The Functionality of the Cysteinyl Leukotriene Receptor 1 (CysLTR1) in the Lung by Metabolomics Analysis of Bronchoalveolar Lavage Fluid

The Functionality of the Cysteinyl Leukotriene Receptor 1 (CysLTR1) in the Lung by Metabolomics Analysis of Bronchoalveolar Lavage Fluid | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Functionality of the Cysteinyl Leukotriene Receptor 1 (CysLTR1) in the Lung by Metabolomics Analysis of Bronchoalveolar Lavage Fluid Wilson Bamise Adeosun, Sibongiseni KL. Poswayo, Suraj P. Parihar, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8052995/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Introduction The cysteinyl leukotriene receptor 1 (CysLTR1) is known as a potent lipid mediator with a well-established role in inflammatory regulation and lung disease. While its involvement in immune cell recruitment has been previously reported, its broader impact on pulmonary metabolism remains poorly understood. Objectives The study aims to investigate the metabolic consequences of a CysLTR1 deletion in mice to elucidate its role in pulmonary metabolic homeostasis. Methods Bronchoalveolar lavage fluid (BALF) was collected from CysLTR1 knockout (KO) and wild-type (WT) mice and analysed using standardized untargeted gas chromatography–time-of-flight mass spectrometry (GC-TOFMS) metabolomics. Results Metabolomics analyses of the BALF collected from the CysLTR1 KO mice presented significantly reduced levels of glucose, glucosamine, and glyceric acid, indicating the role of the CysLTR in lung glucose uptake and consequently lung glycolysis and gluconeogenesis. This is further supported by reductions in myo-inositol and D-chiro-inositol, also supporting previous findings that this occurs due to insulin resistance. Consequential disruption of various glucose-dependent pathways, including the pentose phosphate pathway (reduced gluconic acid, sedoheptulose and xylose) and purine metabolism (reduced 1-methylinosine) indicates a consequential altered nucleotide turnover, and the significantly reduced concentrations of butanoic acid, decan-2-ol, and 1-hexadecanol, indicate changes to fatty acid metabolism in the lung, as a compensatory response to the initial glucose deficiency induced by the CysLTR1 KO. Lastly, the changes to mandelic acid, glutaric acid, tricarballylic acid, and decan-2-ol, furthermore, indicate the role of CysLTR1 in the composition/metabolism of the microbiome. Conclusion This study expands our knowledge on the role of CysLTR1 beyond its role in immune regulation, that may later serve towards a better understanding of CysLTR1 associated lung diseases and in the development of improved therapeutic strategies. cysteinyl leukotriene receptor 1 bronchoalveolar lavage fluid metabolomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Leukotrienes (LTs) and cysteinyl leukotrienes (CysLTs; LTC4, LTD4 and LTE4) are lipid mediators derived from arachidonic acid via the 5-lipoxygenase pathway, and function as regulators of inflammation, vascular permeability, and smooth muscle contraction (Kanaoka & Boyce 2004 ). Leukotrienes, especially leukotriene B4 (LTB4), regulate how macrophages respond to antigens, and are associated with inflammation. This phenomenon is important in the progression of various diseases, including atherosclerosis (Sánchez-Galán et al., 2009 ), obesity and type 2 diabetes (Filgueiras et al., 2015 ; Mothe-Satney et al., 2012 ). Considering the latter, the binding of LTB4 to its receptor results in various inflammatory responses, leading to insulin resistance. Interestingly, elevated concentrations of LTB4 in type 2 diabetes patients have also been shown to be correlated with cardiovascular autonomic dysfunction (Neves et al., 2020 ). The cysteinyl leukotrienes function via the G protein-coupled receptors - CysLTR1, CysLTR2, and CysLTR3, and are best described for their role in various allergic and asthmatic responses and inflammation. For example, the CysLTs regulate the production of proinflammatory cytokines and adipokines, including interleukin 6 (IL-6), monocyte chemotactic protein 1, tumor necrosis factor-α (TNF-α), nuclear factor kappa B (NF-κB), and macrophage inflammatory protein 1 (Coffey et al., 2015 ; Filgueiras et al., 2015 ). Furthermore, CysTLR1 mediates inflammation during infection, and it has been demonstrated that mice lacking CysLTR1 have reduced granuloma, hepatic fibrosis, and liver enzyme release, which in turn is associated with an increased anti-inflammatory cytokine production. Furthermore, treating mice with montelukast (a CysLTR1 antagonist) in combination with praziquantel, resulted in a reduced cellular infiltration into the liver and a reduced egg burden during chronic Schistosoma mansoni infection, and a combination therapy of CysLTR1 inhibition and praziquantel, serves as a prophylactic treatment approach (Mosala et al., 2024 ). Eosinophils, mast cells, and chemosensory epithelial airway tuft cells, are also reportedly activated by CysLTs, leading to the type-2 immune responses, mucus secretion and exacerbation of pulmonary illnesses (Liu et al., 2025 ; Salimi et al., 2017 ). More recently, CysLTR1 inhibition has been shown to modulate intracellular glucose levels in retinal endothelial cells, pericytes and ARPE-19 cells, indicating a possible role in cellular glucose metabolism (Koller et al., 2025). Bronchoalveolar lavage fluid (BALF) is an important biofluid used for researching changes to the lung microenvironment. The procedure involves washing the lower respiratory tract (bronchoalveolar space) with saline solution, which is then recovered. The solute content is composed mostly of alveolar macrophages, lymphocytes, neutrophils, proteins and primary lung metabolites (Kalidhindi et al., 2021 ; Sabounchi-Schütt et al., 2003 ). A metabolomics analysis of BALF would subsequently provide a detailed snapshot of the metabolic changes in the the lung due to a perturbation. This study subsequently investigated the role of CysLTR1 on the lung by comparing the metabolic changes associated with a CysLTR1 KO mouse model with that of a WT control, using GC-TOFMS metabolomics analysis of BALF samples. Materials and methods Reagents Deionized water from a Millipore Milli-Q purification system was used throughout the study. Optima-grade acetonitrile was obtained from Fisher Scientific (Pittsburgh, USA). Unless otherwise stated, all other reagents and organic solvents used in this investigation were sourced from Sigma‒Aldrich (St. Louis, MO, USA). Samples CysLTR1-deficient (Cysltr1-/-) mice were generated by breeding heterozygous (Cysltr1-/+) animals on a C57BL/6 background. Dr. Frank Austen from Harvard Medical School generously provided CysLTR1 heterozygous mice. The mice were housed in ventilated cages under specific-pathogen-free conditions at the research animal facility of the UCT Faculty of Health Science. Mice aged 8–12 weeks were used, with sex matching, unless otherwise specified. The mice were terminally anesthetized, after which a catheter was inserted into the trachea and secured in place. A 1 mL syringe containing sterile saline solution was then connected to the catheter and gently injected. The solution was gently aspirated while the thorax of each mouse was massaged to collect BALF. Approximately 500–800 µL of BALF per mouse was transferred into 2 mL cryotubes, immediately snap-frozen in liquid nitrogen, and stored at − 80°C until analysis. All the animal studies adhered to the rigorous guidelines outlined in the South African National Standard for the Care and Use of Animals for Scientific Purposes (SANS 10386:2008). The study protocol was approved by the Animal Research Ethics Committee (AREC 022/024) at the Faculty of Health Science, University of Cape Town. To monitor analytical reproducibility and ensure data quality, pooled QC samples were prepared by combining equal volume aliquots from each experimental BALF sample included in the study prior to extraction. The pooled QC sample was further aliquoted and treated as a biological sample, undergoing the same sample extraction, and derivatisation steps. Extraction blanks, went through the sample preparation, extraction and derivatisation steps, but did not contain any biological sample. Sample preparation and extraction A 100 µL volume of each BALF sample, was centrifuged at 10, 000 rpm for 10 minutes at room temperature, after the addition of 50 µL of a 3-phenylbutyric acid (Sigma‒Aldrich) internal standard (50ppm) and 300 µL of acetonitrile. The supernatant was subsequently evaporated to dryness before derivatization. The dried metabolic extract was oximated using 50 µL of methoxyamine hydrochloride in pyridine (15 mg/mL) (Merck, Darmstadt, Germany) at 50°C for 1 h, and then silylated using 50 µL of N, O-bis (trimethylsilyl) trifluoroacetamide (BSTFA) with 1% trimethyl chlorosilane (TMCS) (Sigma‒Aldrich, St. Louis, MO, USA) at 60°C for 1 h. The derivatized sample extracts were transferred to 2 mL glass vials containing 250 µL glass inserts. One microliter of each sample extract was injected (1:1 split ratio) into a Pegasus BT GC-TOFMS instrument (Leco Corporation, St. Joseph, MI, USA), equipped with an Agilent 7890A gas chromatograph (Agilent, Atlanta, GA, USA) coupled to a time‒of-flight mass spectrometer (TOFMS) (Leco Corporation, St. Joseph, MI, USA). Separation was achieved via an Rxi-5-MS column (29.690 m, 0.25 mm internal diameter and 0.25 µm film thickness) (Restch GmbH & Co. KG, Haan, Germany). The front inlet temperature was held at a constant 270°C, the transfer line temperature at a constant 250°C, and the ion source temperature at a constant 200°C for the entire run. The initial GC oven temperature was set at 70°C for 1 min, followed by an increase of 5°C/min to a final temperature of 320°C, at which it was held for 3 min. The detector acquisition delay for each run was 420 s and was offset with a filament bias of − 70 eV. Spectra were collected from between 50 to 950 m/z at an acquisition rate of 20 spectra per second. Mass spectral deconvolution, peak alignment and peak identification were performed via Leco Corporation’s ChromaTOF software (version 4.71). Mass spectral deconvolution, peak deconvolution, peak alignment and identification were performed at a signal-to-noise ratio of 30, with a minimum of three apex peaks. To eliminate the effects of retention time shifts and create a data matrix containing the relative abundances of all the compounds present in all the samples, peaks with identical mass spectra and retention times were aligned using ChromaTOF. Mass fragmentation patterns and their respective retention times were screened against the National Institute of Standards and Technology (NIST) mass spectra library, as well as in-house libraries compiled from previously injected standards. Peak annotation was performed using a similarity threshold of at least 70%. All metabolite markers identified as described in section 2.5 were manually checked again by the analyst by comparing their retention times and mass fragment patterns to those of the libraries to confirm their identities before biological interpretation. The extracted BALF samples were randomly injected, ensuring that any residual analytical variation was equally distributed among the groups, thereby minimizing potential bias. The prepared QCs, extraction blanks, and a FAMEs standard, were injected 3 times at the beginning, middle and end of the sample batch. Statistical analysis and metabolite identification The relative concentrations of 201 compounds comprising the combination of quality control (QT), WT and KO samples were statistically analyzed. Standard metabolomics data clean-up procedures were followed (including data pretreatment, spectral deconvolution, missing data input, data normalization (including log transformation) and batch effect correction) (Gromski et al., 2014 ; Sullivan & Feinn, 2012 ), and the cleaned data were further processed using various statistical analyses, including t-test, log 2-fold change (log2FC), and effect size analyses, to determine which metabolites contributed most significantly to the observed changes between the sample groups. A principal component analysis (PCA) was also conducted to determine the compounds contributing most to the observed variability and their relationships with the groups. All data analyses were performed via Metaboanalyst 6.0 (Pang et al., 2024 ). Results Raw data summary A total of 936 compounds were detected via benchtop GC‒TOFMS. The dataset was meticulously cleaned to eliminate compounds without matching mass spectral entries in the libraries, and other artifact compounds of no biological relevance, resulting in the aforementioned 201 compounds (excluding the internal standard), which were subjected to the aforementioned statistical analyses. As indicated in Fig. 1 , the consistent grouping of the QC samples within that of the experimental samples, supports the reliability of the data and suggests that machine drift or analytical variability is absent. These observations affirm the quality and reproducibility of the dataset, validating its suitability for downstream multivariate and univariate statistical analyses. Chemometric analysis results Figure 2 . Principal component analysis score plot for the CysLT1 KO and WT sample groups. The percentages in brackets represent the proportion of variation explained in the observed data by the specific principal component (PC). The clear separation between these groups along both PC1 and PC2 indicates significant metabolic differences between the CysLTR1 KO and WT groups. Statistics and Metabolite Marker Selection Of the aforementioned 201 metabolites in the final data set, 23 metabolites showed a significant t-test p-value of < 0.05, 70 metabolites a log2FC threshold beyond 0.5, and 50 metabolites a Cohen’s d ≥ 0.8, when comparing the groups. The 18 metabolite markers that best describe the variance between CysLTR1 KO-deficient and WT BALF samples were selected via the aforementioned multistatistical selection approach as illustrated in the Venn diagram in Fig. 3 . Table 1 presents the metabolites that most significantly differ when comparing the CysLTR1 KO and WT BALF groups. Table 1 Metabolites with most significance when comparing the CysLTR1 KO and WT BALF sample groups. Metabolite (PubChem ID) Average concentration (ng/L) / (standard deviation) Wild type mice CysLTR1 KO mice Log2FC (threshold 0.5) Cohens (effect size: d-value ≥ 0.8) t-test p-value < 0.05 Tricarballylic acid (14925) 2.650 (1.994) 0.792 (0.100)↓ -4.029 2.911 0.001 Glucosamine (739) 9,707 (10.121) 5,825 (8.381)↓ -2.45 1.958 0.026 Decan-2-ol (2737541) 1.117 (0.987) 0.120 (0.269)↓ -2.435 1.945 0.027 D-Chiro-Inositol (16216949) 125.524 (60.474) 56.153 (14.225)↓ -1.639 2.558 0.013 Glucose (5793) 2.118 (1.647) 0.780 (0.593)↓ -1.440 0.824 0.036 Mandelic acid (22943025) 52.300 (18.357) 38.746 (37.000)↓ -1.366 3.362 0.002 D-xylose (135191) 23.682 (19.270) 9.937 (0.002)↓ -1.713 1.307 0.033 Glutaric acid (90037731) 37.290 (17.780) 20.310 (5.905)↓ -1.343 2.186 0.008 Sedoheptulose (5459879) 19.677 (6.298) 12.138 (4.172)↓ -1.191 2.803 0.002 Glyceric acid (752) 55.715 (12.841) 31.324 (7.776)↓ -1.174 2.939 0.002 Gluconic acid (10690) 222.814 (134.281) 155.146 (80.397)↓ -1.435 1.624 0.022 1-Hexadecanol (2682) 38.672 (19.981) 22.849 (9.901)↓ -1.273 1.758 0.011 (R*,S*)-2,3 Dihydroxy butanoic acid (250402) 8.782 (3.632) 5.921 (1.384)↓ -1.101 2.605 0.006 Butanoic acid (16213394) 325.566 (117.085) 207.031 (36.553)↓ -0.981 2.737 0.007 Myo-inositol (440388) 1912.119 (865.363) 1398.837 (441.522)↓ -0.932 1.676 0.037 Galactose (441035) 25632.153 (6700.214) 20646.841 (2718.550)↓ -0.632 2.766 0.003 Discussion Figure 4 shows a schematic summary of those metabolites most significantly changed in the mice BALF samples collected from the lungs, due to the absence of CysLTR1 (highlighted in green), when compared to the WT mice. The altered pathways in the CysLTR1 KO included changes to glycolysis, the pentose phosphate pathway, purine metabolism, amino acid metabolism, fatty acid metabolism, galactose metabolism, and the hexosamine biosynthesis pathway. The metabolomic analysis of biofluids such as serum, plasma, urine, cerebrospinal fluid, and BALF is a powerful tool for understanding the physiological and pathological states of disease conditions and, in this case, that of the lung (Evans et al., 2014 ; Stringer et al., 2016 ). In clinical research, providing a comprehensive snapshot of metabolic alterations in a biological system / sample /tissue, is important for identifying disease biomarkers, elucidating changes to biochemical pathways, and monitoring therapeutic responses (Dar et al., 2023; Perakakis et al., 2018). The results of the present investigation provide useful insights into the biochemical and physiological changes associated with CysLT1 and its receptor, further highlighting the potential role of CysLT1 in pulmonary physiology and disease mechanisms, as revealed by changes in the metabolic pathways influenced by the absence of CysLTR1 in the KO mice (Fig. 4 ). The most important finding of this study is the significantly reduced in glucose (and galactose) in the BALF of CysLT1 KO mice comparatively, confirming a previously reported association between CysLTs and pulmonary glucose homeostasis (Filgueiras et al., 2015 ; Mothe-Satney et al., 2012 ). Furthermore, Guo et al., ( 2018 ) reported reduction in glucose-stimulated insulin secretion in MIN6 cells, a mouse pancreatic beta-cell line, via CysLTR1. In the absence of CysLTR1, insulin secretion is improved, resulting in improved systemic glucose clearance and glucose uptake into insulin-dependent tissues, including skeletal muscle, adipose tissue and the heart. This phenomenon results in reduced glucose availability to other organs that take up glucose via non-insulin-dependent pathways, including the lung, and hence results in reduced glucose levels in the BALF of CysLTR1 KO mice as seen in this study. This observation provides important insight into the role of CysLTR1 in regulating the metabolic microenvironment of the lung, particularly under inflammatory conditions. CysLTR1, a receptor for the leukotrienes LTC₄, LTD₄, LTE₄ and LTB₄ (Emala, 2018 ), plays a critical role in mediating inflammatory responses in the lung (Figueroa et al., 2001 ; Zhu et al., 2012 ). The absence of CysLTR1 results in a disruption of the normal leukotriene-mediated signaling cascade, and subsequently a reduced inflammatory cell response in the airways. Since the activation of various immune cells (M1 macrophages, neutrophils and T lymphocytes) results in a metabolic reprogramming and a heavy reliance on glycolysis, which subsequently consume substantial amounts of glucose during inflammation (Pająk et al., 2024; Soto-Heredero et al., 2020 ), the observed reduction in glucose levels in the BALF of CysLTR1 KO mice may also be due to a reduced basal pulmonary inflammatory response resulting from the disruption of leukotriene-mediated signaling. Although glucose is a fundamental energy source and a key metabolite in glycolysis, it also serves as a substrate for the pentose phosphate pathway (PPP), hexosamine pathway, and tricarboxylic acid (TCA) cycle. Hence, the reduced levels of glucose in this study led to downstream metabolic perturbations, as observed in the significant reduction of several carbohydrate-related metabolite intermediates, including myo-inositol, D-chiro-inositol, glyceric acid, glucosamine, gluconic acid, and sedoheptulose, all of which are directly or indirectly linked to glucose metabolism (Fig. 4 ). Myo-inositol and D-chiro-inositol are isomeric forms of inositol, a sugar alcohol involved in cellular growth and insulin functionality (DiNicolantonio & H O’Keefe, 2022 ). Both metabolites are synthesized from glucose-6-phosphate, as shown in Fig. 4 . In the absence of an adequate glucose supply, the availability of glucose-6-phosphate is limited, leading to reduced inositol biosynthesis (Lepore et al., 2021 ). Since myo-inositol is the precursor of D-chiro-inositol, a further reduction in the latter supports this finding. The depletion of these intermediates not only indicates impaired glucose metabolism (Lepore et al., 2021 ) but also a reduced insulin signal transduction (DiNicolantonio & H O’Keefe, 2022 ) and an altered membrane phospholipid composition, particularly in tissues such as the lung and liver (Suliman et al., 2022 ). Subsequently, the roles of these metabolites in type 2 diabetes mellitus has been widely investigated. Myo-inositol has been reported to inhibit glucose absorption in the intestine and promote muscle glucose uptake in rats, whereas clinical trials have demonstrated that both myo-inositol and D-chiro-inositol possess insulin-mimetic properties and the ability to improve insulin sensitivity in metabolic conditions associated with insulin resistance in humans (Chukwuma et al., 2016 ; Jeon et al., 2016 ). Glucosamine is a precursor for glycosaminoglycans and glycoprotein synthesis, macromolecules that play crucial roles in various biological processes, including cell signaling, tissue development, and disease progression (Boullanger et al., 1990 ; Linhardt & Toida, 2004 ; Smock & Meijers, 2018 ; Tian & Zhang, 2013 ). The reduction of glucosamine in the absence of CysLTR1 in the KO group in this study, would result in impaired protein glycosylation, which in turn would impact pulmonary function, as glycosylation is crucial for maintaining mucosal barrier integrity and immune responses. The PPP functions by synthesizing nucleotide precursors, including ribose-5-phosphate (Fig. 4 ), and maintaining a balance between NADP⁺ and NADPH (Stincone et al., 2015 ; TeSlaa et al., 2023 ). Since inflammatory cells rely on the PPP to support rapid proliferation and anabolic metabolism, reduced inflammation and glucose supply to the PPP results in diminished PPP flux, which is consistent with the reduction in the levels of some PPP-associated metabolites, such as D-xylose, gluconic acid and sedoheptulose in the CysLTR1 KO mice in this study. D-xylose is metabolized through the nonoxidative branch of the PPP, where it is interconverted via intermediates such as xylulose-5-phosphate to eventually produce ribose-5-phosphate. Similarly, gluconic acid (derived from glucose) also serves as a precursor for ribose-5-phosphate (Stincone et al., 2015 ; TeSlaa et al., 2023 ). The reduced availability of glucose, D-xylose and their resulting PPP precursors limits the capacity of the PPP to generate ribose-5-phosphate, which is required for nucleotide synthesis. Sedoheptulose metabolism functions in NADPH generation, the latter of which is primarily used towards reductive biosynthesis reactions, and for ribose synthesis, for nucleotide biosynthesis (Perl et al., 2011). The resulting decrease in nucleotide synthesis is further substantiated by the reduced levels of 1-methylinosine (Log2FC = − 1.182, effect size = 1.643, and p = 0.088]), a methylated purine nucleoside involved in purine metabolism, in the CysLTR1 KO mice in our study. 1-Hexadecanol and butanoic acid (Fig. 4 ), both intermediates of fatty acid metabolism, were also reduced in BALF of the CysLTR1 KO mice comparatively. This indicates a metabolic adaptation to compensate for lower glucose availability, thereby increasing the utilization of fatty acid derivatives for energy in the lung. Furthermore, as previously mentioned leukotrienes are lipid mediators derived from arachidonic acid (Di Gennaro & Haeggström, 2012 ), and play a key role in inflammatory processes, and disruptions in arachidonic acid pathways have been previously associated with changes in lipid metabolism (Zhang et al., 2023). Glutaric acid is a metabolic product of lysine and tryptophan metabolism (Sauer et al., 2005 ). These amino acids are catabolized to glutaryl-CoA, which is then converted into crotonyl-CoA and eventually enters the TCA cycle (Trefely et al., 2020), possibly indicative of an altered amino acid metabolism. However, it is more likely, considering also the reduction in mandelic acid (Ji et al., 2022 ), tricarballylic acid (TA) (Russell & Forsberg, 1986 ) and decan-2-ol (Riley et al., 2012 ), that the aforementioned glutaric acid is due an altered intestinal microbiome (Cai et al., 2023 ;) in the CysLTR1 KO mice. Recent studies have previously associated these metabolites to microbial activity and dysbiosis under inflammatory conditions (Cai et al., 2023 ; Yu et al., 2025 ). Although there is as yet no direct link between CysLTR1 and the gut or intestinal microbiota, G protein-coupled receptors, the most abundant class of cell surface receptor, and to which CysLTR1 belongs, are known to interact with gut microbiota, influencing various physiological processes (Aleti et al., 2023 ). Therefore, the observed reduction in these metabolites most likely results from changes to microbial metabolism associated with the loss of CysLTR1-mediated inflammatory signaling, which may indirectly modulate the host–microbiome interface, and various inflammatory mediators, including leukotrienes, have been reported to influence the composition and activity of microbial communities (Carlos H. C. Serezani et al., 2005). Conclusion CysLTR1 plays a key role in a variety of metabolic pathways necessary for maintaining lung homeostasis, the most significant of which is glucose uptake and metabolism, with consequential changes to lipid, PPP, nucleotide and microbiome metabolism. These observed changes underscore the central role of CysLT1 in insulin signaling and the inflammatory response, as suggested by previous studies (Guo et al., 2018 ). CysLTR1 expression is reportedly increased in the airway mucosa of asthma patients, particularly during exacerbations (Zhu et al., 2012 ), and an important regulator of mucus secretion, eosinophil recruitment, and airway inflammation, which are all hallmarks of asthma. This study serves as a basis towards a better understanding of the role of CysLTR1 in the lung, and many such studies to follow could be used towards inflammation related lung disease prevention or improved therapeutic strategies for such. Abbreviations BALF Bronchoalveolar lavage fluid BSTFA O-bis (trimethylsilyl) trifluoroacetamide CysLTR1 Cysteinyl leukotriene receptor 1 Cysltr1-/- CysLTR1-deficient Cysltr1-/+ Heterozygous CysLTs Cysteinyl leukotrienes GC-TOFMS Gas chromatography-time of flight mass spectrometry IL-6 Interleukin 6 KO Knockout LTB4 Leukotriene B4 NF-κB Nuclear factor kappa B NIST National Institute of Standards and Technology PCA Principal component analysis PPP Pentose phosphate pathway QT Quality control TA Tricarballylic acid TCA Tricarboxylic acid TMCS Trimethyl chlorosilane TMS Trimethylsilyl TNF-α Tumor necrosis factor-α VIP Variable of importance in projection WT Wild-type Declarations Author information Authors and affiliations Biomedical and Molecular Metabolism (BioMMet), North‒West University, Potchefstroom, South Africa Wilson Bamise Adeosun and Du Toit Loots Division of Medical Microbiology, Institute of Infectious Diseases and Molecular Medicine (IDM), Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa Sibongiseni KL. Poswayo and Suraj P. Parihar Centre for Infectious Disease Research in Africa (CIDRI-Africa), Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa Suraj P. Parihar Competing interests The authors declare no competing financial interests Contributions D.T.L. and S.P.P conceptualize and design the study. S.K.L.P and S.P.P participated in conducting the experiment. W.B.A and D.T.L contributed to metabolomics methodology protocol optimization. W.B.A contributed to GCTOF-MS analysis of biospecimens and data acquisition. D.T.L and W.B.A contributed to data analysis and interpretation of results. W.B.A, S.P.P and D.T.L contributed to drafting, editing and revision of the manuscript. Corresponding author Du Toit Loots [email protected] Ethics declarations Ethics approval and consent to participate This study was approved by the Animal Research Ethics Committee (AREC 022/024) at the Faculty of Health Science, University of Cape Town. Consent for publication Not applicable Funding National Research Foundation (NRF) of South Africa (SRUG2204062208); South African Medical Research Council (SAMRC) doctoral fellowship; NRF Research Development Grants for Y-Rated Researchers (RDYR180413320675); NRF Incentive Funding for Rated Researchers (IFR180305315866); NRF Evaluation and Rating Incentive Funding Award (RA22110567937); NRF support for Competitive Programme For Rated Researchers (SRUG22051611051); SAMRC Self-Initiated Research Grant, the Centre for Infectious Disease Research in Africa (CIDRI-Africa); Fogarty International Centre of the National Institutes of Health under Award Number (K43TW012587). Author Contribution D.T.L. and S.P.P conceptualize and design the study. S.K.L.P and S.P.P participated in conducting the experiment. W.B.A and D.T.L contributed to metabolomics methodology protocol optimization. W.B.A contributed to GCTOF-MS analysis of biospecimens and data acquisition. 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Cysteinyl leukotriene 1 receptor expression associated with bronchial inflammation in severe exacerbations of COPD. Chest , 142 , 347–357. https://doi.org/10.1378/chest.11-1581 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 10 Jan, 2026 Reviews received at journal 08 Jan, 2026 Reviewers agreed at journal 11 Dec, 2025 Reviews received at journal 07 Dec, 2025 Reviewers agreed at journal 14 Nov, 2025 Reviewers invited by journal 12 Nov, 2025 Editor assigned by journal 07 Nov, 2025 Submission checks completed at journal 07 Nov, 2025 First submitted to journal 06 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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1","display":"","copyAsset":false,"role":"figure","size":97308,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis score plot showing distinct clustering of quality control samples (QCs) and separation between BALF experimental groups (samples)\u003c/p\u003e","description":"","filename":"floatimage120.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8052995/v1/26c1c435fa67e341e41538c2.jpeg"},{"id":96634124,"identity":"73554d5d-6f65-4e38-8c21-426777f15d60","added_by":"auto","created_at":"2025-11-24 13:13:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":23222,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis score plot for the CysLT1 KO and WT sample groups. The percentages in brackets represent the proportion of variation explained in the observed data by the specific principal component (PC).\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8052995/v1/63e1c453cbce470658e79e2b.png"},{"id":96708768,"identity":"9f67266a-4541-4184-9f57-c2666860330a","added_by":"auto","created_at":"2025-11-25 10:05:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":128572,"visible":true,"origin":"","legend":"\u003cp\u003eVenn diagram indicating the selection of the 18 metabolite markers contributing most significantly to the differences observed when comparing the CysLTR1-KO and WT BALF sample groups via a multistatistical approach. The selection criteria included metabolites having a t-test p-value \u0026lt; 0.05, a Log2FC of threshold 0.5, and an effect size ≥ 0.8.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8052995/v1/304ab87ab4f70ffbdabf3b02.png"},{"id":96709034,"identity":"ddf29471-7edd-421a-ba80-7d868cdd281b","added_by":"auto","created_at":"2025-11-25 10:07:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":135969,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic summary of the most significantly altered metabolites (highlighted in green) shown to be reduced in the lung BALF samples due to the absence of CysLTR1.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8052995/v1/9074879dd5d0f98029c7cff3.png"},{"id":96712781,"identity":"9ff329b1-2279-4b70-9e31-049a803b38a3","added_by":"auto","created_at":"2025-11-25 10:16:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1643202,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8052995/v1/31e859dc-d582-4dc1-a715-52aae6bae052.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eThe Functionality of the Cysteinyl Leukotriene Receptor 1 (CysLTR1) in the Lung by Metabolomics Analysis of Bronchoalveolar Lavage Fluid\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLeukotrienes (LTs) and cysteinyl leukotrienes (CysLTs; LTC4, LTD4 and LTE4) are lipid mediators derived from arachidonic acid via the 5-lipoxygenase pathway, and function as regulators of inflammation, vascular permeability, and smooth muscle contraction (Kanaoka \u0026amp; Boyce \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Leukotrienes, especially leukotriene B4 (LTB4), regulate how macrophages respond to antigens, and are associated with inflammation. This phenomenon is important in the progression of various diseases, including atherosclerosis (S\u0026aacute;nchez-Gal\u0026aacute;n et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), obesity and type 2 diabetes (Filgueiras et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Mothe-Satney et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Considering the latter, the binding of LTB4 to its receptor results in various inflammatory responses, leading to insulin resistance. Interestingly, elevated concentrations of LTB4 in type 2 diabetes patients have also been shown to be correlated with cardiovascular autonomic dysfunction (Neves et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe cysteinyl leukotrienes function via the G protein-coupled receptors - CysLTR1, CysLTR2, and CysLTR3, and are best described for their role in various allergic and asthmatic responses and inflammation. For example, the CysLTs regulate the production of proinflammatory cytokines and adipokines, including interleukin 6 (IL-6), monocyte chemotactic protein 1, tumor necrosis factor-α (TNF-α), nuclear factor kappa B (NF-κB), and macrophage inflammatory protein 1 (Coffey et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Filgueiras et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Furthermore, CysTLR1 mediates inflammation during infection, and it has been demonstrated that mice lacking CysLTR1 have reduced granuloma, hepatic fibrosis, and liver enzyme release, which in turn is associated with an increased anti-inflammatory cytokine production. Furthermore, treating mice with montelukast (a CysLTR1 antagonist) in combination with praziquantel, resulted in a reduced cellular infiltration into the liver and a reduced egg burden during chronic \u003cem\u003eSchistosoma mansoni\u003c/em\u003e infection, and a combination therapy of CysLTR1 inhibition and praziquantel, serves as a prophylactic treatment approach (Mosala et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Eosinophils, mast cells, and chemosensory epithelial airway tuft cells, are also reportedly activated by CysLTs, leading to the type-2 immune responses, mucus secretion and exacerbation of pulmonary illnesses (Liu et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Salimi et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). More recently, CysLTR1 inhibition has been shown to modulate intracellular glucose levels in retinal endothelial cells, pericytes and ARPE-19 cells, indicating a possible role in cellular glucose metabolism (Koller et al., 2025).\u003c/p\u003e\u003cp\u003eBronchoalveolar lavage fluid (BALF) is an important biofluid used for researching changes to the lung microenvironment. The procedure involves washing the lower respiratory tract (bronchoalveolar space) with saline solution, which is then recovered. The solute content is composed mostly of alveolar macrophages, lymphocytes, neutrophils, proteins and primary lung metabolites (Kalidhindi et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sabounchi-Sch\u0026uuml;tt et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). A metabolomics analysis of BALF would subsequently provide a detailed snapshot of the metabolic changes in the the lung due to a perturbation. This study subsequently investigated the role of CysLTR1 on the lung by comparing the metabolic changes associated with a CysLTR1 KO mouse model with that of a WT control, using GC-TOFMS metabolomics analysis of BALF samples.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eReagents\u003c/h2\u003e\u003cp\u003eDeionized water from a Millipore Milli-Q purification system was used throughout the study. Optima-grade acetonitrile was obtained from Fisher Scientific (Pittsburgh, USA). Unless otherwise stated, all other reagents and organic solvents used in this investigation were sourced from Sigma‒Aldrich (St. Louis, MO, USA).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSamples\u003c/h3\u003e\n\u003cp\u003eCysLTR1-deficient (Cysltr1-/-) mice were generated by breeding heterozygous (Cysltr1-/+) animals on a C57BL/6 background. Dr. Frank Austen from Harvard Medical School generously provided CysLTR1 heterozygous mice. The mice were housed in ventilated cages under specific-pathogen-free conditions at the research animal facility of the UCT Faculty of Health Science. Mice aged 8\u0026ndash;12 weeks were used, with sex matching, unless otherwise specified. The mice were terminally anesthetized, after which a catheter was inserted into the trachea and secured in place. A 1 mL syringe containing sterile saline solution was then connected to the catheter and gently injected. The solution was gently aspirated while the thorax of each mouse was massaged to collect BALF. Approximately 500\u0026ndash;800 \u0026micro;L of BALF per mouse was transferred into 2 mL cryotubes, immediately snap-frozen in liquid nitrogen, and stored at \u0026minus;\u0026thinsp;80\u0026deg;C until analysis. All the animal studies adhered to the rigorous guidelines outlined in the South African National Standard for the Care and Use of Animals for Scientific Purposes (SANS 10386:2008). The study protocol was approved by the Animal Research Ethics Committee (AREC 022/024) at the Faculty of Health Science, University of Cape Town.\u003c/p\u003e\u003cp\u003eTo monitor analytical reproducibility and ensure data quality, pooled QC samples were prepared by combining equal volume aliquots from each experimental BALF sample included in the study prior to extraction. The pooled QC sample was further aliquoted and treated as a biological sample, undergoing the same sample extraction, and derivatisation steps. Extraction blanks, went through the sample preparation, extraction and derivatisation steps, but did not contain any biological sample.\u003c/p\u003e\n\u003ch3\u003eSample preparation and extraction\u003c/h3\u003e\n\u003cp\u003eA 100 \u0026micro;L volume of each BALF sample, was centrifuged at 10, 000 rpm for 10 minutes at room temperature, after the addition of 50 \u0026micro;L of a 3-phenylbutyric acid (Sigma‒Aldrich) internal standard (50ppm) and 300 \u0026micro;L of acetonitrile. The supernatant was subsequently evaporated to dryness before derivatization. The dried metabolic extract was oximated using 50 \u0026micro;L of methoxyamine hydrochloride in pyridine (15 mg/mL) (Merck, Darmstadt, Germany) at 50\u0026deg;C for 1 h, and then silylated using 50 \u0026micro;L of N, O-bis (trimethylsilyl) trifluoroacetamide (BSTFA) with 1% trimethyl chlorosilane (TMCS) (Sigma‒Aldrich, St. Louis, MO, USA) at 60\u0026deg;C for 1 h. The derivatized sample extracts were transferred to 2 mL glass vials containing 250 \u0026micro;L glass inserts.\u003c/p\u003e\u003cp\u003eOne microliter of each sample extract was injected (1:1 split ratio) into a Pegasus BT GC-TOFMS instrument (Leco Corporation, St. Joseph, MI, USA), equipped with an Agilent 7890A gas chromatograph (Agilent, Atlanta, GA, USA) coupled to a time‒of-flight mass spectrometer (TOFMS) (Leco Corporation, St. Joseph, MI, USA). Separation was achieved via an Rxi-5-MS column (29.690 m, 0.25 mm internal diameter and 0.25 \u0026micro;m film thickness) (Restch GmbH \u0026amp; Co. KG, Haan, Germany). The front inlet temperature was held at a constant 270\u0026deg;C, the transfer line temperature at a constant 250\u0026deg;C, and the ion source temperature at a constant 200\u0026deg;C for the entire run. The initial GC oven temperature was set at 70\u0026deg;C for 1 min, followed by an increase of 5\u0026deg;C/min to a final temperature of 320\u0026deg;C, at which it was held for 3 min. The detector acquisition delay for each run was 420 s and was offset with a filament bias of \u0026minus;\u0026thinsp;70 eV. Spectra were collected from between 50 to 950 m/z at an acquisition rate of 20 spectra per second. Mass spectral deconvolution, peak alignment and peak identification were performed via Leco Corporation\u0026rsquo;s ChromaTOF software (version 4.71). Mass spectral deconvolution, peak deconvolution, peak alignment and identification were performed at a signal-to-noise ratio of 30, with a minimum of three apex peaks. To eliminate the effects of retention time shifts and create a data matrix containing the relative abundances of all the compounds present in all the samples, peaks with identical mass spectra and retention times were aligned using ChromaTOF. Mass fragmentation patterns and their respective retention times were screened against the National Institute of Standards and Technology (NIST) mass spectra library, as well as in-house libraries compiled from previously injected standards. Peak annotation was performed using a similarity threshold of at least 70%. All metabolite markers identified as described in section 2.5 were manually checked again by the analyst by comparing their retention times and mass fragment patterns to those of the libraries to confirm their identities before biological interpretation.\u003c/p\u003e\u003cp\u003eThe extracted BALF samples were randomly injected, ensuring that any residual analytical variation was equally distributed among the groups, thereby minimizing potential bias. The prepared QCs, extraction blanks, and a FAMEs standard, were injected 3 times at the beginning, middle and end of the sample batch.\u003c/p\u003e\n\u003ch3\u003eStatistical analysis and metabolite identification\u003c/h3\u003e\n\u003cp\u003eThe relative concentrations of 201 compounds comprising the combination of quality control (QT), WT and KO samples were statistically analyzed. Standard metabolomics data clean-up procedures were followed (including data pretreatment, spectral deconvolution, missing data input, data normalization (including log transformation) and batch effect correction) (Gromski et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sullivan \u0026amp; Feinn, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and the cleaned data were further processed using various statistical analyses, including t-test, log 2-fold change (log2FC), and effect size analyses, to determine which metabolites contributed most significantly to the observed changes between the sample groups. A principal component analysis (PCA) was also conducted to determine the compounds contributing most to the observed variability and their relationships with the groups. All data analyses were performed via Metaboanalyst 6.0 (Pang et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eRaw data summary\u003c/h2\u003e\u003cp\u003eA total of 936 compounds were detected via benchtop GC‒TOFMS. The dataset was meticulously cleaned to eliminate compounds without matching mass spectral entries in the libraries, and other artifact compounds of no biological relevance, resulting in the aforementioned 201 compounds (excluding the internal standard), which were subjected to the aforementioned statistical analyses.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAs indicated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the consistent grouping of the QC samples within that of the experimental samples, supports the reliability of the data and suggests that machine drift or analytical variability is absent. These observations affirm the quality and reproducibility of the dataset, validating its suitability for downstream multivariate and univariate statistical analyses.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eChemometric analysis results\u003c/h3\u003e\n\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Principal component analysis score plot for the CysLT1 KO and WT sample groups. The percentages in brackets represent the proportion of variation explained in the observed data by the specific principal component (PC).\u003c/p\u003e\u003cp\u003eThe clear separation between these groups along both PC1 and PC2 indicates significant metabolic differences between the CysLTR1 KO and WT groups.\u003c/p\u003e\n\u003ch3\u003eStatistics and Metabolite Marker Selection\u003c/h3\u003e\n\u003cp\u003eOf the aforementioned 201 metabolites in the final data set, 23 metabolites showed a significant t-test p-value of \u0026lt;\u0026thinsp;0.05, 70 metabolites a log2FC threshold beyond 0.5, and 50 metabolites a Cohen\u0026rsquo;s d\u0026thinsp;\u0026ge;\u0026thinsp;0.8, when comparing the groups. The 18 metabolite markers that best describe the variance between CysLTR1 KO-deficient and WT BALF samples were selected via the aforementioned multistatistical selection approach as illustrated in the Venn diagram in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the metabolites that most significantly differ when comparing the CysLTR1 KO and WT BALF groups.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMetabolites with most significance when comparing the CysLTR1 KO and WT BALF sample groups.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetabolite\u003c/p\u003e\u003cp\u003e(PubChem ID)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eAverage concentration (ng/L) / (standard deviation)\u003c/p\u003e\u003cp\u003eWild type mice CysLTR1 KO mice\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLog2FC (threshold 0.5)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCohens (effect size:\u003c/p\u003e\u003cp\u003ed-value\u0026thinsp;\u0026ge;\u0026thinsp;0.8)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003et-test p-value\u003c/p\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTricarballylic acid (14925)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.650\u003c/p\u003e\u003cp\u003e(1.994)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.792\u003c/p\u003e\u003cp\u003e(0.100)\u0026darr;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-4.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.911\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlucosamine (739)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9,707\u003c/p\u003e\u003cp\u003e(10.121)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5,825\u003c/p\u003e\u003cp\u003e(8.381)\u0026darr;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-2.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.958\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDecan-2-ol (2737541)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.117\u003c/p\u003e\u003cp\u003e(0.987)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.120\u003c/p\u003e\u003cp\u003e(0.269)\u0026darr;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-2.435\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.945\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD-Chiro-Inositol (16216949)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e125.524\u003c/p\u003e\u003cp\u003e(60.474)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56.153\u003c/p\u003e\u003cp\u003e(14.225)\u0026darr;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.639\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.558\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlucose (5793)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.118\u003c/p\u003e\u003cp\u003e(1.647)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.780\u003c/p\u003e\u003cp\u003e(0.593)\u0026darr;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.440\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.824\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMandelic acid (22943025)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52.300\u003c/p\u003e\u003cp\u003e(18.357)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.746\u003c/p\u003e\u003cp\u003e(37.000)\u0026darr;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.366\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD-xylose (135191)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.682\u003c/p\u003e\u003cp\u003e(19.270)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.937\u003c/p\u003e\u003cp\u003e(0.002)\u0026darr;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.713\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.307\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlutaric acid (90037731)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37.290\u003c/p\u003e\u003cp\u003e(17.780)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.310\u003c/p\u003e\u003cp\u003e(5.905)\u0026darr;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.343\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSedoheptulose (5459879)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19.677\u003c/p\u003e\u003cp\u003e(6.298)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.138\u003c/p\u003e\u003cp\u003e(4.172)\u0026darr;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.803\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlyceric acid (752)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55.715\u003c/p\u003e\u003cp\u003e(12.841)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31.324\u003c/p\u003e\u003cp\u003e(7.776)\u0026darr;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.939\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGluconic acid (10690)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e222.814\u003c/p\u003e\u003cp\u003e(134.281)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e155.146\u003c/p\u003e\u003cp\u003e(80.397)\u0026darr;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.435\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.624\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1-Hexadecanol (2682)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38.672\u003c/p\u003e\u003cp\u003e(19.981)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.849\u003c/p\u003e\u003cp\u003e(9.901)\u0026darr;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.273\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.758\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(R*,S*)-2,3 Dihydroxy butanoic acid (250402)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.782\u003c/p\u003e\u003cp\u003e(3.632)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.921\u003c/p\u003e\u003cp\u003e(1.384)\u0026darr;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.605\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eButanoic acid (16213394)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e325.566\u003c/p\u003e\u003cp\u003e(117.085)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e207.031\u003c/p\u003e\u003cp\u003e(36.553)\u0026darr;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.981\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.737\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMyo-inositol (440388)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1912.119\u003c/p\u003e\u003cp\u003e(865.363)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1398.837\u003c/p\u003e\u003cp\u003e(441.522)\u0026darr;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.932\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.676\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGalactose (441035)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25632.153\u003c/p\u003e\u003cp\u003e(6700.214)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20646.841\u003c/p\u003e\u003cp\u003e(2718.550)\u0026darr;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.632\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.766\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows a schematic summary of those metabolites most significantly changed in the mice BALF samples collected from the lungs, due to the absence of CysLTR1 (highlighted in green), when compared to the WT mice. The altered pathways in the CysLTR1 KO included changes to glycolysis, the pentose phosphate pathway, purine metabolism, amino acid metabolism, fatty acid metabolism, galactose metabolism, and the hexosamine biosynthesis pathway.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe metabolomic analysis of biofluids such as serum, plasma, urine, cerebrospinal fluid, and BALF is a powerful tool for understanding the physiological and pathological states of disease conditions and, in this case, that of the lung (Evans et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Stringer et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In clinical research, providing a comprehensive snapshot of metabolic alterations in a biological system / sample /tissue, is important for identifying disease biomarkers, elucidating changes to biochemical pathways, and monitoring therapeutic responses (Dar et al., 2023; Perakakis et al., 2018). The results of the present investigation provide useful insights into the biochemical and physiological changes associated with CysLT1 and its receptor, further highlighting the potential role of CysLT1 in pulmonary physiology and disease mechanisms, as revealed by changes in the metabolic pathways influenced by the absence of CysLTR1 in the KO mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe most important finding of this study is the significantly reduced in glucose (and galactose) in the BALF of CysLT1 KO mice comparatively, confirming a previously reported association between CysLTs and pulmonary glucose homeostasis (Filgueiras et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Mothe-Satney et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Furthermore, Guo et al., (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) reported reduction in glucose-stimulated insulin secretion in MIN6 cells, a mouse pancreatic beta-cell line, via CysLTR1. In the absence of CysLTR1, insulin secretion is improved, resulting in improved systemic glucose clearance and glucose uptake into insulin-dependent tissues, including skeletal muscle, adipose tissue and the heart. This phenomenon results in reduced glucose availability to other organs that take up glucose via non-insulin-dependent pathways, including the lung, and hence results in reduced glucose levels in the BALF of CysLTR1 KO mice as seen in this study. This observation provides important insight into the role of CysLTR1 in regulating the metabolic microenvironment of the lung, particularly under inflammatory conditions. CysLTR1, a receptor for the leukotrienes LTC₄, LTD₄, LTE₄ and LTB₄ (Emala, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), plays a critical role in mediating inflammatory responses in the lung (Figueroa et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Zhu et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The absence of CysLTR1 results in a disruption of the normal leukotriene-mediated signaling cascade, and subsequently a reduced inflammatory cell response in the airways. Since the activation of various immune cells (M1 macrophages, neutrophils and T lymphocytes) results in a metabolic reprogramming and a heavy reliance on glycolysis, which subsequently consume substantial amounts of glucose during inflammation (Pająk et al., 2024; Soto-Heredero et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the observed reduction in glucose levels in the BALF of CysLTR1 KO mice may also be due to a reduced basal pulmonary inflammatory response resulting from the disruption of leukotriene-mediated signaling.\u003c/p\u003e\u003cp\u003eAlthough glucose is a fundamental energy source and a key metabolite in glycolysis, it also serves as a substrate for the pentose phosphate pathway (PPP), hexosamine pathway, and tricarboxylic acid (TCA) cycle. Hence, the reduced levels of glucose in this study led to downstream metabolic perturbations, as observed in the significant reduction of several carbohydrate-related metabolite intermediates, including myo-inositol, D-chiro-inositol, glyceric acid, glucosamine, gluconic acid, and sedoheptulose, all of which are directly or indirectly linked to glucose metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMyo-inositol and D-chiro-inositol are isomeric forms of inositol, a sugar alcohol involved in cellular growth and insulin functionality (DiNicolantonio \u0026amp; H O\u0026rsquo;Keefe, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Both metabolites are synthesized from glucose-6-phosphate, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. In the absence of an adequate glucose supply, the availability of glucose-6-phosphate is limited, leading to reduced inositol biosynthesis (Lepore et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Since myo-inositol is the precursor of D-chiro-inositol, a further reduction in the latter supports this finding. The depletion of these intermediates not only indicates impaired glucose metabolism (Lepore et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) but also a reduced insulin signal transduction (DiNicolantonio \u0026amp; H O\u0026rsquo;Keefe, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and an altered membrane phospholipid composition, particularly in tissues such as the lung and liver (Suliman et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Subsequently, the roles of these metabolites in type 2 diabetes mellitus has been widely investigated. Myo-inositol has been reported to inhibit glucose absorption in the intestine and promote muscle glucose uptake in rats, whereas clinical trials have demonstrated that both myo-inositol and D-chiro-inositol possess insulin-mimetic properties and the ability to improve insulin sensitivity in metabolic conditions associated with insulin resistance in humans (Chukwuma et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Jeon et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGlucosamine is a precursor for glycosaminoglycans and glycoprotein synthesis, macromolecules that play crucial roles in various biological processes, including cell signaling, tissue development, and disease progression (Boullanger et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Linhardt \u0026amp; Toida, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Smock \u0026amp; Meijers, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Tian \u0026amp; Zhang, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The reduction of glucosamine in the absence of CysLTR1 in the KO group in this study, would result in impaired protein glycosylation, which in turn would impact pulmonary function, as glycosylation is crucial for maintaining mucosal barrier integrity and immune responses.\u003c/p\u003e\u003cp\u003eThe PPP functions by synthesizing nucleotide precursors, including ribose-5-phosphate (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), and maintaining a balance between NADP⁺ and NADPH (Stincone et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; TeSlaa et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Since inflammatory cells rely on the PPP to support rapid proliferation and anabolic metabolism, reduced inflammation and glucose supply to the PPP results in diminished PPP flux, which is consistent with the reduction in the levels of some PPP-associated metabolites, such as D-xylose, gluconic acid and sedoheptulose in the CysLTR1 KO mice in this study. D-xylose is metabolized through the nonoxidative branch of the PPP, where it is interconverted via intermediates such as xylulose-5-phosphate to eventually produce ribose-5-phosphate. Similarly, gluconic acid (derived from glucose) also serves as a precursor for ribose-5-phosphate (Stincone et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; TeSlaa et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The reduced availability of glucose, D-xylose and their resulting PPP precursors limits the capacity of the PPP to generate ribose-5-phosphate, which is required for nucleotide synthesis. Sedoheptulose metabolism functions in NADPH generation, the latter of which is primarily used towards reductive biosynthesis reactions, and for ribose synthesis, for nucleotide biosynthesis (Perl et al., 2011). The resulting decrease in nucleotide synthesis is further substantiated by the reduced levels of 1-methylinosine (Log2FC = \u0026minus;\u0026thinsp;1.182, effect size\u0026thinsp;=\u0026thinsp;1.643, and p\u0026thinsp;=\u0026thinsp;0.088]), a methylated purine nucleoside involved in purine metabolism, in the CysLTR1 KO mice in our study.\u003c/p\u003e\u003cp\u003e1-Hexadecanol and butanoic acid (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), both intermediates of fatty acid metabolism, were also reduced in BALF of the CysLTR1 KO mice comparatively. This indicates a metabolic adaptation to compensate for lower glucose availability, thereby increasing the utilization of fatty acid derivatives for energy in the lung. Furthermore, as previously mentioned leukotrienes are lipid mediators derived from arachidonic acid (Di Gennaro \u0026amp; Haeggstr\u0026ouml;m, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and play a key role in inflammatory processes, and disruptions in arachidonic acid pathways have been previously associated with changes in lipid metabolism (Zhang et al., 2023).\u003c/p\u003e\u003cp\u003eGlutaric acid is a metabolic product of lysine and tryptophan metabolism (Sauer et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). These amino acids are catabolized to glutaryl-CoA, which is then converted into crotonyl-CoA and eventually enters the TCA cycle (Trefely et al., 2020), possibly indicative of an altered amino acid metabolism. However, it is more likely, considering also the reduction in mandelic acid (Ji et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), tricarballylic acid (TA) (Russell \u0026amp; Forsberg, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1986\u003c/span\u003e) and decan-2-ol (Riley et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), that the aforementioned glutaric acid is due an altered intestinal microbiome (Cai et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e;) in the CysLTR1 KO mice. Recent studies have previously associated these metabolites to microbial activity and dysbiosis under inflammatory conditions (Cai et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yu et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Although there is as yet no direct link between CysLTR1 and the gut or intestinal microbiota, G protein-coupled receptors, the most abundant class of cell surface receptor, and to which CysLTR1 belongs, are known to interact with gut microbiota, influencing various physiological processes (Aleti et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, the observed reduction in these metabolites most likely results from changes to microbial metabolism associated with the loss of CysLTR1-mediated inflammatory signaling, which may indirectly modulate the host\u0026ndash;microbiome interface, and various inflammatory mediators, including leukotrienes, have been reported to influence the composition and activity of microbial communities (Carlos H. C. Serezani et al., 2005).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eCysLTR1 plays a key role in a variety of metabolic pathways necessary for maintaining lung homeostasis, the most significant of which is glucose uptake and metabolism, with consequential changes to lipid, PPP, nucleotide and microbiome metabolism. These observed changes underscore the central role of CysLT1 in insulin signaling and the inflammatory response, as suggested by previous studies (Guo et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCysLTR1 expression is reportedly increased in the airway mucosa of asthma patients, particularly during exacerbations (Zhu et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and an important regulator of mucus secretion, eosinophil recruitment, and airway inflammation, which are all hallmarks of asthma. This study serves as a basis towards a better understanding of the role of CysLTR1 in the lung, and many such studies to follow could be used towards inflammation related lung disease prevention or improved therapeutic strategies for such.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eBALF\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBronchoalveolar lavage fluid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eBSTFA\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eO-bis (trimethylsilyl) trifluoroacetamide\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCysLTR1\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCysteinyl leukotriene receptor 1\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCysltr1-/-\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCysLTR1-deficient\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCysltr1-/+\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHeterozygous\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCysLTs\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCysteinyl leukotrienes\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eGC-TOFMS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGas chromatography-time of flight mass spectrometry\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eIL-6\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInterleukin 6\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eKO\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eKnockout\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eLTB4\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLeukotriene B4\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eNF-κB\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNuclear factor kappa B\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eNIST\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNational Institute of Standards and Technology\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003ePCA\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePrincipal component analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003ePPP\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePentose phosphate pathway\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eQT\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eQuality control\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eTA\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTricarballylic acid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eTCA\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTricarboxylic acid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eTMCS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTrimethyl chlorosilane\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eTMS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTrimethylsilyl\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eTNF-α\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTumor necrosis factor-α\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eVIP\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eVariable of importance in projection\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eWT\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWild-type\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor information\u003c/h2\u003e\n\u003cp\u003eAuthors and affiliations\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBiomedical and Molecular Metabolism (BioMMet), North‒West University, Potchefstroom, South Africa\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWilson Bamise Adeosun and Du Toit Loots\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDivision of Medical Microbiology, Institute of Infectious Diseases and Molecular Medicine (IDM), Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSibongiseni KL. Poswayo and Suraj P. Parihar\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCentre for Infectious Disease Research in Africa (CIDRI-Africa), Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSuraj P. Parihar\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing financial interests\u003c/p\u003e\n\u003ch2\u003eContributions\u003c/h2\u003e\n\u003cp\u003eD.T.L. and S.P.P conceptualize and design the study. S.K.L.P and S.P.P participated in conducting the experiment. W.B.A and D.T.L contributed to metabolomics methodology protocol optimization. W.B.A contributed to GCTOF-MS analysis of biospecimens and data acquisition. D.T.L and W.B.A contributed to data analysis and interpretation of results. W.B.A, S.P.P and D.T.L contributed to drafting, editing and revision of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDu Toit Loots [email protected]\u003c/p\u003e\n\u003ch2\u003eEthics declarations\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Animal Research Ethics Committee (AREC 022/024) at the Faculty of Health Science, University of Cape Town.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eNational Research Foundation (NRF) of South Africa (SRUG2204062208); South African Medical Research Council (SAMRC) doctoral fellowship; NRF Research Development Grants for Y-Rated Researchers (RDYR180413320675); NRF Incentive Funding for Rated Researchers (IFR180305315866); NRF Evaluation and Rating Incentive Funding Award (RA22110567937); NRF support for Competitive Programme For Rated Researchers (SRUG22051611051); SAMRC Self-Initiated Research Grant, the Centre for Infectious Disease Research in Africa (CIDRI-Africa); Fogarty International Centre of the National Institutes of Health under Award Number (K43TW012587).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eD.T.L. and S.P.P conceptualize and design the study. S.K.L.P and S.P.P participated in conducting the experiment. W.B.A and D.T.L contributed to metabolomics methodology protocol optimization. W.B.A contributed to GCTOF-MS analysis of biospecimens and data acquisition. D.T.L and W.B.A contributed to data analysis and interpretation of results. W.B.A, S.P.P and D.T.L contributed to drafting, editing and revision of the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eData are available on request from the corresponding author\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAleti, G., Troyer, E. A., \u0026amp; Hong, S. (2023). G protein-coupled receptors: A target for microbial metabolites and a mechanistic link to microbiome-immune-brain interactions. \u003cem\u003eBrain, Behavior, and Immunity \u0026ndash; Health\u003c/em\u003e, 32, 100671\u0026ndash;100686. 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Cysteinyl leukotriene 1 receptor expression associated with bronchial inflammation in severe exacerbations of COPD. \u003cem\u003eChest\u003c/em\u003e, \u003cem\u003e142\u003c/em\u003e, 347\u0026ndash;357. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1378/chest.11-1581\u003c/span\u003e\u003cspan address=\"10.1378/chest.11-1581\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"metabolomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mebo","sideBox":"Learn more about [Metabolomics](http://link.springer.com/journal/11306)","snPcode":"11306","submissionUrl":"https://submission.nature.com/new-submission/11306/3","title":"Metabolomics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"cysteinyl leukotriene receptor 1, bronchoalveolar lavage fluid, metabolomics","lastPublishedDoi":"10.21203/rs.3.rs-8052995/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8052995/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction\u003c/h2\u003e\u003cp\u003eThe cysteinyl leukotriene receptor 1 (CysLTR1) is known as a potent lipid mediator with a well-established role in inflammatory regulation and lung disease. While its involvement in immune cell recruitment has been previously reported, its broader impact on pulmonary metabolism remains poorly understood.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e\u003cp\u003eThe study aims to investigate the metabolic consequences of a CysLTR1 deletion in mice to elucidate its role in pulmonary metabolic homeostasis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eBronchoalveolar lavage fluid (BALF) was collected from CysLTR1 knockout (KO) and wild-type (WT) mice and analysed using standardized untargeted gas chromatography\u0026ndash;time-of-flight mass spectrometry (GC-TOFMS) metabolomics.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eMetabolomics analyses of the BALF collected from the CysLTR1 KO mice presented significantly reduced levels of glucose, glucosamine, and glyceric acid, indicating the role of the CysLTR in lung glucose uptake and consequently lung glycolysis and gluconeogenesis. This is further supported by reductions in myo-inositol and D-chiro-inositol, also supporting previous findings that this occurs due to insulin resistance. Consequential disruption of various glucose-dependent pathways, including the pentose phosphate pathway (reduced gluconic acid, sedoheptulose and xylose) and purine metabolism (reduced 1-methylinosine) indicates a consequential altered nucleotide turnover, and the significantly reduced concentrations of butanoic acid, decan-2-ol, and 1-hexadecanol, indicate changes to fatty acid metabolism in the lung, as a compensatory response to the initial glucose deficiency induced by the CysLTR1 KO. Lastly, the changes to mandelic acid, glutaric acid, tricarballylic acid, and decan-2-ol, furthermore, indicate the role of CysLTR1 in the composition/metabolism of the microbiome.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study expands our knowledge on the role of CysLTR1 beyond its role in immune regulation, that may later serve towards a better understanding of CysLTR1 associated lung diseases and in the development of improved therapeutic strategies.\u003c/p\u003e","manuscriptTitle":"The Functionality of the Cysteinyl Leukotriene Receptor 1 (CysLTR1) in the Lung by Metabolomics Analysis of Bronchoalveolar Lavage Fluid","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-24 13:12:57","doi":"10.21203/rs.3.rs-8052995/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-10T14:13:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-08T20:52:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"38658405855947277665165032348155462720","date":"2025-12-11T23:39:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-08T01:44:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"333033789452069592409305872491811108682","date":"2025-11-15T02:03:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-12T21:05:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-07T05:04:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-07T05:04:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Metabolomics","date":"2025-11-07T04:41:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"metabolomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mebo","sideBox":"Learn more about [Metabolomics](http://link.springer.com/journal/11306)","snPcode":"11306","submissionUrl":"https://submission.nature.com/new-submission/11306/3","title":"Metabolomics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"5f9699b5-2261-4d12-9341-baaa93ec7372","owner":[],"postedDate":"November 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-16T01:23:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-24 13:12:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8052995","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8052995","identity":"rs-8052995","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}