Abstract
Several surveillance studies have reported significantly high level of patulin (PAT), mycotoxin in fruit juices suggesting the possible exposure to human. In vitro studies have showed that PAT can alter the permeability, ion transport and modulates tight junction of intestine. In real scenario, human can be exposed with low levels of PAT for longer duration through different fruits and their products. Hence, keeping this possibility in view, we conducted a study where normal intestinal cells were exposed with non-toxic levels of PAT for longer duration and found that PAT exposure causes cancer-like properties in normal intestinal cells. It is a well-known fact that cancer cells rewired their metabolism for cell growth and survival and metabolites closely depict the phenotypic properties of cells. Here, metabolomic study was performed in the PAT transformed and passage matched non-transformed cells using 1H HRMAS NMR. We have identified 12 significantly up-regulated metabolites, which, interestingly, were majorly amino acids, suggesting that PAT-induced pre-cancerous cells are involved in acquirement of nutrients for high protein turn-over. Furthermore, pathway analysis of metabolomics data indicated that aminoacyl tRNA biosynthesis, D-glutamate metabolism, glyoxylate and dicarboxylate metabolism and nitrogen metabolism were majorly hampered in PAT-induced pre-cancerous properties in normal intestinal cells.
Keywords: patulin, intestinal epithelial cells, neoplastic changes, metabolomics
Introduction
A natural food contaminant, mycotoxin, secondary metabolite of fungus occur worldwide and represent one of the most challenging safety threats due to their potential health risks [1]. Patulin (PAT) is one such mycotoxin that naturally contaminates apples and its products such as juices, jams, purees, jellies and baby food [1]. In fact fruits, other than apples, are also reported to contain PAT contamination [2]. It is produced by genera Penicillium, Aspergillus and Byssochlamys as secondary metabolite [1]. IARC classified patulin as group 3 carcinogen because of insufficient risk assessment data [3], but taking into account the toxic effects of PAT, regulatory agencies set the limit of 50 ppb in different food items available in the market [1]. However, due to its stability at high temperatures and in acidic environment, PAT survived after processing of packaged food and beverages, hence various survey studies reported the significantly higher level of contamination in commercially available food stuffs than permissible limits [2]. PAT is reported to be genotoxic, teratogenic and immunotoxic [2] and several in vivo studies demonstrated its potential to damage vital organs such as liver, intestines and kidney confirming them as target organs of PAT’s toxicity [2].
Gastrointestinal tract is the largest organ coming in contact with xenobiotics ingested via food. With the perspective of food toxicology, the intestinal epithelial cells are the key site to get exposed to toxins and at higher concentrations than other tissues of the body [4]. Earlier, few in vitro studies have reported that patulin exposure leads to alteration in tight junctions, transepithelial electrical resistance and epithelial permeability. It also affects the expression of claudin and ZO-1 and can reduce the goblet cells in intestine [5, 6]. In addition to this, our previous study also reported the PAT’s ability to cause hyper proliferation and inflammation in intestinal in vitro and in vivo experimental approach [7].
Although studies suggesting PAT’s ability to affect intestinal structure and function are available, the concentration of PAT used (high doses) is not environmentally relevant as low level exposure for longer duration to humans is common in real scenario. In this context, we have observed that PAT is capable of inducing neoplastic changes in intestinal epithelial cells at low-dose chronic exposure. Exposure of PAT to intestinal epithelial cells and Wistar rats at 250 nM and 100 μg/kg by wt, respectively, leads to anchorage independent growth and increased migration and invasion capacity in normal intestinal cells. It also caused appearance of aberrant crypt foci in rat colon establishing the neoplastic phenotype of intestine due to PAT exposure (data not published). Here, we have shown that PAT is able to cause morphological changes and enhanced migration ability of intestinal epithelial cells.
Now-a-days metabolomics has emerged as a strong tool for identifying the variations in endogenous metabolites and comprehending the biological mechanisms. Metabolomics is the study of small molecules in a given biological system. The metabolites are the end products of the system and closely depict the physiological characteristics of the cell [8]. The metabolic profiling of a sample reveals the under or over expressed metabolite in a given set of conditions [8]. Considering the scope of metabolomics study, the present study was done to understand the changes occurred at metabolic level due to PAT exposure, as it is well documented that cancer cells showed metabolic reprogramming to support their functions. Here, we used 1H HRMAS NMR to study the metabolomics alteration in PAT exposed cells.
Although the sensitivity of NMR-based metabolomics is lower than that of mass spectrometry, however, it has its own advantages. NMR analysis require minimal sample preparation for the quantification of all abundant compounds present in cell extract or biological fluid or tissues and the data produced is highly reproducible [8]. The 1H HRMAS NMR spectroscopy is having advantage over solution state NMR. It gives us access to analyze the intact cell or tissue without prior to extraction. This differs from the traditional NMR where the extraction of metabolites is a requirement [9].
To the best of our knowledge, this is the first study to report the metabolic changes accompanying PAT induced neoplastic changes in normal intestinal cells. Here, we have detected 12 significantly up regulated metabolites and disturbed metabolic pathways associated with PAT exposure. This study will help us to understand the underlying mechanism at realistic concentration of PAT exposure.
Material and Methods
Cell culture
Rat normal Intestinal Epithelial Cell line (IEC-6) was purchased from American Type Culture Collection. Cells were maintained in Dulbecco’s minimum essential medium with high glucose and supplemented with 10% fetal bovine serum (Thermo Fisher Scientific, USA), 100-U/ml penicillin, 100-U/ml streptomycin and 0.1 Unit/ml insulin Sigma Aldrich Co. (St Louis, MO) at 37°C in a humidified atmosphere of 5% CO2. The cells (n = 3) were treated with patulin for 16 weeks at a concentration of 250 nM. The treated IEC-6 cells were collected after 8 and 16 weeks of exposure and used for 1H HRMAS NMR analysis. The passage matched untreated cells served as control group.
Chemicals
Patulin, D2O (containing .05% w/w TSP-d4) and PBS were procured from Sigma Aldrich (St Louis, MO). All other chemicals used were of high purity and laboratory grade.
In Vitro scratch assay
Scratch assay was performed as described earlier elsewhere. Briefly, 16-week PAT-exposed cells and passage matched control cells were plated onto six-well culture dishes to create a confluent monolayer. Cells were scraped to create a scratch with p200 pipette. Debris was removed by washing with PBS and cells were then incubated in CO2 chamber. Markings were done on plate to obtain the images from the same field. Images were taken at regular interval in Leica DM 1000 microscope (Nussloch, Germany) [10].
Transmission electron miroscopy
Transmission electron microscopy imaging was performed in 16-week PAT-exposed IEC-6 cells. Cell samples were fixed in a solution containing 3% glutaraldehyde +2% paraformaldehyde in 0.1 M cacodylate buffer, containing pH 7.3 for overnight. Samples were washed twice in 0.1 M cacodylate buffer and treated with 0.1% Millipore-filtered (pore size 0.22 μm) tannic acid, post fixed with 1% Osmium tetra-oxide buffer for 45 min and stained the bloc with 1% filtered Uranyl acetate. The samples were washed thrice in milliq water, then dehydrated in increasing concentration of alcohol, infiltered and imbedded in 1% agarose media. The samples were polymerized at room temperature for overnight. Ultrathin sections were cut in a Leica Ultracut microtome (Leica), stained with Uranyl acetate and lead acetate and examined in a transmission electron microscope at an accelerating voltage of 80 kV. Digital images were obtained through advanced microscopy techniques.
Scanning electron microscopy
PAT exposed cells were trypsinized and cell pellet was dissolved in 0.1 M of a fixative solution (2.5% glutaraldehyde in 0.1 M sodium cacodylate buffer) and kept overnight at 40°C. The pellet was then washed with 1% osmium tetraoxide for 4 h. Fixed cells were washed and dehydrated using graded concentration of acetone (15–100%) and embedded in araldite-DDSA mixture (Ladd Research Industries). Afterward, thick section (1 μm) of the sample was cut using ultra microtome (Lieca EM UC7) equipped with a diamond knife and kept on silica plate, pre-coated with platinum under argon gas environment using mini sputter coater (Model SC7620, Quorum, Technologies, UK) and assessed with a field emission of SEM (Quanta 450 FEG, FEI, Netherlands).
Preparation of NMR samples
Approximately 107 (10 million cells) PAT-transformed and passage matched control IEC-6 cells were washed thrice with PBS (pH 7.4) prepared in deuterium oxide and then centrifuged in order to remove the insoluble substances (1000 g, 4°C, 15 min). The cell pellet was resuspended in 30 μl of PBS (pH 7.4) in D2O with TSP-d4 (.05% w/w). D2O was used for locking spectrometer, field homogeneity and TSP-d4 aided for referencing and quantization. The suspended pellet samples were transferred to 4-mm zirconia rotor 50-μl capacity and sealed with KELF cap.
NMR spectroscopy and data processing
All NMR experiments were performed on Bruker Avance-II 400 MHz spectrometer, operating at an 1H frequency of 4.13 MHz, equipped with a 5-mm HR-MAS 13C–1H Z gradient probe with a magic-angle gradient, at a temperature of 298 K and a sample spinning rate of 4000 ± 4 Hz. The spectra were acquired with water suppression variant of 1H NMR experiment NOESYPR1D and CPMGPR1D. NOESYPR1D spectra were acquired with conventional pre-saturation pulse sequence [RD-90°-t1–90°-tm-90°-ACQ] with solvent suppression (relaxation delay = 3.0 s, mixing time = 100 ms; solvent pre-saturation was applied during the relaxation delay and mixing time) at 298 K. Furthermore, to remove the dominancy of macromolecule in the spectra, CPMGPR1D spectra were acquired with conventional pre-saturation pulse sequence [RD – 90° – [τ – 180° – τ] n – Acq] with solvent suppression (relaxation delay = 3.0 s, spin echo time = 200 ms, solvent suppression applied during relaxation delay), which attenuates broad signal by T2 editing. Parameters were used as follows: for NOESYPR1D experiment, time domain (TD) 32 k, spectral width (SW) 8223.68 Hz, water signal irradiation frequency (o1) 1878.50 Hz (nearly same for every sample) and receiver gain (rg) 101 (kept constant for every sample). For CPMGPR1D experiment, TD 32 k, SW 8223.68 Hz, water signal irradiation frequency (o1) 1879.07 (nearly same for every sample) and receiver gain (rg) 9.5 (kept constant for every sample). Pulse calibration was performed by pulsecal command for every sample; value was nearly same for each sample (p1 15.49 s and pl9 55.90 db). Total acquisition time for NOESYPR 1D and CPMGPR1D experiment were 22 min 12 s and 23 min 35 s, respectively.
Identification and quantification of metabolites were performed in Chenomx NMR suite 8.4 (Edmonton, Alberta). HMDB (http://www.hmdb.ca) and BMRB (http://www.bmrb.wisc.edu) database were also utilized for further confirmation of metabolites on the basis of their chemical shift values, coupling constant and splitting pattern. NMR spectra were imported into Chenomx processor module where phase correction, baseline correction, line broadening and calibration were done. Processed spectrum imported into profiler module, 28 metabolites were identified and quantified. Quantification of metabolites was performed by introducing custom CSI signal in Chenomx software [11]. The custom CSI signal was calibrated with respect to known concentration of TSP signal. The assigned concentration of Custom CSI signal was 0.0084 mM. All the parameters of custom CSI signal were kept constant during quantification [12].
Statistical and pathway analysis
Unsupervised analysis and Pathway analysis were performed in metaboanalyst 4.0 web-based server (www.metaboanalyst.ca). Principal component analysis (PCA, unsupervised) was performed, to see the trend in data and finding outliers. Furthermore, one-way analysis of variance (ANOVA), followed by post hoc test (B Turkey), was performed by using SPSS 16 (IBM statistics) to find the statistically significant metabolites.
Results
PAT exposure causes morphological changes in IEC-6 cells
Scanning electron microscopy reflected that cells exposed to patulin for 16 weeks showed microvilli on their surface compared with passage matched control cells (Fig. 1A); however, only a few projections were observed in 8-week patulin exposed IEC-6 cells. As suggested by previous studies, these surface topographical changes are somewhat like observed in tumorogenic cell lines [13]. Moreover, transmission electron microscopy also revealed some protruded short microvilli in patulin exposed cells (Fig. 1A). Although these surface projections are not the confirmation of neoplastic changes, however, such surface projections are being reported in transformed cells [13].
Figure 1.
effect of patulin on cell morphology and migration ability. (A) Scanning and transmission electron photomicrographs of patulin exposed cells (8 and 16 weeks) and passage matched control cells. Patulin exposed cells showed microvilli-like structures on cell surface, while no or a few projections were observed in control cells (Scale bar represents 1 μm). (B) Scratch assay reveals that low-dose chronic exposure to patulin promotes cell migration in IEC-6 cells.
PAT exposure enhanced the cell migration ability of IEC-6 cells
In vitro scratch assay imitates the in vivo cell migration. Thus, it is well established method to assess the cell migration in in vitro. Result of this experiment suggests that migration of PAT-treated cells toward the clear area is significantly higher than passage matched control cells (Fig. 1B).
Cell membrane stability during 1H HRMAS
Cell viability was assessed by the trypan blue dye exclusion method prior to HRMAS NMR analysis and after carrying out the experiment. The cell lysis was in an acceptable range [14] as assessed by the trypan blue dye exclusion method and suggested the metabolic changes among three different group, was because of PAT exposure.
Metabolic profile of PAT exposed cells
To evaluate the possible metabolic effects after PAT exposure, we performed non-targeted NMR profiling in patulin transformed cells. Spectra representing the comparison between PAT exposed cells (8 and 16 weeks) and passage matched control cells (16 weeks) are reported in Fig. 2A–C. The spectrum of cell samples shows the presence of several metabolites comprising amino acids such as glutamine, histidine, methionine, alanine, glutamate and branched chain amino acids and metabolites such as acetate, glucose and lactate. A total of 28 metabolites were identified in analysis (Table 1). A significant quantitative difference can be observed in spectra obtained from PAT exposed cells and passage matched control cells.
Figure 2.
CPMG stacked spectra of the IEC-6 cell samples exposed with patulin and passage matched control cells. (A) The metabolites detected in 16 weeks exposed IEC-6 cells. (B) The metabolites detected in 8 weeks exposed IEC-6 cells. (C) The metabolites detected in passage (16 weeks) matched control cells.
Table 1.
list of metabolites detected from NMR analysis in patulin exposed cells and passage matched control cells and their characteristic parameters; Group 0—passage matched control cells (8 weeks), Group 1—patulin exposed cells (8 weeks) and Group 2—patulin exposed cells (16 weeks)
| Label | Chemical shift | Group 0 | Group 1 | Group 2 | P-value |
|---|---|---|---|---|---|
| Mean ± SD (conc. in μM) | Mean ± SD (conc. in μM) | Mean ± SD (conc. in μM) | |||
| Acetate | 1.9 (s, CH3) | 168.10 ± 26.41 | 346.86 ± 57.58 | 417.8 ± 67.37 | 0.003 |
| Adenine | 8.21(s), 8.17 (s) | 403.06 ± 79.22 | 325.96 ± 108.95 | 608.23 ± 177.37 | 0.084 |
| Alanine | 1.5 (d, CH3) | 149.96 ± 29.64 | 286.36 ± 66.84 | 332.73 ± 91.33 | 0.038 |
| Choline | 3.19 (s, N(CH3)3) | 260.16 ± 47.93 | 265.9 ± 95.62 | 440.33 ± 94.44 | 0.061 |
| Creatine | 3.9(s), 3.0(s) | 448.56 ± 127.41 | 43.56 ± 144.32 | 660.93 ± 40.05 | 0.085 |
| Dimethylamine | 2.7 (s, CH3) | 34.03 ± 6.72 | 40.06 ± 8.15 | 45.96 ± 4.95 | 0.176 |
| Ethanol | 1.2 (t, CH3), 3.6 (q, CH2) | 1765.40 ± 964.79 | 179.26 ± 467.26 | 2399.06 ± 93.52 | 0.591 |
| Ethylene glycol | 3.7 (s, CH2) | 386.43 ± 144.06 | 531.13 ± 229.8 | 732.3 ± 290.37 | 0.257 |
| Glucose | 5.2 (d, C2H), 4.6 (d, C2H), 3.8 (m, C6H), 3.5 (m, C3H), 3.2 (m, C3H) | 506.16 ± 201.41 | 89.56 ± 101.34 | 739.8 ± 140.35 | 0.057 |
| Glutamate | 2.4 (t, γ-CH2), 2.3 (t, γ-CH2) | 1548.83 ± 431.9 | 1244.5 ± 433.7 | 3243.76 ± 234.51 | 0.001 |
| Glutamine | 2.5 (m, γ-CH2), 2.1 (m, β-CH2) | 533.16 ± 210.41 | 408.1 ± 42.9 | 1224.43 ± 117.41 | 0.001 |
| Glycine | 3.5 (s, CH2) | 1124.93 ± 419.05 | 5007.7 ± 4268.92 | 2117.9 ± 101.04 | 0.217 |
| Histidine | 7.1 (s, CH) | 157.43 ± 30.94 | 214.43 ± 23.92 | 252.23 ± 31.12 | 0.019 |
| Isoleucine | 3.7 (d, α-CH), 1.0 (d, β-CH3) | 234.23 ± 68.42 | 252.2 ± 52.12 | 373.4 ± 33.61 | 0.036 |
| Lactate | 4.1 (q), 1.3 (d) | 291.1 ± 123.08 | 538.03 ± 279.09 | 814.7 ± 292.3 | 0.10 |
| Leucine | 1.7 (m, γ-CH), 1.0 (d, δ-CH3), .9 (d, δ-CH3) | 629.26 ± 125.12 | 790.06 ± 142.41 | 1126.86 ± 298.31 | 0.060 |
| Methanol | 3.3 (s, CH3) | 78.93 ± 16.54 | 135.9 ± 42.45 | 247.3 ± 127.46 | 0.094 |
| Methionine | 2.6 (t, S-CH2), 2.2 (s, S-CH3) | 238.03 ± 52.14 | 348.7 ± 46.26 | 480.33 ± 42.37 | 0.002 |
| O-Phosphocholine | 3.2 (s, N(CH3)3) | 1021.60 ± 205.80 | 642.8 ± 274.12 | 1685.5 ± 303.82 | 0.008 |
| Phenylalanine | 7.4 (m, CH), | 367.6 ± 59.28 | 464.93 ± 112.84 | 558 ± 129.03 | 0.164 |
| Serine | 4.0 (m, β-CH2), 3.8 (m, α-CH) | 564.36 ± 229.33 | 783.16 ± 90.87 | 968.43 ± 24.7 | 0.037 |
| Succinate | 2.4 (s, CH2) | 4.30 ± 1.34 | 12.06 ± 17.00 | 21.3 ± 10.31 | 0.270 |
| Taurine | 3.4 (t, CH2) | 351.10 ± 55.77 | 239.86 ± 62.55 | 691.43 ± 130.61 | 0.002 |
| Threonine | 4.3 (m, β-CH2), 3.6 (d, α-CH) | 922.80 ± 407.47 | 1159.66 ± 240.83 | 1595.93 ± 249.77 | 0.091 |
| Tyrosine | 7.2 (d, CH), 6.9 (d, CH) | 488.40 ± 96.82 | 608.4 ± 105.35 | 787.16 ± 90.96 | 0.026 |
| Uracil | 5.8 (d, CH), 7.5 (d, N-CH) | 398.93 ± 84.77 | 513.7 ± 155.45 | 676.3 ± 73.25 | 0.117 |
| Valine | 2.3 (m, β-CH), 1.0 (d, CH3) | 327.60 ± 91.02 | 475.4 ± 27.57 | 583.9 ± 13.5 | 0.004 |
| myo-Inositol | 4.1 (t, CH) | 171.9 ± 67.85 | 135.56 ± 100.19 | 83.6 ± 35.61 | 0.388 |
Multivariate statistical analysis
Multivariate analysis of the recorded spectra of cells was performed to resolve the changes in the metabolic profiles of PAT transformed and passage matched control cells. Unsupervised PCA, which is dimensionality reduction technique, showed that all the three groups are well clustered and can be clearly differentiated from each other (passage matched control cells (0), 8-week PAT exposed cells [1] and 16-week PAT exposed cells [2]). Principal component (PC1) and Principal Component (PC2) explained 76.6% variance in data (Fig. 3). It clearly revealed the metabolic remodeling in PAT transformed cells.
Figure 3.
PCA score plot for patulin exposed cells (8 and 16 weeks) and passage matched control cells, suggesting variations among control and patulin treated groups. PCA model shows good separation between patulin exposed cells (8 and 16 weeks) and passage matched control cells. Principal component (PC1) and Principal component (PC2) explained 76.6% variance in data. PMC-Passage matched control cells (red, 16 weeks), PTC 8-week patulin transformed cells (green, 8 weeks) and PTC 16-week patulin transformed cells (blue, 16 weeks).
Quantitative analysis of metabolites
To find out the statistically significant metabolites (P < 0.05) among the three groups, we performed simple and robust statistical test one-way ANOVA, followed by post hoc test (B Tukey). An analysis was performed to evaluate each metabolite individually. A total of 12 metabolites were found significantly altered in PAT transformed cells and all of the 12 were up regulated (Fig. 4). Interestingly, we have found that significantly altered metabolites are mostly amino acids in PAT transformed cells. This suggests that transformed cells are involved in acquisition of nutrients for high turnover protein needs [15]. Cancer cells required ample amount of amino acids to maintain the proliferative state. Amino acids such as alanine, glutamate, glutamine, histidine, isoleucine, methionine, serine, o-phosphocholine, taurine, tyrosine and valine have shown elevated levels in PAT exposed transformed cells.
Figure 4.
box and whisker plots of metabolites (acetate, alanine, glutamate, glutamine, histidine, isoleucine, methionine, o-phosphocholine, serine, taurine, tyrosine and valine) found significantly increased in patulin exposed cells (16 weeks) compared with patulin exposed cells 8 weeks and passage matched control cells (16 weeks), (P < 0.05). Data were obtained by univariate data analysis (One-way ANOVA) performed by using SPSS 16 (IBM statistics). PMC-Passage matched control cells (green, 16 weeks), PTC 8-week patulin transformed cells (orange, 8 weeks) and PTC 16-week patulin transformed cells (red, 16 weeks).
Identification of altered metabolic pathways
The statistically significant metabolites in univariate test were used as input for Pathway analysis [16]. Detection of altered metabolic pathways describes the changed cellular responses. In the present study, pathways including aminoacyl tRNA biosynthesis, glyoxylate and dicarboxylate metabolism, D-glutamine and D-glutamate metabolism and nitrogen metabolism were found significantly hampered. Mapping of the altered pathways in terms of impact topography was depicted in Fig. 5.
Figure 5.
the aberrant metabolic pathways detected in patulin exposed cells. The altered pathways were obtained by using the relative concentrations of differential metabolites from NMR spectra of cell samples in the pathway analysis module of MetaboAnalyst4.0.
Discussion
Metabolic condition of a cancerous cell differs significantly from normal cells. Elucidation of metabolic reprogramming in cancerous cells helps in revelation of the underlying mechanisms and therapeutic interventions. A number of studies have provided the proof that amino acids have significant involvement in pathways that are essential for growth and survival of cancer cells [15, 17]. Some amino acids act as anaplerotic substrate for TCA cycle intermediates, while others are involved in synthesis of acetyl-CoA. Amino acids provide carbon and nitrogen for macromolecule synthesis and also affect ROS homeostasis [15, 17] as shown in Fig. 6.
Figure 6.
schematic representation of metabolic changes occurred in intestinal epithelial cells after patulin exposure for 16 weeks. Amino acids play significant role in pathways essential for growth and survival of cancer cells. Amino Acids act as anaplerotic substrate for TCA cycle intermediates, helps in nucleotide synthesis, lipid synthesis and helps in maintenance of redox balance. Metabolites detected using HRMAS NMR analysis are in red, BCAA, BCAT (Branched Chain Amino Acid Transaminase), BCKA (Branched Chain Ketoacid), TCA (Tricarboxylic Acid) and MET (Methionine).
In our study, amino acids such as alanine, glutamate, glutamine, histidine, isoleucine, methionine, serine, o-phosphocholine, taurine, tyrosine and valine have shown elevated levels in PAT exposed transformed cells. Glutamine is a well-established hallmark for cancer cell metabolism and a crucial substrate in cancer cells and is required for cancer development, invasion and metastases. It is reported to serve as a source of nitrogen and carbon for biosynthetic process of nucleic acids and other amino acids, respectively. It also plays important role in redox homeostasis, glutathione production [18]. Glutaminase converts the glutamine to glutamate which is then converted to ketoglutarate. That is why, cancer cells are known to be dependent on glutamine. Amino acid transporters SLC1A5 and SLC7A1 are involved in uptake of glutamine and their high levels have been reported in certain cancers cells [19]. Furthermore, studies have shown that colorectal cancer uses glutamine to replenish TCA cycle in vivo and targeting glutamine is an effective approach in treating CRCs [20, 21]. Another amino acid whose involvement in cancer is evaluated is taurine. Recent findings suggest the use of taurine levels in prediction of formation and malignancy of certain tumors [22] and can be used to screen patients of abdominal and gastrointestinal problems and for precancerous patients [22]. A study reported the high levels of taurine in bladder cancer patients [23]. In addition, low serum level of taurine was found in breast cancer patient than high-risk group or control group [24]. Enhanced taurine levels were observed in rectal adenocarcinoma in comparison to healthy mucosa [25]. The literature suggests that expression level of taurine vary according to different types of tumors and the underlying mechanism is still not clear. Similarly, increased serine synthesis is another metabolic change that was reported in cancer cells. Serine is involved in providing carbon to one carbon pool and serves as precursor for synthesis of macromolecules. Thus, serine plays a key role in proliferation of cancer cells [26]. Branched chain amino acids (BCAA) (Leucine and valine) served energy requirements of cancer cells and are utilized for protein synthesis [27] These BCAA also have important role in nitrogen balance. BCAA has a direct effect on growth of cancer; however, there are different metabolic states depending on the type of cancer [28]. Methionine is one of the eight essential amino acids. It is important for synthesis of protein and RNA in all cells. Besides this, it is required in polyamine production, which is involved in cellular division and found in higher concentration in tumors. It is also needed to repair the DNA damage and is important for cell growth [29].
It has been found that in cancer cells, apart from glucose and glutamine, acetate can also contribute toward lipid biosynthesis. Acetate also takes part in other metabolic functions such as energy generation and protein and histone acetylation [30]. Thus, it acts as post-translational modifier. The enzyme Acetyl-CoA synthetase catalyzes its conversion to acetyl co-A. Cancer cells activate the enzyme in nutritional stressed situations [31].
The major abnormal metabolic pathways were the aminoacyl tRNA biosynthesis, glyoxylate and dicarboxylate metabolism, D-glutamine and D-glutamate metabolism and nitrogen metabolism. Aminoacylation of transfer RNAs is essential for protein synthesis and cell viability [32]. The aminoacyl t-RNA synthetases help in the regulation of transcription, translation and various signaling pathways including angiogenesis, proliferation, inflammation and immune activation [32]. The variation in quantity and quality of protein synthesis in cancer cells could be possibly responsible for deregulated pathways [32]. Furthermore, the alteration in glutamine and glutamate pathway implies the oxidative stress in cancer cells. Moreover, cancer cells are dependent on glutamine for macromolecule synthesis, replenishment of TCA substrates, etc. [18]. The amino acids glutamine and glutamate is also the major donor of nitrogen. Nitrogen is required for cell growth and proliferation, and cancer cells use it for biosynthetic purposes. The deficiency of nitrogen leads to the disruption of nitrogen containing molecules; thus, cancer cells rewiring nitrogen metabolism is common [33].
In conclusion, to the best our knowledge, this is the first study revealing that low-dose chronic exposure of PAT to intestinal cells leads to significant metabolic alterations and providing insights into underlying mechanisms. PAT has shown prominent variations in metabolic profiles of exposed cells at realistic doses. PAT exposure significantly affected the amino acid metabolism and pathway analyses showed that majorly hampered pathways were aminoacyl tRNA synthesis, glutamine and glutamate metabolism and nitrogen metabolism (Fig. 6). Notably, these pathways are commonly deregulated in cancer cells. However, further studies evaluating the enzymes involved and deciphering the pathways in detail are required for confirmation of metabolic perturbations observed in this study.
Acknowledgements
Authors are grateful to the Director, CSIR-Indian Institute of Toxicology Research and CSIR-CDRI Lucknow. N.S. and G.S. acknowledged the Department of Biotechnology and UGC New Delhi for fellowship, respectively. SAIF division, CDRI, is gratefully acknowledged for providing the spectroscopic and analytical data. This work was financially supported by Science and Engineering Research Board, Department of Science and Technology, Government of India (SERB No. EMR/2015/000983 and SERB No. EMR/2016/003813). The manuscript is CSIR-IITR communication #3693.
Contributor Information
Neha Singh, Food Toxicology Laboratory, Food, Drug and Chemical Toxicology Group, CSIR-Indian Institute of Toxicology Research, Vishvigyan Bhawan, 31 Mahatma Gandhi Marg, Lucknow 226001, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India.
Gaurav Sharma, Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India; Sophisticated Analytical Instrument Facility, CSIR-Central Drug Research Institute, Lucknow 226031, India.
Indra Dev, Food Toxicology Laboratory, Food, Drug and Chemical Toxicology Group, CSIR-Indian Institute of Toxicology Research, Vishvigyan Bhawan, 31 Mahatma Gandhi Marg, Lucknow 226001, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India.
Sanjeev K Shukla, Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India; Sophisticated Analytical Instrument Facility, CSIR-Central Drug Research Institute, Lucknow 226031, India.
Kausar Mahmood Ansari, Food Toxicology Laboratory, Food, Drug and Chemical Toxicology Group, CSIR-Indian Institute of Toxicology Research, Vishvigyan Bhawan, 31 Mahatma Gandhi Marg, Lucknow 226001, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India.
Conflict of interest statement
Authors declare no conflict of interest.
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