| Literature DB >> 24858832 |
Masuko Kobori1, Yinhua Ni2, Yumiko Takahashi1, Natsumi Watanabe1, Minoru Sugiura3, Kazunori Ogawa4, Mayumi Nagashimada5, Shuichi Kaneko6, Shigehiro Naito1, Tsuguhito Ota2.
Abstract
Recent nutritional epidemiological surveys showed that serum β-cryptoxanthin inversely associates with the risks for insulin resistance and liver dysfunction. Consumption of β-cryptoxanthin possibly prevents nonalcoholic steatohepatitis (NASH), which is suggested to be caused by insulin resistance and oxidative stress from nonalcoholic fatty liver disease. To evaluate the effect of β-cryptoxanthin on diet-induced NASH, we fed a high-cholesterol and high-fat diet (CL diet) with or without 0.003% β-cryptoxanthin to C56BL/6J mice for 12 weeks. After feeding, β-cryptoxanthin attenuated fat accumulation, increases in Kupffer and activated stellate cells, and fibrosis in CL diet-induced NASH in the mice. Comprehensive gene expression analysis showed that although β-cryptoxanthin histochemically reduced steatosis, it was more effective in inhibiting inflammatory gene expression change in NASH. β-Cryptoxanthin reduced the alteration of expression of genes associated with cell death, inflammatory responses, infiltration and activation of macrophages and other leukocytes, quantity of T cells, and free radical scavenging. However, it showed little effect on the expression of genes related to cholesterol and other lipid metabolism. The expression of markers of M1 and M2 macrophages, T helper cells, and cytotoxic T cells was significantly induced in NASH and reduced by β-cryptoxanthin. β-Cryptoxanthin suppressed the expression of lipopolysaccharide (LPS)-inducible and/or TNFα-inducible genes in NASH. Increased levels of the oxidative stress marker thiobarbituric acid reactive substances (TBARS) were reduced by β-cryptoxanthin in NASH. Thus, β-cryptoxanthin suppresses inflammation and the resulting fibrosis probably by primarily suppressing the increase and activation of macrophages and other immune cells. Reducing oxidative stress is likely to be a major mechanism of inflammation and injury suppression in the livers of mice with NASH.Entities:
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Year: 2014 PMID: 24858832 PMCID: PMC4032271 DOI: 10.1371/journal.pone.0098294
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Metabolic parameters of mice fed with different diets for 12 weeks.
| NC | CL | CL+CX | |
| Body weight (g) | 31.7±1.4 | 32.6±0.8 | 32.9±0.6 |
| Food intake (kcal/day/kg BW) | 358.4±25.7 | 369.2±15.3 | 381.9±13.0 |
| Liver weight/BW (%) | 3.85±0.13a | 4.63±0.12b | 4.62±0.11b |
| Hepatic TG (mg/mg protein) | 0.23±0.03a | 0.66±0.05b | 0.43±0.05c |
| Hepatic TC (mg/mg protein) | 0.025±0.002a | 0.248±0.016b | 0.169±0.018c |
| Hepatic TBARS (nmol/mg protein) | 0.16±0.01a | 0.34±0.04b | 0.18±0.04a |
Data are expressed as the arithmetic mean ± SEM (n = 4 (NC) or n = 5 (CL and CL+CX)). Different superscripts indicate significant differences (p<0.05) between the groups.
Figure 1Representative liver histology of mice fed different diets.
C57BL/6J mice were fed a standard chow (NC), a CL diet (CL), or a CL diet containing 0.003% β-cryptoxanthin (CL+CX) for 12 weeks. Liver sections were stained with H&E, Azan, Sirius red, anti-F4/80 antibody, and anti-α-SMA antibody. Original magnification is 200×, and scale bars = 100 µm.
Figure 2β-Cryptoxanthin reduced F4/80-positive hepatic macrophages.
Liver sections of mice fed the CL diet (CL) or the CL diet containing 0.003% β-cryptoxanthin (CL+CX) for 12 weeks were stained with anti-F4/80 antibody. The F4/80-positive area was calculated in 30 fields of 3 slides for each individual mouse using 5 mice for each group as described previously [15]. **p<0.01, Student's t-test.
Figure 3Top 10 biological functions of hepatic genes significantly improved by β-cryptoxanthin in diet-induced NASH in mice.
(a)Top 10 biological functions of hepatic genes significantly up- or downregulated in NASH (b) Top 10 biological functions of hepatic genes significantly improved by β-cryptoxanthin, leading to levels closer to those of the control levels, in NASH. The functions that were most significant in the data set were identified by Ingenuity Pathway Analysis.
Predicted biological functions altered by high cholesterol diets and β-cryptoxanthina.
| Predicted biological functions altered by diet induced-NASH | Predicted biological functions improved by β-cryptoxanthin | ||
| Functions Annotation | Activation State | Functions Annotation | Activation State |
|
| |||
| cell death of liver cells (10 molecules) | ↓ | ||
| necrosis of liver (14) | ↓ | ||
|
| |||
| accumulation of cells (33 molecules) | ↑ | ||
| cell viability of mast cells (6) | ↑ | ||
| proliferation of T lymphocytes (70) | ↑ | ||
| quantity of T lymphocytes (69) | ↑ | quantity of T lymphocytes (27) | ↓ |
| quantity of lymphatic system cells (9) | ↓ | ||
| lack of T lymphocytes (15) | ↑ | ||
| quantity of neutrophils (30) | ↓ | ||
| cytotoxicity of leukocytes (19), lymphocytes (16) | ↑ | cytotoxicity of leukocytes (11), lymphocytes (9) | ↓ |
| cytotoxicity of natural killer cells (10) | ↑ | ||
| activation of cells (99), blood cells (94), macrophages (22) | ↑ | activation of cells (51), blood cells (48), macrophages (12) | ↓ |
| activation of antigen presenting cells (21), leukocytes (45) | ↓ | ||
| inflammatory response (63) | ↑ | inflammatory response (31) | ↓ |
| phagocytosis (16) | ↑ | ||
| phagocytosis of blood cells (19), cells (12), myeloid cells (9), phagocytes (7), by macrophages (4) | ↑ | ||
| engulfment of cells (16) | ↑ | ||
| engulfment of blood cells (13), myeloid cells (12), phagocytes (11), antigen presenting cells (8) | ↑ | engulfment of blood cells (6), myeloid cells (5), phagocytes (5), antigen presenting cells (5) | ↓ |
| endocytosis (5) | ↓ | ||
| attachment of myeloid cells (4), phagocytes (4) | ↑ | ||
| cell movement (109), leukocytes (91), phagocytes (71) | ↑ | cell movement (54), leukocytes (46), phagocytes (33) | ↓ |
| cell movement of mononuclear leukocytes (18), lymphocytes (16) | ↓ | ||
| chemotaxis of leukocytes (45) | ↑ | ||
| leukocyte migration (106) | ↑ | leukocyte migration (52) | ↓ |
| migration of cells (108) | ↑ | migration of cells (53) | ↓ |
| migration of mononuclear leukocytes (15) | ↓ | ||
| Lymphocyte migration (14) | ↓ | ||
| homing of leukocytes (47) | ↑ | homing of leukocytes (21) | ↓ |
| recruitment of leukocytes (40), phagocytes (31), mononuclear leukocytes (14) | ↑ | ||
| transmigration of leukocytes (19) | ↑ | ||
| trafficking of leukocytes (8) | ↑ | ||
| extravasation of myeloid cells (7) | ↑ | ||
| cell spreading of phagocytes (6) | ↓ | ||
| shape change of phagocytes (7) | ↓ | ||
| morphology of cells (74), leukocytes (61), mononuclear leukocytes (34), leukocytes (31), T lymphocytes (18) | ↓ | morphology of cells (32), leukocytes (27), mononuclear leukocytes (18), leukocytes (17), T lymphocytes (11) | ↑ |
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| production of reactive oxygen species (19) | ↑ | production of reactive oxygen species (13) | ↓ |
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| concentration of phosphatidic acid (5) | ↓ | ||
| quantity of carbohydrate (7) | ↑ | ||
The functions and canonical pathways that were most significant to the data set were identified by Ingenuity Pathway Analysis.
Upstream regulators predicted to be activated (a) or inhibited (b) in NASHa.
| Molecule Type | Upstream Regulator |
|
| |
| chemical - endogenous | cholesterol (32 target molecules in dataset), cholic acid (14) |
| ligand-dependent nuclear receptor | Nr1h3 (16), Nr1h4 (14) |
| cytokine | Ifng (49), Ifnb1(25), Tnf (23), Il1b (20), Csf2 (12), Il5 (19), Il1a (4) |
| transcription regulator | Stat1 (16), Myc (19), Irf3 (6), Spi1 (7) |
| transmembrane receptor | Tlr4 (27) |
| peptidase | Plau (9) |
| enzyme | Cd38 (25) |
| kinase | Plk4 (8), Plk2 (8), Map3k7 (8), Tbk1 (10) |
| other | Myd88 (23), Ticam1 (19), Dock8 (13), Samsn1 (15), Arhgap21 (9), Sash1 (12), Tnfaip2 (4) |
|
| |
| transporter | Apoe (15 target molecules in dataset), Atp7b (12) |
| transcription regulator | Hnf4A (23), Smarcb1 (15) |
| phosphatase | Pon1 (4) |
| ligand-dependent nuclear receptor | Nr3C1 (15) |
| other | Scap (19), Socs1 (11) |
The functions and canonical pathways that were most significant to the data set were identified by Ingenuity Pathway Analysis.
Upstream regulators predicted to be inhibited by β-cryptoxanthin in NASHa.
| Upstream Regulator | Molecule Type | Activation z-score | p-value of overlap | Target molecules in dataset |
| Tnf | cytokine | −2.905 | 2.90E-04 | Ccl4, Ccr5, Cd44, Gls, Hmox1, Ncf1, Ncf2, Ppara, Ppargc1a, Rxra, Tnfaip3, Vcam1 |
| Spi1 | transcription regulator | −2.219 | 3.75E-03 | Cd14, Csf1r, Cybb, Emr1, Lyz1/Lyz2 |
| Ticam1 | other | −2.079 | 1.26E-02 | Ccl4, Cd38, Cmpk2, Ifi204 (includes others), Pilra, Slc7a2, Tnfaip3, Vcam1 |
| Myd88 | other | −2.157 | 1.77E-02 | Ccl4, Cd14, Cd38, Cmpk2,Inpp5d, Pilra, Slc7a2, Tnfaip3, Vcam1 |
| Irf8 | transcription regulator | −2.236 | 1.83E-02 | Cebpa, Csf1r, Cxcl16, Emr1, Msr1 |
The functions and canonical pathways that were most significant to the data set were identified by Ingenuity Pathway Analysis.
Top 5 canonical pathways of hepatic genes that were significantly altered by NASH (1) and improved by β-cryptoxanthin (2)a.
| Ingenuity Canonical Pathways | p-value | Ratio |
|
| ||
| LXR/RXR Activation | 5.36E-07 | 28/113 (0.248) |
| LPS/IL-1 Mediated Inhibition of RXR Function | 7.50E-07 | 40/193 (0.207) |
| Superpathway of Cholesterol Biosynthesis | 7.81E-07 | 12/32 (0.375) |
| Antigen Presentation Pathway | 1.61E-05 | 11/28 (0.393) |
| TR/RXR Activation | 2.14E-05 | 20/79 (0.253) |
|
| ||
| Fcγ Receptor-mediated Phagocytosis in Macrophages and Monocytes | 7.54E-06 | 13/92 (0.141) |
| Natural Killer Cell Signaling | 3.06E-05 | 12/92 (0.13) |
| CTLA4 Signaling in Cytotoxic T Lymphocytes | 3.35E-05 | 11/79 (0.139) |
| TR/RXR Activation | 3.79E-05 | 11/79 (0.139) |
| Production of Nitric Oxide and Reactive Oxygen Species in Macrophages | 7.97E-05 | 16/166 (0.096) |
The functions and canonical pathways that were most significant to the data set were identified by Ingenuity Pathway Analysis.
Figure 4Hepatic gene expression levels significantly suppressed by β-cryptoxanthin in NASH.
C57BL/6J mice were fed a standard chow (NC), a CL diet (CL), or a CL diet containing 0.003% β-cryptoxanthin (CL+CX) for 12 weeks. Hepatic gene expression was determined using a DNA microarray (Genopal, Mitsubishi Rayon). The degree of change (fold) was calculated compared with the livers of the control mice (NC). Data are expressed as the arithmetic mean ± SEM of 5 mice in each group. Different superscripts indicate significant differences (p<0.05) between the groups.
Figure 5β-Cryptoxanthin reduced T cell accumulation.
C57BL/6J mice were fed a standard chow (NC), a CL diet (CL), or a CL diet containing 0.003% β-cryptoxanthin (CL+CX) for 12 weeks. Hepatic expression levels of the T cell markers CD3, CD4, and CD8 were determined by quantitative RT-PCR analysis. Data are expressed as the arithmetic mean ± SEM (n = 4 (NC) or n = 5 (CL and CL+CX)). Different superscripts indicate significant differences (p<0.05) between the groups.