Treatment with PPAR Agonist Clofibrate Inhibits the Transcription and Activation of SREBPs and Reduces Triglyceride and Cholesterol Levels in Liver of Broiler Chickens

PPAR agonist clofibrate reduces cholesterol and fatty acid concentrations in rodent liver by an inhibition of SREBP-dependent gene expression. In present study we investigated the regulation mechanisms of the triglyceride-and cholesterol-lowering effect of the PPAR agonist clofibrate in broiler chickens. We observed that PPAR agonist clofibrate decreases the mRNA and protein levels of LXR and the mRNA and both precursor and nuclear protein levels of SREBP1 and SREBP2 as well as the mRNA levels of the SREBP1 (FASN and GPAM) and SREBP2 (HMGCR and LDLR) target genes in the liver of treated broiler chickens compared to control group, whereas the mRNA level of INSIG2, which inhibits SREBP activation, was increased in the liver of treated broiler chickens compared to control group. Taken together, the effects of PPAR agonist clofibrate on lipid metabolism in liver of broiler chickens involve inhibiting transcription and activation of SREBPs and SREBP-dependent lipogenic and cholesterologenic gene expression, thereby resulting in a reduction of the triglyceride and cholesterol levels in liver of broiler chickens

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Integrative analysis of TP53 mutations in lung adenocarcinoma for immunotherapies and prognosis

Abstract Background The TP53 tumor suppressor gene is one of the most mutated genes in lung adenocarcinoma (LUAD) and plays a vital role in regulating the occurrence and progression of cancer. We aimed to elucidate the association between TP53 mutations, response to immunotherapies and the prognosis of LUAD. Methods Genomic, transcriptomic, and clinical data of LUAD were downloaded from The Cancer Genome Atlas (TCGA) dataset. Gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, gene set enrichment analysis (GSEA). Gene set variation analysis (GSVA) were performed to determine the differences in biological pathways. A merged protein–protein interaction (PPI) network was constructed and analyzed. MSIpred was used to analyze the correlation between the expression of the TP53 gene, tumor mutation burden (TMB) and tumor microsatellite instability (MSI). CIBERSORT was used to calculate the abundance of immune cells. Univariate and multivariate Cox regression analyses were used to determine the prognostic value of TP53 mutations in LUAD. Results TP53 was the most frequently mutated in LUAD, with a mutational frequency of 48%. GO and KEGG enrichment analysis, GSEA, and GSVA results showed a significant upregulation of several signaling pathways, including PI3K-AKT mTOR (P < 0.05), Notch (P < 0.05), E2F target (NES = 1.8, P < 0.05), and G2M checkpoint (NES = 1.7, P < 0.05). Moreover, we found a significant correlation between T cells, plasma cells, and TP53 mutations (R2 < 0.01, P = 0.040). Univariate and multivariate Cox regression analyses revealed that the survival prognosis of LUAD patients was related to TP53 mutations (Hazard Ratio (HR) = 0.72 [95% CI, 0.53 to 0.98], P < 0.05), cancer status (P < 0.05), and treatment outcomes (P < 0.05). Lastly, the Cox regression models showed that TP53 exhibited good power in predicting three- and five-year survival rates. Conclusions TP53 may be an independent predictor of response to immunotherapy in LUAD, and patients with TP53 mutations have higher immunogenicity and immune cell infiltration

Genetics STUDY ON RELATIONSHIP OF REX RABBIT RAPD MARKER AND REPRODUCTIVE PERFORMANCES

ABSTRACT RAPD (Random Amplified Polymorphic DNA) marker was applied to study the relationship between some reproductive performances in Rex Rabbit. In the study 15 random primers were selected to PCR for genomes DNA and to detect the amplification product using agarose gel electrophoresis. The study showed some relationship between four primers (OPA1, OPA7, OPA14 and OPA15) with productive performance of Rex Rabbit. From them nine, six, eight and eight bands were obtained respectively. Two groups with or without and №.2 band from OPA1 showed significant (P&lt;0.05) and highly significant (P&lt;0.01) differences in the birth weight and the birth litter size. As for the two groups with or without №.4 band from OPA7 showed distinguished (P&lt;0.05) or significant distinguished (P&lt;0.01) differences in the litter size, living litter size, birth weight and birth litter size. The two groups with or without №.6 band from OPA14 showed significant differences (P&lt;0.05) in the birth weight and birth litter size. The two groups with or without №.2 band from OPA15 showed distinguished significant differences (P&lt;0.01) in litter size, living litter size and birth litter size

Additional file 4 of Integrative analysis of TP53 mutations in lung adenocarcinoma for immunotherapies and prognosis

Additional file 4. SF3. Immune infiltration results of ESITMATE, xCell, EPIC, TIMER, CIBERSORTx, MCPcounter, quanTIseq, and IPS. The columns in the heatmap represent samples; yellow represents TP53 MUT samples, and green represents TP53 ET samples. The rows represent different cells in each immune infiltration analysis, and the content between the parentheses indicates the significance of the difference in immune infiltration between the TP53 MUT and TP53 WT groups (*: p <= 0.05; **: p <= 0.01; ***: p <= 0.001; ****: p <= 0.0001). The heatmap matrix depicts the immune infiltration levels derived from various techniques. In ESTIMATE, red represents high immune infiltration levels, whereas blue represents low immune infiltration levels. In xCell, yellow represents high immune infiltration levels, whereas dark purple represents low immune infiltration levels. In EPIC, red represents high immune infiltration levels, whereas blue represents low immune infiltration levels. In TIMER, light green represents high immune infiltration levels, whereas dark green represents low immune infiltration levels. In CIBERSORTx, light red represents high immune infiltration levels whereas dark red represents low immune infiltration levels. In MCPcounter, yellow represents high immune infiltration levels, whereas deep purple represents low immune infiltration levels. In quanTIseq, yellow represents high immune infiltration levels, whereas dark blue represents low immune infiltration levels. In IPS, yellow represents high immune infiltration levels, whereas darkred represents low immune infiltration levels

Additional file 2 of Integrative analysis of TP53 mutations in lung adenocarcinoma for immunotherapies and prognosis

Additional file 2. ST1. Information of TP53 TOP10 mutation frequency. The top 10 information items of TP53 mutations stored by mutation frequency in TP53 include mutation location, mutation type, codon variation, number of mutation samples, and mutation frequency

Additional file 1 of Integrative analysis of TP53 mutations in lung adenocarcinoma for immunotherapies and prognosis

Additional file 1. SF1. Workflow. TCGA: The Cancer Genome Atlas; LUAD: Lung Adenocarcinoma; GISTIC: Genomic Identification of Significant Targets in Cancer; CNV: Copy Number Variations; TMB: Tumor Mutation Burden; MSI: Microsatellite Instability; PPI: Protein-Protein Interaction; URA: Univariate Regression Analysis; MRA: Multivariate Regression Analysis KM: Kaplan Meier

Additional file 3 of Integrative analysis of TP53 mutations in lung adenocarcinoma for immunotherapies and prognosis

Additional file 3. SF4. Immunohistochemical staining of p53 protein was in normal and tumor tissues. In the HPA database. Immunohistochemical staining of p53 protein was lighter in normal tissues than in tumor tissues