Metabolomic Information Research Articles

Systems biology approaches identify metabolic signatures of dietary lifespan and healthspan across species

Dietary restriction (DR) is a potent method to enhance lifespan and healthspan, but individual responses are influenced by genetic variations. Understanding how metabolism-related genetic differences impact longevity and healthspan are unclear. To investigate this, we used metabolites as markers to reveal how different genotypes respond to diet to influence longevity and healthspan traits. We analyzed data from Drosophila Genetic Reference Panel (DGRP) strains raised under AL and DR conditions, combining metabolomic, phenotypic, and genome-wide information. We employed two computational and complementary methods across species—random forest modeling within the DGRP as our primary analysis and Mendelian randomization in human cohorts as a secondary analysis. We pinpointed key traits with cross-species relevance as well as underlying heterogeneity and pleiotropy that influence lifespan and healthspan. Notably, orotate was linked to parental age at death in humans and blocked the DR lifespan extension in flies, while threonine supplementation extended lifespan, in a strain- and sex-specific manner. Thus, utilizing natural genetic variation data from flies and humans, we employed a systems biology approach to elucidate potential therapeutic pathways and metabolomic targets for diet-dependent changes in lifespan and healthspan.

Meat exudate metabolomics reveals the impact of freeze-thaw cycles on meat quality in pork loins

The aim of this study was to explore the effects of freeze-thaw (FT) cycles on meat quality, myofibrillar protein gelation and emulsification properties, and exudate metabolome changes in pork loins. Meat tenderness improved (P < 0.05), whereas water-holding capacity (WHC), meat color attributes declined (P < 0.05) with FT cycles. Multiple FT accelerated meat lipid and protein oxidations. Decreases in strength and WHC of myofibrillar protein gels with FT cycles were confirmed. Myofibrillar protein emulsions with more FT cycles showed a decrease in the emulsifying activity index (P < 0.001) and larger oil droplets, resulting in poorer storage stability. A total of 501 metabolites were tentatively identified in pork exudates, with 21 metabolites significantly correlated (P < 0.05 and r > 0.6) with meat quality attributes. These results demonstrated the potential of using the metabolomic information from exudates to elaborate on or even predict the FT cycles, or meat quality.

Exposure-associated DNA methylation among people exposed to multiple industrial pollutants

BackgroundCurrent research on the epigenetic repercussions of exposure to a combination of pollutants is limited. This study aims to discern DNA methylation probes associated with exposure to multiple pollutants, serving as early effect markers, and single-nucleotide polymorphisms (SNPs) as surrogate indicators for population susceptibility. The investigation involved the analysis of urine exposure biomarkers for 11 heavy metals (vanadium, arsenic, mercury, cadmium, chromium, nickel, lead, manganese, copper, strontium, thallium), polycyclic aromatic hydrocarbon (PAHs) (1-hydroxypyrene), genome-wide DNA methylation sequencing, and SNPs array on all study participants. The data were integrated with metabolomics information and analyzed both at a community level based on proximity to home addresses relative to the complex and at an individual level based on exposure biomarker concentrations.ResultsOn a community level, 67 exposure-related CpG probes were identified, while 70 CpG probes were associated with urine arsenic concentration, 2 with mercury, and 46 with vanadium on an individual level. These probes were annotated to genes implicated in cancers and chronic kidney disease. Weighted quantile sum regression analysis revealed that vanadium, mercury, and 1-hydroxypyrene contributed the most to cg08238319 hypomethylation. cg08238319 is annotated to the aryl hydrocarbon receptor repressor (AHRR) gene, and AHRR hypomethylation was correlated with an elevated risk of lung cancer. AHRR was further linked to deregulations in phenylalanine metabolism, alanine, aspartate, and glutamate metabolism, along with heightened oxidative stress. Additionally, three SNPs (rs11085020, rs199442, and rs10947050) corresponding to exposure-related CpG probes exhibited significant interaction effects with multiple heavy metals and PAHs exposure, and have been implicated in cancer progression and respiratory diseases.ConclusionOur findings underscore the pivotal role of AHRR methylation in gene-environment interactions and highlight SNPs that could potentially serve as indicators of population susceptibility in regions exposed to multiple heavy metals and PAHs.

Hyperpolarized Magnetic Resonance Imaging, Nuclear Magnetic Resonance Metabolomics, and Artificial Intelligence to Interrogate the Metabolic Evolution of Glioblastoma.

Glioblastoma (GBM) is a malignant Grade VI cancer type with a median survival duration of only 8-16 months. Earlier detection of GBM could enable more effective treatment. Hyperpolarized magnetic resonance spectroscopy (HPMRS) could detect GBM earlier than conventional anatomical MRI in glioblastoma murine models. We further investigated whether artificial intelligence (A.I.) could detect GBM earlier than HPMRS. We developed a deep learning model that combines multiple modalities of cancer data to predict tumor progression, assess treatment effects, and to reconstruct in vivo metabolomic information from ex vivo data. Our model can detect GBM progression two weeks earlier than conventional MRIs and a week earlier than HPMRS alone. Our model accurately predicted in vivo biomarkers from HPMRS, and the results inferred biological relevance. Additionally, the model showed potential for examining treatment effects. Our model successfully detected tumor progression two weeks earlier than conventional MRIs and accurately predicted in vivo biomarkers using ex vivo information such as conventional MRIs, HPMRS, and tumor size data. The accuracy of these predictions is consistent with biological relevance.

Metabolomics analysis of the lactobacillus plantarum ATCC 14917 response to antibiotic stress

BackgroundLactobacillus plantarum has been found to play a significant role in maintaining the balance of intestinal flora in the human gut. However, it is sensitive to commonly used antibiotics and is often incidentally killed during treatment. We attempted to identify a means to protect L. plantarum ATCC14917 from the metabolic changes caused by two commonly used antibiotics, ampicillin, and doxycycline. We examined the metabolic changes under ampicillin and doxycycline treatment and assessed the protective effects of adding key exogenous metabolites.ResultsUsing metabolomics, we found that under the stress of ampicillin or doxycycline, L. plantarum ATCC14917 exhibited reduced metabolic activity, with purine metabolism a key metabolic pathway involved in this change. We then screened the key biomarkers in this metabolic pathway, guanine and adenosine diphosphate (ADP). The exogenous addition of each of these two metabolites significantly reduced the lethality of ampicillin and doxycycline on L. plantarum ATCC14917. Because purine metabolism is closely related to the production of reactive oxygen species (ROS), the results showed that the addition of guanine or ADP reduced intracellular ROS levels in L. plantarum ATCC14917. Moreover, the killing effects of ampicillin and doxycycline on L. plantarum ATCC14917 were restored by the addition of a ROS accelerator in the presence of guanine or ADP.ConclusionsThe metabolic changes of L. plantarum ATCC14917 under antibiotic treatments were determined. Moreover, the metabolome information that was elucidated can be used to help L. plantarum cope with adverse stress, which will help probiotics become less vulnerable to antibiotics during clinical treatment.

Combined transcriptomic and metabolomic analysis revealed that pH changes affected the expression of carbohydrate and ribosome biogenesis-related genes in Aspergillus niger SICU-33.

The process of carbohydrate metabolism and genetic information transfer is an important part of the study on the effects of the external environment on microbial growth and development. As one of the most significant environmental parameters, pH has an important effect on mycelial growth. In this study, the effects of environmental pH on the growth and nutrient composition of Aspergillus niger (A. niger) filaments were determined. The pH values of the medium were 5, 7, and 9, respectively, and the molecular mechanism was further investigated by transcriptomics and metabolomics methods. The results showed that pH 5 and 9 significantly inhibited filament growth and polysaccharide accumulation of A. niger. Further, the mycelium biomass of A. niger and the crude polysaccharide content was higher when the medium's pH was 7. The DEGs related to ribosome biogenesis were the most abundant, and the downregulated expression of genes encoding XRN1, RRM, and RIO1 affected protein translation, modification, and carbohydrate metabolism in fungi. The dynamic changes of pargyline and choline were in response to the oxidative metabolism of A. niger SICU-33. The ribophorin_I enzymes and DL-lactate may be important substances related to pH changes during carbohydrate metabolism of A.niger SICU-33. The results of this study provide useful transcriptomic and metabolomic information for further analyzing the bioinformatic characteristics of A. niger and improving the application in ecological agricultural fermentation.

Dietary pattern modifies the risk of MASLD through metabolomic signature

Background & AimsThe EAT-Lancet Commission in 2019 advocated a plant-centric diet for health and environmental benefits, but its relation to metabolic dysfunction-associated steatotic liver disease (MASLD) is unclear. We aimed to discover the metabolite profile linked to the EAT-Lancet diet and its association with MASLD risk, considering genetic predisposition. MethodsWe analyzed data from 105,752 UK Biobank participants with detailed dietary and metabolomic information. We constructed an EAT-Lancet diet index and derived a corresponding metabolomic signature through elastic net regression. A weighted polygenic risk score for MASLD was computed from associated risk variants. The Cox proportional hazards model was employed to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the risk of MASLD (defined as hospital admission or death). ResultsDuring a median follow-up period of 11.6 years, 1,138 cases of MASLD were documented. The multivariable HR (95% CI) of MASLD for the highest versus the lowest group for the EAT-Lancet diet index was 0.79 (0.66, 0.95). The diet's impact was unaffected by genetic predisposition to MASLD (P = 0.42). Moreover, a robust correlation was found between the metabolomic signature and the EAT-Lancet diet index (Pearson r = 0.29; P <0.0001). Participants with the highest group for the metabolomic signature showing a multivariable HR (95% CI) of 0.46 (0.37, 0.58) for MASLD, in comparison to those with the lowest group. ConclusionsHigher intake of the EAT-Lancet diet and its associated metabolite signature are both linked to a reduced risk of MASLD, independently of traditional risk factors. Impact and implicationsOur analysis leveraging the UK Biobank study showed higher adherence to the EAT-Lancet diet was associated with a reduced risk of MASLD. We identified a unique metabolite signature comprising 81 metabolites associated with the EAT-Lancet diet, potentially underlying the diet's protective mechanism against MASLD. These findings suggest the EAT-Lancet diet may offer substantial protective benefits against MASLD.

Comparative metabolic profiling of different pakchoi cultivars reveals nutritional diversity via widely targeted metabolomics

Pakchoi (Brassica rapa ssp. chinensis) is cultivated for its high nutritional value; however, the nutritional diversity of different pakchoi cultivars is rarely investigated. Herein, we performed widely targeted metabolic profiling analyses of five popular pakchois. A total of 670 metabolites were detected, which could be divided into 13 categories. The accumulation patterns of main nutritional metabolites among the five pakchois were significantly different and complementary. Moreover, the pakchoi cultivar ‘QYC’ showed quite different metabolomic profiles compared with other pakchois. The Venn diagram showed that the 75 differential metabolites were shared among the comparison groups (‘QYC’ vs. ‘MET’/ ‘NBC’/ ‘PPQ’/ ‘XQC’), of which 52 metabolites were upregulated in ‘QYC’. The phenolic acids had the largest variations between ‘QYC’ and the other pakchoi cultivars. These findings expand metabolomic information on different pakchoi cultivars and further provide new insights into the selection and breeding of excellent pakchoi cultivars.

One-Shot Single-Cell Proteome and Metabolome Analysis Strategy for the Same Single Cell.

Characterizing the profiles of proteome and metabolome at the single-cell level is of great significance in single-cell multiomic studies. Herein, we proposed a novel strategy called one-shot single-cell proteome and metabolome analysis (scPMA) to acquire the proteome and metabolome information in a single-cell individual in one injection of LC-MS/MS analysis. Based on the scPMA strategy, a total workflow was developed to achieve the single-cell capture, nanoliter-scale sample pretreatment, one-shot LC injection and separation of the enzyme-digested peptides and metabolites, and dual-zone MS/MS detection for proteome and metabolome profiling. Benefiting from the scPMA strategy, we realized dual-omic analysis of single tumor cells, including A549, HeLa, and HepG2 cells with 816, 578, and 293 protein groups and 72, 91, and 148 metabolites quantified on average. A single-cell perspective experiment for investigating the doxorubicin-induced antitumor effects in both the proteome and metabolome aspects was also performed.

Machine learning identifies fatigue as a key symptom of fibromyalgia reflected in tyrosine, purine, pyrimidine, and glutaminergic metabolism.

Fibromyalgia patients vary in clinical phenotype and treatment can be challenging. The pathophysiology of fibromyalgia is incompletely understood but appears to involve metabolic changes at rest or in response to stress. We enrolled 54 fibromyalgia patients and 31 healthy controls to this prospective study. Symptoms were assessed using the Fibromyalgia Impact Questionnaire (FIQ) and blood samples were collected for metabolomics analysis at baseline and after an oral glucose tolerance test and a cardiopulmonary exercise test. We identified key symptoms of fibromyalgia and related them to changes in metabolic pathways with supervised and unsupervised machine learning methods. Algorithms trained with the FIQ information assigned the fibromyalgia diagnosis in new data with balanced accuracy of 88% while fatigue alone already provided the diagnosis with 86% accuracy. Supervised analyses reduced the metabolomic information from 77 to 13 key markers. With these metabolites, fibromyalgia could be identified in new cases with 79% accuracy. In addition, 5-hydroxyindole-3-acetic acid and glutamine levels correlated with the severity of fatigue. Patients differed from controls at baseline in tyrosine and purine pathways, and in the pyrimidine pathway after the stress challenges. Several key markers are involved in glutaminergic neurotransmission. This data-driven analysis highlights fatigue as a key symptom of fibromyalgia. Fibromyalgia is associated with metabolic changes which also reflect the degree of fatigue. Responses to metabolic and physical stresses result in a metabolic pattern that allows discrimination of fibromyalgia patients from controls and narrows the focus on key pathophysiological processes in fibromyalgia as treatment targets.

Biochemical Signposts: Navigating the Landscape of Early Cancer Diagnosis and Prognostic Insights

This exploration investigates the complicated scene of malignant growth from the perspective of "Biochemical Signs," expecting to upset early analysis and prognostic experiences. Utilizing an exhaustive methodology, biomarkers have distinguished across bosom, lung, and prostate tumors, with values going from 0.45 to 3.10. The coordination of cutting-edge imaging procedures, including PET and X-ray, brought about indicative precision paces of 89% and 85%, separately. Prognostic investigations divulged biomarkers' shifting effects on understanding endurance, with risk proportions going from 0.70 to 1.80 and p-values featuring factual importance. The coordination of genomic, proteomic, and metabolomic information created all-encompassing sub-atomic profiles, associated with unmistakable clinical results. For example, the luminal A subtype showed an incorporated score of 0.85, related to a 75% endurance rate. This study approves the proposed methodology as well as lays out its prevalence in exactness, awareness, and clinical pertinence when contrasted with existing methodologies. The blend of different information types and the vigor of the created demonstrative devices give an establishment to groundbreaking headways in malignant growth research and customized patient consideration.

Deep representation learning of tissue metabolome and computed tomography annotates NSCLC classification and prognosis

The rich chemical information from tissue metabolomics provides a powerful means to elaborate tissue physiology or tumor characteristics at cellular and tumor microenvironment levels. However, the process of obtaining such information requires invasive biopsies, is costly, and can delay clinical patient management. Conversely, computed tomography (CT) is a clinical standard of care but does not intuitively harbor histological or prognostic information. Furthermore, the ability to embed metabolome information into CT to subsequently use the learned representation for classification or prognosis has yet to be described. This study develops a deep learning-based framework -- tissue-metabolomic-radiomic-CT (TMR-CT) by combining 48 paired CT images and tumor/normal tissue metabolite intensities to generate ten image embeddings to infer metabolite-derived representation from CT alone. In clinical NSCLC settings, we ascertain whether TMR-CT results in an enhanced feature generation model solving histology classification/prognosis tasks in an unseen international CT dataset of 742 patients. TMR-CT non-invasively determines histological classes - adenocarcinoma/squamous cell carcinoma with an F1-score = 0.78 and further asserts patients’ prognosis with a c-index = 0.72, surpassing the performance of radiomics models and deep learning on single modality CT feature extraction. Additionally, our work shows the potential to generate informative biology-inspired CT-led features to explore connections between hard-to-obtain tissue metabolic profiles and routine lesion-derived image data.

An insight into tissue culture-induced variation origin shared between anther culture-derived triticale regenerants

BackgroundThe development of the plant in vitro techniques has brought about the variation identified in regenerants known as somaclonal or tissue culture-induced variation (TCIV). S-adenosyl-L-methionine (SAM), glutathione (GSH), low methylated pectins (LMP), and Cu(II) ions may be implicated in green plant regeneration efficiency (GPRE) and TCIV, according to studies in barley (Hordeum vulgare L.) and partially in triticale (× Triticosecale spp. Wittmack ex A. Camus 1927). Using structural equation models (SEM), these metabolites have been connected to the metabolic pathways (Krebs and Yang cycles, glycolysis, transsulfuration), but not for triticale. Using metabolomic and (epi)genetic data, the study sought to develop a triticale regeneration efficiency statistical model. The culture’s induction medium was supplemented with various quantities of Cu(II) and Ag(I) ions for regeneration. The period of plant regeneration has also changed. The donor plant, anther-derived regenerants, and metAFLP were utilized to analyze TCIV concerning DNA in symmetric (CG, CHG) and asymmetric (CHH) sequence contexts. Attenuated Total Reflectance–Fourier Transfer Infrared (ATR-FTIR) spectroscopy was used to gather the metabolomic information on LMP, SAM, and GSH. To frame the data, a structural equation model was employed.ResultsAccording to metAFLP analysis, the average sequence change in the CHH context was 8.65%, and 0.58% was de novo methylation. Absorbances of FTIR spectra in regions specific for LMP, SAM, and GSH were used as variables values introduced to the SEM model. The average number of green regenerants per 100 plated anthers was 2.55.ConclusionsThe amounts of pectin demethylation, SAM, de novo methylation, and GSH are connected in the model to explain GPRE. By altering the concentration of Cu(II) ions in the medium, which influences the amount of pectin, triticale’s GPRE can be increased.

Impact of Cold Ischemia on the Stability of 1H-MRS-Detected Metabolic Profiles of Ovarian Cancer Specimens.

Proton magnetic resonance spectroscopy (1H-MRS) of surgically collected tumor specimens may contribute to investigating cancer metabolism and the significance of the "total choline" (tCho) peak (3.2 ppm) as malignancy and therapy response biomarker. To ensure preservation of intrinsic metabolomic information, standardized handling procedures are needed. The effects of time to freeze (cold ischemia) were evaluated in (a) surgical epithelial ovarian cancer (EOC) specimens using high-resolution (HR) 1H-MRS (9.4 T) of aqueous extracts and (b) preclinical EOC samples (xenografts in SCID mice) investigated by in vivo MRI-guided 1H-MRS (4.7 T) and by HR-1H-MRS (9.4 T) of tumor extracts or intact fragments (using magic-angle-spinning (MAS) technology). No significant changes were found in the levels of 27 of 29 MRS-detected metabolites (including the tCho profile) in clinical specimens up to 2 h cold ischemia, besides an increase in lysine and a decrease in glutathione. EOC xenografts showed a 2-fold increase in free choline within 2 h cold ischemia, without further significant changes for any MRS-detected metabolite (including phosphocholine and tCho) up to 6 h. At shorter times (≤1 h), HR-MAS analyses showed unaltered tCho components, along with significant changes in lactate, glutamate, and glutamine. Our results support the view that a time to freeze of 1 h represents a safe threshold to ensure the maintenance of a reliable tCho profile in EOC specimens.

Predictive metabolites for incident myocardial infarction: a two-step meta-analysis of individual patient data from six cohorts comprising 7897 individuals from the COnsortium of METabolomics Studies.

Myocardial infarction (MI) is a major cause of death and disability worldwide. Most metabolomics studies investigating metabolites predicting MI are limited by the participant number and/or the demographic diversity. We sought to identify biomarkers of incident MI in the COnsortium of METabolomics Studies. We included 7897 individuals aged on average 66 years from six intercontinental cohorts with blood metabolomic profiling (n = 1428 metabolites, of which 168 were present in at least three cohorts with over 80% prevalence) and MI information (1373 cases). We performed a two-stage individual patient data meta-analysis. We first assessed the associations between circulating metabolites and incident MI for each cohort adjusting for traditional risk factors and then performed a fixed effect inverse variance meta-analysis to pull the results together. Finally, we conducted a pathway enrichment analysis to identify potential pathways linked to MI. On meta-analysis, 56 metabolites including 21 lipids and 17 amino acids were associated with incident MI after adjusting for multiple testing (false discovery rate < 0.05), and 10 were novel. The largest increased risk was observed for the carbohydrate mannitol/sorbitol {hazard ratio [HR] [95% confidence interval (CI)] = 1.40 [1.26-1.56], P < 0.001}, whereas the largest decrease in risk was found for glutamine [HR (95% CI) = 0.74 (0.67-0.82), P < 0.001]. Moreover, the identified metabolites were significantly enriched (corrected P < 0.05) in pathways previously linked with cardiovascular diseases, including aminoacyl-tRNA biosynthesis. In the most comprehensive metabolomic study of incident MI to date, 10 novel metabolites were associated with MI. Metabolite profiles might help to identify high-risk individuals before disease onset. Further research is needed to fully understand the mechanisms of action and elaborate pathway findings.

Metabolomic-genomic prediction can improve prediction accuracy of breeding values for malting quality traits in barley

BackgroundMetabolomics measures an intermediate stage between genotype and phenotype, and may therefore be useful for breeding. Our objectives were to investigate genetic parameters and accuracies of predicted breeding values for malting quality (MQ) traits when integrating both genomic and metabolomic information. In total, 2430 plots of 562 malting spring barley lines from three years and two locations were included. Five MQ traits were measured in wort produced from each plot. Metabolomic features used were 24,018 nuclear magnetic resonance intensities measured on each wort sample. Methods for statistical analyses were genomic best linear unbiased prediction (GBLUP) and metabolomic-genomic best linear unbiased prediction (MGBLUP). Accuracies of predicted breeding values were compared using two cross-validation strategies: leave-one-year-out (LOYO) and leave-one-line-out (LOLO), and the increase in accuracy from the successive inclusion of first, metabolomic data on the lines in the validation population (VP), and second, both metabolomic data and phenotypes on the lines in the VP, was investigated using the linear regression (LR) method.ResultsFor all traits, we saw that the metabolome-mediated heritability was substantial. Cross-validation results showed that, in general, prediction accuracies from MGBLUP and GBLUP were similar when phenotypes and metabolomic data were recorded on the same plots. Results from the LR method showed that for all traits, except one, accuracy of MGBLUP increased when including metabolomic data on the lines of the VP, and further increased when including also phenotypes. However, in general the increase in accuracy of MGBLUP when including both metabolomic data and phenotypes on lines of the VP was similar to the increase in accuracy of GBLUP when including phenotypes on the lines of the VP. Therefore, we found that, when metabolomic data were included on the lines of the VP, accuracies substantially increased for lines without phenotypic records, but they did not increase much when phenotypes were already known.ConclusionsMGBLUP is a useful approach to combine phenotypic, genomic and metabolomic data for predicting breeding values for MQ traits. We believe that our results have significant implications for practical breeding of barley and potentially many other species.

Metabolome and transcriptome analyses identify the characteristics and expression of related saponins of the three genealogical plants of bead ginseng.

The classification and clinical usage of the different species of bead ginseng are often confused. Therefore, we conducted an integrated metabolomics and transcriptome analysis of three main species of Panax, including Panax japonicas, Panax pseudoginseng, and Panax pseudo-ginseng var. elegantior. A broad metabolome and transcriptome analysis for three origins of bead ginseng plants was performed using UPLC-ESI-MS/MS, RNA sequencing and annotation, and bioinformatic analysis of transcriptome data. The levels of 830 metabolites were determined. A total of 291 differentially accumulated metabolites (DAMs) between Panax pseudo-ginseng var. elegantior and Panax japonicas (Group A), with 73 upregulated and 218 downregulated. A total of 331 DAMs (110 upregulated and 221 downregulated) were found between Panax pseudoginseng and Panax japonicas (group B). There were 160 DAMs (102 up-regulated and 58 down-regulated) between Panax pseudoginseng and Panax pseudo-ginseng var. elegantior (group C). In addition, RNA sequencing was performed in the above three ways. A total of 16,074 differential expression genes (DEGs) were detected between Group A, in which 7,723 genes were upregulated and 8,351 genes were downregulated by RNA sequencing. Similarly, 15,705 genes were differentially expressed between group B, in which 7,436 genes were upregulated and 8,269 genes were downregulated. However, only 1,294 genes were differentially expressed between group C, in which 531 genes were upregulated and 763 genes were downregulated. We performed differential gene analysis on three groups of samples according to the Venn diagram and found that 181 differential genes were present. A total of 3,698 and 2,834 unique genes were in groups A and B, while 130 unique genes were in group C. This study provides metabolome and transcriptome information for three bead ginseng plants. The analysis of the metabolite content showed differences in the attributes of the three bead ginseng, contained mainly flavonoids, phenolic acids as well as terpenes.

Secondary Metabolomic Analysis and In Vitro Bioactivity Evaluation of Stems Provide a Comprehensive Comparison between Dendrobium chrysotoxum and Dendrobium thyrsiflorum.

The stems of Dendrobium chrysotoxum (DC) are commonly used as health-promoting foods due to their excellent biological activities. However, the stems of D. thyrsiflorum (DT) are often used to meet the scarcity of DC in production because of their highly similar morphology. However, the related metabolomic and bioactive information on the stems of DC and DT are largely deficient. Here, secondary metabolites of DC and DT stems were identified using an ultra-performance liquid chromatography-electrospray ionization-mass spectrometry, and their health-promoting functions were evaluated using several in vitro arrays. A total of 490 metabolites were identified in two stems, and 274 were significantly different. We screened out 10 key metabolites to discriminate the two species, and 36 metabolites were determined as health-promoting constituents. In summary, DT stems with higher extract yield, higher total phenolics and flavonoids, and stronger in vitro antioxidant activities demonstrated considerable potential in food and health fields.

Geoherbalism Metabolomic Analysis of Atractylodes lancea (Thunb.) DC. by LC-Triple TOF-MS/MS and GC-MS.

The rhizome of Atractylodes lancea (Thunb.) DC. (AL), called Maocangzhu in Chinese, is a geoherbalism medical herb in Jiangsu Province that is often used in the prescription of traditional Chinese medicine (TCM), such as for the treatment of COVID-19. The landform and climatic environment of each province varies greatly from south to north, which has an important influence on the chemical constituents in AL. However, there is a lack of research on the significance of its geoherbalism, especially in water-soluble parts other than volatile oil. In this study, eight known compounds were isolated and obtained as reference substances from AL. In addition, liquid chromatography coupled with triple-quadrupole time-of-flight tandem mass spectrometry (LC-triple TOF-MS/MS) and gas chromatography-mass spectrometry (GC-MS) were used to analyze and characterize chemical constituents from different habitats. Moreover, orthogonal partial least-squares discriminant analysis (OPLS-DA) was applied to reveal the differential metabolomics in AL from different habitats based on the qualitative information of the chemical constituents. Results showed that a total of 33 constituents from GC-MS and 106 constituents from LC-triple TOF-MS/MS were identified or inferred, including terpenoids, polyacetylenes, and others; meanwhile, the fragmentation pathways of different types of compounds were preliminarily deduced from the fragmentation behavior of the major constituents. According to the variable importance in projection (VIP) and p-values, only one volatile differential metabolite was identified by GC-MS screening: β-eudesmol. Overall, five differential metabolites were identified by LC-triple TOF-MS/MS screening: sucrose, 4(15),11-eudesmadiene; atractylenolide I, 3,5,11-tridecatriene-7,9-diyne-1,2-diacetate, and (3Z,5E,11E)-tridecatriene-7,9-diynyl-1-O-(E)-ferulate. This study provides metabolomic information for the establishment of a comprehensive quality evaluation system for AL.

Tracking Therapy Response in Glioblastoma Using 1D Convolutional Neural Networks.

Glioblastoma (GB) is a malignant brain tumour that is challenging to treat, often relapsing even after aggressive therapy. Evaluating therapy response relies on magnetic resonance imaging (MRI) following the Response Assessment in Neuro-Oncology (RANO) criteria. However, early assessment is hindered by phenomena such as pseudoprogression and pseudoresponse. Magnetic resonance spectroscopy (MRS/MRSI) provides metabolomics information but is underutilised due to a lack of familiarity and standardisation. This study explores the potential of spectroscopic imaging (MRSI) in combination with several machine learning approaches, including one-dimensional convolutional neural networks (1D-CNNs), to improve therapy response assessment. Preclinical GB (GL261-bearing mice) were studied for method optimisation and validation. The proposed 1D-CNN models successfully identify different regions of tumours sampled by MRSI, i.e., normal brain (N), control/unresponsive tumour (T), and tumour responding to treatment (R). Class activation maps using Grad-CAM enabled the study of the key areas relevant to the models, providing model explainability. The generated colour-coded maps showing the N, T and R regions were highly accurate (according to Dice scores) when compared against ground truth and outperformed our previous method. The proposed methodology may provide new and better opportunities for therapy response assessment, potentially providing earlier hints of tumour relapsing stages.