Metabolome-Microbiome Responses of Growing Pigs Induced by Time-Restricted Feeding

Time-restricted feeding (TRF) mode is a potential strategy in improving the health and production of farm animals. However, the effect of TRF on microbiota and their metabolism in the large intestine of the host remains unclear. Therefore, the present study aimed to investigate the responses of microbiome and metabolome induced by TRF based on a growing-pig model. Twelve crossbred growing barrows were randomly allotted into two groups with six replicates (1 pig/pen), namely, the free-access feeding group (FA) and TRF group. Pigs in the FA group were fed free access while the TRF group were fed free access within a regular time three times per day at 07:00–08:00, 12:00–13:00, and 18:00–19:00, respectively. Results showed that the concentrations of NH4-N, putrescine, cadaverine, spermidine, spermine, total biogenic amines, isobutyrate, butyrate, isovalerate, total SCFA, and lactate were increased while the pH value in the colonic digesta and the concentration of acetate was decreased in the TRF group. The Shannon index was significantly increased in the TRF group; however, no significant effects were found in the Fisher index, Simpson index, ACE index, Chao1 index, and observed species between the two groups. In the TRF group, the relative abundances of Prevotella 1 and Eubacterium ruminantium group were significantly increased while the relative abundances of Clostridium sensu sticto 1, Lactobacillus, and Eubacterium coprostanoligenes group were decreased compared with the FA group. PLS-DA analysis revealed an obvious and regular variation between the FA and TRF groups, further pathway enrichment analysis showed that these differential features were mainly enriched in pyrimidine metabolism, nicotinate and nicotinamide metabolism, glycerolipid metabolism, and fructose and mannose metabolism. In addition, Pearson's correlation analysis indicated that the changes in the microbial genera were correlated with the colonic metabolites. In conclusion, these results together indicated that although the overall microbial composition in the colon was not changed, TRF induced the gradient changes of the nutrients and metabolites which were correlated with certain microbial genera including Lactobacillus, Eubacterium_ruminantium group, Eubacterium coprostanoligenes group, Prevotella 1, and Clostridium sensu sticto 1. However, more studies are needed to understand the impacts of TRF on the health and metabolism of growing pigs

Biological Characteristics of Foam Cell Formation in Smooth Muscle Cells Derived from Bone Marrow Stem Cells

<p>Bone marrow mesenchymal stem cells (BMSC) can differentiate into diverse cell types, including adipogenic, osteogenic, chondrogenic and myogenic lineages. There are lots of BMSC accumulated in atherosclerosis vessels and differentiate into VSMC. However, it is unclear whether VSMC originated from BMSC (BMSC-SMC) could remodel the vessel in new tunica intima or promote the pathogenesis of atherosclerosis. In this study, BMSC were differentiated into VSMC in response to the transforming growth factor &#946; (TGF-&#946;) and shown to express a number of VSMC markers, such as &#945;-smooth muscle actin (&#945;-SMA) and smooth muscle myosin heavy chain1 (SM-MHC1). BMSC-SMC became foam cells after treatment with 80 mg/L ox-LDL for 72 hours. Ox-LDL could upregulate scavenger receptor class A (SR-A) but downregulate the ATP-binding cassette transporter A1 (ABCA1) and caveolin-1 protein expression, suggesting that modulating relative protein activity contributes to smooth muscle foam cell formation in BMSC-SMC. Furthermore, we found that BMSC-SMC have some biological characteristics that are similar to VSMC, such as the ability of proliferation and secretion of extracellular matrix, but, at the same time, retain some biological characteristics of BMSC, such as a high level of migration. These results suggest that BMSC-SMC could be induced to foam cells and be involved in the development of atherosclerosis.</p

MiR-133b regulates the expression of the Actin protein TAGLN2 during oocyte growth and maturation: a potential target for infertility therapy.

Infertility is an area of increasing in life science research. Although follicular maturation disorders and anovulation are the primary causations of infertility, its molecular mechanism is not well understood. Recent research has shown that microRNAs (miRNAs) might play an important role in the regulation of ovarian follicle development and maturation. In this study, the expression of miRNAs in metaphase I (MI) oocytes treated with or without insulin-like growth factor 1 (IGF-1) was observed by microRNA microarray analysis. Results show that 145 miRNAs were up-regulated and 200 miRNAs were down-regulated in MI oocytes after IGF-1 treatment. MiR-133b, which was up-regulated more than 30-fold, was chosen for further research. As a potential target of miR133b, transgelin 2 (TAGLN2) gene was down-regulated, at both transcription and translation levels, in miR-133b- over-expressed 293T cells, but TAGLN2 was up-regulated when the expression of miR-133b was inhibited. Furthermore, the expression level of TAGLN2 in the ovaries of 8-week- old mice was higher than that observed in 4-week-old mice. Immunofluorescence experiments showed that TAGLN2 was located in the cytoplasm. In general, our results indicate that miR-133b may play important roles in the growth and maturation of oocytes by regulating its potential target, TAGLN2, at both transcription and translation levels. Therefore, our research provides a potential new target for infertility therapy

The subcellular location of TAGLN2 was measured by immunofluorescence.

<p>TAGLN2 was labeled with green fluorescence, while cell nuclei and cell membrane were shown as red fluorescence. A)merge; B)Green fluorescence; C)Red fluorescence(10×20, n = 3).</p

Replication Data for: The stove, dome, and umbrella effects of pollutant aerosol on the planetary boundary layer: a large-eddy simulation and observations in Beijing

Dataset for campaign observations and LES input file for the reference case

Target genes of miR-133b.

<p>Three algorithms DIANA, TargetScan 4.0 and PicTar were all used for the prediction of the targets. Only the targets identified by all of them were shown Figure 5A. 168 putative miRNA targets were identified by the Three algorithms. The detail acting site for hsa-miR-133b binding to TAGLN2 was shown Figure 5B. Red rectangle represents miR-133b, and blue circle nodes represent mRNAs.</p

miR-133b targets TAGLN2 in 293T cells.

<p>A) Vertical axis is the ratio of Renilla to firefly luciferase and the horizontal axis is the experiment groups: 1. siCHECK-NP; 2. siCHECK-3UTR+microRNA-NC; 3. siCHECK-3UTR+25 nM microRNA-133b; 4. siCHECK-3UTR+50 nM microRNA-133b; 5. siCHECK-3UTR+100 nM microRA-133b; 6. siCHECK-3UTR-m+100 nM microRNA-133b. B) Protein expression of TAGLN2 in 293T cells after being treated with miR-133b. C) mRNA expression of TAGLN2 in 293T cells after being treated with miR-133b. Each sample was tested in triplicate and the data were presented in mean ± SD. The experiments were repeated twice and similar results were obtained. **<i>P</i><0.01.</p

The expression of TAGLN2 at the human oocyte.

<p>After treatment with IGF-1 for 24 hours, the expression levels of <i>TAGLN2</i> in human ovary were analyzed by real-time PCR and immunofluorescence: A) The expression levels of <i>TAGLN2</i> in human ovary; B) The expression levels of TAGLN2 in human ovary; C) The expression levels of TAGLN2 in human ovary after treatment with IGF-1; D) Average gray value. Each sample was tested in triplicate and the data were presented in mean ± SD. The experiments were repeated twice and similar results were obtained (mean±SD, n = 3, **<i>P</i><0.01).</p

Expression of TAGLN2 in mouse ovarian.

<p>Western blotting was used to measure the expression of TAGLN2 protein in mouse ovary. TAGLN2 had a higher expression level in the 8-week-old mouse ovary than that of the 4-week-old mouse ovary.A)Westernblot:1. 4-week mouse's ovarian, 2. 8-week mouse's ovarian and B) Relative Density(n = 3, mean±SD, **<i>P</i><0.01).</p