Conceived and designed the experiments: EJP. Performed the experiments: SAM XG BLE MSW RW NGR. Analyzed the data: YD SAM JA RW NGR. Contributed reagents/materials/analysis tools: YD JA. Wrote the paper: YD SAM MSW EJP.
Youping Deng, Xin Guan and Barbara Lynn Escalon were employed by SpecPro Inc. This does not alter the authors' adherence to all the PLoS ONE policies on sharing data and materials.
Nitrotoluenes are widely used chemical manufacturing and munitions applications. This group of chemicals has been shown to cause a range of effects from anemia and hypercholesterolemia to testicular atrophy. We have examined the molecular and functional effects of five different, but structurally related, nitrotoluenes on using an integrative systems biology approach to gain insight into common and disparate mechanisms underlying effects caused by these chemicals.
Sprague-Dawley female rats were exposed via gavage to one of five concentrations of one of five nitrotoluenes [2,4,6-trinitrotoluene (TNT), 2-amino-4,6-dinitrotoluene (2ADNT) 4-amino-2,6-dinitrotoulene (4ADNT), 2,4-dinitrotoluene (2,4DNT) and 2,6-dinitrotoluene (2,6DNT)] with necropsy and tissue collection at 24 or 48 h. Gene expression profile results correlated well with clinical data and liver histopathology that lead to the concept that hematotoxicity was followed by hepatotoxicity. Overall, 2,4DNT, 2,6DNT and TNT had stronger effects than 2ADNT and 4ADNT. Common functional terms, gene expression patterns, pathways and networks were regulated across all nitrotoluenes. These pathways included NRF2-mediated oxidative stress response, aryl hydrocarbon receptor signaling, LPS/IL-1 mediated inhibition of RXR function, xenobiotic metabolism signaling and metabolism of xenobiotics by cytochrome P450. One biological process common to all compounds, lipid metabolism, was found to be impacted both at the transcriptional and lipid production level.
A systems biology strategy was used to identify biochemical pathways affected by five nitroaromatic compounds and to integrate data that tie biochemical alterations to pathological changes. An integrative graphical network model was constructed by combining genomic, gene pathway, lipidomic, and physiological endpoint results to better understand mechanisms of liver toxicity and physiological endpoints affected by these compounds.
Nitro-reduction of the munitions compound 2,4,6-trinitrotoluene (TNT) during environmental degradation produces 2-amino-4,6-dinitrotoluene (2A-4,6DNT or 2ADNT) and 4-amino-2,6 dinitrotoluene (and 4A-2,6DNT or 4ADNT). Other structurally similar munitions compounds include the dinitrotoluenes, 2,4-dinitrotoluene (2,4DNT) and its isomer 2,6-dinitrotoluene (2,6DNT). This class of chemicals has been reported to contaminate land, water and retired ammunitions plants as a result of military activities and a series of manufacturing processes
Fathead minnow exhibited that lipid metabolism and oxygen transport pathways were the primary mechanism for 2,4DNT induced toxicity in fish
Although many physiological and toxic responses of these compounds are known, a systematic study using toxicogenomics along with such responses is warranted in order to fully understand the relative toxicity of these nitroaromatics in mammals. Moreover, the underlying mechanisms of toxicity induced by these compounds especially for acute exposures, are largely unknown, and little if any toxicogenomics and systems biology studies have been conducted in exposed mammals. An acute exposure is advantageous in that early biomarkers and pathways may be identified to predict future toxicity induced by these chemicals.
To facilitate study of mechanisms, we chose to profile and integrate genomics, system biology and classical toxicological endpoints from the same biological samples shortly after a single exposure to one of five concentrations of one of the 5 nitrotoluene compounds with sacrifice and tissue collection after 24 or 48 hours. Assessment included physiological endpoint measurements and genomics of liver tissues using microarrays. Overall, we found that the expression results correlated well with toxicological and pathological results. Although each compound affected a distinctive expression pattern, a common group of genes and pathways were significantly affected by most of the compounds. We demonstrate a network mode of action based on the gene expression profiles, which could explain why the physiological responses occurred after exposure to these compounds.
Blood from rats exposed to each of the five nitrotoluenes, TNT, 2ADNT, 4ADNT, 2,6DNT, and 2,4DNT, for either 24 or 48 h was examined for the presence of parent compound and/or possible nitrotoluene metabolites using HPLC. At 24 h, measurable concentrations of nitrotoluenes were found only in blood of the two highest TNT dosed and 3 highest dosed 2ADNT dosed animals (
Liver tissue of exposed and control rats were examined for the presence of parent chemicals and their metabolites 24 and 48 h after exposure. In livers of exposed rats, only the parent compounds TNT, 2ADNT, and 2,4DNT were detected at 24 h, but only in one replicate (
At lower doses of 96 and 48 mg/kg TNT-treatments, 2ADNT was found in liver of one animal for each dose, while no 4ADNT was observed. No measurable concentrations of the other four nitrotoluene parent compounds were found in livers of animals after 48 h (
Although the upper limit of chemical dose ranges were designed to be one-half the published acute oral LD50, lethality was observed within the dose range used for the dinitrotoluenes. The TNT dose was based on the publication of Reddy at al.,
All treated rats expired at the high dose (398 mg/kg) of 2,6DNT. The high dose of 2,4DNT caused deaths of 2 of 5 and 1 of 5 rats at 24 and 48 h, respectively. Systemic toxicity was also indicated for all compounds by decreases in body weight gain over the 48 h observation period. Body weight gain for vehicle-treated rats was 17±1 g (mean±SEM, n = 5) compared to a loss of 6−7 g for 199 mg/kg 2,6DNT and high dose (384 mg/kg) TNT. Weight gains were reduced to between 1 to 4 g for rats treated with 50 and 99 mg/kg 2,6DNT, 398 mg/kg 2,4DNT and 192 mg/kg TNT and to 5−9 g for 48 and 96 mg/kg TNT, 174 and 348 mg/kg 2ADNT, and 374 mg/kg 4ADNT. Body weight loss associated with higher doses of TNT and dinitrotoluenes was possibly related to reduced food consumption as indicated by a coincident decrease in serum alkaline phosphatase (AlkP,
Dose | Time | Albumin | ALT |
AST | AlkP | Glucose | Na |
mg/kg | hr | g/dL | IU/L | IU/L | IU/L | mg/dL | mmol/L |
0 | 24 | 1.38±0.04 | 132.2±2.9 | 237.2±33.9 | 273.2±29.5 | 226.6±14.9 | 143.8±0.8 |
48 | 1.58±0.02 | 104.6±6.6 | 311.8±65.8 | 273.0±21.4 | 260.8±25.0 | 143.4±0.7 | |
5 | 24 | 1.42±0.04 | 128.8±3.7 | 363.8±50.7 | 269.0±20.2 | 255.4±11.3 | 145.0±1.2 |
48 | 1.56±0.02 | 95.0±5.0 | 275.2±58.1 | 262.0±15.5 | 241.4±22.4 | 144.5±0.7 | |
50 | 24 | 1.42±0.04 | 139.4±12.0 | 384.4±87.9 | 310.6±26.8 | 227.2±14.3 | 143.6±0.7 |
48 | 1.54±0.04 | 95.2±11.1 | 316.4±111.4 | 230.0±21.0 | 219.4±8.8 | 144.4±0.8 | |
99 | 24 | 1.30±0.03 | 124.6±15.3 | 506.0±111.4 | 279.0±36.2 | 234.8±19.5 | 140.8±3.8 |
48 | 1.38±0.05** | 97.2±4.1 | 280.4±41.0 | 224.6±28.8 | 221.6±12.3 | 143.2±0.9 | |
198 | 24 | 1.36±0.05 | 96.0±21.5 | 392.4±86.5 | 237.6±15.6 | 225.4±15.1 | 139.2±2.6 |
48 | 0.90±0.06** | 96.0±1.3 | 440.0±35.4 | 112.5±24.3** | 393.5±93.2 | 137.0±1.9** | |
398 | 24 | 1.5, 1.4 | 116, 60 | 514, 200 | 115, 95 | 184, 275 | 142, 139 |
48 | 0.77±0.03** | 132.3±13.0 | 755.0±62.6** | 130.0±7.5** | 686.7±48.2** | 127.7±2.5** | |
0 | 24 | 1.46±0.04 | 144.4±4.2 | 332.0±32.3 | 277.4±19.0 | 257.8±12.7 | 145.2±0.5 |
48 | 1.54±0.06 | 98.4±5.6 | 154.2±12.2 | 250.2±5.1 | 221.3±9.4 | 144.0±2.0 | |
5 | 24 | 1.54±0.02 | 128.8±9.8 | 306.0±47.3 | 318.8±17.1 | 226.0±21.2 | 147.8±1.0 |
48 | 1.64±0.02 | 98.2±7.9 | 161.4±8.4 | 258.0±12.9 | 255.0±2.0 | 147.0±1.0 | |
25 | 24 | 1.52±0.02 | 163.6±16.8 | 567.4±137.3 | 334.6±22.1 | 214.2±24.0 | 145.0±0.8 |
48 | 1.62±0.03 | 95.4±5.0 | 267.2±48.9 | 279.0±27.4 | 211.6±8.6 | 146.4±0.4 | |
50 | 24 | 1.54±0.02 | 138.2±7.1 | 341.8±36.7 | 312.6±22.3 | 246.4±13.2 | 145.4±0.8 |
48 | 1.42±0.05 | 257.8±15.9 | 224.0±11.0 | 144.0±2.0 | |||
99 | 24 | 1.46±0.05 | 174.8±27.7 | 633.6±164.7 | 241.6±18.0 | 193.6±11.1* | 141.8±1.8 |
48 | 1.30±0.14 | 269.0±29.4 | 186.0±12.1 | 145.0±0.6 | |||
199 | 24 | 1.54±0.04 | 150.0±27.9 | 779.0±176.2* | 199.4±27.8 | 293.4±10.6 | 136.6±0.9** |
48 | 1.0 | 208 | 234 | 128 | nd | nd | |
0 | 24 | 1.38±0.02 | 140.4±33.0 | 366.8±52.6 | 294.2±13.7 | 174.4±38.5 | 151.6±6.6 |
48 | 1.52±0.04 | 172.4±24.1 | 463.3±145.0 | 252.8±36.9 | 222.0±20.4 | 141.8±1.2 | |
5 | 24 | 1.34±0.02 | 105.0±8.5 | 309.6±44.9 | 347.0±13.9 | 217.2±9.3 | 146.2±0.4 |
48 | 1.58±0.02 | 147.8±40.0 | 267.5±53.3 | 222.0±27.5 | 246.6±16.0 | 144.4±0.7 | |
48 | 24 | 1.40±0.05 | 81.6±6.5 | 329.8±51.4 | 318.0±10.2 | 209.2±9.5 | 145.4±0.9 |
48 | 1.60±0.03 | 129.8±5.8 | 478.2±90.6 | 247.2±16.9 | 232.4±19.3 | 143.0±1.1 | |
96 | 24 | 1.34±0.02 | 76.0±15.9* | 408.8±108.6 | 284.2±11.7 | 208.8±9.4 | 141.8±1.4 |
48 | 1.50±0.03 | 125.0±18.7 | 433.8±103.6 | 190.6±21.0 | 224.4±14.9 | 144.6±1.2 | |
192 | 24 | 1.40±0.03 | 45.6±6.8** | 251.2±53.0 | 272.2±28.0 | 260.2±18.3 | 141.6±1.1 |
48 | 1.36±0.05* | 128.4±53.0 | 475.2±143.8 | 215.0±20.1 | 239.4±15.3 | 136.8±3.4 | |
384 | 24 | 1.42±0.04 | 42.4±5.8** | 248.2±48.8 | 224.4±20.9* | 228.4±42.9 | 141.8±1.4 |
48 | 1.33±0.02** | 122.5±55.3 | 354.9±68.7 | 208.0±13.2 | 211.8±13.2 | 132.8±1.1** |
Values are means±SEM for n = 5, except for 198 and 398 mg/kg 24DNT 48 h where n = 3 and for 398 mg/kg 24DNT 24 h where individual values are shown. For 199 mg/kg 26DNT 48 h values are from pooled sample for 3 rats. Means that differed from concurrent vehicle controls (0 mg/kg) are indicated by * or ** for p<0.05 or 0.01, respectively.
Abbreviations. AlkP = alkaline phosphatase, ALT and AST = alanine and aspartate aminotransferase, resp., nd = not determined.
Dose | Time | Albumin | ALT |
AST | AlkP | Glucose | Na |
mg/kg | hr | g/dL | IU/L | IU/L | IU/L | mg/dL | mmol/L |
0 | 24 | 1.42±0.04 | 116.8±4.2 | 314.7±72.0 | 359.6±20.4 | 224.8±15.7 | 143.6±0.8 |
48 | 1.60±0.04 | 138.0±7.1 | 225.6±32.6 | 283.8±43.7 | 253.6±9.4 | 141.4±1.1 | |
4 | 24 | 1.36±0.02 | 151.2±41.1 | 403.8±112.1 | 328.4±11.0 | 238.2±12.9 | 141.6±1.4 |
48 | 1.52±0.04 | 143.4±21.0 | 364.8±105.8 | 204.6±29.0 | 200.8±6.6* | 141.6±1.7 | |
44 | 24 | 1.36±0.04 | 92.0±2.5 | 211.6±11.1 | 387.8±30.9 | 228.2±3.4 | 142.8±0.4 |
48 | 1.58±0.07 | 174.6±35.0 | 477.4±107.7 | 203.4±19.4 | 217.0±15.8 | 139.8±2.1 | |
87 | 24 | 1.38±0.04 | 91.6±6.1 | 341.8±58.0 | 338.4±28.9 | 221.0±5.5 | 141.8±0.4 |
48 | 1.56±0.05 | 99.4±4.4 | 128.8±10.1 | 205.8±27.7 | 212.2±8.4 | 144.6±0.4 | |
174 | 24 | 1.34±0.02 | 91.6±2.8 | 316.2±53.4 | 249.0±24.7 | 207.0±10.4 | 142.0±0.8 |
48 | 1.60±0.03 | 109.6±7.1 | 166.0±13.5 | 230.2±11.3 | 226.4±16.9 | 143.6±0.4 | |
348 | 24 | 1.34±0.05 | 89.4±9.6 | 352.8±44.1 | 388.0±13.2 | 241.6±14.8 | 139.8±0.9* |
48 | 1.52±0.04 | 127.4±20.4 | 268.4±124.5 | 212.0±19.4 | 213.0±14.9 | 141.6±1.3 | |
0 | 24 | 1.30±0.01 | 143.2±6.6 | 417.4±37.8 | 342.8±33.3 | 284.4±11.8 | 143.6±0.8 |
48 | 1.42±0.04 | 195.2±18.9 | 630.8±76.4 | 216.8±23.2 | 232.4±11.0 | 136.2±0.5 | |
5 | 24 | 1.34±0.02 | 122.6±6.1 | 364.4±65.7 | 329.0±19.9 | 235.2±6.8 | 144.8±0.4 |
48 | 1.42±0.02 | 202.4±43.5 | 489.8±161.9 | 238.0±21.1 | 253.4±16.0 | 136.6±1.4 | |
47 | 24 | 1.32±0.04 | 86.6±4.7** | 201.0±27.7** | 339.6±25.5 | 225.0±11.9** | 145.0±0.5 |
48 | 1.50±0.04 | 254.6±49.0 | 733.8±150.0 | 307.0±20.7 | 243.6±16.0 | 134.4±1.2 | |
94 | 24 | 1.32±0.04 | 66.6±3.4** | 182.2±14.6** | 342.6±19.7 | 226.0±13.3** | 144.6±0.5 |
48 | 1.46±0.02 | 727.2±253.5* | 1911.8±421.0* | 280.0±33.7 | 324.4±32.9** | 126.2±2.6** | |
187 | 24 | 1.32±0.02 | 56.4±6.6** | 243.6±37.8* | 323.2±35.2 | 202.8±5.3** | 143.4±0.9 |
48 | 1.42±0.06 | 405.0±57.4 | 1505.8±228.2 | 205.8±11.9 | 275.6±13.9 | 130.4±1.8 | |
374 | 24 | 1.42±0.02* | 48.2±4.9** | 283.4±26.7 | 317.4±34.4 | 230.6±8.7** | 142.2±1.2 |
48 | 1.40±0.3 | 271.0±95.0 | 1247.4±305.4 | 377.8±124.6 | 260.8±16.4 | 131.8±1.7 |
Values are means±SEM for n = 5. Means that differed from concurrent vehicle controls (0mg/kg) are indicated by * or ** for p<0.05 or 0.01, respectively.
Abbreviations. AlkP = alkaline phosphatase, ALT and AST = alanine and aspartate aminotransferase, resp.
Additional differences detected from the comprehensive serum metabolic panel included effects on serum albumin, total protein, glucose and Na (
Serum alanine aminotransferase (ALT) activity exhibited a dose-dependent decrease to 33% of vehicle control at 24 h after 4ADNT exposure with doses greater than 5 mg/kg. This effect was not sustained to 48 h post-exposure, but rather an increase was observed for the 94 mg/kg group. A similar trend of lesser magnitude occurred with serum aspartate aminotransferase (AST). Small declines in serum transaminase activities are occasionally observed with some chemicals often due to inhibition of enzymatic utilization of cofactor pyridoxyl-5′-phosphate; however, this effect is thought to be of relatively minor toxicological significance
One rat in each of the 50 and 99 mg/kg 2,6DNT groups (48 h) had serum ALT values in excess of 3000 IU/ml that thus caused their treatment means to exceed 7 times the control mean with ∼200% coefficient of variation (
Photomicrographs are from 5 µm sections stained with hematoxylin and eosin. Livers from one of 5 rats treated with 50 mg/kg 2,6DNT exhibited sloughing of hepatocyte remnants into congested sinusoids (A, arrow) and microvesiculated hepatocytes (A, *). A necrotic hepatocyte with pyknotic nucleus displaced from the liver cord by infiltrating erythrocytes is shown in B (arrow). Numerous apoptotic cells (B, *) were also seen. Amorphous vacuoles occupying a considerable portion of hepatocyte cytoplasms were numerous in livers of 3 of 5 rats treated with 384 mg/kg TNT (C, arrows). One rat treated with 94 mg/kg 4ADNT had focal hemorrhagic areas in the liver (D, arrows), while livers of other rats treated with 94 and 197 mg/kg 4ADNT exhibited ballooning degeneration (E).
Photomicrographs are from 5 µm sections stained with hematoxylin and eosin. Extensive sinusoidal congestion of 2,6DNT-treated rats is shown in panel B and compared to unaffected liver of vehicle treated controls in panel A. Higher power magnification illustrates infiltration of hepatocyte cords by erythrocytes (D, arrows) and microvesiculation (D, *) seen in livers of 2,6DNT-treated rats. Unaffected liver of vehicle-treated controls at high magnification is shown in panel C.
In contrast, hepatocytes of rats treated with the higher dose of 2,6DNT whose serum ALT levels were equivalent to vehicle controls appeared mostly undamaged (
Sinusoidal congestion was also evident with high dose 2,4DNT. Cytoplasmic amorphous inclusions were noted in hepatocytes of 3 livers and microvesicles in another of the 5 rats treated with 384 mg/kg TNT (
Relative liver weights of rats treated with 4ADNT increased from 4.12±0.11% to 4.82±0.05%, 4.77±0.05% and 4.76±0.18% (means±SEM, n = 5) at 48 h after treatment with 93, 187 and 374 mg/kg, respectively. TNT at 384 mg/kg was associated with an increased liver weight of 4.90±0.23% relative to 4.03±0.17% for controls. The 99 mg/kg 2,4DNT group also had an elevated relative liver weight of 4.59±0.09% compared to concurrent vehicle control (3.85±0.08%), but higher doses (199 mg/kg = 4.06±0.25; 389 mg/kg = 4.29±0.18) were unaffected. Liver weights of rats treated with 2,6DNT and 2ADNT were unchanged from vehicle controls at 48 h post-treatment.
The most notable finding from the hematology panel was an elevation of several related parameters indicating erythrocytosis associated with the dinitrotoluenes (
Photomicrographs at high magnification (100X objective with oil) of wedge preparations of blood vitally stained with new methylene blue are shown. Blood from rats treated with 199 (A) and 99 (B) mg/kg 2,6DNT are shown. Reticulocytes with blue stained mRNA are indicated by asterisks and arrows point to erythrocytes with indentations (bite cells).
Dose | Time | Granulocytes | Lymphocytes | Erythrocytes | HbG |
HCt | RDW | Retic |
mg/kg | hr | 106/mL | 106/mL | 109/mL | g/dL | % | % | % |
0 | 24 | 0.23±0.15 | 2.94±0.63 | 7.20±0.14 | 13.7±0.2 | 41.3±0.8 | 10.9±0.1 | 3.0±0.1 |
48 | 0.52±0.13 | 4.21±0.36 | 6.65±0.09 | 13.0±0.1 | 40.3±0.3 | 11.2±0.1 | ||
5 | 24 | 0.52±0.14 | 3.27±0.69 | 7.37±0.18 | 13.9±0.2 | 42.2±0.5 | 11.0±0.1 | 2.5±0.1 |
48 | 0.77±0.25 | 3.11±0.66 | 6.83±0.07 | 12.8±0.3 | 40.1±0.3 | 10.7±0.3 | ||
50 | 24 | 0.51±0.21 | 3.12±0.46 | 7.63±0.11 | 14.3±0.3 | 44.0±0.6* | 11.0±0.1 | 2.0±0.2 |
48 | 0.98±0.20 | 4.31±1.22 | 6.93±0.08 | 13.3±0.1 | 41.6±0.4 | 11.1±0.1 | ||
99 | 24 | 1.50±0.15** | 4.84±0.60 | 7.96±0.08** | 14.3±0.2 | 44.6±0.5** | 11.2±0.2 | 2.3±0.2 |
48 | 1.06±0.15 | 2.54±0.71 | 6.63±0.06 | 12.4±0.3 | 39.8±0.5 | 10.9±0.1 | ||
198 | 24 | 1.02±0.25* | 3.09±0.64 | 7.65±0.04* | 14.2±0.1 | 42.9±0.4 | 10.9±0.1 | 2.5±0.4 |
48 | 4.73±0.60 | 3.01±0.22 | 8.96±0.43** | 17.0±0.9** | 53.6±2.0** | 11.5±0.1 | ||
398 | 24 | 3.6, 2.4 | 1.8, 3.5 | 8.53, 6.04 | 15.3, 12.7 | 45.0, 39.3 | 10.6, 10.6 | 3.3, 3.3 |
48 | 8.38±2.94** | 0.97±0.23* | 8.69±0.16** | 16.5±0.2** | 51.8±0.9** | 12.0±0.7 | ||
0 | 24 | 0.36±0.22 | 6.16±1.23 | 7.39±0.18 | 13.9±0.3 | 43.4±0.9 | 11.3±0.1 | 3.4±0.2 |
48 | 0.68±0.18 | 3.86±0.30 | 7.11±0.11 | 14.1±0.1 | 43.1±0.3 | 11.2±0.1 | ||
5 | 24 | 0.29±0.13 | 5.12±0.85 | 7.57±0.20 | 14.1±0.3 | 44.8±1.1 | 11.0±0.2 | 3.0±0.2 |
48 | 0.58±0.21 | 3.50±0.36 | 7.32±0.13 | 14.1±0.2 | 43.3±0.7 | 11.3±0.2 | ||
25 | 24 | 0.20±0.08 | 5.42±0.82 | 7.94±0.29 | 15.1±0.6 | 46.7±1.8* | 11.0±0.1 | 3.1±0.2 |
48 | 0.49±0.09 | 3.56±0.52 | 6.96±0.12 | 13.6±0.1 | 41.2±0.4 | 10.8±0.1 | ||
50 | 24 | 0.76±0.08 | 5.25±0.90 | 8.03±0.18 | 14.6±0.2 | 46.1±1.1 | 11.1±0.1 | 3.7±0.1 |
48 | 1.45±0.35 | 4.95±0.25 | 7.47±0.26 | 14.4±0.5 | 43.6±1.5 | 11.1±0.2 | ||
99 | 24 | 0.83±0.14 | 3.96±0.37 | 8.43±0.17** | 15.3±0.3* | 48.8±1.1* | 11.0±0.1 | 4.1±0.2* |
48 | 1.36±0.66 | 4.86±0.50 | 7.58±0.21 | 14.7±0.4 | 44.6±1.2 | 11.1±0.1 | ||
199 | 24 | 0.78±0.22 | 1.52±0.39** | 8.02±0.12 | 14.6±0.2 | 45.7±0.8 | 11.0±0.2 | 4.9±0.1** |
48 | 7.58±0.99** | 5.46±2.05 | 11.43±0.94** | 20.4±1.3** | 60.6±4.9** | 12.5±0.4** | ||
0 | 24 | 0.33±0.17 | 6.22±0.79 | 7.62±0.14 | 14.5±0.2 | 44.9±0.8 | 10.9±0.2 | |
48 | 1.06±0.18 | 4.09±0.26 | 7.36±0.38 | 13.6±0.3 | 42.6±1.2 | 10.9±0.4 | ||
5 | 24 | 0.43±0.11 | 5.33±0.73 | 7.08±0.23 | 13.6±0.3 | 41.6±1.4 | 10.6±0.2 | |
48 | 0.97±0.23 | 4.55±0.46 | 7.70±0.41 | 13.8±0.3 | 45.0±2.7 | 11.1±0.3 | ||
48 | 24 | 0.23±0.09 | 5.93±0.82 | 7.85±0.25 | 14.4±0.3 | 46.3±1.4 | 10.8±0.2 | |
48 | 1.00±0.26 | 4.79±0.92 | 7.12±0.07 | 13.7±0.1 | 41.6±0.2 | 10.6±0.1 | ||
96 | 24 | 0.60±0.49 | 4.76±0.37 | 7.79±0.11 | 14.7±0.2 | 45.6±0.7 | 11.0±0.2 | |
48 | 0.99±0.19 | 4.95±0.36 | 7.53±0.51 | 13.7±0.4 | 43.1±3.0 | 11.2±0.2 | ||
192 | 24 | 2.16±1.02 | 2.95±0.46** | 8.61±0.41* | 15.1±0.3 | 48.8±2.3 | 10.9±0.2 | |
48 | 1.42±0.21 | 4.01±0.46 | 7.99±0.63 | 13.1±0.4 | 46.3±3.6 | 11.5±0.2 | ||
384 | 24 | 4.26±2.03* | 1.89±0.30** | 8.31±0.19 | 16.1±0.2** | 45.2±0.5 | 10.7±0.1 | |
48 | 2.60±0.71* | 4.28±0.87 | 7.36±0.38 | 12.8±0.3 | 40.6±1.3 | 12.7±0.2** |
Values are means±SEM for n = 5, except for 199 and 398 mg/kg 2,4DNT at 48 h where n = 4 and 199 mg/kg 2,6DNT at 48 h where n = 3 and for 398 mg/kg 2,4DNT at 24 h where individual values are shown. Means that differed from concurrent vehicle controls (0 mg/kg) are indicated by * or ** for p<0.05 or 0.01, respectively.
Abbreviations. HbG = hemoglobin, HCt = hematocrit, RDW = red blood cell distribution width, Retic = reticulocytes.
Dose | Time | Granulocytes | Lymphocytes | Erythrocytes | HbG |
HCt | RDW |
mg/kg | hr | 106/mL | 106/mL | 109/mL | g/dL | % | % |
0 | 24 | 0.54±0.19 | 5.07±0.56 | 7.70±0.15 | 14.7±0.3 | 43.6±0.7 | 10.6±0.1 |
48 | 0.76±0.19 | 3.75±0.50 | 6.83±0.07 | 13.2±0.1 | 41.2±0.3 | 11.1±0.0 | |
4 | 24 | 0.85±0.06 | 5.75±0.40 | 7.67±0.07 | 14.7±0.1 | 44.0±0.4 | 10.6±0.1 |
48 | 0.40±0.15 | 3.71±0.56 | 6.80±0.10 | 13.1±0.2 | 40.7±0.6 | 11.2±0.1 | |
44 | 24 | 0.60±0.19 | 4.23±0.21 | 8.05±0.11 | 14.5±0.6 | 45.8±0.4 | 10.8±0.1 |
48 | 0.45±0.14 | 4.13±0.36 | 7.05±0.16 | 13.6±0.2 | 42.4±0.8 | 11.0±0.1 | |
87 | 24 | 0.73±0.25 | 3.46±0.37 | 7.41±0.27 | 14.3±0.4 | 42.9±1.7 | 10.6±0.1 |
48 | 0.83±0.11 | 4.01±0.24 | 6.95±0.14 | 13.4±0.3 | 41.7±0.9 | 10.9±0.1 | |
174 | 24 | 0.70±0.17 | 3.94±0.40 | 7.37±0.19 | 14.7±0.3 | 42.7±1.1 | 10.6±0.1 |
48 | 0.92±0.21 | 4.62±0.972 | 7.05±0.19 | 13.3±0.4 | 41.5±1.1 | 10.9±0.1 | |
348 | 24 | 0.50±0.18 | 3.84±0.14 | 7.66±0.14 | 15.3, 12.7 | 43.9±0.9 | 11.0±0.1 |
48 | 0.76±0.10 | 3.40±0.36 | 6.80±0.08 | 12.7±0.1 | 40.3±0.5 | 10.9±0.2 | |
0 | 24 | 1.22±0.17 | 5.26±0.60 | 7.44±0.20 | 14.5±0.3 | 42.8±1.0 | 11.0±0.1 |
48 | 1.09±0.369 | 4.80±0.81 | 7.14±0.12 | 13.4±0.3 | 41.7±0.6 | 10.8±0.1 | |
5 | 24 | 0.88±0.13 | 4.30±0.59 | 7.77±0.11 | 14.9±0.3 | 44.6±0.7 | 10.6±0.1 |
48 | 1.92±0.40 | 5.48±0.62 | 6.67±0.11 | 12.8±0.2 | 39.4±0.5 | 10.4±0.1 | |
47 | 24 | 1.20±0.09 | 4.56±0.31 | 7.82±0.13 | 15.1±0.1 | 45.0±0.4 | 10.9±0.1 |
48 | 1.87±0.24 | 5.52±0.90 | 6.94±0.12 | 13.3±0.1 | 40.9±0.4 | 10.5±0.1 | |
94 | 24 | 1.08±0.15 | 4.30±0.32 | 7.60±0.14 | 14.7±0.4 | 43.6±1.0 | 10.7±0.1 |
48 | 1.77±0.53 | 5.74±1.10 | 6.45±0.17* | 12.2±0.2** | 37.8±0.7** | 10.8±0.1 | |
187 | 24 | 1.08±0.04 | 4.71±0.32 | 7.66±0.05 | 14.9±0.2 | 44.6±0.3 | 10.7±0.1 |
48 | 1.06±0.29 | 4.59±0.36 | 6.63±0.16 | 12.5±0.3* | 39.0±0.8* | 10.6±0.1 | |
374 | 24 | 1.30±0.16 | 3.78±0.30 | 7.76±0.19 | 15.1±0.3 | 44.4±0.9 | 10.7±0.1 |
48 | 2.15±0.37 | 4.97±0.78 | 6.92±0.14 | 13.1±0.2 | 41.0±0.7 | 10.6±0.2 |
Values are means±SEM for n = 5. Means that differed from concurrent vehicle controls (0 mg/kg) are indicated by * or ** for p<0.05 or 0.01, respectively.
Abbreviations. HbG = hemoglobin, HCt = hematocrit, RDW = red blood cell distribution width.
Marked elevation of blood granulocytes were noted at 48 h after high dose 2,4DNT and TNT and 199 mg/kg 2,6DNT (3). Earlier elevations occurred with 2,4DNT at doses of 99 mg/kg and above and with 384 mg/kg TNT. Granulocytosis at 24 h for TNT and at 48 h for 2,4DNT tended to parallel a decrease in lymphocytes as often occurs with corticosteroid-mediated stress
A strong dose responsive change in the total number of differentially expressed genes was observed at both time points for all compounds (
Rats were exposed to one of the 5 compounds for 24 h (blue line) or 48 h (red line), with one of 4 doses plus vehicle controls. Rats were sacrificed and liver tissues were employed for total RNA isolation and microarray hybridization and analysis as described in
While the dose responsive pattern of the number of gene expression was similar for the compounds examined (
Rats were treated with different compounds with variant doses and microarray experiments were performed as described in
Next, we determined the amount of up-regulated or down-regulated transcript numbers for the compounds. To do that, we compared treated and control samples at 24 h or 48 h or both time points together for each compound. Based on significantly regulated transcripts, if the averaged normalized intensity of a gene was higher in the treated group than the control group, we considered the gene relatively up-regulated; and if lower, down-regulated. According to this criterion, we found another common phenomenon: there were much more up-regulated transcripts than down-regulated transcripts at 24h and both time points together (24 h+48 h) (
The overall gene expression profiles induced by the 5 chemicals with various doses and different times were compared by two-way hierarchical clustering of averages of replicates within each of the 40 conditions (
Experimental conditions were based on averaging samples with the same dose treatments of a compound for 24 h or 48 h and total 40 experimental conditions were used for the clustering. Transcripts used for making these conditional trees were selected based on filtering on flags. Only a transcript that was presented in at least 50% samples was chosen for clustering. Pearson correlation algorithm with average linkage was conducted to calculate distances between conditions.
From
We also performed a hierarchical clustering per treatment. Because the two very strong gene regulators, 2,4DNT and 2,6DNT have over 2000 genes that were affected, in order to identify most significantly regulated genes, a more stringent false discovery rate (FDR) with adjusted p-value less than 0.002 was applied resulting in 1418 and 1241 significantly differentially expressed transcripts for 2,4DNT and 2,6DNT, respectively. For the other three compounds, no more stringent FDR was applied because their changed gene numbers were much less than 2000. One-way hierarchical clustering was performed across various doses and times per chemical. Interestingly, the 5 compounds share similar gene expression patterns (
Most significantly regulated transcripts of each compound were used for constructing gene clustering trees across different conditions including control and 4 different doses at 24 h or 48 h for each compound. Pearson correlation algorithm with average linkage was performed to calculate distances between transcripts. Two distinctive gene expression patterns: early up-regulated (early up) and early down-regulated (early down) gene expression patterns were indicated on the right of each cluster.
To gain insight into the functional categories of these significantly changed genes, we performed gene ontology (GO) analyses based on biological processes. Early up-regulated and early down-regulated gene groups for each compound across all doses (
2,4DNTup | 2,6DNTup | 2ADNTup | 4ADNTup | TNTup | |
2,4DNTup | 148(100%) | 90(60.8%) | 7(8.5%) | 80(53.1%) | 44(21.8%) |
2,6DNTup | 148(100%) | 6(7.3%) | 76(50.5%) | 28(25.9%) | |
2ADNTup | 15(100%) | 14(16.6%) | 10(24.1%) | ||
4ADNTup | 153(100%) | 53(50.0%) | |||
TNTup | 68(100%) |
Note: The numbers outside of a bracket are calculated based on the common functional terms between two compounds. The number inside a bracket is the similarity rate which is calculated as that the number of common functional terms divided by the averaged total functional terms of two compared compounds.
Functional terms | 2,4DNT | 2,6DNT | 2ADNT | 4ADNT | TNT |
metabolic process | 5.86E-14 | 4.63E-14 | 0.0062795 | 6.63E-20 | 1.09E-14 |
cellular metabolic process | 3.86E-15 | 7.60E-14 | 0.007577 | 1.46E-22 | 8.44E-13 |
cellular macromolecule metabolic process | 0.0072135 | 2.89E-07 | 4.56E-06 | 0.001380 | |
cellular biosynthetic process | 0.0046218 | 0.02843983 | 2.71E-12 | 1.64E-13 | |
macromolecule metabolic process | 9.03E-13 | 1.37E-15 | 4.79E-08 | 0.039807 | |
cellular macromolecule metabolic process | 0.0072135 | 2.89E-07 | 4.56E-06 | 0.001380 | |
cellular protein metabolic process | 0.0058248 | 2.67E-07 | 3.27E-06 | 0.002308 | |
protein metabolic process | 0.0038298 | 4.97E-09 | 7.26E-06 | 0.003270 | |
cellular protein metabolic process | 0.0058248 | 2.67E-07 | 3.27E-06 | 0.002308 | |
protein folding | 1.10E-05 | 1.99E-04 | 0.083242 | 3.49E-06 | 0.005908 |
RNA metabolic process | 1.47E-11 | 3.04E-05 | 3.33E-04 | 0.004 | |
primary metabolic process | 1.13E-14 | 5.91E-14 | 2.76E-17 | 1.54E-08 | |
protein metabolic process | 0.0038298 | 4.97E-09 | 7.26E-06 | 0.003270 | |
response to xenobiotic stimulus | 0.0680837 | 0.005775329 | 8.52E-07 | 1.96E-04 | 1.43E-04 |
xenobiotic metabolic process | 0.0626892 | 0.004970058 | 7.40E-07 | 1.61E-04 | 1.21E-04 |
macromolecule localization | 4.38E-06 | 2.06E-07 | 3.73E-04 | 0.043349 | |
protein localization | 1.79E-05 | 3.46E-06 | 6.88E-04 | 0.030145 | |
establishment of protein localization | 3.49E-06 | 1.13E-06 | 0.001733 | 0.016687 | |
protein transport | 7.66E-06 | 2.73E-07 | 0.0010188 | 0.007891 | |
Cellular process | |||||
organelle organization and biogenesis | 4.69E-06 | 8.47E-08 | 9.23E-06 | 0.049135 | |
mitochondrion organization and biogenesis | 0.0330679 | 7.40E-04 | 0.0066755 | 0.009797 | |
intracellular transport | 8.49E-07 | 3.94E-12 | 0.0032581 | 0.004579 | |
intracellular protein transport | 3.58E-07 | 1.46E-05 | 0.002939 | 0.008296 | |
protein targeting | 4.59E-07 | 3.97E-04 | 2.44E-04 | 0.013345 |
Twenty significantly up-regulated GO functional terms were shared by 2,4DNT, 2,6DNT, 4ADNT and TNT, among which at least half were child terms of “metabolic process” and “cellular metabolic process”. Primarily, these terms could be categorized as “macromolecule metabolic process”, “RNA metabolic process” and “cellular biosynthetic process”. The major term under macromolecule metabolic process was cellular protein metabolic process, under which a significant common term was protein folding.
The functional term “cellular biosynthetic process” was more enriched in response to TNT, 4A DNT than 2,4DNT and 2,6DNT treatments (
Under the functional term “RNA metabolic process”, pirin (PIR) was up-regulated by 2,6DNT, 2ADNT, 4ADNT and TNT. Tryptophanyl-tRNA synthetase (WARSs) was induced by 2,6DNT, 4ADNT and TNT; small nuclear ribonucleoprotien polypeptide A (SNRPA), NHP2-like protien1 (NHP2L1) was up-regulated by 2,4DNT, 4ADNT and TNT. 2,4DNT, 2,6DNT and 4ADNT shared more induced genes of “RNA metabolic process” which included the gene for absent, small, or homeotic-like gene (ASH2), general transcription factor II H, polypeptide 1 (GTF2H1), EBNA1 binding protein 2 (EBNA1BP2), nucleolar protein 5A (NOL5A), peptidylprolyl isomerase (cyclophilin)-like 3 (PPIL3) and tRNA splicing endonuclease 2 homolog (
Other shared up-regulated functional terms included “mitochondrion organization and biogenesis” as well as “protein transport” (
Further comparison of up-regulated GO functional terms of 2,4DNT, 2,6DNT and 4ADNT revealed a high number of shared terms, 65 terms. “DNA metabolic process,” a child term of “nucleobase, nucleoside, nucleotide and nucleic acid metabolic process” and “biopolymer metabolic process”, was a key term shared by the 3 compounds.
When down-regulated GO biological process terms were compared, 2,4DNT and 2,6DNT were more similar than other compounds (
2,4DNTdown | 2,6DNTdown | 2ADNTdown | 4ADNTdown | TNTdown | |
2,4DNTdown | 127(100%) | 61(54.2%) | 8(11.3%) | 2(2.3%) | 9(12.3%) |
2,6DNTdown | 98(100%) | 6(10.3%) | 2(5.6%) | 7(11.9%) | |
2ADNTdown | 18(100%) | 4(20.0%) | 6(12.4%) | ||
4ADNTdown | 22(100%) | 4(21.1%) | |||
TNTdown | 19(100%) |
Functional terms | 2,4DNT | 2,6DNT | 2ADNT | 4ADNT | TNT |
Lipid metabolic process | 1.39E-14 | 6.79E-12 | 0.00198 | ||
Lipid biosynthetic process | 1.46E-10 | 0.000702 | 0.0159 | 1.46E-22 | 0.0129 |
Immune system process | 1.99E-06 | 8.89E-05 | |||
Immune response | 0.06268 | 0.004970 | 7.40E-07 | 1.61E-04 | 1.21E-04 |
The second common significantly down-regulated GO biological process term was “immune system process” under which “immune response” was also affected. Both terms were more enriched in 4ADNT and TNT exposures than with exposure to the other 3 compounds(
Compounds | Most significantly regulated genes |
2,4DNT | C4A, C5,C9,CXCL12,DPP4,EGR1,ITIH1, KDR, LTB, MASP1, RT1-S3 |
2,6DNT | C4A,C4BPA,C5,C6,CFH,CFI,CXCL12,DPP4,EGR1,FOXN1,H2-T18,ITIH1,LTB,MASP1 |
4ADNT | C1QG,CCL6,CCR5,CD83,CORO1A,ERAF,FCGR3A,GBP2,IL1B,IL6RA,LTB,LY86,PRKR,RT1-DA,RT1-S3,RT1-T24-1,SPN |
TNT | CCL2,CXCL10,CXCL9,CXCL13, CXCL12, OASL2,RT1-BB,RT1-DA,RT1-S3,TNFRSF14 |
To further explore the molecular mechanisms, we used the most significantly differentially expressed transcripts of each chemical exposure to perform canonical pathway analyses. In general, a number of pathways were common between any compound pair (
2,4DNT | 2,6DNT | 2ADNT | 4ADNT | TNT | |
2,4DNT | 44(100%) | 18(46.8%) | 15(38.9%) | 12(31.2%) | 17(43.0%) |
2,6DNT | 30(100%) | 15(47.6%) | 10(31.7%) | 17(52.3%) | |
2ADNT | 33(100%) | 14(42.4%) | 23(67.6%) | ||
4ADNT | 33(100%) | 16(47.1%) | |||
TNT | 35(100%) |
Note: The number outside of a bracket is the common pathway number between two compounds. The number inside a bracket is the similarity rate which is calculated as that the number of common pathways divided by the total averaged pathway terms of two compared compounds.
Top ten pathways were selected to present for each compound. Most significantly regulated transcripts of each compound were chosen to run the Ingenuity pathway tool. The bigger the -log(p-value) of a pathway is, the more significantly the pathway is regulated. The threshold lines represent a p value with 0.05.
Examination of the most significantly regulated genes in pathways reveals that a number of genes from a few different gene families play a repeated role in multiple pathways that were regulated by chemical exposures. These gene families included aldehyde dehydrogenase 1 (ALDH1), gluathione S-transferase (GST), heat shock protein (HSP), and cytochrome P450 (CYP) families. ALDH1 family is involved in all the common pathways except NRF2-mediated oxidative stress response. The most significant differentially expressed genes under this family included ALDH1A1, ALDH1A3, ALDH1B1, ALDH1L1, and ALDH1L2. They were strongly up-regulated by at least two compounds, with some members affected by 3 compounds. For example, the family member ALDH1B1 was regulated by 2,4DNT, 2ADNT, and 4ADNT while ALDH1L1 was significantly affected by 2,4DNT, 4ADNT and TNT. Several genes in the GST family including GSTM1 (muscle), GSTM2, GSTM3, GSTM4 or GSTM7-7, GSTM5, GST pi1(GSTP1) and GST theta 1(GSTT1) participate in at least half of the commonly affected pathways (
The most significantly regulated genes shown in the commonly affected pathways in the HSP family included HSP 90KDa alpha (cytosolic), class A member 1(HSP90AA1), heat shock 27kDa protein 1 (HSPB1), HSP 90kDa alpha (cytosolic), class B member1 (HSP90AB1), and heat shock 22kDa protein 8 (HSPB8). These genes were usually up-regulated and involved in the following common pathways: aryl hydrocarbon receptor signaling, xenobiotic metabolism signaling, and NRF2-mediated oxidative stress response. We have found that HSP genes were highly enriched in the GO biological process term, “response to xenobiotic stimulus”. Since xenobiotic metabolism signaling also encompasses aryl hydrocarbon receptor signaling, and NRF2-mediated oxidative stress response, the pathway analysis results are consistent with the GO analysis indicating that heat shock proteins play an important role in the response to exposure to the 5 compounds.
A large number of differentially expressed genes belonged to the cytochrome P450 family (CYP1A1, CYP1A2, CYP3A5, CYP3A43, CYP4A11, CYP7A1, CYP51A1, CYP2C7, CYP2C44, CYP2C70, CYP2D6, CYP2D12, CYP2D26, CYP4F8, and CYP2J9. They mainly took part in the following common pathways: aryl hydrocarbon receptor signaling, LPS/IL-1 mediated inhibition of RXR function, xenobiotic metabolism signaling and metabolism of xenobiotics by cytochrome P450. These CYP family genes were primarily up-regulated by all the compounds.
Although there was no common significant cell death signaling pathways involved in the expression profiles induced by these compounds, different pathways associated with cell death signaling were strongly impacted. Apoptosis and/or P53 signaling were evidently affected by two and more of these compounds. Three genes which were commonly regulated by two or more compounds were P53, Bcl2-associated X protein (Bax), and caspase 3(CASP3). Some pathways such as estrogen receptor pathway and nucleotide excision repair pathway were only significantly regulated by 2,4DNT and 2,6DNT. Antigen presentation pathway was only evidently affected by TNT.
To further understand the common mechanisms, we looked for differentially expressed genes in common with all five chemical exposures. Fifty-four transcripts corresponding to 47 non-redundant genes were significantly affected by at least four of the 5 compounds (
The common gene network (A) was constructed using commonly significantly regulated genes by at least four of the compounds. For constructing specific gene networks related to liver toxicity of 2,4DNT(B), 2,6DNT(C), 2ADNT(D), 4ADNT(E) and TNT(F), only significantly regulated genes involved in known function to liver or hepatoxicity or liver diseases were selected for each compound. The red and green highlighted genes represent up-regulated and down-regulated genes respectively by one of the compound. The orange highlighted genes are involved in NRF2-mediated oxidative stress response. The up or down-regulated genes in the networks were determined by comparing the averaged treatments with relative controls of a compound. The gene networks were built using the Ingenuity knowledge base tool. The networks scores described in
Based upon evidence that the nitrotoluenes impacted a common gene network related to liver effects, we next examined each chemical exposure for specific gene networks related to liver function. To fulfill this goal, we employed the significantly changed genes by each compound that have been known to be associated with liver function to build gene networks based on Ingenuity knowledge base. Specific significant gene networks that were associated with hepatic system function, liver cholestasis and/or liver damage were generated for each compound (
In the 2ADNT affected gene network, CDKN1A and ABCC3 were greatly connected (
Despite the different topologies and gene compositions of the networks, a number of common pathways that were modulated by all compounds and were also present in all sub networks. These included NRF2-mediated oxidative stress response, aryl hydrocarbon receptor signaling, xenobiotic metabolism signaling, LPS/IL-1 mediated inhibition of RXR function, and xenobiotic metabolism signaling pathways.
To verify the credibility of microarray data, we first selected 96 genes that are responsible for oxidative stress (
Previous studies by Wintz et al.
QRT-PCR analysis of select genes present in gene networks and affected pathways were performed including GSTM4, NRF2, ALDH1A1, and fatty acid binding protein 2 (FABP2) had R2 values of 0.85, 0.83, 0.73, and 0.72, respectively, when compared to microarray data across all samples (
The enrichment in GO biological process related to lipid metabolism and pathways including fatty acid metabolism and NRF2-mediated oxidative stress response in addition to data from other species suggests that nitrotoluenes exposure would significantly impact lipid biosynthesis in liver. To validate this we analyzed lipid metabolites in livers of the exposed rats. Two doses were selected at 24 h to measure lipids levels, the second lowest and the highest, and vehicle control of the 5 compounds. A total of 340 lipid species (
Rats were treated with 2,4DNT, 2,6DNT 2ADNT, 4ADNT or TNT with one of 3 doses including controls for each compound for 24 h. Rats were sacrificed and liver tissues were used for lipid extraction and lipid profile measurement as described in
In the present study, we have used a systems biology approach in order to understand effects of the 5 similarly structured nitrotoluenes on signaling pathways via liver gene alteration and to gain insight into the mechanism of toxicity of this class of compounds. An integrative approach was employed in our study which included clinical toxicology, pathology, transcriptomics, lipidomics, gene function classification, pathway analysis and gene network modeling. Overall, we found that the expression results correlated well with toxic and pathological results. For instance, we have found more genes that were significantly changed in response to 2,4DNT and 2,6DNT than TNT, 4ADNT, and 2ADNT. Interestingly, we found that the number of regulated genes was greater at 24 h than 48 h by all compounds except TNT. Animals might take more time to metabolize TNT to an unidentified reactive metabolite, which could be a reason why TNT impacts more genes at 48 h than 24 h. The difference in the amount of differentially expressed genes, distinctive gene expression patterns, specific regulated gene lists, pathways and unique gene networks, suggest a specific molecular mechanism for each compound. Although we observed clinical and histo-pathological differences caused by the compounds, we could still see some common biological processes that were affected by two or more compounds, which involve DNA damage response and cell death signaling, detoxification response, de-regulation of lipid metabolism and impaired immune response.
Chemicals, irradiation, or other environmental factors can induce DNA damage response which results in cell death via cell cycle arrest
Our study has revealed that DNA metabolic process and DNA damage response are major biological processes affected by these compounds, which seem to be more significant with 2,4DNT, 2,6DNT and 4ADNT than TNT and 2ADNT. One possible reason for the damage caused by these compounds is the ability to impair DNA synthesis and replication through DNA adduction as was discovered in hepatocytes exposed to 2,4DNT and 2,6DNT
Cell death signaling was also found to be induced by these compounds. For instance, caspase 3, known to induce cell shrinkage and DNA fragmentation that leads to cell death, was up-regulated by the compounds
One important pathway that could play a role in oxidative stress and detoxification response is the NRF2-mediated oxidative stress response pathway, which was commonly influenced by all the 5 compounds. NRF2 is a transcription factor which binds to the antioxidant response element in the promoter of NRF2 regulated genes
The NRF2-mediated oxidative stress response has been revealed to reduce toxicity and carcinogenesis during exposure to electrophile or other environmental toxicants or inflammation in mammalian models
Several NRF2 downstream phase I and II metabolizing enzymes including GSTs (GSTM3, GSTM4, etc.), NQO1, Aflatoxin B 1-aldehyde reductases (AKR7A3), epoxide hydrolase 1, microsomal (xenobiotic) (EPHX1) and UGT1A6 were significantly induced by most of the compounds and may prevent cell damage due to oxidative stress.
Another key pathway contributing to the detoxification process is aryl hydrocarbon receptor (AHR) Signaling. AHR ligands bind to the AHR complex inducing the dissociation of interacting proteins, leading to the release of AHR which then complexes with the aryl hydrocarbon receptor nuclear translocator (ARNT) and facilitates transfer of the complex to the nucleus and subsequent regulation of gene expression leading to biochemical and toxic responses
The expression of many CYPs downstream of AHR signaling is significantly regulated by these compounds, which include CYP1A2, CYP3A5, CYP3A43, CYP4A11, CYP7A1, CYP51A1, CYP2C7, CYP2C44, CYP2C70, CYP2D6, CYP2D12, CYP2D26, CYP4F8, and CYP2J9 etc. An induction of CYPs via AHR signaling is to enhance clearance of these compounds. However, if a compound is present in excess it will form an electrophilic nitrenium cation that will adduct nucleophiles of DNA, e.g., guanine, and proteins, e.g., hemoglobin as studied by Sabbioni et al.,
Since these commonly regulated pathways such as NRF2-mediated oxidative stress response, aryl hydrocarbon receptor signaling and metabolism of xenobiotics by cytochrome P450 pathways are part of xenobiotic metabolism signaling, xenobiotic metabolism signaling may play a pivotal role in the protection against the toxicity from the compounds. A variety of phase I and phase II metabolizing enzymes belonging to several gene families including GSTs, CYPs, ALDHs, UGTs, and aflatoxin B1 aldehyde reductase (AFARs) seem to participate in all the xenobiotic pathways.
In addition to NRF2, AHR, and P450 mediated pathways, the constitutive androstane receptor (CAR) and the pregnane x receptor (PXR) mediated pathways are part of xenobiotic pathway as well
GO analyses revealed that differentially expressed gene lists were highly enriched in the biological process “protein folding” by all the compounds. HSPs are well known stress response proteins involved in multiple functions such as protein folding and unfolding, cell survival and cell growth
GO enrichment analyses suggested that “lipid metabolic process” was a process significantly repressed by almost all the compounds. Pathway analysis also found that two pathways with major roles in lipid metabolism were affected: LPS/IL-1 mediated inhibition of RXR function and fatty acid metabolism. Chemical analysis of livers from these compounds exposed rats for 24 h confirmed genomics evidence that lipids were adversely affected including individual lipid species and total lipid classes. Down-regulation of lipid metabolism associated genes leads to the reduction of lipid metabolite products and possible energy production.
An important part of LPS/IL-1 mediated inhibition of RXR function is PPAR/RXR signaling. Fatty acid oxidation in liver and other tissues is regulated via the activation of nuclear hormone receptors, including PPARs and FXR
A target of PPAR/RXR signaling, Acyl-CoA synthetase ACSL5, was strongly down-regulated by 2,4DNT and 2,6DNT. Other members Acyl-CoA synthetases such as ACSL1, ACSL2, ACSL3 and ACSL4 were also affected by 2,4DNT and to some extent 2,6 DNT. Acyl-CoA synthetases catalyze long chain fatty acids to acyl-CoAs. Acyl-CoAs have a variety of metabolic fates in the cell and can be employed to acylate proteins or be metabolized through catabolic pathways such as β-oxidation or anabolic pathways such as
In contrast to Acyl-CoAs, fatty acid binding protein 2 (FABP2), another downstream target of PPAR/RXR signaling
LIPC, FASN, and APOB are primarily downstream genes of FXR/RXR signaling, which is also a part of LPS/IL-1mediated inhibition of RXR function pathway. These genes were more heavily repressed by 2,4DNT and 2,6DNT. Lipase, hepatic (LIPC) is mainly involved in lipoprotein transport and metabolism of HDL while FASN plays a major role in fatty acid synthesis. The down-regulation of APOB supported by both qPCR and microarray is in agreement with the observation in fish exposed to 2,4DNT. The reduction of the expression of these genes could impair normal lipid metabolism. Another recent study by our laboratory has shown that FASN and APOB were also down-regulated in the liver of Northern bobwhite (
Immune response is the most significant functional term for those down-regulated genes by 4ADNT and TNT. The major histocompatibility (MHC) class I E (HLA-E) is a key member of MHC class I. Its expression was repressed by almost all the compounds. HLA-E is a ligand for receptors of both the adaptive and innate immune systems. The binding of self-peptides complexed to HLA-E by the CD94-NKG2A receptor of natural killer (NK) cells is a critical checkpoint for immune surveillance by NK cells. Additionally, HLA-E is recognized by the T-cell receptor of alpha/beta CD8 T cells as well and contributes to the adaptive immune response to invading pathogens or other xenobiotics
Chemokine signaling is a major immune response that was affected by the compounds. It seems different compounds may target different chemokine signaling pathways. Chemokine (C-X-C motif) ligand 12 (CXCL12 or SDF-1) was strongly down-regulated by 2,4 DNT and 2,6DNT. It is a ligand for the G-coupled receptor protein Chemokine (C-X-C motif) receptor CXCR4. Activation of CXCR4 by CXCL12 is involved in many biological functions such as cell migration, growth and survival. The activation can also trigger leukemia cell movement to the marrow microenvironment, where CXCL12 pushes leukemia cells in close contact with marrow stromal cells that lead to growth and drug resistance signals. The inhibition of the expression of CXCL12 could make the cells more susceptible to the nitrotoluenes. It seems there are many chemokine ligands that were repressed by TNT, which include CXCL13, CXCL9, CXCL10, etc. Interestingly CXCL9 and CXCL10 share the same G-protein coupled receptor CXCR3. Therefore, these chemokines possess the capability of maintaining the normal immune function and the protection against infectious agents and other external toxicants
Our toxicity studies have shown that overall dinitrotoluene compounds, 2,4DNT, 2,6DNT and TNT had stronger toxic effects than amino-dinitrotoluene compounds 2ADNT and 4ADNT, among which it seems that 2,4DNT and 2,6DNT are more toxic than TNT. For example, we only observed lethality of rats with 2,4DNT and 2,6DNT treatments. Another unique effect of 2,4DNT, 2,6DNT and TNT exposure was hepatic sinusoidal congestion. We found that erythrocytosis was associated only with dinitrotoluenes 2,4DNT and 2,6DNT. These results were reflected by the overall number of genes whose expression was changed. 2,4DNT, 2,6DNT had more gene expression changed than TNT, 2ADNT and 4ADNT. Our gene expression analysis also showed that 2,4DNT and 2,6DNT share more similar gene expression pattern than the other compounds.
At 48 h, TNT affected more changed gene numbers than 2ADNT and 4ADNT. 2ADNT had the least change in gene number, which may be linked to its weak effects on body and normalized liver weight and no change in RBC parameters or blood granulocytes.
We found that body weight gain was decreased by all the compounds. One probable reason for this observation was the reduction of food consumption due to nitrotoluene exposure. A reduction in food consumption may also play a role in the observed down-regulation of energy metabolism. Our gene expression and lipid profiles clearly show that energetic related lipid metabolism is decreased, which supports this statement.
Liver weight increase, as seen in rats exposed to TNT and 4ADNT at 48 h, is indicative of enzyme induction including cytochrome P450s and GSTs. Many of these enzymes were induced at both 24 h and 48 h by the nitrotoluenes. Lipid metabolism especially fatty acid metabolism,
The most remarkable effect we observed from the hematology assessment was erythrocytosis induced by the dinitrotoluenes. Even though we did not find anemic effects induced by 2,4DNT as have been reported in other studies, we did see decreased hemoglobin and hematocrit in 4ADNT exposed animals after 48 h. One possible reason why anemia was not observed in our studies is that we conducted an acute exposure design; the other studies where anemia was seen are all repeat dose studies.
In our study, expression of several genes related to erythrocytosis and oxygen transport such as hemoglobins and transferrin were affected by the dinitrotoluenes. Numerous blood parameters were unchanged or decreased in contrast to the hematological parameters associated with erythrocytosis. This suggests that the latter did not result from hemoconcentration as might occur if dehydration occurred.
By purposely looking at erythropoietin signaling genes such as erythropoietin and its receptor, we found that their expression was also altered. Hemoglobin adduct formation due to nitrotoluene exposure has been reported and may adversely affect oxygen transport
Hypoxia signaling plays an important role in erythrocytosis
An integrative analysis of physiological endpoints, gene ontology, pathways, and gene networks across different aspects we were able to determine a possible mechanism of liver toxicity due to exposure of these nitrotoluenes (
Environmental chemicals, gene functional terms, pathway cross talks, gene networks, and physiological endpoints were integrated to form a network at the system level to account for the molecular mechanisms of hepatoxicity mediated by these compounds. Red and green highlighted functional terms, genes, and pathways were generally up-regulated and down-regulated respectively by these compounds.
DNA response activates P53 and other cell death signaling which ultimately leads to inflammation and other cell death related diseases. AHR signaling also affects cell death by regulating P53 signaling and controlling cell cycle gene expression. Two pathways, AHR and NRF2, belonging to xenobiotic pathway are primarily responsible for protecting against the toxicity induced by the compounds. Activation of downstream phase I and phase II enzymes in these two pathways contribute to detoxification of xenobiotics leading to cell survival. Another critical commonly regulated pathway is LPS/IL-1 mediated inhibition of RXR function pathway. PPARs such as PPARa and PPARg are involved in this pathway and regulate downstream molecules contributing to lipid metabolism which is an important significant common function term down-regulated by all the compounds, as well as fatty acid metabolism that is a commonly regulated pathway. The reduction of lipid metabolism by the compounds could result in endocrine system disorders, reproductive and hepatic system diseases. LPS/IL-1 mediated pathway is certainly connected with interleukins and cytokines mediated signaling which play a role in defense and immune response, which was a common functional term more heavily down-regulated by TNT and 4ADNT. Inhibition of immune function by the compounds could lead to inflammation related diseases. The production of ROS can also activate NF-kB that connects to ILs and cytokine signaling. The P53 pathway could also connect with hypoxia signaling and play a pivotal role in erythrocytosis (
We also found that liver toxicity could be a secondary effect of primary hematological toxicities caused by these compounds. We found hypoxia signaling could be an important pathway affected by the compounds.
The present study is the first
Our results indicate that an integrative systems biology approach along with physiological findings is an efficient approach to gain insight into both individual and common mechanisms of action of the nitrotoluenes in a mammalian system. Concepts presented in this study can be utilized by others in their research to understand how a toxicant produces its toxicity.
2ADNT (99.9%) and 4ADNT (99.9%) were obtained from SRI International (Menlo Park, CA). TNT (99.9%) was obtained from Eastman Chemical Company (Kingsport, TN). 2,4DNT (97%) and 2,6DNT (98%) were obtained from Sigma-Aldrich (St. Louis, MO).
Female Sprague-Dawley rats (175–225 grams) were from the in-house breeding colony (College of Pharmacy, University of Louisiana at Monroe [ULM] and treated in accordance with the
Groups of rats were weighed and randomly assigned to treatment. Treatments were vehicle (5% v/v DMSO in corn oil), TNT (4.8, 48, 96, 192, and 384 mg/kg), 4ADNT (4.7, 47, 94, 187, and 374 mg/kg), 2ADNT (4.4, 44, 87, 174, 348 mg/kg), 2,4DNT (5, 50, 99, 198 and 398 mg/kg), and 2,6DNT (5.0, 25, 50, 99, 199, 398 mg/kg). Rats were observed continuously for the first hour after dosing, hourly for 8 h and daily thereafter. Moribund rats were euthanized with CO2. All animals died when dosed with the 2,6DNT high dose (398 mg/kg). At 24 or 48 hours after treatment, survivors were anesthetized with CO2 and the sternum was bisected to expose the heart. Blood was collected by cardiac puncture of the left ventricle into EDTA-containing Vacutainer tubes (Becton, Dickinson and Co., Franklin Lakes, NJ) for hematological assessment. Serum was derived from blood drained from the heart after nicking the ventricles. Livers were excised and weighed. A 2 mm slice from the medial lobe was fixed overnight in neutral buffered formalin, then 70% ethanol. A portion of the liver was removed and placed in RNA Later (Ambion) following manufacturer's instruction and later used for genomic analyses. Remaining liver was flash frozen in liquid N2 and stored at −70°C for lipidomic and analytical chemistry analyses.
Upon sacrifice, a portion of liver was flash frozen with liquid nitrogen then transferred to −80°C. Samples were thawed and weighed in bead beater tubes (MP Bio, Solon, OH). Next, 500 µL of high performance liquid chromatography (HPLC)–grade acetonitrile (>99.93%) (Sigma-Aldrich, St. Louis, MO, USA) was added to each bead beater tube before being homogenized using a FastPrep®-24 Instrument (MP Bio, Solon, OH, USA). After lysing, samples were centrifuged at 16,500 g in a microcentrifuge to pellet debris. Each supernatant was then diluted at a ratio of 1∶1 with organic free reagent water. Each diluted extract was then added into a 200 µL glass insert placed in a 1.5 mL amber vial (Agilent Technologies, Santa Clara, CA, USA). Samples were analyzed by HPLC using methods modified from Johnson et al.
Histopathological assessment of livers were performed on sections (5 µm) cut from tissues embedded in paraffin and stained with hematoxylin and eosin (H&E) using routine procedures
Serum was analyzed for a panel of metabolic parameters by the hospital clinical laboratory with an Abbott Architect Ci 8200. Absolute values for albumin of vehicle-treated rats were low relative to published normal values
Clinical endpoints were analyzed for homogeneity of variance with Levene's test and no heteroscedasticity was observed. Data were thus analyzed with ANOVA; post-hoc comparisons of treatment means against concurrent vehicle control were done with Dunnett's test. A square root transformation with 3/8 continuity factor was performed on reticulocyte percent data before analysis. Treatment mean differences from control with p<0.05 were considered significant.
Changes in gene expression were tested using Agilent commercial whole rat genome microarrays (4×44K). Experiments were designed for both purposes of temporal and dose response analysis. For temporal analysis, only two time points for each compound were used, 24 h and 48 h. For dose response analysis, four doses plus a vehicle control were employed for each compound at each time point. A separate control was used for different compounds even at the same time point. The dose selection was based on the LD 50 data for each compound. Four biological replicates of this design were conducted, each using different animals, and four biological replicates were applied, which resulted in a total of 200 microarrays.
Total RNA was extracted from about 30mg of liver tissue. Tissues were homogenized in the lysis buffer with FAST Prep-24 from MP before using RNeasy kits (Qiagen). Total RNA concentrations were measured using NanoDrop® ND-1000 Spectrophotometer (NanoDrop technologies, Wilmington, DE, USA). The integrity and quality of total RNA was checked on an Agilent 2100 Bioanalyzer (Palo Alto, CA). The gel-like images generated by the Bioanalyzer show that total RNAs have two bands, represent 18S and 26S RNA of mammalian RNA. Nuclease-free water (Ambion) was used to elute total RNA.
Rat whole genome oligo arrays in the format of 4X44K were purchased from Agilent Technologies. Sample cRNA synthesis, labeling, hybridization and microarray processing were performed according to manufacturer's protocol “One-Color Microarray-Based Gene Expression Analysis” (version 1.0). The labeling reactions were performed using the Agilent Low RNA Input Linear Amplification Kit in the presence of cyanine 3-CTP. The labeled cRNA from each labeling reaction was hybridized to individual arrays at 65°C for 17 h using Agilent's Gene Expression Hybridization Kit. After washing, the arrays were scanned using a GenePix 4200AL scanner (Molecular Device Inc.). The Feature extraction software (V. 9.5.1) from Agilent was used to automatically find and place microarray grids, reject outlier pixels, accurately determine feature intensities and ratios, flag outlier pixels, and calculate statistical confidences.
Microarray data analyses were processed with GeneSpring version 7.0 and 10.0. The sample quality control was based on the Pearson correlation of a sample with other samples in the whole experiment. If the average Pearson correlation with other samples was less than 80%, the sample was excluded for further analysis. If the scanned intensity was less than 5.0 for a probe, it was transformed to 5. Only one liver sample, TNT at 4.8 mg/ml for 24 h, did not pass the criteria and was excluded. A perchip (within) array normalization was performed using 50 percentile values of all the probe values in the array. Per gene (between) array normalization was also applied using the median value of a gene across all samples in the experiment. Probe features were first filtered using flags. A “present” or “absent” flag was defined using the Agilent
To extract only significantly regulated genes, a One-Way ANOVA was performed across 4 doses and a matched control sample for one compound at a time at either 24 h or 48 h. Benjamini and Hochberg False Discovery (BHF) Rate with a FDR
To compare the overall gene expression profiles induced by the 5 compounds with various doses and different times, the microarray data were first normalized by the median value of all the transcripts per chip. Subsequently each transcript data of all the samples at a time point (24 h or 48 h) for a compound was normalized by the mean value of the transcript of the time matched control samples at the same time point for the same compound. To obtain the comparison of whole genome level expression profiles, we used a total of 22,676 flag filtered transcripts and 40 average conditions to perform a two-way hierarchical clustering to build a condition tree since that data were normalized based on relative control samples, control conditions were excluded in the tree construction. Each dose per time point for one compound was a unique condition, so a total of 40 average unique conditions were in the tree.
Significantly regulated probes were employed for one way hierarchical clustering (only cluster genes) or two-way hierarchical clustering (clustering both genes and samples) using GeneSpring 7.0 and/or 10 (Agilent Technologies, Foster City, CA, USA). A Pearson correlation with average linkage was applied for the clustering. Gene functional categories were classified according to Gene Ontology (GO)
Two-stage RT-QPCR were performed, 1000 ng of total RNA were first reverse transcribed into cDNA in a 20-µl reaction containing 250 ng random primers and SuperScript™ III reverse transcriptase (Invitrogen) following the manufacture's instruction. The synthesized cDNA was diluted to10 ng/µl as cDNA template. QPCR was performed on an Applied Biosystems Incorporated (ABI, Foster City, CA) Sequence Detector 7900. Each 20-µl reaction was run in duplicate and contained 6 µl (10 ng/µl) of synthesized cDNA templates and 3 ul of nuclease-free water along with 1 ul of TaqMan gene specific assay and 10 ul of 2X TaqMan universal PCR Master Mix (ABI). Cycling parameters were 95°C for 15 min to activate the DNA polymerase, then 40 cycles of 95°C for 15 s and 60°C for 1 min.
Lipids were extracted as described
The background of each spectrum was subtracted, the data were smoothed, and peak areas integrated using a custom script and Applied Biosystems Analyst software (Foster City, CA, USA). The lipids in each class were quantified in comparison to the two internal standards of that class. The first and typically every 11th set of mass spectra were acquired on the internal standard mixture only. Peaks corresponding to the target lipids in these spectra were identified and molar amounts calculated in comparison to the internal standards on the same lipid class. To correct for chemical or instrumental noise in the samples, the molar amount of each lipid metabolite detected in the “internal standards only” spectra was subtracted from the molar amount of each metabolite calculated in each set of sample spectra. Finally, the data were corrected for the fraction of the sample analyzed and normalized to the sample “dry weights” to produce data in the units nmol/mg. To compare the different lipids species between groups, data was first filtered based on processed signal in which more than 70% of samples have at least 0.002 nmol/mg for any given lipid species. Otherwise a lipid species was filtered out. Statistic analyses were conducted by One-Way ANOVA across all doses including vehicle controls for a compound. The false discovery rate less than 0.05 after Benjamine-Hochberg correction was taken as the cut off level for significance. A Tukey based post hoc test was applied to identify differentiated lipid species by comparing different groups.
A table (supplementary
(0.97 MB XLSX)
A table (supplementary
(0.04 MB XLSX)
A table (supplementary
(0.44 MB XLSX)
A table (supplementary
(0.46 MB XLSX)
A table (supplementary
(0.02 MB XLSX)
A table (supplementary
(0.02 MB XLSX)
A table (supplementary
(0.20 MB XLSX)
A table (supplementary
(0.02 MB XLSX)
A table (supplementary
(0.02 MB XLSX)
A table (supplementary
(0.10 MB XLSX)
A table (supplementary table 11) providing a list of genes that were commonly significantly regulated by the 5 chemicals.
(0.03 MB XLSX)
A table (supplementary table 12) providing a list of genes in the stress and toxicity pathway that were used for QRT-PCR.
(0.03 MB XLSX)
A table (supplementary table 13) providing a list of lipid species that were measured in the project.
(0.02 MB XLSX)
A PDF including supplementary
(0.82 MB DOC)
We thank Kelly Bailey of St. Francis Hospital, Monroe, LA for blood and serum analyses. We thank Erika L. Knott for helping with some experimental work Mary Roth for technical assistance with the lipidomics analyses, and Choo Y. Ang for carefully reading the manuscript.