Skip to main content
NCBI home page
Frontiers in Molecular Biosciences logoLink to Frontiers in Molecular Biosciences
. 2021 Jul 2;8:688827. doi: 10.3389/fmolb.2021.688827

Carbon Nanotubes Induce Metabolomic Profile Disturbances in Zebrafish: NMR-Based Metabolomics Platform

Raja Ganesan 1,2,3, Prabhakaran Vasantha-Srinivasan 4, Deepa Rani Sadhasivam 5, Raghunandhakumar Subramanian 3, Selvaraj Vimalraj 3,6,*, Ki Tae Suk 1,*
PMCID: PMC8283261  PMID: 34277704

Abstract

The present study aims to investigate the metabolic effects of single-walled carbon nanotubes (SWCNT) on zebrafish (Danio rerio) using 1H nuclear magnetic resonance (1H-NMR) spectroscopy. However, there is no significant information available regarding the characterization of organic molecules, and metabolites with SWCNT exposure. Noninvasive biofluid methods have improved our understanding of SWCNT metabolism in zebrafish in recent years. Here, we used targeted metabolomics to quantify a set of metabolites within biological systems. SWCNT at various concentrations was given to zebrafish, and the metabolites were extracted using two immiscible solvent systems, methanol and chloroform. Metabolomics profiling was used in association with univariate and multivariate data analysis to determine metabolomic phenotyping. The metabolites, malate, oxalacetate, phenylaniline, taurine, sn-glycero-3-phosphate, glycine, N-acetyl mate, lactate, ATP, AMP, valine, pyruvate, ADP, serine, niacinamide are significantly impacted. The metabolism of amino acids, energy and nucleotides are influenced by SWCNT which might indicate a disturbance in metabolic reaction networks. In conclusion, using high-throughput analytical methods, we provide a perspective of metabolic impacts and the underlying associated metabolic pathways.

Keywords: SWCNT, zebrafish, NMR metabolomics, high-throughput omics, metabolic pathways

Introduction

In the past few decades, the carbon nanotubes (CNT) have gained a great attention in biomedical applications that are observed as pipe-shaped atomic layers of carbon particles. It is made up of layer-by-layer orientation of graphite sheets (Cheung et al., 2000; Wang et al., 2021; Zhou et al., 2021). Single-walled carbon nanotubes (SWCNT) and multi-walled carbon nanotubes (MWCNT) have recently applied in lung cancer therapy as a novel drug delivery system, with topographies ranging from 10 to 1,000 nm in size and length ranging from 0.5 to 20 nm. Inner hollow space, electrostatic potential, ultralightweight, drug encapsulating ability, and cellular penetration or transmission abilities are characteristics of CNT (Sahoo et al., 2011; Wu et al., 2011). CNT, polymer nanotubes, and surface altered-CNT have used in clinical applications over two decades (Hirsch, 2002).

The hydrophobic nature of CNT, as well as poor biocompatibility in solvent media, is its drawbacks. Furthermore, surface changes in materials, such as chemical alterations in CNT, may improve solubility in an aqueous environment (Yu et al., 2006; Cho et al., 2015). When working on the surface of CNT, the adsorption, carboxylation, amination, esterification, and surface polymer coating have all are considered (Bhandavat et al., 2013; Yao et al., 2017). The π−π interface between tubes (0.5–2 eV/nm), H-bonding, and van der Waals forces are commonly occurred in the aquatic dispersant. According to previous publications in aquatic cytotoxicity investigations, biocompatible solvent materials of Tween 80, polyethylene glycol (PEG), and dimethyl sulfoxide (DMSO) are reported to use CNT dispersion (Afraz et al., 2014; Chopdey et al., 2015; Carranza et al., 2016). However, water is used as a solvent because of its low cost and greater potential for green chemistry.

The doxorubicin (DOX) and folic acid (FA) coated CNT are achieved higher anti-proliferative effects. The cytotoxicity is found to be associated with DOX and FA in MCF-7 cells (Katwa et al., 2012; Lu et al., 2012; Chall et al., 2021). CNT size, length, sub-lethal concentration (LC50), the path of inhalations, and solvent materials played a lead role in toxicological responses (Han et al., 2010; Ronzani et al., 2012). Recently, MWCNT has employed in animals such as zebrafish, copepod (i.e., Tigriopus japonicas), frogs (i.e., Xenopus laevis), rat, and mice to profile the toxicological responses(Allegri et al., 2016; Girardi et al., 2017; Qi et al., 2017). In in vivo analytical and molecular mechanisms, liver, spleen, and kidney are examined with CNT exposures which could deliver deep knowledge of toxicity levels (Pauluhn, 2010; Kasai et al., 2016).

MWCNT showed impact on apoptosis, DNA damage, oxidative stress, and inflammation, as well as enzyme alteration, protein modification, and gene expression (Ma-Hock et al., 2009; Mercer et al., 2013). Hydrolase and albumin modifications as well as metabolic reactions have studied at the proteome level with CNT exposures (Dong and Ma, 2017; Poulsen et al., 2017; Lee et al., 2018). Free radicals and reactive oxygen species (ROS) are also activated by CNT (O2 −) in dose dependent manner (Shvedova et al., 2003; Monteiro-Riviere et al., 2005; Yuan et al., 2012). Hence, it is speculated that CNT can influence metabolic profile disruption.

To test CNT effect on metabolic profile, zebrafish (Danio rerio) is used as a model organism. Zebrafish is a tiny tropical freshwater fish. Zebrafish are an important vertebrate model organism that is frequently used in toxicological research due to their ease of maintenance in laboratory conditions. (Garcia et al., 2016).

Because of the low cost per sample, lack of requirement for derivatization, and the ability to measure and identify both known and unknown compounds, NMR spectroscopy is the good technology for metabolomics investigations with therapeutic relevance. (Cappello, 2020). The NMR spectroscopy technique describes the chemical input, biosynthetic intermediates, and end products of cellular activity. 1H-edited NMR-based metabolomics profiling in zebrafish has several distinct advantages in terms of quantitative and qualitative metabolites (Fessenden, 2016; Puchades-Carrasco and Pineda-Lucena, 2017; Raja et al., 2020a; Raja et al., 2020b; Raja et al., 2021). Metabolomics analysis allows for more accurate correlations between cellular transition and biochemical pathways. Metabolomics provides a high-throughput global metabolite analysis that is arguably more of an essential platform in an omics science (Blow, 2008; Guijas et al., 2018; Raja et al., 2020a; Raja et al., 2020b) (Dumas et al., 2006). In a separate metabolomics experiments, we assessed the accuracy of metabolic compounds under different conditions. Here, we examined the metabolic phenotypic expression and biochemical reaction that would lead to the identification of new therapeutic metabolites. The present study may provide a perspective of metabolic profile and the underlying associated metabolic pathways influenced by SWCNT in zebrafish model.

Methods and Materials

Chemicals and Materials

Methanol (CH3OH), chloroform (CHCl3), sulfuric acid (H2SO4) and nitric acid (HNO3) were obtained from Carlo Reactifs (SDS, France). SWCNT (Cat No: 775533; CAS No: 308068-56-6; >95% of carbon as SWCNT), and sodium salt of 3-(trimethylsilyl) propionic-2,2,3,3-d4 acid (TSP-d4) were purchased from Sigma-Aldrich, United States. According to datasheet, carbon content (≥95%), color, black; appearance, the powder was confirmed by Thermogravimetric (TGA) investigation. Fish food was obtained from Tera (Melle, Germany). Deuterium water (D2O: 99.8% purity) was obtained from Cambridge Isotope Laboratories, United States. Food was purchased from Tera (Melle, Germany).

Zebrafish and Sample Collection

Zebrafish (Danio rerio; average weight, 0.5–0.9 g; average dimension, 3.6–4.2 cm) were obtained from a local dealer. The age ranges from 5 to 6 months. Zebrafish were maintained for more than two weeks in glass tanks, which contained a fresh 60 L dechlorinated water. Fish were fed 1.0% body weight twice daily with commercial food. All animals were standardized at 26.0 ± 1°C and maintained on 14:10 h light/dark for a reproductive cycle. All examinations were carried out with various dosages of SWCNT (control, 0; 10, 50, and 100 mg/L) for 72 h. Each experimental exposure contains eight fish (n = 8, totally 32) which contains equally in a treated tank. These concentrations were determined based on the previous publications (Cheng et al., 2009; Lee et al., 2015; Lee et al., 2016). The feeding was stopped. After 72 h, all fish was cleaned in additional water, and frozen in liquid nitrogen. Then, the homogenization is done to make a fine powder by mortar and pestle. Finally, fine powder of samples was collected in test tubes. All procedures were carried out on fish were approved by the Institutional Animal Care and Use Committee (IACUC) of the Anna University Center for Biotechnology.

Sample Preparation and Metabolite Extraction

The metabolites were extracted according to previous reports (Bligh and Dyer, 1959; Martin et al., 2007). Briefly, at 4°C, 1,600 µL of 1:1 ratio of ice-cold solvent methanol and chloroform was added to powdered fish. After that, 15 min of sonication and 5 min of vigorous vortexing was done. Then, 1,400 µL of ice water was added to make biphasic conditions. After homogenization, at 4°C, the mixture was centrifuged at 291.79 g (3,000 rpm) for 10 min. Next, the upper part of the aqueous phase was collected and the samples were lyophilized. 700 µL of 99.9% deuterated phosphate buffer (0.2 M, pH 7.0) in heavy water with TSP-d4 was mixed with aqueous samples. Finally, 700 µL of dissolved sample mixture was transferred to 5 mm NMR tubes.

Acquisition

For temperature equilibration, the samples were kept for 10 min inside the NMR probe. NMR spectra were recorded on a 600 MHz spectrometer using one NMR probe. A water-suppressed t2-edited CPMG pulse sequence (RD-90°-[τ-180°-τ]n) was applied to the visualization of small molecules/metabolites. With relation to solvent peaks (TSP at 0 ppm), 1H-NMR chemical shifts were described in ppm (δ). The chemical shifts from −2 to 14 ppm were covered and the multiplicity stated as s, singlet; d, doublet; t, triplet; q, quartet; and m, multiplet. Heavy water acts as field frequency locking. The acquisition time, 3.0 s; relaxation delay, 1.0 s; width, 9,615.6 Hz; and 128 scans were acquired from each spectrum. Total time per sample was taken for 10 min.

Data Processing

All Fourier transform spectra were manually corrected for phase and baseline distortions. TSP calibration at 0 ppm was processed. Mnova software (Mestrelab Research, Santiago de Compostella, Spain) was used to visualize, process the 1D NMR data. To avoid statistical error, the water domain (δ 4.7–4.9 ppm) was removed. δ 0–10 ppm was uniformly divided as buckets that have 0.001 ppm. Further, binned data were exported to MetaboAnalyst software (v 5.0) for multivariate statistical analysis. Initially, to eradicate outlier data, principal component analysis (PCA) was analyzed (data not shown). Using supervised techniques, particle least squares discriminant analysis (PLS-DA) and orthogonal PLS-DA (OPLS-DA) were applied to NMR data. From score data, each point represents a 1H- spectral data.

Data Handling and Statistical Analysis

The data normalization (to avoid concentration differences among metabolites) and validation for score plot analysis was accomplished using the Pareto scaling algorithm, which measured each variable using the square root of its standard deviations. MetaboAnalyst 5.0 program (https://www.metaboanalyst.ca/) was used to analyze all of the samples. The newly aligned and filtered metabolite lists were then processed. The metabolic expression, metabolic networking, and biological interpretation were determined after a logarithmic transformation. GraphPad Prism eight software was used to perform statistical analysis. Numerical values were delivered as the mean ± SD. Two-way analysis of variance (ANOVA) has been used to examine multiple subjects. The significant changes of metabolites were recognized by p < 0.05.

Results

High-Throughput and Highly Reproducible NMR-Based Platform

The chemical structure of SWCNT is shown in Figure 1A. The percentage (%) values, rules, and relevant p-values are mentioned in Table 1. Fold variations (FC > 1.0, increased; FC < 1.0, decreased) are taken into account when determining metabolite levels. Several small molecules, and metabolites (amino acids, polyamines, nucleosides, fatty acids, phosphates, carbohydrates, organic molecules, and other organic mixtures) were observed in spectral chemical changes. The phosphate buffer 1H-resonance at 0 ppm was used as a reference peak. Each metabolite or molecule have its hydrogen-1 nuclei (e.g., −CH, −CH2, and −CH3) profiled and quantified. Mnova software was used to organize the spectral data. Before and after normalization with SWCNT exposed metabolites, Pareto Scaling-based Kernel density plots and Box plots analysis are shown in Supplementary Figure S1. The density plots are based on all samples, while the box plots represent the most quantified metabolites.

FIGURE 1.

FIGURE 1

Open in a new tab

(A) Schematic chemical structure of SWCNT. An OPLS-DA predictive model of zebrafish metabolite profiles after SWCNTs exposure. (B) 0 vs 10 mg/L, 59.9%, Red dot, 0 mg/L; Green dot, 10 mg/L. (C) 0 vs 50 mg/L,58.5%, Red dot, 0 mg/L; Green dot, 50 mg/L. (D) 0 vs 100 mg/L, 61.2%, Red dot, 0 mg/L; Green dot, 100 mg/L. (E) OPLS-DA score plot on normalized metabolite concentrations of 0 vs 10, 50, 100 mg/L. PC can be found in 51.9% of discriminations. Red dot, 0 mg/L; Green dot, 10 mg/L; light blue, 50 mg/L; and dark blue, 100 mg/L.

TABLE 1.

The description of the quantified metabolites in metabolomic fingerprint associated with decompensatory state in SWCNT with three scenarios.

No Name 0 vs 10 0 vs 50 0 vs 100
% Cal Increase or decreases p Values % Cal Increase or decreases p Values % Cal Increase or decreases p Values
1 2-Oxoglutarate 73.70449 Increase 0.092747 10.7,617,737 Increase 0.389,275 74.77,899,388 Increase 0.05272
2 ADP 162.8223 Increase 0.001449 314.7,813,657 Increase 0.097672 513.2,112,066 Increase 0.020734
3 AMP 198.2004 Increase 0.096359 219.947,296 Increase 0.062638 344.1,798,478 Increase 0.029183
4 ATP 7.437,242 Increase 0.431,091 158.8,837,666 Increase 0.046208 81.07,693,581 Increase 0.057041
5 Acetate -16.4859 Decrease 0.421,167 -54.00823,801 Decrease 0.243,334 -36.91,082,151 Decrease 0.317,866
6 Alanine 15.59503 Increase 0.365,929 -14.58,162,808 Decrease 0.374,807 -2.684,656,139 Decrease 0.475,424
7 Anserine -34.607 Decrease 0.130,985 -23.22,183,101 Decrease 0.234,101 -31.87,973,584 Decrease 0.135,223
8 Arginine -9.62773 Decrease 0.395,644 -41.06,335,018 Decrease 0.092192 -9.285,285,347 Decrease 0.389,061
9 Asparagine 104.8953 Increase 0.001219 45.50,601,736 Increase 0.085467 109.4,722,721 Increase 0.003295
10 Aspartate 60.73794 Increase 0.129,267 17.19,689,515 Increase 0.350,938 61.71,678,763 Increase 0.088883
11 Betaine -74.2478 Decrease 0.030403 -81.08,560,073 Decrease 0.017013 -87.29,947,554 Decrease 0.011894
12 Choline -3.72769 Decrease 0.441,084 -15.94,533,167 Decrease 0.257,837 6.133,147,486 Increase 0.408,377
13 Citrate 76.54633 Increase 0.084544 19.49,283,991 Increase 0.292,368 40.0717,263 Increase 0.161,152
14 Creatine 40.32183 Increase 0.055934 42.31,849,693 Increase 0.030331 46.51,900,655 Increase 0.016909
15 Creatine phosphate 13.98336 Increase 0.380,374 -4.797,583,356 Decrease 0.454,801 -2.90,649,314 Decrease 0.471,668
16 Creatinine 188.339 Increase 0.00438 79.80,155,794 Increase 0.110,077 201.647,253 Increase 0.00026
17 Dimethylamine -97.8965 Decrease 0.011613 -88.62,817,939 Decrease 0.020103 -96.41,436,763 Decrease 0.012514
18 Ethanol -77.5631 Decrease 0.008482 -67.1,337,193 Decrease 0.022809 -83.83,115,472 Decrease 0.005507
19 Formate 25.81097 Increase 0.222,801 -24.04,594,779 Decrease 0.17447 -31.44,758,524 Decrease 0.089225
20 Fucose 144.9785 Increase 0.040645 34.82,528,867 Increase 0.274,614 136.3,851,508 Increase 0.023485
21 Fumarate -98.3009 Decrease 0.024533 -90.61,049,235 Decrease 0.035229 -98.65,290,734 Decrease 0.024179
22 GTP -81.3516 Decrease 0.020048 -39.23,261,294 Decrease 0.246,862 -84.14,978,973 Decrease 0.017079
23 Glucose 24.36722 Increase 0.257,163 36.4,047,155 Increase 0.2598 -10.7,777,396 Decrease 0.381,518
24 Glutamate 38.88837 Increase 0.219,095 -15.81,553,187 Decrease 0.356,268 38.5,708,216 Increase 0.185,623
25 Glutamine 24.25334 Increase 0.26184 27.57,064,527 Increase 0.211,586 52.81,697,604 Increase 0.111,108
26 Glutathione -25.4743 Decrease 0.266,799 10.94,984,271 Increase 0.437,511 -11.27,347,073 Decrease 0.395,667
27 Glycerol -23.5594 Decrease 0.324,996 -50.34,917,191 Decrease 0.150,181 -46.73,837,336 Decrease 0.156,496
28 Glycine 133.4323 Increase 0.003889 105.1,663,781 Increase 0.037436 129.3,957,885 Increase 0.004141
29 Histidine 74.85256 Increase 0.024275 56.62,485,047 Increase 0.120,098 75.82,464,931 Increase 0.015855
30 Hypoxanthine -35.6985 Decrease 0.134,947 -45.75,566,132 Decrease 0.074832 -54.45,315,064 Decrease 0.0401
31 Inosine 12.23891 Increase 0.353,662 32.79,363,508 Increase 0.222,122 -19.10,798,266 Decrease 0.255,749
32 Isoleucine -28.8215 Decrease 0.267,945 -71.67,649,692 Decrease 0.026533 -60.78,558,236 Decrease 0.046238
33 Lactate 114.2764 Increase 0.023645 83.76,939,699 Increase 0.08791 87.79,790,125 Increase 0.063472
34 Leucine -9.10471 Decrease 0.450,083 -74.6,175,899 Decrease 0.103,589 -51.55,430,254 Decrease 0.188,734
35 Lysine -23.6233 Decrease 0.365,803 -69.53,369,684 Decrease 0.102,491 -56.27,603,841 Decrease 0.150,371
36 Malate 136.4495 Increase 0.014655 93.6,135,441 Increase 0.04656 270.6,447,781 Increase 0.003909
37 Methionine -30.2841 Decrease 0.297,722 -75.18,860,852 Decrease 0.050906 -67.01,984,485 Decrease 0.069927
38 N-Acetylaspartate -22.321 Decrease 0.191,535 -17.51,070,044 Decrease 0.241,316 -27.39,036,923 Decrease 0.105,742
39 N-Acetylglutamate -98.1887 Decrease 0.028904 -81.39,713,565 Decrease 0.065271 -99.14,360,319 Decrease 0.027846
40 NAD+ 40.94824 Increase 0.174,951 -42.46,688,684 Decrease 0.108,032 -9.34,639,557 Decrease 0.414,604
41 NADPH -86.1827 Decrease 0.023109 -70.85,400,513 Decrease 0.057615 -87.19,187,358 Decrease 0.022166
42 Niacinamide 30.50126 Increase 0.084566 90.58,143,772 Increase 0.006595 79.05,040,869 Increase 0.004627
43 O-Phosphocholine -88.2178 Decrease 0.0245 -60.95,103,293 Decrease 0.126,429 -92.31,146,815 Decrease 0.019682
44 O-Phosphoethanolamine 81.26837 Increase 0.005801 29.64,934,434 Increase 0.152,594 93.51,890,578 Increase 0.004082
45 Oxalacetate 246.0446 Increase 0.001964 213.6,985,716 Increase 0.011506 413.511,089 Increase 3.17E-05
46 Phenylalanine -74.0998 Decrease 0.031956 -90.1,019,858 Decrease 0.008298 -88.17,690,096 Decrease 0.009291
47 Proline 82.14609 Increase 0.094111 -13.42,018,188 Decrease 0.38575 46.6,589,559 Increase 0.172,693
48 Pyruvate 103.5242 Increase 0.031163 103.6,141,295 Increase 0.036117 127.1,389,676 Increase 0.000509
49 Serine 33.88681 Increase 0.245,025 10.72,826,148 Increase 0.400,198 19.90,400,252 Increase 0.304,549
50 Succinate -54.6477 Decrease 0.148,816 -59.82,893,254 Decrease 0.127,362 -66.25,893,766 Decrease 0.105,893
51 Taurine 178.7991 Increase 0.004197 152.5,804,078 Increase 0.010755 216.5,095,771 Increase 0.000156
52 Threonine -47.4423 Decrease 0.067747 27.69,784,522 Increase 0.35361 -29.84,662,431 Decrease 0.171,608
53 Trimethylamine 8.60628 Increase 0.380,823 26.0161,051 Increase 0.147,961 13.02,893,349 Increase 0.293,997
54 Trimethylamine N-oxide -85.3013 Decrease 0.072336 -38.30,154,071 Decrease 0.296,117 -80.02,162,641 Decrease 0.085984
55 Tryptophan -25.9805 Decrease 0.31299 -62.68,571,426 Decrease 0.054649 -59.2,106,272 Decrease 0.063837
56 Tyrosine -58.317 Decrease 0.070382 170.2,144,897 Increase 0.254,126 -72.94,281,412 Decrease 0.022625
57 Uridine -63.7642 Decrease 0.09481 -55.92,116,852 Decrease 0.13265 -73.84,546,235 Decrease 0.065303
58 Valine -40.1231 Decrease 0.153,864 -74.18,575,041 Decrease 0.011754 -57.83,807,623 Decrease 0.035354
59 Xanthine -25.3289 Decrease 0.346,071 80.03,241,069 Increase 0.210,728 -33.66,130,094 Decrease 0.309,344
60 Myo-inositol -47.6804 Decrease 0.1441 -65.48,314,175 Decrease 0.072438 -51.37,657,254 Decrease 0.12524
61 sn-Glycero-3-phosphocholine -82.4237 Decrease 0.033543 -88.18,738,357 Decrease 0.025833 -80.9,428,004 Decrease 0.035817
Open in a new tab

Note and abbreviations: 0, control; 10 mg; 50 mg; 100 mg; % Cal, percentage calculations.

Quantification, Assessment, and Pattern Recognition Analysis

There was a strong distinction between the four classes (i.e., 0, 10, 50, and 100 mg/L), suggesting that zebrafish has a unique metabolic profile. The possible metabolites (FDR 0.05, FC 1 and FC > 1) are highlighted in the volcano map. A univariate OPLS-DA score plot of SWCNT-applied samples shows that they are widely separated from each other and starving fish (Figures 1B–D) (control zebrafish). The metabolic differences in 10 mg/L, 50 mg/L, and 100 mg/L were found to be 59.9%, 58.5 percent, and 61.2 percent, respectively. In addition, the OPLS-DA score plot of SWCNT in zebrafish is shown in Figure 1E. The score plots are demonstrated in the 95% confidence region.

PLS-DA scores were used to select the candidate quantified metabolites with a variable importance of projection (VIP) score >1 (Song et al., 2011). Fifteen metabolites, including malate, oxalacetate, phenylalanine, taurine, sn-glycero-3-phosphate, glycine, N-acetylglutamate, lactate, ATP, AMP, valine, pyruvate, ADP, serine, and niacinamide, are differentiating the metabolic functions according to VIP >1 score analysis (Figure 2A). The heatmap in Figure 2B illustrates the phenotypic delivery of quantified metabolites. The average intensity of variation in the respective sets is represented by painted bars. CNT induces a net increase or decrease in the metabolite amount, as shown by the red and green colors.

FIGURE 2.

FIGURE 2

Open in a new tab

Interactive heatmap analysis with metabolites or small molecules dataset visualization in Zebrafish after SWCNT exposures. The rows and columns display metabolites and the samples, respectively. The green and red colors indicate down- and upregulated metabolites, respectively, in fish fluid samples. The brightness of each color corresponds to the alteration magnitude compared with the average value. The classes of 0, 10, 50, and 100 indicate control, 0 mg/L, 10 mg/ml, 50 mg/ml, and 100 mg/ml, respectively.

As shown in Supplementary Figure S2A, the metabolic correlation map was plotted. The analysis focused on the metabolic expression caused by SWCNT in zebrafish compared to control zebrafish. The metabolite expression from SWCNT-treated zebrafish is shown in the row, while the metabolite expression from control zebrafish is shown in the column. The metabolites whose regulation had basically decreased were shown in green, while those whose regulation had increased were shown in red. This data, when combined with the dendrogram of various leveled bunch investigations, provides a worldwide perspective of metabolite changes in SWCNT-affected zebrafish metabolisms. A significant analysis of metabolites (SAM plot) has been done in Supplementary Figure S2B and screened 12 metabolites as candidate metabolites. The relative expression of metabolites is shown in Supplementary Figure S2C. The cumulative metabolic discrimination is 52.9%. The score plot parameters that R 2 (0.8: 80%) and Q2 (0.4: 40%) revealed satisfactory goodness of fit and goodness prediction, respectively (Supplementary Figure S3A,B). Q2 indicates that a four-component model is the best (marked with a red star). R 2 and Q2 values were indicating near 1 that was considered as robust score plots. The evidence of R 2 and Q2 values from score plots indicate that the metabolic disconcertion was found in fish by SWCNT exposures.

Metabolic Phenotypic Changes and Expression

Adenosine phosphates are involved in energy hydrolysis in cells (ATP to ADP to AMP), and these phosphates serve as a caption of energy yield. AMP, ADP, and ATP are increased in low to high concentrations. These changes also perplexed the ratio of total adenosine phosphate groups, which could disrupt the zebrafish's equilibrium between energy production and ATP consumption. The AMP-activated protein kinase (AMPK) pathway is activated by metabolic changes in adenosine phosphate (Duft et al., 2017; Simpson-Lavy et al., 2017). In cellular environments, demanding ATP production can occur.

Anaerobic conditions can develop in zebrafish as a result of adenosine phosphate degradation. The metabolic disturbance caused by SWCNT exposures, which involves increased ADP, ATP, and decreased glucose, disrupts ATP intake in cellular environments, which serves as cellular energy. When the zebrafish was given large doses of SWCNT, the metabolic chemical reaction took place primarily via the oxygen-dependent pathway. ATP, ADP's primary metabolite, is formed during purine metabolism. As a result, these findings indicate that SWCNT disrupts purine metabolism.

As shown in Figures 3A,B, the original concentration range of an asparagine, glycine, histidine, malate, and oxalacetate were significantly increased by SWCNT exposures in zebrafish. Reversibly, it is important to note that the dimethylamine, fumarate, isoleucine, and NADPH levels were significantly decreased with SWCNT exposures. Thus, different metabolic fractions can affect their biological functions.

FIGURE 3.

FIGURE 3

Open in a new tab

Comparison of the normalized relative intensities of the potential metabolites between control and SWCNT applied zebrafish (A) The employed strategy of the automated assignment of metabolites, asparagine, glycine, histidine, malate, and an oxalacetate with significantly increased concentration from control samples and differences are shown. (B) The significantly decreased metabolites, dimethylamine, fumarate, isoleucine, and NADPH has been listed. t-test analysis; p < 0.05, p < 0.01, p < 0.001 are considered significant metabolites. Data show mean ± SEM.

Metabolites of NAD-utilizing reactions such as oxaloacetate are found to be significantly increased. The NAD involved oxidation in TCA cycle intermediates such as acetyl CoA, alpha-ketoglutarate, and succinyl-CoA can perplex the ATP energy production and metabolic pathway process. Pyruvate, oxaloacetate, NADPH, and NAD + have significantly increased which may lead to an intra and extracellular oxidative chemical reaction. Furthermore, upregulated NADPH/NAD + ratio has been perplexed and lead to ROS development which could affect the oxidative chemical reaction in lipid metabolisms, glycolysis, and galactose metabolism. As shown in Figure 4, the creatine and creatine phosphate has been increased. These two metabolites act as reserve high-energy phosphates in muscles.

FIGURE 4.

FIGURE 4

Open in a new tab

(A) Level of significantly dysregulated creatine and creatine phosphate in zebrafish. (B) The top 15 most metabolic discriminating metabolites between Control and SWCNT. VIP metabolome identified from fish relative to the controls after the SWCNT treatment. The major contributing metabolites are identified using PLS-DA algorithm. The right heatmap shows the concentration of the metabolites. The middle part shows the VIP scores. The left part lists the significant difference between metabolites.

According to the latest updates, carbon nanoparticles and carbon nanotubes (CNTs) uptake causes oxidative stress. The internalization of nanoparticles and their aggregation in cells have been explored (Li et al., 2011; Chiesa et al., 2018). The accumulation of titanium nanomaterials larger than 100 nm in the inner and outer membranes, as well as the intracellular environment (i.e., cytosolic), has been shown to cause stress in mitochondrial and cytoplasmic environments, resulting in autophagy (Bu et al., 2010; Tang et al., 2010).

As shown in Figures 5A–C and Supplementary Tables S1–3, the metabolic pathways in 10 mg/L: glycolysis/gluconeogenesis (26/5), primary bile acid biosynthesis (46/2), phenylalanine metabolism (10/2), phenylalanine, tyrosine, and tryptophan biosynthesis (4/2), taurine and hypotaurine metabolism (8/1), ether lipid metabolism (20/1), citrate cycle (20/6); 50 mg/L: cysteine and methionine metabolism (33/3), ether lipid metabolism (20/1), pantothenate and CoA biosynthesis (19/2), taurine and hypotaurine metabolism (8/1), primary bile acid biosynthesis (46/2), glycine, serine and threonine metabolism (33/7); and 100 mg/L: purine metabolism (65/8), glycolysis/gluconeogenesis (26/5), taurine and hypotaurine metabolism (8/1), citrate cycle (20/6), primary bile acid biosynthesis (46/2) were significantly affected by SWCNT. The values after each metabolic pathway that explained the hits (right) and totals (left) represent the number of metabolites present and total metabolites involved in that metabolism, respectively. To find the pathways phenotypic expression, the metabolites set enrichment analysis (MSEA) has been performed to find the affected highly disturbances in every metabolic pathway.

FIGURE 5.

FIGURE 5

Open in a new tab

Altered metabolic pathways and performance of the automated assignment in zebrafish in (A) 10 mg/L; (B) 50 mg/L; (C) 100 mg/L. The differential metabolites identified by all studies under review were enriched into pathways using MetaboAnalyst software (version 5.0; www.metaboanalyst.ca). Pathways where metabolite sets are ranked according to p-value with hatched lines indicating Holm p-value threshold.

Dicussion

This is the first study to use a zebrafish model to evaluate SWCNT's effects on metabolites and determine if those metabolites are upregulated or downregulated in their metabolism. The density plots that before normalization (left side), after normalization (right side) are delivered based on all the metabolites datasets. These box plots defined the distribution of each variable or metabolite concentrations (before and after normalization). According to pareto-scaling, normalization, and trans-formation, the metabolomic dataset was deferred. Kernel density estimation, which used to estimate the probability density functions. This analysis may yield the practical applications of probability distributions.

A univariate and multivariate analyses of OPLS-DA score plot was constructed, delivering relative clustering positions with SWCNT treatments. Among these, four groupings (Control vs. 10, 50, and 100 mg/L of SWCNT) exhibited clear separation between the groups. The R 2 and Q2 values are reported, which decreases as the score plot becomes robust (i.e., R2-Q2). We first analysed the metabolites and associated metabolic pathways in zebrafish metabolisms with SWCNT exposures. The detection of top fifteen metabolites such as malate, oxalacetate, phenylaniline, taurine, sn-glycero-3-phosphate, glycine, N-acetyl mate, lactate, AMP, ADP, ATP, valine, pyruvate, serine, and niacinamide has been significantly impacted by SWCNT exposures. Here, an asparagine, glycine, histidine, malate, and oxalacetate are increased from control zebrafish. The TCA metabolites, malate, fumarate and oxalacetate contribute to the production of energy (Bruzzone et al., 2020).

The amino acid of histidine is required for protein biogenesis. These metabolites were then selected as prognostic biomarkers through differential expression, survival, and aquatic vertebrate model analysis. Many studies have delivered new therapeutic metabolites for disease prevention, and toxicity with limited success in aquatic zebrafish model (Lu et al., 2016; Kooij et al., 2020). We took advantages from state-of-the-art NMR infrastructure with high reproducibility, quotative capacity, and robustness. This analysis precludes the assignment of 1H-edited NMR-detected fish metabolites.

When analyse each metabolite, we found a pattern of changes in the metabolic intricate of zebrafish. The most remarkable alteration found in our analysis was reduction in the abundance of metabolites that contribute directly or indirectly to metabolic reprograming in zebrafish (Huang et al., 2016). Interestingly, MSEA highlighted the enrichment in glycolysis/gluconeogenesis in zebrafish, an observation in our recent report on rewiring an energy metabolism in various cellular environments (Huang et al., 2016; Lu et al., 2016). In the cellular environments, nanoparticles reported to induce structural transformations within cell such as secondary lysosome formation (Shvedova et al., 2003; Monteiro-Riviere et al., 2005; Yuan et al., 2012). The perturbed NADPH status could inhibit the glutathione cycle (GSH/GSSG) which results in induction of oxidative stress (Gluick and Yadav, 2003).

Remarkably, we found that SWCNT induced major metabolic pathways in zebrafish metabolism and are quantified by MESA investigations. The creatine and creatine phosphate are modified that may fail to serve as energy in cell. Here, purine metabolism might be disconnected from energy metabolisms such as Krebs cycle and glycolysis (Laíns et al., 2019). The enriched metabolic functions with SWCNT treatment have bordered and shown to clearly connected to cell death and survival, proliferation, phenotypic expression, and cellular microenvironment.

According to these data, we quantified a set of metabolic pathways, glycolysis/gluconeogenesis, primary bile acid biosynthesis, phenylalanine metabolism, phenylalanine, tyrosine, and tryptophan biosynthesis, taurine and hypotaurine metabolism, ether lipid metabolism, citrate cycle might play important role in 10 mg/L of SWCNT exposure in zebrafish. The cysteine and methionine metabolism, ether lipid metabolism, pantothenate and CoA biosynthesis, taurine and hypotaurine metabolism, primary bile acid biosynthesis, glycine, serine and threonine metabolism are modified in 50 mg/L of SWCNT treatment in zebrafish. Finaly, we extended the quantification of metabolic pathway in 100 mg/L of SWCNT treatment. Purine metabolism, glycolysis/gluconeogenesis, taurine and hypotaurine metabolism, citrate cycle and primary bile acid biosynthesis are impacted.

Conclusion

This study convincingly demonstrated the impacts of metabolites caused by SWCNT exposure in zebrafish for the first time at the omics scale. According to our findings based on 1H-NMR technique, SWCNT exposure caused significant phenotypic changes in metabolites. 1H-NMR offers significant benefits to the metabolomics field via the detection of metabolites integral to critical cellular metabolism and cell signaling. Malate, oxalacetate, phenylaniline, taurine, sn-glycero-3-phosphate, glycine, N-acetyl mate, lactate, ATP, AMP, valine, pyruvate, ADP, serine, niacinamide are significantly impacted by SWCNT. We believe that these metabolome alterations and metabolic pathways in zebrafish, such as energy, amino acid, and nucleotide metabolisms, may contribute in the development of future treatments that target diseases, especially as SWCNT exposure becomes more important as a pharmacological target. However, more investigations are necessary to better understand the SWCNT exposure and underlying mechanisms.

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.

Ethics Statement

The animal study was reviewed and approved by BRULAC/SDCH/S/MATS/IAEC/3-2021/067.

Author Contributions

RG and PV-S performed all the experiments and analysed the data, and RG drafted the manuscript. DS and RS provided technical support for the work. SV and KS designed the work and approved the final submitted manuscript.

Funding

This work was also supported by Department of Science and Technology, Inspire Faculty Program, Government of India (Grant No: DST/INSPIRE/04/2017/002913 to SV).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2021.688827/full#supplementary-material

Click here for additional data file. (1.2MB, docx)

References

  1. Afraz A., Rafati A. A., Najafi M. (2014). Optimization of Modified Carbon Paste Electrode with Multiwalled Carbon Nanotube/ionic Liquid/cauliflower-like Gold Nanostructures for Simultaneous Determination of Ascorbic Acid, Dopamine and Uric Acid. Mater. Sci. Eng. C 44, 58–68. 10.1016/j.msec.2014.07.065 [DOI] [PubMed] [Google Scholar]
  2. Allegri M., Perivoliotis D. K., Bianchi M. G., Chiu M., Pagliaro A., Koklioti M. A., et al. (2016). Toxicity Determinants of Multi-Walled Carbon Nanotubes: The Relationship between Functionalization and Agglomeration. Toxicol. Rep. 3, 230–243. 10.1016/j.toxrep.2016.01.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Bhandavat R., Feldman A., Cromer C., Lehman J., Singh G. (2013). Very High Laser-Damage Threshold of Polymer-Derived Si(B)CN- Carbon Nanotube Composite Coatings. ACS Appl. Mater. Inter. 5, 2354–2359. 10.1021/am302755x [DOI] [PubMed] [Google Scholar]
  4. Bligh E. G., Dyer W. J. (1959). A Rapid Method of Total Lipid Extraction and Purification. Can. J. Biochem. Physiol. 37, 911–917. 10.1139/y59-099 [DOI] [PubMed] [Google Scholar]
  5. Blow N. (2008). Biochemistry's New Look. Nature 455, 697–698. 10.1038/455697a [DOI] [PubMed] [Google Scholar]
  6. Bruzzone C., Loizaga-Iriarte A., Sánchez-Mosquera P., Gil-Redondo R., Astobiza I., Diercks T., et al. (2020). 1H NMR-Based Urine Metabolomics Reveals Signs of Enhanced Carbon and Nitrogen Recycling in Prostate Cancer. J. Proteome Res. 19, 2419–2428. 10.1021/acs.jproteome.0c00091 [DOI] [PubMed] [Google Scholar]
  7. Bu Q., Yan G., Deng P., Peng F., Lin H., Xu Y., et al. (2010). NMR-based Metabonomic Study of the Sub-acute Toxicity of Titanium Dioxide Nanoparticles in Rats after Oral Administration. Nanotechnology 21, 125105. 10.1088/0957-4484/21/12/125105 [DOI] [PubMed] [Google Scholar]
  8. Cappello T. (2020). eMagRes., 81–100.NMR-based Metabolomics of Aquatic Organisms [Google Scholar]
  9. Carranza A., Pérez-García M. G., Song K., Jeha G. M., Diao Z., Jin R., et al. (2016). Deep-Eutectic Solvents as MWCNT Delivery Vehicles in the Synthesis of Functional Poly(HIPE) Nanocomposites for Applications as Selective Sorbents. ACS Appl. Mater. Inter. 8, 31295–31303. 10.1021/acsami.6b09589 [DOI] [PubMed] [Google Scholar]
  10. Chall A., Stagg J., Mixson A., Gato E., Quirino R. L., Sittaramane V. (2021). Ablation of Cells in Mice Using Antibody-Functionalized Multiwalled Carbon Nanotubes (Ab-MWCNTs) in Combination with Microwaves. Nanotechnology 32, 195102. 10.1088/1361-6528/abe32a [DOI] [PubMed] [Google Scholar]
  11. Cheng J., Chan C. M., Veca L. M., Poon W. L., Chan P. K., Qu L., et al. (2009). Acute and Long-Term Effects after Single Loading of Functionalized Multi-Walled Carbon Nanotubes into Zebrafish (Danio rerio). Toxicol. Appl. Pharmacol. 235, 216–225. 10.1016/j.taap.2008.12.006 [DOI] [PubMed] [Google Scholar]
  12. Cheung C. L., Hafner J. H., Lieber C. M. (2000). Carbon Nanotube Atomic Force Microscopy Tips: Direct Growth by Chemical Vapor Deposition and Application to High-Resolution Imaging. Proc. Natl. Acad. Sci. 97, 3809–3813. 10.1073/pnas.050498597 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Chiesa E., Dorati R., Modena T., Conti B., Genta I. (2018). Multivariate Analysis for the Optimization of Microfluidics-Assisted Nanoprecipitation Method Intended for the Loading of Small Hydrophilic Drugs into PLGA Nanoparticles. Int. J. Pharmaceutics 536, 165–177. 10.1016/j.ijpharm.2017.11.044 [DOI] [PubMed] [Google Scholar]
  14. Cho K. Y., Yeom Y. S., Seo H. Y., Park Y. H., Jang H. N., Baek K.-Y., et al. (2015). Rational Design of Multiamphiphilic Polymer Compatibilizers: Versatile Solubility and Hybridization of Noncovalently Functionalized CNT Nanocomposites. ACS Appl. Mater. Inter. 7, 9841–9850. 10.1021/acsami.5b01849 [DOI] [PubMed] [Google Scholar]
  15. Chopdey P. K., Tekade R. K., Mehra N. K., Mody N., Jain N. K. (2015). Glycyrrhizin Conjugated Dendrimer and Multi-Walled Carbon Nanotubes for Liver Specific Delivery of Doxorubicin. J. Nanosci Nanotechnol 15, 1088–1100. 10.1166/jnn.2015.9039 [DOI] [PubMed] [Google Scholar]
  16. Dong J., Ma Q. (2017). Osteopontin Enhances Multi-Walled Carbon Nanotube-Triggered Lung Fibrosis by Promoting TGF-Beta1 Activation and Myofibroblast Differentiation. Part. Fibre Toxicol. 14, 18. 10.1186/s12989-017-0198-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Duft R. G., Castro A., Bonfante I. L. P., Brunelli D. T., Chacon-Mikahil M. P. T., Cavaglieri C. R. (2017). Metabolomics Approach in the Investigation of Metabolic Changes in Obese Men after 24 Weeks of Combined Training. J. Proteome Res. 16, 2151–2159. 10.1021/acs.jproteome.6b00967 [DOI] [PubMed] [Google Scholar]
  18. Dumas M.-E., Barton R. H., Toye A., Cloarec O., Blancher C., Rothwell A., et al. (2006). Metabolic Profiling Reveals a Contribution of Gut Microbiota to Fatty Liver Phenotype in Insulin-Resistant Mice. Proc. Natl. Acad. Sci. 103, 12511–12516. 10.1073/pnas.0601056103 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Fessenden M. (2016). Metabolomics: Small Molecules, Single Cells. Nature 540, 153–155. 10.1038/540153a [DOI] [PubMed] [Google Scholar]
  20. Garcia G. R., Noyes P. D., Tanguay R. L. (2016). Advancements in Zebrafish Applications for 21st century Toxicology. Pharmacol. Ther. 161, 11–21. 10.1016/j.pharmthera.2016.03.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Girardi F. A., Bruch G. E., Peixoto C. S., Dal Bosco L., Sahoo S. K., Gonçalves C. O. F., et al. (2017). Toxicity of Single-wall Carbon Nanotubes Functionalized with Polyethylene Glycol in Zebrafish (Danio rerio) Embryos. J. Appl. Toxicol. 37, 214–221. 10.1002/jat.3346 [DOI] [PubMed] [Google Scholar]
  22. Gluick T. C., Yadav S. (2003). Trimethylamine N-Oxide Stabilizes RNA Tertiary Structure and Attenuates the Denaturating Effects of Urea. J. Am. Chem. Soc. 125, 4418–4419. 10.1021/ja0292997 [DOI] [PubMed] [Google Scholar]
  23. Guijas C., Montenegro-Burke J. R., Warth B., Spilker M. E., Siuzdak G. (2018). Metabolomics Activity Screening for Identifying Metabolites that Modulate Phenotype. Nat. Biotechnol. 36, 316–320. 10.1038/nbt.4101 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Han S. G., Andrews R., Gairola C. G. (2010). Acute Pulmonary Response of Mice to Multi-wall Carbon Nanotubes. Inhalation Toxicol. 22, 340–347. 10.3109/08958370903359984 [DOI] [PubMed] [Google Scholar]
  25. Hirsch A. (2002). Functionalization of Single-Walled Carbon Nanotubes. Angew. Chem. Int. Ed. 41, 1853–1859. 10.1002/1521-3773(20020603)41:11<1853::aid-anie1853>3.0.co;2-n [DOI] [PubMed] [Google Scholar]
  26. Huang S. S. Y., Benskin J. P., Chandramouli B., Butler H., Helbing C. C., Cosgrove J. R. (2016). Xenobiotics Produce Distinct Metabolomic Responses in Zebrafish Larvae (Danio rerio). Environ. Sci. Technol. 50, 6526–6535. 10.1021/acs.est.6b01128 [DOI] [PubMed] [Google Scholar]
  27. Kasai T., Umeda Y., Ohnishi M., Mine T., Kondo H., Takeuchi T., et al. (2016). Lung Carcinogenicity of Inhaled Multi-Walled Carbon Nanotube in Rats. Part. Fibre Toxicol. 13, 53. 10.1186/s12989-016-0164-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Katwa P., Wang X., Urankar R. N., Podila R., Hilderbrand S. C., Fick R. B., et al. (2012). A Carbon Nanotube Toxicity Paradigm Driven by Mast Cells and the IL-33/ST2 Axis. Small 8, 2904–2912. 10.1002/smll.201200873 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Kooij R., Liu S., Sapmaz A., Xin B.-T., Janssen G. M. C., Van Veelen P. A., et al. (2020). Small-Molecule Activity-Based Probe for Monitoring Ubiquitin C-Terminal Hydrolase L1 (UCHL1) Activity in Live Cells and Zebrafish Embryos. J. Am. Chem. Soc. 142, 16825–16841. 10.1021/jacs.0c07726 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Laíns I., Duarte D., Barros A. S., Martins A. S., Carneiro T. J., Gil J. Q., et al. (2019). Urine Nuclear Magnetic Resonance (NMR) Metabolomics in Age-Related Macular Degeneration. J. Proteome Res. 18, 1278–1288. 10.1021/acs.jproteome.8b00877 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Lee J. W., Choi Y. C., Kim R., Lee S. K. (2015). Multiwall Carbon Nanotube-Induced Apoptosis and Antioxidant Gene Expression in the Gills, Liver, and Intestine of Oryzias latipes . Biomed. Res. Int. 2015, 485343. 10.1155/2015/485343 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Lee J. W., Won E.-J., Kang H.-M., Hwang D.-S., Kim D.-H., Kim R.-K., et al. (2016). Effects of Multi-Walled Carbon Nanotube (MWCNT) on Antioxidant Depletion, the ERK Signaling Pathway, and Copper Bioavailability in the Copepod (Tigriopus Japonicus). Aquat. Toxicol. 171, 9–19. 10.1016/j.aquatox.2015.12.005 [DOI] [PubMed] [Google Scholar]
  33. Lee S. J., Zhu W., Nowicki M., Lee G., Heo D. N., Kim J., et al. (2018). 3D Printing Nano Conductive Multi-Walled Carbon Nanotube Scaffolds for Nerve Regeneration. J. Neural Eng. 15, 016018. 10.1088/1741-2552/aa95a5 [DOI] [PubMed] [Google Scholar]
  34. Li Y., Sun L., Jin M., Du Z., Liu X., Guo C., et al. (2011). Size-dependent Cytotoxicity of Amorphous Silica Nanoparticles in Human Hepatoma HepG2 Cells. Toxicol. Vitro 25, 1343–1352. 10.1016/j.tiv.2011.05.003 [DOI] [PubMed] [Google Scholar]
  35. Lu Y.-J., Wei K.-C., Ma C.-C. M., Yang S.-Y., Chen J.-P. (2012). Dual Targeted Delivery of Doxorubicin to Cancer Cells Using Folate-Conjugated Magnetic Multi-Walled Carbon Nanotubes. Colloids Surf. B: Biointerfaces 89, 1–9. 10.1016/j.colsurfb.2011.08.001 [DOI] [PubMed] [Google Scholar]
  36. Lu Y., Zhang Y., Deng Y., Jiang W., Zhao Y., Geng J., et al. (2016). Uptake and Accumulation of Polystyrene Microplastics in Zebrafish (Danio rerio) and Toxic Effects in Liver. Environ. Sci. Technol. 50, 4054–4060. 10.1021/acs.est.6b00183 [DOI] [PubMed] [Google Scholar]
  37. Ma-Hock L., Treumann S., Strauss V., Brill S., Luizi F., Mertler M., et al. (2009). Inhalation Toxicity of Multiwall Carbon Nanotubes in Rats Exposed for 3 Months. Toxicol. Sci. 112, 468–481. 10.1093/toxsci/kfp146 [DOI] [PubMed] [Google Scholar]
  38. Martin F.-P. J., Wang Y., Sprenger N., Holmes E., Lindon J. C., Kochhar S., et al. (2007). Effects of ProbioticLactobacillusParacaseiTreatment on the Host Gut Tissue Metabolic Profiles ProbedviaMagic-Angle-Spinning NMR Spectroscopy. J. Proteome Res. 6, 1471–1481. 10.1021/pr060596a [DOI] [PubMed] [Google Scholar]
  39. Mercer C. H., Tanton C., Prah P., Erens B., Sonnenberg P., Clifton S., et al. (2013). Changes in Sexual Attitudes and Lifestyles in Britain through the Life Course and over Time: Findings from the National Surveys of Sexual Attitudes and Lifestyles (Natsal). The Lancet 382, 1781–1794. 10.1016/s0140-6736(13)62035-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Monteiro-Riviere N. A., Nemanich R. J., Inman A. O., Wang Y. Y., Riviere J. E. (2005). Multi-walled Carbon Nanotube Interactions with Human Epidermal Keratinocytes. Toxicol. Lett. 155, 377–384. 10.1016/j.toxlet.2004.11.004 [DOI] [PubMed] [Google Scholar]
  41. Pauluhn J. (2010). Subchronic 13-week Inhalation Exposure of Rats to Multiwalled Carbon Nanotubes: Toxic Effects Are Determined by Density of Agglomerate Structures, Not Fibrillar Structures. Toxicol. Sci. 113, 226–242. 10.1093/toxsci/kfp247 [DOI] [PubMed] [Google Scholar]
  42. Poulsen S. S., Knudsen K. B., Jackson P., Weydahl I. E., Saber A. T., Wallin H., et al. (2017). Multi-walled Carbon Nanotube-Physicochemical Properties Predict the Systemic Acute Phase Response Following Pulmonary Exposure in Mice. PLoS One 12, e0174167. 10.1371/journal.pone.0174167 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Puchades-Carrasco L., Pineda-Lucena A. (2017). Metabolomics Applications in Precision Medicine: An Oncological Perspective. Curr. Top. Med. Chem. 17, 2740–2751. 10.2174/1568026617666170707120034 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Qi W., Tian L., An W., Wu Q., Liu J., Jiang C., et al. (2017). Curing the Toxicity of Multi-Walled Carbon Nanotubes through Native Small-Molecule Drugs. Sci. Rep. 7, 2815. 10.1038/s41598-017-02770-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Raja G., Cao S., Kim D.-H., Kim T.-J. (2020a). Mechanoregulation of Titanium Dioxide Nanoparticles in Cancer Therapy. Mater. Sci. Eng. C 107, 110303. 10.1016/j.msec.2019.110303 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Raja G., Gupta H., Gebru Y. A., Youn G. S., Choi Y. R., Kim H. S., et al. (2021). Recent Advances of Microbiome-Associated Metabolomics Profiling in Liver Disease: Principles, Mechanisms, and Applications. Int. J. Mol. Sci. 22. 10.3390/ijms22031160 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Raja G., Jang Y. K., Suh J. S., Kim H. S., Ahn S. H., Kim T. J. (2020b). Microcellular Environmental Regulation of Silver Nanoparticles in Cancer Therapy: A Critical Review. Cancers (Basel) 12. 10.3390/cancers12030664 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Ronzani C., Spiegelhalter C., Vonesch J.-L., Lebeau L., Pons F. (2012). Lung Deposition and Toxicological Responses Evoked by Multi-Walled Carbon Nanotubes Dispersed in a Synthetic Lung Surfactant in the Mouse. Arch. Toxicol. 86, 137–149. 10.1007/s00204-011-0741-y [DOI] [PubMed] [Google Scholar]
  49. Sahoo N. G., Bao H., Pan Y., Pal M., Kakran M., Cheng H. K. F., et al. (2011). Functionalized Carbon Nanomaterials as Nanocarriers for Loading and Delivery of a Poorly Water-Soluble Anticancer Drug: a Comparative Study. Chem. Commun. 47, 5235–5237. 10.1039/c1cc00075f [DOI] [PubMed] [Google Scholar]
  50. Shvedova A., Castranova V., Kisin E., Schwegler-Berry D., Murray A., Gandelsman V., et al. (2003). Exposure to Carbon Nanotube Material: Assessment of Nanotube Cytotoxicity Using Human Keratinocyte Cells. J. Toxicol. Environ. Health A 66, 1909–1926. 10.1080/713853956 [DOI] [PubMed] [Google Scholar]
  51. Simpson-Lavy K., Xu T., Johnston M., Kupiec M. (2017). The Std1 Activator of the Snf1/AMPK Kinase Controls Glucose Response in Yeast by a Regulated Protein Aggregation. Mol. Cel 68, 1120–1133. 10.1016/j.molcel.2017.11.016 [DOI] [PubMed] [Google Scholar]
  52. Song H., Wang L., Liu H. L., Wu X. B., Wang H. S., Liu Z. H., et al. (2011). Tissue Metabolomic Fingerprinting Reveals Metabolic Disorders Associated with Human Gastric Cancer Morbidity. Oncol. Rep. 26, 431–438. 10.3892/or.2011.1302 [DOI] [PubMed] [Google Scholar]
  53. Tang M., Zhang T., Xue Y., Wang S., Huang M., Yang Y., et al. (2010). Dose Dependent In Vivo Metabolic Characteristics of Titanium Dioxide Nanoparticles. J. Nanosci. Nanotech. 10, 8575–8583. 10.1166/jnn.2010.2482 [DOI] [PubMed] [Google Scholar]
  54. Vignet C., Cappello T., Fu Q., Lajoie K., De Marco G., Clérandeau C., et al. (2019). Imidacloprid Induces Adverse Effects on Fish Early Life Stages that Are More Severe in Japanese Medaka (Oryzias latipes) Than in Zebrafish (Danio rerio). Chemosphere 225, 470–478. 10.1016/j.chemosphere.2019.03.002 [DOI] [PubMed] [Google Scholar]
  55. Wang J., Yu J., Fu Q., Yang H., Tong Q., Hao Z., et al. (2021). Unprecedented Nonphotomediated Hole (H+) Oxidation System Constructed from Defective Carbon Nanotubes and Superoxides. ACS Cent. Sci. 7, 355–364. 10.1021/acscentsci.0c01600 [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Wu H., Liu G., Wang X., Zhang J., Chen Y., Shi J., et al. (2011). Solvothermal Synthesis of Cobalt Ferrite Nanoparticles Loaded on Multiwalled Carbon Nanotubes for Magnetic Resonance Imaging and Drug Delivery. Acta Biomater. 7, 3496–3504. 10.1016/j.actbio.2011.05.031 [DOI] [PubMed] [Google Scholar]
  57. Yao W., Bae K.-J., Jung M. Y., Cho Y.-R. (2017). Transparent, Conductive, and Superhydrophobic Nanocomposite Coatings on Polymer Substrate. J. Colloid Interf. Sci. 506, 429–436. 10.1016/j.jcis.2017.07.071 [DOI] [PubMed] [Google Scholar]
  58. Yu B., Zhou F., Liu G., Liang Y., Huck W. T. S., Liu W. (2006). The Electrolyte Switchable Solubility of Multi-Walled Carbon Nanotube/ionic Liquid (MWCNT/IL) Hybrids. Chem. Commun., 2356–2358. 10.1039/b603878f [DOI] [PubMed] [Google Scholar]
  59. Yuan C.-Y., Lee Y.-J., Hsu G.-S. (2012). Aluminum Overload Increases Oxidative Stress in Four Functional Brain Areas of Neonatal Rats. J. Biomed. Sci. 19, 51. 10.1186/1423-0127-19-51 [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Zhou Y., Firkowska-Boden I., Arras M. M. L., Jandt K. D. (2021). Polystyrene Homopolymer Enhances Dispersion of MWCNTs Stabilized in Solution by a PS-B-P2vp Copolymer. Langmuir 37, 391–399. 10.1021/acs.langmuir.0c03042 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Click here for additional data file. (1.2MB, docx)

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.

Articles from Frontiers in Molecular Biosciences are provided here courtesy of Frontiers Media SA

RESOURCES