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75 results for “Sphingolipids”
Sphingolipids are involved in Pieris brassicae egg-induced cell death in Arabidopsis thaliana
<p>This table contains mean + SEM values of sphingolipid levels by LC-MS analysis in Arabidopsis thaliana (wild-type and mutant lines) and Brassica nigra (wild-type) in response to egg extract of Pieris brassicae, as well as P-values for selected comparisons by Welsch t-test. These data were used for Fig. 7 and Fig. 8 of Groux et al. 2022</p> <p> </p> <p> </p> <p> </p>
Substantial contribution of in-situ produced bacterial sphingolipids to the sedimentary lipidome
<p><strong>Abstract:</strong> The sedimentary lipid pool comprises a myriad of components with some specific biomarkers used in paleoclimatic and geobiological reconstructions. However, a comprehensive view of the sedimentary lipidome is lacking. Here we conduct an untargeted analysis of the Black Sea sedimentary lipidome using high resolution mass spectrometry. Besides commonly reported phytoplankton-derived fossil lipids originate from oxic surface water, a diverse and abundant set of sphingolipids, accounting for ~20% of the lipidome, was discovered. These sphingolipids are produced in-situ by sedimentary anaerobic bacteria, likely in place of phospholipids due to the deficiency of phosphate in anoxic sediments. Our results suggest that while phytoplankton-derived lipids contribute 50–60% of the sedimentary lipidome, the importance of bacterial lipids, particularly in-situ produced sphingolipids, has been overlooked.</p> <p>Source data:</p> <p>Data 1. Spt_hits.and.backbone_sequences.MAFFT-L-INS-i.msa</p> <p>Data 2. Spt_hits.and.backbone_sequences.MAFFT-L-INS-i.msa.trimAl</p>
PDB File and MD Simulation results for "The atypical sphingolipid SPB 18:1(14Z);O2 is a biomarker for DEGS1 related hypomyelinating leukodystrophy"
<p>Supplementary structural file for article: "The atypical sphingolipid SPB 18:1(14Z);O2 is a biomarker for DEGS1 related hypomyelinating leukodystrophy":</p> <p>-Predicted Structure of DEGS1 docked to C16 Ceramide in PDB format</p> <p>- 2.5 µsec Molecular Dynamics Simulation of this DEGS1-Ceramide complex embedded in a DPPC membrane and surrounded by TIP3 water and 150 mM NaCl as mp4 (movies) or as original trajectory. In the version with the smaller file size solvent molecules and the membrane are invisible for clarity. </p> <p>Software/Webservices used for generation: AlphaFold, PPM3 web server, CHARM-GUI PDB Manipulator, Maestro/Glide/Ligprep/Desmond (Schrödinger Inc.).</p> <p>Version 1 contained videos in mpeg format that caused error with some players. In Version 2 videos are converted to mp4. </p>
Plasma Sphingolipid Metabolites and Radiotherapy Efficacy in Hepatocellular Carcinoma
ClinicalTrials.gov study NCT06864221. IPD Sharing: NO. Countries: 1. Publications: 11.
Sphingolipid serum profiling in vitamin D deficient and dyslipidemic obese dimorphic adults
<p>Recent studies on Saudi Arabians indicate a prevalence of dyslipidemia and vitamin D deficiency (25(OH)D) in both normal weight and obese subjects. In the present study the sphingolipid pattern was investigated in 23 normolipidemic normal weight (NW), 46 vitamin D deficient dyslipidemic normal weight (-vitDNW) and 60 vitamin D deficient dyslipidemic obese (-vitDO) men and women by HPTLC-primuline profiling and LC-MS analyses. Results indicate higher levels of total ceramide (Cer) and dihydroceramide (dhCers C18–22) and lower levels of total sphingomyelins (SMs) and dihydrosphingomyelin (dhSM) not only in -vitDO subjects compared to NW, but also in –vitDNW individuals. A dependency on body mass index (BMI) was observed analyzing specific Cer acyl chains levels. Lower levels of C20 and 24 were observed in men and C24.2 in women, respectively. Furthermore, LC-MS analyses display dimorphic changes in NW, -vitDNW and –vitDO subjects. In conclusion, LC-MS data identify the independency of the axis high Cers, dhCers and SMs from obesity <em>per se</em>. Furthermore, it indicates that long chains Cers levels are specific target of weight gain and that circulating Cer and SM levels are linked to sexual dimorphism status and can contribute to predict obese related co-morbidities in men and women.</p>
Coupled metalipidomics-metagenomics reveal structurally diverse sphingolipids produced by a wide variety of marine bacteria
<p><strong>Abstract</strong></p> <p><span>Microbial lipids, used as taxonomic markers and physiological indicators, have mainly been studied through cultivation. However, this approach is limited due to the scarcity of cultures of environmental microbes, thereby restricting insights into the diversity of lipids and their ecological roles. Addressing this limitation, here we apply metalipidomics combined with metagenomics in the Black Sea, classifying and tentatively identifying 1</span><span>623 lipid-like species across 18 lipid classes. We discovered over 200 novel, abundant, and structurally diverse sphingolipids in euxinic waters, including unique 1-deoxysphingolipids with long-chain fatty acids and sulfur-containing groups. </span><span>Sphingolipids were thought to be rare in bacteria and their molecular and ecological functions in bacterial membranes remain elusive. However, </span><span>genomic analysis focused on sphingolipid biosynthesis genes revealed that members of 38 bacterial phyla in the Black Sea can synthesize sphingolipids, representing a fourfold increase from previously known capabilities and accounting for up to 25% of the microbial community. These sphingolipids appear to be involved in oxidative stress response, cell wall remodeling and are associated with the metabolism of nitrogen-containing molecules. Our findings underscore the effectiveness of multi-omics approaches in exploring microbial chemical ecology.</span></p> <div><br></div> <p><strong>Repository content:</strong></p> <p><strong>1) metalipidome_sphingolipids.zip: </strong>includes source data and code scripts used for figures regarding metalipidome and sphingolipids abundance, classification and diversity in this study. Files are organized as follows and are associated with the corresponding parts of the manuscript: Fig. 1a, Fig. 1b, Fig. 2b, Fig. 2c, Fig. 2e, Fig. 2f, Fig. 2g, Fig. 2h, Fig. 4d, Supplementary Fig. 2, Supplementary Fig. 3.</p> <p><strong>2) Source data_major lipid classification.xlsx:</strong> includes original tables regarding metalipidome identification, abundance, precursor mass, retention time, classification as well as ID (name) in the molecular network.</p> <p><strong>3) Source data_sphingolipids information.xlsx:</strong> includes information about sphingolipids identification, precusor mass, retention time, peak intensity, elemental composition and etc.</p> <p><strong>4) Black_Sea_2013.code.tar.gz:</strong> contains the directory structure and code used for the metagenomics part of this project. Each directory contains a 'commands.sh', which contains the code to generate the content in that directory. Other shell and python scripts are always run from within 'commands.sh', with the exception of the files within the 'figures' directory which contains Jupyter labs and a python script that were run individually.</p> <p><strong>5) MAGs.tar.gz: </strong>all the MAGs generated by DAS Tool including CheckM and GTDB-Tk analyses (inside the 'binners' directory). Final taxonomic annotations of MAGs (see 'MAG2info.txt' file) are based on BAT annotatations with GTDB as a reference database, source data in the directory 'CAT_and_BAT_with_GTDB_refdb'.</p> <p><strong>6) abundance_profile.tar.gz:</strong> taxonomic annotation (based on CAT and BAT) and abundance of all scaffolds in the file 'big_table.txt'. Columns that start with 'mappings' are the read counts mapping to the scaffold in the sample from which it is assembled. Since the scaffolds were assembled per sample, only one of the 15 samples contains read mappings per scaffold. The last 15 columns (that start with 'BlackSea') are the depth per 1e8 mapped reads based on the all versus all mappings and were used for co-abundance analyses with sphingolipids. The file 'taxon2counts.txt' summarizes the taxonomic composition of the water column based on summing of the read mappings of the samples from which scaffolds were assembled (the 'mappings' columns in 'big_table.txt'), i.e. they represent all reads that could be associated with a certain taxon in that sample. The 'taxon2counts.txt' file used in Fig. 3c,d.</p> <p> </p> <p> </p> <p> </p>
Fig. 5 in BiP-overexpressing soybean plants display accelerated hypersensitivity response (HR) affecting the SA-dependent sphingolipid and flavonoid pathways
Fig. 5. Analysis of 2D Scores Plot by Partial Least Squares Discriminant Analysis (PLS-DA) of characterized flavonoids in soybean leaves from the C9 and WT genotypes, infected (I) or noninfected (NI) by P. s. pv. tomato 36 h after inoculation. Points represent replicates analyzed, whereas ellipses indicate 95% confidence region.
Fig. 9 in BiP-overexpressing soybean plants display accelerated hypersensitivity response (HR) affecting the SA-dependent sphingolipid and flavonoid pathways
Fig. 9. Expression analysis of target genes performed by qRT-PCR involved in plant bacterial interactions from BiP-overexpressing (C9) and wild-type (WT) soybean plants infected (I) or noninfected (NI) with P. syringae pv. tomato. The expression levels were obtained using the 2-ΔCT method. Bars (mean SE; ± n = 4) with the same capital letters indicate no significant difference between control and inoculated treatments and those followed by the same lowercase letters indicate no significant difference among genotypes within the same treatment (Student's test: P <0.05).
Fig. 8 in BiP-overexpressing soybean plants display accelerated hypersensitivity response (HR) affecting the SA-dependent sphingolipid and flavonoid pathways
Fig. 8. Relative abundance of the flavonoid derivatives from BiP-overexpressing (C9) and wild-type (WT) soybean plants infected (I) or noninfected (NI) with P. syringae pv. tomato. Bars (mean ± SE; n = 4) with the same capital letters indicate no significant difference between control and inoculated treatments and those followed by the same lowercase letters indicate no significant difference among genotypes within the same treatment (Student's test: P <0.05).
Fig. 4 in BiP-overexpressing soybean plants display accelerated hypersensitivity response (HR) affecting the SA-dependent sphingolipid and flavonoid pathways
Fig. 4. Absolute concentrations of phytohormones (A) and of flavonoid aglycones (B) by UHPLC/MS QqQ. The data represent the mean ± standard error. Bars (mean ± SE; n = 4) with the same capital letters indicate no significant difference between control and inoculated treatments and those followed by the same lowercase letters indicate no significant difference among genotypes within the same treatment (Student's test: P <0.05).
Fig. 3 in BiP-overexpressing soybean plants display accelerated hypersensitivity response (HR) affecting the SA-dependent sphingolipid and flavonoid pathways
Fig. 3. Clustering analysis by Heat Map method of the characterized metabolites by GC/MS in soybean leaves from the WT and C9 genotypes, infected (I) or noninfected (NI) by P. syringae pv. tomato 36 h after inoculation. Differences in the abundance of the metabolites are indicate in response to treatments.
Fig. 6 in BiP-overexpressing soybean plants display accelerated hypersensitivity response (HR) affecting the SA-dependent sphingolipid and flavonoid pathways
Fig. 6. Clustering analysis by Heat Map method of the characterized flavonoids by LC QqQ in soybean leaves from the WT and C9 genotypes, infected (I) or noninfected (NI) by P. syringae pv. tomato 36 h after inoculation. This shows the differences in the abundance of the flavonoids analyzed by LC-MS in response to bacterial infection. Differences in the abundances of the detected flavonoids are indicate in response to treatments. Green color represents a decrease, and red color an increase.
Fig. 7 in BiP-overexpressing soybean plants display accelerated hypersensitivity response (HR) affecting the SA-dependent sphingolipid and flavonoid pathways
Fig. 7. Schematic overview of flavonoid biosynthesis pathway reconstructed using the characterized compounds from soybean leaves. Each colored square box is indicative of the abundance levels of the metabolites involved in the flavonoid biosynthesis and identified for each WT and C9 genotypes, infected (I) or noninfected (NI) by P. syringae pv. tomato. The main flavonoids of pathway are sketched by continuous line while compounds not detected, but intermediate of the pathway, are sketched by dashed line. Compounds marked by blue asterisk were more abundant in the inoculated C9 genotype while compounds marked by red asterisk were more abundant in the inoculated WT genotype.
Fig. 2 in BiP-overexpressing soybean plants display accelerated hypersensitivity response (HR) affecting the SA-dependent sphingolipid and flavonoid pathways
Fig. 2. Analysis of the metabolic profiles of the C9 and WT genotypes in soybean leaves from the WT and C9 genotypes infected (I) or noninfected (NI) by P. syringae pv tomato 36 h after inoculation. In (A) 2D Scores Plot generated by Partial Least Squares Discriminant Analysis (PLS-DA) of all the metabolites. Points represent analyzed replicates, whereas ellipses indicate 95% confidence region. In (B) Major metabolites responsible for discrimination between inoculated and mock inoculated soybean groups identified by VIP score. Green color represents a decrease, and red color an increase.
Fig. 4 in Function of ceramide synthases on growth, ganoderic acid biosynthesis and sphingolipid homeostasis in Ganoderma lucidum
Fig. 4. Ganoderic acid biosynthesis in G. lucidum is influenced by the lag gene. A Systematic content analysis of ganoderic acid in the lag-silenced, WT and SiControl strains. B–D Relative gene expression of hmgr (B), sqs (C) and osc (D) in the WT, SiControl and lag-silenced strains. There are three independent biological replicates in each column. Error bars represent standard deviations, and asterisks show significant differences from control (WT and Sicontrol) strains according to Student's t-test (**P <0.01, n = 3).
Fig. 3 in Function of ceramide synthases on growth, ganoderic acid biosynthesis and sphingolipid homeostasis in Ganoderma lucidum
Fig. 3. Effect of lag gene silencing on G. lucidum growth. Morphology of fungal colonies in lag-silenced, WT and SiControl strains after cultivation in the dark on CYM medium at 28 ̊C for 5 days. Three independent biological replicates in each column. Error bars represent standard deviations, and asterisks show significant differences from control (WT and Sicontrol) strains according to Student's t-test (**P <0.01, n = 3).
Fig. 1 in Function of ceramide synthases on growth, ganoderic acid biosynthesis and sphingolipid homeostasis in Ganoderma lucidum
Fig. 1. Overview of the sphingolipid biosynthetic pathway in yeast and filamentous fungi. The abbreviations used are as follows: Dihydroxy LCB: dihydroxy longchain (sphingoid) base; Trihydroxy LCB: trihydroxy long-chain sphingoid base; Long chain FA-CoA: long chain fatty acyl-coenzyme A; Very long chain FA-CoA: very long chain fatty acyl-coenzyme A; IPC synthase: inositol phosphorylceramide synthase; IPC mannosyl transferase: inositol phosphorylceramide mannosyl transferase; MIPCs: mannose inositol phosphorylceramides; M (IP)2Cs: mannose (inositol phosphoryl)2- ceramides.
Fig. 2. G in Function of ceramide synthases on growth, ganoderic acid biosynthesis and sphingolipid homeostasis in Ganoderma lucidum
Fig. 2. G. lucidum harbours three ceramide synthases. Phylogenetic analysis of ceramide synthases in eukaryotes. The phylogenetic tree was conducted using MEGA 6 and can be roughly divided into three major groups: human, plant and fungi. The evolutionary history was inferred by the neighbour-joining method from 1000 replicates using MEGA 6.
Fig. 5 in Function of ceramide synthases on growth, ganoderic acid biosynthesis and sphingolipid homeostasis in Ganoderma lucidum
Fig. 5. Sphingolipid profiling of the lag-silenced, WT and SiControl strains. The relative amounts of Cer (A), GlcCer (B), IPC (C), MIPC (D) and M (IP)2C (E) in each strain. Comparison of the sphingolipid intensity ratios between the WT and lag-silenced strains. Each sphingolipid (Cer/GlcCer/IPC/ MIPC/M(IP)2C) intensity ratio was calculated as the percentage of the corresponding total sphingolipid detected in the WT or mutant. The sphingolipid species are indicated as the number of carbon atoms: the unsaturated bond number of the fatty acid: hydroxyl number. For example, Cer 32:0:2 = Cer (d18:0/14:0); Cer 36:0:3 = Cer (t18:0/18:0 or d18:0/18:0 (2-OH)); Cer 36:0:4 = Cer(t18:0/18:0 (2-OH)); Cer 38:1:2 = Cer (d18:0/20:1 or d18:1/ 20:0) and Cer 42:2:2 = (d18:0/24:2, d18:2/24:0 or d18:1/24:1). There are three independent biological replicates in each column. Error bars represent standard deviations, and asterisks show significant differences from control (WT and Sicontrol) strains according to Student's t-test (*P <0.05, **P <0.01, n = 3).
Reducing Circulating Sphingolipid Levels to Optimise Cardiometabolic Health - The SphingoFIT Trial
ClinicalTrials.gov study NCT06024291. IPD Sharing: NO. Countries: 1. Publications: 5.
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