Identification of bile biomarkers of Occult pancreaticobiliary reflux through a liquid chromatography/mass spectrometry-based metabolomic method

doi:10.21203/rs.3.rs-2077593/v1

Download PDF

Research Article

Identification of bile biomarkers of Occult pancreaticobiliary reflux through a liquid chromatography/mass spectrometry-based metabolomic method

https://doi.org/10.21203/rs.3.rs-2077593/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Introduction:

Occult pancreaticobiliary reflux (OPBR) causes chronic inflammation of the gallbladder and associated mucosal malignant changes, but its pathogenic mechanism remains unclear.

Objective

The aim of this study was to investigate the pathogenic mechanism in OPBR through untargeted metabolomic analysis of bile collected during surgery.

Methods

Patients undergoing surgery for gallstones between December 2020 and May 2021 were eligible for inclusion. There were 30 OPBR patients and 338 non-OPBR patients, and 30 non-OPBR patients were randomly selected as normal controls. Metabolomics analysis was performed based on bile samples from patients in the OPBR group.

Results

The principal component analysis by liquid chromatography-mass spectrometry revealed that 30 metabolites were significantly different between patients with OPBR compared to controls. Among these characteristic metabolites, palmitic acid and arachidonic acid are known to be involved in gallstone formation, methionine and lysophosphatidylcholine are known to be associated with carcinogenesis.

Conclusions

The present study suggested that an LC/MS-based metabolomic studies provide an efficient and promising method for identifying potential biomarkers of OPBR. The identified characteristic metabolites in the OPBR group were significantly different to the control group.

Occult pancreaticobiliary reflux

gallstons

bile

carcinogenesis

liquid chromatography-mass spectrometry metabolomics

Most of the knowledge about epithelial injury, hyperplasia, metaplasia and canceration of gallbladder and bile duct related to pancreaticobiliary reflux (PBR) comes from the study of Pancreaticobiliary maljunction (PBM). A series of events caused by PBR were studied in PBM. Recently, some scholars have discussed the phenomenon of pancreatic bile reflux (PBR), called OPBR, which occurs in a normal pancreaticobiliary junction (NPBJ), and has been suggested that could play a role in gallstone formation through damaging the gallbladder mucosa(Beltran, Contreras, Cruces 2010; Beltran, Vracko, Cumsille, Cruces, Almonacid, Danilova 2007; Sai, Suyama, Kubokawa, Tadokoro, Sato, Maehara, Iida et al. 2003). The confluence of pancreatic juice and bile in the bile duct activates to produce carcinogens, which can act on the bile duct for a long time, causing hyperplasia, metaplasia and atypical hyperplasia of the gallbladder and bile duct mucosa, and finally leading to the occurrence of biliary tract tumors(Beltran, Vracko, Cumsille, Cruces, Almonacid, Danilova 2007; Seki, Yanagisawa, Ninomiya, Ninomiya, Ohta, Saiura, Yamamoto et al. 2005; Wang, Zhang, Guo, Xie, Huang, Yu 2022).

A large number of clinical studies have been devoted to studying the pathogenesis of PBR. Continuous PBR causes chronic inflammation and injury of the biliary tract mucosa and the gallbladder mucosa. When the accumulation of bile and pancreatic enzymes reaches a high level, acute and chronic cholecystitis, common bile duct stones, and gallbladder polyps can be caused. The reflux of pancreatic juice will damage the contraction function of the gallbladder, lead to the change of bile components, promote the secretion of mucus proteins, and finally form bile mud and gallstones(Anderson, Hauman, Suriyapa, Schiller 1979; Beltran, Contreras, Cruces 2010).At present, for PBR-related biliary tract tumors, the recognized carcinogenic mechanism is that phospholipase A2 hydrolyzes lecithin into lysophospholipid, thereby injurying the biliary epithelium(Ichikawa, Kamiyama, Sekido, Ishikawa, Miura, Kamiya, Morita et al. 2004; Kamisawa, Kuruma, Chiba, Tabata, Koizumi, Kikuyama 2017). In addition, secondary bile acids, amino acids and activated pancreatic enzymes promote the occurrence of biliary tract cancer under inflammatory conditions(Funabiki, Matsubara, Miyakawa, Ishihara 2009; Wistuba, Albores-Saavedra 1999). There are K-ras gene mutations, p53 gene mutations and p53 overexpression in gallbladder cancer and non gallbladder cancer tissues of PBR patients(Funabiki, Matsubara, Miyakawa, Ishihara 2009; Kasuya, Nagakawa, Matsudo, Ozawa, Tsuchida, Aoki, Itoi et al. 2009; Tsuchida, Itoi, Aoki, Koyanagi 2003). So PBR patients should be considered as high-risk groups of biliary tract tumors. The mutation of tumor suppressor gene, microsatellite instability and mRNA index were significantly increased in the biliary epithelium of PBR patients, which indicated that pancreatic juice reflux caused the production of mutagenic metabolites and promoted epithelial carcinogenesis(Chao, Wang, Jan, Chen, Chen 1999; Kamisawa, Kuruma, Chiba, Tabata, Koizumi, Kikuyama 2017; Seki, Yanagisawa, Ninomiya, Ninomiya, Ohta, Saiura, Yamamoto et al. 2005). However, the pathogenic mechanism of benign and malignant biliary diseases in OPBR patients is still unclear.

Metabolomics is a newly developed discipline after genomics and proteomics, which simultaneously performs qualitative and quantitative analysis of all low molecular weight metabolites in an organism during a specific physiological period. This new technology can help us find new biomarkers, discover new metabolic pathways, or gain a deeper understanding of these currently known metabolic pathways, which have been demonstrated be one of the effective means to elucidate pathophysiology, disease diagnosis of various diseases(Goutman, Boss, Guo, Alakwaa, Patterson, Kim, Savelieff et al. 2020; Spratlin, Serkova, Eckhardt 2009; Sreekumar, Poisson, Rajendiran, Khan, Cao, Yu, Laxman et al. 2009). Bile metabolism plays a key role in the pathogenesis of biliary diseases, but bile metabolomic studies have never been performed in OPBR patients.

This study is the first to extensively analyze changes in different metabolites in bile samples obtained from OPBR patients and non-OPBR patients using a liquid chromatography-mass spectrometry (LC-MS) metabolomic approach. We identify potential biomarkers that may provide a rationale for elucidating the pathogenic mechanism of OPBR.

2.1 Patients

This prospective study was consecutively included 400 patients who underwent surgery for gallstones between December 2020 and May 2021 in our hospital.Excluded base on preoperative assessment：（1）acute cholecystitis，(2)cholangitis, (3)acute or chronic pancreatic disease, (4) abnormal serum amylase and lipase values, (5)common bile duct stone, (5)anatomic biliary tract anomalies or pancreaticobiliary maljunction, (6)had undergone preoperative endoscopic cholangiography or sphincterotomy,and (7) inability to obtain written informed consent; Excluded base on intraoperative assessment: (1)gallbladder filled with thick sticky bile,(2) small shrunken and atrophic gallbladder, (3)gallbladder with no bile content at all, (4)cystic duct was occluded, gallbladder was hydropic.

2.2 Bile specimen pretreatment

During the operation, at least 5 ml bile was obtained from the gallbladder using a syringe, 1 ml bile was stored in a sterile tube at 4℃,the rest of bile immediately preserved at -80˚C before preparation and analysis of sample and sent to the laboratory on the next day. All samples were processed and measured by laboratory technicians who were not informed of the study and source of samples using a Roche Cobas c702 (Roche Diagnostics, Basel, Switzerland); the normal value of serum amylase was 30–110 U/L.Although there is no consensus on normal amylase levels in either the gallbladder or biliary tract, this study, like most investigations, used normal plasma amylase levels as a reference, and bile amylase levels higher than the normal plasma amylase levels as indicative of PBR(Beltran 2011; Beltran, Contreras, Cruces 2010; Beltran, Vracko, Cumsille, Cruces, Almonacid, Danilova 2007; Kamisawa, Anjiki, Egawa, Kurata, Honda, Tsuruta 2008). In this study, 30 patients in the OPBR group and 338 patients in the non-OPBR group were obtained, and Thirty patients with normal bile amylase were selected as Normal group by coin tossing method.

2.3 Metabolite extraction

100μL of sample was transferred to an EP tube. After the addition of 400μL ofextract solution (acetonitrile:methanol=1:1, containing isotopically-labelled internal standard mixture), the samples were vortexed for 30 s, sonicated for 10 min in ice-water bath, and incubated for 1 h at -40℃ to precipitate proteins. Then the sample was centrifuged at 12000 rpm(RCF=13800(×g),R= 8.6cm) for 15 min at 4℃. The resulting supernatant was transferred to a fresh glass vial for analysis. The quality control (QC) sample was prepared by mixing an equal aliquot of the supernatants from all of the samples.

2.4 Metabolic profling of bile

LC-MS/MS analyses were performed using an UHPLC system (Vanquish, Thermo Fisher Scientific) with a UPLC BEH Amide column (2.1 mm×100 mm, 1.7μm) coupled to Q Exactive HFX mass spectrometer (Orbitrap MS, Thermo). The mobile phase consisted of 25 mmol/L ammonium acetate and 25 ammonia hydroxide in water（pH = 9.75）(A) and acetonitrile (B). The auto-sampler temperature was 4℃, and the injection volume was 2μL. The QE HFX mass spectrometer was used for its ability to acquire MS/MS spectra on information-dependent acquisition (IDA) mode in the control of the acquisition software (Xcalibur, Thermo). In this mode, the acquisition software continuously evaluates the full scan MS spectrum. The ESI source conditions were set as following: sheath gas flow rate as 30 Arb, Aux gas flow rate as 25 Arb, capillary temperature 350℃, full MS resolution as 60000, MS/MS resolution as 7500, collision energy as 10/30/60 in NCE mode, spray Voltage as 3.6 kV (positive) or -3.2 kV (negative), respectively.

2.5 Bioinformatics and statistical analysis

The Simca-p 14.1 (umetrics, Umea, Sweden) was used for pattern recognition. After the data were preprocessed by Pareto scaling, multivariate statistical analysis was performed, including unsupervised principal component analysis (PCA), supervised partial least squares discriminant analysis (PLS-DA) and orthogonal projections to latent structures- discriminant analysis（OPLS-DA）.Univariate statistical analysis included student's t-test and multiple of variation analysis .To determine the metabolites with the best identification ability(Variable Importance in the Projection, VIP > 1 and P value less than 0.05). In addition, commercial databases, including KEGG (http:// www.genome.jp/kegg/), was used to search for the pathways of metabolites. In all tests, P <0.05 was considered statistically significant.

3.1. Multivariate analysis of bile metabolites

The bile samples were characterized by LC/MS in the positive and negative ion modes and the mass spectra of all two groups of bile samples were obtained. The mass spectral data were then processed by multivariate analysis, which mainly including PCA, PLS-DA and OPLS-DA, to observe the stability of the whole analysis process and the distribution between samples. The PCA score chart shows that the sample is basically within the Hotelling's t-squared ellipse and there is a certain separation trend between the samples under the positive and negative ion modes data (Fig. 1A , B) .The PLS-DA model was then used for data analysis. In the present study, the R2 and Q2 intercepts were ≥ 0.05 using PLS-DA, demonstrating that the model is stable and reliable. (Fig. 1C, D)

In addition, we established the OPLS-DA model to further verify the separation trend of the metabolic spectrum between the two groups. The result revealed obvious separation between the PBR group and the benign biliary tract disease group in the metabolomic 3D scores plot. (Fig. 1E, F). The permutation test of OPLS-DA model demonstrated that the modes had good explanatory and predictive capabilities. (Fig 1G, H)

3.2 Screening and identifcation of diferential metabolites

In the analysis of differential metabolites between the two groups of samples, the paired-samples student t test was employed to screen the differential metabolites. In this study, we taken VIP values greater than 1 and P values of the t test less than 0.05 as the criteria for screening differential metabolites. After material identification, 40 metabolites showed reliable results (supplementary Table 1). Among these different metabolites, four exhibited a gradual downward trend, and the levels of the other 36 metabolites increased significantly compared with those in the normal group. These compounds mainly include amino acids and lipids. In the PBR group, the levels of phosphatidylcholine and PC (20:3 (8Z, 11z, 14z)/14:0) were significantly decreased, while the levels of bile acids sulfolitholylglycine and lysoPC were significantly increased. In addition, an increase in the levels of palmitoleic acid and arachidonic acid was observed. The levels of various amino acids including leucine, methionine and phenylalanine in the OPBR group signifcantly increased.

3.3 Pathway enrichment and metabolic pathway analysis of the potential metabolic mechanism

We used the expression of qualitatively significant difference metabolites to perform hierarchical clustering on the two groups of samples, and the Heatmap intuitively displayed the diferences in metabolic profles between two groups. (Fig. 2A). The pathway enrichment analysis was performed to visualize the afected metabolic and signal transduction pathways after OPBR. The pathway with an impact-value threshold above 0.10 was regarded as the potential target pathway(Wang, Yang, Sun, Zhang 2012). As shown in Fig. 2B, protein digestion and absorption, mineral absorption, lysine degradation, linoleic acid metadata, D-amino acid metadata, central carbon metadata in cancer, arginine and proline metadata, aminoacyl-tRNA biosynthesis, alanine, aspartate and glutamate metadata and ABC transporters as the crucial pathways to promote biliary mucosal injury, stone formation, epithelial metaplasia and carcinogenesis after OPBR. The matching status, P value, -log10 (P value) and rich factor of each pathway are shown in Table 2, which indicates that the metabolic pathways of lipids and some amino acids changed significantly after OPBR.

Pancreaticobiliary maljunction(PBM) is closely related to the occurrence and development of gallbladder stones and bile duct cancer(Funabiki, Matsubara, Miyakawa, Ishihara 2009; Tashiro, Imaizumi, Ohkawa, Okada, Katoh, Kawaharada, Shimada et al. 2003). PBM and long common passage of pancreatic juice reflux into the biliary tract are related to proliferation changes, metaplasia and cancer of bile duct and gallbladder epithelium(Funabiki, Matsubara, Miyakawa, Ishihara 2009; Ichikawa, Kamiyama, Sekido, Ishikawa, Miura, Kamiya, Morita et al. 2004). Some scholars have studied the causes of biliary tract cancer and found that the cancer development rate of patients with PBM is 200 times higher than that of normal pancreaticobiliary junction in Japan(Matsuda, Marugame, Kamo, Katanoda, Ajiki, Sobue, Japan Cancer Surveillance Research 2009; Morine, Shimada, Takamatsu, Araida, Endo, Kubota, Toki et al. 2013). Recently, it has been recognized that patients with normal pancreaticobiliary junction may have OPBR, just like pancreaticobiliary reflux associated with PBM .OPBR could lead to informative changes of the biliary epithelium and progress towards the development of precancerous biliary changes and gallader cancer(McKee 2007; Mizuno, Kato, Koyama 1996). However, the underlying molecular mechanism of gallstone formation and cancer development in OPBR is still unclear.

Metabolomics is a newly developed subject after genomics and proteomics, and it is a promising technology. Metabolomics studies usually use liquid chromatography-mass spectrometry (LC/MS) for organic metabolites and the screening of biomarkers among them(Dunn, Bailey, Johnson 2005; Lu, Zhao, Bai, Zhao, Lu, Xu 2008). Some scholars reported that metabolomics analysis can be used as a new method for the diagnosis of breast cancer, biliary tract cancer and PBM(Claudino, Quattrone, Biganzoli, Pestrin, Bertini, Di Leo 2007; Mori, Morine, Mawatari, Chiba, Yamada, Saito, Ishibashi et al. 2021; Xu, Cheng, Ding, Lv, Chen, Wu, Zheng 2015). In the past, metabolomics methods were only applied in biliary tract cancer and PBM, and few studies were applied, including small sample sizes, different results and different methods(Mori, Morine, Mawatari, Chiba, Yamada, Saito, Ishibashi et al. 2021; Sharif, Williams, Lampejo, Khan, Bansi, Westaby, Thillainayagam et al. 2010). In this study, we applied LC/MS method to metabolomic analysis of bile samples from large study samples of 60 patients. The results of metabolomics analysis showed that there was a certain separation trend between PBR group and Normal group in the metabolomic 2D and 3D score plots.. These results reflect the reliability of the present study and the applied model, which was further confirmed using the OPLS-DA model established from the positive and negative ion mode data of this experiment was good-fit and is statistically vaild.

A strong association has been found between gallstone formation and gallbladder cancer and higher levels of amylase in the gallbladder bile of patients with PBR and NPBJ(Beltran, Vracko, Cumsille, Cruces, Almonacid, Danilova 2007; Horaguchi, Fujita, Noda, Kobayashi, Ito, Takasawa, Obana et al. 2008; Sai, Suyama, Nobukawa, Kubokawa, Sato 2005). Biliary tract cancer associated with PBR usually results from the reflux of pancreatic juice into the biliary tract leading to metabolic disturbances of compounds in the bile causing chronic inflammation and damage to the biliary mucosa, especially amino acid and lipid metabolism disturbances(Funabiki, Matsubara, Miyakawa, Ishihara 2009; Mizuno, Kato, Koyama 1996; Mori, Morine, Mawatari, Chiba, Yamada, Saito, Ishibashi et al. 2021). LC/MS metabolomics analysis was used to determine the characteristics of bile metabolism in the OPBR. PCA and OPLS-DA models showed sufficient sensitivity and specificity to distinguish the OPBR group from the Normal group. Compared with the normal group, the levels of 106 metabolites related to energy homeostasis, amino acid metabolism, Ba metabolism and lipid synthesis in the bile metabolomics were changed significantly in OPBR group. In addition, potential biomarkers with the greatest contribution to this discrimination were then selected according to the VIP values and were identified. The results showed that the levels of phosphatidylcholine and PC (20:3 (8Z, 11z, 14z) / 14:0) were significantly lower in opbr group, while the levels of lysoPC (16:1 (9z) / 0:0), lysoPC (15:0), lysoPC (16:0), palmitic acid, leucine, methionine, L-tyrosine and phenylalanine were significantly higher in OPBR group.

Phenylalanine is an essential amino acid, synthesizes important neurotransmitters and hormones together with tyrosine, and participate in the body's glucose metabolism and fat metabolism. Tyrosine is oxidized from phenylalanine by phenylalanine hydroxylase in vivo and it is an important precursor of melanin, catecholamine neurotransmitter and thyroid hormone. Some studys reported that the abnormal proliferation of malignant tumors in the early and middle stages of lung cancer caused normal cell stress reactions, resulting in abnormal amino acid metabolism, such as a significant increase in tyrosine concentration(An, Chen, Zhang, Song, Sun, He, Bai et al. 2010). Furthermore, phenylalanine and tyrosine biosynthesis were shown to be significantly increased at the stage of severe dysplasia of the stomach and gastric cancer(Lario, Ramirez-Lazaro, Sanjuan-Herraez, Brunet-Vega, Pericay, Gombau, Junquera et al. 2017), anti androgen prostate cancer cells present high levels of phenylalanine and tyrosine(Corsetti, Rabl, McGloin, Nabi 2018). Other studies have confirmed that a diet restricted with phenylalanine inhibits the growth and metastasis of several malignancies(Elstad, Meadows 1990) (Elstad, Meadows, Abdallah 1990). The results of this study suggest that phenylalanine and tyrosine may be related to biliary tract lesions in OPBR, which is similar to the results of a metabolomic study of biliary tract cancer(Mori, Morine, Mawatari, Chiba, Yamada, Saito, Ishibashi et al. 2021).

The analysis results of this study revealed that the circulating leucine levels in OPBR were significantly higher than those in Normal (Table I). Branched chain amino acids (BCAA) have been reported to be associated with a variety of cancers. These BCCA include valine, leucine and isoleucine. An adequate intake of BCAAs is required for protein synthesis and several metabolic and signalling functions, including insulin metabolism(Wiggins, Kumar, Markar, Antonowicz, Hanna 2015). The catabolism of BCAA is an important process in amino acid synthesis(Holecek 2021). In order to support sustained biomass accumulation, cancer cells need to increase the level of nutrients such as amino acids. Indeed, cancer cells have increased BCAAs uptake(Hattori, Tsunoda, Konuma, Kobayashi, Nagy, Glushka, Tayyari et al. 2017), and BCAA aminotransferases (BCATs) that catalyze the first reaction in the catabolism of BCAAs are overexpressed in cancer cells(Fickert, Fuchsbichler, Wagner, Zollner, Kaser, Tilg, Krause et al. 2004; Tonjes, Barbus, Park, Wang, Schlotter, Lindroth, Pleier et al. 2013). Therefore, the increase of leucine in OPBR patients may be because both the host and the tumor need energy and proliferation. Similarly, other studies have reported that there is a positive correlation between higher intake of BCCA in diet and all-cause mortality of colorectal cancer patients(Long, Yang, Liu, Tobias, Katagiri, Wu, Jin et al. 2020). In PDAC cells, bcat2 protein levels are significantly increased, and downregulation of bcat2 can inhibit the proliferation of these cells(Dey, Baddour, Muller, Wu, Wang, Liao, Lan et al. 2017). Serum leucine and isoleucine levels of lung cancer patients are increased(Zhang, Zhu, Wang, Zhang, Cai 2016), and BCAA play an important role in cancer metabolism by regulating the bioenergetics and biosynthesis of cells through the tricarboxylic acid cycle(Reyes-Castellanos, Masoud, Carrier 2020).

Many studies have demonstrated that the disorders of lipid profles played a pivotal pathogenetic role in the initiation and progression of gallstons and GBC(Lee, Lee, Kweon, Kim 2019; Mori, Morine, Mawatari, Chiba, Yamada, Saito, Ishibashi et al. 2021). Lyso-PC is one of the major lysophospholipids and is mainly generated by hydrolysis of phosphatidylcholine. Phosphatidylcholine is the main phospholipid in mammalian cells and plays an important role in cell membrane structure, signal transduction and lipoprotein metabolism(Billah, Anthes 1990). It is synthesized in hepatocytes and subsequently transported to bile ducts by flippase multidrug resistance protein 3. Its main function in bile is to form mixed micelles with bile acids and cholesterol, which is necessary for fat emulsification. Phosphatidylcholine has also been confirmed to have cytoprotective effects and reduce the damaging effect of bile acids on biliary epithelium(Barrios, Lichtenberger 2000; Komichi, Tazuma, Nishioka, Hyogo, Une, Chayama 2003). Decreased phosphatidylcholine in bile will lead to the formation of bile acid monomers, which are more damaging to biliary cell membranes and predispose to biliary malignancies(Komichi, Tazuma, Nishioka, Hyogo, Chayama 2005). Phosphatidylcholine reduction has been found in the bile of biliary cancer(Albiin, Smith, Arnelo, Lindberg, Bergquist, Dolenko, Bryksina et al. 2008; Hashim Abdalla, Taylor-Robinson, Sharif, Williams, Crossey, Badra, Thillainayagam et al. 2011). Lyso-PC can damage vascular endothelial cells, promote the proliferation and migration of tumor cells(Alpini, Invernizzi, Gaudio, Venter, Kopriva, Bernuzzi, Onori et al. 2008; Dial, Dawson, Lichtenberger 2015), exhibit cytotoxicity by inducing biliary epithelial cell damage, and also cause cholangiocyte senescence, which may ultimately lead to biliary tumors(Fujita, Sugiyama, Otoshi, Taogoshi, Kimura, Kishikawa, Kodama et al. 2014). In present study, lyso-PC in bile of OPBR patients was significantly higher than that of Normal.

As for the mechanism of gallstone formation, it is believed that the changes of bile composition caused by various reasons is one of the main reasons of gallstone formation, including cholesterol, phospholipids and free fatty acids(FFA)(Carey, Lamont 1992; Portincasa, Moschetta, Palasciano 2006). Bile FFA play an important role in maintaining the stable dissolution of cholesterol, and the main component is palmitic acid(Fujino, Tamura, Uraya, Yonaga 1987). Some studies have revealed that the total FFA content in bile of patients with gallstones is significantly higher than that of normal groups. With the increase of bile FFA concentration, it will promote cholesterol crystallization and ultimately stone formation. In addition, a large number of unsaturated FFA can cause damage to the gallbladder mucosa, damage the gallbladder contraction function, and promote the high secretion of gallbladder mucin. These factors are all considered to play a positive role in the formation of gallstones(Schone, Jungst, Meyer, Hernandez-Richter, Fischer 2000). Similarly, we found that palmitic acid was significantly elevated in OPBR patients, which may be one of the reasons for gallstone formation. Meanwhile, arachidonic acid in bile of OPBR patients was higher. Phospholipase A2 plays a crucial role in arachidonic acid metabolism and secretion and is upregulated in patients with multiple cholesterol stones(Shoda, Ueda, Ikegami, Matsuzaki, Satoh, Kano, Matsuura et al. 1997). Arachidonic acid metabolism may play an active role in the formation of gallstones OPBR patients.

In this study, we performed a bile metabolomic analysis to compare the metabolites of bile obtained from patients with OPBR and benign diseases. Studys using metabolomics for the pathogenesis of OPBR has not been reported thus far. This is the first study on the metabolomic analysis of OPBR bile, and also a highly original study with clinical application value. In addition, we found that there were significant differences in bile lithogenic and carcinogenic metabolites between OPBR and benign diseases. This study can actually identify carcinogenic related metabolites, which is helpful to identify high-risk groups of clinical biliary tract tumors. All of the above are the advantages of this study and have strong clinical applicability. However, the current study has some limitations. Firstly, one limitation was that the sample size was small. Therefore, it is necessary to carry out validation studies. We are collecting more bile samples from OPBR patients and OPBR patients with biliary tract tumors. Another limitation is that there is no detection of markers of precancerous lesions in the gallbladder resected from OPBR patients. Although this is a preliminary study, our results provide a basis for further study of the biliary carcinogenesis mechanism of OPBR.Because bile metabolites involve complex interaction networks in the process of cancer development, further targeted metabolomics research is needed to analyze the association between changes in amino acid, bile acid and lipid metabolism and the occurrence and progression of cancer.

In conclusion, the results of this study demonstrate that LC/MS-based metabolomic approaches are effective and promising methods to differentiate OPBR from benign biliary diseases and to identify specific metabolites. The results of this study may help to elucidate the oncogenic mechanism of the disease in the future. Bile metabolites were significantly altered in OPBR patients compared with healthy controls. Therefore, OPBR patients can be considered as a high-risk group for biliary tract tumors.

Author contributions

In this study, YLY main researcher, helped to develop the research idea, analyzed the data. XBD and YKX supervised data analysis and drafted the manuscript. HH, XYK,GZ,JLC, and AHH collected clinical data. CZ, CQH,CQ,BNL and HLZ supervised the experiment’s execution and revised the manuscript. All authors have read and agreed to the published version of the manuscript.

DISCLOSURES

The authors declare that they have no conflicts of interest.

GRANTS

This work was supported by the Fund from Shanghai East Hospital (rant no. DFRC2018014) and the Featured Clinical Discipline Project of Shanghai Pudong (rant no. PWYts2021-06).

Albiin, N., et al. (2008). Detection of cholangiocarcinoma with magnetic resonance spectroscopy of bile in patients with and without primary sclerosing cholangitis. Acta Radiologica, 49, 855–862. doi:10.1080/02841850802220092
Alpini, G., et al. (2008). Serotonin metabolism is dysregulated in cholangiocarcinoma, which has implications for tumor growth. Cancer Research, 68, 9184–9193. doi:10.1158/0008-5472.CAN-08-2133
An, Z., et al. (2010). Integrated ionization approach for RRLC-MS/MS-based metabonomics: finding potential biomarkers for lung cancer. Journal Of Proteome Research, 9, 4071–4081. doi:10.1021/pr100265g
Anderson, M. C., Hauman, R. L., Suriyapa, C., & Schiller, W. R. (1979). Pancreatic enzyme levels in bile of patients with extrahepatic biliary tract disease. American Journal Of Surgery, 137, 301–306. doi:10.1016/0002-9610(79)90055-2
Barrios, J. M., & Lichtenberger, L. M. (2000). Role of biliary phosphatidylcholine in bile acid protection and NSAID injury of the ileal mucosa in rats. Gastroenterology, 118, 1179–1186. doi:10.1016/s0016-5085(00)70371-4
Beltran, M. A. (2011). Pancreaticobiliary reflux in patients with a normal pancreaticobiliary junction: Pathologic implications. World Journal Of Gastroenterology, 17, 953–962. doi:10.3748/wjg.v17.i8.953
Beltran, M. A., Contreras, M. A., & Cruces, K. S. (2010). Pancreaticobiliary reflux in patients with and without cholelithiasis: is it a normal phenomenon? World Journal Of Surgery, 34, 2915–2921. doi:10.1007/s00268-010-0771-2
Beltran, M. A., Vracko, J., Cumsille, M. A., Cruces, K. S., Almonacid, J., & Danilova, T. (2007). Occult pancreaticobiliary reflux in gallbladder cancer and benign gallbladder diseases. Journal Of Surgical Oncology, 96, 26–31. doi:10.1002/jso.20756
Billah, M. M., & Anthes, J. C. (1990). The regulation and cellular functions of phosphatidylcholine hydrolysis. The Biochemical Journal, 269, 281–291. doi:10.1042/bj2690281
Carey, M. C., & Lamont, J. T. (1992). Cholesterol gallstone formation. 1. Physical-chemistry of bile and biliary lipid secretion. Progress In Liver Diseases, 10, 139–163
Chao, T. C., Wang, C. S., Jan, Y. Y., Chen, H. M., & Chen, M. F. (1999). Carcinogenesis in the biliary system associated with APDJ. Journal Of Hepatobiliary Pancreatic Surgery, 6, 218–222. doi:10.1007/s005340050110
Claudino, W. M., Quattrone, A., Biganzoli, L., Pestrin, M., Bertini, I., & Di Leo, A. (2007). Metabolomics: available results, current research projects in breast cancer, and future applications. Journal Of Clinical Oncology, 25, 2840–2846. doi:10.1200/JCO.2006.09.7550
Corsetti, S., Rabl, T., McGloin, D., & Nabi, G. (2018). Raman spectroscopy for accurately characterizing biomolecular changes in androgen-independent prostate cancer cells. Journal Of Biophotonics, 11, doi:10.1002/jbio.201700166
Dey, P., et al. (2017). Genomic deletion of malic enzyme 2 confers collateral lethality in pancreatic cancer. Nature, 542, 119–123. doi:10.1038/nature21052
Dial, E. J., Dawson, P. A., & Lichtenberger, L. M. (2015). In vitro evidence that phosphatidylcholine protects against indomethacin/bile acid-induced injury to cells. American Journal Of Physiology. Gastrointestinal And Liver Physiology, 308, G217–G222. doi:10.1152/ajpgi.00322.2014
Dunn, W. B., Bailey, N. J., & Johnson, H. E. (2005). Measuring the metabolome: current analytical technologies. The Analyst, 130, 606–625. doi:10.1039/b418288j
Elstad, C. A., & Meadows, G. G. (1990). Phenotypic stability of B16-BL6 melanoma exposed to low levels of tyrosine and phenylalanine. Anticancer Research, 10, 1313–1318
Elstad, C. A., Meadows, G. G., & Abdallah, R. M. (1990). Specificity of the suppression of metastatic phenotype by tyrosine and phenylalanine restriction. Clinical & Experimental Metastasis, 8, 393–416. doi:10.1007/BF00058152
Fickert, P., et al. (2004). Regurgitation of bile acids from leaky bile ducts causes sclerosing cholangitis in Mdr2 (Abcb4) knockout mice. Gastroenterology, 127, 261–274. doi:10.1053/j.gastro.2004.04.009
Fujino, Y., Tamura, R., Uraya, T., & Yonaga, T. (1987). Free fatty acids in gallstones in mice fed a diet containing cholic acid. Japanese Journal Of Pharmacology, 45, 525–531. doi:10.1254/jjp.45.525
Fujita, K., et al. (2014). Partial characterization of proapoptotic action of biliary deteriorated lipids on biliary epithelial cells in pancreaticobiliary diseases. J Hepatobiliary Pancreat Sci, 21, 212–218. doi:10.1002/jhbp.20
Funabiki, T., Matsubara, T., Miyakawa, S., & Ishihara, S. (2009). Pancreaticobiliary maljunction and carcinogenesis to biliary and pancreatic malignancy. Langenbecks Arch Surg, 394, 159–169. doi:10.1007/s00423-008-0336-0
Goutman, S. A., et al. (2020). Untargeted metabolomics yields insight into ALS disease mechanisms. Journal Of Neurology, Neurosurgery And Psychiatry, 91, 1329–1338. doi:10.1136/jnnp-2020-323611
Hashim Abdalla, M. S., et al. (2011). Differences in phosphatidylcholine and bile acids in bile from Egyptian and UK patients with and without cholangiocarcinoma. HPB (Oxford), 13, 385–390. doi:10.1111/j.1477-2574.2011.00296.x
Hattori, A., et al. (2017). Cancer progression by reprogrammed BCAA metabolism in myeloid leukaemia. Nature, 545, 500–504. doi:10.1038/nature22314
Holecek, M. (2021). The role of skeletal muscle in the pathogenesis of altered concentrations of branched-chain amino acids (valine, leucine, and isoleucine) in liver cirrhosis, diabetes, and other diseases. Physiological Research, 70, 293–305. doi:10.33549/physiolres.934648
Horaguchi, J., et al. (2008). Amylase levels in bile in patients with a morphologically normal pancreaticobiliary ductal arrangement. Journal Of Gastroenterology, 43, 305–311. doi:10.1007/s00535-008-2158-9
Ichikawa, Y., et al. (2004). Telomerase activity and Bcl-2 expression in gallbladders of pancreaticobiliary maljunction patients: a preliminary study. Journal Of Hepatobiliary Pancreatic Surgery, 11, 34–39. doi:10.1007/s00534-003-0860-9
Kamisawa, T., Anjiki, H., Egawa, N., Kurata, M., Honda, G., & Tsuruta, K. (2008). Diagnosis and clinical implications of pancreatobiliary reflux. World Journal Of Gastroenterology, 14, 6622–6626. doi:10.3748/wjg.14.6622
Kamisawa, T., Kuruma, S., Chiba, K., Tabata, T., Koizumi, S., & Kikuyama, M. (2017). Biliary carcinogenesis in pancreaticobiliary maljunction. Journal Of Gastroenterology, 52, 158–163. doi:10.1007/s00535-016-1268-z
Kasuya, K., et al. (2009). p53 gene mutation and p53 protein overexpression in a patient with simultaneous double cancer of the gallbladder and bile duct associated with pancreaticobiliary maljunction. Journal Of Hepatobiliary Pancreatic Surgery, 16, 376–381. doi:10.1007/s00534-008-0030-1
Komichi, D., Tazuma, S., Nishioka, T., Hyogo, H., & Chayama, K. (2005). Glycochenodeoxycholate plays a carcinogenic role in immortalized mouse cholangiocytes via oxidative DNA damage. Free Radical Biology And Medicine, 39, 1418–1427. doi:10.1016/j.freeradbiomed.2005.07.005
Komichi, D., Tazuma, S., Nishioka, T., Hyogo, H., Une, M., & Chayama, K. (2003). Unique inhibition of bile salt-induced apoptosis by lecithins and cytoprotective bile salts in immortalized mouse cholangiocytes. Digestive Diseases And Sciences, 48, 2315–2322. doi:10.1023/b:ddas.0000007869.67105.27
Lario, S., et al. (2017). Plasma sample based analysis of gastric cancer progression using targeted metabolomics. Scientific Reports, 7, 17774. doi:10.1038/s41598-017-17921-x
Lee, S., Lee, A., Kweon, O. K., & Kim, W. H. (2019). Changes in pre- and postoperative serum leptin concentrations in dogs with gallbladder mucocele and cholelithiasis. Bmc Veterinary Research, 15, 215. doi:10.1186/s12917-019-1964-z
Long, L., et al. (2020). Dietary intake of branched-chain amino acids and survival after colorectal cancer diagnosis. International Journal Of Cancer. doi:10.1002/ijc.33449
Lu, X., Zhao, X., Bai, C., Zhao, C., Lu, G., & Xu, G. (2008). LC-MS-based metabonomics analysis. Journal Of Chromatography. B, Analytical Technologies In The Biomedical And Life Sciences, 866, 64–76. doi:10.1016/j.jchromb.2007.10.022
Matsuda, T., et al. (2009). Cancer incidence and incidence rates in Japan in 2003: based on data from 13 population-based cancer registries in the Monitoring of Cancer Incidence in Japan (MCIJ) Project. Japanese Journal Of Clinical Oncology, 39, 850–858. doi:10.1093/jjco/hyp106
McKee, M. D. (2007). Pancreaticobiliary reflux and gallbladder cancer. Journal Of Surgical Oncology, 96, 455–456. doi:10.1002/jso.20755
Mizuno, M., Kato, T., & Koyama, K. (1996). An analysis of mutagens in the contents of the biliary tract in pancreaticobiliary maljunction. Surgery Today, 26, 597–602. doi:10.1007/BF00311663
Mori, H., et al. (2021). Bile Metabolites and Risk of Carcinogenesis in Patients With Pancreaticobiliary Maljunction: A Pilot Study. Anticancer Research, 41, 327–334. doi:10.21873/anticanres.14779
Morine, Y., et al. (2013). Clinical features of pancreaticobiliary maljunction: update analysis of 2nd Japan-nationwide survey. J Hepatobiliary Pancreat Sci, 20, 472–480. doi:10.1007/s00534-013-0606-2
Portincasa, P., Moschetta, A., & Palasciano, G. (2006). Cholesterol gallstone disease. Lancet, 368, 230–239. doi:10.1016/S0140-6736(06)69044-2
Reyes-Castellanos, G., Masoud, R., & Carrier, A. (2020). Mitochondrial Metabolism in PDAC: From Better Knowledge to New Targeting Strategies. Biomedicines, 8, doi:10.3390/biomedicines8080270
Sai, J. K., et al. (2003). Occult pancreatobiliary reflux in patients with a normal pancreaticobiliary junction. Gastrointestinal Endoscopy, 57, 364–368. doi:10.1067/mge.2003.53
Sai, J. K., Suyama, M., Nobukawa, B., Kubokawa, Y., & Sato, N. (2005). Severe dysplasia of the gallbladder associated with occult pancreatobiliary reflux. Journal Of Gastroenterology, 40, 756–760. doi:10.1007/s00535-005-1621-0
Schone, A., Jungst, D., Meyer, G., Hernandez-Richter, T., & Fischer, S. (2000). Effects of phospholipase A2, free fatty acids and 2-lysolecithin on the crystallization of cholesterol in gallbladder bile. European Journal Of Clinical Investigation, 30, 715–721. doi:10.1046/j.1365-2362.2000.00703.x
Seki, M., et al. (2005). Clinicopathology of pancreaticobiliary maljunction: relationship between alterations in background biliary epithelium and neoplastic development. Journal Of Hepatobiliary Pancreatic Surgery, 12, 254–262. doi:10.1007/s00534-004-0966-8
Sharif, A. W., et al. (2010). Metabolic profiling of bile in cholangiocarcinoma using in vitro magnetic resonance spectroscopy. HPB (Oxford), 12, 396–402. doi:10.1111/j.1477-2574.2010.00185.x
Shoda, J., et al. (1997). Increased biliary group II phospholipase A2 and altered gallbladder bile in patients with multiple cholesterol stones. Gastroenterology, 112, 2036–2047. doi:10.1053/gast.1997.v112.pm9178697
Spratlin, J. L., Serkova, N. J., & Eckhardt, S. G. (2009). Clinical applications of metabolomics in oncology: a review. Clinical Cancer Research, 15, 431–440. doi:10.1158/1078-0432.CCR-08-1059
Sreekumar, A., et al. (2009). Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature, 457, 910–914. doi:10.1038/nature07762
Tashiro, S., et al. (2003). Pancreaticobiliary maljunction: retrospective and nationwide survey in Japan. Journal Of Hepatobiliary Pancreatic Surgery, 10, 345–351. doi:10.1007/s00534-002-0741-7
Tonjes, M., et al. (2013). BCAT1 promotes cell proliferation through amino acid catabolism in gliomas carrying wild-type IDH1. Nature Medicine, 19, 901–908. doi:10.1038/nm.3217
Tsuchida, A., Itoi, T., Aoki, T., & Koyanagi, Y. (2003). Carcinogenetic process in gallbladder mucosa with pancreaticobiliary maljunction (Review). Oncology Reports, 10, 1693–1699. doi:10.3892/or.10.6.1693
Wang, L., Zhang, Z. W., Guo, T., Xie, P., Huang, X. R., & Yu, Y. H. (2022). Occult pancreaticobiliary reflux is a pathogenic factor of some benign biliary diseases and gallbladder cancer. Hepatobiliary Pancreat Dis Int. doi:10.1016/j.hbpd.2022.08.010
Wang, X., Yang, B., Sun, H., & Zhang, A. (2012). Pattern recognition approaches and computational systems tools for ultra performance liquid chromatography-mass spectrometry-based comprehensive metabolomic profiling and pathways analysis of biological data sets. Analytical Chemistry, 84, 428–439. doi:10.1021/ac202828r
Wiggins, T., Kumar, S., Markar, S. R., Antonowicz, S., & Hanna, G. B. (2015). Tyrosine, phenylalanine, and tryptophan in gastroesophageal malignancy: a systematic review. Cancer Epidemiology, Biomarkers & Prevention, 24, 32–38. doi:10.1158/1055-9965.EPI-14-0980
Wistuba, I. I., & Albores-Saavedra, J. (1999). Genetic abnormalities involved in the pathogenesis of gallbladder carcinoma. Journal Of Hepatobiliary Pancreatic Surgery, 6, 237–244. doi:10.1007/s005340050113
Xu, X., et al. (2015). Identification of bile biomarkers of biliary tract cancer through a liquid chromatography/mass spectrometry-based metabolomic method. Molecular Medicine Reports, 11, 2191–2198. doi:10.3892/mmr.2014.2973
Zhang, X., Zhu, X., Wang, C., Zhang, H., & Cai, Z. (2016). Non-targeted and targeted metabolomics approaches to diagnosing lung cancer and predicting patient prognosis. Oncotarget, 7, 63437–63448. doi:10.18632/oncotarget.11521

Table 1 is not available with this version.

Table 2 Results of pathway enrichment analysis of signifcant metabolites

No.	Pathway	Total	Hits number	Raw p	-log10(p)	Rich factor
1	D-Amino acid metabolism	40	15	9.88538E-17	16.0050	0.224
2	Protein digestion and absorption	40	13	7.66515E-16	15.1154	0.277
3	Central carbon metabolism in cancer	40	11	7.23559E-14	13.1405	0.297
4	Aminoacyl-tRNA biosynthesis	40	12	1.32113E-13	12.8790	0.231
5	Mineral absorption	40	7	1.98608E-08	7.70200	0.241
6	ABC transporters	40	11	2.38156E-07	6.62314	0.079
7	Arginine and proline metabolism	40	6	0.0001193	3.92311	0.086
8	Lysine degradation	40	5	0.0002249	3.64786	0.1
9	Alanine, aspartate and glutamate metabolism	40	4	0.0002512	3.59984	0.143
10	Linoleic acid metabolism	40	4	0.0002512	3.59984	0.143

No competing interests reported.

SupplementalTable1.docx

Download PDF

Version 1

posted

You are reading this latest preprint version

Identification of bile biomarkers of Occult pancreaticobiliary reflux through a liquid chromatography/mass spectrometry-based metabolomic method

Status:

Version 1

Abstract

Introduction:

Objective

Methods

Results

Conclusions

Figures

1. Introduction

2. Patients And Methods

3. Results

4. Discussion

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1