Neutrophils play a key role in the pathophysiology of rheumatoid arthritis (RA) where release of ROS and proteases directly causes damage to joints and tissues. Neutrophil function can be modulated by Janus Kinase (JAK) inhibitor drugs, including tofacitinib and baricitinib, which are clinically effective treatments for RA. However, clinical trials have reported increased infection rates and transient neutropenia during therapy. The subtle differences in the mode of action, efficacy and safety of JAK inhibitors have been the primary research topic of many clinical trials and systematic reviews, to provide a more precise and targeted treatment to patients. The aim of this study was to determine both the differences in the metabolome of neutrophils from healthy controls and people with RA, and the effect of different JAK inhibitors on the metabolome of healthy and RA neutrophils. Isolated neutrophils from healthy controls (HC) (n = 6) and people with RA (n = 7) were incubated with baricitinib, tofacitinib or a pan-JAK inhibitor (all 200 ng/mL) for 2 h. Metabolites were extracted, and ¹H nuclear magnetic resonance (NMR) was applied to study the metabolic changes. Multivariate analyses and machine learning models showed a divergent metabolic pattern in RA neutrophils compared to HC at 0 h (F1 score = 86.7%) driven by energy metabolites (ATP, ADP, GTP and glucose). No difference was observed in the neutrophil metabolome when treated with JAK inhibitors. However, JAK inhibitors significantly inhibited ROS production and baricitinib decreased NET production (p < 0.05). Bacterial killing was not impaired by JAK inhibitors, indicating that the effect of JAK inhibitors on neutrophils can inhibit joint damage in RA without impairing host defence. This study highlights altered energy metabolism in RA neutrophils which may explain the cause of their dysregulation in inflammatory disease.

The nuclear deubiquitylase BRCA1-associated protein 1 (BAP1) is frequently inactivated in malignant pleural mesothelioma (MPM) and germline BAP1 mutation predisposes to cancers including MPM. To explore the influence on cell physiology and drug sensitivity, we sequentially edited a predisposition mutation (w-) and a promoter trap (KO) into human mesothelial cells. BAP1w-/KO MeT5A cells express less BAP1 protein and phenocopy key aspects of BAP1 loss in MPM. Stable isotope labeling with amino acids in cell culture–mass spectrometry revealed evidence of metabolic adaptation, with concomitant alteration of cellular metabolites. In MeT5A, BAP1 deficiency reduces glycolytic enzyme levels but increases enzymes involved in the tricarboxylic acid cycle and anaplerotic pathways. Notably both argininosuccinate synthase 1 (ASS1), essential for cellular synthesis of arginine, and its substrate aspartate, are elevated in BAP1w-/KO MeT5A cells. Likewise, ASS1 expression is higher in BAP1-altered MPM cell lines, and inversely correlates with BAP1 in The Cancer Genome Atlas MESO dataset. Elevated ASS1 is also evident by IHC staining in epithelioid MPM lacking nuclear BAP1 expression, with improved survival among patients with BAP1-negative/ASS1-expressing tumors. Alterations in arginine metabolism may sensitize cells to metabolic drugs and we find that BAP1-negative/ASS1-expressing MPM cell lines are more sensitive to ASS1 inhibition, although not to inhibition of purine synthesis by mizoribine. Importantly, BAP1w-/KO MeT5A become desensitized to arginine deprivation by pegylated arginine deiminase (ADI-PEG20), phenocopying BAP1-negative/ASS1-expressing MPM cell lines. Implications: Our data reveal an interrelationship between BAP1 and arginine metabolism, providing a potential means of identifying patients with epithelioid MPM likely to benefit from ADI-PEG20.

Oenocytes are an insect cell type having diverse physiological functions ranging from cuticular hydrocarbon (CHC) production to insecticide detoxification that may impact their capacity to transmit pathogens. To develop functional genetic tools to study Anopheles gambiae oenocytes, we have trapped an oenocyte enhancer to create a transgenic mosquito Gal4 driver line that mediates tissue-specific expression. After crossing with UAS-reporter lines, An. gambiae oenocytes are fluorescently tagged through all life stages and demonstrate clearly the two characteristic oenocyte cell-types arising during development. The driver was then used to characterise the function of two oenocyte expressed An. gambiae cyp4g genes through tissue-specific expression of UAS-RNAi constructs. Silencing of cyp4g16 or cyp4g17 caused lethality in pupae of differing timing and penetrance. Surviving cyp4g16 knockdown adults showed increased sensitivity to desiccation. Total cuticular hydrocarbon levels were reduced by approximately 80% or 50% in both single gene knockdowns when assayed in young pupa or surviving adults respectively, indicating both genes are required for complete CHC production in An. gambiae and demonstrate synergistic activity in young pupae. Comparative CHC profiles were very similar for the two knockdowns, indicating overlapping substrate specificities of the two enzymes. Differences were observed for example with reduced abundance of shorter chain CHCs in CYP4G16 knockdowns, and reduction in longer, branched chained CHCs in CYP4G17 knockdown adults. This is the first time that two cyp4g s have both been shown to be required for complete CHC production in an insect. Moreover, the generation of tagged cells and identification of an enhancer region can expediate oenocyte specific transcriptomics. The novel driver line can also be used to explore oenocyte roles in pheromone production, mating behaviour and longevity in the malaria mosquito.

Background/Objectives: Untargeted ¹H NMR metabolomics is a robust and reproducible approach used to study the metabolism in biological samples, providing unprecedented insight into altered cellular processes associated with human diseases. Metabolomics is increasingly used alongside other techniques to detect an instantaneous altered cellular function, for example, the role of neutrophils in the inflammatory response. However, in some clinical settings, blood samples may be limited, restricting the amount of cellular material available for a metabolomic analysis. In this study, we wanted to establish an optimal 1D ¹H NMR metabolomic pipeline for use with human neutrophil samples with low amounts of input material. Methods: We compared the effect of different neutrophil isolation protocols on metabolite profiles. We also compared the effect of the absolute cell counts (100,000 to 5,000,000) on the identities of metabolites that were detected with an increasing number of scans (NS) from 256 to 2048. Results/Conclusions: The variance in the neutrophil profile was equivalent between the isolation methods, and the choice of isolation method did not significantly alter the metabolite profile. The minimum number of cells required for the detection of neutrophil metabolites was 400,000 at an NS of 256 for the spectra acquired with a cryoprobe (700 MHz). Increasing the NS to 2048 increased metabolite detection at the very lowest cell counts (<400,000 neutrophils); however, this was associated with a significant increase in the analysis time, which would be rate-limiting for large studies. The application of a correlation-reliability-score-filtering method to the spectral bins preserved the essential discriminatory features of the PLS-DA models whilst improving the dataset robustness and analytical precision.

Fig. S1. SNP6.0 analysis of parental MeT5A-BAP1+/+ cells.Fig. S2. Expression of catalytically active BAP1 in parental MeT5A.Fig. S3. Characterisation of BAP1 expression in gene-edited MeT5A.Fig. S4. Whole genome sequencing of gene-edited MeT5A.Fig. S5. Proliferative profiles for gene-edited MeT5A.Fig. S6. Enriched GO terms in gene-edited MeT5A SILAC-MS.Fig. S7. Enriched KEGG pathways in gene-edited MeT5A SILAC-MS include EMT.Fig. S8. Metabolite responses to BAP1 mutation in isogenic MeT5A.Fig. S9. Immunoblotting for differentially expressed metabolic enzymes identified by SILAC-MS in isogenic MeT5A cell lines.Fig. S10. Characterisation of BAP1-status for MPM cell panel.Fig. S11. Evaluating correlation between BAP1 and selected metabolic enzymes in a panel of MPM cell lines and the TCGA MESO pan-cancer dataset.Fig. S12. Validation of ASS1 response to BAP1 alteration.Fig. S13. Improved prognosis for epithelioid MPM patients with loss of nBAP1 and increased expression of ASS1.Fig. S14. Relationship between BAP1 and ASS1 transcripts in the TCGA Pan-Cancer datasets for other cancer types.Fig. S15. The influence of BAP1-status on response to inhibition of purine metabolism.Fig. S16. The influence of BAP1-status on response to ASS1 inhibition.