Differential Serum Metabolites Research Articles

Analysis of differential metabolites in serum metabolomics of patients with aortic dissection

BackgroundPathogenesis and diagnostic biomarkers of aortic dissection (AD) can be categorized through the analysis of differential metabolites in serum. Analysis of differential metabolites in serum provides new methods for exploring the early diagnosis and treatment of aortic dissection.ObjectivesThis study examined affected metabolic pathways to assess the diagnostic value of metabolomics biomarkers in clients with AD.MethodThe serum from 30 patients with AD and 30 healthy people was collected. The most diagnostic metabolite markers were determined using metabolomic analysis and related metabolic pathways were explored.ResultsIn total, 71 differential metabolites were identified. The altered metabolic pathways included reduced phospholipid catabolism and four different metabolites considered of most diagnostic value including N2-gamma-glutamylglutamine, PC(phocholines) (20:4(5Z,8Z,11Z,14Z)/15:0), propionyl carnitine, and taurine. These four predictive metabolic biomarkers accurately classified AD patient and healthy control (HC) samples with an area under the curve (AUC) of 0.9875. Based on the value of the four different metabolites, a formula was created to calculate the risk of aortic dissection. Risk score = (N2-gamma-glutamylglutamine × -0.684) + (PC (20:4(5Z,8Z,11Z,14Z)/15:0) × 0.427) + (propionyl carnitine × 0.523) + (taurine × -1.242). An additional metabolic pathways model related to aortic dissection was explored.ConclusionMetabolomics can assist in investigating the metabolic disorders associated with AD and facilitate a more in-depth search for potential metabolic biomarkers.

Danggui Jixueteng decoction for the treatment of myelosuppression after chemotherapy: A combined metabolomics and network pharmacology analysis

ObjectiveThis study aimed to explore the mechanism of the Danggui Jixueteng decoction (DJD) in treating Myelosuppression after chemotherapy (MAC) through network pharmacology and metabolomics. MethodsWe obtained the chemical structures of DJD compounds from TCMSP and PubMed. SwissTargetPrediction, STITCH, CTD, GeneCards, and OMIM were utilized to acquire component targets and MAC-related targets. We identified the key compounds, core targets, main biological processes, and signaling pathways related to DJD by constructing and analyzing related networks. The main active compounds and key proteins of DJD in treating AA were confirmed by molecular docking. A MAC rat model was established through intraperitoneal injection of cyclophosphamide to confirm DJD's effect on the bone marrow hematopoietic system. Untargeted metabolomics analyzed serum metabolite differences between MAC rats and the control group, and before and after DJD treatment, to explore DJD's mechanism in treating MAC. ResultsOf the 93 active compounds identified under screening conditions, 275 compound targets and 3113 MAC-related targets were obtained, including 95 intersecting targets; AKT1, STAT3, CASP3, and JUN were key proteins in MAC treatment. The phosphatidylinositol-3-kinase/RAC-alpha serine/threonine-protein kinase (PI3K/AKT) signaling pathway may play a crucial role in MAC treatment with DJD. Molecular docking results showed good docking effects of key protein AKT1 with luteolin, β-sitosterol, kaempferol, and glycyrrhizal chalcone A. In vivo experiments indicated that, compared to the model group, in the DJD group, levels of WBCs, RBCs, HGB, and PLTs in peripheral blood cells, thymus index increased, spleen index decreased, serum IL-3, GM-CSF levels increased, and IL-6, TNF-α, and VEGF levels decreased (p < 0.01); the pathological morphology of femoral bone marrow improved. Eleven differential metabolites were identified as differential serum metabolites, mainly concentrated in phenylalanine, tyrosine, and tryptophan biosynthesis pathways, phenylalanine metabolism, and arachidonic acid metabolism. ConclusionThis study revealed that DJD's therapeutic effects are due to multiple ingredients, targets, and pathways. DJD may activate the PI3K/AKT signaling pathway, promote hematopoietic-related cytokine production, regulate related metabolic pathways, and effectively alleviate cyclophosphamide-induced myelosuppression after chemotherapy in rats.

Gut microbiota and metabolic profiles in chronic intermittent hypoxia-induced rats: disease-associated dysbiosis and metabolic disturbances.

Chronic intermittent hypoxia (CIH) is a key characteristic of obstructive sleep apnea (OSA) syndrome, a chronic respiratory disorder. The mechanisms of CIH-induced metabolic disturbance and histopathological damage remain unclear. CIH-induced rats underwent daily 8-h CIH, characterized by oxygen levels decreasing from 21% to 8.5% over 4min, remaining for 2min, and quickly returning to 21% for 1min. The control rats received a continuous 21% oxygen supply. The levels of hypersensitive C reactive protein (h-CRP), tumor necrosis factor-α (TNF-α), interleukin 6 (IL-6), interleukin 8 (IL-8), and nuclear factor kappa-B (NF-κB) were measured by ELISA. Histological analysis of the soft palates was conducted using HE staining. The microbial profiling of fecal samples was carried out by Accu16STM assay. Untargeted metabolomics of serum and soft palate tissue samples were analyzed by UPLC-MS. The protein expression of cAMP-related pathways in the soft palate was determined by Western blot. After 28h of CIH induction, a significant increase in pro-inflammatory cytokines was observed in the serum, along with mucosal layer thickening and soft palate tissue hypertrophy. CIH induction altered the diversity and composition of fecal microbiota, specifically reducing beneficial bacteria while increasing harmful bacteria/opportunistic pathogens. Notably, CIH induction led to a significant enrichment of genera such as Dorea, Oscillibacter, Enteractinococcus, Paenibacillus, Globicatella, and Flaviflexus genera. Meanwhile, Additionally, CIH induction had a notable impact on 108 serum marker metabolites. These marker metabolites, primarily involving amino acids, organic acids, and a limited number of flavonoids or sterols, were associated with protein transport, digestion and absorption, amino acid synthesis and metabolism, as well as cancer development. Furthermore, these differential serum metabolites significantly affected 175 differential metabolites in soft palate tissue, mainly related to cancer development, signaling pathways, amino acid metabolism, nucleotide precursor or intermediate metabolism, respiratory processes, and disease. Importantly, CIH induction could significantly affect the expression of the cAMP pathway in soft palate tissue. Our findings suggest that targeting differential metabolites in serum and soft palate tissue may represent a new approach to clinical intervention and treatment of OSA simulated by the CIH.

Assessing the risk of prenatal depressive symptoms in Chinese women: an integrated evaluation of serum metabolome, multivitamin supplement intake, and clinical blood indicators.

Prenatal depressive symptoms (PDS) is a serious public health problem. This study aimed to develop an integrated panel and nomogram to assess at-risk populations by examining the association of PDS with the serum metabolome, multivitamin supplement intake, and clinical blood indicators. This study comprised 221 pregnant women, categorized into PDS and non-PDS groups based on the Edinburgh postnatal depression scale. The participants were divided into training and test sets according to their enrollment time. We conducted logistic regression analysis to identify risk factors, and employed liquid chromatography/high resolution mass spectrometry-based serum metabolome analysis to identify metabolic biomarkers. Multiple factor analysis was used to combine risk factors, clinical blood indicators and key metabolites, and then a nomogram was developed to estimate the probability of PDS. We identified 36 important differential serum metabolites as PDS biomarkers, mainly involved in amino acid metabolism and lipid metabolism. Multivitamin intake works as a protective factor for PDS. The nomogram model, including multivitamin intake, HDL-C and three key metabolites (histidine, estrone and valylasparagine), exhibited an AUC of 0.855 in the training set and 0.774 in the test set, and the calibration curves showed good agreement, indicating that the model had good stability. Our approach integrates multiple models to identify metabolic biomarkers for PDS, ensuring their robustness. Furthermore, the inclusion of dietary factors and clinical blood indicators allows for a comprehensive characterization of each participant. The analysis culminated in an intuitive nomogram based on multimodal data, displaying potential performance in initial PDS risk assessment.

Metabolomic Insights into the Mechanisms of Ganoderic Acid: Protection against α-Amanitin-Induced Liver Injury.

α-Amanitin is a representative toxin found in the Amanita genus of mushrooms, and the consumption of mushrooms containing α-Amanitin can lead to severe liver damage. In this study, we conduct toxicological experiments to validate the protective effects of Ganoderic acid A against α-amanitin-induced liver damage. By establishing animal models with different durations of Ganoderic acid A treatment and conducting a metabolomic analysis of the serum samples, we further confirmed the differences in serum metabolites between the AMA+GA and AMA groups. The analysis of differential serum metabolites after the Ganoderic acid A intervention suggests that Ganoderic acid A may intervene in α-amanitin-induced liver damage by participating in the regulation of retinol metabolism, tyrosine and tryptophan biosynthesis, fatty acid biosynthesis, sphingosine biosynthesis, spermidine and spermine biosynthesis, and branched-chain amino acid metabolism. This provides initial insights into the protective intervention mechanisms of GA against α-amanitin-induced liver damage and offers new avenues for the development of therapeutic drugs for α-Amanitin poisoning.

Abstract 12591: 12,13-dihome and Noradrenaline Are Associated With the Occurrence of Acute Myocardial Infarction in Patients With Type 2 Diabetes Mellitus

Introduction: Acute myocardial infarction (AMI) is the most prevalent cause of mortality and morbidity in patients with type 2 diabetes mellitus (T2DM). However, strict blood glucose control does not always prevent the development and progression of AMI. Aims: Therefore, the present study aimed to explore potential new biomarkers associated with the occurrence of AMI in T2DM patients. Methods: A total of 82 participants were recruited, including the control group (n=28), T2DM without AMI group (T2DM, n=30) and T2DM with initial AMI group (T2DM+AMI, n=24). The untargeted metabolomics using LC-MS analysis was performed to evaluate the changes in serum metabolites. Then, candidate metabolites were determined using ELISA method in the validation study (n=126/T2DM group, n=122/T2DM+AMI group). Results: The results showed that 146 differential serum metabolites were identified among the control, T2DM and T2DM+AMI, Moreover, 16 differentially-expressed metabolites were significantly altered in T2DM+AMI compared to T2DM. Furthermore, three candidate differential metabolites, 12,13-diHOME, noradrenaline (NE) and estrone sulfate (ES), were selected for validation study. Serum levels of 12,13-diHOME and NE in T2DM+AMI were significantly higher than those in T2DM. Multivariate logistic analyses showed that 12,13-diHOME (OR, 1.491; 95% CI, 1.230-1.807, P &lt;0.001) and NE (OR, 8.636; 95% CI, 2.303-32.392, P =0.001) were independent risk factors for AMI occurrence in T2T2DM patients. The area under receiver operating characteristic (ROC) curve (AUC) were 0.757 (95% CI:0.697-0.817, P &lt;0.001) and 0.711(95% CI:0.648-0.775, P &lt;0.001), respectively. The combination of both significantly improved the AUC to 0.816 (95% CI:0.763-0.869, P &lt;0.001). Conclusions: 12,13-diHOME and NE may lead to understanding the possible metabolic alterations associated with AMI onset in T2DM population and serve as promising risk factors and therapeutic targets.

Integrated Macrogenomics and Metabolomics Explore Alterations and Correlation between Gut Microbiota and Serum Metabolites in Adult Epileptic Patients: A Pilot Study.

Epilepsy (EP) is a complex brain disorder showing a lot of unknows reasons. Recent studies showed that gut microbiota can influence epilepsy via the brain-gut axis. Nevertheless, the mechanism by which gut microbiota affects adult epilepsy still remains unclear. In this study, fecal and serum samples were obtained from patients with epilepsy and normal controls. Using an integrated analysis, sequencing was performed by macrogenomics and high-throughput targeted metabolomics with various bioinformatics approaches. The macrogenomic sequencing revealed significant changes in microbial structure in patients suffering from epilepsy. For example, at the phylum level, the relative abundance of Actinobacteria, Bacteroidetes and Proteobacteria showed an increase in the patients with epilepsy, whereas that of Firmicutes decreased. In addition, the patients with epilepsy had significantly differential metabolite profiles compared to normal controls, and five clusters with 21 metabolites, mainly containing the upregulation of some fatty acids and downregulation of some amino acids. Tryptophan (AUC = 91.81, p < 0.0001), kynurenine (AUC = 79.09, p < 0.01) and 7Z,10Z,13Z,16Z-Docosatetraenoic acid (AUC = 80.95, p < 0.01) may be used as potential diagnostic markers for epilepsy. Differential serum metabolites have effects on tryptophan metabolism, iron death and other pathways. Furthermore, a multiomic joint analysis observed a statistically significant correlation between the differential flora and the differential serum metabolites. In our findings, a macrogenomic analysis revealed the presence of dysregulated intestinal flora species and function in adult epileptic patients. Deeper metabolomic analyses revealed differences in serum metabolites between patients with epilepsy and healthy populations. Meanwhile, the multiomic combination showed connection between the gut microbes and circulating metabolites in the EP patients, which may be potential therapeutic targets.

Serum metabolomics analysis of biomarkers and metabolic pathways in patients with colorectal cancer associated with spleen-deficiency and qi-stagnation syndrome or damp-heat syndrome: a prospective cohort study.

To profile the serum metabolites and metabolic pathways in colorectal cancer (CRC) patients associated with spleen-deficiency and qi-stagnation syndrome (SDQSS) or damp-heat syndrome (DHS). From May 2020 to January 2021, CRC patients diagnosed with traditional Chinese medicine (TCM) syndromes of SDQSS or DHS were enrolled. The clinicopathological data of the SDQSS and DHS groups were compared. The serum samples were analyzed by liquid chromatography-mass spectrometry (LC-MS). The variable importance in the projection >1, fold change ≥3 or ≤0.333, and P value ≤0.05 were used to identify differential metabolites between the two groups. Furthermore, areas under the receiver operating characteristic (ROC) curve > 0.9 were applied to select biomarkers with good predictive performance. The enrichment metabolic pathways were searched through the database of Kyoto Encyclopedia of Genes and Genomes. 60 CRC patients were included (30 SDQSS and 30 DHS). The level of alanine aminotransferase was marginally significantly higher in the DHS group than the SDQSS group (P = 0.051). The other baseline clinicopathological characteristics were all comparable between the two groups. 23 differential serum metabolites were identified, among which 16 were significantly up-regulated and 7 were significantly down-regulated in the SDQSS group compared with the DHS group. ROC curve analysis showed that (S)-3-methyl-2-oxopentanoic acid, neocembrene, 1-aminocyclopropanecarboxylic acid, 3-methyl-3-hydroxypentanedioate, and nicotine were symbolic differential metabolites with higher predictive power. The top five enrichment signalling pathways were valine, leucine and isoleucine biosynthesis; lysosome; nicotine addiction; fructose and mannose metabolism; and pertussis. Our study identifies the differential metabolites and characteristic metabolic pathways among CRC patients with SDQSS or DHS, offering the possibility of accurate and objective syndrome differentiation and TCM treatment for CRC patients.

Serum metabolomics detected by LDI-TOF-MS can be used to distinguish between diabetic patients with and without diabetic kidney disease.

Diabetic kidney disease (DKD) is an important cause of end-stage renal disease with changes in metabolic characteristics. The objective of this study was to study changes in serum metabolic characteristics in patients with DKD and to examine metabolite panels to distinguish DKD from diabetes with MALDI-TOF-MS. We recruited 40 T2DM patients with or without DKD from a single center for a cross-sectional study. Serum metabolic profiling was performed with MALDI-TOF-MS using a vertical silicon nanowire array. Differential metabolites between DKD and diabetes patients were selected, and their relevance to the clinical parameters of DKD was analyzed. We applied machine learning methods to the differential metabolite panels to distinguish DKD patients from diabetes patients. Twenty-four differential serum metabolites between DKD patients and diabetes patients were identified, which were mainly enriched in butyrate metabolism, TCA cycle, and alanine, aspartate and glutamate metabolism. Among the metabolites, L-kynurenine was positively correlated with urinary microalbumin, urinary microalbumin/creatinine ratio (UACR), creatinine and urea nitrogen content. L-serine, pimelic acid, 5-methylfuran-2-carboxylic acid, 4-methylbenzaldehyde and dihydrouracil were associated with Glomerular filtration rate (eGFR). The panel of differential metabolites could be used to distinguish between DKD and diabetes patients with an AUC value reaching 0.9899-0.9949. Among the differential metabolites, L-kynurenine was related to the progression of DKD. The differential metabolites exhibited excellent performance at distinguishing between DKD and diabetes. This study provides a novel direction for metabolomics-based clinical detection of DKD.

Serum metabolomics study of Psoraleae Fructus in improving learning and memory ability of APP/PS1 mice

This study aimed to investigate the mechanism of Psoraleae Fructus in improving the learning and memory ability of APP/PS1 mice by serum metabolomics, screen the differential metabolites of Psoraleae Fructus on APP/PS1 mice, and reveal its influence on the metabolic pathway of APP/PS1 mice. Thirty 3-month-old APP/PS1 mice were randomly divided into a model group and a Psoraleae Fructus extract group, and another 15 C57BL/6 mice of the same age were assigned to the blank group. The learning and memory ability of mice was evaluated by the Morris water maze and novel object recognition tests, and metabolomics was used to analyze the metabolites in mouse serum. The results of the Morris water maze test showed that Psoraleae Fructus shortened the escape latency of APP/PS1 mice(P&lt;0.01), and increased the number of platform crossing and residence time in the target quadrant(P&lt;0.01). The results of the novel object recognition test showed that Psoraleae Fructus could improve the novel object recognition index of APP/PS1 mice(P&lt;0.01). Eighteen differential metabolites in serum were screened out by metabolomics, among which the levels of arachidonic acid, tryptophan, and glycerophospholipid decreased after drug administration, while the levels of glutamyltyrosine increased after drug administration. The metabolic pathways involved included arachidonic acid metabolism, glycerophospholipid metabolism, tryptophan metabolism, linoleic acid metabolism, α-linolenic acid metabolism, and glycerolipid metabolism. Therefore, Psoraleae Fructus can improve the learning and memory ability of APP/PS1 mice, and its mechanism may be related to the effects in promoting energy metabolism, reducing oxidative damage, protecting central nervous system, reducing neuroinflammation, and reducing Aβ deposition. This study is expected to provide references for Psoraleae Fructus in the treatment of Alzheimer's disease(AD) and further explain the mechanism of Psoraleae Fructus in the treatment of AD.

Differences in serum metabolome profile explain individual variation in growth performance of young goats

High growth rates and body weight are important traits of young dairy goats that can shorten generation intervals, improve animal performance, and increase economic benefits. In the present study, ninety-nine, 6-month-old, female goats were fed with the same diet and kept under the same management condition. The ten goats with highest average daily gain (ADG, HADG, 135.27 ± 4.59 g/d) and ten goats with lowest ADG (LADG, 87.74 ± 3.13 g/d) were selected to identify the key serum metabolites associated with ADG, and to investigate the relationships of serum metabolome profiles with digestive tract microbiota. The results showed that a total of 125 serum metabolites were significantly different between HADG and LADG. Of these, 43 serum metabolites were significantly higher levels in HADG, including D-ornithine, l-glutamine, L-histidine, carnosine, LysoPC (16:1(9Z)/0:0), DCTP and hydroxylysine, while, 82 serum metabolites were significantly higher levels in LADG, including P-salicylic acid and deoxycholic acid 3-glucuronide. Pathway analysis indicated that these different metabolites were mainly involved in amino acid and lipid metabolism. Furthermore, Spearman's rank correlation analysis revealed that these differential serum metabolites were correlated with ADG and ADG-related bacteria. Notably, serum hydroxylysine and L-histidine could be used as biomarkers for distinguishing HADG and LADG goats, with an accuracy of >92.0%. SignificanceOur study confirms that individual microbiota and metabolic differences contribute to the variations of growth rate in young goats. Some serum metabolites may be useful in improving the growth performance of young goats, which provides directions for developing further nutritional regulation in the goat industry to achieve healthy feeding and efficiency enhancement.

12,13-diHOME and noradrenaline are associated with the occurrence of acute myocardial infarction in patients with type 2 diabetes mellitus

BackgroundAcute myocardial infarction (AMI) is the most prevalent cause of mortality and morbidity in patients with type 2 diabetes mellitus (T2DM). However, strict blood glucose control does not always prevent the development and progression of AMI. Therefore, the present study aimed to explore potential new biomarkers associated with the occurrence of AMI in T2DM patients.MethodsA total of 82 participants were recruited, including the control group (n = 28), T2DM without AMI group (T2DM, n = 30) and T2DM with initial AMI group (T2DM + AMI, n = 24). The untargeted metabolomics using liquid chromatography-mass spectrometry (LC–MS) analysis was performed to evaluate the changes in serum metabolites. Then, candidate metabolites were determined using ELISA method in the validation study (n = 126/T2DM group, n = 122/T2DM + AMI group).ResultsThe results showed that 146 differential serum metabolites were identified among the control, T2DM and T2DM + AMI, Moreover, 16 differentially-expressed metabolites were significantly altered in T2DM + AMI compared to T2DM. Amino acid and lipid pathways were the major involved pathways. Furthermore, three candidate differential metabolites, 12,13-dihydroxy-9Z-octadecenoic acid (12,13-diHOME), noradrenaline (NE) and estrone sulfate (ES), were selected for validation study. Serum levels of 12,13-diHOME and NE in T2DM + AMI were significantly higher than those in T2DM. Multivariate logistic analyses showed that 12,13-diHOME (OR, 1.491; 95% CI 1.230–1.807, P < 0.001) and NE (OR, 8.636; 95% CI 2.303–32.392, P = 0.001) were independent risk factors for AMI occurrence in T2T2DM patients. The area under receiver operating characteristic (ROC) curve (AUC) were 0.757 (95% CI 0.697–0.817, P < 0.001) and 0.711(95% CI 0.648–0.775, P < 0.001), respectively. The combination of both significantly improved the AUC to 0.816 (95% CI 0.763–0.869, P < 0.001).Conclusions12,13-diHOME and NE may lead to understanding the possible metabolic alterations associated with AMI onset in T2DM population and serve as promising risk factors and therapeutic targets.

Mongolian medicine formulae Ruda-6 alleviates indomethacin-induced gastric ulcer by regulating gut microbiome and serum metabolomics in rats

Ethnopharmacological relevanceRuda-6 (RD-6), a typical traditional Mongolian medicine formulae consisting of 6 herbs, has been traditionally used in treating gastric disorders. Even though it has been shown to protect against gastric ulcers (GU) in animal models, the gut microbiome and serum metabololite-related mechanisms that prevent GU are not well understood. Aim of the studyThis study was conducted to evaluate the gastroprotective mechanism of RD-6 associated with the alteration of the gut microbiome and serum metabolic profiles in GU rats. Materials and methodsRD-6 (0.27, 1.35 and 2.7 g/kg) or ranitidine (40 mg/kg) were orally administered in rats for three weeks before the induction of gastric ulcer using indomethacin (30 mg/kg, single oral dose). The gastric ulcer index, ulcer area, H&E staining, and the levels of TNF-α, iNOS, MPO and MDA were quantified to evaluate the ulcer inhibitory effects of RD-6. Then, 16S rRNA gene sequencing combined with LC-MS metabolic profiling was performed to investigate the effect of RD-6 on the gut microbiota and serum metabolites in rats. Moreover, a spearman analysis was used to calculate the correlation coefficient between the different microbiota and the metabolites. ResultsRD-6 inhibited the gastric lesion damage caused by indomethacin in rats, decreased the ulcer index by 50.29% (p < 0.05), reduced the levels of TNF-α, iNOS, MDA and MPO in gastric tissue. Additionally, RD-6 reshaped the diversity and microbial composition, and reversed the reduced bacteria including [Eubacterium]_xylanophilum group, Sellimonas, Desulfovibrio, and UCG-009, and the increased bacteria Aquamicrobium caused by indomethacin induction. Furthermore, RD-6 regulated the levels of metabolites including amino acids and organic acids, and these affected metabolites were involved in taurine and hypotaurine metabolism and tryptophan metabolism. Spearman analysis revealed that the perturbed gut microbiota were closely related to the changes in differential serum metabolites. ConclusionIn view of the 16S rRNA gene sequencing and LC-MS metabolic results, the present study suggests the mechanism of RD-6 ameliorating GU via modulating intestinal microbiota and their metabolites.

Fecal and serum metabolomic signatures and gut microbiota characteristics of allergic rhinitis mice model.

The etiology of allergic rhinitis (AR) is complicated. Traditional therapy of AR still has challenges, such as low long-term treatment compliance, unsatisfactory therapeutic outcomes, and a high financial burden. It is urgent to investigate the pathophysiology of allergic rhinitis from different perspectives and explore brand-new possible preventative or treatment initiatives. The aim is to apply a multi-group technique and correlation analysis to explore more about the pathogenesis of AR from the perspectives of gut microbiota, fecal metabolites, and serum metabolism. Thirty BALB/c mice were randomly divided into the AR and Con(control) groups. A standardized Ovalbumin (OVA)-induced AR mouse model was established by intraperitoneal OVA injection followed by nasal excitation. We detected the serum IL-4, IL-5, and IgE by enzyme-linked immunosorbent assay (ELISA), evaluated the histological characteristics of the nasal tissues by the hematoxylin and eosin (H&E) staining, and observed the nasal symptoms (rubs and sneezes) to evaluate the reliability of the AR mouse model. The colonic NF-κB protein was detected by Western Blot, and the colonic histological characteristics were observed by the H&E staining to evaluate inflammation of colon tissue. We analyzed the V3 and V4 regions of the 16S ribosomal DNA (rDNA) gene from the feces (colon contents) through 16S rDNA sequencing technology. Untargeted metabolomics was used to examine fecal and serum samples to find differential metabolites. Finally, through comparison and correlation analysis of differential gut microbiota, fecal metabolites, and serum metabolites, we further explore the overall impact of AR on gut microbiota, fecal metabolites, and host serum metabolism and its correlation. In the AR group, the IL-4, IL-5, IgE, eosinophil infiltration, and the times of rubs and sneezes were significantly higher than those in the Con group, indicating the successful establishment of the AR model. No differences in diversity were detected between the AR and Con groups. However, there were modifications in the microbiota's structure. At the phylum level, the proportion of Firmicutes and Proteobacteria in the AR group increased significantly, while the proportion of Bacteroides decreased significantly, and the ratio of Firmicutes/Bacteroides was higher. The key differential genera, such as Ruminococcus, were increased significantly in the AR group, while the other key differential genera, such as Lactobacillus, Bacteroides, and Prevotella, were significantly decreased in the Con group. Untargeted metabolomics analysis identified 28 upregulated and 4 downregulated differential metabolites in feces and 11 upregulated and 16 downregulated differential metabolites in serum under AR conditions. Interestingly, one of the significant difference metabolites, α-Linoleic acid (ALA), decreased consistently in feces and serum of AR. KEGG functional enrichment analysis and correlation analysis showed a close relationship between differential serum metabolites and fecal metabolites, and changes in fecal and serum metabolic patterns are associated with altered gut microbiota in AR. The NF-κB protein and inflammatory infiltration of the colon increased considerably in the AR group. Our study reveals that AR alters fecal and serum metabolomic signatures and gut microbiota characteristics, and there is a striking correlation between the three. The correlation analysis of the microbiome and metabolome provides a deeper understanding of AR's pathogenesis, which may provide a theoretical basis for AR's potential prevention and treatment strategies.

Metabolomic profiles in serum and urine uncover novel biomarkers in children with nephrotic syndrome.

Nephrotic syndrome is common in children and adults worldwide, and steroid-sensitive nephrotic syndrome (SSNS) accounts for 80%. Aberrant metabolism involvement in early SSNS is sparsely studied, and its pathogenesis remains unclear. Therefore, the goal of this study was to investigate the changes in initiated SSNS patients-related metabolites through serum and urine metabolomics and discover the novel potential metabolites and metabolic pathways. Serum samples (27 SSNS and 56 controls) and urine samples (17 SSNS and 24 controls) were collected. Meanwhile, the non-targeted analyses were performed by ultra-high-performance liquid chromatography-quadrupole time of flight-mass spectrometry (UHPLC-QTOF-MS) to determine the changes in SSNS. We applied the causal inference model, the DoWhy model, to assess the causal effects of several selected metabolites. An ultraperformance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was used to validate hits (D-mannitol, dulcitol, D-sorbitol, XMP, NADPH, NAD, bilirubin, and α-KG-like) in 41 SSNS and 43 controls. In addition, the metabolic pathways were explored. Compared to urine, the metabolism analysis of serum samples was more clearly discriminated at SSNS. 194 differential serum metabolites and five metabolic pathways were obtained in the SSNS group. Eight differential metabolites were identified by establishing the diagnostic model for SSNS, and four variables had a positive causal effect. After validation by targeted MS, except XMP, others have similar trends like the untargeted metabolic analysis. With untargeted metabolomics analysis and further targeted quantitative analysis, we found seven metabolites may be new biomarkers for risk prediction and early diagnosis for SSNS.

Gas Chromatography-Mass Spectrometry Technology: Application in the Study of Inflammatory Mechanism in COVID-19 Patients.

SARS-CoV-2 infection in the human body induces a severe storm of inflammatory factors. However, its specific mechanism is still not clear. Gas chromatography–mass spectrometry (GC–MS) technology is expected to explain the possible mechanism of the disease by detecting differential metabolites. 15 COVID-19 patients and healthy controls were included in this study. Immune indicators such as hs CRP and cytokines were detected to reflect the level of inflammation in patients with COVID-19. The distribution of lymphocytes and subpopulations in peripheral whole blood were detected using flow cytometry to assess the immune function of COVID-19 patients. The expression of differential metabolites in serum was analyzed using GC–MS non-targeted metabolomics. The results showed that hs CRP, IL-5/6/8/10 and IFN-α in the serum of COVID-19 patients increased to varying degrees, and CD3/4/8+ T lymphocytes decreased. Additionally, 53 metabolites in the serum of COVID-19 patients were up regulated, 18 metabolites were down regulated, and 8 metabolites remained unchanged. Increased Cholesterol, Lactic Acid and 1-Monopalmitin may be the mechanism that causes excessive inflammation in COVID-19 patients. The increase of D-Allose may be involved in the process of lymphocyte decrease. In conclusion, the significance of our study is to reveal the possible mechanism of inflammatory response in patients with COVID-19 from the perspective of metabolomics. This provided a new idea for the treatment of COVID-19.

High-risk genotypes for type 1 diabetes are associated with the imbalance of gut microbiome and serum metabolites.

The profile of gut microbiota, serum metabolites, and lipids of type 1 diabetes (T1D) patients with different human leukocyte antigen (HLA) genotypes remains unknown. We aimed to explore gut microbiota, serum metabolites, and lipids signatures in individuals with T1D typed by HLA genotypes. We did a cross-sectional study that included 73 T1D adult patients. Patients were categorized into two groups according to the HLA haplotypes they carried: those with any two of three susceptibility haplotypes (DR3, DR4, DR9) and without any of the protective haplotypes (DR8, DR11, DR12, DR15, DR16) were defined as high-risk HLA genotypes group (HR, n=30); those with just one or without susceptibility haplotypes as the non-high-risk HLA genotypes group (NHR, n=43). We characterized the gut microbiome profile with 16S rRNA gene amplicon sequencing and analyzed serum metabolites with liquid chromatography-mass spectrometry. Study individuals were 32.5 (8.18) years old, and 60.3% were female. Compared to NHR, the gut microbiota of HR patients were characterized by elevated abundances of Prevotella copri and lowered abundances of Parabacteroides distasonis. Differential serum metabolites (hypoxanthine, inosine, and guanine) which increased in HR were involved in purine metabolism. Different lipids, phosphatidylcholines and phosphatidylethanolamines, decreased in HR group. Notably, Parabacteroides distasonis was negatively associated (p ≤ 0.01) with hypoxanthine involved in purine metabolic pathways. The present findings enabled a better understanding of the changes in gut microbiome and serum metabolome in T1D patients with HLA risk genotypes. Alterations of the gut microbiota and serum metabolites may provide some information for distinguishing T1D patients with different HLA risk genotypes.

Effect of dietary protein content shift on aging in elderly rats by comprehensive quantitative score and metabolomics analysis.

In the protein nutrition strategy of middle-aged and elderly people, some believe that low protein is good for health, while others believe high protein is good for health. Facing the contradictory situation, the following hypothesis is proposed. There is a process of change from lower to higher ratio of protein nutritional requirements that are good for health in the human body after about 50 years of age, and the age at which the switch occurs is around 65 years of age. Hence, in this study, 50, 25-month-old male rats were randomly divided into five groups: Control (basal diet), LP (low-protein diet with a 30% decrease in protein content compared to the basal diet), HP (high-protein diet with a 30% increase in protein content compared to the basal diet), Model 1 (switched from LP to HP feed at week 4), and Model 2 (switched from LP to HP feed at week 7). After a total of 10 weeks intervention, the liver and serum samples were examined for aging-related indicators, and a newly comprehensive quantitative score was generated using principal component analysis (PCA). The effects of the five protein nutritional modalities were quantified in descending order: Model 1 > HP > LP > Control > Model 2. Furthermore, the differential metabolites in serum and feces were determined by orthogonal partial least squares discriminant analysis, and 15 differential metabolites, significantly associated with protein intake, were identified by Spearman's correlation analysis (p < 0.05). Among the fecal metabolites, 10 were positively correlated and 3 were negatively correlated. In the serum, tyrosine and lactate levels were positively correlated, and acetate levels were negatively correlated. MetaboAnalyst analysis identified that the metabolic pathways influenced by protein intake were mainly related to amino acid and carbohydrate metabolism. The results of metabolomic analysis elucidate the mechanisms underlying the preceding effects to some degree. These efforts not only contribute to a unified protein nutrition strategy but also positively impact the building of a wiser approach to protein nutrition, thereby helping middle-aged and older populations achieve healthy aging.

Cadmium perturbed lipid profile and induced liver dysfunction in mice through phosphatidylcholine remodeling and promoting arachidonic acid synthesis and metabolism

Cadmium ion (Cd2+) exposure has been reported to associate with the prevalence of dyslipidemia, and contribute to the initiation and progression of nonalcoholic fatty liver disease (NAFLD). However, Cd2+ exposure perturbed specific metabolic pathways and underlying mechanisms are still unclear. In the present study, through lipidomics analyses of differential metabolites in serum between the Cd2+-exposed mice and the control group, 179 differential metabolites were identified, among which phosphatidylcholines (PCs) accounted for 49 % metabolites. Moreover, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment assay indicates that PCs participate in the metabolic pathways, including the Arachidonic Acid (AA) metabolism, which also could be potential NAFLD biomarkers. Moreover, in vivo and in vitro results suggested that Cd2+ exposure induced PC synthesis and remodeling, and increased AA level by promoting fatty acid desaturase 1 (FADS1) to catalyze synthesis process instead of cytosolic phospholipase A2 (cPLA2) mediated release pathway. Inhibition of FADS1 by T3364366 could reverse Cd-induced AA, prostaglandin E2 (PGE2) and triglyceride (TAG) levels, and it also reduce cisplatin resistance in HepG2 cells. This study provides new evidence of Cd2+-induced dyslipidemia and reveals underlying molecular mechanism involved in liver dysfunction of Cd2+ exposure.

Metabolic reprogramming enables the auxiliary diagnosis of breast cancer by automated breast volume scanner.

Breast cancer is the leading cause of female cancer-related deaths worldwide. New technologies with enhanced sensitivity and specificity for early diagnosis and monitoring of postoperative recurrence are in critical demand. Automatic breast full volume scanning system (ABVS) is an emerging technology used as an alternative imaging method for breast cancer screening. Despite its improved detection rate of malignant tumors, ABVS cannot accurately stage breast cancer preoperatively in 30–40% of cases. As a major hallmark of breast cancer, the characteristic metabolic reprogramming may provide potential biomarkers as an auxiliary method for ABVS.ObjectiveThe objective of this study was to identify differential metabolomic signatures between benign and malignant breast tumors and among different subtypes of breast cancer patients based on untargeted metabolomics and improve breast cancer detection rate by combining key metabolites and ABVS.MethodsUntargeted metabolomics approach was used to profile serum samples from 70 patients with different subtypes of breast cancer and benign breast tumor to determine specific metabolomic profiles through univariate and multivariate statistical data analysis.ResultsMetabolic profiles correctly distinguished benign and malignant breast tumors patients, and a total of 791 metabolites were identified. There were 54 different metabolites between benign and malignant breast tumors and 17 different metabolites between invasive and non-invasive breast cancer. Notably, the missed diagnosis rate of ABVS could be reduced by differential metabolite analysis. Moreover, the diagnostic performance analyses of combined metabolites (pelargonic acid, N-acetylasparagine, and cysteine-S-sulfate) with ABVS performance gave a ROC area under the curve of 0.967 (95% CI: 0.926, 0.993).ConclusionsOur study identified metabolic features both in benign and malignant breast tumors and in invasive and non-invasive breast cancer. Combined ultrasound ABVS and a panel of differential serum metabolites could further improve the accuracy of preoperative diagnosis of breast cancer and guide surgical therapy.