Biology subjects

Deng, D.

Publications and source records attributed to Deng, D..

4 recordsLinked to original sources

bioRxiv2018

Hydroxymethylated-P16 Allele Is Transcription-Inactive

Background5-Methylcytosine can be oxidized into 5-hydroxymethylcytosine (5hmC) in the genome. Methylated-P16 (P16M) can be oxidized into completely hydroxymethylated-P16 (P16H) in human cancer and precancer cells. The aim of this study is to investigate the biological function of P16H.\n\nMethodsTrue P16M and P16H were analyzed using bisulfite/TAB-based assays. A ZFP-based P16-specific dioxygenase (P16-TET) was constructed and used to induce P16H. Cell proliferation and migration were determined with a series of biological analyses.\n\nResults(A) The 5hmCs were enriched in the antisense-strand of the P16 exon-1 in HCT116 and AGS cells containing methylated-P16 alleles (P16M). (B) P16-TET induced both P16H and P16 demethylation in H1299 and AGS cells and reactivated P16 expression. Notably, P16H was only detectable in the sorted P16-TET H1299 and AGS cells that did not show P16 expression. (C) P16-TET significantly inhibited the xenograft growth derived from H1299 cells in NOD-SCID mice, but did not inhibit the growth of P16-deleted A549 control cells. P16-siRNA knockdown could rescue P16-TET-inhibited cell migration.\n\nConclusionHydroxymethylated P16 alleles are transcriptionally inactive.\n\nAUTHOR SUMMARYIt is well known that 5-methylcytosine (5mC) in genomic DNA of mammalian cells can be oxidized into 5-hydroxymethylcytosine (5hmC) and other derivates by DNA dioxygenase TETs. While conversion of 5mC to 5hmC plays an important role in active DNA demethylation through further oxidations, a certain proportion of 5hmCs remain in the genome. Although it is supposed that occurrence of 5hmCs may contribute to the flexibility of chromatin and the protection of the bivalent promoters from hypermethylation, the direct effect of 5hmCs on gene transcription is unknown. In the present study, we engineered a zinc-finger protein-based P16-specific DNA dioxygenase and used it to induce P16 hydroxymethylation and demethylation in cancer cells. Our results demonstrate, for the first time, that the hydroxymethylated P16 alleles retain transcriptionally inactive. This is supported by our recent findings that mRNAs are always transcribed only from the unmethylated P16 strands, but not from the hydroxymethylated/methylated strands in HCT116 cells, and that the risks for malignant transformation are similar for patients with the P16 methylation-positive oral epithelial dysplasia with and without P16 hydroxymethylation in a prospective study.

molecular biology↗

bioRxiv2018

Effects of P16 DNA Methylation on Proliferation, Senescence, and Lifespan of Human Fibroblasts

The aim is to study the effects of P16 DNA methylation on lifespan of normal cells. An expression-controllable pTRIPZ vector expressing P26-specific zinc finger binding protein-based methyltransferase (P16-Dnmt) was used to induce P16 methylation in primary CCD-I8C0 fibroblasts via stable transfection. Long-term dynamic IncuCyte analysis showed that CCD-I8C0 fibroblasts expressing baseline P16-Dnmt continued proliferating until passage-26 in the 53th post-transfection week, while vector control cells stopped proliferating at passage-6 and completely died 2 weeks later. The proliferation rate of baseline P16-Dnmt cells was significantly higher than that of vector control cells. The proportion of P-galactosidase-positive staining cells was significantly decreased in baseline P16-Dnmt cells compared to vector control cells. The P16 expression was lost in baseline P16-Dnmt cells at and after passage-6. The average telomere length in baseline P16-Dnmt cells also gradually decreased. In conclusion, P16 methylation could prevent senescence, promote proliferation, and expand lifespan of human fibroblasts, which may play a role in cancer development.\n\nSummaryA zinc finger protein-based DNA methyltransferase (P16-Dnmt) expressed at the baseline level could specifically methylate P16 promoter CpG islands. P16 methylation induced by baseline P16-Dnmt could significantly prevent senescence, promote proliferation, and expand lifespan of primary human fibroblasts.

cell biology↗

bioRxiv2018

bpRNA: Large-scale Automated Annotation and Analysis of RNA Secondary Structure

While RNA secondary structure prediction from sequence data has made remarkable progress, there is a need for improved strategies for annotating the features of RNA secondary structures. Here we present bpRNA, a novel annotation tool capable of parsing RNA structures, including complex pseudoknot-containing RNAs, to yield an objective, precise, compact, unambiguous, easily-interpretable description of all loops, stems, and pseudoknots, along with the positions, sequence, and flanking base pairs of each such structural feature. We also introduce several new informative representations of RNA structure types to improve structure visualization and interpretation. We have further used bpRNA to generate a web-accessible meta-database, \"bpRNA-1m\", of over 100,000 single-molecule, known secondary structures; this is both more fully and accurately annotated and over 20-times larger than existing databases. We use a subset of the database with highly similar ([≥]90% identical) sequences filtered out to report on statistical trends in sequence, flanking base pairs, and length. Both the bpRNA method and the bpRNA-1m database will be valuable resources both for specific analysis of individual RNA molecules and large-scale analyses such as are useful for updating RNA energy parameters for computational thermodynamic predictions, improving machine learning models for structure prediction, and for benchmarking structure-prediction algorithms.

bioinformatics↗

bioRxiv2018

LinearFold: Linear-Time Prediction of RNA Secondary Structures

Predicting the secondary structure of an RNA sequence with speed and accuracy is useful in many applications such as drug design. The state-of-the-art predictors have a fundamental limitation: they have a run time that scales cubically with the length of the input sequence, which is slow for longer RNAs and limits the use of secondary structure prediction in genome-wide applications. To address this bottleneck, we designed the first linear-time algorithm for this problem. which can be used with both thermodynamic and machine-learned scoring functions. Our algorithm, like previous work, is based on dynamic programming (DP), but with two crucial differences: (a) we incrementally process the sequence in a left-to-right rather than in a bottom-up fashion, and (b) because of this incremental processing, we can further employ beam search pruning to ensure linear run time in practice (with the cost of exact search). Even though our search is approximate, surprisingly, it results in even higher overall accuracy on a diverse database of sequences with known structures. More interestingly, it leads to significantly more accurate predictions on the longest sequence families in that database (16S and 23S Ribosomal RNAs), as well as improved accuracies for long-range base pairs (500+ nucleotides apart).

bioinformatics↗