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At least 415 records · Page 23Linked to original sources

Multi-Omics Analysis of Experimentally Evolved Candida auris Isolates Reveals Modulation of Sterols, Sphingolipids, and Oxidative Stress in Acquired Amphotericin B Resistance.

Clinical isolates of Candida auris show a high prevalence of resistance to Amphotericin B (AmB)-an uncommon trait in most Candida species. Alterations in ergosterol biosynthesis can contribute to acquired AmB resistance in C. auris laboratory strains but are rarely seen in clinical isolates. In this study, we experimentally evolved two drug-susceptible Clade II isolates of C. auris to develop AmB resistance. The evolved strains displayed a four to eight fold increase in MIC50 compared to the parental cells. We analyzed changes in their karyotype, genome, lipidome, and transcriptome associated with this acquired resistance. In one lineage, AOX2 was upregulated, and its deletion reversed the AmB resistance phenotype. The aox2Δ mutant also failed to evolve AmB resistance under experimental conditions. In the same lineage, restoring the UPC2 S332R and RTG3 S101T mutations to the wild-type allele restored AmB susceptibility. In another lineage, the ergosterol and sphingolipid pathways were observed to play a critical role, and upregulation of the ERG genes elevated the total sterol content, while significant downregulation of HSX11 (glucosylceramide synthase) resulted in lower levels of glucosylceramides. To our knowledge, this study is the first to show that AmB resistance in C. auris can be acquired through mechanisms both dependent on or independent of sterol content modulation, highlighting Aox2 and Upc2 as key regulators of amphotericin resistance.

Amphotericin B↗

Multi-Omics Analyses Reveal the Red and Far-Red Light Combination Enhancing Heterologous Protein and Metabolite Production in Nicotiana benthamiana.

Transient expression of exogenous protein in Nicotiana benthamiana leaves via agroinfiltration offers a rapid and efficient platform for functional gene discovery and heterologous production of valuable eukaryotic proteins and metabolites. Though light quality is an important factor for plant photomorphogenesis, its impact on the efficiency of transient expression remains unexplored. In this study, we examined the influence of five representative light qualities with varying wavelength mix on the N. benthamiana growth and recombinant green fluorescent protein (GFP) production. Plants with red and far-red light treatment (LED-red) showed the highest GFP expression, 57.4% higher than white light. Further study showed that a higher dosage of post-infiltration Agrobacterium and the resulting increase in the number of transcripts contribute to the expression rate enhancement. Moreover, as for exogenous metabolites, a 76.5% increase of accumulated taxadiene was also observed in LED-red group. Integrated transcriptomic, proteomic and metabolomic revealed that LED-red plants reduced the resistance pathways before infiltration, inducing a higher dosage of post-agroinfiltration Agrobacterium. Our results suggest that N. benthamiana grown under LED-red creates a more favorable environment for Agrobacterium growth, enhancing heterologous protein and metabolite production. This study highlights the potential utilization of light quality as an implementable tool in plant synthetic biology.

Nicotiana↗

Integrative Multi-Omics Analysis of Stem Growth Habit Divergence in Wild Soybean (Glycine soja).

Stem architecture is a major determinant of lodging resistance, biomass accumulation, and harvest efficiency in soybean. However, the molecular features associated with contrasting stem growth habits in wild soybean remain incompletely characterised. Here, we performed an integrated transcriptomic, metabolomic, and epigenomic analysis of stem growth-habit divergence in wild soybean, comparing the wild-type accession ZYD7068 with contrasting vining and erect mutant lines derived from carbon-ion beam mutagenesis. Pairwise transcriptomic comparisons identified between 20 311 and 28 705 differentially expressed genes per contrast, with a core set of 2672 genes consistently altered across the comparisons. Functional enrichment, gene set variation analysis, and gene set enrichment analysis converged on xylem and phloem pattern formation as a prominent molecular pathway associated with growth-habit divergence. Random forest analysis identified BBR-BPC and ARF transcription factor families as major molecular discriminators, while metabolomic profiling revealed distinct metabolic profiles involving amino-acid-derived and lipid-associated metabolites. Whole-genome bisulfite sequencing revealed context-specific DNA methylation differences, including substantial variation in CHG methylation among erect mutant lines. Integrated network and in silico perturbation analyses prioritised four candidate genes associated with vascular development for future functional validation. Together, these results provide a multi-layer molecular resource for investigating stem growth-habit divergence in G. soja and establish testable candidate pathways and genes for subsequent functional studies and soybean improvement.

glycine soja↗

Multi-omics analyses provide insights into the molecular basis for salt tolerance of Phyla nodiflora.

The perennial herbaceous plant, Phyla nodiflora (Verbenaceae), which possesses natural resistance to multiple abiotic stresses, is widely used as a pioneer species in island ecological restoration. Due to the lack of information about its genome, the mechanism underlying its tolerance to environmental stresses, such as salinity, is almost entirely unknown. Here, we report on the high-quality genome of P. nodiflora that is 403.07 Mb in size, and which was assembled and anchored onto 18 pseudo-chromosomes. Genomic synteny revealed that P. nodiflora underwent two whole genome duplication events, which promoted the expansion of genes related to environmental adaptation and the biosynthesis of secondary metabolites. An integrated genomic and transcriptomic analysis suggested that salt stress tolerance in P. nodiflora is associated with the expansion and activated expression of genes related to abscisic acid (ABA) homeostasis and signaling. The expansion of ZEP family genes may contribute to the consistent increase in ABA levels under salt stress. Lysine acetylomic analysis revealed that exposure to salt led to widespread protein deacetylation, with these proteins primarily involved in signal transduction, carbohydrate transport and metabolism, and transcription regulation. Deacetylation of glutathione S-transferase increased enzymatic activities in response to salt-induced oxidative stress. Collectively, the genomic, transcriptomic, and lysine acetylomic analyses provide profound insight into the molecular basis of the adaptation of P. nodiflora to salt stress, and will be helpful to engineer salt-tolerant plants for ecological restoration.

Salt Tolerance↗

Multi-omics analysis of ubiquitin E2 genes in Setaria: evidence for the roles of E2 genes in various aspects of plant development, stress tolerance, and domestication.

Ubiquitin E2 enzymes (E2s) are critical mediators in the ubiquitination cascade, a post-translational modification process that regulates protein stability, activity, localization, and degradation. Here, we analyzed the E2 gene family in foxtail millet (Setaria italica), integrating comparative genomics, transcriptomics, and functional studies. A total of 52 E2 genes were identified and classified into four subfamilies (UBC, UEV, SCE, and RCE) based on phylogenetic analysis across 49 species. Notably, foxtail millet exhibited significant gene expansion. Tissue-specific expression profiling revealed distinct roles of E2 genes in growth and development. Haplotype and quantitative trait loci analyses demonstrated that several E2 genes, including SiUBC39, are associated with key agronomic traits, such as plant height, flowering time, and stress tolerance. Using CRISPR/Cas9, we validated the functional role of SiUBC39, finding that its disruption led to phenotypes resembling wild species (Setaria viridis), such as early flowering and reduced plant height and grain yield. IP-MS and transcriptome analysis revealed SiUBC39's involvement in growth and development regulation, drought stress response, and immune response. SiPIP2;1 and SiEhd2 were identified as interactors of SiUBC39, explaining its roles in blast resistance and flowering time control. Furthermore, domestication analysis identified an A/G mutation in the SiUBC39 promoter TATA box, distinguishing domesticated and wild haplotypes and highlighting its role in domestication selection. This study underscores the essential roles of E2 genes in regulating crop agronomic traits and stress responses, providing valuable insights for genetic improvement in foxtail millet and other cereals.

Setaria Plant↗

Aging-related research in the "-omics" age.

The application of high-throughput technologies to aging-related research has the potential to dramatically enhance our understanding of how longevity is determined at a molecular level. Genome-scale studies are being carried out in every major model system used for aging-related research, and new technologies are being developed to rapidly identify mutations or small-molecules that increase life span. A meta-analysis of data derived from genome-wide studies of aging in simple eukaryotes will allow the identification of conserved determinants of longevity that can be tested in mammals.

Aging↗

Multi-omics analysis of glucocorticoid receptor crosstalk with Type I and Type II inflammatory signaling in human airway smooth muscle cells.

Airway smooth muscle (ASM) dysfunction in obstructive airway disease is treated with glucocorticoids. Through RNA-seq analysis of cultured human ASM, we identified repressive effects of dexamethasone, a glucocorticoid, on the baseline expression of a subset of genes that are induced by either IL1B or IL13, which model Type I and Type II inflammation, respectively. ChIP-seq analysis of glucocorticoid receptor (GR) and the p65 subunit of NFkB occupancy indicated canonical motifs for both factors occur at sites of p65 occupancy but did not provide biochemical support for significant repressive tethering between GR and p65. Instead, ATAC-seq revealed significant chromatin remodeling and increased accessibility at binding motifs for the NFkB complex in association with dex + IL1B co-treatment in comparison to IL1B treatment alone. Our data support a competition-based primary repressive effect of glucocorticoids on both IL1B and IL13 signaling and provide evidence for transcriptional cooperation between GR and NFkB on a genome-wide basis in ASM, including at regulatory elements that control expression of anti-inflammatorygenes.

chromatin↗

Epistemological issues in omics and high-dimensional biology: give the people what they want.

Gene expression microarrays have been the vanguard of new analytic approaches in high-dimensional biology. Draft sequences of several genomes coupled with new technologies allow study of the influences and responses of entire genomes rather than isolated genes. This has opened a new realm of highly dimensional biology where questions involve multiplicity at unprecedented scales: thousands of genetic polymorphisms, gene expression levels, protein measurements, genetic sequences, or any combination of these and their interactions. Such situations demand creative approaches to the processes of inference, estimation, prediction, classification, and study design. Although bench scientists intuitively grasp the need for flexibility in the inferential process, the elaboration of formal supporting statistical frameworks is just at the very start. Here, we will discuss some of the unique statistical challenges facing investigators studying high-dimensional biology, describe some approaches being developed by statistical scientists, and offer an epistemological framework for the validation of proffered statistical procedures. A key theme will be the challenge in providing methods that a statistician judges to be sound and a biologist finds informative. The shift from family-wise error rate control to false discovery rate estimation and to assessment of ranking and other forms of stability will be portrayed as illustrative of approaches to this challenge.

Computational Biology↗

Unveiling m7G modification patterns and causal drivers governing intracranial aneurysm rupture risk through multi-omics validation and m7G-MeRIP-seq profiling.

Intracranial aneurysm (IA) rupture causes severe brain hemorrhage with high mortality, yet its molecular drivers remain unclear and better risk prediction is urgently needed. Using transcriptomics, single-cell analysis, and genetic data, we investigated the role of N7-methylguanosine (m7G) RNA modification in IA. We identified distinct m7G modification patterns, validated their methylation features in patient samples, and incorporated these patterns into a machine learning-based rupture prediction model. The presence and characteristics of m7G patterns significantly improved model performance, achieving high predictive accuracy across three independent cohorts (AUC 0.91-0.95). Genetic analyses further identified three causal m7G-related genes (NSUN2, IFIT5, SNUPN), and laboratory experiments confirmed their altered expression and methylation in ruptured aneurysms. Overall, our findings demonstrate that m7G modifications play a key role in IA rupture. The validated prediction model offers strong clinical potential for rupture risk assessment, and the identified genes represent promising therapeutic targets.

Humans↗

Multi‑omics identification of a novel signature for serous ovarian carcinoma in the context of 3P medicine and based on twelve programmed cell death patterns: a multi-cohort machine learning study.

BACKGROUND: Predictive, preventive, and personalized medicine (PPPM/3PM) is a strategy aimed at improving the prognosis of cancer, and programmed cell death (PCD) is increasingly recognized as a potential target in cancer therapy and prognosis. However, a PCD-based predictive model for serous ovarian carcinoma (SOC) is lacking. In the present study, we aimed to establish a cell death index (CDI)-based model using PCD-related genes. METHODS: We included 1254 genes from 12 PCD patterns in our analysis. Differentially expressed genes (DEGs) from the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) were screened. Subsequently, 14 PCD-related genes were included in the PCD-gene-based CDI model. Genomics, single-cell transcriptomes, bulk transcriptomes, spatial transcriptomes, and clinical information from TCGA-OV, GSE26193, GSE63885, and GSE140082 were collected and analyzed to verify the prediction model. RESULTS: The CDI was recognized as an independent prognostic risk factor for patients with SOC. Patients with SOC and a high CDI had lower survival rates and poorer prognoses than those with a low CDI. Specific clinical parameters and the CDI were combined to establish a nomogram that accurately assessed patient survival. We used the PCD-genes model to observe differences between high and low CDI groups. The results showed that patients with SOC and a high CDI showed immunosuppression and hardly benefited from immunotherapy; therefore, trametinib_1372 and BMS-754807 may be potential therapeutic agents for these patients. CONCLUSIONS: The CDI-based model, which was established using 14 PCD-related genes, accurately predicted the tumor microenvironment, immunotherapy response, and drug sensitivity of patients with SOC. Thus this model may help improve the diagnostic and therapeutic efficacy of PPPM.

Humans↗

The tissue-specific effects of glucose-lowering drug targets on aging mediated through DNA methylation: a multi-omics genetic study.

BACKGROUND: DNA methylation plays a key role in mediating the anti-aging effects of glucose-lowering drugs. This study aims to systematically explore the potential anti-aging effects of target genes of FDA-approved glucose-lowering drugs and the underlying epigenetic mediators. METHODS: We conducted a two-sample Mendelian randomization (MR) study to investigate the putative causal relationships between the gene expression levels of glucose-lowering drug targets and 10 aging-related phenotypes, followed by a two-step MR to estimate the mediation effect of DNA methylation. Drug candidates were selected according to the latest review of clinical drug use for type 2 diabetes, and their target genes were obtained from the DGIdb. Tissue-specific cis-expression quantitative trait loci (eQTLs) from GTEx Consortium were selected as genetic instruments to proxy the expression level of drug-target genes. Glycemic phenotypes were used as positive controls to validate the instruments. The cis- and trans-methylation QTLs of Cytosine-phosphate-Guanine sites near the drug target genes were obtained from GoDMC Consortium. Additionally, we performed enrichment analyses focused on tissue specificity and aging pathways to further corroborate our findings. RESULTS: We obtained 194 target genes interacting with 36 FDA-approved anti-diabetic drugs, of which the tissue-specific eQTLs were used to proxy the drug target effects. MR showed strong evidence that nine interacting genes of six glucose-lowering drugs showed anti-aging potential on one or more aging-related phenotypes mediated by DNA methylation: EHMT2, HSPA4, IGF2BP2, IRS1, LPL, NDUFAF1, NDUFS3, SLC22A3, and TCF7L2. These genes were distributed in 17 tissues, especially in the central nervous system, suggesting a potential neural component in their anti-aging effects. For instance, expression of EHMT2 in several brain basal ganglia regions, where the gene interacted with Tolazamide, showed a protective effect on frailty (odds ratio (OR) in caudate = 1.02, 95%CI = 1.01-1.04, FDR adjusted P = 1.69 × 10-2; OR in putamen = 1.02, 95% CI = 1.01-1.03, PFDR = 3.37 × 10-2, OR in nucleus accumbens = 1.02, 95% CI = 1.01-1.04, PFDR = 3.37 × 10-2). These associations were externally validated by searching literature evidence in existing EWAS and TWAS studies, as well as evidence from enrichment analyses. CONCLUSIONS: This study prioritizes nine glucose-lowering genes as anti-aging drug targets in specific tissues and prioritizes their epigenetic regulation through DNA methylation for future drug development.

DNA Methylation↗

Gut microbiota dysbiosis and host metabolite-immune crosstalk drives the pathogenesis of neonatal lupus erythematosus: a multi-omics analysis.

BACKGROUND: Neonatal lupus erythematosus (NLE) is a rare autoimmune condition triggered by the transplacental transfer of maternal antibodies. Despite its recognized clinical manifestations, the underlying pathogenesis remains incompletely understood. This study seeks to explore the disruption of the gut microbiota-host metabolism-immune axis in anti-Ro/La-positive neonates, and to assess its potential role in the development of NLE. METHODS: This multicenter, cross-sectional study included 90 neonates, divided into three groups: 30 with neonatal lupus erythematosus (NLE), 30 with positive antibodies but without clinical manifestations (No-NLE), and 30 healthy controls. We performed 16 S rRNA sequencing to analyze gut microbiota composition, untargeted plasma metabolomic profiling, and proteomic analysis to identify alterations associated with the pathogenesis of NLE. RESULTS: We identified significant alterations in the gut microbiota, plasma metabolome, and proteome profiles of anti-Ro/La-positive neonates. NLE infants exhibited marked enrichment of Enterobacteriaceae and depletion of Bifidobacterium and Clostridium butyricum. Metabolomic analysis revealed hyperactivation of β-alanine and purine metabolism, along with impaired α-linolenic acid metabolism and endocannabinoid signaling. Proteomic profiling indicated aberrant protein expression that modulated IFN signaling, particularly within the C-type lectin receptor pathway. Dysregulation of the spleen tyrosine kinase (SYK) and high-affinity immunoglobulin epsilon receptor subunit gamma (FCER1G) decoupling was observed, correlating with elevated IFN-α and NF-κB p65 levels. Integrated correlation analysis revealed significant associations among differential microbial taxa, plasma metabolites, and proteins. Notably, E. coli-associated metabolites and proteins displayed inverse relationships with those associated with C. butyricum. CONCLUSIONS: These findings represent comprehensive evidence of dysregulation along the "gut microbiota-host metabolism-immune" axis in neonatal lupus erythematosus (NLE), providing novel insights into the disease's underlying heterogeneity.

Humans↗

Sugar-sweetened beverage consumption and incident depression: an exploratory multi-omics analysis of candidate biological mediators.

BACKGROUND: Depression is a leading cause of mental and physical disability globally, with its onset and progression influenced by a complex interplay of dietary, psychological, and biological factors. Recent research suggests a link between sugar-sweetened beverage (SSB) consumption and depression risk, although the potential biological pathways underlying this association remain poorly understood. METHODS: This study utilized data from 192,045 participants in the UK Biobank to examine the prospective association between SSB consumption and incident depression using Cox proportional hazards models. SSBs were defined as the sum of five beverage categories assessed via the Oxford WebQ 24-hour dietary recall. Directional consistency of the association was further examined across three external supporting datasets encompassing diverse populations: NHANES, YRBSS, and the Lianyungang Municipal School Health and Risk Factor Surveillance Study Dataset. We further investigated whether proteins, metabolites, inflammatory markers, and brain imaging phenotypes may serve as candidate mediators statistically consistent with mediation of the SSB-depression association. RESULTS: High SSB consumption was associated with an 18% higher risk of incident depression compared with non-consumers (HR = 1.18; 95% CI: 1.11-1.25), with consistent directional associations observed across external supporting datasets. A plasma proteomic signature comprising 229 proteins was constructed using elastic net regularization and was associated with an increased risk of incident depression. Exploratory mediation analyses identified 72 proteins, 36 metabolites, and 5 inflammatory markers as candidate mediators, with IL1RN showing the strongest protein-level candidate mediating effect (9.6%), and Unsaturation and neutrophil count showing the strongest metabolite- and inflammatory marker-level effects, respectively. CONCLUSIONS: This study provides preliminary evidence that proteins, metabolites, and inflammatory markers may serve as candidate mediators statistically consistent with mediation of the association between SSB consumption and incident depression. These findings are exploratory and hypothesis-generating, and future experimental studies are needed to validate these candidate pathways and assess their potential as targets for dietary interventions in depression prevention.

Humans↗

Identification and evaluation of glutamine-related gene characteristics based on multi-omics to predict the prognosis of patients with colorectal cancer.

BACKGROUND: Colorectal cancer (CRC), a prevalent malignancy of the gastrointestinal tract, ranks among the leading causes of cancer-related morbidity and mortality. Its clinical course is marked by high fatality and poor prognosis. Elucidating the mechanisms underlying CRC initiation and recurrence is therefore critical for identifying novel therapeutic targets. METHODS: This study incorporated two datasets, TCGA-CRC and GSE17537. A total of 84 glutamine metabolism-related genes (GMRGs) were identified, and differential expression analysis was conducted using the TCGA-CRC dataset. Weighted Gene Co-expression Network Analysis (WGCNA) was applied to determine gene modules most strongly associated with GMRG scores. Single-cell RNA sequencing (scRNA-seq) was utilized to characterize key cellular clusters and to identify differentially expressed genes (DEGs) between high and low glutamine metabolism (GM) groups. Overlapping GMRGs were visualized using the ggVennDiagram package in R. A CRC risk prediction model was developed through Cox proportional hazards and LASSO regression analyses, with performance evaluated by ROC curves. Cell type enrichment across 64 immune and stromal populations was assessed via xCell, and intergroup differences were tested using the Wilcoxon rank-sum test. TIDE scores were used to estimate immunotherapy responsiveness, while oncoPredict facilitated drug sensitivity profiling. PCOLCE2 expression in CRC was validated by RT-qPCR and Western blotting. Its functional role was examined through CCK-8 assays, invasion and migration tests, flow cytometry, and glutamate quantification. RESULTS: ScRNA-seq analysis identified two key cell populations and 437 DEGs associated with GM status. WGCNA pinpointed the MEgreen module as most significantly correlated with GMRG scores, encompassing 1075 genes. Integration of DEGs, module genes, and GM-related DEGs yielded 60 candidate genes for downstream analysis. A GMRG-based prognostic model comprising six genes (SRPX, CXCL1, GPX3, PCOLCE2, CLU, SEMA3E) demonstrated strong predictive performance. Prognostic gene expression correlated with immune and stromal infiltration patterns, as indicated by Spearman correlation analysis. The high-risk group exhibited diminished predicted response to immunotherapy (TIDE scores). Drug sensitivity analysis identified four compounds—Dasatinib-51, WH-4-023-56, TWS-119-366, and LDN-193189-478—with elevated efficacy in high-risk CRC cases. PCOLCE2 expression was significantly reduced in CRC tissues. Functional assays revealed that PCOLCE2 knockdown did not substantially affect cell proliferation but significantly impaired invasion and migration in CRC cells, increased apoptosis, and suppressed both glutamine uptake and glutamate production—highlighting its oncogenic role. CONCLUSION: Six GMRGs—SRPX, CXCL1, GPX3, PCOLCE2, CLU, and SEMA3E—were identified as key components of a robust prognostic model for CRC. These findings offer valuable insights into CRC pathogenesis and potential therapeutic strategies. Notably, this study provides the first evidence implicating PCOLCE2 as a tumor-promoting factor in CRC.

Glutamine↗

Multi-omics characterization of a GPRC5A+ epithelial subpopulation associated with malignant features in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) exhibits marked cellular heterogeneity, and the cellular context of malignancy-associated epithelial programs remains incompletely defined. METHODS: We integrated 2,993 CRC samples spanning bulk RNA-seq (n = 2,568; two OS/RFS cohorts), scRNA-seq (281,961 cells/152 specimens), spatial transcriptomics (n = 6), and proteomics (n = 267). Analyses included single-cell integration/annotation, GSVA/HALLMARK, interactome, pseudotime, and ligand-receptor mapping; functional CRISPR assays, EMT immunoblotting, and xenografts; TF profiling (SCENIC/JASPAR/ChIP-qPCR); and exploratory drug-response prediction (OncoPredict), cell-sensitivity assays, and docking/MD modeling. RESULTS: We constructed a stage-stratified single-cell atlas and resolved eleven malignant epithelial subsets, characterizing Epi_4 as late-stage-enriched with EMT, hypoxia, and inflammatory programs and adverse OS/RFS. GPRC5A marked this subset, which we define as GPRC5A+Epi; its expression rose from stage I→IV and was associated with poor outcomes across cohorts, with concordant spatial/proteomic observations. GPRC5A perturbation affected CRC proliferation, migration/invasion, EMT, and xenograft tumorigenicity, supporting a functionally important role in the tested models. SCENIC and ChIP-qPCR supported FOSL1 as an upstream regulator that occupies the GPRC5A promoter. Spatial and ligand-receptor analyses predicted close association and potentially reciprocal signaling between GPRC5A+Epi and POSTN+fibroblasts (COL1A1-SDC4, COL1A1/1A2-ITGA2/ITGB1, PPIA-BSG); concurrent high GPRC5A+Epi/POSTN+Fib signatures were associated with inferior OS/RFS. Drug-response analyses identified an association between GPRC5A status and trametinib sensitivity. Docking/MD produced a computational model of a possible trametinib-GPRC5A interaction, which remains experimentally unvalidated. CONCLUSIONS: GPRC5A⁺Epi is a malignancy-associated epithelial state in CRC, and GPRC5A is functionally important for malignant phenotypes in the tested models. Its inferred relationships with POSTN⁺ fibroblasts and the trametinib findings should be regarded as hypothesis-generating pending functional crosstalk, direct-binding, and therapeutic validation.

Humans↗

Multi-omics revealed the effects of rumen to blood path on early lactation performance in transition dairy cows.

BACKGROUND: The transition period is vitally important to the life cycle of dairy cows. However, the function of the microbiota during both pre- and post-partum and their relationship with ruminal, plasma, and milk metabolites still require systematic investigation. To address this, the 7 highest- and 7 lowest-performing animals among a cohort of 100 dairy cows were selected based on their postpartum energy-corrected milk yield. Rumen fluid and plasma samples were collected during both pre- and post-partum periods, whereas milk samples were obtained postpartum. Shotgun metagenomics of rumen contents in addition to metabolomics of rumen, plasma, and milk samples were performed to evaluate the associations between ruminal microbes and early lactation performance in transition dairy cows. RESULTS: Compared with prepartum cows, postpartum high-yield cows had greater concentrations of ruminal volatile fatty acids and plasma total bile acid. Moreover, plasma urea nitrogen and most amino acids, peptides, and their derivatives in plasma and milk were increased in postpartum high-yield cows, relative to postpartum low-yield cows. Metagenomic analysis revealed that the relative abundances of several species within the Prevotella, Succinimonas, Succinatimonas, and Methanosphaera increased, while other bacteria belong to Alistipes and Bacteroides, and archaeal Methanobrevibacter species decreased in postpartum cows, particularly in postpartum high-yield cows. Co-occurrence network and correlation analysis suggested that Prevotella and Succinatimonas were negatively correlated to Alistipes, Bacteroides, and Methanobrevibacter, potentially contributing to the nutritionally efficient phenotype of postpartum high-yield cows. A metabolic pathway analysis of our metagenomic data revealed that postpartum high-yield cows possessed more microbial genes involved in starch utilization and amino acid synthesis, while a wide range of microbial genes involved in cellulose utilization, acetogenesis, and amino acid degradation were found in prepartum cows with low-yield in postpartum. A structural equation model analysis showed that the increased relative abundances of Prevotella tf.2-5 and Succinatimonas CAG_777 were related to greater concentrations of plasma chenodeoxycholic acid glycine conjugate, milk 5-Methoxytryptophan, and energy-corrected milk yield. Finally, pan-genomic analysis confirmed that Alistipes, Bacteroides, and Methanobrevibacter possess genetic conservation of both hydrogenases and dehydrogenases, which may contribute to energy loss in the rumen via hydrogen dissipation. CONCLUSION: In summary, our findings provide a fundamental understanding of how microbiome-dependent mechanisms contribute to early lactation performance in dairy cows during the transition period. The increased abundance of Prevotella, Succinimonas, and Succinatimonas in postpartum cows suggest that they are important microbes during the transition period and may help in coping with metabolic challenges, while improving nutrient utilization efficiency during this period. Our study underscores the importance of the ruminal microbiome during the transition period and highlights the need for rumen-based nutritional intervention strategies to improve production efficiency in ruminants. Video Abstract.

Animals↗