The endotoxin content of PM 2.5 and its relationship with oxidative stress biomarkers in urine after subchronic inhalation exposure in a rat model

The endotoxin content of PM 2.5 and its relationship with oxidative stress biomarkers in urine after subchronic inhalation exposure in a rat model | 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 endotoxin content of PM 2.5 and its relationship with oxidative stress biomarkers in urine after subchronic inhalation exposure in a rat model Jessica Baldriche-Acosta, Marisela Uribe-Ramírez, Juana Narváez-Morales, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4428140/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Currently, our understanding of the impact of particulate matter on nephrotoxicity is limited. Oxidative stress has been identified as a mechanism involved in the adverse health effects due to exposure to this air pollutant, to their inorganic, organic, and aerobiological constituents (e.g. endotoxin). The goal of the present study was to correlate the endotoxin content of particulate matter with urinary oxidative stress biomarkers to explain early decline in renal dysfunction. Adult male Sprague-Dawley rats exposed to subchronic inhalation to particles less 2.5 micrometers in aerodynamic diameter, also known as fine particles or PM 2.5 (8 weeks, 4 days/week, 5 hours/day). The control group was exposed to filtered air. Biomarkers of oxidative stress were assessed in urine samples per week harvested by metabolic cage. The assessed oxidative stress biomarkers were methylglyoxal, non-esterified fatty acids, malondialdehyde, advanced oxidative protein products, arginase, myeloperoxidase, glutathione-S-transferase, and gamma-glutamyl transferase. Subchronic exposure to PM 2.5 increased five evaluated biomarkers in urine. Endotoxin content in PM 2.5 positively correlated with urinary oxidative stress biomarkers evaluated. Positively correlation of urinary oxidative stress biomarkers was found with urinary early kidney damage biomarkers (e.g., β-2-microglobulin and cystatin-C). The subchronic inhalation exposure to PM 2.5 induce the presence of oxidative stress reflected in urine, based on statistical correlations, suggests early kidney damage related to endotoxin content. Fine particles or PM2.5 oxidative stress nephrotoxicity biomarkers Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Exposure to high atmospheric particulate matter (PM) causes cardiorespiratory diseases and impaired lung function (Liebers et al., 2008 ; Mukherjee and Agrawal, 2017; Rylander, 2002 ). Smaller particles are more likely to enter the respiratory tract and lungs after inhalation. It is suggested that particles with a diameter less than or equal to 2.5 µm, known as fine particles or PM 2.5 can be deposited in the alveolar sacs and cross the alveolar-capillary membrane, reaching distant organs (Snow et al. 2014 ). Most studies focus on respiratory damage, and systemic effects remain poorly understood. It has been reported that PM toxicity is mainly attributed to the metal(loid), and endotoxin content (Valavanidis, Fiotakis, and Vlachogianni 2008). PM toxicity has recently associated with metabolic and kidney diseases (Clementi et al. 2019 ; Reyes-Caballero et al. 2019 ). Oxidative stress (OxS) and inflammation are the main pathophysiological mechanisms to explain the damage to health by particle exposure (N. Li, Xia, and Nel 2008; Osornio-Vargas et al. 2003 ). The OxS is the imbalance between the production of free radicals, accumulation of oxidative products, and their ability to detoxify these products in the cells (Betteridge 2000 ). Free radicals at low concentrations play essential roles for the organism involving the signaling process, metabolism, differentiation, development, and proliferation can have mitogenic effects, can mimic and amplify the action of growth factors, and the immunological defense against pathogens in the respiratory burst. However, they are highly reactive and can react with carbohydrates, lipids, proteins, and nucleic acids, altering their structure and function and leading to cell damage. Thus, oxidative damage is associated with broad diseases (respiratory, cardiovascular, and neurodegenerative, among others), ischemic processes, inflammation, and cancer (Pizzino et al. 2017 ). Some molecules are indicators of the redox state in the organism. These biomarkers include host components of the antioxidant system, which change in response to increased oxidation-reduction stress, and molecules modified by interacting with free radicals (Ho et al. 2013 ). Among the latter, lipid, and protein oxidation biomarkers such as malondialdehyde (MDA) and advanced protein oxidation products (AOPPs), respectively, are widely used in clinical and research (Del Rio, Stewart, and Pellegrini 2005). There are experimental findings that OxS may be involved in the pathogenesis of different kidney diseases. Some OxS biomarkers have been used as indicators of kidney damage in bladder cancer (Xu et al. 2022 ), urinary tract infections (Smith et al. 2020 ), tubular damage (McMahon, Koyner, and Murray 2010 ), and ischemia-induced acute kidney injury (Tsikas 2017 ). It has also been proposed that proximal oxidative metabolism in acute kidney injury may lead to chronic kidney disease (CKD). In patients with CKD and uremia, there is an increase in the production of reactive oxygen species (ROS) and a decrease in the antioxidant systems of the kidney (Schaub, Venkatachalam, and Weinberg 2021). Recently, it was reported that subchronic inhalation of PM 2.5 increases the urinary excretion of early kidney damage biomarkers in rats with renal cortex impaired antioxidant response (Aztatzi-Aguilar et al. 2016). However, PM 2.5 is a heterogeneous mixture of substances, and not all its components seem to be equally responsible for its toxicity. The biological fraction composing PM 2.5 is responsible for multiple allergies and respiratory diseases (Khan et al., 2018 ). However, it is unknown whether the inhalation of PM 2.5 or aerobiological components, such as endotoxin, can cause adverse effects beyond cardiorespiratory ones. Specifically, endotoxin induces kidney injury when administered intravenously and intraperitoneally in rodent models, but the nephrotoxic and oxidative stress effect of this aerobiological when inhaled remain unknown. This study aims to assess OxS through urinary biomarkers after subchronic exposure to PM 2.5 inhalation and establish the relationship of urinary OxS biomarkers with the endotoxin content in PM 2.5 to explain renal effects by air pollution. 2. Materials and Methods 2.1. Subchronic inhalation exposure to PM 2.5 All the experimental protocols were designed in accordance with the Guide for the Care and Use of Laboratory Animals issued by the National Institute of Health and with the Mexican guidelines (NOM-062-ZOO-1999); and with the approval of the institution's Animal Care and Use Committee (UPEAL-CINVESTAV). The animals were maintained in a freestanding clean room with a changing station docking port (bioBubble®, Colorado, USA). The inhalation exposure was carried out from June to August 2013 at Cinvestav-IPN by Aztatzi-Aguilar et al., 2016. Briefly, male adult Sprague Dawley rats (purchased from Harlan, Mexico) were exposed subchronically (8 weeks, 4 days/week, 5 h/day) to PM 2.5 in whole-body chambers associated with a particulate concentrator, a control group was exposed to filtered air (FA) at air flow of 2.5 L/min. Urine samples were collected in metabolic cages with ice bedding to ensure integrity. For each group, six rats were randomly selected, and housed one per cage for 12 h, with a previous recovery period of 8 hours after the last weekly exposure (Aztatzi-Aguilar et al. 2016). It was obtained the body weight gain, the water consumption, and the urinary flow rate. 2.2. Endotoxin quantification Monitoring in parallel was carried out with a miniVol equipment to estimate the air ambient PM 2.5 concentrations. The endotoxin content in PM 2.5 was determined in particles collected by HiVol equipment. Samples were collected weekly at the same exposure time while the animals remained in the chambers. The Limulus Amebocyte Lysate Pyrochrome Chromogenic Test Kit (Pyrochrome Associates of Cape Cod Incorporated, Falmouth, MA, USA) was used as the manufacturer recommended to determinate the Endotoxin content. 2.3. Oxidative stress biomarkers evaluation Eight oxidative stress (OxS) biomarkers were evaluated in the urine of Sprague Dawley rats after subchronic inhalation exposure to PM 2.5 , previously described. The biomarkers assessed were arginase (EC: 3.5.3.1), myeloperoxidase (MPO, EC: 1.11.2.2), gamma-glutamyl transferase (GGT, EC: 2.3.2.2), glutathione S-transferase (GST, EC: 2.5.1.18), Methylglyoxal (MGO), malondialdehyde (MDA), non-esterified fatty acids (NEFAs), and advanced oxidation protein products (AOPPs). All methods were adapted to microplate reader. 2.3.1. Arginase (EC: 3.5.3.1) The arginase activity in the samples was determined from the urea production. A first dilution of the urine samples was made in distilled water (1:40; v:v). A second dilution (1:1; v:v) was then performed in PBS 1X pH 7.4. The final urine dilution was incubated at 55°C for 10 minutes in a dry block heater (Benchmark BSH1001). From each dilution sample, 50 µL/well was plated to a 96-well plate; after that, 50 µL of arginine solution (0.5 M, pH 9.7) was added and incubated at 37°C for 1 h. Later, 140 µL of the acid mixture (H 2 SO 4 , H 3 PO 4 , and H 2 O 1:3:7 v:v:v) and 10 µL of 9% α-isonitrosopropiophenone were added. It was incubated at 100°C for 45 min, and the absorbance was read at 540 nm in a microplate reader (Labsystems, Multiskan MS). A urea standard curve was used and processed as well as samples (Corraliza et al. 1994 ). 2.3.2. Myeloperoxidase (MPO, EC: 1.11.2.2) MPO activity, a peroxidase with antimicrobial action, was evaluated spectrophotometric through the oxidation of the 3,3′,5,5′-Tetramethylbenzidine (TMB). Undissolved urine (20 µL) was incubated with 90 µL of mixed reaction solution prepared as follows (76.77µL of PBS 0.1 M pH 5.4, 9.45 µL of TMB 1.6 mM, and 3.78 µL of H 2 O 2 0.3 mM), at 37°C for 3 min in the dark. Then, the reaction was stopped by adding 150 µL of cold glacial acetic acid 0.4 M, pH 3. Samples were read at 570 nm in a microplate reader (Labsystems, Multiskan MS). For data processing, the average of the blank reaction wells (bidistilled water) was determined and subtracted from each sample absorbance well. MPO activity was then determined considering that 1U of activity corresponds to the change of 0.1 in the absorbance (Suzuki et al. 1983 ). 2.3.3. Glutathione S-transferase (GST, EC: 2.5.1.18) The GST enzymatic activity was conducted by the conjugation of glutathione with the acceptor 1-chloro-2,4-dinitrobenzene (DCNB). To 10 uL of undissolved urine, was added 230 µL of the mixed reaction solution (4580 mL of PBS 1X, 360 µL of GSH 10 mM, and 60 µL of DCNB 60 mM) and read at 340 nm for 10 min every minute in a Beckman Coulter DU-800 Spectrophotometer. GST activity was expressed in nmol/min/mg protein (Habig, Pabst, and Jakboy 1974). 2.3.4. Gamma-glutamyl transferase (GGT, EC: 2.3.2.2) The GGT activity was determined by the transference of gamma-glutamyl of L-γ-glutamyl-p-nitroanilide to the peptide glycine-glycine (Gly-Gly), realizing the p-nitroanilide which detected spectrophotometric. To 10 µL of undissolved urine, 190 µL of reaction mix (170 µL Buffer Tris Gly-Gly pH 8.2 and 20 µL L-γ-glutamyl-p-nitroanilide 10 mM) was added. The absorbance 405 nm was read for 10 min every 30 seconds in a Beckman Coulter DU-800 Spectrophotometer. Activity was calculated according to Persijn (Persijn and van der Slik 1976). 2.3.5. Methylglyoxal (MGO) The MGO is a dicarbonyl intermediate of no enzymatic glycans, it is considered a precursor of advanced glycation end products, and it is related with impaired metabolism. To quantify MGO, 50 µL of undissolved urine was incubated with 100 µL of DNFH 0.9 mM at 37°C for 10 min. Subsequently, 100 µL of NaOH 1.5 N was added, and the absorbance at 540 nm was read in a microplate reader (Labsystems, Multiskan MS). To carry out the quantification of MGO, a standard MGO curve was made (Fields and Dixon 1971 ). 2.3.6. Non-esterified fatty acids (NEFAs) NEFAs or free fatty acids are essential constituents of the structure of lipids in membranes and lipoproteins. To quantified NEFAs 50 µL of undissolved urine was added to 200 µL of fatty acid extraction solution (heptane, methanol, and chloroform: 24.5:1 v:v:v). After removing and discarding the micelle, was added 100 µL of a cupric solution (cupric nitrate 0.5 M, triethanolamine 1M, NaOH 1N, NaCl in deionized H 2 O). The micelle was removed and was added 5 µL of sodium diethyldithiocarbamate at the time of reading at 450 nm in a microplate reader (Labsystems, Multiskan MS). To calculate the concentration of NEFAS, a standard palmitic acid curve was constructed (Duncombe 1964 ). 2.3.7. Malondialdehyde (MDA) The MDA detection is based on the acidic reaction of the chromogen 1-Methyl-2-phenylindole (MPI) with the MDA at mid-temperature condition. To perform the assay 50 µL of undissolved urine were added 185 µL of MPI 10 mM in acetonitrile: methanol (3:1, V: V) and 40 µL of HCl 37% in the dark. It was shaken and incubated at 45°C for 40 min. Then, it was centrifuged at 9,000 rpm for 15 min. Supernatants were recovered and read at 570 nm in a microplate reader (Labsystems, Multiskan MS). To determine the concentration of MDA, a standard curve of 1,1,3,3-tetramethoxypropane (malonaldehyde bis (dimethyl acetal) was constructed (Esterbauer, Schaur, and Helmward 1991). 2.3.8. Advanced Oxidation Protein Products (AOPPs) The AOPPs are uremic toxins created during OxS through the reaction of plasma proteins with chlorinated oxidants such as chloramines or hypochlorous acid. This assay was carried out with 40 µL of undissolved urine, mixed with 120 µL of PBS 1X and 40 µL of pure acetic acid. It was shaken and incubated for 10 minutes at room temperature in the dark and was read at 340 nm in a Beckman Coulter DU-800 Spectrophotometer. To determine the concentration of AOPPs, a calibration curve of chloramine T was made (Witko-Sarsat et al. 1996). 2.4. Statistical analysis The statistical processing and data analysis were performed in GraphPad Prism version 8.01. The body weight gain, the water consumption, and the urinary flow rate was expressed as percentage.Data for each biomarker were corrected by the urinary volume. Non-parametric statistical analysis was performed to compare the groups using the U-Mann-Whitney test. A Pearson correlation tests were performed between PM 2.5 endotoxin content with OxS biomarkers. In addition, the OxS biomarkers evaluated were correlated with early kidney damage conventional biomarkers reported by Aztatzi-Aguilar et al., 2016. A statistically significant difference was considered with a p < 0.05. 3. Results The PM 2.5 environmental concentrations and the endotoxin content in PM 2.5 are shown in Table 1 . It was observed a constant PM 2.5 concentration a long of first seven weeks, and a low concentration at eighth week. The maximum Endotoxin content in PM 2.5 was observed at the second and third weeks. The lowest Endotoxin content was observed at seventh week. There was not observed a relation between particle concentration and Endotoxin content. General health animal parameters of exposure groups are present in the supplementary material ( Figure S1 ). Where no body weight changes were observed between PM 2.5 group respectively control group (FA). Changes in the water consumption and urinary flow rate were observed in the PM2.5 group respect the FA group. Table 1 Environmental concentration of PM2.5 per week and the endotoxin content in particles. Week Mass (ug/m 3 ) Endotoxin (UE/mg) § 1 18.59 2 ± 1.1 2 21.28 157 ± 24.9 3 22.95 140.25 ± 4.6 4 20.30 2.22 ± 0.4 5 15.44 2.1 ± 0.3 6 18.03 3.1 ± 0.5 7 15.95 1.5 ± 0.4 8 8.20 18.6 ± 4.2 § Average ± SD of triplicates by sample. We show the percentages of body weight gain, water consumption, and urinary flow rate of the PM 2.5 group compared with the control group. Statistical differences were observed in the water consumption on weeks 2, 3, 5, 6, and 8; nevertheless, the urinary flow rate presents the first increment at week 2 and the rest of weeks show a constant and statistically significant augment. We observed a statistical tendency in endotoxin content correlation with body weight and water consumption, however, statistical significance was observed in the urinary flow rate (Table 2 ). Table 2 Pearson correlation differences between PM2.5 endotoxin content and PM2.5 gravimetry mass over the general animal condition Variable Endotoxin PM 2.5 Rho p-value Rho p-value Body weight (g) -0.295 0.057 -0.209 0.133 Water consumption (µL/g body weight) 0.255 0.087 -0.005 0.489 Urinary flow rate (µL/min/100g body weight) 0.538 0.001 0.034 0.428 Oxidative stress biomarkers were grouped in 1) cell metabolism (MGO and NEFAs) and oxidative products (MDA and AOPPs); 2) oxidative stress related with the immune response (Arginase and MPO); and 3) Glutathione antioxidant response (GST and GGT). As biomarkers of cellular metabolism MGO and NEFAS were evaluated (Fig. 1 ). A significant increase in MGO levels were observed during all exposure weeks to PM 2.5 (Fig. 1 A). A high increase in NEFAs levels were observed in weeks 1, 2, 4, 5, 7 and 8 (Fig. 1 B). The MDA levels increased at week three (p = 0.057) and it was statistically significative at fourth week of exposure; after that, it showed a decreasing trend, which was significant at weeks 5 and 6 (Fig. 2 A). On the other hand, AOPPs increased significantly at second week and showed a significant decrease at fifth week (Fig. 2 B). Regarding OxS biomarkers related to immune response. The urinary activity of Arginase and MPO enzymes was evaluated as evidence of cell damage and inflammation during subchronic exposure to PM 2.5 (Fig. 3 ). Arginase enzymatic activity shows after subchronic exposure to PM 2.5 , a statistically significant increase on weeks two, three, and six concerning the control group (Fig. 3 A). Statistical differences in MPO enzymatic activity were observed partially in all weeks, except for week three; the urinary MPO enzymatic activity increased in the PM 2.5 group compared to FA (Fig. 3 B). Respect Glutathione antioxidant response the urinary activity of the GST and GGT enzymes was tested. It was observed the presence of both glutathione-dependent enzymes in urinary samples (Fig. 4 ). Our results show a significant increase in GST from the first to sixth week after PM 2.5 exposure (Fig. 4 A), while GGT shows a statistically significant decrease after the fourth week of exposure to PM 2.5 (Fig. 4 B). A correlation between PM 2.5 endotoxin content and urinary levels of the OxS biomarkers were performed. A positive and statistically significant correlation was obtained for five of eight OxS biomarkers with the PM 2.5 endotoxin content (Table 3 ). Table 3 Pearson correlation test between weekly PM2.5 endotoxin content and the urinary excretion of OxS biomarkers OxS biomarker Rho p-value Arginase 0.0059 0.3547 GST 0.0221 0.2442 MPO 0.0540 0.1266 GGT 0.3919 0.0003 MGO 0.6502 < 0.0001 MDA 0.3474 0.0019 NEFAs 0.3825 0.0006 POAPs 0.3798 0.0002 The data are shown as Pearson Correlation Coefficient (Rho) and statistical significance p < 0.05. On the other hand, to strengthen our results of urinary OxS biomarkers after PM 2.5 exposure with the kidney damage we performed correlations between them with the early kidney damage biomarkers reported by Aztatzi-Aguilar et al. , (2016). A positive and statistically significant correlation was found between the early kidney damage biomarkers reported previously and the urinary levels of the OxS biomarkers (Table 4 ). The highest correlation coefficient values with statistical significance were observed for MGO and NEFAs. 4. Discussion It has been recently reported epidemiological and in vivo evidence that supports PM 2.5 air pollution not only causes cardiorespiratory conditions but is also linked to other effects such as the development of autoimmune processes (Adami et al. 2022 ; Gawda et al. 2017 ), metabolic diseases (Zheng et al. 2022 ), and nephrotoxicity (Aztatzi-Aguilar et al. 2021; Aztatzi-Aguilar et al. 2016). These effects are attributable to the PM 2.5 size, which can penetrate deeply into the respiratory tract, causing inflammation and OxS (Gawda et al. 2017 ). Both responses can amplify and trigger systemic effects that can affect organs distant from the site of exposure (de Camargo et al. 2021; Polidoro et al. 2020 ). Table 4 Pearson correlation test between early kidney damage biomarkers and urinary excretion of OxS biomarkers. Early Kidney damage biomarker OxS biomarker Rho p-value β2M MPO 0.406 0.013 MGO 0.637 < 0.0001 NEFAs 0.704 < 0.0001 Cys-C MPO 0.463 0.005 GGT 0.322 0.042 MDA 0.337 0.037 POAPs 0.357 0.026 MGO 0.642 < 0.0001 NEFAs 0.718 < 0.0001 2-NGAL MPO 0.431 0.014 GGT 0.402 0.021 MDA 0.558 0.0035 POAPs 0.366 0.028 MGO 0.717 < 0.0001 NEFAs 0.640 0.0004 EGF MPO 0.479 0.007 GGT 0.544 0.002 MDA 0.786 < 0.0001 POAPs 0.634 0.0001 MGO 0.749 < 0.0001 NEFAs 0.692 < 0.0001 AGP MDA 0.646 0.0006 MGO 0.607 0.0011 NEFAs 0.529 0.004 The data are shown as Pearson Correlation Coefficient (Rho) and statistical significance p < 0.05. β-2-Microglobulin (β2M); Cistatin-C (Cys-C); neutrophil gelatinase-associated lipocalin (2-NGAL); Epithermal Growth Factor (EGF); and α-1-glycoprotein (AGP) There are recently preclinical and epidemiological studies on the toxic effect of PM inhalation on kidney physiology decline. Recent published studies have shed light on the inhalation exposure to atmospheric particles, particularly PM 2.5 , as a potential cause of renal damage (Yuan et al. 2022 ; Huang et al. 2020 ; Aztatzi-Aguilar et al. 2016; Aztatzi-Aguilar et al. 2021; Rasking et al. 2022 ). Some even regard it as an important environmental risk factor for the development of chronic kidney disease (Zhang, Liu, and Liu 2021; Ghazi, Drawz, and Berman 2022). However, this remains an underexplored field that offers many opportunities for new insights. For example, regarding the mechanisms involved in the deterioration of renal physiology, and which components of atmospheric particles may be primarily responsible for nephrotoxicity. In this respect, the present study assessed whether OxS biomarkers are present in urine after PM 2.5 inhalation exposure and if these biomarkers correlate with the PM 2.5 endotoxin content. The importance of urinary OxS biomarkers in relation to kidney damage was confirmed based on their correlation with early kidney damage biomarkers previously reported by Aztatzi-Aguilar et al. (2016). Our results show significant differences in the urinary excretion of all OxS biomarkers evaluated in the PM 2.5 group compared to the FA control. MGO and NEFAs as biomarkers of cellular metabolism show higher differences to PM 2.5 exposure because they significantly increased their urinary levels from the first to the last week of exposure. MGO is primarily a byproduct of glycolysis, which increases during hyperglycemia, inflammation, and hypoxia, because glycolysis increases under these conditions (Hanssen, Stehouwer, and Schalkwijk 2019). MGO causes high OxS and cell damage and its increases in tissues is the leading cause of microvascular damage in diabetic patients (Schalkwijk and Stehouwer 2020 ). Its accumulation in the kidney has been related to tubular atrophy in models of acid nephropathy in mice, linked with a decrease in GSH reserves compromises the antioxidant defense and detoxification (Li et al. 2012 ). On the other hand, the increase in NEFAs is mainly due to the lipolysis of triacylglycerides in adipose tissue due to greater energy demand (Schelling 2022 ), and have been associated with obesity, insulin resistance and inflammation(Nicholas et al. 2024 ) Exposure to xenobiotics alters tissue fat metabolism. For instance, in the liver, it stimulates the uptake of NEFAs while reduces fatty acid oxidation (Massart et al. 2022 ). However, the kidney seems to exhibit distinct behavior in this regard. The kidney proximal tubules are sites that undergo ischemic damage during acute renal failure, where NEFAs accumulation has been observed after hypoxia-ischemia and reoxygenation-reperfusion in proximal tubules (Feldkamp et al. 2006 ). The presence of NEFAs in urine could result from tissue damage by PM 2.5 exposure, which may indicate the kidney's high metabolic rate in animals exposed. Another possible explanation for the increased urinary release of NEFAs could be linked to fatty acid binding enzyme-1 (FAB-1); this cytoplasmic protein is found in proximal tubular cells, where it facilitates the uptake of fatty acids by the cell and directs them towards different metabolic pathways in the cytoplasm. FAB-1 has been reported as a highly sensitive early kidney damage marker in other animal models because its range of urinary excretion depends on the generated kidney damage (Noiri et al. 2009 ). The reuptake of fatty acids and their cytoplasmic transport by FAB-1 must be affected by the tissue's decrease in protein due to cell damage. In addition, the transport of fatty acids to FAB-1 must also be affected by the loss of the brush border of the proximal tubule. On this, transporter-2 (FATP2) has been reported as the main membrane transporter that mediates the uptake of NEFAs in the apical membrane of rat proximal tubular epithelial cells (S. Khan et al. 2018 ). Urinary levels of MDA and AOPPs were measured as biomarkers of OxS related to lipid and protein oxidation, respectively. Notably, both biomarkers behavior is similar and seems related to the endotoxin concentration fluctuations in the PM 2.5 samples. This suggests that oxidative stress products in the kidney are related to the endotoxin content of PM 2.5 ; that is, endotoxin exposure could contribute to induce acute kidney damage. It may be that the endotoxin crosses the alveolar-capillary barrier and enters the systemic circulation, being able to reach other organs such as the kidneys, or that the kidney impact is due to the amplification of the damage and/or the pulmonary inflammatory response caused by exposure. In other words, inhalation exposure to lipopolysaccharide (LPS) causes damage and inflammation in the lung, with the consequent production of mediators that can reach the bloodstream and the rest of the body, amplifying the lung response to exposure. This could explain the occurrence of lipoperoxidation and protein oxidation at the renal level. It should be noted that the increase in MDA appears one week after the peak of endotoxin. This time difference between the exposure and the onset of the effect, called lag, has been reported in exposure to PM, and it is suggested that it may be 1 to 6 days. The space-time variation of PM deposit, composition, and concentrations mainly explains the lag effect. The biological responses to PM show different behavior patterns among patients. Its adverse effects take several days to become evident (Chien, Chen, and Yu 2018). The decrease in MDA and AOPPs after fifth week suggest that the kidney can compensate for the oxidation of lipids and proteins, for example, by the activation of renal antioxidant pathways, which need further study in this experimental model. Increased activity of Arginase and MPO was observed in the PM 2.5 group. Both enzymes are related to OxS and the immune response. ARG is also found in mammalian innate immune system cells such as macrophages, where it participates in an oxygen-independent defense mechanism, depleting L-arginine during pathogen phagocytosis (Munder 2009 ). We consider that the increase in the urinary activity of ARG may show cellular damage in the kidney due to exposure. This would explain a greater release of ARG into the urine because of increased cellular degradation of the renal tissues. The activity of ARG in urine has recently been reported as a specific and sensitive biomarker in the progression of bladder cancer since it increases in the most advanced stages due to greater cell damage (Kalaf, Ewadh, and Abood 2020). We consider that its increased activity indicates the presence of renal inflammation with possible leukocyturia because of exposure. MPO is stored in azurophilic granules of polymorphonuclear leukocytes, neutrophils, monocytes, and macrophages. It is released in response to leukocyte activation, and it is related to OxS because it catalyzes the production of hypochlorous acid, a powerful oxidant and halogenating agent of proteins, which has been linked to tissue damage due to inflammation (Aratani 2018 ). The increase in urinary MPO activity was also obtained. An increase in plasma MPO has been related to ischemic heart disease since MPO oxidizes low-density lipoproteins (LDL), contributing to atherosclerotic plaque formation (Delporte et al. 2013 ). However, its increase in urine has been a helpful biomarker in diagnosing bacterial infections in the urinary tract in a canine model (Smith et al. 2020 ). In our case, having found its increased activity in urine throughout exposure to PM 2.5 can be due to intratubular immune cell infiltration, which was reported by Aztatzi-Aguilar et al. (2016). Regarding the renal antioxidant response, significant changes were observed in the enzymatic activities of GST and GGT in the urine of the group exposed to PM 2.5 compared to AF. In the kidney two isoforms of GST are abundant, α-GST and π-GST, which are in 7the proximal and distal tubule, respectively. Both are released exclusively in the urine during kidney damage and are very early indicators of tubular damage (Shu et al. 2016 ). They have been reported as biomarkers of acute kidney injury after cardiac surgery (McMahon, Koyner, and Murray 2010 ). The α-GST isoform is also present in the liver, but its increase in serum has been reported in liver damage, transplantation, and viral infections since it does not cross the glomerular filtration barrier (McMahon, Koyner, and Murray 2010 ). On the other hand, GGT activity showed a significant decrease after week 4 of exposure. GGT, unlike GST, is a membrane enzyme localized to the apical membrane of the proximal tubular cells in the kidney. The drop in its activity could reflect the possible loss of the brush border in the proximal tubule. Khundmiri et al., ( 1997 ) reported decreased urinary GGT enzymatic activity due to tubular damage and loss of cellular integrity. Since GST and GGT participate in the biochemical cycle of GSH, both may show opposite behavior in their enzymatic activities because they compete for the same biochemical sources. If so, it is possible the increase in the activity of one implies the decrease in the activity of the other. This biochemical regulation for both proteins in isolate cell model need to be study. According to the correlation results, the OxS biomarkers in urine is attributable to the PM 2.5 endotoxin content. Particularly MGO and NEFAs are helpful indicators of early renal damage since they were positively correlated with previously conventional early renal damage biomarkers reported by Aztatzi-Aguilar et al., (2016). Both MGO and NEFAs increase during inflammation. Thus, their increased urinary levels resulting from renal damage caused by the inhalation of atmospheric particles can be attributed to the inflammatory component of the particles, the endotoxin. Endotoxin in the blood (endotoxemia) can lead to kidney damage due to inflammation, affecting the tubular system, vasculature, and causing an enlargement of glomerular pores. Podocytes possess endotoxin receptors such as TLR-4 and CD14, contributing to this process (Peng et al. 2019 ; Tinti et al. 2021 ). It's important to highlight that endotoxin, as an immunogenic and inflammation-inducing molecule, maintains both properties when it is present in ambient air. Furthermore, there are no regulations governing its levels in outdoor air. 4.1. Conclusions The inhalation of fine particulate matter induces the presence of OxS biomarkers in urine following subchronic exposure in a rat model. Endotoxin, an aerobiological component of particulate matter, correlates with the nephrotoxic effects of inhaling atmospheric particles due to its inflammatory potential and induction of renal OxS. This leads to elevated urinary levels of oxidative compounds (MGO, NEFAs, MDA, and POAPs), modifying the activity of oxidative enzymes (GST and GGT), and as well as inflammation-related biomarkers (arginase and MPO). Moreover, our findings highlight the potential of the assessed OxS biomarkers, particularly MGO and NEFAs, as indicators of kidney damage. Collectively, these results together shed new light on the possible involvement of inhaling aerobiological atmospheric pollutants in the development of nephropathies. Abbreviations PM particulate matter PM 2.5 fine particles with an aerodynamic diameter of 2.5 µm or less FA filtered air OxS oxidative stress LPS lipopolysaccharide MGO methylglyoxal, NEFAs:non-esterified fatty acids MDA malondialdehyde AOPPs advanced oxidative protein products MPO myeloperoxidase GST glutathione S-transferase GGT gamma-glutamyl transferase β2M β-2-microglobulin and Cys-C:Cystatin-C. Declarations Competing Interest declaration All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. Funding sources This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contribution J.B.A. Writing- Original draft preparation, Investigation, Formal analysis, Data curation. M.U.R. Investigation, Formal analysis. J.N.M. Investigation, Formal analysis. A.V.R. Project administration, Resources, Writing - Review & Editing. O.C.B: Project administration, Resources, Writing - Review & Editing, Supervision. O.G.A.A. Conceptualization, Visualization, Methodology, Data curation, Writing - Review & Editing, Supervision. Acknowledgement We want to thank the Department of Research in Toxicology and Environmental Medicine of the National Institute of Respiratory Diseases in Mexico City; for the equipment for evaluating biomarkers during the COVID-19 pandemic. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4428140","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":305174116,"identity":"7364087b-d898-4d10-8d21-e646df789606","order_by":0,"name":"Jessica Baldriche-Acosta","email":"","orcid":"","institution":"Center for Research and Advanced Studies of the National Polytechnic Institute","correspondingAuthor":false,"prefix":"","firstName":"Jessica","middleName":"","lastName":"Baldriche-Acosta","suffix":""},{"id":305174117,"identity":"81df8729-e605-4c9f-9132-934e558d245b","order_by":1,"name":"Marisela Uribe-Ramírez","email":"","orcid":"","institution":"Center for Research and Advanced Studies of the National Polytechnic Institute","correspondingAuthor":false,"prefix":"","firstName":"Marisela","middleName":"","lastName":"Uribe-Ramírez","suffix":""},{"id":305174119,"identity":"2014058e-57c2-45ec-9e9c-a45bed73c680","order_by":2,"name":"Juana Narváez-Morales","email":"","orcid":"","institution":"Center for Research and Advanced Studies of the National Polytechnic Institute","correspondingAuthor":false,"prefix":"","firstName":"Juana","middleName":"","lastName":"Narváez-Morales","suffix":""},{"id":305174120,"identity":"08e51b35-bbf1-4d8e-b61f-f0db85b4688f","order_by":3,"name":"Andrea De Vizcaya-Ruiz","email":"","orcid":"","institution":"University of California Irvine","correspondingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"","lastName":"De Vizcaya-Ruiz","suffix":""},{"id":305174121,"identity":"a8c3ca11-73f6-48cc-bd9f-c23574381d74","order_by":4,"name":"Olivier Christophe Barbier","email":"","orcid":"","institution":"Center for Research and Advanced Studies of the National Polytechnic Institute","correspondingAuthor":false,"prefix":"","firstName":"Olivier","middleName":"Christophe","lastName":"Barbier","suffix":""},{"id":305174126,"identity":"469655f1-54e4-40b1-8437-647fab523449","order_by":5,"name":"Octavio Gamaliel Aztatzi-Aguilar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIie3SMYoCMRSA4QRxtnmQ9gVErxAYsBK8yhsEvYLFEjIsxMYDjHgK8QIjA6YZsB2xURa0sdBuF5ZFxVai222Rrwu8n5DwGAuCfwwE4+bl6esoMZDmnuDLCVP5/fQ8EW/usP3+0o3YFfYThlqLjOpz8CRyTEk6pgjaZTKKoSwQK4rWmSdROS0MEEC74lZObI6szKPq5EtWuzT9IYQ4uyW/Glu3hHxJ1eMfQAoUcotnU0PljP8Wme35tNEnwDKxeFoWcuZS632LEIPt+djRXTFye6R3LZpFbbnx/dgjf1iDIAiC4LELbGhNnipK0JoAAAAASUVORK5CYII=","orcid":"","institution":"Center for Research and Advanced Studies of the National Polytechnic Institute","correspondingAuthor":true,"prefix":"","firstName":"Octavio","middleName":"Gamaliel","lastName":"Aztatzi-Aguilar","suffix":""}],"badges":[],"createdAt":"2024-05-16 03:29:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4428140/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4428140/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57634493,"identity":"d00f50c7-dacc-4019-b850-6bf31a9d3b7e","added_by":"auto","created_at":"2024-06-03 15:38:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":15535,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSubchronic inhalation exposure to PM\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2.5 \u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003eincreases the urinary excretion of OxS biomarkers related to cellular metabolism.\u003c/strong\u003e\u0026nbsp;OxS biomarkers were determined in 12-hours urine collected from rats (n=6) at the end of each week during eight weeks of exposure to PM2.5 by the metabolic cage.\u0026nbsp;\u003cstrong\u003eA)\u0026nbsp;\u003c/strong\u003eMethylglyoxal (MGO) concentration increased in all weeks of exposure.\u0026nbsp;\u003cstrong\u003eB)\u0026nbsp;\u003c/strong\u003eNon-esterified fatty acids (NEFAs) increased in 1, 2, 4, 5, 7, and 8 weeks. The comparison of groups was performed by U–Mann-Whitney statistical test. In box-and-whisker plots, asterisks (*) indicate statistically significant differences (p\u0026lt;0.05).\u0026nbsp;\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4428140/v1/dd457135a90fa188c2a26aed.png"},{"id":57634492,"identity":"fb92b1c9-9b9a-4bca-982d-0407d4868bba","added_by":"auto","created_at":"2024-06-03 15:38:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":14383,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSubchronic inhalation exposure to PM\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2.5 \u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003einduces the urinary excretion of lipid and protein oxidation biomarkers.\u003c/strong\u003e\u0026nbsp;OxS biomarkers were determined in 12-hours urine collected from rats (n=6) at the end of each week of exposure to PM2.5 by the metabolic cage.\u0026nbsp;\u003cstrong\u003eA)\u003c/strong\u003e\u0026nbsp;Malondialdehyde (MDA) increased in the fourth week and decreased in the next two weeks (5 and 6).\u0026nbsp;\u003cstrong\u003eB)\u003c/strong\u003e\u0026nbsp;Advanced Oxidative Protein Products (AOPP) show a similar pattern increasing in week 2 and then decreasing in week 5. The comparison of groups was performed by U–Mann-Whitney statistical test. In box-and-whisker plots, asterisks (*) indicate statistically significant differences (p\u0026lt;0.05).\u0026nbsp;\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4428140/v1/4f46beb41901056e487a6f99.png"},{"id":57634489,"identity":"afc56671-eb1b-4ecc-a985-1cab2f38ce8e","added_by":"auto","created_at":"2024-06-03 15:38:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":15623,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSubchronic inhalation exposure to PM\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2.5\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e increases the urinary excretion of OxS biomarkers related to immune response.\u003c/strong\u003e OxS biomarkers were determined in 12-hours urine collected from rats (n=6) at the end of each week of exposure to PM2.5 by the metabolic cage. \u003cstrong\u003eA)\u003c/strong\u003e Arginase activity increased at weeks 2, 3, and 6.\u0026nbsp;\u003cstrong\u003eB)\u0026nbsp;\u003c/strong\u003eMyeloperoxidase (MPO) increase in all weeks except week 3. The comparison of groups was performed by U–Mann-Whitney statistical test. In box-and-whisker plots, asterisks (*) indicate statistically significant differences (p\u0026lt;0.05).\u0026nbsp;\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4428140/v1/00cb18d3d3ef9ba266912b83.png"},{"id":57634491,"identity":"63ad1d53-02af-451f-bc04-48cba30fcb96","added_by":"auto","created_at":"2024-06-03 15:38:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":15631,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSubchronic inhalation exposure to PM\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2.5\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e modifies the urinary excretion of OxS biomarkers related to the glutathione antioxidant system.\u003c/strong\u003e OxS biomarkers were determined in 12-hours urine collected from rats (n=6) at the end of each week of exposure to PM\u003csub\u003e2.5\u003c/sub\u003e by the metabolic cage. \u003cstrong\u003eA)\u003c/strong\u003e Glutathione S-transferase (GST) increased from week 1 to 6. In contrast, \u003cstrong\u003eB)\u003c/strong\u003e gamma-glutamyl transferase (GGT) (B) decreased from week 6 to the end of exposure in week 8. The comparison of groups was performed by U–Mann-Whitney statistical test. In box-and-whisker plots, asterisks (*) indicate statistically significant differences (p\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4428140/v1/f3a0d474349052ccb8887389.png"},{"id":57635111,"identity":"5b883535-2ed6-4885-8af6-40212418d465","added_by":"auto","created_at":"2024-06-03 15:46:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":947211,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4428140/v1/0cb4eee9-33c2-4a16-b391-1dcc636fd8ab.pdf"},{"id":57634490,"identity":"04bb5d88-ad27-42d5-81a0-8d2ea00cb7a6","added_by":"auto","created_at":"2024-06-03 15:38:49","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":242285,"visible":true,"origin":"","legend":"","description":"","filename":"Supmaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4428140/v1/7227a7c8c445798e4f48a192.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The endotoxin content of PM 2.5 and its relationship with oxidative stress biomarkers in urine after subchronic inhalation exposure in a rat model","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eExposure to high atmospheric particulate matter (PM) causes cardiorespiratory diseases and impaired lung function (Liebers et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Mukherjee and Agrawal, 2017; Rylander, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Smaller particles are more likely to enter the respiratory tract and lungs after inhalation. It is suggested that particles with a diameter less than or equal to 2.5 \u0026micro;m, known as fine particles or PM\u003csub\u003e2.5\u003c/sub\u003e can be deposited in the alveolar sacs and cross the alveolar-capillary membrane, reaching distant organs (Snow et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMost studies focus on respiratory damage, and systemic effects remain poorly understood. It has been reported that PM toxicity is mainly attributed to the metal(loid), and endotoxin content (Valavanidis, Fiotakis, and Vlachogianni 2008). PM toxicity has recently associated with metabolic and kidney diseases (Clementi et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Reyes-Caballero et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOxidative stress (OxS) and inflammation are the main pathophysiological mechanisms to explain the damage to health by particle exposure (N. Li, Xia, and Nel 2008; Osornio-Vargas et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The OxS is the imbalance between the production of free radicals, accumulation of oxidative products, and their ability to detoxify these products in the cells (Betteridge \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Free radicals at low concentrations play essential roles for the organism involving the signaling process, metabolism, differentiation, development, and proliferation can have mitogenic effects, can mimic and amplify the action of growth factors, and the immunological defense against pathogens in the respiratory burst. However, they are highly reactive and can react with carbohydrates, lipids, proteins, and nucleic acids, altering their structure and function and leading to cell damage. Thus, oxidative damage is associated with broad diseases (respiratory, cardiovascular, and neurodegenerative, among others), ischemic processes, inflammation, and cancer (Pizzino et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSome molecules are indicators of the redox state in the organism. These biomarkers include host components of the antioxidant system, which change in response to increased oxidation-reduction stress, and molecules modified by interacting with free radicals (Ho et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Among the latter, lipid, and protein oxidation biomarkers such as malondialdehyde (MDA) and advanced protein oxidation products (AOPPs), respectively, are widely used in clinical and research (Del Rio, Stewart, and Pellegrini 2005).\u003c/p\u003e \u003cp\u003eThere are experimental findings that OxS may be involved in the pathogenesis of different kidney diseases. Some OxS biomarkers have been used as indicators of kidney damage in bladder cancer (Xu et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), urinary tract infections (Smith et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), tubular damage (McMahon, Koyner, and Murray \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), and ischemia-induced acute kidney injury (Tsikas \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). It has also been proposed that proximal oxidative metabolism in acute kidney injury may lead to chronic kidney disease (CKD). In patients with CKD and uremia, there is an increase in the production of reactive oxygen species (ROS) and a decrease in the antioxidant systems of the kidney (Schaub, Venkatachalam, and Weinberg 2021).\u003c/p\u003e \u003cp\u003eRecently, it was reported that subchronic inhalation of PM\u003csub\u003e2.5\u003c/sub\u003e increases the urinary excretion of early kidney damage biomarkers in rats with renal cortex impaired antioxidant response (Aztatzi-Aguilar et al. 2016). However, PM\u003csub\u003e2.5\u003c/sub\u003e is a heterogeneous mixture of substances, and not all its components seem to be equally responsible for its toxicity. The biological fraction composing PM\u003csub\u003e2.5\u003c/sub\u003e is responsible for multiple allergies and respiratory diseases (Khan et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, it is unknown whether the inhalation of PM\u003csub\u003e2.5\u003c/sub\u003e or aerobiological components, such as endotoxin, can cause adverse effects beyond cardiorespiratory ones. Specifically, endotoxin induces kidney injury when administered intravenously and intraperitoneally in rodent models, but the nephrotoxic and oxidative stress effect of this aerobiological when inhaled remain unknown. This study aims to assess OxS through urinary biomarkers after subchronic exposure to PM\u003csub\u003e2.5\u003c/sub\u003e inhalation and establish the relationship of urinary OxS biomarkers with the endotoxin content in PM\u003csub\u003e2.5\u003c/sub\u003e to explain renal effects by air pollution.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Subchronic inhalation exposure to PM\u003csub\u003e2.5\u003c/sub\u003e\u003c/h2\u003e \u003cp\u003e All the experimental protocols were designed in accordance with the Guide for the Care and Use of Laboratory Animals issued by the National Institute of Health and with the Mexican guidelines (NOM-062-ZOO-1999); and with the approval of the institution's Animal Care and Use Committee (UPEAL-CINVESTAV). The animals were maintained in a freestanding clean room with a changing station docking port (bioBubble\u0026reg;, Colorado, USA). The inhalation exposure was carried out from June to August 2013 at Cinvestav-IPN by Aztatzi-Aguilar et al., 2016. Briefly, male adult Sprague Dawley rats (purchased from Harlan, Mexico) were exposed subchronically (8 weeks, 4 days/week, 5 h/day) to PM\u003csub\u003e2.5\u003c/sub\u003e in whole-body chambers associated with a particulate concentrator, a control group was exposed to filtered air (FA) at air flow of 2.5 L/min.\u003c/p\u003e \u003cp\u003eUrine samples were collected in metabolic cages with ice bedding to ensure integrity. For each group, six rats were randomly selected, and housed one per cage for 12 h, with a previous recovery period of 8 hours after the last weekly exposure (Aztatzi-Aguilar et al. 2016). It was obtained the body weight gain, the water consumption, and the urinary flow rate.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Endotoxin quantification\u003c/h2\u003e \u003cp\u003eMonitoring in parallel was carried out with a miniVol equipment to estimate the air ambient PM\u003csub\u003e2.5\u003c/sub\u003e concentrations. The endotoxin content in PM\u003csub\u003e2.5\u003c/sub\u003e was determined in particles collected by HiVol equipment. Samples were collected weekly at the same exposure time while the animals remained in the chambers. The Limulus Amebocyte Lysate Pyrochrome Chromogenic Test Kit (Pyrochrome Associates of Cape Cod Incorporated, Falmouth, MA, USA) was used as the manufacturer recommended to determinate the Endotoxin content.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Oxidative stress biomarkers evaluation\u003c/h2\u003e \u003cp\u003eEight oxidative stress (OxS) biomarkers were evaluated in the urine of Sprague Dawley rats after subchronic inhalation exposure to PM\u003csub\u003e2.5\u003c/sub\u003e, previously described. The biomarkers assessed were arginase (EC: 3.5.3.1), myeloperoxidase (MPO, EC: 1.11.2.2), gamma-glutamyl transferase (GGT, EC: 2.3.2.2), glutathione S-transferase (GST, EC: 2.5.1.18), Methylglyoxal (MGO), malondialdehyde (MDA), non-esterified fatty acids (NEFAs), and advanced oxidation protein products (AOPPs). All methods were adapted to microplate reader.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1. Arginase (EC: 3.5.3.1)\u003c/h2\u003e \u003cp\u003eThe arginase activity in the samples was determined from the urea production. A first dilution of the urine samples was made in distilled water (1:40; v:v). A second dilution (1:1; v:v) was then performed in PBS 1X pH 7.4. The final urine dilution was incubated at 55\u0026deg;C for 10 minutes in a dry block heater (Benchmark BSH1001). From each dilution sample, 50 \u0026micro;L/well was plated to a 96-well plate; after that, 50 \u0026micro;L of arginine solution (0.5 M, pH 9.7) was added and incubated at 37\u0026deg;C for 1 h. Later, 140 \u0026micro;L of the acid mixture (H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e, H\u003csub\u003e3\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e, and H\u003csub\u003e2\u003c/sub\u003eO 1:3:7 v:v:v) and 10 \u0026micro;L of 9% α-isonitrosopropiophenone were added. It was incubated at 100\u0026deg;C for 45 min, and the absorbance was read at 540 nm in a microplate reader (Labsystems, Multiskan MS). A urea standard curve was used and processed as well as samples (Corraliza et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1994\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2. Myeloperoxidase (MPO, EC: 1.11.2.2)\u003c/h2\u003e \u003cp\u003eMPO activity, a peroxidase with antimicrobial action, was evaluated spectrophotometric through the oxidation of the 3,3\u0026prime;,5,5\u0026prime;-Tetramethylbenzidine (TMB). Undissolved urine (20 \u0026micro;L) was incubated with 90 \u0026micro;L of mixed reaction solution prepared as follows (76.77\u0026micro;L of PBS 0.1 M pH 5.4, 9.45 \u0026micro;L of TMB 1.6 mM, and 3.78 \u0026micro;L of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e 0.3 mM), at 37\u0026deg;C for 3 min in the dark. Then, the reaction was stopped by adding 150 \u0026micro;L of cold glacial acetic acid 0.4 M, pH 3. Samples were read at 570 nm in a microplate reader (Labsystems, Multiskan MS). For data processing, the average of the blank reaction wells (bidistilled water) was determined and subtracted from each sample absorbance well. MPO activity was then determined considering that 1U of activity corresponds to the change of 0.1 in the absorbance (Suzuki et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1983\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3. Glutathione S-transferase (GST, EC: 2.5.1.18)\u003c/h2\u003e \u003cp\u003eThe GST enzymatic activity was conducted by the conjugation of glutathione with the acceptor 1-chloro-2,4-dinitrobenzene (DCNB). To 10 uL of undissolved urine, was added 230 \u0026micro;L of the mixed reaction solution (4580 mL of PBS 1X, 360 \u0026micro;L of GSH 10 mM, and 60 \u0026micro;L of DCNB 60 mM) and read at 340 nm for 10 min every minute in a Beckman Coulter DU-800 Spectrophotometer. GST activity was expressed in nmol/min/mg protein (Habig, Pabst, and Jakboy 1974).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4. Gamma-glutamyl transferase (GGT, EC: 2.3.2.2)\u003c/h2\u003e \u003cp\u003eThe GGT activity was determined by the transference of gamma-glutamyl of L-γ-glutamyl-p-nitroanilide to the peptide glycine-glycine (Gly-Gly), realizing the p-nitroanilide which detected spectrophotometric. To 10 \u0026micro;L of undissolved urine, 190 \u0026micro;L of reaction mix (170 \u0026micro;L Buffer Tris Gly-Gly pH 8.2 and 20 \u0026micro;L L-γ-glutamyl-p-nitroanilide 10 mM) was added. The absorbance 405 nm was read for 10 min every 30 seconds in a Beckman Coulter DU-800 Spectrophotometer. Activity was calculated according to Persijn (Persijn and van der Slik 1976).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.3.5. Methylglyoxal (MGO)\u003c/h2\u003e \u003cp\u003eThe MGO is a dicarbonyl intermediate of no enzymatic glycans, it is considered a precursor of advanced glycation end products, and it is related with impaired metabolism. To quantify MGO, 50 \u0026micro;L of undissolved urine was incubated with 100 \u0026micro;L of DNFH 0.9 mM at 37\u0026deg;C for 10 min. Subsequently, 100 \u0026micro;L of NaOH 1.5 N was added, and the absorbance at 540 nm was read in a microplate reader (Labsystems, Multiskan MS). To carry out the quantification of MGO, a standard MGO curve was made (Fields and Dixon \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1971\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.3.6. Non-esterified fatty acids (NEFAs)\u003c/h2\u003e \u003cp\u003eNEFAs or free fatty acids are essential constituents of the structure of lipids in membranes and lipoproteins. To quantified NEFAs 50 \u0026micro;L of undissolved urine was added to 200 \u0026micro;L of fatty acid extraction solution (heptane, methanol, and chloroform: 24.5:1 v:v:v). After removing and discarding the micelle, was added 100 \u0026micro;L of a cupric solution (cupric nitrate 0.5 M, triethanolamine 1M, NaOH 1N, NaCl in deionized H\u003csub\u003e2\u003c/sub\u003eO). The micelle was removed and was added 5 \u0026micro;L of sodium diethyldithiocarbamate at the time of reading at 450 nm in a microplate reader (Labsystems, Multiskan MS). To calculate the concentration of NEFAS, a standard palmitic acid curve was constructed (Duncombe \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1964\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.3.7. Malondialdehyde (MDA)\u003c/h2\u003e \u003cp\u003eThe MDA detection is based on the acidic reaction of the chromogen 1-Methyl-2-phenylindole (MPI) with the MDA at mid-temperature condition. To perform the assay 50 \u0026micro;L of undissolved urine were added 185 \u0026micro;L of MPI 10 mM in acetonitrile: methanol (3:1, V: V) and 40 \u0026micro;L of HCl 37% in the dark. It was shaken and incubated at 45\u0026deg;C for 40 min. Then, it was centrifuged at 9,000 rpm for 15 min. Supernatants were recovered and read at 570 nm in a microplate reader (Labsystems, Multiskan MS). To determine the concentration of MDA, a standard curve of 1,1,3,3-tetramethoxypropane (malonaldehyde bis (dimethyl acetal) was constructed (Esterbauer, Schaur, and Helmward 1991).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.3.8. Advanced Oxidation Protein Products (AOPPs)\u003c/h2\u003e \u003cp\u003eThe AOPPs are uremic toxins created during OxS through the reaction of plasma proteins with chlorinated oxidants such as chloramines or hypochlorous acid. This assay was carried out with 40 \u0026micro;L of undissolved urine, mixed with 120 \u0026micro;L of PBS 1X and 40 \u0026micro;L of pure acetic acid. It was shaken and incubated for 10 minutes at room temperature in the dark and was read at 340 nm in a Beckman Coulter DU-800 Spectrophotometer. To determine the concentration of AOPPs, a calibration curve of chloramine T was made (Witko-Sarsat et al. 1996).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Statistical analysis\u003c/h2\u003e \u003cp\u003eThe statistical processing and data analysis were performed in GraphPad Prism version 8.01. The body weight gain, the water consumption, and the urinary flow rate was expressed as percentage.Data for each biomarker were corrected by the urinary volume. Non-parametric statistical analysis was performed to compare the groups using the U-Mann-Whitney test. A Pearson correlation tests were performed between PM\u003csub\u003e2.5\u003c/sub\u003e endotoxin content with OxS biomarkers. In addition, the OxS biomarkers evaluated were correlated with early kidney damage conventional biomarkers reported by Aztatzi-Aguilar et al., 2016. A statistically significant difference was considered with a p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe PM\u003csub\u003e2.5\u003c/sub\u003e environmental concentrations and the endotoxin content in PM\u003csub\u003e2.5\u003c/sub\u003e are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. It was observed a constant PM\u003csub\u003e2.5\u003c/sub\u003e concentration a long of first seven weeks, and a low concentration at eighth week. The maximum Endotoxin content in PM\u003csub\u003e2.5\u003c/sub\u003e was observed at the second and third weeks. The lowest Endotoxin content was observed at seventh week. There was not observed a relation between particle concentration and Endotoxin content.\u003c/p\u003e \u003cp\u003eGeneral health animal parameters of exposure groups are present in the supplementary material (\u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Where no body weight changes were observed between PM\u003csub\u003e2.5\u003c/sub\u003e group respectively control group (FA). Changes in the water consumption and urinary flow rate were observed in the PM2.5 group respect the FA group.\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\u003eEnvironmental concentration of PM2.5 per week and the endotoxin content in particles.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeek\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMass (ug/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEndotoxin (UE/mg)\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e157\u0026thinsp;\u0026plusmn;\u0026thinsp;24.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e140.25\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e18.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003e\u0026sect;\u003c/sup\u003e Average\u0026thinsp;\u0026plusmn;\u0026thinsp;SD of triplicates by sample.\u003c/p\u003e \u003cp\u003eWe show the percentages of body weight gain, water consumption, and urinary flow rate of the PM\u003csub\u003e2.5\u003c/sub\u003e group compared with the control group. Statistical differences were observed in the water consumption on weeks 2, 3, 5, 6, and 8; nevertheless, the urinary flow rate presents the first increment at week 2 and the rest of weeks show a constant and statistically significant augment. We observed a statistical tendency in endotoxin content correlation with body weight and water consumption, however, statistical significance was observed in the urinary flow rate (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePearson correlation differences between PM2.5 endotoxin content and PM2.5 gravimetry mass over the general animal condition\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eEndotoxin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRho\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRho\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody weight (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater consumption\u003c/p\u003e \u003cp\u003e(\u0026micro;L/g body weight)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.489\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrinary flow rate\u003c/p\u003e \u003cp\u003e(\u0026micro;L/min/100g body weight)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.428\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOxidative stress biomarkers were grouped in 1) cell metabolism (MGO and NEFAs) and oxidative products (MDA and AOPPs); 2) oxidative stress related with the immune response (Arginase and MPO); and 3) Glutathione antioxidant response (GST and GGT).\u003c/p\u003e \u003cp\u003eAs biomarkers of cellular metabolism MGO and NEFAS were evaluated (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A significant increase in MGO levels were observed during all exposure weeks to PM\u003csub\u003e2.5\u003c/sub\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). A high increase in NEFAs levels were observed in weeks 1, 2, 4, 5, 7 and 8 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe MDA levels increased at week three (p\u0026thinsp;=\u0026thinsp;0.057) and it was statistically significative at fourth week of exposure; after that, it showed a decreasing trend, which was significant at weeks 5 and 6 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). On the other hand, AOPPs increased significantly at second week and showed a significant decrease at fifth week (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRegarding OxS biomarkers related to immune response. The urinary activity of Arginase and MPO enzymes was evaluated as evidence of cell damage and inflammation during subchronic exposure to PM\u003csub\u003e2.5\u003c/sub\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Arginase enzymatic activity shows after subchronic exposure to PM\u003csub\u003e2.5\u003c/sub\u003e, a statistically significant increase on weeks two, three, and six concerning the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Statistical differences in MPO enzymatic activity were observed partially in all weeks, except for week three; the urinary MPO enzymatic activity increased in the PM\u003csub\u003e2.5\u003c/sub\u003e group compared to FA (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRespect Glutathione antioxidant response the urinary activity of the GST and GGT enzymes was tested. It was observed the presence of both glutathione-dependent enzymes in urinary samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Our results show a significant increase in GST from the first to sixth week after PM\u003csub\u003e2.5\u003c/sub\u003e exposure (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), while GGT shows a statistically significant decrease after the fourth week of exposure to PM\u003csub\u003e2.5\u003c/sub\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA correlation between PM\u003csub\u003e2.5\u003c/sub\u003e endotoxin content and urinary levels of the OxS biomarkers were performed. A positive and statistically significant correlation was obtained for five of eight OxS biomarkers with the PM\u003csub\u003e2.5\u003c/sub\u003e endotoxin content (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePearson correlation test between weekly PM2.5 endotoxin content and the urinary excretion of OxS biomarkers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOxS biomarker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRho\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArginase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3547\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2442\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1266\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMGO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEFAs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePOAPs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eThe data are shown as Pearson Correlation Coefficient (Rho) and statistical significance p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOn the other hand, to strengthen our results of urinary OxS biomarkers after PM\u003csub\u003e2.5\u003c/sub\u003e exposure with the kidney damage we performed correlations between them with the early kidney damage biomarkers reported by Aztatzi-Aguilar \u003cem\u003eet al.\u003c/em\u003e, (2016). A positive and statistically significant correlation was found between the early kidney damage biomarkers reported previously and the urinary levels of the OxS biomarkers (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The highest correlation coefficient values with statistical significance were observed for MGO and NEFAs.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIt has been recently reported epidemiological and \u003cem\u003ein vivo\u003c/em\u003e evidence that supports PM\u003csub\u003e2.5\u003c/sub\u003e air pollution not only causes cardiorespiratory conditions but is also linked to other effects such as the development of autoimmune processes (Adami et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Gawda et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), metabolic diseases (Zheng et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and nephrotoxicity (Aztatzi-Aguilar et al. 2021; Aztatzi-Aguilar et al. 2016). These effects are attributable to the PM\u003csub\u003e2.5\u003c/sub\u003e size, which can penetrate deeply into the respiratory tract, causing inflammation and OxS (Gawda et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Both responses can amplify and trigger systemic effects that can affect organs distant from the site of exposure (de Camargo et al. 2021; Polidoro et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePearson correlation test between early kidney damage biomarkers and urinary excretion of OxS biomarkers.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly Kidney\u003c/p\u003e \u003cp\u003edamage biomarker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOxS biomarker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRho\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eβ2M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMPO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMGO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNEFAs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eCys-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMPO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePOAPs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMGO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNEFAs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e2-NGAL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMPO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePOAPs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMGO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNEFAs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eEGF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMPO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePOAPs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMGO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNEFAs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAGP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMGO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNEFAs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eThe data are shown as Pearson Correlation Coefficient (Rho) and statistical significance p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. β-2-Microglobulin (β2M); Cistatin-C (Cys-C); neutrophil gelatinase-associated lipocalin (2-NGAL); Epithermal Growth Factor (EGF); and α-1-glycoprotein (AGP)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThere are recently preclinical and epidemiological studies on the toxic effect of PM inhalation on kidney physiology decline. Recent published studies have shed light on the inhalation exposure to atmospheric particles, particularly PM\u003csub\u003e2.5\u003c/sub\u003e, as a potential cause of renal damage (Yuan et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Huang et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Aztatzi-Aguilar et al. 2016; Aztatzi-Aguilar et al. 2021; Rasking et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Some even regard it as an important environmental risk factor for the development of chronic kidney disease (Zhang, Liu, and Liu 2021; Ghazi, Drawz, and Berman 2022). However, this remains an underexplored field that offers many opportunities for new insights. For example, regarding the mechanisms involved in the deterioration of renal physiology, and which components of atmospheric particles may be primarily responsible for nephrotoxicity. In this respect, the present study assessed whether OxS biomarkers are present in urine after PM\u003csub\u003e2.5\u003c/sub\u003e inhalation exposure and if these biomarkers correlate with the PM\u003csub\u003e2.5\u003c/sub\u003e endotoxin content. The importance of urinary OxS biomarkers in relation to kidney damage was confirmed based on their correlation with early kidney damage biomarkers previously reported by Aztatzi-Aguilar et al. (2016). Our results show significant differences in the urinary excretion of all OxS biomarkers evaluated in the PM\u003csub\u003e2.5\u003c/sub\u003e group compared to the FA control.\u003c/p\u003e \u003cp\u003eMGO and NEFAs as biomarkers of cellular metabolism show higher differences to PM\u003csub\u003e2.5\u003c/sub\u003e exposure because they significantly increased their urinary levels from the first to the last week of exposure. MGO is primarily a byproduct of glycolysis, which increases during hyperglycemia, inflammation, and hypoxia, because glycolysis increases under these conditions (Hanssen, Stehouwer, and Schalkwijk 2019). MGO causes high OxS and cell damage and its increases in tissues is the leading cause of microvascular damage in diabetic patients (Schalkwijk and Stehouwer \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Its accumulation in the kidney has been related to tubular atrophy in models of acid nephropathy in mice, linked with a decrease in GSH reserves compromises the antioxidant defense and detoxification (Li et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOn the other hand, the increase in NEFAs is mainly due to the lipolysis of triacylglycerides in adipose tissue due to greater energy demand (Schelling \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and have been associated with obesity, insulin resistance and inflammation(Nicholas et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) Exposure to xenobiotics alters tissue fat metabolism. For instance, in the liver, it stimulates the uptake of NEFAs while reduces fatty acid oxidation (Massart et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, the kidney seems to exhibit distinct behavior in this regard. The kidney proximal tubules are sites that undergo ischemic damage during acute renal failure, where NEFAs accumulation has been observed after hypoxia-ischemia and reoxygenation-reperfusion in proximal tubules (Feldkamp et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The presence of NEFAs in urine could result from tissue damage by PM\u003csub\u003e2.5\u003c/sub\u003e exposure, which may indicate the kidney's high metabolic rate in animals exposed. Another possible explanation for the increased urinary release of NEFAs could be linked to fatty acid binding enzyme-1 (FAB-1); this cytoplasmic protein is found in proximal tubular cells, where it facilitates the uptake of fatty acids by the cell and directs them towards different metabolic pathways in the cytoplasm. FAB-1 has been reported as a highly sensitive early kidney damage marker in other animal models because its range of urinary excretion depends on the generated kidney damage (Noiri et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The reuptake of fatty acids and their cytoplasmic transport by FAB-1 must be affected by the tissue's decrease in protein due to cell damage. In addition, the transport of fatty acids to FAB-1 must also be affected by the loss of the brush border of the proximal tubule. On this, transporter-2 (FATP2) has been reported as the main membrane transporter that mediates the uptake of NEFAs in the apical membrane of rat proximal tubular epithelial cells (S. Khan et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUrinary levels of MDA and AOPPs were measured as biomarkers of OxS related to lipid and protein oxidation, respectively. Notably, both biomarkers behavior is similar and seems related to the endotoxin concentration fluctuations in the PM\u003csub\u003e2.5\u003c/sub\u003e samples. This suggests that oxidative stress products in the kidney are related to the endotoxin content of PM\u003csub\u003e2.5\u003c/sub\u003e; that is, endotoxin exposure could contribute to induce acute kidney damage. It may be that the endotoxin crosses the alveolar-capillary barrier and enters the systemic circulation, being able to reach other organs such as the kidneys, or that the kidney impact is due to the amplification of the damage and/or the pulmonary inflammatory response caused by exposure. In other words, inhalation exposure to lipopolysaccharide (LPS) causes damage and inflammation in the lung, with the consequent production of mediators that can reach the bloodstream and the rest of the body, amplifying the lung response to exposure. This could explain the occurrence of lipoperoxidation and protein oxidation at the renal level.\u003c/p\u003e \u003cp\u003eIt should be noted that the increase in MDA appears one week after the peak of endotoxin. This time difference between the exposure and the onset of the effect, called lag, has been reported in exposure to PM, and it is suggested that it may be 1 to 6 days. The space-time variation of PM deposit, composition, and concentrations mainly explains the lag effect. The biological responses to PM show different behavior patterns among patients. Its adverse effects take several days to become evident (Chien, Chen, and Yu 2018). The decrease in MDA and AOPPs after fifth week suggest that the kidney can compensate for the oxidation of lipids and proteins, for example, by the activation of renal antioxidant pathways, which need further study in this experimental model.\u003c/p\u003e \u003cp\u003eIncreased activity of Arginase and MPO was observed in the PM\u003csub\u003e2.5\u003c/sub\u003e group. Both enzymes are related to OxS and the immune response. ARG is also found in mammalian innate immune system cells such as macrophages, where it participates in an oxygen-independent defense mechanism, depleting L-arginine during pathogen phagocytosis (Munder \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). We consider that the increase in the urinary activity of ARG may show cellular damage in the kidney due to exposure. This would explain a greater release of ARG into the urine because of increased cellular degradation of the renal tissues. The activity of ARG in urine has recently been reported as a specific and sensitive biomarker in the progression of bladder cancer since it increases in the most advanced stages due to greater cell damage (Kalaf, Ewadh, and Abood 2020). We consider that its increased activity indicates the presence of renal inflammation with possible leukocyturia because of exposure.\u003c/p\u003e \u003cp\u003eMPO is stored in azurophilic granules of polymorphonuclear leukocytes, neutrophils, monocytes, and macrophages. It is released in response to leukocyte activation, and it is related to OxS because it catalyzes the production of hypochlorous acid, a powerful oxidant and halogenating agent of proteins, which has been linked to tissue damage due to inflammation (Aratani \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The increase in urinary MPO activity was also obtained. An increase in plasma MPO has been related to ischemic heart disease since MPO oxidizes low-density lipoproteins (LDL), contributing to atherosclerotic plaque formation (Delporte et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). However, its increase in urine has been a helpful biomarker in diagnosing bacterial infections in the urinary tract in a canine model (Smith et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In our case, having found its increased activity in urine throughout exposure to PM\u003csub\u003e2.5\u003c/sub\u003e can be due to intratubular immune cell infiltration, which was reported by Aztatzi-Aguilar et al. (2016).\u003c/p\u003e \u003cp\u003eRegarding the renal antioxidant response, significant changes were observed in the enzymatic activities of GST and GGT in the urine of the group exposed to PM\u003csub\u003e2.5\u003c/sub\u003e compared to AF. In the kidney two isoforms of GST are abundant, α-GST and π-GST, which are in 7the proximal and distal tubule, respectively. Both are released exclusively in the urine during kidney damage and are very early indicators of tubular damage (Shu et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). They have been reported as biomarkers of acute kidney injury after cardiac surgery (McMahon, Koyner, and Murray \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The α-GST isoform is also present in the liver, but its increase in serum has been reported in liver damage, transplantation, and viral infections since it does not cross the glomerular filtration barrier (McMahon, Koyner, and Murray \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOn the other hand, GGT activity showed a significant decrease after week 4 of exposure. GGT, unlike GST, is a membrane enzyme localized to the apical membrane of the proximal tubular cells in the kidney. The drop in its activity could reflect the possible loss of the brush border in the proximal tubule. Khundmiri et al., (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) reported decreased urinary GGT enzymatic activity due to tubular damage and loss of cellular integrity. Since GST and GGT participate in the biochemical cycle of GSH, both may show opposite behavior in their enzymatic activities because they compete for the same biochemical sources. If so, it is possible the increase in the activity of one implies the decrease in the activity of the other. This biochemical regulation for both proteins in isolate cell model need to be study.\u003c/p\u003e \u003cp\u003eAccording to the correlation results, the OxS biomarkers in urine is attributable to the PM\u003csub\u003e2.5\u003c/sub\u003e endotoxin content. Particularly MGO and NEFAs are helpful indicators of early renal damage since they were positively correlated with previously conventional early renal damage biomarkers reported by Aztatzi-Aguilar et al., (2016). Both MGO and NEFAs increase during inflammation. Thus, their increased urinary levels resulting from renal damage caused by the inhalation of atmospheric particles can be attributed to the inflammatory component of the particles, the endotoxin. Endotoxin in the blood (endotoxemia) can lead to kidney damage due to inflammation, affecting the tubular system, vasculature, and causing an enlargement of glomerular pores. Podocytes possess endotoxin receptors such as TLR-4 and CD14, contributing to this process (Peng et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tinti et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It's important to highlight that endotoxin, as an immunogenic and inflammation-inducing molecule, maintains both properties when it is present in ambient air. Furthermore, there are no regulations governing its levels in outdoor air.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Conclusions\u003c/h2\u003e \u003cp\u003eThe inhalation of fine particulate matter induces the presence of OxS biomarkers in urine following subchronic exposure in a rat model. Endotoxin, an aerobiological component of particulate matter, correlates with the nephrotoxic effects of inhaling atmospheric particles due to its inflammatory potential and induction of renal OxS. This leads to elevated urinary levels of oxidative compounds (MGO, NEFAs, MDA, and POAPs), modifying the activity of oxidative enzymes (GST and GGT), and as well as inflammation-related biomarkers (arginase and MPO). Moreover, our findings highlight the potential of the assessed OxS biomarkers, particularly MGO and NEFAs, as indicators of kidney damage. Collectively, these results together shed new light on the possible involvement of inhaling aerobiological atmospheric pollutants in the development of nephropathies.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eparticulate matter\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efine particles with an aerodynamic diameter of 2.5 \u0026micro;m or less\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efiltered air\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOxS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eoxidative stress\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLPS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elipopolysaccharide\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMGO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emethylglyoxal, NEFAs:non-esterified fatty acids\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMDA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emalondialdehyde\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAOPPs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eadvanced oxidative protein products\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMPO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emyeloperoxidase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eglutathione S-transferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGGT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003egamma-glutamyl transferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eβ2M\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eβ-2-microglobulin and Cys-C:Cystatin-C.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting Interest declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding sources\u003c/h2\u003e \u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJ.B.A. Writing- Original draft preparation, Investigation, Formal analysis, Data curation. M.U.R. Investigation, Formal analysis. J.N.M. Investigation, Formal analysis. A.V.R. Project administration, Resources, Writing - Review \u0026amp; Editing. O.C.B: Project administration, Resources, Writing - Review \u0026amp; Editing, Supervision. O.G.A.A. Conceptualization, Visualization, Methodology, Data curation, Writing - Review \u0026amp; Editing, Supervision.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe want to thank the Department of Research in Toxicology and Environmental Medicine of the National Institute of Respiratory Diseases in Mexico City; for the equipment for evaluating biomarkers during the COVID-19 pandemic.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdami, Giovanni, Marco Pontalti, Giorgio Cattani, Maurizio Rossini, Ombretta Viapiana, Giovanni Orsolini, Camilla Benini, et al. 2022. \u0026ldquo;Association between Long-Term Exposure to Air Pollution and Immune-Mediated Diseases: A Population-Based Cohort Study.\u0026rdquo; RMD Open 8 (e002055): 1\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/rmdopen-2021-002055\u003c/span\u003e\u003cspan address=\"10.1136/rmdopen-2021-002055\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAratani, Yasuaki. 2018. \u0026ldquo;Myeloperoxidase: Its Role for Host Defense, Inflammation, and Neutrophil Function.\u0026rdquo; Arch. 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Source 21 (84): 1\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12940-022-00888-2\u003c/span\u003e\u003cspan address=\"10.1186/s12940-022-00888-2\" 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":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Fine particles or PM2.5, oxidative stress, nephrotoxicity, biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-4428140/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4428140/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCurrently, our understanding of the impact of particulate matter on nephrotoxicity is limited. Oxidative stress has been identified as a mechanism involved in the adverse health effects due to exposure to this air pollutant, to their inorganic, organic, and aerobiological constituents (e.g. endotoxin). The goal of the present study was to correlate the endotoxin content of particulate matter with urinary oxidative stress biomarkers to explain early decline in renal dysfunction. Adult male Sprague-Dawley rats exposed to subchronic inhalation to particles less 2.5 micrometers in aerodynamic diameter, also known as fine particles or PM\u003csub\u003e2.5\u003c/sub\u003e (8 weeks, 4 days/week, 5 hours/day). The control group was exposed to filtered air. Biomarkers of oxidative stress were assessed in urine samples per week harvested by metabolic cage. The assessed oxidative stress biomarkers were methylglyoxal, non-esterified fatty acids, malondialdehyde, advanced oxidative protein products, arginase, myeloperoxidase, glutathione-S-transferase, and gamma-glutamyl transferase. Subchronic exposure to PM\u003csub\u003e2.5\u003c/sub\u003e increased five evaluated biomarkers in urine. Endotoxin content in PM\u003csub\u003e2.5\u003c/sub\u003e positively correlated with urinary oxidative stress biomarkers evaluated. Positively correlation of urinary oxidative stress biomarkers was found with urinary early kidney damage biomarkers (e.g., β-2-microglobulin and cystatin-C). The subchronic inhalation exposure to PM\u003csub\u003e2.5\u003c/sub\u003e induce the presence of oxidative stress reflected in urine, based on statistical correlations, suggests early kidney damage related to endotoxin content.\u003c/p\u003e","manuscriptTitle":"The endotoxin content of PM 2.5 and its relationship with oxidative stress biomarkers in urine after subchronic inhalation exposure in a rat model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-03 15:38:44","doi":"10.21203/rs.3.rs-4428140/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"adbd68f8-bc2d-40dd-b910-357e0d5ece81","owner":[],"postedDate":"June 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-03T15:38:47+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-03 15:38:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4428140","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4428140","identity":"rs-4428140","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}