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ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS6259/m_MTBLS6259_NMR___metabolite_profiling_v2_maf.tsvftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS6259/s_MTBLS6259.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS6259/a_MTBLS6259_NMR___metabolite_profiling.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS6259/i_Investigation.txtprimaryOK200ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS6259Following established guidelines and practice recommendations, manual annotation of neutrophil metabolites was performed using in house tools (tameNMR; https://github.com/PGB-LIV/tameNMR) and profiling software NMR Procflow v1.4 (https://nmrprocflow.org/) [DOI: 10.1007/s11306-017-1178-y], Metabolite peaks were then compared against several open-access metabolite databases, including HMDB (http://www.hmdb.ca/) [DOI: 10.1093/nar/gkab1062], Biological Magnetic Resonance dataBank (BMRB; bmrb.io) [DOI:10.1093/nar/gkac1050], Chenomx library (Chenomx NMR Suite v8.2), in-house reference library, and prior publications on intracellular neutrophil metabolomics. Then, annotation accuracy and consistency were ensured through guidance from an expert spectroscopist. Moreover, biological context was considered crucial when assigning identities to previously unidentified peaks. Spectral binning process defining left and right boundaries for each peak multiplet was employed to select and integrate peaks and peak multiplets accommodating minimal chemical misalignments of the computed area under the peak in each aligned spectra. Where possible peaks were annotated to known metabolites or marked as unknown. A representative peak bin for each annotated metabolite was selected via correlation reliability score ‘CRS’ filtering (Grosman 2019) through comparison of Pearson correlation matrices using R v4.2. This approach yielded a single representative bucket for each annotated metabolite reducing the number of variables but also removing unknown NMR peaks. As such analysis of the fully integrated spectra (containing information on known and unknown metabolite peaks) as well as the filtered spectra containing only representative peaks from all annotated metabolites present were both compared in subsequent spectral analysis. MetaboLightsPublicNuclear Magnetic Resonance (NMR) -Multiomics analysis of neutrophils in SLE: insights from adult and paediatric disease. 10.1093/cei/uxaf077. PMID:41363550NMR were acquired on a 700 MHz Avance IIIHD Bruker NMR spectrometer equipped with TCI cryoprobe and chilled SampleJet autosampler. Spectrometer quality assurance was completed daily via temperature calibration to 25 °C (with margin error of 0.1 °C) by a methanol thermometer (cat number Z10627 99.8% Methanol-d4, 5 mm, Bruker, UK) [Findeisen [https://doi.org/10.1002/mrc.1941] and 3D shimming on Bruker standard 2 mM sucrose (cat number Z10902 2; mM Sucrose 0.5 mM DSS 2 mM NaN3 in H2O/D2O 90/10, 5 mm Bruker, UK) to ensure linewidth half height of DSS reference peak is within acceptance criteria (<1 Hz).Marie Margaret PhelanUniversity of LiverpoolneutrophilNMR spectroscopy assayNeutrophils were pelleted by centrifugation at 1,000 g at 25 °C for 2 min and supernatant was aspirated. Cell pellets were heated at 100 °C for 1 min prior to being snap-frozen using liquid nitrogen and stored at -80 °C for further analysis.Neutrophil metabolites were extracted using an established method [Chokesuwattanaskul 2018]. A mixture of 50:50 v/v ice cold HPLC grade acetonitrile:water (ddH2O) was added to each sample (500 μL per cell pellet), followed by a 10 min incubation on ice. Samples were sonicated three times for 30 sec at 23 kHz and 10 μm amplitude using an exponential probe with 30 sec rest in between of sonication in an ice water bath. Sonicated samples were centrifuged at 12,000 g for 5 mins at 4 °C, followed by transfer of the supernatants to cryovials and flash frozen in liquid nitrogen prior to lyophilisation. All lyophilised samples were stored at -80 °C prior to analysis in spectrometer. Homo sapienshttps://www.ebi.ac.uk/metabolights/MTBLS6259Helen Wright. University of Liverpool. Institute of Life Course and Medical Sciences, University of Liverpool, UK. hlwright@liverpool.ac.uk.Marie Phelan. University of Liverpool. HF-NMR Facility, Liv-SRF, Institute of Systems Molecular and Integrative Biology, Crown Street, Liverpool, L697ZB, UK. mphelan@liv.ac.uk.Nattiya Hirankarn.Grace Filbertine. University of Liverpool.Lucy Gill.Isobel Kynoch.Zoe McLaren.Tawatchai Deekajorndech.Rudi Grosman.Genna A Abdullah.Direkrit Chiewchengchol.All acquired spectra were automatically phased referenced to 3-(trimethylsilyl) propionic-2,2,3,3-d4 acid sodium salt (deuterated trimethylsilyl propionate; TSP-d4) at 0 ppm, phasing and baseline corrected using automated macro (apk0.noe) provided within TopSpin v4.4.1 (Bruker Corporation) followed by compliance check to minimum quality criteria outlined by the Metabolomics Standards Initiative (MSI) to suppress the macromolecule signals, selectively accentuate small molecule metabolite signals, ensure consistent linewidths, baseline corrections, and water suppression. All spectra were thoroughly evaluated to ensure they met the current best practice of Metabolomics Society [DOI: 10.1038/nprot.2007.376 DOI:10.1007/s11306-007-0082-2 DOI:10.1007/978-1-4939-9690-2_25 DOI:10.1016/j.aca.2017.05.011 ], ensuring a robust consistency in phasing, baseline correction, peak alignment, chemical shift reference to TSP-d4 at 0 ppm, line width, and water suppression conditions for neutrophil intracellular metabolomic reporting. Only technical replicates with the lowest line width at half height from each sample which passed quality control process including the flat baseline, consistent line widths, and water suppression consistency were selected. Any spectra that did not meet the requirements of recommended reporting standards were subsequently removed from further processing [DOI:10.1007/s11306-007-0082-2 ].Following spectral acquisition and preliminary quality control in TopSpin v4.4.1, the highest-quality spectra from each sample underwent baseline correction using MestreNova (MNova) v15.1 (Mestrelab Research S.L., Santiago de Compostela, Spain) accessed through nmrbox (nmrbox.nmrhub.org) [DOI:10.1016/j.bpj.2017.03.011] to address baseline distortions around residual water signal region (4.40-5.00 1H chemical shift range ppm) as observed in spectrum with weak signal due to smaller sample quantity [DOI:10.1007/978-1-4939-9690-2_25, DOI: 10.1038/s41596-020-0343-3]. MNova v15.1 provides a semi-automated baseline-correcting algorithms [DOI:10.1016/j.pnmrs.2011.02.001 DOI:10.1016/j.jmr.2018.02.012 DOI:10.1021/ac303233c] using Bernstein Polynomials approach as demonstrated in previous metabonomic studies [DOI:10.1016/j.jevs.2011.03.134 DOI:10.1007/s43450-024-00603-x]. Employing this method polynomial shapes were manually adjusted using between 10-20 inflection points defined along the baseline per spectrum to fit the undulations and distortions in the baseline prior to auto adjustment.Systemic lupus erythematosusNumber of cellsAgeNumber of scansmphelan@liv.ac.ukThis study was subject to ethical approval in Thailand and the UK. Recruitment of people with SLE and healthy controls in Thailand was approved by the Ethics Committee Institutional Review Board in the Faculty of Medicine, Chulalongkorn University, Thailand (IRB No.0551/65). Transfer of human tissue samples to from the UK to Thailand was approved by the University of Liverpool Central University Research Ethics Committee (No. 11635). Recruitment of people with SLE in the UK was approved by the NRES Committee North West (Greater Manchester West, UK, Ref: 11/NW/0206) under the Inflammatory Signalling Pathways study, and by NRES West Midlands Solihull Research Ethics Committee (Ref 23/WM/0170) Pathways in Inflammation study. Recruitment of healthy controls in the UK was approved by the University of Liverpool Central University Research Ethics Committee (No. 10956). All study participants gave written informed consent in accordance with the Declaration of Helsinki. Clinical demographics of adult (aSLE) and juvenile (jSLE) SLE patients are sown in Supplementary Table 1 and details of prescription medications for each group of participants are shown in Supplementary Table 2. Human peripheral blood samples were obtained by venipuncture into lithium-heparin vacutainers. Neutrophils were isolated using Ficoll-Paque as previously described [MTBLS6220]. Contaminating erythrocytes were removed with hypotonic ammonium chloride lysis buffer and cells resuspended in RPMI-1640 media. Neutrophil purity was confirmed with Wright Giemsa staining.Spectra were acquired using standard (vendor supplied) 1D 1H Carr-Purcell-Meiboom-Gill (CPMG; cpmgpr1d) spectra with 256 or 2048 transients as specified, a 15-ppm spectral width, 32K points, 9.6 ms echo time, 3.1 s acquisition time and 4 s interscan delay.Metabolomicsuntargeted analysisuntargeted metabolitesHomo sapiensNeutrophilBruker AVANCE III HD 700 MHz spectrometerPediatricexperimental sampleuntargeted analysisuntargeted metabolitesHomo sapiensNeutrophilBruker AVANCE III HD 700 MHz spectrometerPediatricexperimental sampleLyophilised metabolite pellets were resuspended in 200 μL of 100 mM deuterated sodium phosphate buffer pH 7.4 with 100 μM of TSP-d4 and 0.05% NaN3. All samples were vortexed for 20 sec and centrifuged at 12,000 g for 1 min at 20 °C, prior transferring into 3 mm (outer diameter) NMR tubes and loaded into a sampleJet rack.Benzoic acidTaurineIndolelactic acidL-PhenylalanineDiethylhexyl adipateADPL-IsoleucineL-HomoserineL-ThreonineFormic acidL-AlanineDimethylamineSaccharopineAcetoacetic acidL-HistidineL-LeucineEthanolPropionic acidGlycerol2-Hydroxy-3-methylbutyric acidPhosphorylcholine2-Hydroxy-3-methylpentanoic acidL-ProlineIsopropyl alcoholL-MethionineAcetaminophenL-Aspartic acidL-LysineAcetic acidL-ArgininePropylene glycolD-Glucose2-HydroxybutyricacidProline2-Hydroxyvaleric acidL-AsparagineunknownMyoinositolL-CysteinePyroglutamic acidNADPAdenosine triphosphateImidazolepropionic acidGlutathioneL-Glutamic acidmannoseAdenosine monophosphateAcetamideNADGuanosine triphosphateL-GlutamineCholineSarcosineL-SerineLactic acidL-Valine3-Hydroxybutyric acidAcetoneNeutrophils contribute to systemic lupus erythematosus (SLE) pathogenesis through reactive oxygen species and neutrophil extracellular trap (NET) production, and increased apoptotic debris which causes autoantibody production and immune complex formation. These processes drive inflammation and tissue damage. The aim of this study was to perform integrated transcriptomic and metabolomic analyses comparing paediatric and adult SLE neutrophils. Adult (aSLE) and paediatric (jSLE) patient and healthy adult (HA) and juvenile (HJ) control neutrophils were subjected to RNAseq and 1H-NMR metabolomics. Univariate, multivariate and multiomics enrichment analyses were conducted in R and with ingenuity pathway analysis (IPA). Transcriptomic analysis revealed distinct gene expression profiles. Adult and juvenile SLE neutrophils were enriched for genes regulating interferon (IFN)-α/β signalling, neutrophil degranulation and NET signalling pathways (IPA, adj.P-value <0.01). Gene Ontology analysis revealed enrichment in cell cycle and interferon signalling in aSLE and angiogenesis and tissue-specific development in jSLE. Metabolomic profiling identified distinct metabolic alterations in aSLE, with a greater complexity of metabolic changes in jSLE. Multivariate PLS-DA demonstrated group discrimination, particularly in aSLE (balanced accuracy 80%, sensitivity 80%). Variable importance in the projection >1 metabolites were enriched in taurine/hypotaurine and amino acid metabolism in aSLE. Integrating transcriptomic and metabolomic data strengthened IFN-α/β signalling, neutrophil degranulation and NET signalling (adj. P < 0.001). Additional metabolic pathways uniquely down-regulated in aSLE included glutamate and glutamine metabolism, nucleotide biosynthesis and tryptophan catabolism (adj.P< 0.01). In summary, neutrophils from SLE patients, especially in jSLE, displayed complex transcriptomic and metabolic profiles, with aberrant IFN responses and neutrophil activation.Multiomics analysis of neutrophils in SLE: insights from adult and paediatric disease.Filbertine Grace G, Kynoch Isobel I, Abdullah Genna A GA, Gill Lucy L, Grosman Rudi R, Phelan Marie M MM, McLaren Zoe Z, Deekajorndech Tawatchai T, Chiewchengchol Direkrit D, Hirankarn Nattiya N, Wright Helen L HLfalseMultiomics analysis of neutrophils in SLE: insights from adult and pediatric diseaseNeutrophils contribute to systemic lupus erythematosus (SLE) pathogenesis through ROS and NET production, and increased apoptotic debris which causes autoantibody production and immune complex formation. These processes drive inflammation and tissue damage. The aim of this study was to perform integrated transcriptomic and metabolomic analyses comparing paediatric and adult SLE neutrophils. Adult (aSLE) and pediatric (jSLE) patient and healthy control neutrophils were subjected to RNAseq and 1H-NMR metabolomics. Univariate, multivariate and multiomics enrichment analyses were conducted in R and with Ingenuity Pathway Analysis (IPA). Transcriptomic analysis revealed distinct gene expression profiles. aSLE was enriched in LTF and ARHGEF12, while jSLE showed elevated SIGLEC1, OTOF, and IRF7. MMP8, OLFM4, and IFI27 were upregulated across both SLE groups (adj.p-value<0.05). Gene Ontology analysis revealed enrichment in cell cycle and interferon signalling in aSLE, and angiogenesis and tissue-specific development in jSLE. Adult and juvenile SLE neutrophils were enriched for IFN-α/β signalling, neutrophil degranulation and NET signalling pathways (IPA, adj.p-value<0.01). Metabolomic profiling identified distinct metabolic alterations in aSLE, with a greater complexity of metabolic changes in jSLE. Multivariate PLS-DA improved group discrimination, particularly in aSLE (balanced accuracy 80%, sensitivity 80%). VIP>1 metabolites were enriched in taurine/hypotaurine and amino acid metabolism. Integrating transcriptomic and metabolomic data strengthened IFN-α/β signalling, neutrophil degranulation and NET signalling (adj. p <0.001). Additional metabolic pathways uniquely enriched in aSLE included glutamate and glutamine metabolism and nucleotide biosynthesis (adj.p<0.01). In summary, neutrophils from SLE patients, especially in jSLE, displayed complex transcriptomic and metabolic profiles, with aberrant IFN responses and neutrophil activation. 2026-04-222025-08-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