Metabolic Reprogramming by Folate Restriction Leads to a Less Aggressive Cancer Phenotype

Folate coenzymes are involved in biochemical reactions of one-carbon transfer, and deficiency of this vitamin impairs cellular proliferation, migration and survival in many cell types. Here the effect of folate restriction on mammary cancer was evaluated using three distinct breast cancer subtypes differing in their aggressiveness and metastatic potential: non-invasive basal-like (E-Wnt), invasive but minimally metastatic claudin-low (M-Wnt), and highly metastatic claudin-low (metM-Wntliver) cell lines, each derived from the same pool of MMTV-Wnt-1 transgenic mouse mammary tumors. NMR-based metabolomics was used to quantitate 41 major metabolites in cells grown in folate-free medium versus standard medium. Each cell line demonstrated metabolic reprogramming when grown in folate-free medium. In E-Wnt, M-Wnt and metM-Wntliver cells 12, 29, and 25 metabolites, respectively, were significantly different (p<0.05 and at least 1.5-fold change). The levels of eight metabolites (aspartate, ATP, creatine, creatine phosphate, formate, serine, taurine and β-alanine) were changed in each folate-restricted cell line. Increased glucose, decreased lactate, and inhibition of glycolysis, cellular proliferation, migration and invasion occurred in M-Wnt and metM-Wntliver cells (but not E-Wnt cells) grown in folate-free versus standard medium. These effects were accompanied by altered levels of several folate-metabolizing enzymes, indicating that the observed metabolic reprogramming may result from both decreased folate availability and altered folate metabolism. These findings reveal that folate restriction results in metabolic and bioenergetic changes and a less aggressive cancer cell phenotype

Maternal Serum Metabolomics in Mid-Pregnancy Identifies Lipid Pathways as a Key Link to Offspring Obesity in Early Childhood

Maternal metabolism during pregnancy shapes offspring health via in utero programming. In the Healthy Start study, we identified five subgroups of pregnant women based on conventional metabolic biomarkers: Reference (n = 360); High HDL-C (n = 289); Dyslipidemic–High TG (n = 149); Dyslipidemic–High FFA (n = 180); Insulin Resistant (IR)–Hyperglycemic (n = 87). These subgroups not only captured metabolic heterogeneity among pregnant participants but were also associated with offspring obesity in early childhood, even among women without obesity or diabetes. Here, we utilize metabolomics data to enrich characterization of the metabolic subgroups and identify key compounds driving between-group differences. We analyzed fasting blood samples from 1065 pregnant women at 18 gestational weeks using untargeted metabolomics. We used weighted gene correlation network analysis (WGCNA) to derive a global network based on the Reference subgroup and characterized distinct metabolite modules representative of the different metabolomic profiles. We used the mummichog algorithm for pathway enrichment and identified key compounds that differed across the subgroups. Eight metabolite modules representing pathways such as the carnitine–acylcarnitine translocase system, fatty acid biosynthesis and activation, and glycerophospholipid metabolism were identified. A module that included 189 compounds related to DHA peroxidation, oxidative stress, and sex hormone biosynthesis was elevated in the Insulin Resistant–Hyperglycemic vs. the Reference subgroup. This module was positively correlated with total cholesterol (R:0.10; p-value < 0.0001) and free fatty acids (R:0.07; p-value < 0.05). Oxidative stress and inflammatory pathways may underlie insulin resistance during pregnancy, even below clinical diabetes thresholds. These findings highlight potential therapeutic targets and strategies for pregnancy risk stratification and reveal mechanisms underlying the developmental origins of metabolic disease risk

Association Analysis of NLRP3 Inflammation-Related Gene Promotor Methylation as Well as Mediating Effects on T2DM and Vascular Complications in a Southern Han Chinese Population

Objective: To explore the association between the methylation levels in the promoter regions of the NLRP3, AIM2, and ASC genes and T2DM and its vascular complications in a Southern Han Chinese population and further analyze their interaction and mediating effects with environmental factors in T2DM.Methods: A case-control study was used to determine the association between population characteristics, the methylation level in the promoter region of the NLRP3, AIM2, and ASC genes and T2DM and vascular complications. A mediating effect among genes-environment-T2DM and the interaction of gene-gene or gene-environment factors was explored.Results: In the logistic regression model with adjusted covariants, healthy people with lower total methylation levels in the AIM2 promoter region exhibited a 2.29-fold [OR: 2.29 (1.28~6.66), P = 0.011] increased risk of developing T2DM compared with higher-methylation individuals. T2DM patients without any vascular complications who had lower methylation levels (&lt;methylation median) in NLRP3 CpG2 and AIM2 total methylation had 6.45 (OR: 6.45, 95% CI: 1.05~39.78, P = 0.011) and 9.48 (OR: 9.48, 95% CI: 1.14~79.00, P = 0.038) times higher risks, respectively, of developing diabetic microvascular complications than T2DM patients with higher methylation. Similar associations were also found between the lower total methylation of the NLRP3 and AIM2 promoter regions and macrovascular complication risk (NLRP3 OR: 36.03, 95% CI: 3.11~417.06, P = 0.004; AIM2 OR: 30.90, 95% CI: 2.59~368.49, P = 0.007). Lower NLRP3 promoter total methylation was related to a 17.78-fold increased risk of micro-macrovascular complications (OR: 17.78, 95% CI: 2.04~155.28, P = 0.009). Lower ASC CpG1 or CpG3 methylation levels had significant partial mediating effects on T2DM vascular complications caused by higher age (ASC CpG1 explained approximately 52.8% or 32.9% of the mediating effect of age on macrovascular or macro-microvascular complications; ASC CpG3 explained approximately 38.9% of the mediating effect of age on macrovascular complications). No gene-gene or gene-environment interaction was identified in T2DM.Conclusion: Lower levels of AIM2 promoter total methylation might increase the risk of T2DM. NLRP3, AIM2, and ASC promoter total methylation or some CpG methylation loss might increase the risk of T2DM vascular complications, which merits further study to support the robustness of these findings

Integrative analysis of multimodal mass spectrometry data in MZmine 3

3 Pág.We thank Christopher Jensen and Gauthier Boaglio for their contributions to the MZmine codebase. We thank Jianbo Zhang and Zachary Russ for their donations to MZmine development. The MZmine 3 logo was designed by the Bioinformatics & Research Computing group at the Whitehead Institute for Biomedical Research. T.P. is supported by Czech Science Foundation (GA CR) grant 21-11563M and by the European Union’s Horizon 2020 research and innovation programme under Marie Skłodowska-Curie grant agreement 891397. Support for P.C.D. was from US NIH U19 AG063744, P50HD106463, 1U24DK133658 and BBSRC-NSF award 2152526. T.S. acknowledges funding by Deutsche Forschungsgemeinschaft (441958208). M. Wang acknowledges the US Department of Energy Joint Genome Institute ( https://ror.org/04xm1d337 , a DOE Office of Science User Facility) and is supported by the Office of Science of the US Department of Energy operated under subcontract No. 7601660. E.R. and H.H. thank Wen Jiang (HILICON AB) for providing the iHILIC Fusion(+) column for HILIC measurements. M.F., K.D. and S.B. are supported by Deutsche Forschungsgemeinschaft (BO 1910/20). L.-F.N. is supported by the Swiss National Science Foundation (project 189921). D.P. was supported through the Deutsche Forschungsgemeinschaft (German Research Foundation) through the CMFI Cluster of Excellence (EXC-2124 — 390838134 project-ID 1-03.006_0) and the Collaborative Research Center CellMap (TRR 261 - 398967434). J.-K.W. acknowledges the US National Science Foundation (MCB-1818132), the US Department of Agriculture, and the Chan Zuckerberg Initiative. MZmine developers have received support from the European COST Action CA19105 — Pan-European Network in Lipidomics and EpiLipidomics (EpiLipidNET). We acknowledge the support of the Google Summer of Code (GSoC) program, which has funded the development of several MZmine modules through student projects. We thank Adam Tenderholt for introducing MZmine to the GSoC program.Peer reviewe

SPECTRAL DECONVOLUTION FOR GAS CHROMATOGRAPHY MASS SPECTROMETRY-BASED METABOLOMICS: CURRENT STATUS AND FUTURE PERSPECTIVES

Mass spectrometry coupled to gas chromatography (GC-MS) has been widely applied in the field of metabolomics. Success of this application has benefited greatly from computational workflows that process the complex raw mass spectrometry data and extract the qualitative and quantitative information of metabolites. Among the computational algorithms within a workflow, deconvolution is critical since it reconstructs a pure mass spectrum for each component that the mass spectrometer observes. Based on the pure spectrum, the corresponding component can be eventually identified and quantified. Deconvolution is challenging due to the existence of co-elution. In this review, we focus on progress that has been made in the development of deconvolution algorithms and provide thoughts on future developments that will expand the application of GC-MS in metabolomics

Information-Theoretic Analysis of Turtle Cortical Waves

In this paper, we describe two approaches to the problem of encoding cortical waves of turtle visual cortex. The first approach relies on representing the response of individual pyramidal cells using various temporal scales (multiresolution analysis). In the second approach, we consider decomposing the visual cortex into various spatial grids. Each of these spatial grid contains several neurons and the Kullback-Leibler distance between the spiking patterns in response to different stimuli is computed. Such distance measure, we hope, would eventually be used to detect the stimuli. Keywords: maximal overlap discrete wavelet transform, Kullback-Leibler distance, maximum likelihood ratio testing, detection

Memory-Efficient Searching of Gas-Chromatography Mass Spectra Accelerated by Prescreening

The number of metabolomics studies and spectral libraries for compound annotation (i.e., assigning possible compound identities to a fragmentation spectrum) has been growing steadily in recent years. Accompanying this growth is the number of mass spectra available for searching through those libraries. As the size of spectral libraries grows, accurate and fast compound annotation becomes more challenging. We herein report a prescreening algorithm that was developed to address the speed of spectral search under the constraint of low memory requirements. This prescreening has been incorporated into the Automated Data Analysis Pipeline Spectral Knowledgebase (ADAP-KDB) and can be applied to compound annotation by searching other spectral libraries as well. Performance of the prescreening algorithm was evaluated for different sets of parameters and compared to the original ADAP-KDB spectral search and the MSSearch software. The comparison has demonstrated that the new algorithm is about four-times faster than the original when searching for low-resolution mass spectra, and about as fast as the original when searching for high-resolution mass spectra. However, the new algorithm is still slower than MSSearch due to the relational database design of the former. The new search workflow can be tried out at the ADAP-KDB web portal

An integrated cross-linking-MS approach to investigate cell penetrating peptides interacting partners

Cell penetrating peptides (CPPs) are attracting attention because of their ability to deliver biologically active molecules into cells. On their way they can interact with membrane and intracellular proteins. To fully understand and improve CPP efficiency as drug delivery tools, their partners need to be identified. To investigate CPP-protein complexes, chemical cross-linking coupled to mass spectrometry is a relevant method. With this aim, we developed an original approach based on two parallel strategies, an intact complex analysis and a bottom-up one, to have a global characterization of the cross-linked complexes composition as well as a detailed mapping of the interaction zones. Biological significance: The robust and efficient cross-linking-MS workflow presented here can easily be adapted to any CPP-protein interacting system and could thus contribute to a better understanding of CPPs activity as cell-specific drug delivery tools. We validated the relevancy of this cross-linking-MS approach with two biologically active CPPs, (R/W)9 and (R/W)16, and two interacting protein partners, actin and albumin, previously reported using isothermal titration calorimetry (ITC) and NMR. Cross-linking-MS results obtained on these previous studies allowed us to go further by providing a detailed mapping of the interaction zones. The identified interaction zones between actin and CPPs (R/W)9 and (R/W)16 are biologically meaningful. Two cross-linked zones [46–57] and [202–210] of actin are indeed involved in the modulation of its dynamics. Moreover, [46–57] domain has also been described as one interaction domain for thymosin β4 whose actin binding can be displaced by competition with (R/W)16 (NMR experiments)