Additional file 1 of The difference of intestinal microbiota composition between Lantang and Landrace newborn piglets

Figure S1 Stacked bar chart. Bar plot shows the relative abundance of jejunal microbiota at the phylum level in each group. Figure S2 Non-metric multi-dimensional scaling (NMDS). The NMDS analysis was based on the Bray–Curtis distance. Each point in the figure represents a sample, and the samples in the same group are represented by the same color. Figure S3 On the left is the UPGMA cluster tree structure of each sample at the OTU level, and on the right is the relative abundance distribution map of each sample at the genus level. Figure S4 Comparison of the classification of rumen microbiota between two groups by linear discriminant analysis effect size (LefSe) method. The LDA value distribution histogram (left) shows the species with significant differences in abundance in the two groups, and the length of the histogram represents the impact of different species. In the taxonomic cladogram (right), the circles radiating from the inside to the outside represent the classification level from phylum to species. Figure S5 Sparcc network diagram and heat map. Different nodes in the network diagram represent different dominant genera. The connection between nodes indicates that there is correlation between the two genera. The thickness of the line indicates the strength of the correlation, and the size of the node indicates the number of other bacteria associated with the bacterium. Figure S6 Functional prediction STAMP difference analysis. The analysis results show the top 30 differential classification Clusters between the two group in COG function pathways (P < 0.05, 95% confidence interval)

Additional file 4 of The difference of intestinal microbiota composition between Lantang and Landrace newborn piglets

Tables S3 (A) Differences between two groups identified at Phylum-taxa level. (B) Differences between two groups identified at Class-taxa level. (C) Differences between two groups identified at Order-taxa level. (D) Differences between two groups identified at Family-taxa level. (E) Differences between two groups identified at Genus-taxa level. (F) Differences between two groups identified at Species-taxa level

Additional file 2 of The difference of intestinal microbiota composition between Lantang and Landrace newborn piglets

Tables S1 Quality control and preprocessing of metagenomic datasets

Additional file 5 of The difference of intestinal microbiota composition between Lantang and Landrace newborn piglets

Tables S4 (A) Function prediction results in the COG database. (B) Function prediction results in the KO database. (C) Differential functional prediction analysis of each sample in in the COG databas. (D) Differential functional prediction analysis of each sample in in the KO database

Additional file 3 of The difference of intestinal microbiota composition between Lantang and Landrace newborn piglets

Tables S2 (A) Sequence composition of each sample at each level based on the SILVA and NT-16S database. (B) The aligned percentages that annotated at Phylum level. (C) The aligned percentages that annotated at Class level. (D) The aligned percentages that annotated at Order level. (E) The aligned percentages that annotated at Family level. (F) The aligned percentages that annotated at Genus level. (G) The aligned percentages that annotated at Species level

MiRNA-181a Regulates Adipogenesis by Targeting Tumor Necrosis Factor-α (TNF-α) in the Porcine Model

<div><p>Adipogenesis is tightly regulated by altering gene expression, and TNF-α is a multifunctional cytokine that plays an important role in regulating lipogenesis. MicroRNAs are strong post-transcriptional regulators of cell differentiation. In our previous work, we found high expression of <i>miR-181a</i> in a fat-rich pig breed. Using bioinformatic analysis, <i>miR-181a</i> was identified as a potential regulator of TNF-α. Here, we validated TNF-α as the target of <i>miR-181a</i> by a dual luciferase assay. In response to adipogenesis, a mimic or inhibitor was used to overexpress or reduce <i>miR-181a</i> expression in porcine pre-adipocytes, which were then induced into mature adipocytes. Overexpression of <i>miR-181a</i> accelerated accumulation of lipid droplets, increased the amount of triglycerides, and repressed TNF-α protein expression, while the inhibitor had the opposite effect. At the same time, TNF-alpha rescued the increased lipogenesis by miR181a mimics. Additionally, <i>miR-181a</i> suppression decreased the expression of fatty synthesis associated genes <i>PDE3B</i> (phosphodiesterase 3B), LPL (lipoprotein lipase), <i>PPARγ</i> (proliferator-activated receptor-γ), <i>GLUT1</i>(glucose transporter), <i>GLUT4</i>, <i>adiponectin</i> and <i>FASN</i> (fatty acid synthase), as well as key lipolytic genes HSL (hormone-sensitive lipase) and <i>ATGL</i> (adipose triglyceride lipase) as revealed by quantitative real-time PCR. Our study provides the first evidence of the role of <i>miR-181a</i> in adipocyte differentiation by regulation of TNF-α, which may became a new therapeutic target for anti-obesity drugs.</p> </div

Changes in <i>miR-181a</i> levels modulate adipocyte differentiation.

<p>After transfection of protected <i>miR-181a</i> mimic or inhibitor, pre-adipocytes were stimulated to differentiate, taking TNF-α siRNA, TNF-α inhibitor and TNF-α as the reference or cotransfected controls. After 8 days, cells were harvested for analysis. (A) Formation of lipid droplets in the cells that transfected with miR-181a mimics and inhibitor were observed by staining with Oil Red O. (B) Formation of lipid droplets in the cells that treated with TNF-α siRNA, TNF-α inhibitor and TNF-α were observed as miR-181a treatment. (C) Formation of lipid droplets in the cells that contransfection with TNF-α were observed as miR-181a mimics treatment. (D) TNF-α protein abundance 8 days post-induction was assessed by Western blot analysis and quantified using gray scale scanning. (E,F) The degrees of differentiation in concorresponding to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0071568#pone-0071568-g005" target="_blank">Figure 5B,5C</a> treatment, respectively, were also determined by measuring the TG level, represented as the means SD, and each sample was assayed in triplicate (*<i>P</i> < 0.05, **<i>P</i> < 0.01, n=8).</p

Level of <i>miR-181a</i> in porcine adipocytes after transfection of its mimic.

<p>The <i>miR-181a</i> mimic was transfected using Lipofectmine 2000, and endogenous <i>miR-181a</i> and mimics were quantified using qRT-PCR (n = 6). (A) Concentration of measured <i>miR-181a</i> in porcine adipocytes declined from day 0 to day 8. (B) At day 8, the measured <i>miR-181a</i> concentration in the <i>miR-181a</i> mimic transfected group remained significantly higher vs. control (<i>P</i> < 0.01), while transfection of the <i>miR-181a</i> inhibitor resulted in a significantly lower <i>miR-181a</i> level vs. the control (<i>P</i> < 0.01), n = 6.</p

MiR-181aa suppresses TNF-α protein level in Hela cells detected by Western blot.

<p>MiR-181aa suppresses TNF-α protein level in Hela cells detected by Western blot.</p

Lauric Acid Accelerates Glycolytic Muscle Fiber Formation through TLR4 Signaling

Lauric acid (LA), which is the primary fatty acid in coconut oil, was reported to have many metabolic benefits. TLR4 is a common receptor of lipopolysaccharides and involved mainly in inflammation responses. Here, we focused on the effects of LA on skeletal muscle fiber types and metabolism. We found that 200 μM LA treatment in C2C12 or dietary supplementation of 1% LA increased MHCIIb protein expression and the proportion of type IIb muscle fibers from 0.452 ± 0.0165 to 0.572 ± 0.0153, increasing the mRNA expression of genes involved in glycolysis, such as HK2 and LDH2 (from 1.00 ± 0.110 to 1.35 ± 0.0843 and from 1.00 ± 0.123 to 1.71 ± 0.302 <i>in vivo</i>, respectively), decreasing the catalytic activity of lactate dehydrogenase (LDH), and transforming lactic acid to pyruvic acid. Furthermore, LA activated TLR4 signaling, and TLR4 knockdown reversed the effect of LA on muscle fiber type and glycolysis. Thus, we inferred that LA promoted glycolytic fiber formation through TLR4 signaling