Metabolic rewiring underlies macrophage effector functions, but the mechanisms involved remain incompletely defined. Here, using unbiased metabolomics and stable isotope-assisted tracing, we show induction of an inflammatory aspartate-argininosuccinate shunt following LPS stimulation. The shunt, supported by increased ASS1 expression, also leads to increased cytosolic fumarate levels and fumarate-mediated protein succination. Pharmacologic inhibition and genetic ablation of the TCA cycle enzyme FH further elevates intracellular fumarate levels, suppresses mitochondrial respiration, and increases mitochondrial membrane potential. RNA sequencing and proteomic analysis demonstrate profound inflammatory effects resulting from FH inhibition. Of note, acute FH inhibition suppresses IL-10 expression leading to increased TNF-α secretion, an effect recapitulated by fumarate esters. Unexpectedly, FH inhibition, but not fumarate esters, also increases IFN-β production through mechanisms that are driven by mitochondrial RNA (mtRNA) release and activation of the RNA sensors TLR7 and RIG-I/MDA5. This effect is recapitulated endogenously when FH is suppressed following prolonged LPS stimulation. Furthermore, cells from SLE patients also exhibit FH suppression, indicating a potential pathogenic role for this process in human disease. We therefore identify a protective role for FH in maintaining appropriate macrophage cytokine and interferon responses.

Data from this study that is included in this Dryad submission is as follows:

1. RNA sequencing of non-stimulated (with vehicle DMSO) or lipopolysaccaharide-stimulated (4 h) murine bone marrow-derived macrophages (BMDMs) pre-treated with vehicle (DMSO), 20 micromolar fumarate hydratase inhibitor 1 (FHIN1) or 25 micromolar dimethylfumarate (DMF) for 3 h. Three biological replicates per condition.

2. Label-free proteomics of lipopolysaccaharide-stimulated (4 h) murine bone marrow-derived macrophages (BMDMs) pre-treated with vehicle (DMSO), 20 micromolar fumarate hydratase inhibitor 1 (FHIN1) or 25 micromolar dimethylfumarate (DMF) for 3 h. Five biological replicates per condition. 

3. Metabolomics source data used for the study.

RNA sequencing 

RNA extraction from cells was carried out using an Purelink^TM RNA kit (Invitrogen) according to the manufacturer’s instructions. BMDMs were treated as required, and following treatments were instantly lysed in 350 μl RNA lysis buffer. Isolated RNA was quantified using a NanoDrop 2000 spectrophotometer, and RNA concentration was normalised to the lowest concentration across all samples with RNAse-free water. If necessary, samples were DNAse-treated after quantification using DNAse I (Thermo Fisher) according to the manufacturer’s instructions. BMDMs (3 independent mice) were treated as indicated and RNA was extracted as detailed. mRNA was extracted from total RNA using poly-T-oligo-attached magnetic beads. After fragmentation, the first strand cDNA was synthesized using random hexamer primers, followed by the second strand cDNA synthesis. The library was checked with Qubit and real-time PCR for quantification and bioanalyzer for size distribution detection. Quantified libraries were pooled and sequenced on the NovaSeq 6000 S4 (Illumina). Differential expression analysis of two conditions/groups was performed using counted reads and the DESeq2 R package. Pathway enrichment analyses were performed as indicated in quantification and statistical analysis. 

Proteomics

Sample Preparation

BMDMs (from 5 independent mice) were plated onto 10-cm dishes and treated as indicated. At the experimental endpoint, cells were washed with PBS on ice and centrifuged at 1500 rpm for 5 mins at 4°C  and frozen at -80°C. Cell pellets were lysed, reduced and alkylated in 50 µl of 6M Gu-HCl, 200 mM Tris-HCl pH 8.5, 10 mM TCEP, 15 mM Chloroacetamide by probe sonication and heating to 95ºC for 5 min. Protein concentration was measured by a Bradford assay and initially digested with LysC (Wako) with an enzyme-to-substrate ratio of 1/200 for 4 h at 37ºC. Subsequently, the samples were diluted tenfold with water and digested with porcine trypsin (Promega) at 37ºC overnight. Samples were acidified to 1% TFA, cleared by centrifugation (16,000 g at RT) and approximately 20 µg of the sample was desalted using a Stage-tip. Eluted peptides were lyophilized and resuspended in 0.1% TFA/water, and the peptide concentration was measured by A280 on a nanodrop instrument (Thermo). The sample was diluted to 2 µg/ 5 µl for subsequent analysis.

MS Analysis

The tryptic peptides were analysed on a Fusion Lumos mass spectrometer connected to an Ultimate Ultra3000 chromatography system (both Thermo Scientific) incorporating an autosampler. 2 µg of de-salted peptides were loaded onto a 50 cm emitter packed with 1.9 µm ReproSil-Pur 200 C18-AQ (Dr Maisch, Germany) using a RSLC-nano uHPLC systems connected to a Fusion Lumos mass spectrometer (both Thermo, UK). Peptides were separated by a 140 min linear gradient from 5% to 30% acetonitrile, 0.5% acetic acid. The mass spectrometer was operated in DIA mode, acquiring a MS 350-1650 Da at 120k resolution followed by MS/MS on 45 windows with 0.5 Da overlap (200-2000 Da) at 30k with a NCE setting of 27.

Data Analysis

Raw files were analysed and quantified by searching against the Uniprot Mus musculus database using DIA-NN 1.8 (https://github.com/vdemichev/DiaNN). Library-free search was selected, and the precursor ion spectra were generated from the FASTA file using the deep learning option. Default settings were used throughout apart from using “Robust LC (high precision)”. In brief, Carbamidomethylation was specified as fixed modification while acetylation of protein N-termini was specified as variable. Peptide length was set to minimum 7 amino acids, precursor FDR was set to 1%.  Subsequently, missing values were replaced by a normal distribution (1.8 π shifted with a distribution of 0.3 π) in order to allow the following statistical analysis.  Protein-wise linear models combined with empirical Bayes statistics are used for the differential expression analyses. We use the Bioconductor package limma to carry out the analysis using an R-based online tool.

Liquid-Chromatography-Mass Spectrometry (LC-MS)

Steady-State Metabolomics

BMDMs (3 independent mice) were plated at 0.5 x 10⁶ cells/well in 12-well plates in technical triplicate per condition, treated as indicated, snap frozen and stored at -80°C. For metabolomics on cytosolic fraction, BMDMs were plated at 10 x 10⁶ cells/10 cm dish and rapid fractionation was performed as previously reported. Metabolite extraction solution (MES) (methanol/acetonitrile/water, 50:30:20 v/v/v) was added (0.5 mL per 1 x 10⁶ cells) and samples were incubated for 15 min on dry ice. The resulting suspension was transferred to ice-cold microcentrifuge tubes. Samples were agitated for 20 min at 4°C in a thermomixer and then incubated at -20°C for 1 h. Samples were centrifuged at maximum speed for 10 min at 4°C. The supernatant was transferred into a new tube and centrifuged again at maximum speed for 10 min at 4°C. The supernatant was transferred to autosampler vials and stored at -80°C prior to analysis by LC-MS.

 HILIC chromatographic separation of metabolites was achieved using a Millipore Sequant ZIC-pHILIC analytical column (5 µm, 2.1 × 150 mm) equipped with a 2.1 × 20 mm guard column (both 5 mm particle size) with a binary solvent system. Solvent A was 20 mM ammonium carbonate, 0.05% ammonium hydroxide; Solvent B was acetonitrile. The column oven and autosampler tray were held at 40°C and 4°C, respectively. The chromatographic gradient was run at a flow rate of 0.200 mL/min as follows: 0–2 min: 80% B; 2–17 min: linear gradient from 80% B to 20% B; 17–17.1 min: linear gradient from 20% B to 80% B; 17.1-22.5 min: hold at 80% B. Samples were randomized and analysed with LC–MS in a blinded manner and the injection volume was 5 µl. Pooled samples were generated from an equal mixture of all individual samples and analysed interspersed at regular intervals within sample sequence as a quality control. Metabolites were measured with a Thermo Scientific Q Exactive Hybrid Quadrupole-Orbitrap Mass spectrometer (HRMS) coupled to a Dionex Ultimate 3000 UHPLC or with Vanquish Horizon UHPLC coupled to an Orbitrap Exploris 240 mass spectrometer (both Thermo Fisher Scientific) via a heated electrospray ionization source.

For Thermo Scientific Q Exactive Hybrid Quadrupole-Orbitrap Mass spectrometer (HRMS) coupled to a Dionex Ultimate 3000 UHPLC, the mass spectrometer was operated in full-scan, polarity-switching mode, with the spray voltage set to +4.5 kV/-3.5 kV, the heated capillary held at 280°C and the heated electrospray ionization probe held at 320°C. The sheath gas flow was set to 40 units, the auxiliary gas flow was set to 15 units, and the sweep gas flow was set to 0 unit. HRMS data acquisition was performed in a range of m/z = 70–900, with the resolution set at 70,000, the AGC target at 1 × 106, and the maximum injection time (Max IT) at 120 ms. Metabolite identities were confirmed using two parameters: (1) precursor ion m/z was matched within 5 ppm of theoretical mass predicted by the chemical formula; (2) the retention time of metabolites was within 5% of the retention time of a purified standard run with the same chromatographic method. Chromatogram review and peak area integration were performed using the Thermo Fisher software XCalibur Qual Browser, XCalibur Quan Browser software and Tracefinder 5.0 and the peak area for each detected metabolite was normalized against the total ion count (TIC) of that sample to correct any variations introduced from sample handling through instrument analysis. Absolute quantification of 2SC was performed by interpolation of the corresponding standard curve obtained from serial dilutions of commercially available standards (Sigma Aldrich) running with the same batch of samples.

For the Orbitrap Exploris 240 mass spectrometer, MS1 scans, mass range was set to m/z=70–900, AGC target set to standard and maximum injection time (IT) set to auto. Data acquisition for experimental samples used full scan mode with polarity switching at an Orbitrap resolution of 120000. Data acquisition for untargeted metabolite identification was performed using the AcquireX Deep Scan workflow, an iterative data-dependent acquisition (DDA) strategy using multiple injections of the pooled sample. In brief, sample was first injected in full scan-only mode in single polarity to create an automated inclusion list. MS2 acquisition was then carried out in triplicate, where ions on the inclusion list were prioritized for fragmentation in each run, after which both the exclusion and inclusion lists were updated in a manner where fragmented ions from the inclusion list were moved to exclusion list for the next run. DDA full scan-ddMS2 method for AcquireX workflow used the following parameters: full scan resolution was set to 60000, fragmentation resolution to 30000, fragmentation intensity threshold to 5.0e3. Dynamic exclusion was enabled after 1 time and exclusion duration was 10s. Mass tolerance was set to 5ppm. Isolation window was set to 1.2 m/z. Normalized HCD collision energies were set to stepped mode with values at 30, 50 and 150. Fragmentation scan range was set to auto, AGC target at standard and max IT at auto. Xcalibur AcquireX method modification was on. Mild trapping was enabled. 

Metabolite identification was performed in the Compound Discoverer software (v 3.2, Thermo Fisher Scientific). Metabolites were annotated at the MS2 level using both an in-house mzVault spectral database curated from 1051 authentic compound standards and the online spectral library mzCloud. The precursor mass tolerance was set to 5 ppm and fragment mass tolerance was set to 10 ppm. Only metabolites with mzVault or mzCloud best match score above 50% and 75%, respectively, and RT tolerance within 0.5 min to that of a purified standard run with the same chromatographic method were exported to generate a list including compound names, molecular formula and RT. The curated list was then used for further processing in the Tracefinder software (v 5.0, Thermo Fisher Scientific), where extracted ion chromatographs for all compounds were examined and manually integrated if necessary. False positive, noise or chromatographically unresolved compounds were removed. The peak area for each detected metabolite was then normalized against the total ion count (TIC) of that sample to correct any variations introduced from sample handling through instrument analysis. The normalized areas were used as variables for further statistical data analysis. Statistical analysis was performed using MetaboAnalyst 5.0.

Stable isotope-assisted tracing

BMDMs (3 independent mice) were plated at 0.5 x 10⁶ cells/well in 12-well plates in technical triplicate per condition, treated as indicated in glutamine-free DMEM supplemented with U-¹³C-glutamine or ¹⁵N₂-glutamine, respectively. For ¹³C- and ¹⁵N-tracing analysis, the theoretical masses of ¹³C and ¹⁵N isotopes were calculated and added to a library of predicted isotopes in Tracefinder 5.0. These masses were then searched with a 5-ppm tolerance and integrated only if the peak apex showed less than 1% deviation in retention time from the [U-¹²C or ¹⁴N] monoisotopic mass in the same chromatogram. The raw data obtained for each isotopologue were corrected for natural isotope abundances using the AccuCor algorithm (https://github.com/lparsons/accucor) before further statistical analysis.

1. RNA sequencing of lipopolysaccaharide-stimulated (4 h) murine bone marrow-derived macrophages (BMDMs) pre-treated with vehicle (DMSO), 20 micromolar fumarate hydratase inhibitor 1 (FHIN1) or 25 micromolar dimethylfumarate (DMF). Three biological replicates per condition.

1_Fpkm_genename.txt

2. Label-free proteomics of lipopolysaccaharide-stimulated (4 h) murine bone marrow-derived macrophages (BMDMs) pre-treated with vehicle (DMSO), 20 micromolar fumarate hydratase inhibitor 1 (FHIN1) or 25 micromolar dimethylfumarate (DMF) for 3 h. Five biological replicates per condition. 

2_diann.pg_matrix.tsv.txt

3. Metabolomics source data - 11 different metabolomics datasets containing the total ion count (TIC)-normalised signal intensity or fraction labelling for the indicated metabolites. All technical and biological replicates are indicated in the file. All experiment conditions are defined in the manuscript. 

3_Metabolomics_data.xlsx

README file describes the 3 types of data in the dataset mentioned above:  1_Fpkm_genename.txt; 2_diann.pg_matrix.tsv.txt; 3_Metabolomics_data.xlsx