Pyrosequencing of Plaque Microflora In Twin Children with Discordant Caries Phenotypes

<div><p>Despite recent successes in the control of dental caries, the mechanism of caries development remains unclear. To investigate the causes of dental decay, especially in early childhood caries, the supragingival microflora composition of 20 twins with discordant caries phenotypes were analyzed using high-throughput pyrosequencing. In addition, the parents completed a lifestyle questionnaire. A total of 228,789 sequencing reads revealed 10 phyla, 84 genera, and 155 species of microflora, the relative abundances of these strains varied dramatically among the children, Comparative analysis between groups revealed that <i>Veillonella</i>, <i>Corynebacterium</i> and <i>Actinomyces</i> were presumed to be caries-related genera, <i>Fusobacterium</i>, <i>Kingella</i> and <i>Leptotrichia</i> were presumed to be healthy-related genus, yet this six genera were not statistically significant (P>0.05). Moreover, a cluster analysis revealed that the microbial composition of samples in the same group was often dissimilar but that the microbial composition observed in twins was usually similar. Although the genetic and environmental factors that strongly influence the microbial composition of dental caries remains unknown, we speculate that genetic factors primarily influence the individual's susceptibility to dental caries and that environmental factors primarily regulate the microbial composition of the dental plaque and the progression to caries. By using improved twins models and increased sample sizes, our study can be extended to analyze the specific genetic and environmental factors that affect the development of caries.</p></div

Genera and species with significant differences in representation (P<0.05).

<p>A: Comparisons between different kindergartens. B: Comparisons between different samples in the same group.</p

Weighted Unifrac clustering results of the samples.

<p>Samples having similar plaque microbiota compositions are usually clustered in a sub-branch. The different colors denote different kindergartens.</p

Principal Component Analysis results on individual samples.

<p>Principal Component Analysis (PCA) results on all individual samples at the level of OTUs clustering sequences at a 3% difference: A) the plot of the PCA axis 1 (accounting for 30.26% of intersample variation) and the axis 2 (17.56% of intersample variation); B) the plot of the PCA axis 1 and the axis 3 (13.93% of intersample variation). Blue dots—samples(Square) from group H1, orange dots—samples(Triangle) from groupH2, red dots—samples(Round) from group C2. Data were normalized to an equal number of reads per sample and log2 transformed.</p

The barplot graph of samples microorganisms and the predominant bacteria of three groups.

<p>(A, B, C) Abundance and prevalence of bacteria at the phylum, genus, and species level in the 30 plaque samples. (a, b, c) Mean levels of the predominant bacteria in groups H1, H2 and C2 at the phylum, genus, and species level.</p

Circular maximum likelihood phylogenetic tree at level of genus.

<p>The inner band shows genera colored by their corresponding phylum (see key for taxa with multiple members), the outer band shows overall relative abundance of each genus in different groups. The tree was constructed in iTOL (Letunic and Bork, 2007).</p

Discovery of Novel, Selective Prostaglandin EP4 Receptor Antagonists with Efficacy in Cancer Models

Prostaglandin E2 (PGE2) receptor 4 (EP4) is one of four EP receptors commonly upregulated in the tumor microenvironment and plays vital roles in stimulating cell proliferation, invasion, and metastasis. Biochemical blockade of the PGE2–EP4 signaling pathway is a promising strategy for controlling inflammatory and immune related disorders. Recently combination therapies of EP4 antagonists with anti-PD-1 or chemotherapy agents have emerged in clinical studies for lung, breast, colon, and pancreatic cancers. Herein, a novel series of indole-2-carboxamide derivatives were identified as selective EP4 antagonists, and SAR studies led to the discovery of the potent compound 36. Due to favorable pharmacokinetics properties and good oral bioavailability (F = 76%), compound 36 was chosen for in vivo efficacy studies. Compound 36 inhibited tumor growth in a CT-26 colon cancer xenograft better than E7046 and a combination of 36 with capecitabine significantly suppressed tumor growth (TGI up to 94.26%) in mouse models