHEA sulfate, alanine, threonine and ornithine, were lower. Among RA patients undergoing MTX treatment pre-treatment (M0), EGT values were significantly lower in non-responders. In conclusion, low RBC levels of EGT, a food-derived AA barely detectable in plasma, characterize DMARD naïve RA patients and lack of response to MTX treatment. [ABSTRACT FROM AUTHOR]

Digitalis purpurea L. is one of the important plant species of Nilgiris, Kashmir and Darjeeling regions of India, belonging to the family Plantaginaceae, with well-known pharmacological applications. In the present investigation, an in vitro culture technique of indirect shoot organogenesis of D. purpurea is being explored; the biochemical attributes, the antioxidant activities and the metabolomic analyses were made by utilizing untargeted Gas Chromatography-Mass Spectrometry (GC-MS) and Ultra Performance Liquid Chromatography coupled with electronspray ionization/quadrupole-time-of-flight-mass spectrometry (UPLC-ESI-QTOF-MS) approaches. Initially, the leaf explants were used for callus induction and proliferation and maximum callusing frequency (94.44%) and fresh biomass (4.9 g) were obtained on MS, fortified with 8.8 µM BAP (6-benzyl amino purine) + 0.9 µM 2,4-D (2,4-dichlorophenoxyacetic acid), subsequently shoot formation (indirect organogenesis) was noted on the same MS medium with a shoot induction frequency of 83.33%. Later on, the biochemical and antioxidant potential of in vivo-, in vitro grown leaf and leaf derived callus were assessed. Significantly higher total phenol, flavonoid, DPPH (2,2-diphenyl-1-picrylhydrazyl), POD (peroxidase) and SOD (superoxide dismutase) activities were noticed in in vitro grown callus and leaf tissues compared with field grown leaf. The GC-MS analysis of each methanolic extract (in vivo-, in vitro derived leaf and leaf derived callus) displayed the presence of more than 75 bioactive compounds viz loliolide, stigmasterin, alpha-tocopherol, squalene, palmitic acid, linoleic acid, beta-amyrin, campesterol etc. possessing immense therapeutic importance. The UPLC-MS based metabolite fingerprinting of each methanolic extracts were conducted in both positive and negative ionization mode. The obtained results revealed variation in phytochemical composition in field - and laboratory grown tissues, indicating the impact of in vitro culture conditions on plant tissues. The detected phytocompounds belongs to various classes such as flavonoids, steroids, terpenoids, carbohydrates, tannins, lignans etc. The medicinally important metabolites identified were 20, 22-dihydrodigoxigenin, digoxigenin monodigitoxoside, apigenin, luteolin, kaempferide, rosmarinic acid, nepitrin and others. The results of the present study suggest that in vitro culture of D. purpurea could successfully be utilized for the novel drug discovery by producing such important phytocompounds of commercial interest in shorter duration without harming the plants' natural population. [ABSTRACT FROM AUTHOR]

Bath sponges, included in the Spongiidae (Demospongiae: Dictyoceratida), are distributed across the oceans of the world, with a greater abundance in temperate, subtropical, and tropical zones. Their harvest started during ancient times in the Mediterranean Sea, which shed light on the whole family. Most of the Mediterranean Spongiidae have been reported repeatedly from the Northeast Atlantic, notably along the Iberian peninsula. The aim of this study was to clarify the taxonomic status of these sponges after a recent sampling effort in the French Pays Basque and the Spanish Cantabria, complemented by several Mediterranean Sea stations, including the Strait of Gibraltar. An integrative taxonomic approach was adopted for the first time on a set of Spongiidae samples from the Northeast Atlantic, assessing the congruence of analyses of: (i) the morphology of the spongin skeleton; (ii) two different parts of the genome (mitochondrial and nuclear); and (iii) metabolomic fingerprints, to delimit the frontiers between species better. A new bath sponge species is described herein, and significant advances and changes are proposed for the systematics of the family. [ABSTRACT FROM AUTHOR]

To comply with a more circular and environmentally friendly European common agricultural policy, while also valorising sunflower by-products, an ultrasound assisted extraction (UAE) was tested to optimise ethanol-wash solutes (EWS). Furthermore, the capabilities of DART-HRMS as a rapid and cost-effective tool for determining the biochemical changes after valorisation of these defatted sunflower EWS were investigated. Three batches of EWS were doubly processed into optimised EWS (OEWS) samples, which were analysed via DART-HRMS. Then, the metabolic profiles were submitted to a univariate analysis followed by a partial least square discriminant analysis (PLS-DA) allowing the identification of the 15 most informative ions. The assessment of the metabolomic fingerprinting characterising EWS and OEWS resulted in an accurate and well-defined spatial clusterization based on the retrieved pool of informative ions. The outcomes highlighted a significantly higher relative abundance of phenolipid hydroxycinnamoyl-glyceric acid and a lower incidence of free fatty acids and diglycerides due to the ultrasound treatment. These resulting biochemical changes might turn OEWS into a natural antioxidant supplement useful for controlling lipid oxidation and to prolong the shelf-life of foods and feeds. A standardised processing leading to a selective concentration of the desirable bioactive compounds is also advisable. [ABSTRACT FROM AUTHOR]

This article reviews innovative diagnostic approaches for diabetic retinopathy as the prevalence of diabetes mellitus and its complications continue to escalate. Novel techniques focus on early disease detection. Technological innovations, such as teleophthalmology, smartphone-based photography, artificial intelligence with deep learning, or widefield photography, can enhance diagnostic accuracy and accelerate the treatment. The review highlights teleophthalmology and handheld photography as promising solutions for remote eye care. These methods revolutionize diabetic retinopathy screening, offering cost-effective and accessible solutions. However, the use of these techniques may be limited by insurance coverage in certain world regions. Ultra-widefield photography offers a comprehensive view of up to 80.0% of the retina in a single image, compared to the 34.0% coverage of the traditional seven-field imaging protocol. It allows retinal imaging without pupil dilation, especially for individuals with compromised mydriasis. However, they also have drawbacks, including high costs, artifacts from eyelashes, eyelid margins, and peripheral distortion. Recent advances in artificial intelligence and machine learning, particularly through convolutional neural networks, are revolutionizing diabetic retinopathy diagnostics, enhancing screening efficiency and accuracy. FDA-approved Artificial Intelligence-powered devices such as LumineticsCore™, EyeArt, and AEYE Diagnostic Screening demonstrate high sensitivity and specificity in diabetic retinopathy detection. While Artificial Intelligence offers the potential to improve patient outcomes and reduce treatment costs, challenges such as dataset biases, high initial costs, and cybersecurity risks must be considered to ensure safety and efficiency. Nanotechnology advancements further enhance diagnosis, offering highly branched polyethyleneimine particles with fluorescein sodium (PEI-NHAc-FS) for better fluorescein angiography or vanadium oxide-based metabolic fingerprinting for early detection. [ABSTRACT FROM AUTHOR]

Cellular senescence is characterized by stable cell cycle arrest. Senescent cells exhibit a senescence‐associated secretory phenotype that can promote tumor progression. The aim of our study was to identify specific nuclear magnetic resonance (NMR) spectroscopy‐based markers of cancer cell senescence. For metabolic studies, we employed murine liver carcinoma Harvey Rat Sarcoma Virus (H‐Ras) cells, in which reactivation of p53 expression induces senescence. Senescent and nonsenescent cell extracts were subjected to high‐resolution proton (1H)‐NMR spectroscopy‐based metabolomics, and dynamic metabolic changes during senescence were analyzed using a magnetic resonance spectroscopy (MRS)‐compatible cell perfusion system. Additionally, the ability of intact senescent cells to degrade the extracellular matrix (ECM) was quantified in the cell perfusion system. Analysis of senescent H‐Ras cell extracts revealed elevated sn‐glycero‐3‐phosphocholine, myoinositol, taurine, and creatine levels, with decreases in glycine, o‐phosphocholine, threonine, and valine. These metabolic findings were accompanied by a greater degradation index of the ECM in senescent H‐Ras cells than in control H‐Ras cells. MRS studies with the cell perfusion system revealed elevated creatine levels in senescent cells on Day 4, confirming the 1H‐NMR results. These senescence‐associated changes in metabolism and ECM degradation strongly impact growth and redox metabolism and reveal potential MRS signals for detecting senescent cancer cells in vivo. [ABSTRACT FROM AUTHOR]

Multiple sclerosis (MS) is the most common inflammatory neurodegenerative disease of the central nervous system (CNS) in young adults and results in progressive neurological defects. The relapsing-remitting phenotype (RRMS) is the most common disease course in MS, which ultimately progresses to secondary progressive MS (SPMS), while primary progressive MS (PPMS) is a type of MS that worsens gradually over time without remissions. There is a gap in knowledge regarding whether the relapsing form can be distinguished from the progressive course, or healthy subjects (HS) based on an altered serum metabolite profile. In this study, we performed global untargeted metabolomics with the 2D GC-GC-MS platform to identify altered metabolites between RRMS, PPMS, and HS. We profiled 235 metabolites in the serum of patients with RRMS (n = 41), PPMS (n = 31), and HS (n = 91). A comparison of RRMS and HS patients revealed 22 significantly altered metabolites at p < 0.05 (false-discovery rate [FDR] = 0.3). The PPMS and HS comparisons revealed 28 altered metabolites at p < 0.05 (FDR = 0.2). Pathway analysis using MetaboAnalyst revealed enrichment of four metabolic pathways in both RRMS and PPMS (hypergeometric test p < 0.05): (1) galactose metabolism; (2) amino sugar and nucleotide sugar metabolism; (3) phenylalanine, tyrosine, and tryptophan biosynthesis; and (4) aminoacyl-tRNA biosynthesis. The Qiagen IPA enrichment test identified the sulfatase 2 (SULF2) (p = 0.0033) and integrin subunit beta 1 binding protein 1 (ITGB1BP1) (p = 0.0067) genes as upstream regulators of altered metabolites in the RRMS vs. HS groups. However, in the PPMS vs. HS comparison, valine was enriched in the neurodegeneration of brain cells (p = 0.05), and heptadecanoic acid, alpha-ketoisocaproic acid, and glycerol participated in inflammation in the CNS (p = 0.03). Overall, our study suggests that RRMS and PPMS may contribute metabolic fingerprints in the form of unique altered metabolites for discriminating MS disease from HS, with the potential for constructing a metabolite panel for progressive autoimmune diseases such as MS. [ABSTRACT FROM AUTHOR]

Pregnancy is a complex process involving complex molecular interaction networks, such as between miRNA-protein, protein-protein, metabolite-metabolite, and protein-metabolite interactions. Advances in technology have led to the identification of many pregnancy-associated microRNA (miRNA), protein, and metabolite fingerprints in dairy cows. An array of miRNA, protein, and metabolite fingerprints produced during the early pregnancy of dairy cows were described. We have found the in silico interaction networks between miRNA-protein, protein-protein, metabolite-metabolite, and protein-metabolite. We have manually constructed miRNA-protein-metabolite interaction networks such as bta-miR-423-3p-IGFBP2-PGF2α interactomes. This interactome is obtained by manually combining the interaction network formed between bta-miR-423-3p-IGFBP2 and the interaction network between IGFBP2-PGF2α with IGFBP2 as a common interactor with bta-miR-423-3p and PGF2α with the provided sources of evidence. The interaction between bta-miR-423-3p and IGFBP2 has many sources of evidence including a high miRanda score of 169, minimumfree energy (MFE) score of -25.14, binding probability (p) of 1, and energy of -25.5. The interaction between IGFBP2 and PGF2α occurs at high confidence scores (≥0.7 or 70%). Interestingly, PGF2α is also found to interact with different metabolites, such as PGF2α-PGD2, PGF2α-thromboxane B2, PGF2α-PGE2, and PGF2α-6-keto-PGF1α at high confidence scores (≥0.7 or 70%). Furthermore, the interactions between C3-PGE2, C3-PGD2, PGE2-PGD2, PGD2-thromboxane B2, PGE2-thromboxane B2, 6-keto-PGF1α-thromboxane B2, and PGE2-6-keto-PGF1α were also obtained at high confidence scores (≥0.7 or 70%). Therefore, we propose that miRNA-protein-metabolite interactomes involving miRNA, protein, andmetabolite fingerprints of early pregnancy of dairy cows such as bta-miR-423-3p, IGFBP2, PGF2α, PGD2, C3, PGE2, 6-keto-PGF1 alpha, and thromboxane B2may form the key regulatory networks and players of pregnancy regulation in dairy cows. This is the first study involving miRNA-protein-metabolite interactomes obtained in the early pregnancy stage of dairy cows. [ABSTRACT FROM AUTHOR]

Introduction: Exploring metabolic changes within host E. coli through an untargeted metabolomic study of T7L variants overexpression to optimize engineered endolysins for clinical/therapeutic use. Aim and objective: This study aims to assess the impact of overexpressing T7L variants on the metabolic profiles of E. coli. The two variants considered include T7L-H37A, which has enhanced lytic activity compared to its wild-type protein, and T7L-H48K, a dead mutant with no significant activity. Methods: 1H NMR-based metabolomics was employed to compare the metabolic profiles of E. coli cells overexpressing T7L wild-type protein and its variants. Results: Overexpression of the T7L wild-type (T7L-WT) protein and its variants (T7L-H48K and T7L-H37A) was compared to RNAP overexpression in E. coli cells using 1H NMR-based metabolomics, analyzing a total of 75 annotated metabolites, including organic acids, amino acids, sugars, and nucleic acids. The results showed distinct clustering patterns for the two T7L variant groups compared with the WT, in which the dead mutant (H48K) group showed clustering close to that of RNAP. Pathway impact analysis revealed different effects of T7L variants on E. coli metabolic profiles, with T7L-H48K showing minimal alterations in energy and amino acid pathways linked to osmotic stress compared to noticeable alterations in these pathways for both T7L-H37A and T7L-WT. Conclusions: This study uncovered distinct metabolic fingerprints when comparing the overexpression of active and inactive mutants of T7L lytic enzymes in E. coli cells. These findings could contribute to the optimization and enhancement of suitable endolysins as potential alternatives to antibiotics. [ABSTRACT FROM AUTHOR]

The ecological niche of aphids is largely defined by the phloem sap of plants, which they consume. Previous studies revealed that aphids may change the phloem sap metabolome and that a previous aphid infestation may benefit them. As aphids reproduce parthenogenetically, the performance and preference of aphids of the same monoclonal aphid lineage can be compared between previously uninfested and previously aphid‐infested plants.Wheat (Triticum aestivum) leaves were infested by clonal lineages of Sitobion avenae aphids for 9–10 days. Leaf phloem exudates were analysed using amino acid profiling and metabolic fingerprinting, capturing mainly specialised metabolites. It was investigated whether aphid colony sizes differ between leaves that were previously uninfested or infested by aphids of a different clonal lineage or by aphids of the same clonal lineage and whether aphids show a preference for any of these leaves.The relative concentrations of metabolites in the phloem exudates were not affected by aphid infestation. Aphid colonies were partly larger on previously aphid‐infested leaves than on control leaves. However, this effect was transient and finally colonies had similar sizes. Given a choice between previously aphid‐infested and uninfested leaves, S. avenae nymphs showed no preferences.The findings that positive effects of previous aphid infestation on aphid performance are transient and that the performance but not the preference of the aphids was affected by previous aphid infestation highlight the temporal dynamics and complexity of aphid–plant interactions. Niche alteration processes may be partly involved in these interactions. [ABSTRACT FROM AUTHOR]

Bioelectrochemical systems offer unique opportunities to remove recalcitrant environmental pollutants in a net positive energy process, although it remains challenging because of the toxic character of such compounds. In this study, microbial fuel cell (MFC) technology was applied to investigate the benzene degradation process for more than 160 days, where glucose was used as a cometabolite and a control. We have applied an inoculation strategy that led to the development of 10 individual microbial communities. The electrochemical dynamics of MFC efficiency was observed, along with their 1H NMR metabolic fingerprints and analysis of the microbial community. The highest power density of 120 mW/m2 was recorded in the final period of the experiment when benzene/glucose was used as fuel. This is the highest value reported in a benzene/co-substrate system. Metabolite analysis confirmed the full removal of benzene, while the dominance of fermentation products indicated the strong occurrence of non-electrogenic reactions. Based on 16S rRNA gene amplicon sequencing, bacterial community analysis revealed several petroleumdegrading microorganisms, electroactive species and biosurfactant producers. The dominant species were recognised as Citrobacter freundii and Arcobacter faecis. Strong, positive impact of the presence of benzene on the alpha diversity was recorded, underlining the high complexity of the bioelectrochemically supported degradation of petroleum compounds. This study reveals the importance of supporting the bioelectrochemical degradation process with auxiliary substrates and inoculation strategies that allow the communities to reach sufficient diversity to improve the power output and degradation efficiency in MFCs beyond the previously known limits. This study, for the first time, provides an outlook on the syntrophic activity of biosurfactant producers and petroleum degraders towards the efficient removal and conversion of recalcitrant hydrophobic compounds into electricity in MFCs. [ABSTRACT FROM AUTHOR]

Background: Angelman syndrome (AS) is a rare neurodevelopmental genetic disorder caused by the loss of function of the ubiquitin ligase E3A (UBE3A) gene, affecting approximately 1:15,000 live births. We have recently shown that mitochondrial function in AS is altered during mid to late embryonic brain development leading to increased oxidative stress and enhanced apoptosis of neural precursor cells. However, the overall alterations of metabolic processes are still unknown. Hence, as a follow-up, we aim to investigate the metabolic profiles of wild-type (WT) and AS littermates and to identify which metabolic processes are aberrant in the brain of AS model mice during embryonic development. Methods: We collected brain tissue samples from mice embryos at E16.5 and performed metabolomic analyses using proton nuclear magnetic resonance (1H-NMR) spectroscopy. Multivariate and Univariate analyses were performed to determine the significantly altered metabolites in AS mice. Pathways associated with the altered metabolites were identified using metabolite set enrichment analysis. Results: Our analysis showed that overall, the metabolomic fingerprint of AS embryonic brains differed from those of their WT littermates. Moreover, we revealed a significant elevation of distinct metabolites, such as acetate, lactate, and succinate in the AS samples compared to the WT samples. The elevated metabolites were significantly associated with the pyruvate metabolism and glycolytic pathways. Limitations: Only 14 metabolites were successfully identified and investigated in the present study. The effect of unidentified metabolites and their unresolved peaks was not determined. Additionally, we conducted the metabolomic study on whole brain tissue samples. Employing high-resolution NMR studies on different brain regions could further expand our knowledge regarding metabolic alterations in the AS brain. Furthermore, increasing the sample size could reveal the involvement of more significantly altered metabolites in the pathophysiology of the AS brain. Conclusions: Ube3a loss of function alters bioenergy-related metabolism in the AS brain during embryonic development. Furthermore, these neurochemical changes could be linked to the mitochondrial reactive oxygen species and oxidative stress that occurs during the AS embryonic development. [ABSTRACT FROM AUTHOR]

Introduction: Metabolic reprogramming is a hallmark feature of pancreatic ductal adenocarcinoma (PDAC). A pancreatic juice (PJ) metabolic signature has been reported to be prognostic of oncological outcome for PDAC. Integration of PJ profiling with transcriptomic and spatial characterization of the tumor microenvironment would help in identifying PDACs with peculiar vulnerabilities. Methods: We performed a transcriptomic analysis of 26 PDAC samples grouped into 3 metabolic clusters (M_CL) according to their PJ metabolic profile. We analyzed molecular subtypes and transcriptional differences. Validation was performed by multidimensional imaging on tumor slides. Results: Pancreatic juice metabolic profiling was associated with PDAC transcriptomic molecular subtypes (p=0.004). Tumors identified as M_CL1 exhibited a non-squamous molecular phenotype and demonstrated longer survival. Enrichment analysis revealed the upregulation of immune genes and pathways in M_CL1 samples compared to M_CL2, the group with worse prognosis, a difference confirmed by immunofluorescence on tissue slides. Enrichment analysis of 39 immune signatures by xCell confirmed decreased immune signatures in M_CL2 compared to M_CL1 and allowed a stratification of patients associated with longer survival. Discussion: PJ metabolic fingerprints reflect PDAC molecular subtypes and the immune microenvironment, confirming PJ as a promising source of biomarkers for personalized therapy. [ABSTRACT FROM AUTHOR]

High‐depth whole‐genome resequencing of 53 diverse fig tree genotypes yielded a rich dataset of genetic variants. We successfully identified 5,501,460 single‐nucleotide polymorphisms (SNPs) and 1,228,537 insertions and deletions (InDels), providing a high‐density and excellent‐quality genetic map of the fig tree. We also performed a detailed population structure analysis, dividing the 53 genotypes into three geographical groups and assessing their genetic diversity and divergence. Analysis of structural variants (SVs) and copy number variations (CNVs) revealed their potential functional impact, particularly in plant‐pathogen interaction and secondary metabolism. Metabolomic fingerprinting of fig genotypes uncovered extensive variation in primary metabolites and polyphenolic compounds, highlighting the influence of genotype on fruit quality traits such as nutritional content and bioactive compound composition. The genome‐wide association study (GWAS) identified critical SNPs associated with fruit quality and morphological features. The discovery of significant candidate genes, such as AGL62, GDSL, and COBRA‐like protein 4 genes, offers promising targets for marker‐assisted selection and genome editing approaches to improve fig fruit morphological and quality traits. This extensive genomic analysis of fig trees enhances our understanding of the genetic basis of important agronomic traits and provides a rich resource for future research in this economically and nutritionally significant fruit. [ABSTRACT FROM AUTHOR]

Azadirachta excelsa is a plant belonging to the same genus as the Indian neem (Azadirachta indica) which is expected to have similar biological activities. However, its active components and pharmacological effects are limited. The composition and quantity of these metabolites in A. excelsa may differ due to different polarities of extracting solvents, so selecting an effective extractive solvent with a high level of biological activity is important. Therefore, in this study, we examined differences in the metabolite finger printing using FTIR-based metabolomics, as well as evaluated their antibacterial activity against Staphylococcus aureus (ATCC 6538) and Escherichia coli (ATCC 8739). A. excelsa was extracted using chloroform, ethyl acetate, ethanol p.a., 70% and 50% ethanol. Extracts obtained were analyzed using FTIR and the inhibition zones were then determined. The results showed that principal component analysis (PCA) could distinguish each sample based on the extraction solvent. In this study, we found 50% and 70% ethanol extracts had similar metabolite compositions and concentrations based on their respective FTIR spectrum. The inhibition zone of A. excelsa extracts ranged from 12.37-17.20 mm and 13.88-15.89 mm against S. aureus and E.coli, respectively. The chloroform extract was more effective against both bacteria. Duncan's further test showed that chloroform extract reduced E. coli similarly to ethyl acetate but not S. aureus. While 50%, 70%, and ethanol p.a. extracts inhibited S. aureus and E. coli equally. Based on these results, the polarity of the extracting solvent had an important influence on the metabolite profile and antibacterial activity of A. excelsa. [ABSTRACT FROM AUTHOR]

Skeletal muscle metabolism has implications for swine feed efficiency (FE); however, it remains unclear if the metabolic profile of skeletal muscle changes during postnatal growth. To assess the metabolic changes, samples were collected from the longissimus dorsi (LD, glycolytic muscle), latissimus dorsi (LAT, mixed muscle), and masseter (MS, oxidative muscle) at 20, 53, 87, 120, and 180 days of age from barrows. Muscles were assessed to determine the abundance of several metabolic enzymes. Lactate dehydrogenase (LDHα) decreased in all muscles from 20 to 87 d (p < 0.01), which may be attributed to the muscles being more glycolytic at weaning from a milk-based diet. Pyruvate carboxylase (PC) increased in all muscles at 53 d compared to the other time points (p < 0.01), while pyruvate dehydrogenase α 1 (PDHα1) increased at 87 and 180 d in MS compared to LD (p < 0.05), indicating that potential changes occur in pyruvate entry into the tricarboxylic acid (TCA) cycle during growth. Isolated mitochondria from each muscle were incubated with 13C-labeled metabolites to assess isotopomer enrichment patterns of TCA intermediates. Citrate M + 2 and M + 4 derived from [13C3]-pyruvate increased at 87 d in LAT and MS mitochondria compared to LD mitochondria (p < 0.05). Regardless of the muscle, citrate M+3 increased at 87 d compared to 20, 53, and 120 d, while 180 d showed intermediate values (p < 0.01). These data support the notion that pyruvate metabolism is dynamic during growth. Our findings establish a metabolic fingerprint associated with postnatal muscle hypertrophy. [ABSTRACT FROM AUTHOR]

Background: Rhizoctonia solani is an important plant pathogen worldwide, and causes serious tobacco target spot in tobacco in the last five years. This research studied the biological characteristics of four different anastomosis groups strains (AG-3, AG-5, AG-6, AG-1-IB) of R. solani from tobacco. Using metabolic phenotype technology analyzed the metabolic phenotype differences of these strains. Results: The results showed that the suitable temperature for mycelial growth of four anastomosis group strains were from 20 to 30oC, and for sclerotia formation were from 20 to 25oC. Under different lighting conditions, R. solani AG-6 strains produced the most sclerotium, followed by R. solani AG-3, R. solani AG-5 and R. solani AG-1-IB. All strains had strong oligotrophic survivability, and can grow on water agar medium without any nitrutions. They exhibited three types of sclerotia distribution form, including dispersed type (R. solani AG-5 and AG-6), peripheral type (R. solani AG-1-IB), and central type (R. solani AG-3). They all presented different pathogenicities in tobacco leaves, with the most virulent was noted by R. solani AG-6, followed by R. solani AG-5 and AG-1-IB, finally was R. solani AG-3. R. solani AG-1-IB strains firstly present symptom after inoculation. Metabolic fingerprints of four anastomosis groups were different to each other. R. solani AG-3, AG-6, AG-5 and AG-1-IB strains efficiently metabolized 88, 94, 71 and 92 carbon substrates, respectively. Nitrogen substrates of amino acids and peptides were the significant utilization patterns for R. solani AG-3. R. solani AG-3 and AG-6 showed a large range of adaptabilities and were still able to metabolize substrates in the presence of the osmolytes, including up to 8% sodium lactate. Four anastomosis groups all showed active metabolism in environments with pH values from 4 to 6 and exhibited decarboxylase activities. Conclusions: The biological characteristics of different anastomosis group strains varies, and there were significant differences in the metabolic phenotype characteristics of different anastomosis group strains towards carbon source, nitrogen source, pH, and osmotic pressure. Highlights: Phenotyping differences in carbon, nitrogen, and pH metabolism, as well in osmotic pressure among four anastomosis groups (AG-3, AG-5, AG-6, AG-1-IB) of R. solani were different. Nitrogen substrates of amino acids and peptides were the main metabolic substrates for R. solani AG-3. R. solani AG-3 and AG-6 showed a wide range of adaptability. [ABSTRACT FROM AUTHOR]

Introduction: We assessed the in vitro anti-chlamydial activity of fresh vaginal secretions, deciphering the microbial and metabolic components able to counteract Chlamydia trachomatis viability. Methods: Forty vaginal samples were collected from a group of reproductiveaged women and their anti-chlamydial activity was evaluated by inhibition experiments. Each sample underwent 16S rRNA metabarcoding sequencing to determine the bacterial composition, as well as ¹H-NMR spectroscopy to detect and quantify the presence of vaginal metabolites. Results: Samples characterized by a high anti-chlamydial activity were enriched in Lactobacillus, especially Lactobacillus crispatus and Lactobacillus iners, while notactive samples exhibited a significant reduction of lactobacilli, along with higher relative abundances of Streptococcus and Olegusella. Lactobacillus gasseri showed an opposite behavior compared to L. crispatus, being more prevalent in not-active vaginal samples. Higher concentrations of several amino acids (i.e., isoleucine, leucine, and aspartate; positively correlated to the abundance of L. crispatus and L. jensenii) lactate, and 4-aminobutyrate were the most significant metabolic fingerprints of highly active samples. Acetate and formate concentrations, on the other hand, were related to the abundances of a group of anaerobic opportunistic bacteria (including Prevotella, Dialister, Olegusella, Peptostreptococcus, Peptoniphilus, Finegoldia and Anaerococcus). Finally, glucose, correlated to Streptococcus, Lachnospira and Alloscardovia genera, emerged as a key molecule of the vaginal environment: indeed, the antichlamydial effect of vaginal fluids decreased as glucose concentrations increased. Discussion: These findings could pave the way for novel strategies in the prevention and treatment of chlamydial urogenital infections, such as lactobacilli probiotic formulations or lactobacilli-derived postbiotics. [ABSTRACT FROM AUTHOR]

Raw milk cheeses harbor complex microbial communities. Some of these microorganisms are technologically essential, but undesirable microorganisms can also be present. While most of the microbial dynamics and cross-talking studies involving interaction between food-derived bacteria have been carried out on agar plates in laboratory-controlled conditions, the present study evaluated the modulation of the resident microbiota and the changes of metabolite production directly in ripening raw milk cheese inoculated with Listeria innocua strains. Using a proxy of the pathogenic Listeria monocytogenes, we aimed to establish the key microbiota players and chemical signals that characterize Latteria raw milk cheese over 60 days of ripening time. The microbiota of both the control and Listeria-inoculated cheeses was analyzed using 16S rRNA targeted amplicon sequencing, while direct analysis in real time mass spectrometry (DART-HRMS) was applied to investigate the differences in the metabolic profiles of the cheeses. The diversity analysis showed the same microbial diversity trend in both the control cheese and the inoculated cheese, while the taxonomic analysis highlighted the most representative genera of bacteria in both the control and inoculated cheese: Lactobacillus and Streptococcus. On the other hand, the metabolic fingerprints revealed that the complex interactions between resident microbiota and L. innocua were governed by continuously changing chemical signals. Changes in the amounts of small organic acids, hydroxyl fatty acids, and antimicrobial compounds, including pyroglutamic acid, hydroxy-isocaproic acid, malic acid, phenyllactic acid, and lactic acid, were observed over time in the L. innocua-inoculated cheese. In cheese that was inoculated with L. innocua, Streptococcus was significantly correlated with the volatile compounds carboxylbenzaldheyde and cyclohexanecarboxylic acid, while Lactobacillus was positively correlated with some volatile and flavor compounds (cyclohexanecarboxylic acid, pyroxidal acid, aminobenzoic acid, and vanillic acid). Therefore, we determined the metabolic markers that characterize a raw milk cheese inoculated with L. innocua, the changes in these markers with the ripening time, and the positive correlation of flavor and volatile compounds with the resident microbiota. This multi-omics approach could suggest innovative food safety strategies based on the enhanced management of undesirable microorganisms by means of strain selection in raw matrices and the addition of specific antimicrobial metabolites to prevent the growth of undesirable microorganisms. [ABSTRACT FROM AUTHOR]

Flowers are gaining considerable interest among consumers as ingredients in food, beverages, cosmetics, and natural health products. The supply chain trades in multiple forms of botanicals, including fresh whole flowers, which are easier to identify than dried flowers or flowers processed as powdered or liquid extracts. There is a gap in the scientific methods available for the verification of flower species ingredients traded in the supply chains of multiple markets. The objective of this paper is to develop methods for flower species ingredient verification using two orthogonal methods. More specifically, the objectives of this study employed both (1) DNA-based molecular diagnostic methods and (2) NMR metabolite fingerprint methods in the identification of 23 common flower species ingredients. NMR data analysis reveals considerable information on the variation in metabolites present in different flower species, including color variants within species. This study provides a comprehensive comparison of two orthogonal methods for verifying flower species ingredient supply chains to ensure the highest quality products. By thoroughly analyzing the benefits and limitations of each approach, this research offers valuable insights to support quality assurance and improve consumer confidence. [ABSTRACT FROM AUTHOR]

Introduction: European mistletoe (Viscum album L.) has been gaining increasing interest in the field of oncology as a clinically relevant adjunctive treatment in many forms of cancer. In the field of phytopharmacology, harvesting time is pivotal. In the last century, a form of metabolomic fingerprinting based on pattern formation was proposed as a way to determine optimal harvesting times to ensure high quality of mistletoe as raw material for pharmaceutical use. In order to further evaluate the information obtained with this metabolomic fingerprinting method, we analysed a large time series of previously undigitised daily mistletoe chromatograms dating back to the 1950s. Methods: These chromatograms were scanned and evaluated using computerized image analysis, resulting in 12 descriptors for each individual chromatogram. We performed a statistical analysis of the data obtained, investigating statistical distributions, cross-correlations and time selfcorrelations. Results: The analysed dataset spanning about 27 years, contains 19,037 evaluable chromatograms in daily resolution. Based on the distribution and crosscorrelation analyses, the 12 descriptors could be clustered into six independent groups describing different aspects of the chromatograms. One descriptor was found to mirror the annual rhythm being well correlated with temperature and a phase shift of 10 days. The time self-correlation analysis showed that most other descriptors had a characteristic self-correlation of ~50 days, which points to further infradian rhythms (i.e., more than 24 h). Discussion: To our knowledge, this dataset is the largest of its type. The combination of this form of metabolomic fingerprinting with the proposed computer analysis seems to be a promising tool to characterise biological variations of mistletoe. Additional research is underway to further analyse the different rhythms present in this dataset. [ABSTRACT FROM AUTHOR]

Elephantorrhiza elephantina, a wild plant in southern Africa, is utilized in traditional medicine for various ailments, leading to its endangerment and listing on the Red List of South African Plants. To date, there have been no reports on bacterial endophytes from this plant, their classes of secondary metabolites, and potential medicinal properties. This study presents (i) taxonomic characterization of bacterial endophytes in leaf and root tissues using 16S rRNA, (ii) bacterial isolation, morphological, and phylogenetic characterization, (iii) bacterial growth, metabolite extraction, and LC-MS-based metabolite fingerprinting, and (iv) antimicrobial testing of bacterial crude extracts. Next-generation sequencing yielded 693 and 2,459 DNA read counts for the rhizomes and leaves, respectively, detecting phyla including Proteobacteria, Bacteroidota, Gemmatimonadota, Actinobacteriota, Verrucomicrobiota, Dependentiae, Firmicutes, and Armatimonodata. At the genus level, Novosphingobium, Mesorhizobium, Methylobacterium, and Ralstonia were the most dominant in both leaves and rhizomes. From root tissues, four bacterial isolates were selected, and 16S rRNA-based phylogenetic characterization identified two closely related Pseudomonas sp. (strain BNWU4 and 5), Microbacterium oxydans BNWU2, and Stenotrophomonas maltophilia BNWU1. The ethyl acetate:chloroform (1:1 v/v) organic extract from each isolate exhibited antimicrobial activity against all selected bacterial pathogens. Strain BNWU5 displayed the highest activity, with minimum inhibitory concentrations ranging from 62.5 µg/mL to 250 µg/mL against diarrhoeagenic Escherichia coli, Escherichia coli O157:H7, Salmonella enterica, antibiotic-resistant Vibrio cholerae, Staphylococcus aureus, Bacillus cereus, and Enterococcus durans. LC-MS analysis of the crude extract revealed common antimicrobial metabolites produced by all isolates, including Phenoxomethylpenicilloyl (penicilloyl V), cis-11-Eicosenamide, 3-Hydroxy-3-phenacyloxindole, and 9-Octadecenamide. [ABSTRACT FROM AUTHOR]

Introduction: Aspalathus linearis (commonly known as rooibos) is endemic to the Cape Floristic Region of South Africa and is a popular herbal drink and skin phytotherapeutic ingredient, with health benefits derived primarily from its unique phenolic content. Several, seemingly habitat-specific ecotypes from the Cederberg (Western Cape) and Northern Cape have morphological, ecological, genetic and biochemical differences. Objectives and methods: Despite the commercial popularity of the cultivated variety, the uncultivated ecotypes are largely understudied. To address gaps in knowledge about the biochemical constituency, ultra-performance liquid chromatography-mass spectrometry analysis of fifteen populations was performed, enabling high-throughput metabolomic fingerprinting of 50% (v/v) methanolic extracts. Antioxidant screening of selected populations was performed via three assays and antimicrobial activity on two microbial species was assessed. The metabolomic results were corroborated with total phenolic and flavonoid screening of the extracts. Results and discussion: Site-specific chemical lineages of rooibos ecotypes were confirmed via multivariate data analyses. Important features identified via PLS-DA disclosed higher relative abundances of certain tentative metabolites (e.g., rutin, aspalathin and apiin) present in the Dobbelaarskop, Blomfontein, Welbedacht and Eselbank sites, in comparison to other locations. Several unknown novel metabolites (e.g., m/z 155.0369, 231.0513, 443.1197, 695.2883) are responsible for metabolomic separation of the populations, four of which showed higher amounts of key metabolites and were thus selected for bioactivity analysis. The Welbedacht and Eselbank site 2 populations consistently displayed higher antioxidant activities, with 2,2-azino-bis (3-ethylbenzothiazoline-6-sulphonic acid) (ABTS) radical scavenging activities of 679.894 ± 3.427 µmol Trolox/g dry matter and 635.066 ± 5.140 µmol Trolox/g dry matter, respectively, in correlation with a high number of phenolic and flavonoid compounds. The contribution of the individual metabolites to the pharmacological effectiveness of rooibos remains unknown and as such, further structural elucidation and phytopharmacological testing is thus urgently needed. [ABSTRACT FROM AUTHOR]

This study investigated the chemical and sensory distinctions in tomato fruits from three Physalis species (P. ixocarpa, P. angulata, and P. philadelphica) found in Michoacán, Mexico, using metabolomic fingerprinting through GC-MS analysis. The objective was to identify organoleptic differences that could influence consumer preferences, highlighting the significance of these species' unique traits. These species represented a valuable genetic reservoir for potential hybridization or selection aimed at enhancing commercial varieties by focusing on organoleptic properties rather than traditional selection criteria like fruit size or yield. This research emphasizes the importance of preserving Mexican biodiversity and providing insights into domestication processes that prioritize flavor and sensory qualities. By analyzing metabolite profiles and their correlation with taste preferences, this study contributes to understanding how these differences could be leveraged in breeding programs to develop new tomato varieties with preferred flavors. It was suggested that variations in taste among the species are mainly due to differences in metabolite expression. This knowledge underscores the importance of organoleptic properties in the selection and domestication of edible fruits, offering a pathway toward the conservation and enhancement of tomato varieties through the exploitation of genetic diversity for organoleptic improvement. [ABSTRACT FROM AUTHOR]

Background: Carbon nano sol (CNS) can markedly affect the plant growth and development. However, few systematic analyses have been conducted on the underlying regulatory mechanisms in plants, including tobacco (Nicotiana tabacum L.). Results: Integrated analyses of phenome, ionome, transcriptome, and metabolome were performed in this study to elucidate the physiological and molecular mechanisms underlying the CNS-promoting growth of tobacco plants. We found that 0.3% CNS, facilitating the shoot and root growth of tobacco plants, significantly increased shoot potassium concentrations. Antioxidant, metabolite, and phytohormone profiles showed that 0.3% CNS obviously reduced reactive oxygen species production and increased antioxidant enzyme activity and auxin accumulation. Comparative transcriptomics revealed that the GO and KEGG terms involving responses to oxidative stress, DNA binding, and photosynthesis were highly enriched in response to exogenous CNS application. Differential expression profiling showed that NtNPF7.3/NtNRT1.5, potentially involved in potassium/auxin transport, was significantly upregulated under the 0.3% CNS treatment. High-resolution metabolic fingerprints showed that 141 and 163 metabolites, some of which were proposed as growth regulators, were differentially accumulated in the roots and shoots under the 0.3% CNS treatment, respectively. Conclusions: Taken together, this study revealed the physiological and molecular mechanism underlying CNS-mediated growth promotion in tobacco plants, and these findings provide potential support for improving plant growth through the use of CNS. [ABSTRACT FROM AUTHOR]

Background: Ulcerative colitis (UC) is a multifactorial chronic inflammatory bowel disease (IBD) that affects the large intestine with superficial mucosal inflammation. A dysbiotic gut microbial profile has been associated with UC. Our study aimed to characterize the UC gut bacterial, fungal, and metabolic fingerprints by omic approaches. Methods: The 16S rRNA- and ITS2-based metataxonomics and gas chromatography-mass spectrometry/solid phase microextraction (GC-MS/SPME) metabolomic analysis were performed on stool samples of 53 UC patients and 37 healthy subjects (CTRL). Univariate and multivariate approaches were applied to separated and integrated omic data, to define microbiota, mycobiota, and metabolic signatures in UC. The interaction between gut bacteria and fungi was investigated by network analysis. Results: In the UC cohort, we reported the increase of Streptococcus, B ifidobacterium, Enterobacteriaceae, TM7-3, Granulicatella, Peptostreptococcus, Lactobacillus, Veillonella, Enterococcus, Peptoniphilus, Gemellaceae, and phenylethyl alcohol; and we also reported the decrease of Akkermansia; Ruminococcaceae; Ruminococcus; Gemmiger; Methanobrevibacter; Oscillospira; Coprococus; Christensenellaceae; Clavispora; Vishniacozyma; Quambalaria; hexadecane; cyclopentadecane; 5- hepten-2-ol, 6 methyl; 3-carene; caryophyllene; p-Cresol; 2-butenal; indole, 3- methyl-; 6-methyl-3,5-heptadiene-2-one; 5-octadecene; and 5-hepten-2-one, 6 methyl. The integration of the multi-omic data confirmed the presence of a distinctive bacterial, fungal, and metabolic fingerprint in UC gut microbiota Moreover, the network analysis highlighted bacterial and fungal synergistic and/or divergent interkingdom interactions. Conclusion: In this study, we identified intestinal bacterial, fungal, and metabolic UC-associated biomarkers. Furthermore, evidence on the relationships between bacterial and fungal ecosystems provides a comprehensive perspective on intestinal dysbiosis and ecological interactions between microorganisms in the framework of UC. [ABSTRACT FROM AUTHOR]

Herein, the link between rearing environmental condition and metabolism was explored. Metabolite fingerprint datasets of black tiger shrimp (Penaeus monodon) from three production sites were collected and studied using matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) and HPLC-MS/MS. Two compounds, benzisothiazolinone and hippuric acid, were identified to be potentially related to pollution in the rearing environment and showed different abundances in the analysed shrimp samples with different origins. Furthermore, metabolomic analysis on three shrimp species, black tiger shrimp, kuruma shrimp (Penaeus japonicus) and sword shrimp (Parapenaeopsis hardwickii), under an identical rearing environment was also conducted. Two compounds, diethanolamine and benzisothiazolinone, potentially linked with pollution in the rearing environment were identified. The present protocol holds promise to be extended to the studies of exploring the relationship between rearing environmental conditions and metabolism. Furthermore, the analysis of single-blind samples was conducted. The results show that specific metabolites can be utilized as markers for tracing the origins of shrimp samples. The present protocol holds potential for application in tracing the origin and species of certain seafoods. [ABSTRACT FROM AUTHOR]

Curcuma caesia Roxb. is an ethnomedicinally important, essential oil (EO) yielding aromatic plant. A total of twelve accessions of this plant rhizome were collected from six different agro-climatic zones of West Bengal, India and evaluated for their antimicrobial activities against eight disease-causing, multi-drug-resistant pathogenic strains of urinary-tract infection and respiratory-tract infection. The EO and extracts demonstrated antibacterial activity, with the highest inhibition zone of 18.00 ± 0.08 and 17.50 ± 0.14 mm against Klebsiella pneumoniae by accession 06, even where all the broad-spectrum antibiotics failed to respond. In this study, we employed high-performance thin-layer chromatography (HPTLC) to quantify curcumin, the primary secondary metabolite of C. caesia, and the highest 0.228 mg/gm of curcumin resulted from accession 06. Hence, on the basis of all aspects, accession 06 was identified as the elite chemotype among all twelve accessions. The chemical profiling of EO from accession 06 was done using gas chromatography-mass spectroscopy (GC–MS). Conceivably, about 13 medicinally significant compounds were detected. As this plant species is seasonal and has difficulties in conventional breeding due to dormancy, it must be conserved through in vitro tissue culture for a steady supply throughout the year in massive amounts for agricultural demand. A maximum number of 19.28 ± 0.37 shoots has been obtained in MS medium fortified with 6-Benzylaminopurine, Kinetin, and Naphthalene acetic acid. The genetic uniformity of the plants has been studied through Start Codon Targeted Polymorphism. Therefore, this study must help meet the need for essential phytoactive compounds through a simple, validated, and reproducible plant tissue culture protocol throughout the year. [ABSTRACT FROM AUTHOR]

Alzheimer's disease is strongly linked to metabolic abnormalities. We aimed to distinguish amyloid-positive people who progressed to cognitive decline from those who remained cognitively intact. We performed untargeted metabolomics of blood samples from amyloid-positive individuals, before any sign of cognitive decline, to distinguish individuals who progressed to cognitive decline from those who remained cognitively intact. A plasma-derived metabolite signature was developed from Supercritical Fluid chromatography coupled with high-resolution mass spectrometry (SFC-HRMS) and nuclear magnetic resonance (NMR) metabolomics. The 2 metabolomics data sets were analyzed by Data Integration Analysis for Biomarker discovery using Latent approaches for Omics studies (DIABLO), to identify a minimum set of metabolites that could describe cognitive decline status. NMR or SFC-HRMS data alone cannot predict cognitive decline. However, among the 320 metabolites identified, a statistical method that integrated the 2 data sets enabled the identification of a minimal signature of 9 metabolites (3-hydroxybutyrate, citrate, succinate, acetone, methionine, glucose, serine, sphingomyelin d18:1/C26:0 and triglyceride C48:3) with a statistically significant ability to predict cognitive decline more than 3 years before decline. This metabolic fingerprint obtained during this exploratory study may help to predict amyloid-positive individuals who will develop cognitive decline. Due to the high prevalence of brain amyloid-positivity in older adults, identifying adults who will have cognitive decline will enable the development of personalized and early interventions. [ABSTRACT FROM AUTHOR]

The profile of secondary metabolites present in the apple cuticular layer is not only characteristic of a particular apple cultivar; it also dynamically reflects various external factors in the growing environment. In this study, the possibility of authenticating apple samples by analyzing their cuticular layer extracts was investigated. Ultra-high-performance liquid chromatography coupled with high-resolution tandem mass spectrometry (UHPLC-HRMS/MS) was employed for obtaining metabolomic fingerprints. A total of 274 authentic apple samples from four cultivars harvested in the Czech Republic and Poland between 2020 and 2022 were analyzed. The complex data generated, processed using univariate and multivariate statistical methods, enabled the building of classification models to distinguish apple cultivars as well as their geographical origin. The models showed very good performance in discriminating Czech and Polish samples for three out of four cultivars: "Gala", "Golden Delicious" and "Idared". Moreover, the validity of the models was tested over several harvest seasons. In addition to metabolites of the triterpene biosynthetic pathway, the diagnostic markers were mainly wax esters. "Jonagold", which is known to be susceptible to mutations, was the only cultivar for which an unambiguous classification of geographical origin was not possible. [ABSTRACT FROM AUTHOR]

In this study, 1H-NMR spectroscopy coupled with chemometrics was applied to study the wine metabolome and to classify wines according to different grape varieties and different terroirs. By obtaining the metabolomic fingerprinting and profiling of the wines, it was possible to assess the metabolic biomarkers leading the classification (i.e., phenolic compounds, aroma compounds, amino acids, and organic acids). Moreover, information about the influence of the soil in shaping wine metabolome was obtained. For instance, the relationship between the soil texture and the content of amino acids and organic acids in wines was highlighted. The analysis conducted in this study allowed extraction of relevant spectral information not only from the most populated and concentrated spectral areas (e.g., aliphatic and carbinolic areas), but also from crowded spectral areas held by lowly concentrated compounds (i.e., polyphenols). This may be due to a successful combination between the parameters used for data reduction, preprocessing and elaboration. The metabolomic fingerprinting also allowed exploration of the H-bonds network inside the wines, which affects both gustatory and olfactory perceptions, by modulating the way how solutes interact with the human sensory receptors. These findings may have important implications in the context of food traceability and quality control, providing information about the chemical composition and biomolecular markers from a holistic point of view. [ABSTRACT FROM AUTHOR]

Torin1, a selective kinase inhibitor targeting the mammalian target of rapamycin (mTOR), remains widely used in autophagy research due to its potent autophagy-inducing abilities, regardless of its unspecific properties. Recognizing the impact of mTOR inhibition on metabolism, our objective was to develop a reliable and thorough untargeted metabolomics workflow to study torin1-induced metabolic changes in mouse embryonic fibroblast (MEF) cells. Crucially, our quality assurance and quality control (QA/QC) protocols were designed to increase confidence in the reported findings by reducing the likelihood of false positives, including a validation experiment replicating all experimental steps from sample preparation to data analysis. This study investigated the metabolic fingerprint of torin1 exposure by using liquid chromatography—high resolution mass spectrometry (LC-HRMS)-based untargeted metabolomics platforms. Our workflow identified 67 altered metabolites after torin1 exposure, combining univariate and multivariate statistics and the implementation of a validation experiment. In particular, intracellular ceramides, diglycerides, phosphatidylcholines, phosphatidylethanolamines, glutathione, and 5′-methylthioadenosine were downregulated. Lyso-phosphatidylcholines, lyso-phosphatidylethanolamines, glycerophosphocholine, triglycerides, inosine, and hypoxanthine were upregulated. Further biochemical pathway analyses provided deeper insights into the reported changes. Ultimately, our study provides a valuable workflow that can be implemented for future investigations into the effects of other compounds, including more specific autophagy modulators. [ABSTRACT FROM AUTHOR]

The loss of leaves and needles in tree crowns and tree mortality are increasing worldwide, mostly as a result of more frequent and severe drought stress. Scots pine (Pinus sylvestris L.) is a tree species that is strongly affected by these developments in many regions of Europe and Asia. So far, changes in metabolic pathways and metabolite profiles in needles and roots on the trajectory toward mortality are unknown, although they could contribute to a better understanding of the mortality mechanisms. Therefore, we linked long-term observations of canopy defoliation and tree mortality with the characterization of the primary metabolite profile in needles and fine roots of Scots pines from a forest site in the Swiss Rhone valley. Our results show that Scots pines are able to maintain metabolic homeostasis in needles over a wide range of canopy defoliation levels. However, there is a metabolic tipping point at around 80–85% needle loss. Above this threshold, many stress-related metabolites (particularly osmoprotectants, defense compounds and antioxidants) increase in the needles, whereas they decrease in the fine roots. If this defoliation tipping point is exceeded, the trees are very likely to die within a few years. The different patterns between needles and roots indicate that mainly belowground carbon starvation impairs key functions for tree survival and suggest that this is an important factor explaining the increasing mortality of Scots pines. [ABSTRACT FROM AUTHOR]

Introduction: Acute lymphoblastic leukemia (ALL) is the most prevalent childhood malignancy. Despite high cure rates, several questions remain regarding predisposition, response to treatment, and prognosis of the disease. The role of intermediary metabolism in the individualized mechanistic pathways of the disease is unclear. We have hypothesized that children with any (sub)type of ALL have a distinct metabolomic fingerprint at diagnosis when compared: (i) to a control group; (ii) to children with a different (sub)type of ALL; (iii) to the end of the induction treatment. Materials and Methods: In this prospective case–control study (NCT03035344), plasma and urinary metabolites were analyzed in 34 children with ALL before the beginning (D0) and at the end of the induction treatment (D33). Their metabolic fingerprint was defined by targeted analysis of 106 metabolites and compared to that of an equal number of matched controls. Multivariate and univariate statistical analyses were performed using SIMCAP and scripts under the R programming language. Results: Metabolomic analysis showed distinct changes in patients with ALL compared to controls on both D0 and D33. The metabolomic fingerprint within the patient group differed significantly between common B-ALL and pre-B ALL and between D0 and D33, reflecting the effect of treatment. We have further identified the major components of this metabolic dysregulation, indicating shifts in fatty acid synthesis, transfer and oxidation, in amino acid and glycerophospholipid metabolism, and in the glutaminolysis/TCA cycle. Conclusions: The disease type and time point-specific metabolic alterations observed in pediatric ALL are of particular interest as they may offer potential for the discovery of new prognostic biomarkers and therapeutic targets. [ABSTRACT FROM AUTHOR]

To explore the metabolic fingerprints of diabetic retinopathy (DR) in individuals with type 2 diabetes using a newly-developed laser desorption/ionization mass spectrometry (LDI-MS) platform assisted by ferric particles. Metabolic fingerprinting was performed using a ferric particle-assisted LDI-MS platform. A nested population-based case-control study was performed on 216 DR cases and 216 control individuals with type 2 diabetes. DR cases and control individuals with type 2 diabetes were comparable for a list of clinical factors. The newly-developed LDI-MS platform allowed us to draw the blueprint of plasma metabolic fingerprints from participants with and without DR. The neural network afforded diagnostic performance with an average area under curve value of 0.928 for discovery cohort and 0.905 for validation cohort (95 % confidence interval: 0.902–0.954 and 0.845–0.965, respectively). Tandem MS and Fourier transform ion cyclotron resonance MS with ultrahigh resolution identified seven specific metabolites that were significantly associated with DR in fully adjusted models. Of these metabolites, dihydrobiopterin, phosphoserine, N-arachidonoylglycine, and 3-methylhistamine levels in plasma were first reported to show the associations. This work advances the design of metabolic analysis for DR and holds the potential to promise as an efficient tool for clinical management of DR. [ABSTRACT FROM AUTHOR]

Triple-negative breast cancer (TNBC) is a subtype of breast cancer with a poor prognosis and lacks effective therapeutic targets. The use of gambogic acid (GA), a class of active ingredients in traditional Chinese medicine with anti-tumour potential, is limited in tumour therapy owing to its drawbacks and unclear organ toxicity. In this study, we used the pH-responsive amphiphilic block copolymer, PEOz-PCL, to create nanodrugs for GA delivery to MDA-MB-231 cells. The pH-responsive GA-loaded micelles were prepared through nanoprecipitation with a more homogeneous size. The average particle size was 42.29 ± 1.74 nm, and the zeta potential value was 9.88 ± 0.17 mV. The encapsulation rate was 85.06%, and the drug loading rate was 10.63%. The process was reproducible, and sustained release reached 80% in 96 h at acid pH 5.0. Furthermore, cellular tests using CCK-8, TUNEL, and flow cytometry revealed that pH-responsive GA-loaded micelles killed MDA-MB-231 cells more effectively and had much higher activity and targeting compared with free drugs. Metabolomic analysis of the changes in differential metabolites revealed that pH-responsive GA-loaded micelles may inhibit TNBC cells by causing amino acid anabolism, nucleotide metabolism, and glucose metabolism, as well as by affecting their energy sources. The study outcomes will help understand the mechanism of action and the therapeutic efficacy of pH-responsive GA-loaded micelles in vivo. [ABSTRACT FROM AUTHOR]

Ectopic fat accumulation in non-adipose tissues is closely related to diabetes-related myocardial dysfunction. Nevertheless, the complete picture of the lipid metabolites involved in the metabolic-related myocardial alterations is not fully characterized. The aim of this study was to characterize the specific lipid profile in hearts in an animal model of obesity/insulin resistance induced by a high-fat diet (HFD). The cardiac lipidome profiles were assessed via liquid chromatography–mass spectrometry (LC–MS)/MS-MS and laser desorption/ionization–mass spectrometry (LDI–MS) tissue imaging in hearts from C57BL/6J mice fed with an HFD or standard-diet (STD) for 12 weeks. Targeted lipidome analysis identified a total of 63 lipids (i.e., 48 triacylglycerols (TG), 5 diacylglycerols (DG), 1 sphingomyelin (SM), 3 phosphatidylcholines (PC), 1 DihydroPC, and 5 carnitines) modified in hearts from HFD-fed mice compared to animals fed with STD. Whereas most of the TG were up-regulated in hearts from animals fed with an HFD, most of the carnitines were down-regulated, thereby suggesting a reduction in the mitochondrial β-oxidation. Roughly 30% of the identified metabolites were oxidated, pointing to an increase in lipid peroxidation. Cardiac lipidome was associated with a specific biochemical profile and a specific liver TG pattern. Overall, our study reveals a specific cardiac lipid fingerprint associated with metabolic alterations induced by HFD. [ABSTRACT FROM AUTHOR]

Suitable light intensities were studied to enable sustainable and efficient cultivation of Houttuynia cordata with a high yield of bioactive compounds which later can be a good quality material for fermentation. To achieve this goal, the evaluation of physiological adaptation and elucidation of the metabolic alterations of Houttuynia cordata in response to different light intensities were conducted. Four-month-old H. cordata was exposed to 3 different light intensities: high light, control, and low light (100, 50 and 20 % natural irradiances, respectively). Physiological responses were measured using leaf gas exchange. The same leaf samples were either methanolic extracted for LC-QTOF-MS analysis to generate metabolite fingerprints or fermented for 3 months prior to quercetin quantification. High light treatment had the greatest effects on both the physiology and metabolisms of H. cordata compared to control and low light treatments. Compared to the control, the key results observed in high-light-grown H. cordata were a significant increase in photosynthesis, a decrease in relative chlorophyll contents, the enrichment of the top 3 metabolic pathways (phenylpropanoid biosynthesis, pyruvate metabolism and fatty acid degradation) and increasing quercetin concentrations in both fresh and fermented leaves. Metabolite fingerprints of high light-grown H. cordata were distinctively different from those of the other 2 treatments according to PCA and hierarchical clustering analysis. In conclusion, H. cordata responded to high light intensity by enriching the phenylpropanoid biosynthetic pathway to elevate sunscreen pigments. Additional acetyl-CoA generated from pyruvate metabolism and fatty acid degradation was a key supporting factor in the above response. As high quercetin concentration was observed in both fresh and fermented leaves, this will lead to quality improvement of the fermented products. [ABSTRACT FROM AUTHOR]

This study investigated whether metabolomic fingerprints of bovine embryo growth media improve the prediction of successful embryo implantation. In this prospective cohort study, the metabolome from in vitro-produced day 7 blastocysts with successful implantation (n = 11), blastocysts with failed implantation (n = 10), and plain culture media without embryos (n = 5) were included. Samples were analyzed using an AbsoluteIDQ® p180 Targeted Metabolomics Kit with LC-MS/MS, and a total of 189 metabolites were analyzed from each sample. Blastocysts that resulted in successful embryo implantation had significantly higher levels of methionine sulfoxide (p < 0.001), DOPA (p < 0.05), spermidine (p < 0.001), acetylcarnitine-to-free-carnitine ratio (p < 0.05), C2 + C3-to-free-carnitine ratio (p < 0.05), and lower levels of threonine (nep < 0.001) and phosphatidylcholine PC ae C30:0 (p < 0.001) compared to control media. However, when compared to embryos that failed to implant, only DOPA, spermidine, C2/C0, (C2 + C3)/C0, and PC ae C30:0 levels differentiated significantly. In summary, our study identifies a panel of differential metabolites in the culture media of bovine blastocysts that could act as potential biomarkers for the selection of viable blastocysts before embryo transfer. [ABSTRACT FROM AUTHOR]

Introduction: European mistletoe (Viscum album L.) has been gaining increasing interest in the field of oncology as a clinically relevant adjunctive treatment in many forms of cancer. In the field of phytopharmacology, harvesting time is pivotal. In the last century, a form of metabolomic fingerprinting based on pattern formation was proposed as a way to determine optimal harvesting times to ensure high quality of mistletoe as raw material for pharmaceutical use. In order to further evaluate the information obtained with this metabolomic fingerprinting method, we analysed a large time series of previously undigitised daily mistletoe chromatograms dating back to the 1950s.Methods: These chromatograms were scanned and evaluated using computerized image analysis, resulting in 12 descriptors for each individual chromatogram. We performed a statistical analysis of the data obtained, investigating statistical distributions, cross-correlations and time self-correlations.Results: The analysed dataset spanning about 27 years, contains 19,037 evaluable chromatograms in daily resolution. Based on the distribution and cross-correlation analyses, the 12 descriptors could be clustered into six independent groups describing different aspects of the chromatograms. One descriptor was found to mirror the annual rhythm being well correlated with temperature and a phase shift of 10 days. The time self-correlation analysis showed that most other descriptors had a characteristic self-correlation of ∼50 days, which points to further infradian rhythms (i.e., more than 24 h).Discussion: To our knowledge, this dataset is the largest of its type. The combination of this form of metabolomic fingerprinting with the proposed computer analysis seems to be a promising tool to characterise biological variations of mistletoe. Additional research is underway to further analyse the different rhythms present in this dataset.

The article discusses the impact of cachexia, a condition characterized by weight loss and muscle wasting, on the quality of life and survival of lung cancer patients. It highlights the high prevalence of cachexia in lung cancer patients and the need for effective treatment approaches. The article introduces a research project called LuCaPET, which aims to use advanced imaging techniques and artificial intelligence algorithms to detect metabolic patterns associated with cachexia in lung cancer patients. The project also addresses the ethical challenges of using AI in clinical decision-making and aims to make the AI-driven models transparent and comprehensible for clinicians. The ultimate goal of the project is to improve the diagnosis and treatment of cachexia in lung cancer patients and enhance their quality of life and survival. [Extracted from the article]

Metabolic fingerprints in serum characterize diverse diseases for diagnostics and biomarker discovery. The identification of systemic lupus erythematosus (SLE) by serum metabolic fingerprints (SMFs) will facilitate precision medicine in SLE in an early and designed manner. Here, a discovery cohort of 731 individuals including 357 SLE patients and 374 healthy controls (HCs), and a validation cohort of 184 individuals (SLE/HC, 91/93) are constructed. Each SMF is directly recorded by nano‐assisted laser desorption/ionization mass spectrometry (LDI MS) within 1 minute using 1 µL of native serum, which contains 908 mass to charge features. Sparse learning of SMFs achieves the SLE identification with sensitivity/specificity and area‐under‐the‐curve (AUC) up to 86.0%/92.0% and 0.950 for the discovery cohort. For the independent validation cohort, it exhibits no performance loss by affording the sensitivity/specificity and AUC of 89.0%/100.0% and 0.992. Notably, a metabolic biomarker panel is screened out from the SMFs, demonstrating the unique metabolic pattern of SLE patients different from both HCs and rheumatoid arthritis patients. In conclusion, SMFs characterize SLE by revealing its unique metabolic pattern. Different regulation of small molecule metabolites contributes to the precise diagnosis of autoimmune disease and further exploration of the pathogenic mechanisms. [ABSTRACT FROM AUTHOR]