Effects of Tumor-Associated E. coli Metabolites on Migration of Colorectal Cancer Cells

Effects of Tumor-Associated E. coli Metabolites on Migration of Colorectal Cancer Cells | 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 Article Effects of Tumor-Associated E. coli Metabolites on Migration of Colorectal Cancer Cells Nadezhda Ignatova, Maria Pryazhnikova, Andrey Seliverstov, Alina Abidullina, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4724160/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 Colorectal tumors have a close connection with the gut microbiome. Correlation between rearrangement in microbiome composition and disease progression has already been shown. However, the questions about the mechanisms underlying microorganisms and cancer cells interaction and the immediate effects of tumor-associated microbiomes on cancer cells remain open. In this work, we investigated the effects of metabolites of tumor-associated E.coli strains on the growth and migration of human colorectal cancer cell lines (HCT116, SW480 and HT29). Differences in the spectrum of synthesized organic acids from tumor-associated and probiotic M-17 strains were revealed. Specifically, tumor-associated E.coli produced more fumaric, malic and maleic acids, whereas the M-17 - more propionic, 2-oxobutyric and α-ketoglutaric acids. Upon exposure to metabolites from tumor-associated E.coli strains, HCT116 and SW480 cells showed an increased migration activity and HT29 cells - decreased migration activity in 2D and 3D culture models. Immunocytochemistry assay revealed decrease of E-cadherin in HCT116 and SW480 cells and FAK- in HT29, which explain different effects of E.coli metabolites on migratory capacity of colorectal cancer cells. Therefore, these results suggest that the effect of tumor-associated E.coli strains on cancer cells migration depends on their innate type of migration - single-cell or collective migration. Escherichia coli metabolites colon cancer migration E-cadherin FAK Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Colorectal cancer is one of the most common malignancies worldwide. Metastatic colorectal cancer with a highly invasive phenotype remains a lethal disease with a 5-year survival rate of approximately 14% [ 1 ]. Its development is multifactorial and influenced by the host genetic and environmental factors [ 2 ]. In the case of colorectal cancer, microbiota and its metabolites are considered an essential player in the disease progression [ 3 ]. In the human gastrointestinal tract, there are about 40 trillion microbes constituting more than 1000 species, the majority of which inhabit the colon [ 4 ]. The composition of the microbiome in the human colon may change over time as a result of a number of chronic inflammatory diseases, including type I and type II diabetes, Crohn's disease, ulcerative colitis and psoriasis, and cancer [ 5 ]. It is generally accepted that there are close associations between the gut microbiota composition and its metabolites and the progression of colorectal cancer [ 6 ]. For example, some toxic and genotoxic metabolites produced by Fusobacterium nucleatum and Bacteroides fragilis was shown to have direct carcinogenicity [ 7 ], leading to the accumulation of mutational changes in the intestinal cells and microsatellite instability. The crosstalk between the host cancer cells and gut microbiota mediated by metabolites can favor cancer progression, enhanced invasiveness and cancer cell survivability. However, the direct effects of microorganisms and their metabolites on cancer cells have been poorly described. Escherichia coli (E. coli) is the most common representative of colon microbiota. Commensal E. coli is known to promote the regeneration of damaged colon epithelial cells [ 8 ]. However, some of E. coli strains may cause inflammatory bowel diseases, which are the risk factors for colorectal cancer [ 9 ]. An increase in the colonization of colon mucosa by mucosa-associated E. coli has been found in patients with colorectal cancer [ 10 ]. E. coli metabolites can cause programmed cell aging, arrest the cell cycle and reprogram cells to produce pro-inflammatory cytokines, toxins and growth factors [ 11 , 12 ]. Bacterial metabolites can also play a role in metastasis formation. Specifically, lipopolysaccharide, produced by E. coli can increase secretion of cathepsin K, which mediates “M2-like” polarization of macrophages and promotes metastasis in colorectal cancer [ 13 ]. Notably, the majority of studies are based on the analysis of microbiota composition and its correlation with clinical-pathological characteristics of tumors [ 14 ]. Our study was designed to understand the role of E.coli metabolites isolated from patients’ tumor samples in regulation of migratory capacity of colorectal cancer cells. The E.coli strains were tested for antibiotic susceptibility, biofilms production and biochemical properties. Composition of E.coli metabolites was identified using the liquid chromatography – mass spectrometry analysis. The effects of the metabolites on cancer cells migration were examined on cell monolayers and 3D tumor spheroids. We used three colorectal cancer cell lines that are different in cell morphology, mutation status and chemosensitivity. The expression of E-cadherin and focal adhesion kinase (FAK) markers was assessed using immunocitochemistry. Results Characterization of isolated E. coli strains Four bacteria species were isolated from patients’ tumor and, using a combination of biochemical assay and MALDI ToF spectroscopy, identified as E. coli . For the tumor-associated E. coli strains the data on antibiotic sensitivity and biochemical enzyme activity were obtained and compared with standard probiotic M17 strain. Unlike M17, all the tumor-associated E.coli strains were resistant to at least one of the antibiotics tested. Three out of four strains (Col-101, Col-102, Col-103) showed resistance to ampicillin, and one (Col-93) - to amoxicillin (Fig. 1 A). Analysis of the biofilm activity showed that three tumor-associated strains (Col-93, Col-101, Col-103) increased biomass and synthesized a matrix less actively compared to M17 strain (Fig. 1 B). One strain (Col-102) had statistically higher reproduction rate and produced the same volume of matrix as M17. Biochemical activity of the strains has fluctuations in some amino acids and especially in sucrose (Supl.1). Most of the strains associated with colorectal cancer were unable to utilize sucrose, which is typically observed in only a few E.coli that can are typically slow growing or pathogenic strains [ 15 ]. The differences in the proportion and composition of the synthesized metabolites were found between tumor-associated and the probiotic strains. The proportion of dominant metabolites (mg/ml) was similar in all strains (Fig. 1 , C), tumor-associated and control. While the balance of minor components (µg/ml) was different for the tumor-associated E.coli strains compared to M17 control (Fig. 1 , D). We found statistically higher levels (p ≤ 0.05) of butyric, fumaric, maleic, and glycolic acids in tumor-associated strains’ metabolites compared to probiotic strain M17 (Table 2 ). The amount of malic acid was increased in Col-101 metabolites more than 5 times compared to other strains and M17. Pyruvic acid level was higher in tumor-associated strains, but statistical significance was shown only for Col-102 and Col-103. The level of valeric acid was higher for Col-101, Col-102 and Col-103 metabolites (p ≤ 0.05). Daily production of short-chain fatty acids (SCFAs) such as 2-oxybutyrate, propionate, α-Ketoglutaric acid (AKG) and isobutyric acid by tumor-associated strains was lower compared to probiotic strain M17 (Table 2 ). The production of propionic, malonic, 2-hydroxyglutaric, 2-oxobutyric a and isobutyric acids was dramatically, > 2–5 times decreased for all tumor-associated strains. Extremely low level of α-ketoglutaric acid (AKG) was found for Col-103 strain (> 90-fold decrease) as well as for other tumor-associated strains. Lactic acid concentration was lower in Col-93 and Col-103 strains. Succinic acid was decreased in all patients’ strain metabolites. No changes were observed for isovaleric acid and glyoxylic acid. Table 1. Patient’s sample characteristics. Sample Age Sex TNM classification Tumor type Localisation Grade Col-93 76 F T3N1М0 Adenocarcinoma Sigmoid colon G2 Col-101 73 F рT4bN1bМ1c Adenocarcinoma Sigmoid colon G2 Col-102 78 M рT4bN1bМ0 Adenocarcinoma Sigmoid colon G2 Col-103 76 M рT3N0М0 Adenocarcinoma Transverse colon G1 Table 2 Daily production of the selected metabolites by E.coli strains (* p ≤ 0.05 with M17). Analyte M17 Col-093 Col-101 Col-102 Col-103 Lactic acid, mg/l 138.6 ± 13.3 102.2 ± 39.4 * 123.8 ± 23.9 124.6 ± 8.1 112.5 ± 3.7 * Acetic acid, mg/l 1.7 ± 0.1 2.0 ± 0.8 1.9 ± 0.2 2.1 ± 0.1 * 2.3 ± 0.0 * Succinic acid, mg/l 170.8 ± 18.5 120.5 ± 40.7 * 118.2 ± 11.4 * 147.4 ± 2.1 * 144.6 ± 4.1 * Pyruvic acid, mg/l 8.9 ± 0.9 12.6 ± 4.4 10.1 ± 1.2 11.6 ± 0.6 * 12.7 ± 0.2 * Malic acid, mg/l 1.8 ± 0.3 1.8 ± 0.6 9.7 ± 1.3 * 0.5 ± 0.03 * 0.3 ± 0.02 * Valeric acid, mg/l 0.60 ± 0.09 0.6 ± 0.2 0.8 ± 0.09 * 0.8 ± 0.01 * 0.8 ± 0.04 * β-Hydroxybutyric acid, µg/l 31.4 ± 2.9 43.6 ± 19.0 35.8 ± 6.1 70.0 ± 8.3 * 30.2 ± 5.3 Propionic acid, µg/l 122.7 ± 23.0 24.2 ± 8.5 * 37.3 ± 4.3 * 37.0 ± 2.9 * 21.0 ± 2.6 * Isobutyric acid, µg/l 12.7 ± 2.4 6.1 ± 0.1 * 6.1 ± 0.6 * 6.9 ± 2.0 * 8.5 ± 1.6 * Butyric acid, µg/l 19.1 ± 3.8 27.3 ± 10.3 36.1 ± 4.1 * 36.4 ± 4.0 * 27.3 ± 1.4 * 2-Hydroxyglutaric acid, µg/l 47.0 ± 6.1 98.7 ± 43.0 * 11.9 ± 0.8 * 6.9 ± 2.8 * 15.2 ± 1.8 * Isovaleric acid, µg/l 5.2 ± 1.0 4.2 ± 1.9 5.9 ± 1.8 5.1 ± 1.3 6.5 ± 0.5 Fumaric acid, µg/l 170.1 ± 16.1 325.6 ± 114.8 * 372.3 ± 53.2 * 263.2 ± 27.2 * 476.8 ± 15.5 * Maleic acid, µg/l 101.8 ± 6.1 366.3 ± 229.3 * 446.1 ± 59.6 * 337.4 ± 15.9 * 465.1 ± 63.5 * Glyoxylic acid, µg/l 38.9 ± 3.3 36.9 ± 11.9 42.1 ± 5.2 37.8 ± 8.4 41.6 ± 7.4 2-Oxobutyric acid, µg/l 59.0 ± 5.3 6.0 ± 2.1 * 9.1 ± 3.6 * 11.5 ± 0.6 * 8.8 ± 0.7 * α-Ketoglutaric acid, µg/l 45.2 ± 16.5 6.3 ± 5.7 * 9.9 ± 1.9 * 0.5 ± 0.2 * 3.4 ± 1.0 * Glycolic acid, µg/l 235.8 ± 35.3 264.7 ± 72.9 257.9 ± 34.0 * 301.5 ± 2.5 * 286.4 ± 10.1 * Malonic acid, µg/l 11.2 ± 2.8 2.0 ± 1.3 * 2.9 ± 0.4 * 3.0 ± 1.0 * 3.3 ± 1.5 * Effect of E.coli metabolites on tumor spheroids growth The effect of E.coli metabolites on the growth of tumor spheroids was assessed using colorectal cancer cell lines HCT116 and HT29. The SW480 line lacked the ability for spheroid formation. It was found that in the presence of E.coli metabolites the growth of both HCT116 and HT29 spheroids was inhibited compared with a control without metabolites (Fig. 2 , A, B). At that, no differences were observed in the effects of M17 and tumor-associated strains. We also assessed proliferative activity of all cell lines in the presence of metabolites of E.coli strains (Fig. 2 , C). A significant increase of the doubling time in the presence of metabolites M17 (p = 0.034) and Col-101 (p = 0.039) was shown for HT29 cells. The metabolites of Col-103 strain extended the doubling time of SW480 (p = 0.042). For other lines, the presence of metabolites only slightly increased the doubling time, without statistical significance. Effect of E.coli metabolites on cancer cells migration Metabolites of the probiotic strain M17 did not affect migration of HCT116 cells from the spheroids and slightly (p = 0.035) inhibited migration of HT29 cells compared to control without metabolites (Fig. 3 ). Tumor-associated E.coli strains metabolites had different effects on migration of HCT116 and HT29 cells. HCT116 cell line showed higher migration activity in the presence of metabolites from all tumor-associated strains compared to control without metabolites and M17 strain (Fig. 3 , A). The largest migration area was observed upon exposure to the Col-101 metabolites. The opposite effects were observed for HT29 cells, whichactively migrated in control without metabolites but inhibited migration in the presence of E.coli metabolites. Of the strains used, M17 metabolites inhibited migration of HT29 cells in a lower degree in comparison with tumor-associated strains metabolites (Fig. 3 , B). Analysis of cell migration in the model of monolayer “wound healing” was performed for the three cell lines HCT116, SW480 and HT29. Two E.coli strains, Col-101 and Col-102, were selected for this test as they demonstrated the most notable effects among other strains obtained from the patients. Similar to migration from the spheroids, patient-derived E.coli metabolites stimulated migration of HCT116 and SW480 cells and inhibited migration of HT29 cells in the “wound healing” model (Fig. 4 ). The M17 metabolites did not change migration of HCT116 and SW480 cells and inhibited migration of HT29 cells. Therefore, the experiments on the cell monolayers and tumor spheroids revealed that tumor-associated E.coli metabolites affected migratory capacity of colorectal cancer cells and could either increase or decrease it depending on the specifics of cancer cells. Immunocytochemical analysis of the migration-associated markers To identify the molecular mechanisms through which E.coli metabolites had different effects on colorectal cancer cell lines, the expression levels of E-cadherin and the focal adhesion kinase (FAK) were analyzed using immunofluorescence. E-cadherin expression statistically decreased in HCT116 and SW480 cell lines upon exposure to metabolites of the Col-101 strain (Fig. 5 ). The probiotic M17 strain induced a marked increase in E-cadherin level only in HT29 cells, and had no effect on two other cell lines. Since down-regulation of E-cadherin, a major component of adherens junctions, facilitates cell motility and migration, its lower level in HCT116 and SW480 cells correlated with their highest migratory activity upon incubation with Col-101 metabolites. A significant decrease in FAK expression was noted in HT29 cells under the bacterial metabolites of all tested strains, which explains a decrease of migratory capacity of this cell line. Inhibition of FAK activity is known to decrease cell motility due to suppression of cell-matrix attachment. Of note, the initial FAK activity in HT29 cells was higher compared with other cell lines, suggesting their higher migratory potential. In HCT116 and SW480 cell lines FAK level did not change after incubation with bacterial metabolites (Fig. 5 , B). These results suggest that changes in migratory capacity of colorectal cancer cells under the exposure to tumor-associated E.coli metabolites can be mediated by both the loss of cadherin-based cell − cell adhesions and attenuation of the FAK signaling. Discussion It is known that tumor-associated microorganisms play a critical role in the progression of colorectal cancer [ 16 ]. Numerous studies demonstrate correlation between microbiome composition and different cancer features, such as stage, invasiveness, drug resistance, etc. [ 17 – 20 ]. However, the mechanisms underlying the impact of microbiome on tumor progression remain poorly elucidated. Here, we analyzed the effect of probiotic and tumor-associated E. coli strains on migratory capacity of colorectal cancer cells in vitro. First, we compared general characteristics of E.coli strains obtained from colorectal cancer patients with the standard probiotic strain M17 and found numerous differences between them. Specifically, the tumor-associated strains showed resistance to at least one antibiotic tested, primarily β-lactam antibiotics (ampicillin, amoxicillin), which can be associated with their ability to intracellularly survive during antibiotic treatment [ 21 ]. In addition, their biomass growth and biofilm matrix synthesis were reduced compared to M17, indicating that growth on the enterocytes surface is not a dominant type of growth for tumor-associated strains. Also, it was noticed that tumor-associated strains were adapted to utilize glucose, rather than complex disaccharides due to decreased sucrose-related saccharolytic activity, which can be explained by the competition for the intracellular glucose with cancer cells. Since the E.coli strains were isolated directly from surgical samples of patients' tumors, the observed features may indicate their adaptation to the conditions of intracellular interaction with cancer cells, rather than survival on the abiotic surfaces. Previously, it has been shown that intracellular localization provides numerous advantages to the invading microbes, including the immune escape and a favorable nutritional environment. A low microbial biomass is consistently present in colorectal tumors, and they play an important role in cancer development [ 21 ]. Сharacterization of the spectrum of organic acids synthesized by different E. coli strains during their metabolism showed that the proportion and the level of synthesized substances were different for the probiotic and tumor-associated strains. The positive impact of microbiota is mostly connected with the production of SCFAs as a result of fermentation of dietary fibers, which are commonly indigestible by the human enzymes [ 22 ]. SCFAs are thought to serve as anti-inflammatory substances in the gut, improving the intestinal epithelium barrier and gut homeostasis [ 23 – 25 ]. Also, SCFAs can promote antitumor effects and even induce apoptosis of colorectal cancer cells [ 22 , 26 , 27 ]. An excess of malonic acid and AKG was produced by the probiotic strain. Malonic acid inhibites succinate dehydrogenase. The succinate and succinate dehydrogenase complex are the central link of the Krebs cycle and the main structure of the antihypoxic ensemble for all somatic cells. This complex regulates the mitochondrial respiratory chain and provides antioxidant defense by binding excess iron ions [ 28 ]. It has been reported that AKG mediates DNA demethylation and aberrant epigenetic modifications in HT-29/Caco-2, down regulates cell differentiation in colorectal tumor models [ 29 , 30 ]. Antitumor effect of AKG was demonstrated on human lung carcinoma H460 and colon adenocarcinoma cell lines HCT116 [ 31 ]. Multiple studies have shown that metabolites such as butyrate, propionate, acetate, and niacin contribute to protection of the host against malignation and represent an energy source for the colon epithelial cells [ 32 ]. However, the oncometabolites like the lactate, glutamate, fumarate, and succinate are involved in tumor survival and progression [ 33 – 35 ]. Oncometabolites make the tumor microenvironment more favorable for cell migration [ 36 ]. It has been shown that elevated plasma concentrations of succinic and maleic acid are associated with the development of lung cancer [ 37 ]. In the study by Ternes et al. it was shown that gut microbial metabolite formate, produced by F. nucleatum , enhanced migration potential of HCT116 cells through the formation of focal adhesion points [ 38 ]. Sciacovelli et al. showed on renal cancer patients that fumaric acid inhibits Tet-mediated demethylation of a regulatory region of the antimetastatic miRNA cluster miR-200ba429, leading to the expression of EMT (epithelial-mesenchymal transition)-related transcription factors, which, in its turn, results in enhanced migratory properties and poor clinical outcome [ 39 ]. Malic acid was increased in malignant prostatic hyperplasia, suggesting that this metabolite can be used as a biomarker of prostate cancer [ 40 ]. The results of our study showed that metabolites of tumor-associated E.coli strains had different effects of migration capacity of colorectal cancer cells depending on their original properties. In our previous in vitro study the higher migrational potential and proliferation rate of HCT116 compared to HT29 line were shown. SW480 is characterized by the inability to form spheroids and fast growth rate [ 41 ]. Here, we observed more active migration of HCT116 and SW480 cells and inhibition of migration of HT29 cells in the monolayer “healing” model under E.coli metabolites. Similar results were obtained using the model of 3D tumor spheroids. We assume that these differences could be associated with different expressions of proteins involved in the processes of intercellular adhesion and adhesion to the substrate, such as E-cadherin and focal adhesion kinase (FAK). According to “The human Protein atlas” HT29 cell line initially has high expression of genes, associated with collective migration, such as CDH1 (E-cadherin) and PDK2 (focal adhesion kinase (FAK) and low expression of single-cell migration-associated markers, such as ROCK1 , EZR and TALIN . While HCT116 and SW480 have significantly lower expression of CDH1 and PDK2 and higher ROCK1 , EZR and TALIN , indicating different types of cell migration specific to these cells. Our previous studies demonstrated that HT29 cells have a lower migration potential compared to HCT116 and SW480 cells [ 41 ]. In HCT116 and SW480 cells, for which a single-cell migration is typical, the loss of E-cadherin was the main effect of E.coli metabolites. Also, it is worth noting that the most pronounced effect was shown for E.coli strain Col-101. It was isolated from the tumor with the most invasive phenotype with the presence of the distant metastases to the liver. Loss of E-cadherin expression results in loss of contact inhibition, increase of cell motility and subsequent single-cell migration [ 42 ]. In the work by Tarashi et al. similar effect of B. fragilis toxin that is associated with colorectal cancer on cleavage of E-cadherin and formation of invasive phenotype was demonstrated [ 43 ]. Also, it was shown that ammonia produced by H.pylori disrupted cellular tight junctions, based on E-cadherin, thus affecting cell integrity and damaging the gastric epithelium, which resulted in gastric cancer development [ 44 ]. Thirunavukkarasan et al. showed that SCFAs increased the level of expression of E-cadherin, and therefore, prevented the formation of the invasive phenotype of cancer cells [ 45 ]. In collective cell migration, E-cadherin mediates epithelial cell–cell adhesion and its expression is required to maintain intercellular junctions [ 42 ]. In HT29 cells, characterized by collective migration, the main effect of bacterial metabolites was the loss of FAK, while the expression of E-cadherin did not change. It is known that FAK, when it becomes constitutively active due to mutations or elevated activity of alternative signaling pathways, exerts oncogenic properties and allows cancer cells growth and survival without anchorage to the ECM, which is an important step during metastatic process [ 46 ]. The limitation of our investigation is the low number of samples and focus on the one type of microorganism - E.coli from the whole microbiome. However, we obtained the detailed metabolic profile and clearly demonstrated the effect of tumor-associated E.coli on migratory potential of tumor cells. Although this effect may be enhanced by other representatives of the microbiome, the results of such studies are important for understanding the possible role of individual representatives of the microbiome in the progression of cancer that is difficult to separate in vivo. Our research showed that metabolites from tumor-associated E.coli strains enhanced FAK-dependent single-cell migration accompanied by the loss of E-cadherin in cancer cells with initially low FAK expression. At the same time, this effect was not observed in cancer cells with collective migration phenotype. Further studies of the effects of tumor-associated strains on migratory potential of different cancer types are important for development of microbiome correction strategies to improve cancer prognosis. Methods Bacterial strains and cultivation E.coli strains (Col-93, Col-101, Col-102, Col-103) were isolated from colorectal cancer biopsy samples. Human colon samples were collected at Nizhny Novgorod Regional Oncologic Hospital (Russia). The study with the use of patients’ material was approved by the ethics committee of the Privolzhsky Research Medical University (approval № 09 from 30.06.2023). All methods were carried out in accordance with relevant guidelines and regulations. Informed consent was obtained from all subjects and/or their legal guardians.The clinical information about patients’ tumors is presented in Table 1 . The species identity was determined by matrix assisted laser desorption ionization-time of flight mass spectrometry (MALDI ToF Autoflex speed, Bruker Daltonik GmbH, Germany). Biochemical properties were studied by bacterial biochemical identification kits (RPC Diagnostic Systems, Nizhny Novgorod, Russia) (Suppl.1). Routine cultivation was carried out using nutrient agar (24 h, 37°C). Probiotic strain of E.coli M17, which is widely used as a component of probiotic drugs to correct dysbiotic conditions, served as a positive control in all the experiments. Metabolite preparation The bacterial strains were seeded in DMEM with 4.5 mg/L glucose (PanEco, Russia) in a concentration of 1×10 6 CFU/ml and cultured for 24 h at 37°C. Then metabolites were obtained by filtration of the growth media through bacterial filter 0.2 µm (Corning, USA). The resulting solution was diluted 1:1.5 with DMEM with 5% fetal bovine serum. The final medium was checked for pH, which was in neutral range (7.0-7.2). Target liquid chromatography – mass spectrometry analysis Cell-free metabolites were prepared by centrifuging the culture at 20,000 rcf for 20 min. Finally, they were filtered through 0.2 µm filters. High-performance liquid chromatography with electrospray ionization triple quadrupole tandem mass spectrometry was used for detection of short-chain fatty acids and organic acids in supernatant liquid. The samples of metabolites were prepared according to the manual (Shimadzu Corporation) and analyzed using a mass spectrometer LCMS-8050 coupled with the Nexera XR liquid chromatography system (Shimadzu, Japan). The detailed description of the procedure is presented in the Supplementary information (Suppl.2). Antibiotic susceptibility testing The disk diffusion susceptibility test was used to determine the susceptibility of isolates of E. coli to the nine antibiotics - ampicillin, trimethoprim, amoxicillin, norfloxacin, ciprofloxacin, ofloxacin, cefotaxime, ceftriaxone and ceftazidime. The test was performed using the standard protocol [ 47 ]. A bacterial inoculum 1–2 × 10 8 CFU/mL was applied to the surface of a nutrient agar plate with a diameter of 60 mm. A commercially prepared, fixed-concentration paper antibiotic disk was placed on the inoculated agar surface. The results were assessed after 18–24 h incubation of the plates at 37°C. The zones of growth inhibition surrounding the antibiotic disk were measured to the nearest millimeter. The zone diameters of each drug were interpreted according to the manufacturer's instruction (NICF, St. Petersburg, Russia). Matrix production assay To analyze matrix production, the biofilms were grown for 48 h in DMEM with 4.5mg/L glucose (PanEco, Russia) in 96-well plates. The bacterial concentration was 1×10 6 CFU/ml. Then the bacterial biofilms were washed three times by phosphate buffered saline (PBS) and stained by Congo red for 15 min. The staining solution containing 1% Congo red and 10% Twin 80 was prepared in PBS. After staining, plates were washed three times by PBS and ethyl alcohol was used for extraction of Congo red from the cells. The optical density was measured at a multichannel spectrophotometer BioTek Synergy Mx (BioTek, USA) at a wavelength of 500 nm. Bacterial biomass growth The biofilms were washed three times by PBS, fixed with 96% ethyl alcohol for 15 minutes, stained by 0.1% gentian violet solution (3 min). Next, the dye was eluted by 96% ethyl alcohol at constant shaking (10 min) and the optical density was measured using a multichannel spectrophotometer at a wavelength of 570 nm. Colon cancer cell lines Human colon adenocarcinoma cell lines НСТ116, SW480 and HT29 were routinely cultured in DMEM (PanEco, Russia) with 5% fetal bovine serum (HyClone, USA) and passed twice a week. The cells were cultured in a CO 2 -incubator, 37°C, RH 80%, CO 2 5%. The cell lines were obtained from the cell collection of the Ivanovskiy Institute of Virology (Moscow, Russia). Spheroids formation and cell migration assay To obtain tumor spheroids, HCT116 and HT29 cancer cells were seeded in low attachment 96-well plates (Corning, USA) in the amount of 200 cells in 200 µl /well and cultured in the presence of bacterial metabolites (1:1.5). Spheroids grown in a pure DMEM medium (200 µl per well) were used as a negative control. The size of spheroids was measured in 3, 5 and 7 days for HCT116 and in 4 and 7 days for HT29, because the latter have lower cell division rate. For migration assay the spheroids after 5 days of culturing were used. They were gently transferred in a small Petri dish (3.5 mm) and incubated for either 2 (HCT116) or 5 days (HT29). Light microscopy images of the spheroids were acquired after their attachment using DMIL microscope (Leica, Germany) at magnification 100x, and the zones of cell migration were measured in ImageJ (V 1.4.3.67) software. Cell proliferation assay To assess the proliferative activity, the cells were seeded in 6-well plates (5 × 10 5 cells per well for HT29 and 2 x 10 4 cells per well for HCT116 and SW480). Cells were incubated for 7 days in 2 mL of DMEM with or without metabolites. Then cells were counted using a TC20 automated cell counter (Bio-Rad, Hercules, CA, USA). Proliferation was assessed totally in 9 wells for each experimental group. The doubling time (DT) was calculated using the formula DT = h × LN(2)/LN(C2/C1), where C1—initial cell number, C2—final cell number, h—cultivation time (hours). “Wound healing” assay The study of migration activity was carried out on the model of "wound healing" using cultural inserts Culture-Insert 2 Well (Ibidi, USA). The cell suspension (70 µl) with a concentration of 1×10 5 cells/ml was placed into the wells, incubated for 24 h (37° C, 5% CO 2 ), and silicone liners were removed after the formation of the monolayer. Light microscopy images (Leica, Germany) of the "wounds" were obtained in 1–5 days after removal of the liners at magnification 100x. Migration zones were measured using ImageJ (V 1.4.3.67) software. Immunostaining assay For immunocytochemical staining, the cells were cultured in 96-well plates for 24 h after seeding and fixed in 4% formaldehyde for 15 min. The following primary antibodies were used: rabbit antibodies against E-cadherin (ab15148, Abcam, USA), rabbit antibodies against focal adhesion kinase (FAK) (ab131435, Abcam, USA). Subsequently cells were stained with Alexa555-labeled goat anti-rabbit IgG secondary antibody (ab6825, Abcam, USA). Staining was performed in accordance with the manufacturer's protocol. In addition, the cells were stained with DAPI to visualize the cell nuclei. Fluorescence images were observed using DMIL fluorescence microscope (Leica, Germany) equipped with the following filters: A4 UV BP 360/40 400 BP 470/40 for DAPI and TX2 green BP 560/40 595 BP 645/75 for Alexa. Statistical analysis Statistical analysis was performed using Statistica 10 (StatSoft. Inc., Tusla, OK, USA). P-values ≤ 0.05 were considered statistically significant. The nonparametric Mann-Whitney U-test was used to compare the data. Declarations Author Contributions: Conceptualization, Ignatova N. and Druzhkova I.; methodology, Pryazhnikova M., Seliverstov A. and Ignatova N.; investigation, Pryazhnikova M., Ignatova N., Abidullina A. and Druzhkova I; writing—original draft preparation, Ignatova N., Pryazhnikova M. and Druzhkova I; writing - review and editing, Shirmanova M., Gamayunov S.; project administration, Shirmanova M.; funding acquisition, Ignatova N. All authors reviewed the manuscript. Funding: This work was supported by the Russian Science Foundation under grant No. 23-74-00045. Acknowledgments: The authors are thankful to Denis Sohin (Privolzhsky Research Medical University, Russia) for assistance in MALDI ToF identification of bacterial strains and Vitaliy Terekhov for the providing surgical samples. Competing interests: The authors declare no conflict of interest. Data availability statement: Data is provided within the manuscript or supplementary information files. All data supporting the findings of this study are available within the paper and its Supplementary Information. Biochemical activity of E.coli strains is provided in Supplementary Table 1, Target liquid chromatography in Supplementary 2, Transcription of genes associated with migration (data from https://www.proteinatlas.org/) in Supplementary 3. References Rumpold H., Niedersüß-Beke D., Heiler C., et al. 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Circulating succinate-modifying metabolites accurately classify and reflect the status of fumarate hydratase-deficient renal cell carcinoma. J Clin Invest . 2023 ;133(11):e165028. doi:10.1172/JCI165028 Giallongo S., Costa F., Longhitano L., et al. The Pleiotropic Effects of Fumarate: From Mitochondrial Respiration to Epigenetic Rewiring and DNA Repair Mechanisms. Metabolites . 2023 ;13(7):880. doi:10.3390/metabo13070880 Roupar D., González A., Martins JT., et al. Modulation of Designed Gut Bacterial Communities by Prebiotics and the Impact of Their Metabolites on Intestinal Cells. Foods . 2023 ;12(23):4216. doi:10.3390/foods12234216 Baryła M., Semeniuk-Wojtaś A., Róg L., Kraj L., Małyszko M., Stec R. Oncometabolites-A Link between Cancer Cells and Tumor Microenvironment. Biology (Basel) . 2022 ;11(2):270. doi:10.3390/biology11020270 Liu JJ., Shen WB., Qin QR., et al. Prediction of positive pulmonary nodules based on machine learning algorithm combined with central carbon metabolism data. J Cancer Res Clin Oncol . 2024 ;150(2):33. doi:10.1007/s00432-024-05610-y Ternes D., Tsenkova M., Pozdeev VI., et al. The gut microbial metabolite formate exacerbates colorectal cancer progression [published correction appears in Nat Metab . 2023 ;5(9):1638]. Nat Metab . 2022 ;4(4):458-475. doi:10.1038/s42255-022-00558-0 Sciacovelli M., Gonçalves E., Johnson TI., et al. Fumarate is an epigenetic modifier that elicits epithelial-to-mesenchymal transition [published correction appears in Nature . 2016 ;540(7631):150]. Nature . 2016 ;537(7621):544-547. doi:10.1038/nature19353 He J., Han Z., Luo W., et al. Serum organic acid metabolites can be used as potential biomarkers to identify prostatitis, benign prostatic hyperplasia, and prostate cancer. Front Immunol . 2023 ;13:998447. doi:10.3389/fimmu.2022.998447 Druzhkova I., Shirmanova M., Ignatova N., et al. Expression of EMT-Related Genes in Hybrid E/M Colorectal Cancer Cells Determines Fibroblast Activation and Collagen Remodeling. Int J Mol Sci . 2020 ;21(21):8119. doi:10.3390/ijms21218119 Lu YW., Hou XL., Koo HM., Chao WT. Dasatinib suppresses collective cell migration through the coordination of focal adhesion and E-cadherin in colon cancer cells. Heliyon . 2023 ;10(1):e23501. doi:10.1016/j.heliyon.2023.e23501 Tarashi S., Siadat SD., Ahmadi Badi S., et al. Gut Bacteria and their Metabolites: Which One Is the Defendant for Colorectal Cancer? Microorganisms. 2019 ;7(11):561. doi:10.3390/microorganisms7110561 Ansari S., Yamaoka Y. Helicobacter pylori Virulence Factors Exploiting Gastric Colonization and its Pathogenicity. Toxins (Basel) . 2019 ;11(11):677. doi:10.3390/toxins11110677 Thirunavukkarasan M., Wang C., Rao A., et al. Short-chain fatty acid receptors inhibit invasive phenotypes in breast cancer cells. PLoS One . 2017 ;12(10):e0186334. doi:10.1371/journal.pone.0186334 Schiller HB., Fässler R. Mechanosensitivity and compositional dynamics of cell-matrix adhesions. EMBO Rep . 2013 ;14(6):509-519. doi:10.1038/embor.2013.49 Wayne, P.A. Performance Standards for Antimicrobial Disk Susceptibility Tests; 12th ed.; CLSI document M02-A12; Clinical and Laboratory Standards Institute: Pennsylvania 19087, USA, 2015 . 230p. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-4724160","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":334115364,"identity":"64d00edc-c8d0-46c4-b897-12ddef8fcf57","order_by":0,"name":"Nadezhda Ignatova","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYFACxgY46wGQ4OEjUosBiMUMInnYiLQKrIVNAkwSUmvOwNzA8LHtjxy/RPKzyq85djJsDMwPH93Ao8WygbGBcWabgbHkjDSz27LbkoEOYzM2zsHnogOMDcw8ZwwSN5w5YHZbchszUAsPmzRBLX/OGNTvP3P8W7HktnoitTBUGCQYsPeYMX7cdpgILYcZGw72VBgbzjjeUyzNuO04DxszIb8cb3/44IeBnDx/M/vGjz+3Vdvzszc/fIxPCwMzA8MBOJsHKkI8YPxBiupRMApGwSgYMQAARFhBkFDwrQMAAAAASUVORK5CYII=","orcid":"","institution":"Privolzhsky Research Medical University","correspondingAuthor":true,"prefix":"","firstName":"Nadezhda","middleName":"","lastName":"Ignatova","suffix":""},{"id":334115370,"identity":"c6f283b7-ad8c-4634-b02d-47f650a959a9","order_by":1,"name":"Maria Pryazhnikova","email":"","orcid":"","institution":"Privolzhsky Research Medical University","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"","lastName":"Pryazhnikova","suffix":""},{"id":334115373,"identity":"784dba28-d141-4da8-a66d-a217f7470529","order_by":2,"name":"Andrey Seliverstov","email":"","orcid":"","institution":"Privolzhsky Research Medical University","correspondingAuthor":false,"prefix":"","firstName":"Andrey","middleName":"","lastName":"Seliverstov","suffix":""},{"id":334115374,"identity":"e82b7811-fbff-44f5-b7f3-9956d17c2d3d","order_by":3,"name":"Alina Abidullina","email":"","orcid":"","institution":"Privolzhsky Research Medical University","correspondingAuthor":false,"prefix":"","firstName":"Alina","middleName":"","lastName":"Abidullina","suffix":""},{"id":334115375,"identity":"20ef65a8-af60-48dd-97ba-5900739fbe0e","order_by":4,"name":"Sergey Gamayunov","email":"","orcid":"","institution":"Nizhny Novgorod Regional Oncologic Hospital","correspondingAuthor":false,"prefix":"","firstName":"Sergey","middleName":"","lastName":"Gamayunov","suffix":""},{"id":334115378,"identity":"8b92fc0e-9716-4956-adfc-c85699b999a6","order_by":5,"name":"Marina Shirmanova","email":"","orcid":"","institution":"Privolzhsky Research Medical University","correspondingAuthor":false,"prefix":"","firstName":"Marina","middleName":"","lastName":"Shirmanova","suffix":""},{"id":334115380,"identity":"8a7e4ef7-8e94-4f44-9025-a8955e39dac4","order_by":6,"name":"Irina Druzhkova","email":"","orcid":"","institution":"Privolzhsky Research Medical University","correspondingAuthor":false,"prefix":"","firstName":"Irina","middleName":"","lastName":"Druzhkova","suffix":""}],"badges":[],"createdAt":"2024-07-11 12:36:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4724160/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4724160/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61801942,"identity":"1807b5bd-6c96-4017-8e2a-d34061c5dec9","added_by":"auto","created_at":"2024-08-05 17:51:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":347946,"visible":true,"origin":"","legend":"\u003cp\u003eCharacterization of patients’ derived \u003cem\u003eE.coli\u003c/em\u003e strains. Antibiotic susceptibility (A), biomass and matrix production of different \u003cem\u003eE.coli \u003c/em\u003estrains (B), proportion of selected major (C) and minor (D) metabolites produced by \u003cem\u003eE.coli\u003c/em\u003e strains (C). Mean±SD, n=10, * p≤0.05 with M17.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-4724160/v1/cbe624f58f705720d8fcfadb.png"},{"id":61802337,"identity":"1e3051fb-abe9-4da5-8d98-cc4fcebdc71d","added_by":"auto","created_at":"2024-08-05 17:59:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":429055,"visible":true,"origin":"","legend":"\u003cp\u003eTumor spheroid growth under exposure to bacterial metabolites.\u003c/p\u003e\n\u003cp\u003eA - Light microscopy images of tumor spheroids HCT116 and the size of spheroids HCT116; B - Light microscopy images of tumor spheroids HT29 and the size of spheroids HT29; C - Doubling time of different cell lines. Mean±SD, n=9, * p≤0.05 with control without metabolites\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-4724160/v1/a0b2e4fa4c1433c3a4bc66a7.png"},{"id":61801939,"identity":"78a8080f-398b-4785-8a0a-e7f647e20aaa","added_by":"auto","created_at":"2024-08-05 17:51:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":629593,"visible":true,"origin":"","legend":"\u003cp\u003eMigration of colorectal cancer cells from the spheroids in the presence of bacterial metabolites. Light microscopy images and the migration area (square) of HCT116 (A) and HT29 (B) spheroids. Mean±SD, n=7, * p≤0.05 with control without metabolites.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-4724160/v1/25d294a2430ec8542ff53fac.png"},{"id":61801945,"identity":"ee531b25-4f44-4a0d-9dfe-87c379c95ee5","added_by":"auto","created_at":"2024-08-05 17:51:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":976377,"visible":true,"origin":"","legend":"\u003cp\u003eThe “wound healing” assay in the presence of \u003cem\u003eE.coli\u003c/em\u003e metabolites. (A) Light microscopy images of HCT116, SW480 and HT29 cells; (B) The relative square of the “wound” in the cell monolayer. Mean±SD, n=5, * p≤0.05 with control without metabolites at the same day\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-4724160/v1/69ad79e4a756df4f5b06cac9.png"},{"id":61801943,"identity":"84c7de4f-215f-4421-9934-2fc96c1a7cca","added_by":"auto","created_at":"2024-08-05 17:51:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":813167,"visible":true,"origin":"","legend":"\u003cp\u003eE-cadherin and FAK expression under exposure of cancer cells to \u003cem\u003eE.coli\u003c/em\u003e metabolites. (A) Fluorescence microscopy images of E-cadherin expression and quantification of fluorescence intensity. (B) Fluorescence microscopy images of FAK expression and quantification of fluorescence intensity. Scale bar 50 μm for all images, Mean±SD, n=25, * p≤0.05 with control without metabolites.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-4724160/v1/b219a863cf431ab019eb5396.png"},{"id":64357013,"identity":"3def855e-02dc-4598-ad9a-75d08b3cb055","added_by":"auto","created_at":"2024-09-12 06:07:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4181855,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4724160/v1/8ee6a10c-94f2-4b01-bd2e-1fd641f2bb3e.pdf"},{"id":61801941,"identity":"235fb6c2-c839-421f-91d5-c13b9bd8364b","added_by":"auto","created_at":"2024-08-05 17:51:07","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":154261,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4724160/v1/17182a2dd502d99707109637.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effects of Tumor-Associated E. coli Metabolites on Migration of Colorectal Cancer Cells","fulltext":[{"header":"Introduction","content":"\u003cp\u003eColorectal cancer is one of the most common malignancies worldwide. Metastatic colorectal cancer with a highly invasive phenotype remains a lethal disease with a 5-year survival rate of approximately 14% [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Its development is multifactorial and influenced by the host genetic and environmental factors [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In the case of colorectal cancer, microbiota and its metabolites are considered an essential player in the disease progression [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In the human gastrointestinal tract, there are about 40 trillion microbes constituting more than 1000 species, the majority of which inhabit the colon [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The composition of the microbiome in the human colon may change over time as a result of a number of chronic inflammatory diseases, including type I and type II diabetes, Crohn's disease, ulcerative colitis and psoriasis, and cancer [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. It is generally accepted that there are close associations between the gut microbiota composition and its metabolites and the progression of colorectal cancer [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. For example, some toxic and genotoxic metabolites produced by \u003cem\u003eFusobacterium nucleatum and Bacteroides fragilis\u003c/em\u003e was shown to have direct carcinogenicity [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], leading to the accumulation of mutational changes in the intestinal cells and microsatellite instability. The crosstalk between the host cancer cells and gut microbiota mediated by metabolites can favor cancer progression, enhanced invasiveness and cancer cell survivability. However, the direct effects of microorganisms and their metabolites on cancer cells have been poorly described.\u003c/p\u003e \u003cp\u003e \u003cem\u003eEscherichia coli (E. coli)\u003c/em\u003e is the most common representative of colon microbiota. Commensal \u003cem\u003eE. coli\u003c/em\u003e is known to promote the regeneration of damaged colon epithelial cells [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, some of \u003cem\u003eE. coli\u003c/em\u003e strains may cause inflammatory bowel diseases, which are the risk factors for colorectal cancer [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. An increase in the colonization of colon mucosa by mucosa-associated \u003cem\u003eE. coli\u003c/em\u003e has been found in patients with colorectal cancer [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. \u003cem\u003eE. coli\u003c/em\u003e metabolites can cause programmed cell aging, arrest the cell cycle and reprogram cells to produce pro-inflammatory cytokines, toxins and growth factors [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Bacterial metabolites can also play a role in metastasis formation. Specifically, lipopolysaccharide, produced by \u003cem\u003eE. coli\u003c/em\u003e can increase secretion of cathepsin K, which mediates \u0026ldquo;M2-like\u0026rdquo; polarization of macrophages and promotes metastasis in colorectal cancer [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNotably, the majority of studies are based on the analysis of microbiota composition and its correlation with clinical-pathological characteristics of tumors [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Our study was designed to understand the role of \u003cem\u003eE.coli\u003c/em\u003e metabolites isolated from patients\u0026rsquo; tumor samples in regulation of migratory capacity of colorectal cancer cells. The \u003cem\u003eE.coli\u003c/em\u003e strains were tested for antibiotic susceptibility, biofilms production and biochemical properties. Composition of \u003cem\u003eE.coli\u003c/em\u003e metabolites was identified using the liquid chromatography \u0026ndash; mass spectrometry analysis. The effects of the metabolites on cancer cells migration were examined on cell monolayers and 3D tumor spheroids. We used three colorectal cancer cell lines that are different in cell morphology, mutation status and chemosensitivity. The expression of E-cadherin and focal adhesion kinase (FAK) markers was assessed using immunocitochemistry.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eCharacterization of isolated\u003c/strong\u003e \u003cstrong\u003eE. coli\u003c/strong\u003e \u003cstrong\u003estrains\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFour bacteria species were isolated from patients\u0026rsquo; tumor and, using a combination of biochemical assay and MALDI ToF spectroscopy, identified as \u003cem\u003eE. coli\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eFor the tumor-associated \u003cem\u003eE. coli\u003c/em\u003e strains the data on antibiotic sensitivity and biochemical enzyme activity were obtained and compared with standard probiotic M17 strain. Unlike M17, all the tumor-associated \u003cem\u003eE.coli\u003c/em\u003e strains were resistant to at least one of the antibiotics tested. Three out of four strains (Col-101, Col-102, Col-103) showed resistance to ampicillin, and one (Col-93) - to amoxicillin (Fig. \u003cspan\u003e1\u003c/span\u003eA).\u003c/p\u003e\n\u003cp\u003eAnalysis of the biofilm activity showed that three tumor-associated strains (Col-93, Col-101, Col-103) increased biomass and synthesized a matrix less actively compared to M17 strain (Fig. \u003cspan\u003e1\u003c/span\u003eB). One strain (Col-102) had statistically higher reproduction rate and produced the same volume of matrix as M17.\u003c/p\u003e\n\u003cp\u003eBiochemical activity of the strains has fluctuations in some amino acids and especially in sucrose (Supl.1). Most of the strains associated with colorectal cancer were unable to utilize sucrose, which is typically observed in only a few \u003cem\u003eE.coli\u003c/em\u003e that can are typically slow growing or pathogenic strains [\u003cspan\u003e15\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThe differences in the proportion and composition of the synthesized metabolites were found between tumor-associated and the probiotic strains. The proportion of dominant metabolites (mg/ml) was similar in all strains (Fig. \u003cspan\u003e1\u003c/span\u003e, C), tumor-associated and control. While the balance of minor components (\u0026micro;g/ml) was different for the tumor-associated \u003cem\u003eE.coli\u003c/em\u003e strains compared to M17 control (Fig. \u003cspan\u003e1\u003c/span\u003e, D).\u003c/p\u003e\n\u003cp\u003eWe found statistically higher levels (p\u0026thinsp;\u0026le;\u0026thinsp;0.05) of butyric, fumaric, maleic, and glycolic acids in tumor-associated strains\u0026rsquo; metabolites compared to probiotic strain M17 (Table \u003cspan\u003e2\u003c/span\u003e). The amount of malic acid was increased in Col-101 metabolites more than 5 times compared to other strains and M17. Pyruvic acid level was higher in tumor-associated strains, but statistical significance was shown only for Col-102 and Col-103. The level of valeric acid was higher for Col-101, Col-102 and Col-103 metabolites (p\u0026thinsp;\u0026le;\u0026thinsp;0.05).\u003c/p\u003e\n\u003cp\u003eDaily production of short-chain fatty acids (SCFAs) such as 2-oxybutyrate, propionate, \u0026alpha;-Ketoglutaric acid (AKG) and isobutyric acid by tumor-associated strains was lower compared to probiotic strain M17 (Table \u003cspan\u003e2\u003c/span\u003e). The production of propionic, malonic, 2-hydroxyglutaric, 2-oxobutyric a and isobutyric acids was dramatically, \u0026gt; 2\u0026ndash;5 times decreased for all tumor-associated strains.\u003c/p\u003e\n\u003cp\u003eExtremely low level of \u0026alpha;-ketoglutaric acid (AKG) was found for Col-103 strain (\u0026gt;\u0026thinsp;90-fold decrease) as well as for other tumor-associated strains. Lactic acid concentration was lower in Col-93 and Col-103 strains. Succinic acid was decreased in all patients\u0026rsquo; strain metabolites. No changes were observed for isovaleric acid and glyoxylic acid.\u003c/p\u003e\u003cp\u003eTable 1. Patient\u0026rsquo;s sample characteristics.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"628\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.828025477707007%\" valign=\"top\"\u003e\n \u003cp\u003eSample\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.687898089171974%\" valign=\"top\"\u003e\n \u003cp\u003eAge\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.528662420382165%\" valign=\"top\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.88535031847134%\" valign=\"top\"\u003e\n \u003cp\u003eTNM classification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.585987261146496%\" valign=\"top\"\u003e\n \u003cp\u003eTumor type\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.611464968152866%\" valign=\"top\"\u003e\n \u003cp\u003eLocalisation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.872611464968152%\" valign=\"top\"\u003e\n \u003cp\u003eGrade\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.828025477707007%\" valign=\"top\"\u003e\n \u003cp\u003eCol-93\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.687898089171974%\" valign=\"top\"\u003e\n \u003cp\u003e76\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.528662420382165%\" valign=\"top\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.88535031847134%\" valign=\"top\"\u003e\n \u003cp\u003eT3N1М0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.585987261146496%\" valign=\"top\"\u003e\n \u003cp\u003eAdenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.611464968152866%\" valign=\"top\"\u003e\n \u003cp\u003eSigmoid colon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.872611464968152%\" valign=\"top\"\u003e\n \u003cp\u003eG2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.828025477707007%\" valign=\"top\"\u003e\n \u003cp\u003eCol-101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.687898089171974%\" valign=\"top\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.528662420382165%\" valign=\"top\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.88535031847134%\" valign=\"top\"\u003e\n \u003cp\u003eрT4bN1bМ1c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.585987261146496%\" valign=\"top\"\u003e\n \u003cp\u003eAdenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.611464968152866%\" valign=\"top\"\u003e\n \u003cp\u003eSigmoid colon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.872611464968152%\" valign=\"top\"\u003e\n \u003cp\u003eG2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.828025477707007%\" valign=\"top\"\u003e\n \u003cp\u003eCol-102 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.687898089171974%\" valign=\"top\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.528662420382165%\" valign=\"top\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.88535031847134%\" valign=\"top\"\u003e\n \u003cp\u003eрT4bN1bМ0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.585987261146496%\" valign=\"top\"\u003e\n \u003cp\u003eAdenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.611464968152866%\" valign=\"top\"\u003e\n \u003cp\u003eSigmoid colon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.872611464968152%\" valign=\"top\"\u003e\n \u003cp\u003eG2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.828025477707007%\" valign=\"top\"\u003e\n \u003cp\u003eCol-103\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.687898089171974%\" valign=\"top\"\u003e\n \u003cp\u003e76\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.528662420382165%\" valign=\"top\"\u003e\n \u003cp\u003eM\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.88535031847134%\" valign=\"top\"\u003e\n \u003cp\u003eрT3N0М0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.585987261146496%\" valign=\"top\"\u003e\n \u003cp\u003eAdenocarcinoma\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.611464968152866%\" valign=\"top\"\u003e\n \u003cp\u003eTransverse colon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.872611464968152%\" valign=\"top\"\u003e\n \u003cp\u003eG1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDaily production of the selected metabolites by \u003cem\u003eE.coli\u003c/em\u003e strains (* p\u0026thinsp;\u0026le;\u0026thinsp;0.05 with M17).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAnalyte\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eM17\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCol-093\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCol-101\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCol-102\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCol-103\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLactic acid, mg/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e138.6\u0026thinsp;\u0026plusmn;\u0026thinsp;13.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e102.2\u0026thinsp;\u0026plusmn;\u0026thinsp;39.4 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e123.8\u0026thinsp;\u0026plusmn;\u0026thinsp;23.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e124.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e112.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7 *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcetic acid, mg/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0 *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSuccinic acid, mg/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e170.8\u0026thinsp;\u0026plusmn;\u0026thinsp;18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e120.5\u0026thinsp;\u0026plusmn;\u0026thinsp;40.7 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e118.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.4 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e147.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e144.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1 *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePyruvic acid, mg/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMalic acid, mg/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValeric acid, mg/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04 *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026beta;-Hydroxybutyric acid, \u0026micro;g/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.6\u0026thinsp;\u0026plusmn;\u0026thinsp;19.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70.0\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePropionic acid, \u0026micro;g/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e122.7\u0026thinsp;\u0026plusmn;\u0026thinsp;23.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6 *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIsobutyric acid, \u0026micro;g/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6 *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eButyric acid, \u0026micro;g/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.3\u0026thinsp;\u0026plusmn;\u0026thinsp;10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4 *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2-Hydroxyglutaric acid, \u0026micro;g/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98.7\u0026thinsp;\u0026plusmn;\u0026thinsp;43.0 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8 *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIsovaleric acid, \u0026micro;g/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFumaric acid, \u0026micro;g/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e170.1\u0026thinsp;\u0026plusmn;\u0026thinsp;16.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e325.6\u0026thinsp;\u0026plusmn;\u0026thinsp;114.8 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e372.3\u0026thinsp;\u0026plusmn;\u0026thinsp;53.2 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e263.2\u0026thinsp;\u0026plusmn;\u0026thinsp;27.2 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e476.8\u0026thinsp;\u0026plusmn;\u0026thinsp;15.5 *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaleic acid, \u0026micro;g/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e101.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e366.3\u0026thinsp;\u0026plusmn;\u0026thinsp;229.3 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e446.1\u0026thinsp;\u0026plusmn;\u0026thinsp;59.6 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e337.4\u0026thinsp;\u0026plusmn;\u0026thinsp;15.9 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e465.1\u0026thinsp;\u0026plusmn;\u0026thinsp;63.5 *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlyoxylic acid, \u0026micro;g/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.9\u0026thinsp;\u0026plusmn;\u0026thinsp;11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2-Oxobutyric acid, \u0026micro;g/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7 *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026alpha;-Ketoglutaric acid, \u0026micro;g/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.2\u0026thinsp;\u0026plusmn;\u0026thinsp;16.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlycolic acid, \u0026micro;g/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e235.8\u0026thinsp;\u0026plusmn;\u0026thinsp;35.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e264.7\u0026thinsp;\u0026plusmn;\u0026thinsp;72.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e257.9\u0026thinsp;\u0026plusmn;\u0026thinsp;34.0 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e301.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e286.4\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1 *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMalonic acid, \u0026micro;g/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5 *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEffect of\u003c/strong\u003e \u003cstrong\u003eE.coli\u003c/strong\u003e \u003cstrong\u003emetabolites on tumor spheroids growth\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe effect of \u003cem\u003eE.coli\u003c/em\u003e metabolites on the growth of tumor spheroids was assessed using colorectal cancer cell lines HCT116 and HT29. The SW480 line lacked the ability for spheroid formation.\u003c/p\u003e\n\u003cp\u003eIt was found that in the presence of \u003cem\u003eE.coli\u003c/em\u003e metabolites the growth of both HCT116 and HT29 spheroids was inhibited compared with a control without metabolites (Fig. \u003cspan\u003e2\u003c/span\u003e, A, B). At that, no differences were observed in the effects of M17 and tumor-associated strains.\u003c/p\u003e\n\u003cp\u003eWe also assessed proliferative activity of all cell lines in the presence of metabolites of \u003cem\u003eE.coli\u003c/em\u003e strains (Fig. \u003cspan\u003e2\u003c/span\u003e, C). A significant increase of the doubling time in the presence of metabolites M17 (p\u0026thinsp;=\u0026thinsp;0.034) and Col-101 (p\u0026thinsp;=\u0026thinsp;0.039) was shown for HT29 cells. The metabolites of Col-103 strain extended the doubling time of SW480 (p\u0026thinsp;=\u0026thinsp;0.042). For other lines, the presence of metabolites only slightly increased the doubling time, without statistical significance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEffect of\u003c/strong\u003e \u003cstrong\u003eE.coli\u003c/strong\u003e \u003cstrong\u003emetabolites on cancer cells migration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMetabolites of the probiotic strain M17 did not affect migration of HCT116 cells from the spheroids and slightly (p\u0026thinsp;=\u0026thinsp;0.035) inhibited migration of HT29 cells compared to control without metabolites (Fig. \u003cspan\u003e3\u003c/span\u003e). Tumor-associated \u003cem\u003eE.coli\u003c/em\u003e strains metabolites had different effects on migration of HCT116 and HT29 cells. HCT116 cell line showed higher migration activity in the presence of metabolites from all tumor-associated strains compared to control without metabolites and M17 strain (Fig. \u003cspan\u003e3\u003c/span\u003e, A). The largest migration area was observed upon exposure to the Col-101 metabolites.\u003c/p\u003e\n\u003cp\u003eThe opposite effects were observed for HT29 cells, whichactively migrated in control without metabolites but inhibited migration in the presence of \u003cem\u003eE.coli\u003c/em\u003e metabolites. Of the strains used, M17 metabolites inhibited migration of HT29 cells in a lower degree in comparison with tumor-associated strains metabolites (Fig. \u003cspan\u003e3\u003c/span\u003e, B).\u003c/p\u003e\n\u003cp\u003eAnalysis of cell migration in the model of monolayer \u0026ldquo;wound healing\u0026rdquo; was performed for the three cell lines HCT116, SW480 and HT29. Two \u003cem\u003eE.coli\u003c/em\u003e strains, Col-101 and Col-102, were selected for this test as they demonstrated the most notable effects among other strains obtained from the patients.\u003c/p\u003e\n\u003cp\u003eSimilar to migration from the spheroids, patient-derived \u003cem\u003eE.coli\u003c/em\u003e metabolites stimulated migration of HCT116 and SW480 cells and inhibited migration of HT29 cells in the \u0026ldquo;wound healing\u0026rdquo; model (Fig. \u003cspan\u003e4\u003c/span\u003e). The M17 metabolites did not change migration of HCT116 and SW480 cells and inhibited migration of HT29 cells.\u003c/p\u003e\n\u003cp\u003eTherefore, the experiments on the cell monolayers and tumor spheroids revealed that tumor-associated \u003cem\u003eE.coli\u003c/em\u003e metabolites affected migratory capacity of colorectal cancer cells and could either increase or decrease it depending on the specifics of cancer cells.\u003c/p\u003e\n\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003eImmunocytochemical analysis of the migration-associated markers\u003c/h2\u003e\n \u003cp\u003eTo identify the molecular mechanisms through which \u003cem\u003eE.coli\u003c/em\u003e metabolites had different effects on colorectal cancer cell lines, the expression levels of E-cadherin and the focal adhesion kinase (FAK) were analyzed using immunofluorescence.\u003c/p\u003e\n \u003cp\u003eE-cadherin expression statistically decreased in HCT116 and SW480 cell lines upon exposure to metabolites of the Col-101 strain (Fig. \u003cspan\u003e5\u003c/span\u003e). The probiotic M17 strain induced a marked increase in E-cadherin level only in HT29 cells, and had no effect on two other cell lines. Since down-regulation of E-cadherin, a major component of adherens junctions, facilitates cell motility and migration, its lower level in HCT116 and SW480 cells correlated with their highest migratory activity upon incubation with Col-101 metabolites.\u003c/p\u003e\n \u003cp\u003eA significant decrease in FAK expression was noted in HT29 cells under the bacterial metabolites of all tested strains, which explains a decrease of migratory capacity of this cell line. Inhibition of FAK activity is known to decrease cell motility due to suppression of cell-matrix attachment. Of note, the initial FAK activity in HT29 cells was higher compared with other cell lines, suggesting their higher migratory potential. In HCT116 and SW480 cell lines FAK level did not change after incubation with bacterial metabolites (Fig. \u003cspan\u003e5\u003c/span\u003e, B).\u003c/p\u003e\n \u003cp\u003eThese results suggest that changes in migratory capacity of colorectal cancer cells under the exposure to tumor-associated \u003cem\u003eE.coli\u003c/em\u003e metabolites can be mediated by both the loss of cadherin-based cell\u0026thinsp;\u0026minus;\u0026thinsp;cell adhesions and attenuation of the FAK signaling.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIt is known that tumor-associated microorganisms play a critical role in the progression of colorectal cancer [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Numerous studies demonstrate correlation between microbiome composition and different cancer features, such as stage, invasiveness, drug resistance, etc. [\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, the mechanisms underlying the impact of microbiome on tumor progression remain poorly elucidated. Here, we analyzed the effect of probiotic and tumor-associated E. coli strains on migratory capacity of colorectal cancer cells in vitro.\u003c/p\u003e \u003cp\u003eFirst, we compared general characteristics of \u003cem\u003eE.coli\u003c/em\u003e strains obtained from colorectal cancer patients with the standard probiotic strain M17 and found numerous differences between them. Specifically, the tumor-associated strains showed resistance to at least one antibiotic tested, primarily β-lactam antibiotics (ampicillin, amoxicillin), which can be associated with their ability to intracellularly survive during antibiotic treatment [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In addition, their biomass growth and biofilm matrix synthesis were reduced compared to M17, indicating that growth on the enterocytes surface is not a dominant type of growth for tumor-associated strains. Also, it was noticed that tumor-associated strains were adapted to utilize glucose, rather than complex disaccharides due to decreased sucrose-related saccharolytic activity, which can be explained by the competition for the intracellular glucose with cancer cells. Since the \u003cem\u003eE.coli\u003c/em\u003e strains were isolated directly from surgical samples of patients' tumors, the observed features may indicate their adaptation to the conditions of intracellular interaction with cancer cells, rather than survival on the abiotic surfaces. Previously, it has been shown that intracellular localization provides numerous advantages to the invading microbes, including the immune escape and a favorable nutritional environment. A low microbial biomass is consistently present in colorectal tumors, and they play an important role in cancer development [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eСharacterization of the spectrum of organic acids synthesized by different \u003cem\u003eE. coli\u003c/em\u003e strains during their metabolism showed that the proportion and the level of synthesized substances were different for the probiotic and tumor-associated strains. The positive impact of microbiota is mostly connected with the production of SCFAs as a result of fermentation of dietary fibers, which are commonly indigestible by the human enzymes [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. SCFAs are thought to serve as anti-inflammatory substances in the gut, improving the intestinal epithelium barrier and gut homeostasis [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Also, SCFAs can promote antitumor effects and even induce apoptosis of colorectal cancer cells [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAn excess of malonic acid and AKG was produced by the probiotic strain. Malonic acid inhibites succinate dehydrogenase. The succinate and succinate dehydrogenase complex are the central link of the Krebs cycle and the main structure of the antihypoxic ensemble for all somatic cells. This complex regulates the mitochondrial respiratory chain and provides antioxidant defense by binding excess iron ions [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. It has been reported that AKG mediates DNA demethylation and aberrant epigenetic modifications in HT-29/Caco-2, down regulates cell differentiation in colorectal tumor models [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Antitumor effect of AKG was demonstrated on human lung carcinoma H460 and colon adenocarcinoma cell lines HCT116 [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMultiple studies have shown that metabolites such as butyrate, propionate, acetate, and niacin contribute to protection of the host against malignation and represent an energy source for the colon epithelial cells [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. However, the oncometabolites like the lactate, glutamate, fumarate, and succinate are involved in tumor survival and progression [\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Oncometabolites make the tumor microenvironment more favorable for cell migration [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. It has been shown that elevated plasma concentrations of succinic and maleic acid are associated with the development of lung cancer [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In the study by Ternes et al. it was shown that gut microbial metabolite formate, produced by \u003cem\u003eF. nucleatum\u003c/em\u003e, enhanced migration potential of HCT116 cells through the formation of focal adhesion points [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Sciacovelli et al. showed on renal cancer patients that fumaric acid inhibits Tet-mediated demethylation of a regulatory region of the antimetastatic miRNA cluster miR-200ba429, leading to the expression of EMT (epithelial-mesenchymal transition)-related transcription factors, which, in its turn, results in enhanced migratory properties and poor clinical outcome [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Malic acid was increased in malignant prostatic hyperplasia, suggesting that this metabolite can be used as a biomarker of prostate cancer [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe results of our study showed that metabolites of tumor-associated \u003cem\u003eE.coli\u003c/em\u003e strains had different effects of migration capacity of colorectal cancer cells depending on their original properties.\u003c/p\u003e \u003cp\u003eIn our previous in vitro study the higher migrational potential and proliferation rate of HCT116 compared to HT29 line were shown. SW480 is characterized by the inability to form spheroids and fast growth rate [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Here, we observed more active migration of HCT116 and SW480 cells and inhibition of migration of HT29 cells in the monolayer \u0026ldquo;healing\u0026rdquo; model under E.coli metabolites. Similar results were obtained using the model of 3D tumor spheroids. We assume that these differences could be associated with different expressions of proteins involved in the processes of intercellular adhesion and adhesion to the substrate, such as E-cadherin and focal adhesion kinase (FAK). According to \u0026ldquo;The human Protein atlas\u0026rdquo; HT29 cell line initially has high expression of genes, associated with collective migration, such as \u003cem\u003eCDH1\u003c/em\u003e (E-cadherin) and \u003cem\u003ePDK2\u003c/em\u003e (focal adhesion kinase (FAK) and low expression of single-cell migration-associated markers, such as \u003cem\u003eROCK1\u003c/em\u003e, \u003cem\u003eEZR\u003c/em\u003e and \u003cem\u003eTALIN\u003c/em\u003e. While HCT116 and SW480 have significantly lower expression of \u003cem\u003eCDH1\u003c/em\u003e and \u003cem\u003ePDK2\u003c/em\u003e and higher \u003cem\u003eROCK1\u003c/em\u003e, \u003cem\u003eEZR\u003c/em\u003e and \u003cem\u003eTALIN\u003c/em\u003e, indicating different types of cell migration specific to these cells. Our previous studies demonstrated that HT29 cells have a lower migration potential compared to HCT116 and SW480 cells [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn HCT116 and SW480 cells, for which a single-cell migration is typical, the loss of E-cadherin was the main effect of \u003cem\u003eE.coli\u003c/em\u003e metabolites. Also, it is worth noting that the most pronounced effect was shown for \u003cem\u003eE.coli\u003c/em\u003e strain Col-101. It was isolated from the tumor with the most invasive phenotype with the presence of the distant metastases to the liver.\u003c/p\u003e \u003cp\u003eLoss of E-cadherin expression results in loss of contact inhibition, increase of cell motility and subsequent single-cell migration [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In the work by Tarashi et al. similar effect of \u003cem\u003eB. fragilis\u003c/em\u003e toxin that is associated with colorectal cancer on cleavage of E-cadherin and formation of invasive phenotype was demonstrated [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Also, it was shown that ammonia produced by \u003cem\u003eH.pylori\u003c/em\u003e disrupted cellular tight junctions, based on E-cadherin, thus affecting cell integrity and damaging the gastric epithelium, which resulted in gastric cancer development [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Thirunavukkarasan et al. showed that SCFAs increased the level of expression of E-cadherin, and therefore, prevented the formation of the invasive phenotype of cancer cells [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn collective cell migration, E-cadherin mediates epithelial cell\u0026ndash;cell adhesion and its expression is required to maintain intercellular junctions [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In HT29 cells, characterized by collective migration, the main effect of bacterial metabolites was the loss of FAK, while the expression of E-cadherin did not change. It is known that FAK, when it becomes constitutively active due to mutations or elevated activity of alternative signaling pathways, exerts oncogenic properties and allows cancer cells growth and survival without anchorage to the ECM, which is an important step during metastatic process [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe limitation of our investigation is the low number of samples and focus on the one type of microorganism - \u003cem\u003eE.coli\u003c/em\u003e from the whole microbiome. However, we obtained the detailed metabolic profile and clearly demonstrated the effect of tumor-associated \u003cem\u003eE.coli\u003c/em\u003e on migratory potential of tumor cells. Although this effect may be enhanced by other representatives of the microbiome, the results of such studies are important for understanding the possible role of individual representatives of the microbiome in the progression of cancer that is difficult to separate in vivo.\u003c/p\u003e \u003cp\u003eOur research showed that metabolites from tumor-associated \u003cem\u003eE.coli\u003c/em\u003e strains enhanced FAK-dependent single-cell migration accompanied by the loss of E-cadherin in cancer cells with initially low FAK expression. At the same time, this effect was not observed in cancer cells with collective migration phenotype. Further studies of the effects of tumor-associated strains on migratory potential of different cancer types are important for development of microbiome correction strategies to improve cancer prognosis.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eBacterial strains and cultivation\u003c/h2\u003e\n \u003cp\u003e\u003cem\u003eE.coli\u003c/em\u003e strains (Col-93, Col-101, Col-102, Col-103) were isolated from colorectal cancer biopsy samples. Human colon samples were collected at Nizhny Novgorod Regional Oncologic Hospital (Russia). The study with the use of patients\u0026rsquo; material was approved by the ethics committee of the Privolzhsky Research Medical University (approval № 09 from 30.06.2023). All methods were carried out in accordance with relevant guidelines and regulations. Informed consent was obtained from all subjects and/or their legal guardians.The clinical information about patients\u0026rsquo; tumors is presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eThe species identity was determined by matrix assisted laser desorption ionization-time of flight mass spectrometry (MALDI ToF Autoflex speed, Bruker Daltonik GmbH, Germany). Biochemical properties were studied by bacterial biochemical identification kits (RPC Diagnostic Systems, Nizhny Novgorod, Russia) (Suppl.1). Routine cultivation was carried out using nutrient agar (24 h, 37\u0026deg;C). Probiotic strain of \u003cem\u003eE.coli\u003c/em\u003e M17, which is widely used as a component of probiotic drugs to correct dysbiotic conditions, served as a positive control in all the experiments.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eMetabolite preparation\u003c/h2\u003e\n \u003cp\u003eThe bacterial strains were seeded in DMEM with 4.5 mg/L glucose (PanEco, Russia) in a concentration of 1\u0026times;10\u003csup\u003e6\u003c/sup\u003e CFU/ml and cultured for 24 h at 37\u0026deg;C. Then metabolites were obtained by filtration of the growth media through bacterial filter 0.2 \u0026micro;m (Corning, USA). The resulting solution was diluted 1:1.5 with DMEM with 5% fetal bovine serum. The final medium was checked for pH, which was in neutral range (7.0-7.2).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eTarget liquid chromatography \u0026ndash; mass spectrometry analysis\u003c/h3\u003e\n\u003cp\u003eCell-free metabolites were prepared by centrifuging the culture at 20,000 rcf for 20 min. Finally, they were filtered through 0.2 \u0026micro;m filters.\u003c/p\u003e\n\u003cp\u003eHigh-performance liquid chromatography with electrospray ionization triple quadrupole tandem mass spectrometry was used for detection of short-chain fatty acids and organic acids in supernatant liquid. The samples of metabolites were prepared according to the manual (Shimadzu Corporation) and analyzed using a mass spectrometer LCMS-8050 coupled with the Nexera XR liquid chromatography system (Shimadzu, Japan). The detailed description of the procedure is presented in the Supplementary information (Suppl.2).\u003c/p\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eAntibiotic susceptibility testing\u003c/h2\u003e\n \u003cp\u003eThe disk diffusion susceptibility test was used to determine the susceptibility of isolates of \u003cem\u003eE. coli\u003c/em\u003e to the nine antibiotics - ampicillin, trimethoprim, amoxicillin, norfloxacin, ciprofloxacin, ofloxacin, cefotaxime, ceftriaxone and ceftazidime. The test was performed using the standard protocol [\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e]. A bacterial inoculum 1\u0026ndash;2 \u0026times; 10\u003csup\u003e8\u003c/sup\u003e CFU/mL was applied to the surface of a nutrient agar plate with a diameter of 60 mm. A commercially prepared, fixed-concentration paper antibiotic disk was placed on the inoculated agar surface. The results were assessed after 18\u0026ndash;24 h incubation of the plates at 37\u0026deg;C. The zones of growth inhibition surrounding the antibiotic disk were measured to the nearest millimeter. The zone diameters of each drug were interpreted according to the manufacturer\u0026apos;s instruction (NICF, St. Petersburg, Russia).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eMatrix production assay\u003c/h2\u003e\n \u003cp\u003eTo analyze matrix production, the biofilms were grown for 48 h in DMEM with 4.5mg/L glucose (PanEco, Russia) in 96-well plates. The bacterial concentration was 1\u0026times;10\u003csup\u003e6\u003c/sup\u003e CFU/ml. Then the bacterial biofilms were washed three times by phosphate buffered saline (PBS) and stained by Congo red for 15 min. The staining solution containing 1% Congo red and 10% Twin 80 was prepared in PBS. After staining, plates were washed three times by PBS and ethyl alcohol was used for extraction of Congo red from the cells. The optical density was measured at a multichannel spectrophotometer BioTek Synergy Mx (BioTek, USA) at a wavelength of 500 nm.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eBacterial biomass growth\u003c/h2\u003e\n \u003cp\u003eThe biofilms were washed three times by PBS, fixed with 96% ethyl alcohol for 15 minutes, stained by 0.1% gentian violet solution (3 min). Next, the dye was eluted by 96% ethyl alcohol at constant shaking (10 min) and the optical density was measured using a multichannel spectrophotometer at a wavelength of 570 nm.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eColon cancer cell lines\u003c/h2\u003e\n \u003cp\u003eHuman colon adenocarcinoma cell lines НСТ116, SW480 and HT29 were routinely cultured in DMEM (PanEco, Russia) with 5% fetal bovine serum (HyClone, USA) and passed twice a week. The cells were cultured in a CO\u003csub\u003e2\u003c/sub\u003e-incubator, 37\u0026deg;C, RH 80%, CO\u003csub\u003e2\u003c/sub\u003e 5%. The cell lines were obtained from the cell collection of the Ivanovskiy Institute of Virology (Moscow, Russia).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eSpheroids formation and cell migration assay\u003c/h2\u003e\n \u003cp\u003eTo obtain tumor spheroids, HCT116 and HT29 cancer cells were seeded in low attachment 96-well plates (Corning, USA) in the amount of 200 cells in 200 \u0026micro;l /well and cultured in the presence of bacterial metabolites (1:1.5). Spheroids grown in a pure DMEM medium (200 \u0026micro;l per well) were used as a negative control. The size of spheroids was measured in 3, 5 and 7 days for HCT116 and in 4 and 7 days for HT29, because the latter have lower cell division rate.\u003c/p\u003e\n \u003cp\u003eFor migration assay the spheroids after 5 days of culturing were used. They were gently transferred in a small Petri dish (3.5 mm) and incubated for either 2 (HCT116) or 5 days (HT29). Light microscopy images of the spheroids were acquired after their attachment using DMIL microscope (Leica, Germany) at magnification 100x, and the zones of cell migration were measured in ImageJ (V 1.4.3.67) software.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eCell proliferation assay\u003c/h2\u003e\n \u003cp\u003eTo assess the proliferative activity, the cells were seeded in 6-well plates (5 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e cells per well for HT29 and 2 x 10\u003csup\u003e4\u003c/sup\u003e cells per well for HCT116 and SW480). Cells were incubated for 7 days in 2 mL of DMEM with or without metabolites. Then cells were counted using a TC20 automated cell counter (Bio-Rad, Hercules, CA, USA). Proliferation was assessed totally in 9 wells for each experimental group. The doubling time (DT) was calculated using the formula DT\u0026thinsp;=\u0026thinsp;h \u0026times; LN(2)/LN(C2/C1), where C1\u0026mdash;initial cell number, C2\u0026mdash;final cell number, h\u0026mdash;cultivation time (hours).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ldquo;Wound healing\u0026rdquo; assay\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe study of migration activity was carried out on the model of \u0026quot;wound healing\u0026quot; using cultural inserts Culture-Insert 2 Well (Ibidi, USA). The cell suspension (70 \u0026micro;l) with a concentration of 1\u0026times;10\u003csup\u003e5\u003c/sup\u003e cells/ml was placed into the wells, incubated for 24 h (37\u0026deg; C, 5% CO\u003csub\u003e2\u003c/sub\u003e), and silicone liners were removed after the formation of the monolayer. Light microscopy images (Leica, Germany) of the \u0026quot;wounds\u0026quot; were obtained in 1\u0026ndash;5 days after removal of the liners at magnification 100x. Migration zones were measured using ImageJ (V 1.4.3.67) software.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eImmunostaining assay\u003c/h2\u003e\n \u003cp\u003eFor immunocytochemical staining, the cells were cultured in 96-well plates for 24 h after seeding and fixed in 4% formaldehyde for 15 min. The following primary antibodies were used: rabbit antibodies against E-cadherin (ab15148, Abcam, USA), rabbit antibodies against focal adhesion kinase (FAK) (ab131435, Abcam, USA). Subsequently cells were stained with Alexa555-labeled goat anti-rabbit IgG secondary antibody (ab6825, Abcam, USA). Staining was performed in accordance with the manufacturer\u0026apos;s protocol. In addition, the cells were stained with DAPI to visualize the cell nuclei. Fluorescence images were observed using DMIL fluorescence microscope (Leica, Germany) equipped with the following filters: A4 UV BP 360/40 400 BP 470/40 for DAPI and TX2 green BP 560/40 595 BP 645/75 for Alexa.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eStatistical analysis was performed using Statistica 10 (StatSoft. Inc., Tusla, OK, USA). P-values\u0026thinsp;\u0026le;\u0026thinsp;0.05 were considered statistically significant. The nonparametric Mann-Whitney U-test was used to compare the data.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e Conceptualization, Ignatova N. and Druzhkova I.; methodology, Pryazhnikova M., Seliverstov A. and Ignatova N.; investigation, Pryazhnikova M., Ignatova N., Abidullina A. and Druzhkova I; writing\u0026mdash;original draft preparation, Ignatova N., Pryazhnikova M. and Druzhkova I; writing - review and editing, Shirmanova M., Gamayunov S.; project administration, Shirmanova M.; funding acquisition, Ignatova N. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This work was supported by the Russian Science Foundation under grant No. 23-74-00045.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e The authors are thankful to Denis Sohin (Privolzhsky Research Medical University, Russia) for assistance in MALDI ToF identification of bacterial strains and Vitaliy Terekhov for the providing surgical samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement:\u0026nbsp;\u003c/strong\u003eData is provided within the manuscript or supplementary information files. All data supporting the findings of this study are available within the paper and its Supplementary Information. Biochemical activity of E.coli strains is provided in Supplementary Table 1, Target liquid chromatography in Supplementary 2, Transcription of genes associated with migration (data from https://www.proteinatlas.org/) in Supplementary 3.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRumpold H., Nieders\u0026uuml;\u0026szlig;-Beke D., Heiler C., et al. Prediction of mortality in metastatic colorectal cancer in a real-life population: a multicenter explorative analysis. \u003cem\u003eBMC Cancer\u003c/em\u003e. \u003cstrong\u003e2020\u003c/strong\u003e;20(1):1149. doi:10.1186/s12885-020-07656-w \u003c/li\u003e\n\u003cli\u003eBlanpain C. Tracing the cellular origin of cancer. \u003cem\u003eNat Cell Biol\u003c/em\u003e. \u003cstrong\u003e2013\u003c/strong\u003e;15(2):126-134. doi:10.1038/ncb2657\u003c/li\u003e\n\u003cli\u003eHanus M., Parada-Venegas D., Landskron G., Wielandt AM., et al. 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Performance Standards for Antimicrobial Disk Susceptibility Tests; 12th ed.; CLSI document M02-A12; Clinical and Laboratory Standards Institute: Pennsylvania 19087, USA, \u003cstrong\u003e2015\u003c/strong\u003e. 230p. \u003c/li\u003e\n\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":"Escherichia coli, metabolites, colon cancer, migration, E-cadherin, FAK","lastPublishedDoi":"10.21203/rs.3.rs-4724160/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4724160/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eColorectal tumors have a close connection with the gut microbiome. Correlation between rearrangement in microbiome composition and disease progression has already been shown. However, the questions about the mechanisms underlying microorganisms and cancer cells interaction and the immediate effects of tumor-associated microbiomes on cancer cells remain open. In this work, we investigated the effects of metabolites of tumor-associated \u003cem\u003eE.coli\u003c/em\u003e strains on the growth and migration of human colorectal cancer cell lines (HCT116, SW480 and HT29). Differences in the spectrum of synthesized organic acids from tumor-associated and probiotic M-17 strains were revealed. Specifically, tumor-associated \u003cem\u003eE.coli\u003c/em\u003e produced more fumaric, malic and maleic acids, whereas the M-17 - more propionic, 2-oxobutyric and α-ketoglutaric acids. Upon exposure to metabolites from tumor-associated \u003cem\u003eE.coli\u003c/em\u003e strains, HCT116 and SW480 cells showed an increased migration activity and HT29 cells - decreased migration activity in 2D and 3D culture models. Immunocytochemistry assay revealed decrease of E-cadherin in HCT116 and SW480 cells and FAK- in HT29, which explain different effects of \u003cem\u003eE.coli\u003c/em\u003e metabolites on migratory capacity of colorectal cancer cells. Therefore, these results suggest that the effect of tumor-associated \u003cem\u003eE.coli\u003c/em\u003e strains on cancer cells migration depends on their innate type of migration - single-cell or collective migration.\u003c/p\u003e","manuscriptTitle":"Effects of Tumor-Associated E. coli Metabolites on Migration of Colorectal Cancer Cells","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-05 17:51:02","doi":"10.21203/rs.3.rs-4724160/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":"20020fa9-d7a9-4253-9ca5-959979262bb9","owner":[],"postedDate":"August 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-12T05:42:51+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-05 17:51:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4724160","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4724160","identity":"rs-4724160","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}