PubMed

Nat Commun. 2023 Dec 11;14(1):8188. doi: 10.1038/s41467-023-43909-5.ABSTRACTRetention time (RT) alignment is a crucial step in liquid chromatography-mass spectrometry (LC-MS)-based proteomic and metabolomic experiments, especially for large cohort studies. The most popular alignment tools are based on warping function method and direct matching method. However, existing tools can hardly handle monotonic and non-monotonic RT shifts simultaneously. Here, we develop a deep learning-based RT alignment tool, DeepRTAlign, for large cohort LC-MS data analysis. DeepRTAlign has been demonstrated to have improved performances by benchmarking it against current state-of-the-art approaches on multiple real-world and simulated proteomic and metabolomic datasets. The results also show that DeepRTAlign can improve identification sensitivity without compromising quantitative accuracy. Furthermore, using the MS features aligned by DeepRTAlign, we trained and validated a robust classifier to predict the early recurrence of hepatocellular carcinoma. DeepRTAlign provides an advanced solution to RT alignment in large cohort LC-MS studies, which is currently a major bottleneck in proteomics and metabolomics research.PMID:38081814 | DOI:10.1038/s41467-023-43909-5

ACS Chem Biol. 2023 Dec 11. doi: 10.1021/acschembio.3c00627. Online ahead of print.ABSTRACTRed algae or seaweeds produce highly distinctive halogenated terpenoid compounds, including the pentabromochlorinated monoterpene halomon that was once heralded as a promising anticancer agent. The first dedicated step in the biosynthesis of these natural product molecules is expected to be catalyzed by terpene synthase (TS) enzymes. Recent work has demonstrated an emerging class of type I TSs in red algal terpene biosynthesis. However, only one such enzyme from a notoriously haloterpenoid-producing red alga (Laurencia pacifica) has been functionally characterized and the product structure is not related to halogenated terpenoids. Herein, we report 10 new type I TSs from the red algae Portieria hornemannii, Plocamium pacificum, L. pacifica, and Laurencia subopposita that produce a diversity of halogenated mono- and sesquiterpenes. We used a combination of genome sequencing, terpenoid metabolomics, in vitro biochemistry, and bioinformatics to establish red algal TSs in all four species, including those associated with the selective production of key halogenated terpene precursors myrcene, trans-β-ocimene, and germacrene D-4-ol. These results expand on a small but growing number of characterized red algal TSs and offer insight into the biosynthesis of iconic halogenated algal compounds that are not without precedence elsewhere in biology.PMID:38081799 | DOI:10.1021/acschembio.3c00627

Nanotechnology. 2023 Dec 11. doi: 10.1088/1361-6528/ad1448. Online ahead of print.ABSTRACTTriple-negative breast cancer (TNBC) is a subtype of breast cancer with a poor prognosis and lacks effective therapeutic targets. The use of gambogic acid (GA), a class of active ingredients in traditional Chinese medicine with anti-tumour potential, is limited in tumour therapy owing to its drawbacks and unclear organ toxicity. In this study, we used the pH-responsive amphiphilic block copolymer, PEOz-PCL, to create nanodrugs for GA delivery to MDA-MB-231 cells. The pH-responsive GA-loaded micelles were prepared through nanoprecipitation with a more homogeneous size. The average particle size was 42.29 ± 1.74 nm, and the zeta potential value was 9.88 ± 0.17 mV. The encapsulation rate was 85.06%, and the drug loading rate was 10.63%. The process was reproducible, and sustained release reached 80% in 96 h at pH 5.0. Furthermore, cellular tests using CCK-8, TUNEL, and flow cytometry revealed that pH-responsive GA-loaded micelles killed MDA-MB-231 cells more effectively and had much higher activity and targeting compared with free drugs. Metabolomic analysis of the changes in differential metabolites revealed that pH-responsive GA-loaded micelles may inhibit TNBC cells by causing amino acid anabolism, nucleotide metabolism, and glucose metabolism, as well as by affecting their energy sources. The study outcomes will help understand the mechanism of action and the therapeutic efficacy of pH-responsive GA-loaded micelles in vivo.&#xD.PMID:38081078 | DOI:10.1088/1361-6528/ad1448

Diabetes. 2023 Dec 11:db230447. doi: 10.2337/db23-0447. Online ahead of print.ABSTRACTHeterogeneity in host and gut microbiota hampers microbial precision intervention of type 2 diabetes mellitus (T2DM). Here, we investigate novel features for patient-stratification and bacterial modulators for intervention, using cross-sectional patient cohorts and animal experiments. We collected stool/blood/urine samples from 103 recent-onset T2DM patients and 25 healthy controls (HCs), performed gut microbial composition/metabolite profiling, and combined it with host-transcriptome/metabolome/cytokines and clinical data. Stool-type (dry/loose-stool), a feature of stool-microenvironment recently explored in microbiome studies, was used for T2DM patientstratification as it explained most of the variation in multi-omics dataset among all clinical parameters in our covariate analysis. T2DM with dry-stool (DM-DS) and loose-stool (DM-LS) were clearly differentiated from HC and each other by LightGBM-models, optimal among multiple machine-learning models. Compared to DM-DS, DM-LS exhibited discordant gut microbial taxonomic and functional profiles, severe host metabolic disorder, and excessive insulin secretion. Further cross-measurement-association-analysis linked the differential microbial profiles, in particular Blautia abundances, to T2DM phenotypes in our stratified multi-omics dataset. Notably, oral supplementation of Blautia to T2DM mice induced inhibitory effects on lipid accumulation, weight gain, and blood-glucose elevation with simultaneous modulation of gut bacterial composition, revealing the therapeutic potential of Blautia. Our study highlights the clinical implications of stool-microenvironment stratification and Blautia supplementation in T2DM, offering promising prospects for microbial precision treatment of metabolic diseases.PMID:38079576 | DOI:10.2337/db23-0447

J Chem Inf Model. 2023 Dec 11. doi: 10.1021/acs.jcim.3c01525. Online ahead of print.ABSTRACTMulticlass metabolomic studies have become popular for revealing the differences in multiple stages of complex diseases, various lifestyles, or the effects of specific treatments. In multiclass metabolomics, there are multiple data manipulation steps for analyzing raw data, which consist of data filtering, the imputation of missing values, data normalization, marker identification, sample separation, classification, and so on. In each step, several to dozens of machine learning methods can be chosen for the given data set, with potentially hundreds or thousands of method combinations in the whole data processing chain. Therefore, a clear understanding of these machine learning methods is helpful for selecting an appropriate method combination for obtaining stable and reliable analytical results of specific data. However, there has rarely been an overall introduction or evaluation of these methods based on multiclass metabolomic data. Herein, detailed descriptions of these machine learning methods in multiple data manipulation steps are reviewed. Moreover, an assessment of these methods was performed using a benchmark data set for multiclass metabolomics. First, 12 imputation methods for imputing missing values were evaluated based on the PSS (Procrustes statistical shape analysis) and NRMSE (normalized root-mean-square error) values. Second, 17 normalization methods for processing multiclass metabolomic data were evaluated by applying the PMAD (pooled median absolute deviation) value. Third, different methods of identifying markers of multiclass metabolomics were evaluated based on the CWrel (relative weighted consistency) value. Fourth, nine classification methods for constructing multiclass models were assessed using the AUC (area under the curve) value. Performance evaluations of machine learning methods are highly recommended to select the most appropriate method combination before performing the final analysis of the given data. Overall, detailed descriptions and evaluation of various machine learning methods are expected to improve analyses of multiclass metabolomic data.PMID:38079572 | DOI:10.1021/acs.jcim.3c01525

Biotechnol Bioeng. 2023 Dec 11. doi: 10.1002/bit.28613. Online ahead of print.ABSTRACTChinese Hamster Ovary (CHO) cells have rapidly become a cornerstone in biopharmaceutical production. Recently, a reinvigoration of perfusion culture mode in CHO cell cultivation has been observed. However, most cell lines currently in use have been engineered and adapted for fed-batch culture methods, and may not perform optimally under perfusion conditions. To improve the cell's resilience and viability during perfusion culture, we cultured a triple knockout CHO cell line, deficient in three apoptosis related genes BAX, BAK, and BOK in a perfusion system. After 20 days of culture, the cells exhibited a halt in cell proliferation. Interestingly, following this phase of growth arrest, the cells entered a second growth phase. During this phase, the cell numbers nearly doubled, but cell specific productivity decreased. We performed a proteomics investigation, elucidating a distinct correlation between growth arrest and cell cycle arrest and showing an upregulation of the central carbon metabolism and oxidative phosphorylation. The upregulation was partially reverted during the second growth phase, likely caused by intragenerational adaptations to stresses encountered. A phase-dependent response to oxidative stress was noted, indicating glutathione has only a secondary role during cell cycle arrest. Our data provides evidence of metabolic regulation under high cell density culturing conditions and demonstrates that cell growth arrest can be overcome. The acquired insights have the potential to not only enhance our understanding of cellular metabolism but also contribute to the development of superior cell lines for perfusion cultivation.PMID:38079117 | DOI:10.1002/bit.28613

Probiotics Antimicrob Proteins. 2023 Dec 11. doi: 10.1007/s12602-023-10196-x. Online ahead of print.ABSTRACTRecently, it has been reported that oral probiotics improve the apparent digestibility of nitrogen in weaned piglets; however, the underlying mechanism is unclear. A total of 12 crossbred piglets (Yorkshire × Landrace; 28 days old) were randomly divided into two groups. The control (Con) group was fed with a basic diet + Luria-Bertani (LB; sterile; 10 mL), whereas the subject (Sub) group was fed with a basic diet + B. subtilis JATP-3 (1 × 109 CFU/mL; 10 mL). The results showed that feeding B. subtilis JATP-3 increased the final body weight and nitrogen deposition rate of weaned piglets (P < 0.05); while the daily weight gain showed an upward trend (P < 0.1). The abundance of Pedicoccus, Collinella, Turiciator, Veillonella, Clostridium, and Escherichia were significantly increased in the jejunum (P < 0.05). The abundance of Olsenella and Pediococcus were significantly increased in the ileum (P < 0.05). The metabolomics analysis showed that the levels of l-lactic acid and Alpha-ketoglutaric acid (AKG) in portal vein plasma were significantly increased (P < 0.05). In addition, the content of AKG in muscle and liver increased significantly (P < 0.01). The metagenomics analysis showed that Veillonella encoded the functional genes of 2-oxoglutarate synthase and promoted AKG production. The protein expression of eIF4E-binding protein 1 (4EBP1) phosphorylated in the skeletal muscle increased (P < 0.05). In summary, B. subtilis JATP-3 promotes dietary nitrogen metabolism and skeletal muscle synthesis by modulating the intestinal microbiota and its metabolites, in which AKG may be one of the main mediators of the therapeutic effects of B. subtilis JATP-3.PMID:38079031 | DOI:10.1007/s12602-023-10196-x

Chem Res Toxicol. 2023 Dec 11. doi: 10.1021/acs.chemrestox.3c00152. Online ahead of print.ABSTRACTSince the focus in regulatory toxicology has drifted toward the identification of endocrine disruptors, the improvement in determination of alterations in the thyroid hormone system has become more important. THs are involved in several molecular processes important for a proper pre- and postnatal development so that disturbances can inter alia lead to incorrect brain maturation and/or disturbed metabolic processes (thermogenesis or lipolysis). In this publication, a new automated online solid-phase extraction (SPE)-liquid chromatography (LC)-tandem mass spectrometry (MS/MS, xLC-MS/MS) is introduced which simultaneously analyzes total T4, T3, rT3, T2, and T1. Method validation parameters are presented, and the method was positively verified by analyzing control and PTU-treated rat plasma samples (time points day 7, 14, and 28) for their total TH content. The obtained results were compared to published results by using a radioimmunoassay method. The automated SPE system ensures a consistent unified sample preparation, and this method overall showed sufficient specificity and accuracy to detect the given analytes in rat plasma. For the preparation of 50 μL of rat plasma, the following LOQs were established: 0.020 nM for T1, 0.029 nM for T2, 0.023 nM for rT3 and T3, and 3.22 nM for T4. This method is suitable to assess the identification of mechanisms leading to adverse effects, such as disturbed TH metabolism and regulation.PMID:38078760 | DOI:10.1021/acs.chemrestox.3c00152

mSystems. 2023 Dec 11:e0097223. doi: 10.1128/msystems.00972-23. Online ahead of print.ABSTRACTA quest for novel antibiotics and revitalizing older ones (such as nitrofurantoin) for treatment of difficult-to-treat Gram-negative bacterial infections has become increasingly popular. The precise antibacterial activity of nitrofurantoin is still not fully understood. Furthermore, although the prevalence of nitrofurantoin resistance remains low currently, the drug's fast-growing consumption worldwide highlights the need to comprehend the emerging resistance mechanisms. Here, we used multidisciplinary techniques to discern the exact mechanism of nitrofurantoin action and high-level resistance in Klebsiella pneumoniae, a common cause of urinary tract infections for which nitrofurantoin is the recommended treatment. We found that the expression of multiple genes related to membrane transport (including active efflux and passive diffusion of drug molecules) and nitroreductase activity was modified in nitrofurantoin-resistant strains, including oqxR, the transcriptional regulator of the oqxAB efflux pump. Furthermore, complex interconnected metabolic pathways that potentially govern the nitrofurantoin-killing mechanisms (e.g., aminoacyl-tRNA biosynthesis) and nitrofurantoin resistance (riboflavin metabolism) were significantly inhibited following nitrofurantoin treatment. Our study could help inform the improvement of nitrofuran derivatives, the development of new pharmacophores, or drug combinations to support the resurgence of nitrofurantoin in the management of multidrug resistant K. pneumouniae infection.PMID:38078757 | DOI:10.1128/msystems.00972-23

Crit Rev Food Sci Nutr. 2023 Dec 11:1-18. doi: 10.1080/10408398.2023.2291188. Online ahead of print.ABSTRACTLactic acid bacteria (LAB) plays a crucial role in the establishment and maintenance of host health, as well as the improvement of some diseases. One of the major modes is the secretion of metabolites that may be intermediate or end products of the LAB's metabolism. In this review, we summarized some common metabolites (particularly short-chain fatty acids [SCFAs], bacteriocin, and exopolysaccharide [EPS]) from LAB in fermented foods and the gut for the first time. The effects of LAB-derived metabolites (LABM) on inflammation, oxidative stress, the intestinal barrier, and gut microbiota in inflammatory bowel disease (IBD) model are also discussed. The discovery of LABM and identification of IBD biomarkers are mainly attributed to the development of metabolomics technologies, especially nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS), and liquid chromatography tandem mass spectrometry (LC-MS). The application of these metabolomics technologies in identification of LABM and IBD biomarkers are also summarized and analyzed. Although the beneficial effects of some LABM have been explored, undiscovered metabolites and their functions still need further investigations.PMID:38078699 | DOI:10.1080/10408398.2023.2291188

Gut Microbes. 2023 Dec;15(2):2281011. doi: 10.1080/19490976.2023.2281011. Epub 2023 Dec 11.ABSTRACTGut bacteria provide benefits to the host and have been implicated in inflammatory bowel disease (IBD), where adherent-invasive E. coli (AIEC) pathobionts (e.g., strain LF82) are associated with Crohn's disease. E. coli-LF82 causes fragmentation of the epithelial mitochondrial network, leading to increased epithelial permeability. We hypothesized that butyrate would limit the epithelial mitochondrial disruption caused by E. coli-LF82. Human colonic organoids and the T84 epithelial cell line infected with E. coli-LF82 (MOI = 100, 4 h) showed a significant increase in mitochondrial network fission that was reduced by butyrate (10 mM) co-treatment. Butyrate reduced the loss of mitochondrial membrane potential caused by E. coli-LF82 and increased expression of PGC-1α mRNA, the master regulator of mitochondrial biogenesis. Metabolomics revealed that butyrate significantly altered E. coli-LF82 central carbon metabolism leading to diminished glucose uptake and increased succinate secretion. Correlating with preservation of mitochondrial network form/function, butyrate reduced E. coli-LF82 transcytosis across T84-cell monolayers. The use of the G-protein inhibitor, pertussis toxin, implicated GPCR signaling as critical to the effect of butyrate, and the free fatty acid receptor three (FFAR3, GPR41) agonist, AR420626, reproduced butyrate's effect in terms of ameliorating the loss of barrier function and reducing the mitochondrial fragmentation observed in E. coli-LF82 infected T84-cells and organoids. These data indicate that butyrate helps maintain epithelial mitochondrial form/function when challenged by E. coli-LF82 and that this occurs, at least in part, via FFAR3. Thus, loss of butyrate-producing bacteria in IBD in the context of pathobionts would contribute to loss of epithelial mitochondrial and barrier functions that could evoke disease and/or exaggerate a low-grade inflammation.PMID:38078655 | DOI:10.1080/19490976.2023.2281011

Analyst. 2023 Dec 11. doi: 10.1039/d3an01735d. Online ahead of print.ABSTRACTDysfunctional lipid metabolism plays a crucial role in the development and progression of various diseases. Accurate measurement of lipidomes can help uncover the complex interactions between genes, proteins, and lipids in health and diseases. The prediction of retention time (RT) has become increasingly important in both targeted and untargeted metabolomics. However, the potential impact of RT prediction on targeted LC-MS based lipidomics is still not fully understood. Herein, we propose a simplified workflow for predicting RT in phospholipidomics. Our approach involves utilizing the fatty acyl chain length or carbon-carbon double bond (DB) number in combination with multiple reaction monitoring (MRM) validation. We found that our model's predictive capacity for RT was comparable to that of a publicly accessible program (QSRR Automator). Additionally, MRM validation helped in further mitigating the interference in signal recognition. Using this developed workflow, we conducted phospholipidomics of sorafenib resistant hepatocellular carcinoma (HCC) cell lines, namely MHCC97H and Hep3B. Our findings revealed an abundance of monounsaturated fatty acyl (MUFA) or polyunsaturated fatty acyl (PUFA) phospholipids in these cell lines after developing drug resistance. In both cell lines, a total of 29 lipids were found to be co-upregulated and 5 lipids were co-downregulated. Further validation was conducted on seven of the upregulated lipids using an independent dataset, which demonstrates the potential for translation of the established workflow or the lipid biomarkers.PMID:38078496 | DOI:10.1039/d3an01735d

Front Plant Sci. 2023 Nov 21;14:1332622. doi: 10.3389/fpls.2023.1332622. eCollection 2023.NO ABSTRACTPMID:38078090 | PMC:PMC10703365 | DOI:10.3389/fpls.2023.1332622

Front Plant Sci. 2023 Nov 23;14:1140829. doi: 10.3389/fpls.2023.1140829. eCollection 2023.ABSTRACTINTRODUCTION: Flux phenotypes from different organisms and growth conditions allow better understanding of differential metabolic networks functions. Fluxes of metabolic reactions represent the integrated outcome of transcription, translation, and post-translational modifications, and directly affect growth and fitness. However, fluxes of intracellular metabolic reactions cannot be directly measured, but are estimated via metabolic flux analysis (MFA) that integrates data on isotope labeling patterns of metabolites with metabolic models. While the application of metabolomics technologies in photosynthetic organisms have resulted in unprecedented data from 13CO2-labeling experiments, the bottleneck in flux estimation remains the application of isotopically nonstationary MFA (INST-MFA). INST-MFA entails fitting a (large) system of coupled ordinary differential equations, with metabolite pools and reaction fluxes as parameters. Here, we focus on the Calvin-Benson cycle (CBC) as a key pathway for carbon fixation in photosynthesizing organisms and ask if approaches other than classical INST-MFA can provide reliable estimation of fluxes for reactions comprising this pathway.METHODS: First, we show that flux estimation with the labeling patterns of all CBC intermediates can be formulated as a single constrained regression problem, avoiding the need for repeated simulation of time-resolved labeling patterns.RESULTS: We then compare the flux estimates of the simulation-free constrained regression approach with those obtained from the classical INST-MFA based on labeling patterns of metabolites from the microalgae Chlamydomonas reinhardtii, Chlorella sorokiniana and Chlorella ohadii under different growth conditions.DISCUSSION: Our findings indicate that, in data-rich scenarios, simulation-free regression-based approaches provide a suitable alternative for flux estimation from classical INST-MFA since we observe a high qualitative agreement (rs=0.89) to predictions obtained from INCA, a state-of-the-art tool for INST-MFA.PMID:38078077 | PMC:PMC10702240 | DOI:10.3389/fpls.2023.1140829

Clin Cosmet Investig Dermatol. 2023 Dec 5;16:3465-3480. doi: 10.2147/CCID.S441000. eCollection 2023.ABSTRACTPURPOSE: This study investigated microbiome and metabolome differences between ulcerated tissues and normal skin from the lower limbs of participants with leprosy.PATIENTS AND METHODS: Ulcerated tissues and surrounding normal skin were collected from the lower limbs of 28 participants with leprosy who had been cured. The 16S ribosomal DNA sequencing analysis of the samples was conducted with the Illumina NovaSeq platform to analyze the community structure and diversity of microorganisms on the skin surface, followed by non-targeted metabolomic analysis with LC-MS technology. Next, differential metabolites were statistically screened, followed by metabolic pathway analysis. The Spearman method was used to analyze the correlation between differential microbiota and differential metabolites.RESULTS: Compared to normal skin, ulcerated tissues showed a decrease in microbial α diversity (species richness, homogeneity, and sequencing depth), without significant differences (observed species, Chao1, Shannon, Simpson, and Pielou's evenness index; P > 0.05). Conversely, Jaccard distance demonstrated that sample β-diversity exhibited a certain degree of clustering (P < 0.05), with significant differences between the two groups. The results of LEfSe analysis revealed that compared to the normal skin, the ulcerated tissues had significantly decreased microbial abundance of Flavobacteriaceae, Flavobacteriales, Lachnospiraceae, Lachnospirales, Enterobacterales, Acinetobacter, and Moraxellaceae, which might be associated with the ulcerative state. The Spearman correlation analysis suggested a strong correlation between skin metabolome and skin microbiome.CONCLUSION: For participants with leprosy sequelae, skin microecology and metabolites are disturbed and species diversity and homogeneity are reduced in lower-limb ulcers, and the types of skin metabolites are dependent on the microbiota.PMID:38077917 | PMC:PMC10710263 | DOI:10.2147/CCID.S441000

Res Pract Thromb Haemost. 2023 Sep 28;7(7):102213. doi: 10.1016/j.rpth.2023.102213. eCollection 2023 Oct.ABSTRACTBACKGROUND: Alterations in platelet function have been implicated in the pathophysiology of COVID-19 since the beginning of the pandemic. While early reports linked hyperactivated platelets to thromboembolic events in COVID-19, subsequent investigations demonstrated hyporeactive platelets with a procoagulant phenotype. Mitochondria are important for energy metabolism and the function of platelets.OBJECTIVES: Here, we sought to map the energy metabolism of platelets in a cohort of noncritically ill COVID-19 patients and assess platelet mitochondrial function, activation status, and responsiveness to external stimuli.METHODS: We enrolled hospitalized COVID-19 patients and controls between October 2020 and December 2021. Platelets function and metabolism was analyzed by flow cytometry, metabolomics, glucose fluxomics, electron and fluorescence microscopy and western blot.RESULTS: Platelets from COVID-19 patients showed increased phosphatidylserine externalization indicating a procoagulant phenotype and hyporeactivity to ex vivo stimuli, associated with profound mitochondrial dysfunction characterized by mitochondrial depolarization, lower mitochondrial DNA-encoded transcript levels, an altered mitochondrial morphology consistent with increased mitochondrial fission, and increased pyruvate/lactate ratios in platelet supernatants. Metabolic profiling by untargeted metabolomics revealed NADH, NAD+, and ATP among the top decreased metabolites in patients' platelets, suggestive of energy metabolism failure. Consistently, platelet fluxomics analyses showed a strongly reduced utilization of 13C-glucose in all major energy pathways together with a rerouting of glucose to de novo generation of purine metabolites. Patients' platelets further showed evidence of oxidative stress, together with increased glutathione oxidation and synthesis. Addition of plasma from COVID-19 patients to normal platelets partially reproduced the phenotype of patients' platelets and disclosed a temporal relationship between mitochondrial decay and (subsequent) phosphatidylserine exposure and hyporeactivity.CONCLUSION: These data link energy metabolism failure in platelets from COVID-19 patients with a prothrombotic platelet phenotype with features matching cell death.PMID:38077825 | PMC:PMC10700394 | DOI:10.1016/j.rpth.2023.102213

Infect Med (Beijing). 2022 Aug 24;1(3):180-191. doi: 10.1016/j.imj.2022.08.004. eCollection 2022 Sep.ABSTRACTHuman gut microbiome is a major source of human bacterial population and a significant contribution to both positive and harmful effects. Due to its involvement in a variety of interactions, gut microorganisms have a great impact on our health throughout our lives. The impact of gut microbial population is been studied intensively in last two decades. Extensive literature survey focusing developments in the field were searched in English language Electronic Databases like PubMed, Google Scholar, Pubag, Google books, and Research Gate were mostly used to understand the role of human gut mirobiome and its role in different human diseases. Gut microbiome in healthy subjects differs from those who suffer from diseases. Type 2 diabetes, obesity, non-alcoholic liver disease, and cardiometabolic diseases have all been linked to dysbiosis of the gut microbiota. Pathogenesis of many disorders is also linked to changes in gut microbiota. Other diseases like cancer, arithritis, autism, depression, anxiety, sleep disorder, HIV, hypertension, and gout are also related to gut microbiota dysbiosis. We focus in this review on recent studies looking into the link between gut microbiome dysbiosis and disease etiology. Research on how gut microbiota affects host metabolism has been changed in past decades from descriptive analyses to high throughput integrative omics data analysis such as metagenomics and metabolomics. Identification of molecular mechanisms behind reported associations is been carried out in human, animals, and cells for measure of host physiology and mechanics. Still many the mechanisms are not completely understood.PMID:38077626 | PMC:PMC10699709 | DOI:10.1016/j.imj.2022.08.004

bioRxiv. 2023 Nov 28:2023.11.27.568931. doi: 10.1101/2023.11.27.568931. Preprint.ABSTRACTHigh-resolution spatial imaging is transforming our understanding of foundational biology. Spatial metabolomics is an emerging field that enables the dissection of the complex metabolic landscape and heterogeneity from a thin tissue section. Currently, spatial metabolism highlights the remarkable complexity in two-dimensional space and is poised to be extended into the three-dimensional world of biology. Here, we introduce MetaVision3D, a novel pipeline driven by computer vision techniques for the transformation of serial 2D MALDI mass spectrometry imaging sections into a high-resolution 3D spatial metabolome. Our framework employs advanced algorithms for image registration, normalization, and interpolation to enable the integration of serial 2D tissue sections, thereby generating a comprehensive 3D model of unique diverse metabolites across host tissues at mesoscale. As a proof of principle, MetaVision3D was utilized to generate the mouse brain 3D metabolome atlas (available at https://metavision3d.rc.ufl.edu/ ) as an interactive online database and web server to further advance brain metabolism and related research.PMID:38077043 | PMC:PMC10705265 | DOI:10.1101/2023.11.27.568931

bioRxiv. 2023 Dec 1:2023.11.29.569274. doi: 10.1101/2023.11.29.569274. Preprint.ABSTRACTIntrauterine metabolic reprogramming occurs in obese mothers during gestation, putting the offspring at high risk of developing obesity and associated metabolic disorders even before birth. We have generated a mouse model of maternal high-fat diet-induced obesity that recapitulates the metabolic changes seen in humans. Here, we profiled and compared the metabolic characteristics of bone marrow cells of newly weaned 3-week-old offspring of dams fed either a high-fat (Off-HFD) or a regular diet (Off-RD). We utilized a state-of-the-art targeted metabolomics approach coupled with a Seahorse metabolic analyzer. We revealed significant metabolic perturbation in the offspring of HFD-fed vs. RD-fed dams, including utilization of glucose primarily via oxidative phosphorylation, and reduction in levels of amino acids, a phenomenon previously linked to aging. Furthermore, in the bone marrow of three-week-old offspring of high-fat diet-fed mothers, we identified a unique B cell population expressing CD19 and CD11b, and found increased expression of Cyclooxygenase-2 (COX-2) on myeloid CD11b, and on CD11b hi B cells, with all the populations being significantly more abundant in offspring of dams fed HFD but not a regular diet. Altogether, we demonstrate that the offspring of obese mothers show metabolic and immune changes in the bone marrow at a very young age and prior to any symptomatic metabolic disease.PMID:38077037 | PMC:PMC10705475 | DOI:10.1101/2023.11.29.569274

bioRxiv. 2023 Nov 29:2023.11.28.569095. doi: 10.1101/2023.11.28.569095. Preprint.ABSTRACTThis study, utilizing SBF-SEM, reveals structural alterations in mitochondria and myofibrils in human heart failure (HF). Mitochondria in HF show changes in structure, while myofibrils exhibit increased cross-sectional area and branching. Metabolomic and lipidomic analyses indicate concomitant dysregulation in key pathways. The findings underscore the need for personalized treatments considering individualized structural changes in HF.PMID:38076993 | PMC:PMC10705476 | DOI:10.1101/2023.11.28.569095