Glucose selectively drives a rapid oxidative burst and immunometabolic reprogramming in human neutrophils during Mycobacterium tuberculosis infection

Description

Abstract

Neutrophil functions have been linked to tuberculosis (TB)-associated tissue damage; however, the mechanisms driving immunopathology in the human TB lung remain poorly understood, due partly to the scarcity of human tissue for study. Here, we examine the metabolic and bioenergetic reprogramming of human neutrophils in response to Mycobacterium tuberculosis (Mtb) infection. In human necrotic TB granulomas, levels of NETosis-associated proteins are increased and co-localize with GLUT3, linking nutrient uptake to tissue damage. In vitro, Mtb elicits an immediate, contact-dependent oxidative burst in human neutrophils, and the magnitude of this response is carbon source-dependent. Glucose enables the most robust responses, indicating that glucose metabolism is a key driver of neutrophil-mediated inflammatory damage during TB. Mtb-induced responses are distinct from those induced by PMA, non-tuberculous mycobacteria, or other pathogenic intracellular bacteria, and are mediated through multiple neutrophil surface receptors. Notably, our data show that while the oxidative burst is carbon source-dependent, cytokine production is not. Further, Mtb infection reprograms neutrophil metabolism from glycolysis to the pentose phosphate pathway (PPP), generating NADPH required for the oxidative burst. Inhibiting G6PD, NADPH oxidase, or PAD4 significantly reduces this response, highlighting the PPP as a promising host target for mitigating TB immunopathology.

Methods

Laser-capture microdissection and protein extraction

FFPE human TB lung tissues were cut into 30 µm-thick sections and mounted on membrane-coated slides. Regions of interest (ROI) were excised and collected by laser capture microdissection (LCM) using a Nikon Ti Eclipse microscope equipped with the MMI CellCut system, controlled through MMI software, and fitted with a Nikon Digital Sight DS-U2 camera for imaging. Each ROI was transferred to a well of a protein LoBind 96-well plate (Eppendorf, cat# 0030129504) containing 50 µL of lysis buffer consisting of 50 mM triethylammonium bicarbonate (TEAB; Thermo Fisher, cat# 90114) and 5% SDS (Thermo Scientific, cat# 15553027). Samples were sonicated using a BeatBox™ (PreOmics GmbH) at high power for 10 minutes, briefly centrifuged, and heated at 90 °C for 1 hour for protein denaturation. The plate was sonicated again for an additional 10-minute high-power cycle, followed by centrifugation at 3,000 rpm. Protein concentration was determined using a Pierce™ BCA Protein Assay kit (Thermo Scientific, 23225).

Sample preparation for LC–MS/MS

Proteins were reduced by adding 0.5 µL of 500 mM Tris (2-carboxyethyl) phosphine hydrochloride (TCEP; Sigma-Aldrich, C4706) to a final concentration of 5 mM and incubating samples at 56 °C for 15 minutes. Alkylation was performed by adding 2 µL of 500 mM chloroacetamide (CAA; Millipore) to a final concentration of 20 mM, followed by incubation for 30 minutes in the dark. Samples were acidified by adding 5 µL of 27.5% aqueous phosphoric acid to a final concentration of 1.6%. Protein digestion and cleanup were performed using the S-Trap™ 96-well MS Sample Prep Kit (Protifi, NC1508276) according to the manufacturer’s protocol. Briefly, 350 µL of S-Trap binding buffer (90% methanol, 100 mM TEAB, pH 7.1) was added to the acidified lysate, mixed thoroughly, and transferred to the S-Trap plate positioned atop a 96-well receiver plate, followed by centrifugation at 1,500 g for 2 minutes. As recommended for FFPE tissues, a chloroform wash was performed using 150 µL of 50% methanol/50% chloroform, followed by centrifugation at 1,500 g for 2 minutes. Captured proteins were washed three times with 200 µL of S-Trap binding buffer. For on-column digestion, the S-Trap plate was transferred to a clean receiver plate, and 125 µL of digestion buffer containing TPCK-treated trypsin (Thermo Scientific, 20233) at a 1:10 enzyme-to-protein ratio was added. Samples were incubated overnight at 37 °C. Peptides were sequentially eluted by centrifugation following the addition of 80 µL of digestion buffer, 80 µL of 0.2% aqueous formic acid, and 80 µL of 50% aqueous acetonitrile containing 0.2% formic acid, resulting in a final acetonitrile concentration of approximately 10% (v/v). Eluted peptides were dried using a vacuum centrifuge. Dried peptides were resuspended in 0.1% aqueous formic acid, and peptide concentration was determined using a Pierce™ Quantitative Fluorometric Peptide Assay kit (Thermo Scientific, 23290). Peptides were further desalted using Pierce™ C18 Spin Columns (Thermo Scientific, 89870) according to the manufacturer’s instructions. Columns were activated twice with 200 µL of 50% methanol and equilibrated twice with 200 µL of 0.5% trifluoroacetic acid (TFA) in 5% acetonitrile (ACN). Samples were loaded onto the columns, with the flow-through reapplied once to maximize binding, washed twice with 0.5% TFA in 5% ACN, and eluted twice with 20 µL of 70% ACN. Eluted peptides were dried using a vacuum evaporator, reconstituted in 30 µL of 2% ACN containing 0.2% formic acid, and diluted to a final concentration of 100 ng/µL for LC–MS/MS analysis.

Liquid chromatography-tandem mass spectrometry

For proteomic analysis, 200 ng of peptides per sample were separated on an Aurora Ultimate UHPLC column (25 cm × 75 µm, 1.7 µm C18; IonOpticks) maintained at 50 °C. Samples were loaded directly onto the analytical column without a trapping column to maximize sensitivity. Peptide separation was performed on a Vanquish Neo UHPLC system (Thermo Fisher) coupled to an Orbitrap Exploris 480 mass spectrometer (Thermo Fisher) equipped with a Nanospray Flex ion source, using a 123-minute linear gradient at a flow rate of 0.22 µL/min. Solvent A consisted of 0.2% formic acid in 2% acetonitrile, and solvent B consisted of 0.2% formic acid in 80% acetonitrile. The gradient was initiated at 3% solvent B and held for 7 minutes, followed by a linear increase to 25% solvent B over 83 minutes. Solvent B was then increased to 41% over the next 32 minutes, followed by a rapid ramp to 95% solvent B over 1 minute for column washing. The column was subsequently re-equilibrated to initial conditions prior to the next injection. Data-dependent acquisition was performed in positive ion mode with a spray voltage of 1,600 V and an ion transfer tube temperature of 300 °C. Full MS scans were acquired at a resolution of 60,000 over an m/z range of 375–1,200, with a 3-second cycle time, an AGC target of 300%, and automatic injection time. Precursor ions with charge states of +2 to +6 and intensities above 5 × 10³ were selected for fragmentation, with dynamic exclusion applied after a single acquisition (45 seconds, 10 ppm). MS/MS spectra were acquired in the Orbitrap at 30,000 resolution using a 1.6 m/z isolation window, higher-energy collisional dissociation (HCD) with a normalized collision energy of 28%, an AGC target of 200%, and automatic injection time. Data were acquired using Xcalibur software (Thermo Scientific).

LC-MS/MS data analysis and statistics

LC–MS/MS data were acquired in data-dependent acquisition (DDA) mode and processed using the FragPipe computational pipeline. Raw mass spectrometry files were analyzed with FragPipe v21.192,93, incorporating MSFragger v3.894 for database searching and Philosopher v5.1.195 for downstream processing. Spectra were searched against the UniProt human reference proteome (20,631 entries) along with Mycobacterium tuberculosis proteomes (strains ATCC 25177, ATCC 25618, ATCC 35801, ATCC 35801, CDC 1551, and K85 totaling 25407 protein sequence entries) with trypsin as the proteolytic enzyme, allowing up to two missed cleavages. Carbamidomethylation of cysteine residues was specified as a fixed modification, while oxidation of methionine and acetylation of protein N-termini were considered variable modifications. The precursor mass tolerance was set to ±20 ppm for both initial and main searches, and the fragment mass tolerance was set to 20 ppm, with peptide lengths restricted to 7–50 amino acids. False discovery rates for peptide-spectrum matches, peptides, and proteins were controlled at 5% using the target–decoy strategy implemented in Philosopher.

Label-free protein quantification was performed using IonQuant v1.10.1196 within FragPipe. Quantification was based on precursor-level extracted ion chromatograms with a mass tolerance of 10 ppm, retention time tolerance of 0.4 min, and isotope mass tolerance of 20 ppm. Protein abundances were estimated using the top three peptide intensities per protein, and global intensity-based normalization was applied to reduce systematic variation across samples. Processed quantitative protein tables were subsequently imported into the tidyproteomics R package (v1.8.8) for statistical and exploratory analysis. Data were filtered to retain proteins identified by at least two unique peptides and quantified in the majority of samples per condition. Intensities were log₂-transformed and normalized using an SVM regression to correct for remaining technical variation. Principal component analysis and hierarchical clustering were used to assess sample-level variation and reproducibility. Differential protein abundance was evaluated Student’s t-test, and p-values were adjusted for multiple testing using the Benjamini–Hochberg procedure to control the false discovery rate.