Nucleotide Research Articles

Mass Spectrometry Imaging Reveals Spatial Metabolic Alterations and Salidroside’s Effects in Diabetic Encephalopathy

Background: Diabetic encephalopathy (DE) is a neurological complication of diabetes marked by cognitive decline and complex metabolic disturbances. Salidroside (SAL), a natural compound with antioxidant and neuroprotective properties, has shown promise in alleviating diabetic complications. Exploring the spatial metabolic reprogramming in DE and elucidating SAL’s metabolic effects are critical for deepening our understanding of its pathogenesis and developing effective therapeutic strategies. Methods: Air-flow-assisted desorption electrospray ionization–mass spectrometry imaging (AFADESI-MSI) was employed to investigate spatial metabolic alterations in the brains of db/db mice, a spontaneous DE model. The mice were treated with SAL (30 and 150 mg/kg, orally) for 12 weeks. Differential metabolites were identified and characterized using high-resolution mass spectrometry and validated against public databases. Results: Our AFADESI-MSI analysis revealed significant changes in 26 metabolites in the brains of DE mice compared to the controls. These metabolic changes indicated disruptions in glucose, glutamate-glutamine, nucleotide, lipid, choline, aspartate, and L-carnitine metabolism. Notably, glucose 6-phosphate (G6P), glutamine, adenosine, L-carnitine, and choline exhibited similar trends in both db/db mice and STZ-induced rat models of DE, suggesting their potential as reliable biomarkers. Twelve weeks of SAL treatment demonstrated a positive regulatory effect on glucose metabolism, the glutamate–glutamine cycle, and lipid metabolism. Conclusions: This study identifies key metabolic alterations in DE and demonstrates the therapeutic potential of SAL in modulating these disturbances, offering valuable insights for targeted interventions in diabetic complications.

Combined transcriptome and metabolome analysis reveals the mechanism of high nitrite tolerance in freshwater mussel Anodonta woodiana

Nitrite contamination and stress on aquatic organisms are increasingly emphasized in freshwater ecosystems. Freshwater bivalves exhibit high tolerance to nitrite; however, the underlying mechanism is unknown. Accordingly, this study investigated the tolerance mechanism of the globally occurring freshwater bivalve Anodonta woodiana. A. woodiana were exposed to nominal concentrations of 0, 250, 500, 1000, 2000, and 4000 mg/L nitrite for 96 h to calculate the 96-h median lethal concentration (96-h LC50). A combined transcriptome and metabolome analysis of the hemolymph (the most vital component of the bivalve immune system) was performed after exposing A. woodiana to 300 mg/L nitrite (approximately half the 96-h LC50) for 96 h. The 96-h LC50 of nitrite in A. woodiana was 618.7 mg/L. Transcriptome analysis identified 5600 differentially expressed genes (DEGs) primarily related to ribosomes, lysosomes, DNA replication, and nucleotide excision repair. Metabolome analysis identified 216 differentially expressed metabolites (DEMs) primarily involved in biosynthesis of amino acids, 2-oxocarboxylic acid metabolism, protein digestion and absorption, aminoacyl-tRNA biosynthesis, nucleotide metabolism, ABC transporters, and valine, leucine and isoleucine degradation. Combined transcriptome and metabolome analysis revealed that DEGs and DEMs were primarily associated with nucleotide (purine and pyrimidine) and amino acid metabolism (including aminoacyl-tRNA biosynthesis, cysteine and methionine metabolism, arginine and proline metabolism, and valine, leucine and isoleucine degradation) as well as the immune system (necroptosis and glutathione metabolism). This study is the first to describe the high tolerance of A. woodiana to nitrite and elucidate the molecular mechanisms underlying high nitrite tolerance in mussels.

Exploration of the biological mechanisms of CENPA as an oncogene in glioma: Screening based on cancer functional status

AbstractGlioma is the most common primary tumour in central nervous system, characterized by high invasiveness, a high recurrence rate and extremely poor prognosis. Machine learning based on cancer functional state helps to combine multi‐omics methods to screen for key gene, such as CENPA, that influences the phenotype of glioma and patients' prognosis. Based on 14 CFS, glioma was divided into three subtypes. Bioinformatics and machine learning methods were utilized to develop an enhanced prognostic prediction signature based on three subtypes. We selected CENPA as a hub biomarker and conducted in vitro experiments such as IHC, western blot, Coip, transwell, cck8, flow cytometry, scratch assay, qPCR, AlphaFold, MOE and in vivo experiments. We identified three subtypes of glioma based on the 14 CFS. The C subtype exhibits poor clinical outcomes, increased carbohydrate and nucleotide metabolism, high infiltration of immune cells, high CNV and tumour mutation burden (p < 0.05). The differential expression of gene between three subtypes were used to construct a novel signature with improved performance in prognostic prediction via machine learning. CENPA was selected as the hub gene, in vitro experiments such as ihc, western blot and qPCR showed that CENPA had high expression in tissues and cell lines (p < 0.05). The scratch assay, edu, cck8, flow cytometry and transwell after CENPA knockdown or overexpression had significant effects on the functions of glioma. Meanwhile, CENPA was regulated by EZH2 and influenced downstream wnt pathway, affecting phosphorylation of two sites, Ser675 and Ser552, on β‐catenin. The effect of CENPA knockdown was reversed by drug CHIR‐99021. Animal experiments indicated that the tumour volume of control and overexpression group increased faster, especially the overexpression group, which was significantly faster (p < 0.001). Machine learning based on CFS is beneficial for the selection of key genes and disease assessment. In glioma, CENPA is positively correlated with WHO grading at both the gene and protein levels, and high CENPA affects patients' poor prognosis. Regulating CENPA can affect functions of glioma, and these phenomena may act through the EZH2/CENPA/β‐catenin signalling axis. CENPA knockdown can be reversed by the drug CHIR‐99021. CENPA may become one of the therapeutic targets in glioma.

Network pharmacology and metabolomics elucidate the underlying effects and mechanisms of maackiain against endometrial cancer

Endometrial carcinoma (EC), a prevalent gynecological cancer, is characterized by rising incidence and mortality rates, highlighting the need for novel treatments to improve patient outcomes. Maackiain (MA) is a natural compound isolated from common herbal medicines, that has been reported to have MA's anti-cancer effects. However, the underlying roles and mechanisms concerning EC remain unclear. This study focused on deeply exploring the potential roles and mechanisms of MA against EC by network pharmacology, experimentally validated, metabolomics, and molecular docking. A total of 86 potential targets of MA against EC were identified by network pharmacology. In vitro experiments further confirmed network pharmacology' predictions. In addition to suppressing EC cell proliferation, MA also paused the cell cycle at the G2/M phase in a dose-dependent manner. This effect is accompanied by increased p21 and phospho-p53 expression, as well as reduced expression of CDK1 and CCNB1. Furthermore, cell metabolomics analysis revealed that 285 metabolites were changed before and after MA administration, which majorly affects glycerophospholipid metabolism, nucleotide metabolism, choline metabolism in cancer, and purine metabolism. Combination network pharmacology, metabolomics, and molecular docking, PLA2G10, PDE4D, and PDE5A were found to be potential targets for therapeutic intervention. These findings underline that MA has anti-EC potential by modulating multiple targets including PLA2G10, PDE4D, and PDE5A, inhibiting EC cell proliferation, inducing G2/M phase arrest, and causing metabolic shifts. This study provides theoretical support for advanced experimental research on its clinical applications.

Integrated metabolomics and transcriptomics reveal metabolic alterations of Ophiocordyceps sinensis from different geographical regions

Ophiocordyceps sinensis is a prized edible and medicinal fungus due to its high nutritional value and broad pharmacological properties. The quality of O. sinensis from Nagqu in Tibet has been regarded as superior to those from other regions. However, the mechanisms underlying these differences remain unclear. This study investigated the metabolic profiles and gene expression patterns of O. sinensis from five major production areas on the Tibetan Plateau in China using metabolomics and transcriptomics. A total of 349 differentially accumulated metabolites (DAMs) and 2983 differentially expressed genes (DEGs) were identified. O. sinensis from Nagqu was characterized by 42 DAMs, primarily including amino acids, nucleosides, fatty acids, and vitamins. Integrated metabolomic and transcriptomic data indicated that DAMs and DEGs were co-enriched in pathways associated with amino acid metabolism, carbohydrate metabolism, lipid metabolism, and nucleotide metabolism. Additionally, the genes ADE5, pgm, purC, IMPDH, ADSS, AK, CarA, CarB, and OMPD were suggested to play critical roles in the biosynthesis of nucleosides, while the genes LSS, CYP51-1, and ERG25 were implicated in steroid biosynthesis. These findings enhance our understanding of the regulatory mechanisms of bioactive compound synthesis in O. sinensis.

Multi-omics Combined with Machine Learning Facilitating the Diagnosis of Gastric Cancer.

Gastric cancer (GC) is a highly intricate gastrointestinal malignancy. Early detection of gastric cancer forms the cornerstone of precision medicine. Several studies have been conducted to investigate early biomarkers of gastric cancer using genomics, transcriptomics, proteomics, and metabolomics, respectively. However, endogenous substances associated with various omics are concurrently altered during gastric cancer development. Furthermore, environmental exposures and family history can also induce modifications in endogenous substances. Therefore, in this study, we primarily investigated alterations in DNA mutation, DNA methylation, mRNA, lncRNA, miRNA, circRNA, and protein, as well as glucose, amino acid, nucleotide, and lipid metabolism levels in the context of GC development, employing genomics, transcriptomics, proteomics, and metabolomics. Additionally, we elucidate the impact of exposure factors, including HP, EBV, nitrosamines, smoking, alcohol consumption, and family history, on diagnostic biomarkers of gastric cancer. Lastly, we provide a summary of the application of machine learning in integrating multi-omics data. Thus, this review aims to elucidate: i) the biomarkers of gastric cancer related to genomics, transcriptomics, proteomics, and metabolomics; ii) the influence of environmental exposure and family history on multiomics data; iii) the integrated analysis of multi-omics data using machine learning techniques.

Cadmium alters the cellular metabolome of human ovarian granulosa cells

Cadmium (Cd) is a toxic heavy metal that has been extensively implicated in disordered folliculogenesis, but the mechanisms underlying the ovarian toxicity of Cd remain to be explored fully. Granulosa cells are key players in ovarian follicular development and are the primary cells affected by Cd exposure-induced damage and dysfunction. In this study, we investigated how various levels of exposure of Cd (3 and 10 μM) to human granulosa cells (KGN cells) impacted the metabolism of the KGN cells utilizing a non-targeted metabolomics methodology. In vitro cell experiments revealed that Cd exposure dose-dependently diminished the viability of KGN cells. Metabolomics analysis revealed the presence of 296 (182 elevated and 114 reduced) and 397 (244 elevated and 153 reduced) differentially expressed metabolites after exposure to 3 and 10 μM, respectively. Cd exposure was found to significantly enrich nucleotide metabolism, sphingolipid metabolism, and ABC transporters in both groups. Although amino acid metabolic pathways exhibited significant enrichment across all groups, only glutathione, cysteine, and methionine metabolism were notably enriched in KGN cells exposed to 3 μM Cd, while glutathione and tryptophan metabolism were significantly enriched in the 10 μM Cd exposure cohort. The outcomes of this study provide mechanistic clues for elucidating Cd’s cytotoxic impact on granulosa cells, and deepen our understanding of the ovarian toxicity of Cd.

Proteome-wide bioinformatic annotation and functional validation of the monotopic phosphoglycosyl transferase superfamily

Phosphoglycosyl transferases (PGTs) are membrane proteins that initiate glycoconjugate biosynthesis by transferring a phospho-sugar moiety from a soluble nucleoside diphosphate sugar to a membrane-embedded polyprenol phosphate acceptor. The centrality of PGTs in complex glycan assembly and the current lack of functional information make these enzymes high-value targets for biochemical investigation. In particular, the small monotopic PGT family is exclusively bacterial and represents the minimal functional unit of the monotopic PGT superfamily. Here, we combine a sequence similarity network analysis with a generalizable, luminescence-based activity assay to probe the substrate specificity of this family of monoPGTs in the bacterial cell-membrane fraction. This strategy allows us to identify specificity on a far more significant scale than previously achievable and correlate preferred substrate specificities with predicted structural differences within the conserved monoPGT fold. Finally, we present the proof-of-concept for a small-scale inhibitor screen (eight nucleoside analogs) with four monoPGTs of diverse substrate specificity, thus building a foundation for future inhibitor discovery initiatives.

PRKDC regulates cGAMP to enhance immune response in lung cancer treatment

BackgroundDespite its involvement in nucleotide metabolism, tumor immune landscape, and immunotherapy response, the role of 2’-3’-cyclic guanosine monophosphate–adenosine monophosphate (2’,3’-cGAMP) in lung adenocarcinoma (LUAD) remails unelucidated. This study aimed to investigate the antitumor effects of 2’,3’-cGAMP in LUAD.MethodHerein, patients with LUAD were screened for prognostic biomarkers, which were then assessed for sensitivity to immunotherapy and chemotherapy utilizing the “TIDE” algorithm and CellMiner database. The results were validated using a mouse xenograft model. Additionally, macrophages and lung cancer cells were co-cultured, and macrophage polarization and apoptosis levels in the lung cancer cells were detected through flow cytometry. Protein levels were analyzed through western blotting and immunofluorescence. Finally, drug-encapsulated nanoparticles were designed to systematically examine the antitumor efficacy of the treatment against LUAD.ResultNotably, 2’,3’-cGAMP-mediated protein kinase, DNA-activated, catalytic subunit (PRKDC) inhibition induced macrophage polarization toward the M1 phenotype, thereby triggering apoptosis in LUAD cells. Furthermore, in vivo experiments showed that M1 macrophage infiltration enhancement and apoptosis induction in lung cancer cells were achieved by suppressing PRKDC expression via 2’,3’-cGAMP, which inhibited lung cancer growth. The machine-learning approaches revealed SB505124 to be an effective antitumor agent in LUAD cells with high PRKDC levels owing to its ability to promote 2’,3’-cGAMP-mediated apoptosis. Encapsulation of 2’,3’-cGAMP, and SB505124 within a nano-delivery system markedly reduced tumor volumes in murine lung cancer tissues compared with that by individual agents.ConclusionThe findings of this study reveal that PRKDC can predict poor survival of patients with LUAD. Additionally, SB505124 enhances the efficacy of 2’,3’-cGAMP-based immunotherapy in patients exhibiting a high PRKDC expression.

Serum metabolomics reveals the anti-aging effect of royal jelly in D-galactose induced aging mice

Aging, marked by a gradual decline in physiological functions, presents significant global health challenges. While royal jelly (RJ) is known for its biological activities, its anti-aging mechanisms require further investigation. Using a D-galactose (D-gal)-induced aging mouse model, this study applied untargeted serum metabolomics to explore RJ's effects. RJ administration significantly (P < 0.05) improved locomotor and cognitive abilities impaired by D-gal-induced aging. Metabolomic analysis showed that RJ modulates key pathways in amino acid, lipid, and nucleotide metabolism, which are essential for cellular function and energy supply. RJ enhanced the tricarboxylic acid cycle (P < 0.05), increased essential amino acids, and elevated nucleotide availability, supporting cognitive function and energy metabolism in aging mice. Notably, RJ had a stronger effect on male mice, particularly in improving lipid and nucleotide metabolism. These findings highlight RJ's potential as a promising anti-aging intervention, warranting further investigation for therapeutic applications in aging.

Semen Cuscutae flavonoids activated the cAMP-PKA-CREB-BDNF pathway and exerted an antidepressant effect in mice

BackgroundSemen Cuscutae flavonoids (SCFs) constitute a class of metabolites of Semen Cuscutae, a botanical drug that was recently found to have an anti-depression effect. This study aimed to evaluate the anti-depression effects of SCFs in chronic unpredictable mild stress (CUMS)-induced mice and to interrogate the underlying mechanisms.Materials and methodsThe CUMS mice were used for assessing the effects of SCFs treatments on depression. Mice were randomly divided into five groups. Four groups were subjected to the CUMS induction and concomitantly administered orally with either the vehicle or with a high-, medium-, and low-dose of SCFs, once per day for 4 weeks. One group was kept untreated as a control. The mice were then assessed for their statuses of a number of depression-related parameters, including body weight, food intake, sucrose preference test (SPT), open field test (OFT), tail suspension test (TST), and forced swim test (FST). In addition, a day after the completion of these tests, biopsies from the hippocampus were harvested and used to perform metabolomics by HPLC-MS/MS and to assess the levels of cAMP by ELISA and the levels of PKA, CREB, p-CREB, and BDNF by Western blot analyses.ResultsSCFs resulted in significant increases in both body weight and food intake and in the amelioration of the depressive-like behaviors in CUMS mice. A high-dose SCFs treatment led to significant alterations in 72 metabolites, of which 26 were identified as potential biomarkers for the SCFs treatment. These metabolites are associated with lipid, amino acid, and nucleotide metabolism. Among 26 metabolites, cAMP was positively correlated with body weight, SPT, OFT-total distance, and OFT-central residence time, while negatively correlated with immobility time in TST and FST, linking a change in cAMP with the SCFs treatment and the significant improvement in depressive symptoms in CUMS mice. Further analyses revealed that the levels of cAMP, PKA, CREB, p-CREB, and BDNF were reduced in the hippocampus of CUMS mice but were all increased following the SCFs treatments.ConclusionSCFs could ameliorate hippocampal metabolic disturbances and depressive behaviors and cause the activation of the cAMP-PKA-CREB-BDNF signaling pathway in the hippocampus of CUMS mice.

Unveiling biomarker detection in Alzheimer's disease: a computational approach to microarray analysis.

Alzheimer's disease (AD) is a major neurodegenerative condition that affects a significant number of people around the world, making understanding the underlying molecular mechanisms fundamental for identifying predictive biomarkers and therapeutic targets for treating AD. Analysis of the gene expression profile GSE5281, consisting of 161 samples (87 AD and 74 control samples) revealed differentially expressed genes (DEGs) used for KEGG screening to connect dysregulated genes to metabolic pathways or other neurological diseases including Parkinson's, prion, and Huntington's and construction of a protein interaction network. Protein-protein interaction (PPI) network and module analysis uncovered the hub genes ACTB, ACTG1, ATP5A1, CCT2, CDC42, EGFR, FN1, GAPDH, GFAP, GRIA1, HSP90AB1, MAPK1, PSMA3, PSMD14, SNAP25, SNCA, SOD1, SOX2, TPI1, and YWHAZ. The analysis revealed a link between dysregulated genes and processes in AD pathology, including the promotion of osteoporosis, an altered nucleotide metabolism, microtubule stability, and the dysfunctionality of the blood-brain barrier (BBB). These targets might be used as predictive biomarkers or to develop curative and preventive therapeutic approaches for treating AD.

Endophytic bacteria in Halogeton glomeratus from mining areas are mainly Sphingomonas pseudosanguinis, with a Cyanobacteria moving from roots to leaves to avoid heavy metals

In the cold and arid mining areas of Northwest China, Halogeton glomeratus C. Meyer (Amaranthaceae) is a promising plant for the remediation of heavy metal pollution. In this study, samples correspond to a gradient of nickel and copper pollution, and this study aims to analyze the characteristics of endophytic bacteria in H. glomeratus under such pollution gradients. Samples of the plant H. glomeratus and their corresponding rhizosphere soil were collected from a smelting area, a mining area, and a control area within the Jinchang mine. In mining and smelting areas, Ni in H. glomeratus rhizosphere soils was 95 and 6 times, and Cu was 40 and 94 times higher than control area. Ni in H. glomeratus from these areas was 27 and 4 times, and Cu was 4.2 and 4.6 times greater than control area. The endophytic bacteria predominantly found in H. glomeratus from nickel-copper mining regions was Sphingomonas pseudosanguinis. Our findings might corroborate the notion that heavy metal stress in the soil can markedly facilitate the migration of an unclassified second most abundant species of Cyanobacteria, residing within the roots of H. glomeratus, to aerial tissues, where the stress from heavy metals was diminished. RDA indicated that the migration and enrichment of nickel and copper into the tissues of H. glomeratus in smelting and mining areas influenced changes in the community structure of endophytic bacteria. Under varying levels of nickel and copper stress, endophytic bacteria underwent alterations in their metabolic characteristics, aiding H. glomeratus in withstanding heavy metal stress through processes such as lipid, nucleotide, and amino acid metabolism, xenobiotics biodegradation and metabolism, protein folding, sorting, and degradation, as well as replication and repair. The identification of plant growth-promoting traits, including the ability to release phosphorus, produce IAA and ACC deaminase, and exhibit tolerance to nickel and copper, among culturable dominant strains, had shown that Pseudomonas oryzihabitans K2l-2-LB and Pseudomonas putida K2r-3-R2A possess significant potential for application. These strains could be effectively utilized as microbial inoculants to promote plant growth during the restoration of vegetation in nickel and copper-contaminated mine sites.

Inclusion of Hybrid Pennisetum and Probiotics Enhanced Anaerobic Fermentation Quality and Bacterial Diversity of Alfalfa Silage

This study aims to assess the impact of Bacillus subtilis (BS) and Lactobacillus buchneri (LB) on the fermentation quality, microbial communities, and predicted metabolic pathways in mixed silage made from alfalfa and hybrid Pennisetum. We prepared mixed silage from fresh alfalfa and hybrid Pennisetum in a 1:1 ratio and inoculated it with BS, LB, or a combination of both (BSLB) or left it untreated as a control. The silage was fermented for 30 and 60 days. The results showed that inoculation with BS, LB, or their combination increased the lactic acid and crude-protein content while reducing the fiber content compared to the control. Additionally, BS and LB inoculation raised (p &lt; 0.05) the acetic acid content, and the combination of both strains increased (p &lt; 0.05) the ratio of lactic acid to acetic acid. LB alone and the combined inoculation also increased the relative abundance of Lactobacillus during the pre-silage period. Functional analysis through the Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed considerable variations among the different probiotic treatments. The silage process reduced nucleotide metabolism but enhanced carbohydrate, amino acid, energy, cofactor, and vitamin nucleotide metabolism. High-throughput sequencing combined with KEGG functional prediction demonstrated significant differences in community composition and functional changes at 30 and 60 days of fermentation. These findings enhance our understanding of bacterial communities and functional changes in mixed silage of alfalfa and hybrid Pennisetum, offering valuable insights into the fermentation mechanisms of legume and grass silage and informing practices for producing high-quality mixed silage.

Influence of Uric Acid on Vascular and Cognitive Functions: Evidence for an Ambivalent Relationship

The growing recognition of the public health impact of cognitive impairment and dementia has sparked a global initiative to identify risk factors and develop strategies to prevent or slow the progression of these cognitive disorders. Uric acid, the end product of the metabolism of purine nucleotides, has been reported as a key factor of many conditions potentially involved in cognitive dysfunction/dementia. In addition, some studies support the hypothesis that elevated uric acid levels could reduce the risk of Alzheimer’s disease, slow down the decline of cognition, and delay the progression of Alzheimer’s disease, while other evidence achieves opposite positions. These discrepancies might reflect a biological ambivalence for uric acid depending on a very complex interplay of factors that include its concentrations achieved in biological fluids, the nature, and concentration of free radicals, the presence and concentration of other antioxidant molecules, potentially responsible for bi-directional effects of uric acid on brain health/functioning. In this narrative review, we attempt to elucidate the influential role of uric acid metabolism in cognitive functioning by discussing pathophysiological mechanisms putatively involved, being well aware that none of them can be considered one-sided due to the complexity of the human organism.

Proteomic profiling of gliomas unveils immune and metabolism-driven subtypes with implications for anti-nucleotide metabolism therapy

Gliomas exhibit high heterogeneity and poor prognosis. Despite substantial progress has been made at the genomic and transcriptomic levels, comprehensive proteomic characterization and its implications remain largely unexplored. In this study, we perform proteomic profiling of gliomas using 343 formalin-fixed and paraffin-embedded tumor samples and 53 normal-appearing brain samples from 188 patients, integrating these data with genomic panel information and clinical outcomes. The proteomic analysis uncovers two distinct subgroups: Subgroup 1, the metabolic neural subgroup, enriched in metabolic enzymes and neurotransmitter receptor proteins, and Subgroup 2, the immune subgroup, marked by upregulation of immune and inflammatory proteins. These proteomic subgroups show significant differences in prognosis, tumorigenesis, microenvironment dysregulation, and potential therapeutics, highlighting the critical roles of metabolic and immune processes in glioma biology and patient outcomes. Through a detailed investigation of metabolic pathways guided by our proteomic findings, dihydropyrimidine dehydrogenase (DPYD) and thymidine phosphorylase (TYMP) emerge as potential prognostic biomarkers linked to the reprogramming of nucleotide metabolism. Functional validation in patient-derived glioma stem cells and animal models highlights nucleotide metabolism as a promising therapy target for gliomas. This integrated multi-omics analysis introduces a proteomic classification for gliomas and identifies DPYD and TYMP as key metabolic biomarkers, offering insights into glioma pathogenesis and potential treatment strategies.

Prebiotic Nucleoside Phosphorylation in a Simulated Deep-Sea Supercritical Carbon Dioxide-Water Two-Phase Environment.

Prebiotic synthesis of complex organic molecules in water-rich environments has been a long-standing challenge. In the modern deep sea, emission of liquid CO2 has been observed in multiple locations, which indicates the existence of benthic CO2 pools. Recently, a liquid/supercritical CO2 (ScCO2) hypothesis has been proposed that a two-phase ScCO2-water environment could lead to efficient dehydration and condensation of organics. To confirm this hypothesis, we conducted a nucleoside phosphorylation reaction in a hydrothermal reactor creating ScCO2-water two-phase environment. After 120 h of uridine, cytosine, guanosine, and adenosine phosphorylation at 68.9°C, various nucleoside monophosphates (NMPs), nucleotide diphosphates, and carbamoyl nucleosides were produced. The addition of urea enhanced the overall production of phosphorylated species with 5'-NMPs, the major products that reached over 10% yield. As predicted, phosphorylation did not proceed in the fully aqueous environment without ScCO2. Further, a glass window reactor was introduced for direct observation of the two-phase environment, where the escape of water into the ScCO2 phase was observed. These results are similar to those of a wet-dry cycle experiment simulating the terrestrial hot spring environment, indicating that the presence of ScCO2 can create a comparatively dry condition in the deep sea. In addition, the high acidity present in the aqueous phase further supports nucleotide synthesis by enabling the release of orthophosphate from the hydroxyapatite mineral solving the phosphate problem. Thus, the present study highlights the potential of the unique ScCO2-water two-phase environment to drive prebiotic nucleotide synthesis and likely induce condensation reactions of various organic and inorganic compounds in the deep-sea CO2 pool on Earth and potentially other ocean worlds.

Polyamines mediate cellular energetics and lipid metabolism through mitochondrial respiration to facilitate virus replication.

Polyamines are critical cellular components that regulate a variety of processes, including translation, cell cycling, and nucleic acid metabolism. The polyamines, putrescine, spermidine, and spermine, are found abundantly within cells and are positively-charged at physiological pH. Polyamine metabolism is connected to distinct other metabolic pathways, including nucleotide and amino acid metabolism. However, the breadth of the effect of polyamines on cellular metabolism remains to be fully understood. We recently demonstrated a role for polyamines in cholesterol metabolism, and following these studies, we measured the impact of polyamines on global lipid metabolism. We find that lipid droplets increase in number and size with polyamine depletion. We further demonstrate that lipid anabolism is markedly decreased, and lipid accumulation is due to reduced mitochondrial fatty acid oxidation. In fact, mitochondrial structure and function are largely ablated with polyamine depletion. To compensate, cells depleted of polyamines switch from aerobic respiration to glycolysis in a polyamine depletion-mediated Warburg-like effect. Finally, we show that inhibitors of lipid metabolism are broadly antiviral, suggesting that polyamines and lipids are promising antiviral targets. Together, these data demonstrate a novel role for polyamines in mitochondrial function, lipid metabolism, and cellular energetics.

Genomic determinants in pathogenicity of SARS-CoV-2 versa common cold coronaviruses

Determination of the different short oligonucleotide features in the full genome of fatal and mild coronavirus strains can show the researchers how these viruses evolved and became virulent strains. To this aim, at first, in the full genome of all coronavirus strains included in this study, the observed and expected frequency of dinucleotide to hexanucleotide was obtained using Markov method. Then odds ratio (observed/expected abundances) of short oligonucleotide was computed and considered as the raw data (features). Finally, ten distinct weighting algorithms approaches (Information Gain, Information Gain Ratio, Rule, Deviation, Chi Squared, Gini Index, Uncertainty, Relief, Support Vector Machine (SVM), and PCA) was employed on the features to identify oligonucleotide distribution differences across the full genome of SARS-related viruses compared to common cold coronaviruses. Totally among 5440 features (16 dinucleotides, 64 trinucleotides, 256 tetra nucleotides, 1024 penta-nucleotides, and 4096 Hexa-nucleotides), CC, CCA, CCAC, ACCAC, and CACCAC motifs were selected by 80 -90% of all weighting algorithms models to distinguish virulent strains from mild coronaviruses. These remarkable oligonucleotides might point toward the existence of some particular RNA elements that might be involved in viral virulence and thus can be targeted for viral treatment in the future.

Global metabolomic profiling of tumor tissue and paired serum samples to identify biomarkers for response to neoadjuvant FOLFIRINOX treatment of human pancreatic cancer.

Neoadjuvant chemotherapy (NAT) is increasingly used for the treatment of non-metastatic pancreatic ductal adenocarcinoma (PDAC) and is established as a standard of care for borderline resectable and locally advanced PDAC. However, full exploitation of its clinical benefits is limited by the lack of biomarkers that assess treatment response. To address this unmet need, global metabolomic profiling was performed on tumor tissue and paired serum samples from patients with treatment-naïve (TN; n = 18) and neoadjuvant leucovorin calcium (folinic acid), fluorouracil, irinotecan hydrochloride and oxaliplatin (FOLFIRINOX)-treated (NAT; n = 17) PDAC using liquid chromatography mass spectrometry. Differentially abundant metabolites (DAMs) in TN versus NAT groups were identified and their correlation with various clinical parameters was assessed. Metabolomics profiling identified 40 tissue and five serum DAMs in TN versus NAT PDAC. In general, DAMs associated with amino acid andnucleotide metabolism were lower in NAT compared to TN. Four DAMs-3-hydroxybutyric acid (BHB), 3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid (CMPF), glycochenodeoxycholate and citrulline-were common to both tissue and serum and showed a similar pattern of differential abundance in both groups. A strong positive correlation was observed between serum carbohydrate 19-9 antigen (CA 19-9) and tissue carnitines (C12, C18, C18:2) and N8-acetylspermidine. The reduction in CA 19-9 following NAT correlated negatively with serum deoxycholate levels, and the latter correlated positively with survival. This study revealed neoadjuvant-chemotherapy-induced changes in metabolic pathways in PDAC, mainly amino acid and nucleotide metabolism, and these correlated with reduced CA 19-9 following neoadjuvant FOLFIRINOX treatment.