PubMed

Insects. 2024 Mar 6;15(3):177. doi: 10.3390/insects15030177.ABSTRACTX-ray irradiation and modified atmospheres (MAs) provide eco-friendly, chemical-free methods for pest management. Although a low-oxygen atmospheric treatment improves the performance of some irradiated insects, its influence on the irradiation of quarantine insects and its impacts on pest control efficacy have yet to be investigated. Based on bioassay results, this study employed direct immersion solid-phase microextraction (DI-SPME) combined with gas chromatography-mass spectrometry (GC-MS) to determine metabolic profiles of late third-instar B. dorsalis larvae under normoxia (CON, Air), hypoxia (95% N2 + 5% O2, HY), super-hypoxia (99.5% N2 + 0.5% O2, Sup-HY), irradiation-alone (116 Gy, IR-alone), hypoxia + irradiation (HY + IR) and super-hypoxia + irradiation (Sup-HY + IR). Our findings reveal that, compared to the IR-alone group, the IR treatment under HY and Sup-HY (HY + IR and Sup-HY + IR) increases the larval pupation of B. dorsalis, and weakens the delaying effect of IR on the larval developmental stage. However, these 3 groups further hinder adult emergence under the phytosanitary IR dose of 116 Gy. Moreover, all IR-treated groups, including IR-alone, HY + IR, and Sup-HY + IR, lead to insect death as a coarctate larvae or pupae. Pathway analysis identified changed metabolic pathways across treatment groups. Specifically, changes in lipid metabolism-related pathways were observed: 3 in HY vs. CON, 2 in Sup-HY vs. CON, and 5 each in IR-alone vs. CON, HY + IR vs. CON, and Sup-HY + IR vs. CON. The treatments of IR-alone, HY + IR, and Sup-HY + IR induce comparable modifications in metabolic pathways. However, in the HY + IR, and Sup-HY + IR groups, the third-instar larvae of B. dorsalis demonstrate significantly fewer changes. Our research suggests that a low-oxygen environment (HY and Sup-HY) might enhance the radiation tolerance in B. dorsalis larvae by stabilizing lipid metabolism pathways at biologically feasible levels. Additionally, our findings indicate that the current phytosanitary IR dose contributes to the effective management of B. dorsalis, without being influenced by radioprotective effects. These results hold significant importance for understanding the biological effects of radiation on B. dorsalis and for developing IR-specific regulatory guidelines under MA environments.PMID:38535372 | DOI:10.3390/insects15030177

Metabolites. 2024 Mar 20;14(3):173. doi: 10.3390/metabo14030173.ABSTRACTUrinary tract cancers, including those of the bladder, the kidneys, and the prostate, represent over 12% of all cancers, with significant global incidence and mortality rates. The continuous challenge that these cancers present necessitates the development of innovative diagnostic and prognostic methods, such as identifying specific biomarkers indicative of cancer. Biomarkers, which can be genes, proteins, metabolites, or lipids, are vital for various clinical purposes including early detection and prognosis. Mass spectrometry (MS), particularly soft ionization techniques such as electrospray ionization (ESI) and laser desorption/ionization (LDI), has emerged as a key tool in metabolic profiling for biomarker discovery, due to its high resolution, sensitivity, and ability to analyze complex biological samples. Among the LDI techniques, matrix-assisted laser desorption/ionization (MALDI) and surface-assisted laser desorption/ionization (SALDI) should be mentioned. While MALDI methodology, which uses organic compounds as matrices, is effective for larger molecules, SALDI, based on the various types of nanoparticles and nanostructures, is preferred for smaller metabolites and lipids due to its reduced spectral interference. This study highlights the application of LDI techniques, along with mass spectrometry imaging (MSI), in identifying potential metabolic and lipid biomarkers for urological cancers, focusing on the most common bladder, kidney, and prostate cancers.PMID:38535333 | DOI:10.3390/metabo14030173

Metabolites. 2024 Mar 18;14(3):168. doi: 10.3390/metabo14030168.ABSTRACTThe clinical blood metabogram (CBM) was developed to match a tailored analysis of the blood metabolome to the time, cost, and reproducibility constraints of clinical laboratory testing. By analyzing the main blood metabolite groups, CBM offers clinically relevant information about the intake of low-molecular substances into the organism, humoral regulation, liver function, amino acid level, and the lipid and carbohydrate metabolism. The purpose of this work was to investigate the relevance of using the CBM in patients with diabetes mellitus. For this, a CBM was obtained for 18 healthy individuals, 12 individuals with prediabetes, and 64 individuals with type 2 diabetes mellitus, separated into groups according to fasting blood glucose and oral glucose tolerance tests. The results showed that the CBM reveals diabetes-associated metabolic alterations in the blood, including changes in the levels of carbohydrates, ketone bodies, eicosanoids, phospholipids, and amino acids, which are consistent with the scientific data available to date. The CBM enabled the separation of diabetic patients according to their metabolic metabotypes, providing both a general overview of their metabolic alterations and detailing their individual metabolic characteristics. It was concluded that the CBM is a precise and clinically applicable test for assessing an individual's metabolic status in diabetes mellitus for diagnostic and treatment purposes.PMID:38535328 | DOI:10.3390/metabo14030168

Metabolites. 2024 Mar 9;14(3):159. doi: 10.3390/metabo14030159.ABSTRACTType 2 diabetes (T2D) is a global public health issue characterized by excess weight, abdominal obesity, dyslipidemia, hyperglycemia, and a progressive increase in insulin resistance. Human population studies of T2D development and its effects on systemic metabolism are confounded by many factors that cannot be controlled, complicating the interpretation of results and the identification of early biomarkers. Aged, sedentary, and overweight/obese non-human primates (NHPs) are one of the best animal models to mimic spontaneous T2D development in humans. We sought to identify and distinguish a set of plasma and/or fecal metabolite biomarkers, that have earlier disease onset predictability, and that could be evaluated for their predictability in subsequent T2D studies in human cohorts. In this study, a single plasma and fecal sample was collected from each animal in a colony of 57 healthy and dysmetabolic NHPs and analyzed for metabolomics and lipidomics. The samples were comprehensively analyzed using untargeted and targeted LC/MS/MS. The changes in each animal's disease phenotype were monitored using IVGTT, HbA1c, and other clinical metrics, and correlated with their metabolic profile. The plasma and fecal lipids, as well as bile acid profiles, from Healthy, Dysmetabolic (Dys), and Diabetic (Dia) animals were compared. Following univariate and multivariate analyses, including adjustments for weight, age, and sex, several plasma lipid species were identified to be significantly different between these animal groups. Medium and long-chain plasma phosphatidylcholines (PCs) ranked highest at distinguishing Healthy from Dys animals, whereas plasma triglycerides (TG) primarily distinguished Dia from Dys animals. Random Forest (RF) analysis of fecal bile acids showed a reduction in the secondary bile acid glycoconjugate, GCDCA, in diseased animals (AUC 0.76[0.64, 0.89]). Moreover, metagenomics results revealed several bacterial species, belonging to the genera Roseburia, Ruminococcus, Clostridium, and Streptococcus, to be both significantly enriched in non-healthy animals and associated with secondary bile acid levels. In summary, our results highlight the detection of several elevated circulating plasma PCs and microbial species associated with fecal secondary bile acids in NHP dysmetabolic states. The lipids and metabolites we have identified may help researchers to differentiate individual NHPs more precisely between dysmetabolic and overtly diabetic states. This could help assign animals to study groups that are more likely to respond to potential therapies where a difference in efficacy might be anticipated between early vs. advanced disease.PMID:38535319 | DOI:10.3390/metabo14030159

Metabolites. 2024 Mar 7;14(3):157. doi: 10.3390/metabo14030157.ABSTRACTThe skin is a complex ecosystem colonized by millions of microorganisms, the skin microbiota, which are crucial in regulating not only the physiological functions of the skin but also the metabolic changes underlying the onset of skin diseases. The high microbial colonization together with a low diversity at the phylum level and a high diversity at the species level of the skin is very similar to that of the gastrointestinal tract. Moreover, there is an important communication pathway along the gut-brain-skin axis, especially associated with the modulation of neurotransmitters by the microbiota. Therefore, it is evident that the high complexity of the skin system, due not only to the genetics of the host but also to the interaction of the host with resident microbes and between microbe and microbe, requires a multi-omics approach to be deeply understood. Therefore, an integrated analysis, with high-throughput technologies, of the consequences of microbial interaction with the host through the study of gene expression (genomics and metagenomics), transcription (transcriptomics and meta-transcriptomics), and protein production (proteomics and meta-proteomics) and metabolite formation (metabolomics and lipidomics) would be useful. Although to date very few studies have integrated skin metabolomics data with at least one other 'omics' technology, in the future, this approach will be able to provide simple and fast tests that can be routinely applied in both clinical and cosmetic settings for the identification of numerous skin diseases and conditions. It will also be possible to create large archives of multi-omics data that can predict individual responses to pharmacological treatments and the efficacy of different cosmetic products on individual subjects by means of specific allotypes, with a view to increasingly tailor-made medicine. In this review, after analyzing the complexity of the skin ecosystem, we have highlighted the usefulness of this emerging integrated omics approach for the analysis of skin problems, starting with one of the latest 'omics' sciences, metabolomics, which can photograph the expression of the genome during its interaction with the environment.PMID:38535317 | DOI:10.3390/metabo14030157

Metabolites. 2024 Mar 4;14(3):152. doi: 10.3390/metabo14030152.ABSTRACTMulti-omics approaches, which integrate genomics, transcriptomics, proteomics, and metabolomics, have emerged as powerful tools in the diagnosis of rare diseases. We used untargeted metabolomics and whole-genome sequencing (WGS) to gain a more comprehensive understanding of a rare disease with a complex presentation affecting female twins from a consanguineous family. The sisters presented with polymicrogyria, a Dandy-Walker malformation, respiratory distress, and multiorgan dysfunctions. Through WGS, we identified two rare homozygous variants in both subjects, a pathogenic variant in ADGRG1(p.Arg565Trp) and a novel variant in CNTNAP1(p.Glu910Val). These genes have been previously associated with autosomal recessive polymicrogyria and hypomyelinating neuropathy with/without contractures, respectively. The twins exhibited symptoms that overlapped with both of these conditions. The results of the untargeted metabolomics analysis revealed significant metabolic perturbations relating to neurodevelopmental abnormalities, kidney dysfunction, and microbiome. The significant metabolites belong to essential pathways such as lipids and amino acid metabolism. The identification of variants in two genes, combined with the support of metabolic perturbation, demonstrates the rarity and complexity of this phenotype and provides valuable insights into its underlying mechanisms.PMID:38535312 | DOI:10.3390/metabo14030152

Metabolites. 2024 Feb 29;14(3):149. doi: 10.3390/metabo14030149.ABSTRACTThis paper aimed at devising an intelligence-based method to select compounds that can distinguish between open-angle glaucoma patients, type 2 diabetes patients, and healthy controls. Taking the concentration of 188 compounds measured in the aqueous humour (AH) of patients and controls, linear discriminant analysis (LDA) was used to identify the right combination of compounds that could lead to accurate diagnosis. All possibilities, using the leave-one-out approach, were considered through ad hoc programming and in silico massive data production and statistical analysis. Our proof of concept led to the selection of four molecules: acetyl-ornithine (Ac-Orn), C3 acyl-carnitine (C3), diacyl C42:6 phosphatidylcholine (PC aa C42:6), and C3-DC (C4-OH) acyl-carnitine (C3-DC (C4-OH)) that, taken in combination, would lead to a 95% discriminative success. 100% success was obtained with a non-linear combination of the concentration of three of these four compounds. By discarding younger controls to adjust by age, results were similar although one control was misclassified as a diabetes patient. Methods based on the consideration of individual clinical chemical parameters have limitations in the ability to make a reliable diagnosis, stratify patients, and assess disease progression. Leveraging human AH metabolomic data, we developed a procedure that selects a minimal number of metabolites (3-5) and designs algorithms that maximize the overall accuracy evaluating both positive predictive (PPV) and negative predictive (NPV) values. Our approach of simultaneously considering the levels of a few metabolites can be extended to any other body fluid and has potential to advance precision medicine. Artificial intelligence is expected to use algorithms that use the concentration of three to five molecules to correctly diagnose diseases, also allowing stratification of patients and evaluation of disease progression. In addition, this significant advance shifts focus from a single-molecule biomarker approach to that of an appropriate combination of metabolites.PMID:38535309 | DOI:10.3390/metabo14030149

Metabolites. 2024 Feb 29;14(3):148. doi: 10.3390/metabo14030148.ABSTRACTWith 64,050 new diagnoses and 50,550 deaths in the US in 2023, pancreatic ductal adenocarcinoma (PDAC) is among the most lethal of all human malignancies. Early detection and improved prognostication remain critical unmet needs. We applied next-generation metabolomics, using quantitative tandem mass spectrometry on plasma, to develop biochemical signatures that identify PDAC. We first compared plasma from 10 PDAC patients to 169 samples from healthy controls. Using metabolomic algorithms and machine learning, we identified ratios that incorporate amino acids, biogenic amines, lysophosphatidylcholines, phosphatidylcholines and acylcarnitines that distinguished PDAC from normal controls. A confirmatory analysis then applied the algorithms to 30 PDACs compared with 60 age- and sex-matched controls. Metabolic signatures were then analyzed to compare survival, measured in months, from date of diagnosis to date of death that identified metabolite ratios that stratified PDACs into distinct survival groups. The results suggest that metabolic signatures could provide PDAC diagnoses earlier than tumor markers or radiographic measures and offer insights into disease severity that could allow more judicious use of therapy by stratifying patients into metabolic-risk subgroups.PMID:38535308 | DOI:10.3390/metabo14030148

Metabolites. 2024 Feb 28;14(3):145. doi: 10.3390/metabo14030145.ABSTRACTThe neuropathological sequelae of stroke and subsequent recovery are incompletely understood. Here, we investigated the metabolic dynamics following stroke to advance the understanding of the pathophysiological mechanisms orchestrating stroke recovery. Using a nuclear magnetic resonance (NMR)-driven metabolomic profiling approach for urine samples obtained from a clinical group, the objective of this research was to (1) identify novel biomarkers indicative of severity and recovery following stroke, and (2) uncover the biochemical pathways underlying repair and functional recovery after stroke. Urine samples and clinical stroke assessments were collected during the acute (2-11 days) and chronic phases (6 months) of stroke. Using a 700 MHz 1H NMR spectrometer, metabolomic profiles were acquired followed by a combination of univariate and multivariate statistical analyses, along with biological pathway analysis and clinical correlations. The results revealed changes in phenylalanine, tyrosine, tryptophan, purine, and glycerophospholipid biosynthesis and metabolism during stroke recovery. Pseudouridine was associated with a change in post-stroke motor recovery. Thus, NMR-based metabolomics is able to provide novel insights into post-stroke cellular functions and establish a foundational framework for future investigations to develop targeted therapeutic interventions, advance stroke diagnosis and management, and enhance overall quality of life for individuals with stroke.PMID:38535305 | DOI:10.3390/metabo14030145

Metabolites. 2024 Feb 27;14(3):143. doi: 10.3390/metabo14030143.ABSTRACTIn vitro fertilization (IVF) is a highly effective treatment for infertility; however, it poses challenges for women with decreased ovarian reserve (DOR). Despite the importance of understanding the impact of DOR on IVF outcomes, limited research has explored this relationship, particularly using omics approaches. Hence, we conducted a study to investigate the association between DOR and IVF outcomes, employing a metabolomic approach. We analyzed serum samples from 207 women undergoing IVF treatment, including 89 with DOR and 118 with normal ovarian reserve (NOR). Our findings revealed that DOR was significantly associated with unfavorable IVF outcomes, characterized by a reduced oocyte count, lower embryo quality, and decreased rates of pregnancy and live births. Furthermore, we identified 82 metabolites that displayed significant alterations in DOR patients, impacting diverse metabolic pathways. Notably, a distinct panel of metabolites, including palmitic acid, stearic acid, LysoPC(9:0(CHO)/0:0), PC(18:0/9:0(CHO)), and PC(16:0/9:0(CHO)), exhibited discriminatory power between the DOR and NOR groups, showcasing a strong correlation with IVF outcomes. These findings emphasize the crucial role of metabolomic disruptions in influencing IVF outcomes among women with DOR.PMID:38535303 | DOI:10.3390/metabo14030143

Metabolites. 2024 Feb 26;14(3):140. doi: 10.3390/metabo14030140.ABSTRACTCalendula officinalis L. is a well-known plant widely used in traditional medicine due to the presence of various biologically active compounds. The main raw material for the production of medicinal preparations is the inflorescence, which consists of ligulate and tubular flowers. However, the characteristics of the metabolome of these flowers are not fully understood. This study identified and compared the levels of major metabolites in the ligulate and tubular flowers of two C. officinalis cultivars, 'Golden Sea' (GS) and 'Paradise Garden' (PG). The metabolome was analysed using ultra-performance liquid chromatography with photodiode array detection and a Q Exactive Orbitrap high-resolution mass spectrometer. It was found that the tubular flowers of both PG and GS cultivars had higher levels of lipids, phenolamides and caffeoylquinic acids and lower levels of triterpenoid glycosides than the ligulate flowers. It was also shown that the inflorescences of the GS, which had a 35% higher proportion of tubular flowers, contained 30% more phenolic compounds and 50% more lipids than the PG. Thus, the results obtained extend our understanding of the features in the metabolomes of ligulate and tubular flowers and suggest that the quality of inflorescences of C. officinalis cultivars, as a source of medicinal preparations, is strongly influenced by the proportion of ligulate and tubular flowers.PMID:38535300 | DOI:10.3390/metabo14030140

Metabolites. 2024 Feb 26;14(3):138. doi: 10.3390/metabo14030138.ABSTRACTTo study the effects of different feed additives on the weaning stress of Tibetan piglets, we selected 28 healthy, 30-day-old Tibetan weaned piglets and divided them into four groups, namely, the control group (basal feed without any antibiotic additions) (Nor), the group with the addition of the antibiotic lincomycin (Ant), the group with the addition of fifteen-flavor black pills of Tibetan medicine (Tib), and the group with the addition of fecal bacterial supernatant (Fec). We measured growth performance, blood physiological indexes, and metabolomics. The results showed that the Ant, Tib, and Fec groups significantly reduced the ratio of diarrhea to feed/weight (F/G) and increased the average daily gain (ADG) compared with the Nor group (p < 0.01). The Nor group had significantly lower leukocyte counts, hemoglobin levels, and erythrocyte counts compared with the other three groups at 21 d (p < 0.05). These physiological indexes tended to stabilize at 42 d. We found that there were beneficial metabolites and metabolic pathways for gastrointestinal function. Specifically, the porphyrin metabolic pathway was elevated in the Ant group, and the tryptophan metabolic pathway was significantly elevated in the Tib and Fec groups compared with the Nor group (p < 0.05). In conclusion, adding fecal bacterial supernatant and fifteen-flavor black pills of Tibetan medicine to the feed reduced the rate of diarrhea and improved the growth performance of the piglets. Moreover, it had an effect on the microorganisms and their metabolites and pathways in the gastrointestinal tract of the animals, which might be the main reason for influencing the diarrhea rate of weaned Tibetan piglets and the growth and development of the piglets. This study provides a new approach for anti-stress applications in weaned Tibetan piglets and the development of substitute anti-products.PMID:38535298 | DOI:10.3390/metabo14030138

Metabolites. 2024 Feb 24;14(3):136. doi: 10.3390/metabo14030136.ABSTRACTVertical transmission of metabolic constituents from mother to child contributes to the manifestation of disease phenotypes in early life. This study probes the vertical transmission of metabolites from mothers to offspring by utilizing machine learning techniques to differentiate between true mother-child dyads and randomly paired non-dyads. Employing random forests (RF), light gradient boosting machine (LGBM), and logistic regression (Elasticnet) models, we analyzed metabolite concentration discrepancies in mother-child pairs, with maternal plasma sampled at 24 weeks of gestation and children's plasma at 6 months. The propensity of vertical transfer was quantified, reflecting the likelihood of accurate mother-child matching. Our findings were substantiated against an external test set and further verified through statistical tests, while the models were explained using permutation importance and SHapley Additive exPlanations (SHAP). The best model was achieved using RF, while xenobiotics were shown to be highly relevant in transfer. The study reaffirms the transmission of certain metabolites, such as perfluorooctanoic acid (PFOA), but also reveals additional insights into the maternal influence on the child's metabolome. We also discuss the multifaceted nature of vertical transfer. These machine learning-driven insights complement conventional epidemiological findings and offer a novel perspective on using machine learning as a methodology for understanding metabolic interactions.PMID:38535296 | DOI:10.3390/metabo14030136

Metabolites. 2024 Feb 21;14(3):131. doi: 10.3390/metabo14030131.ABSTRACTWith its high resolving power and sensitivity, mass spectrometry is considered the most informative technique for metabolite qualitation and quantification in the plant sciences. However, the spatial location information, which is crucial for the exploration of plant physiological mechanisms, is lost. Mass spectrometry imaging (MSI) is able to visualize the spatial distribution of a large number of metabolites from the complex sample surface in a single experiment. In this paper, a flexible and low-cost laser desorption-dielectric barrier discharge ionization-MSI (LD-DBDI-MSI) platform was constructed by combining an LD system with an in-line DBDI source, a high-precision sample translation stage, and an ambient mass spectrometer. It can be operated at a spatial resolution of 20 μm in an atmospheric environment and requires minimal sample preparation. This study presents images of in-situ metabolic profiling of two kinds of plants from different origins, a wild and a farmed Rheum palmatum L. From the screen of these two root sections, the wild one presented five more endogenous molecules than the farmed one, which provides information about the differences in metabolomics.PMID:38535291 | DOI:10.3390/metabo14030131

Membranes (Basel). 2024 Mar 20;14(3):70. doi: 10.3390/membranes14030070.ABSTRACTTransport systems play a pivotal role in bacterial physiology and represent potential targets for medical and biotechnological applications. However, even in well-studied organisms like Escherichia coli, a notable proportion of transporters, exceeding as many as 30%, remain classified as orphans due to their lack of known substrates. This study leveraged high-resolution LC-MS-based untargeted metabolomics to identify candidate substrates for these orphan transporters. Human serum, including a diverse array of biologically relevant molecules, served as an unbiased source for substrate exposure. The analysis encompassed 26 paired transporter mutant contrasts (i.e., knockout vs. overexpression), compared with the wild type, revealing distinct patterns of substrate uptake and excretion across various mutants. The convergence of candidate substrates across mutant scenarios provided robust validation, shedding light on novel transporter-substrate relationships, including those involving yeaV, hsrA, ydjE, and yddA. Furthermore, several substrates were contingent upon the specific mutants employed. This investigation underscores the utility of untargeted metabolomics for substrate identification in the absence of prior knowledge and lays the groundwork for subsequent validation experiments, holding significant implications for both medical and biotechnological advancements.PMID:38535289 | DOI:10.3390/membranes14030070

Diseases. 2024 Feb 23;12(3):43. doi: 10.3390/diseases12030043.ABSTRACTThe immune response to infectious diseases is directly influenced by metabolic activities. COVID-19 is a disease that affects the entire body and can significantly impact cellular metabolism. Recent studies have focused their analysis on the potential connections between post-infection stages of SARS-CoV2 and different metabolic pathways. The spike S1 antigen was found to have in vitro IgG antibody memory for PBMCs when obtaining PBMC cultures 60-90 days post infection, and a significant increase in S-adenosyl homocysteine, sarcosine, and arginine was detected by mass spectrometric analysis. The involvement of these metabolites in physiological recovery from viral infections and immune activity is well documented, and they may provide a new and simple method to better comprehend the impact of SARS-CoV2 on leukocytes. Moreover, there was a significant change in the metabolism of the tryptophan and urea cycle pathways in leukocytes with IgG memory. With these data, together with results from the literature, it seems that leukocyte metabolism is reprogrammed after viral pathogenesis by activating certain amino acid pathways, which may be related to protective immunity against SARS-CoV2.PMID:38534967 | DOI:10.3390/diseases12030043

Curr Issues Mol Biol. 2024 Mar 13;46(3):2402-2443. doi: 10.3390/cimb46030153.ABSTRACTGliomas constitute a diverse and complex array of tumors within the central nervous system (CNS), characterized by a wide range of prognostic outcomes and responses to therapeutic interventions. This literature review endeavors to conduct a thorough investigation of gliomas, with a particular emphasis on glioblastoma (GBM), beginning with their classification and epidemiological characteristics, evaluating their relative importance within the CNS tumor spectrum. We examine the immunological context of gliomas, unveiling the intricate immune environment and its ramifications for disease progression and therapeutic strategies. Moreover, we accentuate critical developments in understanding tumor behavior, focusing on recent research breakthroughs in treatment responses and the elucidation of cellular signaling pathways. Analyzing the most novel transcriptomic studies, we investigate the variations in gene expression patterns in glioma cells, assessing the prognostic and therapeutic implications of these genetic alterations. Furthermore, the role of epigenetic modifications in the pathogenesis of gliomas is underscored, suggesting that such changes are fundamental to tumor evolution and possible therapeutic advancements. In the end, this comparative oncological analysis situates GBM within the wider context of neoplasms, delineating both distinct and shared characteristics with other types of tumors.PMID:38534769 | DOI:10.3390/cimb46030153

Curr Issues Mol Biol. 2024 Mar 3;46(3):2027-2042. doi: 10.3390/cimb46030131.ABSTRACTGlucose is a major energy substrate for porcine adipocytes and also serves as a regulatory signal for adipogenesis and lipid metabolism. In this study, we combined transcriptome and metabolome analyses to reveal the underlying regulatory mechanisms of high glucose (HG) on adipogenesis by comparing differentially expressed genes (DEGs) and differentially accumulated metabolites (DAMs) identified in porcine adipocytes. Results showed that HG (20 mmol/L) significantly increased fat accumulation in porcine adipocytes compared to low glucose (LG, 5 mmol/L). A total of 843 DEGs and 365 DAMs were identified. Functional enrichment analyses of DEGs found that multiple pathways were related to adipogenesis, lipid metabolism, and immune-inflammatory responses. PPARγ, C/EBPα, ChREBP, and FOS were identified as the key hub genes through module 3 analysis, and PPARγ acted as a central regulator by linking genes involved in lipid metabolism and immune-inflammatory responses. Gene-metabolite networks found that PPARγ-13-HODE was the most important interaction relationship. These results revealed that PPARγ could mediate the cross-talk between adipogenesis and the immune-inflammatory response during adipocyte maturation. This work provides a comprehensive view of the regulatory mechanisms of glucose on adipogenesis in porcine adipocytes.PMID:38534747 | DOI:10.3390/cimb46030131

Biology (Basel). 2024 Mar 20;13(3):197. doi: 10.3390/biology13030197.ABSTRACTOphiopogon japonicus, a plant that thrives in river alluvial dams, often faces waterlogging stress due to sustained rainfall and flood seasons, which significantly impacts its growth and development. Currently, the mechanisms of waterlogging tolerance in Ophiopogon japonicus are still unclear. This study analyzed the transcriptome and metabolome data for Ophiopogon japonicus in the Sichuan region (referred to as CMD) under varying degrees of waterlogging stress: mild, moderate, and severe. The results indicate that the group exposed to flooding stress exhibited a higher number of differentially expressed genes (DEGs) compared to the control group. Notably, most DEGs were downregulated and primarily enriched in phenylpropanoid biosynthesis, starch and sucrose metabolism, and plant hormone signal transduction pathways. A total of 5151 differentially accumulated metabolites (DAMs) were identified, with significantly upregulated DAMs annotated to two clusters, namely flavonoids such as apiin, pelargonin, and others. Furthermore, our study revealed significant upregulation in the expression of C2H2 (C2H2 zinc finger proteins) and AP2/ERF-ERF (the subfamily ERF proteins of APETALA2/ethylene-responsive element binding factors) transcription factors in CMD under flooding stress, suggesting their critical roles in enabling CMD to adapt to these conditions. In conclusion, this research provides insights into the intricate molecular mechanisms underlying CMD's response to flooding stress and reports valuable genetic data for the development of transgenic plants with improved waterlogging tolerance.PMID:38534466 | DOI:10.3390/biology13030197

Cells. 2024 Mar 7;13(6):475. doi: 10.3390/cells13060475.ABSTRACTChronic Obstructive Pulmonary Disease (COPD) is a pathological condition of the respiratory system characterized by chronic airflow obstruction, associated with changes in the lung parenchyma (pulmonary emphysema), bronchi (chronic bronchitis) and bronchioles (small airways disease). In the last years, the importance of phenotyping and endotyping COPD patients has strongly emerged. Metabolomics refers to the study of metabolites (both intermediate or final products) and their biological processes in biomatrices. The application of metabolomics to respiratory diseases and, particularly, to COPD started more than one decade ago and since then the number of scientific publications on the topic has constantly grown. In respiratory diseases, metabolomic studies have focused on the detection of metabolites derived from biomatrices such as exhaled breath condensate, bronchoalveolar lavage, and also plasma, serum and urine. Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy are powerful tools in the precise identification of potentially prognostic and treatment response biomarkers. The aim of this article was to comprehensively review the relevant literature regarding the applications of metabolomics in COPD, clarifying the potential clinical utility of the metabolomic profile from several biologic matrices in detecting biomarkers of disease and prognosis for COPD. Meanwhile, a complete description of the technological instruments and techniques currently adopted in the metabolomics research will be described.PMID:38534319 | DOI:10.3390/cells13060475