A complex and highly orchestrated gene expression program chiefly establishes the properties that define the adipocyte phenotype, in which the vast majority of factors are involved in transcriptional regulation. However, the mechanisms by post-transcriptional modulation are poorly understood. Here, we showed that zinc finger protein (Zfp217) couples gene transcription to m6A mRNA modification to facilitate adipogenesis. Zfp217 modulates m6A mRNA methylation by activating the transcription of m6A demethylase FTO. Consistently, depletion of Zfp217 compromises adipogenic differentiation of 3T3L1 cells and results in a global increase of m6A modification. Moreover, the interaction of Zfp217 with YTHDF2 is critical for allowing FTO to maintain its interaction with m6A sites on various mRNAs, as loss of Zfp217 leads to FTO decrease and augmented m6A levels. These findings highlight a role for Zfp217-dependent m6A modification to coordinate transcriptional and post-transcriptional regulation and thus promote adipogenic differentiation.

Sirtuin 1 (SIRT1) regulates adipocyte and osteoblast differentiation. However, the underlying mechanism should be investigated. This study revealed that SIRT1 acts as a crucial repressor of adipogenesis. RNA-interference-mediated SIRT1 knockdown or genetic ablation enhances adipogenic potential, whereas SIRT1 overexpression inhibits adipogenesis in mesenchymal stem cells (MSCs). SIRT1 also deacetylates the histones of sFRP1, sFRP2, and Dact1 promoters; inhibits the mRNA expression of sFRP1, sFRP2, and Dact1; activates Wnt signaling pathways; and suppresses adipogenesis. SIRT1 deacetylates β-catenin to promote its accumulation in the nucleus and thus induces the transcription of genes that block MSC adipogenesis. In mice, the partial absence of SIRT1 promotes the formation of white adipose tissues without affecting the development of the body of mice. Our study described the regulatory role of SIRT1 in Wnt signaling and proposed a regulatory mechanism of adipogenesis.

BackgroundEvidence based on genomic sequences is urgently needed to confirm the phylogenetic relationship between Mesorhizobium strain MAFF303099 and M. huakuii. To define underlying causes for the rather striking difference in host specificity between M. huakuii strain 7653R and MAFF303099, several probable determinants also require comparison at the genomic level. An improved understanding of mobile genetic elements that can be integrated into the main chromosomes of Mesorhizobium to form genomic islands would enrich our knowledge of how genome dynamics may contribute to Mesorhizobium evolution in general.ResultsIn this study, we sequenced the complete genome of 7653R and compared it with five other Mesorhizobium genomes. Genomes of 7653R and MAFF303099 were found to share a large set of orthologs and, most importantly, a conserved chromosomal backbone and even larger perfectly conserved synteny blocks. We also identified candidate molecular differences responsible for the different host specificities of these two strains. Finally, we reconstructed an ancestral Mesorhizobium genomic island that has evolved into diverse forms in different Mesorhizobium species.ConclusionsOur ortholog and synteny analyses firmly establish MAFF303099 as a strain of M. huakuii. Differences in nodulation factors and secretion systems T3SS, T4SS, and T6SS may be responsible for the unique host specificities of 7653R and MAFF303099 strains. The plasmids of 7653R may have arisen by excision of the original genomic island from the 7653R chromosome.Electronic supplementary materialThe online version of this article (doi: 10.1186/1471-2164-15-440) contains supplementary material, which is available to authorized users.

It is well known that obesity-induced white adipose tissue inflammation is an important reason for insulin-resistance and type 2 diabetes mellitus. Sirtuin-1 (SIRT1) is an important regulator of inflammtion response pathways in white adipose tissue. Here, we found that miR-221 negatively regulated SIRT1 in white adipose tissue during inflammation and HFD-induced obesity. MiR-221 is a putative oncogene which has been found overexpressed in a number of human tumors. Recently, it has also found that miR-221 was increased in obese adipose tissue and may be involved in inflammation and insulin-resistance. However the specific mechanism remains to be elucidated. In our present study, we found that overexpression of miR-221 decreased the protein abundance of SIRT1 and caused inflammation and insulin-resistance in differentiated 3T3-L1 cells. Conversely, miR-221 inhibition increased the protein levels, ameliorated inflammation, and improved insulin sensitivity. Moreover, inhibition of SIRT1 by EX527 significantly diminished the downregulation of the inflammation and insulin-resistance levels induced by the miR-221 inhibitor. In conclusion, our data suggest that miR-221 promotes white adipose tissue inflammation and decreases insulin sensitivity in obesity, at least in part, through suppressing SIRT1.

Digital dentistry plays a pivotal role in dental health care. A critical step in many digital dental systems is to accurately delineate individual teeth and the gingiva in the 3-dimension intraoral scanned mesh data. However, previous state-of-the-art methods are either time-consuming or error prone, hence hindering their clinical applicability. This article presents an accurate, efficient, and fully automated deep learning model trained on a data set of 4,000 intraoral scanned data annotated by experienced human experts. On a holdout data set of 200 scans, our model achieves a per-face accuracy, average-area accuracy, and area under the receiver operating characteristic curve of 96.94%, 98.26%, and 0.9991, respectively, significantly outperforming the state-of-the-art baselines. In addition, our model takes only about 24 s to generate segmentation outputs, as opposed to >5 min by the baseline and 15 min by human experts. A clinical performance test of 500 patients with malocclusion and/or abnormal teeth shows that 96.9% of the segmentations are satisfactory for clinical applications, 2.9% automatically trigger alarms for human improvement, and only 0.2% of them need rework. Our research demonstrates the potential for deep learning to improve the efficacy and efficiency of dental treatment and digital dentistry.