PubMed

Sci Rep. 2024 Jun 13;14(1):13630. doi: 10.1038/s41598-024-64561-z.ABSTRACTThis cross-sectional study investigated differences in the plasma metabolome in two groups of adults that were of similar age but varied markedly in body composition and dietary and physical activity patterns. Study participants included 52 adults in the lifestyle group (LIFE) (28 males, 24 females) and 52 in the control group (CON) (27 males, 25 females). The results using an extensive untargeted ultra high-performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) metabolomics analysis with 10,535 metabolite peaks identified 486 important metabolites (variable influence on projections scores of VIP ≥ 1) and 16 significantly enriched metabolic pathways that differentiated LIFE and CON groups. A novel metabolite signature of positive lifestyle habits emerged from this analysis highlighted by lower plasma levels of numerous bile acids, an amino acid profile characterized by higher histidine and lower glutamic acid, glutamine, β-alanine, phenylalanine, tyrosine, and proline, an elevated vitamin D status, higher levels of beneficial fatty acids and gut microbiome catabolism metabolites from plant substrates, and reduced levels of N-glycan degradation metabolites and environmental contaminants. This study established that the plasma metabolome is strongly associated with body composition and lifestyle habits. The robust lifestyle metabolite signature identified in this study is consistent with an improved life expectancy and a reduced risk for chronic disease.PMID:38871777 | DOI:10.1038/s41598-024-64561-z

Sci Rep. 2024 Jun 13;14(1):13621. doi: 10.1038/s41598-024-62369-5.ABSTRACTIn the current study, we evaluated the in vitro antibacterial efficacy of the roots' extracts of Jasminum officinale, Rosa damascene and Paeonia officinalis against MRSA (methicillin-resistant Staphylococcus aureus) by well diffusion technique. The root extract of P. officinalis exerted a potent anti-MRSA with MIC 0.4673 µg/ml, while both J. officinale and R. damascene exhibited very weak activity. Therefore, chemical profiling of the crude extract P. officinalis roots assisted by LC-HR-ESI-MS was performed and led to the dereplication of twenty metabolites of different classes, in which terpenes are the most abundant compounds. On a molecular level, network pharmacology was used to determine the targets of active metabolites to bacterial infections, particularly MRSA. Online databases PubChem, UniProt, STRING, and Swiss Target Prediction were used. In addition to using CYTOSCAPE software to display and analyze the findings, ShinyGO and FunRich tools were used to identify the gene enrichment analysis to the set of recognized genes. The results detected the identified metabolites were annotated by 254 targets. ALB, ACHE, TYMS, PRKCD, PLG, MMP9, MMP2, ERN1, EDNRA, BRD4 were found to be associated with MRSA infection. The top KEGG pathway was the vascular smooth muscle contraction pathway according to enrichment FDR. The present study suggested a possible implication of P. officinalis roots as a potent candidate having a powerful antibacterial activity against MRSA.PMID:38871725 | DOI:10.1038/s41598-024-62369-5

Oncogenesis. 2024 Jun 13;13(1):22. doi: 10.1038/s41389-024-00521-6.ABSTRACTBreast cancer (BC) is a leading cause of cancer-related death worldwide. The diverse nature and heterogeneous biology of BC pose challenges for survival prediction, as patients with similar diagnoses often respond differently to treatment. Clinically relevant BC intrinsic subtypes have been established through gene expression profiling and are implemented in the clinic. While these intrinsic subtypes show a significant association with clinical outcomes, their long-term survival prediction beyond 5 years often deviates from expected clinical outcomes. This study aimed to identify naturally occurring long-term prognostic subgroups of BC based on an integrated multi-omics analysis. This study incorporates a clinical cohort of 335 untreated BC patients from the Oslo2 study with long-term follow-up (>12 years). Multi-Omics Factor Analysis (MOFA+) was employed to integrate transcriptomic, proteomic, and metabolomic data obtained from the tumor tissues. Our analysis revealed three prominent multi-omics clusters of BC patients with significantly different long-term prognoses (p = 0.005). The multi-omics clusters were validated in two independent large cohorts, METABRIC and TCGA. Importantly, a lack of prognostic association to long-term follow-up above 12 years in the previously established intrinsic subtypes was shown for these cohorts. Through a systems-biology approach, we identified varying enrichment levels of cell-cycle and immune-related pathways among the prognostic clusters. Integrated multi-omics analysis of BC revealed three distinct clusters with unique clinical and biological characteristics. Notably, these multi-omics clusters displayed robust associations with long-term survival, outperforming the established intrinsic subtypes.PMID:38871719 | DOI:10.1038/s41389-024-00521-6

Phytochem Anal. 2024 Jun 13. doi: 10.1002/pca.3403. Online ahead of print.ABSTRACTINTRODUCTION: Taxus media (Taxus × media Rehder) is renowned for its high paclitaxel content, serving as a major source for industrial paclitaxel production. In addition to paclitaxel, T. media contains a diverse range of metabolites, including flavonoids, alkaloids, and terpenoids, which have been shown to possess antioxidant, antibacterial, anti-inflammatory, and immunomodulatory effects. However, these compounds have not been thoroughly studied as key metabolites in T. media.OBJECTIVE: The untargeted metabolomics analysis of six T. media tissues provides new insights into the development and utilization of T. media metabolites.METHOD: The extracts from six tissues of T. media were analyzed and subjected to analysis using high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (HPLC-Q-TOF-MS/MS) and chemometric techniques.RESULTS: Using a reliable HPLC-Q-TOF-MS/MS method, we identified 312 compounds in six T. media tissues, including 214 previously unreported in T. media. To identify characteristic compounds across different tissues, 34 metabolites were further screened. KEGG metabolic pathway analysis revealed that these compounds primarily occur in the metabolic pathways of terpene glycosides, flavans, and O-methylated flavonoids.CONCLUSION: This study initially utilized an HPLC-QTOF-MS/MS-based metabolomics approach to assess the metabolites in different tissues of T. media, providing a basis for their utilization and management.PMID:38870256 | DOI:10.1002/pca.3403

J Proteome Res. 2024 Jun 13. doi: 10.1021/acs.jproteome.4c00198. Online ahead of print.ABSTRACTThe application of innovative spatial proteomics techniques, such as those based upon matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) technology, has the potential to impact research in the field of nephropathology. Notwithstanding, the possibility to apply this technology in more routine diagnostic contexts remains limited by the alternative fixatives employed by this ultraspecialized diagnostic field, where most nephropathology laboratories worldwide use bouin-fixed paraffin-embedded (BFPE) samples. Here, the feasibility of performing MALDI-MSI on BFPE renal tissue is explored, evaluating variability within the trypsin-digested proteome as a result of different preanalytical conditions and comparing them with the more standardized formalin-fixed paraffin-embedded (FFPE) counterparts. A large proportion of the features (270, 68.9%) was detected in both BFPE and FFPE renal samples, demonstrating only limited variability in signal intensity (10.22-10.06%). Samples processed with either fixative were able to discriminate the principal parenchyma regions along with diverse renal substructures, such as glomeruli, tubules, and vessels. This was observed when performing an additional "stress test", showing comparable results in both BFPE and FFPE samples when the distribution of several amyloid fingerprint proteins was mapped. These results suggest the utility of BFPE tissue specimens in MSI-based nephropathology research, further widening their application in this field.PMID:38869849 | DOI:10.1021/acs.jproteome.4c00198

Methods Mol Biol. 2024;2832:171-182. doi: 10.1007/978-1-0716-3973-3_12.ABSTRACTStress can affect different groups of plant metabolites and multiple signaling pathways. Untargeted metabolomics enables the collection of whole-spectrum data for the entire metabolite content present in plant tissues at that point in time. We present a thorough approach for large-scale, untargeted metabolomics of plant tissues using reverse-phase liquid chromatography connected to high-resolution mass spectrometry (LC-MS) of dilute methanolic extract. MZmine is a specialized computer software that automates the alignment and baseline modification of all derived mass peaks across all samples, resulting in precise information on the relative abundance of hundreds of metabolites reflected by thousands of mass signals. Further processing with statistic and bioinformatic techniques will provide a comprehensive perspective of the variations and connections among groups of samples.PMID:38869795 | DOI:10.1007/978-1-0716-3973-3_12

J Asian Nat Prod Res. 2024 Jun 13:1-10. doi: 10.1080/10286020.2024.2366010. Online ahead of print.ABSTRACTLiquiritigenin is a natural medicine. However, its inhibitory effect and its potential mechanism on bladder cancer (BCa) remain to be explored. It was found that it could be visualized that the transplanted tumours in the low-dose liquiritigenin -treated group and the high-dose liquiritigenin -treated group were smaller than those in the model group. Liquiritigenin treatment led to alterations in Lachnoclostridium, Escherichia-Shigella, Alistipes and Akkermansia. Non-targeted metabolomics analysis showed that a total of multiple differential metabolites were identified between the model group and the high-dose liquiritigenin-treated group. This provides a new direction and rationale for the antitumour effects of liquiritigenin.PMID:38869213 | DOI:10.1080/10286020.2024.2366010

Rev Med Liege. 2024 Jun;79(5-6):297-303.ABSTRACTIn order to improve our healthcare system, it is undeniable that the future of modern medicine must focus on a more preventive and personalized approach, notably based on the individual characteristics specific to each patient. In this perspective, clinical metabolomics, which focuses on metabolites, emerges as a particularly interesting and promising approach. Indeed, this science reflects the internal and external stimuli received by an individual, thus capturing their physiological and/or pathological state. Close to the phenotype, it represents the interface between the patient, their genes, and their environment in the broadest sense. Its translational nature requires the conjunction of several expertise areas, both in analytical, biostatistical, and clinical levels. Combined with other data, it allows the generation of predictive or diagnostic models useful for early detection and monitoring of pathologies, taking into account notably the individual characteristics of patients. There are, of course, many obstacles and challenges to overcome for metabolomics to transition into clinical practice, but it is evident that this innovative approach will, in the years to come, find its place among the tools available to clinicians in a more personalized vision of patient care.PMID:38869115

Gut Microbes. 2024 Jan-Dec;16(1):2361491. doi: 10.1080/19490976.2024.2361491. Epub 2024 Jun 13.ABSTRACTMetformin is widely used for treating type 2 diabetes mellitus (T2D). However, the efficacy of metformin monotherapy is highly variable within the human population. Understanding the potential indirect or synergistic effects of metformin on gut microbiota composition and encoded functions could potentially offer new insights into predicting treatment efficacy and designing more personalized treatments in the future. We combined targeted metabolomics and metagenomic profiling of gut microbiomes in newly diagnosed T2D patients before and after metformin therapy to identify potential pre-treatment biomarkers and functional signatures for metformin efficacy and induced changes in metformin therapy responders. Our sequencing data were largely corroborated by our metabolic profiling and identified that pre-treatment enrichment of gut microbial functions encoding purine degradation and glutamate biosynthesis was associated with good therapy response. Furthermore, we identified changes in glutamine-associated amino acid (arginine, ornithine, putrescine) metabolism that characterize differences in metformin efficacy before and after the therapy. Moreover, metformin Responders' microbiota displayed a shifted balance between bacterial lipidA synthesis and degradation as well as alterations in glutamate-dependent metabolism of N-acetyl-galactosamine and its derivatives (e.g. CMP-pseudaminate) which suggest potential modulation of bacterial cell walls and human gut barrier, thus mediating changes in microbiome composition. Together, our data suggest that glutamine and associated amino acid metabolism as well as purine degradation products may potentially condition metformin activity via its multiple effects on microbiome functional composition and therefore serve as important biomarkers for predicting metformin efficacy.PMID:38868903 | DOI:10.1080/19490976.2024.2361491

Clin Lab. 2024 Jun 1;70(6). doi: 10.7754/Clin.Lab.2023.231105.ABSTRACTBACKGROUND: Malignant pleural effusion (MPE) is a common complication of non-small cell lung cancer (NSCLC). Patients with NSCLC exhibit a high rate of epidermal growth factor receptor (EGFR) mutations. The detection of EGFR mutations is usually time-consuming and costly. This study aimed at identifying potential biomarkers of EGFR mutations in MPE of NSCLC patients by metabolomics.METHODS: In total, 58 MPE samples from 30 EGFR mutant and from 28 wild-type NSCLC patients were collected and analyzed by using hydrogen nuclear magnetic resonance (1H NMR) based metabolomics and UPLC-MS/MS based amino acid analysis.RESULTS: Our 1H NMR study showed a significant increase in the lysine levels but a significant decrease in the alanine levels in MPE of NSCLC patients with EGFR-mutant. Twelve amino acids in MPE were further determined by UPLC-MS/MS. It showed that alanine in MPE (6.34 ± 1.88 vs. 8.73 ± 3.68) were significantly decreased and leucine (3.13 ± 0.57 vs. 2.22 ± 0.13), lysine (2.19 ± 0.50 vs. 1.53 ± 0.40), and tyrosine (2.69 ± 0.71 vs. 1.89 ± 0.46) were increased in the EGFR mutation group; leucine (2.19 ± 0.50 vs. 1.53 ± 0.40), methionine (2.19 ± 0.50 vs. 1.53 ± 0.40), and threonine (2.19 ± 0.50 vs. 1.53 ± 0.40) in MPE were significantly lower in the EGRF 19 mutation compared with 21 mutation patients. The area under the receiver operating characteristic curve of 0.851 and 0.931 would be achieved by the logistic model for classification of EGFR-mutant patients from the wild-type controls or the exon 19 from exon 21 mutant patients.CONCLUSIONS: Amino acids in MPE are significantly altered and helpful in the diagnosis of EGFR-mutant patients from the wild-type controls or the exon 19 from exon 21 mutant patients with high accuracy, which is worthy of further study.PMID:38868885 | DOI:10.7754/Clin.Lab.2023.231105

Clin Lab. 2024 Jun 1;70(6). doi: 10.7754/Clin.Lab.2023.230527.ABSTRACTBACKGROUND: The goal was to analyze serums of GDM patients and healthy pregnant women using HPLC-MS and preliminarily screen differential metabolites by metabolomics.METHOD: Sixty pregnant women who underwent elective cesarean section at term in Dongguan Dalang Hospital from January 2023 to April 2023 were selected and divided into the GDM group and healthy pregnancy group. Pre-pregnancy and pregnancy examination information, such as age, BMI, OGTT results, triglyceride, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and other clinical data were col-lected for statistical analysis. Non-targeted metabolomics of serum from 30 GDM patients and 30 healthy pregnant women were studied by HPLC-MS, and different ions were searched. The structures of differential metabolites were identified by HMDB database. The metabolic pathways of differential metabolites were analyzed by KEGG database.RESULTS: The OGTT result, pCO2, pO2, HCO3, BE, Apgar score, and bilirubin levels in the GDM group were higher than those in the healthy pregnancy group (p < 0.05). However, there were no significant differences in age, triglyceride, total cholesterol, newborn birth weight, newborn birth blood glucose, and blood gas pH between the two groups (all p > 0.05). Using p < 0.05 as the screening standard, 55 differential metabolites were identified in serum, mainly including fatty acyl, carboxylic acids and their derivatives, steroids and their derivatives, ketoacids and their derivatives, and pyrimidine nucleosides, etc., all of which were up-regulated or down-regulated to varying degrees. The 55 metabolites were mainly involved in the metabolism of pyrimidine, pyruvate, alanine, aspartic acid, glutamic acid, and arachidonic acid, glycolysis, and biosynthesis of unsaturated fatty acids.CONCLUSIONS: The discovery of these metabolites provides a theoretical basis for an indepth understanding of GDM pathogenesis. Non-targeted metabonomics analysis of blood metabonomics research technology has shown great potential value in the early diagnosis of obstetric diseases and the study of disease mechanisms.PMID:38868866 | DOI:10.7754/Clin.Lab.2023.230527

Front Immunol. 2024 May 28;15:1406234. doi: 10.3389/fimmu.2024.1406234. eCollection 2024.ABSTRACTOBJECTIVE: This study employed Mendelian Randomization (MR) to investigate the causal relationships among immune cells, COPD, and potential metabolic mediators.METHODS: Utilizing summary data from genome-wide association studies, we analyzed 731 immune cell phenotypes, 1,400 plasma metabolites, and COPD. Bidirectional MR analysis was conducted to explore the causal links between immune cells and COPD, complemented by two-step mediation analysis and multivariable MR to identify potential mediating metabolites.RESULTS: Causal relationships were identified between 41 immune cell phenotypes and COPD, with 6 exhibiting reverse causality. Additionally, 21 metabolites were causally related to COPD. Through two-step MR and multivariable MR analyses, 8 cell phenotypes were found to have causal relationships with COPD mediated by 8 plasma metabolites (including one unidentified), with 1-methylnicotinamide levels showing the highest mediation proportion at 26.4%.CONCLUSION: We have identified causal relationships between 8 immune cell phenotypes and COPD, mediated by 8 metabolites. These findings contribute to the screening of individuals at high risk for COPD and offer insights into early prevention and the precocious diagnosis of Pre-COPD.PMID:38868780 | PMC:PMC11168115 | DOI:10.3389/fimmu.2024.1406234

Imeta. 2022 Jun 13;1(3):e31. doi: 10.1002/imt2.31. eCollection 2022 Sep.ABSTRACTOrobanche cumana Wallr. is a holoparasite weed that extracts water and nutrients from its host the sunflower, thereby causing yield reductions and quality losses. However, the number of O. cumana parasites in the same farmland is distinctly different. The roots of some hosts have been heavily parasitized, while others have not been parasitized. What are the factors contributing to this phenomenon? Is it possible that sunflower interroot microorganisms are playing a regulatory role in this phenomenon? The role of the microbial community in this remains unclear. In this study, we investigated the rhizosphere soil microbiome for sunflowers with different degrees of O. cumana parasitism, that is, healthy, light infection, moderate infection, and severe infection on the sunflower roots. The microbial structures differed significantly according to the degree of parasitism, where Xanthomonadaceae was enriched in severe infections. Metagenomic analyses revealed that amino acid, carbohydrate, energy, and lipid metabolism were increased in the rhizosphere soils of severely infected sunflowers, which were attributed to the proliferation of Lysobacter. Lysobacter antibioticus (HX79) was isolated and its capacity to promote O. cumana seed germination and increase the germ tube length was confirmed by germination and pot experiments. Cyclo(Pro-Val), an active metabolite of strain HX79, was identified and metabolomic and molecular docking approaches confirmed it was responsible for promoting O. cumana seed germination and growth. And we found that Pseudomonas mandelii HX1 inhibited the growth of O. cumana in the host rhizosphere soil. Our findings clarify the role of rhizosphere microbiota in regulating the parasite O. cumana to possibly facilitate the development of a new weed suppression strategy.PMID:38868712 | PMC:PMC10989955 | DOI:10.1002/imt2.31

Imeta. 2022 Mar 16;1(2):e12. doi: 10.1002/imt2.12. eCollection 2022 Jun.ABSTRACTThe platform consists of three modules, which are pre-configured bioinformatic pipelines, cloud toolsets, and online omics' courses. The pre-configured bioinformatic pipelines not only combine analytic tools for metagenomics, genomes, transcriptome, proteomics and metabolomics, but also provide users with powerful and convenient interactive analysis reports, which allow them to analyze and mine data independently. As a useful supplement to the bioinformatics pipelines, a wide range of cloud toolsets can further meet the needs of users for daily biological data processing, statistics, and visualization. The rich online courses of multi-omics also provide a state-of-art platform to researchers in interactive communication and knowledge sharing.PMID:38868573 | PMC:PMC10989754 | DOI:10.1002/imt2.12

Imeta. 2022 Apr 5;1(2):e17. doi: 10.1002/imt2.17. eCollection 2022 Jun.ABSTRACTThe commercial aquatic animal microbiome may markedly affect the successful host's farming in various aquaculture systems. However, very little was known about it. Here, two different aquaculture systems, the rice-fish culture (RFC) and intensive pond culture (IPC) systems, were compared to deconstruct the skin, oral, and gut microbiome, as well as the gut metabolome of juvenile Chinese softshell turtle (Pelodiscus sinensis). Higher alpha-diversity and functional redundancy of P. sinensis microbial community were found in the RFC than those of the IPC. The aquaculture systems have the strongest influence on the gut microbiome, followed by the skin microbiome, and finally the oral microbiome. Source-tracking analysis showed that the RFC's microbial community originated from more unknown sources than that of the IPC across all body regions. Strikingly, the RFC's oral and skin microbiome exhibited a significantly higher proportion of generalists and broader habitat niche breadth than those of the IPC, but not the gut. Null model analysis revealed that the RFC's oral and skin microbial community assembly was governed by a significantly greater proportion of deterministic processes than that of the IPC, but not the gut. We further identified the key gene and microbial contribution to five significantly changed gut metabolites, 2-oxoglutarate, N-acetyl-d-mannosamine, cis-4-hydroxy-d-proline, nicotinamide, and l-alanine, which were significantly correlated with important categories of microbe-mediated processes, including the amino acid metabolism, GABAergic synapse, ABC transporters, biosynthesis of unsaturated fatty acids, as well as citrate cycle. Moreover, different aquaculture systems have a significant impact on the hepatic lipid metabolism and body shape of P. sinensis. Our results provide new insight into the influence of aquaculture systems on the microbial community structure feature and assembly mechanism in an aquatic animal, also highlighting the key microbiome and gene contributions to the metabolite variation in the gut microbiome-metabolome association.PMID:38868566 | PMC:PMC10989827 | DOI:10.1002/imt2.17

Front Nutr. 2024 May 29;11:1356038. doi: 10.3389/fnut.2024.1356038. eCollection 2024.ABSTRACTINTRODUCTION: Obesity is a multi-factorial disease frequently associated with poor nutritional habits and linked to many detrimental health outcomes. Individuals with obesity are more likely to have increased levels of persistent inflammatory and metabolic dysregulation. The goal of this study was to compare four dietary patterns differentiated by macronutrient content in a postmenopausal model. Dietary patterns were high carbohydrate (HC), high fat (HF), high carbohydrate plus high fat (HCHF), and high protein (HP) with higher fiber.METHODS: Changes in body weight and glucose levels were measured in female, ovariectomized C57BL/6 mice after 15 weeks of feeding. One group of five mice fed the HCHF diet was crossed over to the HP diet on day 84, modeling a 21-day intervention. In a follow-up study comparing the HCHF versus HP dietary patterns, systemic changes in inflammation, using an 80-cytokine array and metabolism, by untargeted liquid chromatography-mass spectrometry (LCMS)-based metabolomics were evaluated.RESULTS: Only the HF and HCHF diets resulted in obesity, shown by significant differences in body weights compared to the HP diet. Body weight gains during the two-diet follow-up study were consistent with the four-diet study. On Day 105 of the 4-diet study, glucose levels were significantly lower for mice fed the HP diet than for those fed the HC and HF diets. Mice switched from the HCHF to the HP diet lost an average of 3.7 grams by the end of the 21-day intervention, but this corresponded with decreased food consumption. The HCHF pattern resulted in dramatic inflammatory dysregulation, as all 80 cytokines were elevated significantly in the livers of these mice after 15 weeks of HCHF diet exposure. Comparatively, only 32 markers changed significantly on the HP diet (24 up, 8 down). Metabolic perturbations in several endogenous biological pathways were also observed based on macronutrient differences and revealed dysfunction in several nutritionally relevant biosynthetic pathways.CONCLUSION: Overall, the HCHF diet promoted detrimental impacts and changes linked to several diseases, including arthritis or breast neoplasms. Identification of dietary pattern-specific impacts in this model provides a means to monitor the effects of disease risk and test interventions to prevent poor health outcomes through nutritional modification.PMID:38868554 | PMC:PMC11168494 | DOI:10.3389/fnut.2024.1356038

Front Vet Sci. 2024 May 29;11:1403493. doi: 10.3389/fvets.2024.1403493. eCollection 2024.ABSTRACTThe genetic trait of residual feed intake (RFI) holds considerable importance in the swine industry. Recent research indicates that the gut microbiota of pigs plays a pivotal role in the manifestation of the RFI trait. Nevertheless, the metabolic pathways involved in the functioning of these microorganisms remain elusive. Thus, based on the ranking of the RFI trait in Duroc pigs, the present study selected the top 10 and bottom 10 pigs as the experimental subjects. The distribution and metabolite differences of cecal microbiota were analyzed using 16S rRNA gene sequencing and liquid chromatography-tandem mass spectrometry (LC-MS/MS) techniques. The low RFI cecal group was named LRC, and the high RFI cecal group was named HRC. The results indicate that the LRC group had lower RFI, feed conversion ratio (FCR), average daily feed intake (ADFI) (p < 0.001), and thinner backfat (p < 0.05) compared with the HRC group. We simultaneously recorded the foraging behavior as well, the LRC group had a significant increase in total time spent at the feeder per day (TPD) (p < 0.05) and a significant increase in average feed intake per mins (AFI) and the number of visits to the feeder per day (NVD) compared to the HRC group (p < 0.001). Clostridium_XVIII, Bulleidia, and Intestinimonas were significantly enriched in the LRC group (p < 0.01), while Sutterella, Fusobacterium, and Bacteroides were significantly increased in the HRC group (p < 0.01). In the metabolome, we detected 390 (248 metabolites up and 142 down in the LRC compared with HRC), and 200 (97 metabolites up and 103 down in the LRC compared with HRC) differential metabolites in positive and negative ionization modes. The comprehensive analysis found that in the LRC group, Escherichia and Eubacterium in the gut may increase serotonin content, respectively. Bacteroides may deplete serotonin. We suggest that the RFI may be partly achieved through tryptophan metabolism in gut microbes. In individuals with low RFI, gut microbes may enhance feed efficiency by enhancing host synthesis and metabolism of tryptophan-related metabolites.PMID:38868499 | PMC:PMC11168206 | DOI:10.3389/fvets.2024.1403493

Imeta. 2023 Apr 25;2(2):e105. doi: 10.1002/imt2.105. eCollection 2023 May.ABSTRACTRevealing the assembly and succession of the chicken gut microbiota is critical for a better understanding of its role in chicken physiology and metabolism. However, few studies have examined dynamic changes of absolute chicken gut microbes using the quantitative microbiome profiling (QMP) method. Here, we revealed the developmental trajectory of the broiler chicken gut bacteriome and mycobiome by combining high-throughput sequencing with a microbial load quantification assay. We showed that chicken gut microbiota abundance and diversity reached a plateau at 7 days posthatch (DPH), forming segment-specific community types after 1 DPH. The bacteriome was more impacted by deterministic processes, and the mycobiome was more affected by stochastic processes. We also observed stage-specific microbes in different gut segments, and three microbial occurrence patterns including "colonization," "disappearance," and "core" were defined. The microbial co-occurrence networks were very different among gut segments, with more positive associations than negative associations. Furthermore, we provided links between the absolute changes in chicken gut microbiota and their serum metabolite variations. Time-course untargeted metabolomics revealed six metabolite clusters with different changing patterns of abundance. The foregut microbiota had more connections with chicken serum metabolites, and the gut microbes were closely related to chicken lipid and amino acid metabolism. The present study provided a full landscape of chicken gut microbiota development in a quantitative manner, and the associations between gut microbes and chicken serum metabolites further highlight the impact of gut microbiota in chicken growth and development.PMID:38868437 | PMC:PMC10989779 | DOI:10.1002/imt2.105

Imeta. 2023 Jan 9;2(1):e73. doi: 10.1002/imt2.73. eCollection 2023 Feb.ABSTRACTRather than a "short-term tenant," the tumor microbiome has been shown to play a vital role as a "permanent resident," affecting carcinogenesis, cancer development, metastasis, and cancer therapies. As the tumor microbiome has great potential to become a target for the early diagnosis and treatment of cancer, recent research on the relevance of the tumor microbiota has attracted a wide range of attention from various scientific fields, resulting in remarkable progress that benefits from the development of interdisciplinary technologies. However, there are still a great variety of challenges in this emerging area, such as the low biomass of intratumoral bacteria and unculturable character of some microbial species. Due to the complexity of tumor microbiome research (e.g., the heterogeneity of tumor microenvironment), new methods with high spatial and temporal resolution are urgently needed. Among these developing methods, multi-omics technologies (combinations of genomics, transcriptomics, proteomics, and metabolomics) are powerful approaches that can facilitate the understanding of the tumor microbiome on different levels of the central dogma. Therefore, multi-omics (especially single-cell omics) will make enormous impacts on the future studies of the interplay between microbes and tumor microenvironment. In this review, we have systematically summarized the advances in multi-omics and their existing and potential applications in tumor microbiome research, thus providing an omics toolbox for investigators to reference in the future.PMID:38868335 | PMC:PMC10989946 | DOI:10.1002/imt2.73

MedComm (2020). 2024 Jun 12;5(6):e588. doi: 10.1002/mco2.588. eCollection 2024 Jun.ABSTRACTTo identify the mechanism underlying macrosteatosis (MaS)-related graft failure (GF) in liver transplantation (LT) by multi-omics network analysis. The transcriptome and metabolome were assayed in graft and recipient plasma in discovery (n = 68) and validation (n = 89) cohorts. Differentially expressed molecules were identified by MaS and GF status. Transcriptional regulatory networks were generated to explore the mechanism for MaS-related inferior post-transplant prognosis. The differentially expressed molecules associated with MaS and GF were enriched in ferroptosis and peroxisome-related pathways. Core features of MaS-related GF were presented on decreased transferrin and impaired anti-oxidative capacity dependent upon dysregulation of transcription factors hepatocyte nuclear factor 4A (HNF4A) and hypoxia-inducible factor 1A (HIF1A). Furthermore, miR-362-3p and miR-299-5p inhibited transferrin and HIF1A expression, respectively. Lower M2 macrophages but higher memory CD4 T cells were observed in MaS-related GF cases. These results were validated in clinical specimens and cellular models. Systemic analysis of multi-omics data depicted a panorama of biological pathways deregulated in MaS-related GF. Transcriptional regulatory networks centered on transferrin and anti-oxidant responses were associated with poor MaS graft quality, qualifying as potential targets to improve prognosis of patients after LT.PMID:38868330 | PMC:PMC11167151 | DOI:10.1002/mco2.588