Impact of Porphyromonas gingivalis-odontogenic infection on the pathogenesis of non-alcoholic fatty liver disease

AbstractAim: Non-alcoholic fatty liver disease is characterized by diffuse hepatic steatosis and has quickly risen to become the most prevalent chronic liver disease. Its incidence is increasing yearly, but the pathogenesis is still not fully understood. Porphyromonas gingivalis (P. gingivalis) is a major pathogen widely prevalent in periodontitis patients. Its infection has been reported to be a risk factor for developing insulin resistance, non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH), and metabolic syndrome. The aim of this review is to evaluate the association between P. gingivalis infection and NAFLD, identify the possible etiopathogenetic mechanisms, and raise public awareness of oral health to prevent and improve NAFLD.Methods: After searching in PubMed and Web of Science databases using ‘Porphyromonas gingivalis’, ‘non-alcoholic fatty liver disease’, and ‘hepatic steatosis’ as keywords, studies related were compiled and examined.Results: P. gingivalis infection is a direct risk factor for NAFLD based on clinical and basic research. Moreover, it induces systematic changes and systemic abnormalities by disrupting metabolic, inflammatory, and immunologic homeostasis.Conclusion: P. gingivalis-odontogenic infection promotes the occurrence and development of NAFLD. Further concerns are needed to emphasize oral health and maintain good oral hygiene for the prevention and treatment of NAFLD

Reference gene stability of a synanthropic fly, Chrysomya megacephala

Abstract Background Stable reference genes are essential for accurate normalization in gene expression studies with reverse transcription quantitative polymerase chain reaction (qPCR). A synanthropic fly, Chrysomya megacephala, is a well known medical vector and forensic indicator. Unfortunately, previous studies did not look at the stability of reference genes used in C. megacephala. Results In this study, the expression level of Actin, ribosomal protein L8 (Rpl8), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), elongation factor 1α (EF1), α-tubulin (α-TUB), β-tubulin (β-TUB), TATA binding box (TBP), 18S rRNA (18S) and ribosomal protein S7 (Rps7) were evaluated for their stability using online software RefFinder, which combines the normal software of the ΔCt method, BestKeeper, Normfinder, and geNorm. Moreover the number of suitable reference gene pairs was also suggested by Excel-based geNorm. The expression levels of these reference genes were evaluated under different experimental conditions with special perspectives of forensic applications: developmental stages (eggs, first, second and third instar larvae, pupae and adults); food sources of larvae (pork, fish and chicken); feeding larvae with drugs (untreated control, Estazolam and Marvelon); feeding larvae with heavy metals (untreated control, cadmium and zinc); tissues of adults (head, thorax, abdomen, legs and wings). According to RefFinder, EF1 was the most suitable reference gene of developmental stages, food and tissues; 18S and GAPDH were the most suitable reference genes for drugs and heavy metals, respectively, which could be widely used for quantification of target gene expression with qPCR in C. megacephala. Suitable reference gene pairs were also suggested by geNorm. Conclusion This fundamental but vital work should facilitate the gene studies of related biological processes and deepen the understanding in physiology, toxicology, and especially medical and forensic entomology of C. megacephala

Bulletin mensuel de statistique / Institut national de la statistique et des études économiques...

octobre 19651965/10 (N10,A16)

Additional file 3: Table S3. of Reference gene stability of a synanthropic fly, Chrysomya megacephala

Ranking orders of the candidate reference genes of C. megacephala within all pupal samples. Ct values within all pupal samples were combined together, and ranking orders of the candidate reference genes were calculated by RefFinder. (DOCX 17 kb

Comprehensive Transcriptome Analyses of the Fructose-Fed Syrian Golden Hamster Liver Provides Novel Insights into Lipid Metabolism

<div><p>Dyslipidemia has been widely proven to contribute to cardiovascular diseases and other metabolic disorders, especially in insulin resistance and type 2 diabetes. The overproduction of VLDL is a significant characteristic of dyslipidemia, indicating the dysfunction of hepatic lipid metabolism, from triglyceride synthesis to transport. The fructose-fed Syrian golden hamster is an established animal model for the study of VLDL assembly with insulin resistance, however, it remains unknown how VLDL production is regulated at the transcriptional level due to the absence of a complete hamster genome. Here, we performed deep sequencing and constructed an mRNA-miRNA-lncRNA interaction network of Syrian golden hamster liver in order to reveal the global transcription profile and find potential RNA molecular regulation of VLDL production. We identified 4,450 novel multi-exon hamster lncRNAs and 755 miRNAs expressed in liver. Additionally, 146 differentially expressed coding genes, 27 differentially expressed lncRNA genes, as well as 16 differentially expressed miRNAs were identified. We then constructed an mRNA-miRNA-lncRNA interaction network that may potentially regulate VLDL production, and interestingly found several microRNA-centered regulatory networks. In order to verify our interpretation, miR-486 was selected for further experiments. Overexpression or down-regulation of miR-486 in fructose-fed hamsters resulted in altered hepatic expression of proteins involved in VLDL production, and in modulated levels of circulating VLDL. Our findings implicated that miR-486 is a potential regulator of circulating VLDL levels. These results provide new insights and a valuable resource for further study of the molecular mechanisms of VLDL secretion.</p></div

Profiling of differentially expressed circRNAs and functional prediction in peripheral blood mononuclear cells from patients with rheumatoid arthritis

AbstractBackground Rheumatoid arthritis (RA) is a chronic autoimmune disease associated with an increased risk of death, but its underlying mechanisms are not fully understood. Circular RNAs (circRNAs) have recently been implicated in various biological processes. The aim of this study was to investigate the key circRNAs related to RA.Methods A microarray assay was used to identify the differentially expressed circRNAs (DEcircRNAs) in peripheral blood mononuclear cells (PBMCs) from patients with RA compared to patients with osteoarthritis (OA) and healthy controls. Then, quantitative real-time PCR was applied to verify the DEcircRNAs, and correlations between the levels of DEcircRNAs and laboratory indices were analysed. We also performed extensive bioinformatic analyses including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genome (KEGG) pathway and potential circRNA–miRNA–mRNA network analyses to predict the function of these DEcircRNAs.Results A total of 35,342 and 6146 DEcircRNAs were detected in RA patients compared to controls and OA patients, respectively. Nine out of the DEcircRNAs in RA were validated by real-time PCR. There were correlations between the levels of DEcircRNAs and some of the laboratory indices. GO analyses revealed that these DEcircRNAs in RA were closely related to cellular protein metabolic processes, gene expression, the immune system, cell cycle, posttranslational protein modification and collagen formation. Functional annotation of host genes of these DEcircRNAs was implicated in several significantly enriched pathways, including metabolic pathways, ECM–receptor interaction, the PI3K–Akt signalling pathway, the AMPK signalling pathway, leukocyte transendothelial migration, platelet activation and the cAMP signalling pathway, which might be responsible for the pathophysiology of RA.Conclusions The findings of this study may help to elucidate the role of circRNAs in the specific mechanism underlying RA.Key messagesMicroarray assays showed that a total of 35,342 and 6146 DEcircRNAs were detected in RA patients compared to controls and OA patients, respectively.Nine out of the DEcircRNAs in RA were validated by real-time PCR, and the levels of the DEcircRNAs were related to some of the laboratory indices.GO analyses revealed that the DEcircRNAs in RA were closely related to cellular protein metabolic processes, gene expression, the immune system, etc.Functional annotation of host genes of the DEcircRNAs in RA was implicated in several significantly enriched pathways, including metabolic pathways, ECM–receptor interaction, the PI3K–Akt signalling pathway, etc

miR-486 regulates the production of VLDL through the PI3K-Akt-Signaling Pathway by targeting PTEN and Foxo1a.

<p>(a) Hepatic miR-486 levels determined by quantitative RT-PCR in hamsters injected with Adenovirus harboring miR-486 mimics and miR-486 antago, as well as a scrambled control. n = 6 per group. (b) Fast protein liquid chromatography (FPLC) analysis of sera from fructose-fed Syrian golden hamsters (n = 6) injected with lentivirus expressing miR-486. (c) Mean area under the curve (AUC) values calculated for VLDL fractions 14–16 isolated by FPLC from b. (d, h) Immunoblot analysis of hepatic Foxo1, Pten (d), and Mttp (h) in liver tissue from fructose-fed hamsters treated with miR-486 mimics and miR-486 antagonist showing expression levels of Foxo1. β-actin was used as a loading control. (e-g) Hepatic expression of Foxo1 (e), Pten (f), and Mttp (g) in hamsters injected with Adenovirus harboring miR-486 mimics and miR-486 antagonist, as well as a scrambled control. n = 6 per group.</p

Differentially expressed liver miRNAs and the interaction network.

<p>(a) Differentially expressed liver miRNAs. (b) KEGG pathway analysis of the identified differentially expressed miRNAs. (c) qPCR results for miRNAs. <i>(Data were analyzed by the Δ Δ Ct method</i>, <i>the values represent the means with SD</i>, <i>n = 10)</i>. (d) Validation of selected miRNAs. miRNAs from M to 6: marker (DL 2000), miR-10a-5p, miR-28-3p, miR-92-3p, miR-150-5p, miR-182-5p, miR-192-5p. (e) Differentially expressed mRNAs, miRNAs and lncRNAs interactions on VLDL secretion. This interaction network diagram was made by using Cytoscape.</p

Primer pairs selected for validation by qRT-PCR.

<p>Primer pairs selected for validation by qRT-PCR.</p

Overview of the data processing pipeline.

<p>Overview of the data processing pipeline.</p