No abstract

Lysine crotonylation (Kcr) is a newly discovered posttranslational modification (PTM) existing in mammals. A global crotonylome analysis was undertaken in rice ( L. ) using high accuracy nano-LC-MS/MS in combination with crotonylated peptide enrichment. A total of 1,265 lysine crotonylation sites were identified on 690 proteins in rice seedlings. Subcellular localization analysis revealed that 51% of the crotonylated proteins identified were localized in chloroplasts. The photosynthesis-associated proteins were also mostly enriched in total crotonylated proteins. In addition, a genomic localization analysis of histone Kcr by ChIP-seq was performed to assess the relevance between histone Kcr and the genome. Of the 10,923 identified peak regions, the majority (86.7%) of the enriched peaks were located in gene body, especially exons. Furthermore, the degree of histone Kcr modification was positively correlated with gene expression in genic regions. Compared with other published histone modification data, the Kcr was co-located with the active histone modifications. Interestingly, histone Kcr-facilitated expression of genes with existing active histone modifications. In addition, 77% of histone Kcr modifications overlapped with DNase hypersensitive sites (DHSs) in intergenic regions of the rice genome and might mark other cis-regulatory DNA elements that are different from IPA1, a transcription activator in rice seedlings. Overall, our results provide a comprehensive understanding of the biological functions of the crotonylome and new active histone modification in transcriptional regulation in plants.

Highlights d Microbially derived secondary bile acid isoalloLCA enhances T reg differentiation d A biosynthetic gene cluster in gut Bacteroidetes converts 3-oxoLCA to isoalloLCA d NR4A1 is required for the isoalloLCA-mediated differentiation of Treg cells d Levels of isoalloLCA and corresponding genes are negatively correlated with IBD

Histone marks control many cellular processes including DNA damage repair. This review will focus primarily on the active histone mark H3K36me3 in the regulation of DNA damage repair and the maintenance of genomic stability after DNA damage. There are diverse clues showing H3K36me3 participates in DNA damage response by directly recruiting DNA repair machinery to set the chromatin at a "ready" status, leading to a quick response upon damage. Reduced H3K36me3 is associated with low DNA repair efficiency. This review will also place a main emphasis on the H3K36me3-mediated DNA damage repair in the tumorigenesis of the newly found oncohistone mutant tumors. Gaining an understanding of different aspects of H3K36me3 in DNA damage repair, especially in cancers, would share the knowledge of chromatin and DNA repair to serve to the drug discovery and patient care.

The expression profile of seven lncRNAs can effectively predict ER after surgical resection for HCC.

The elucidation of epigenetic responses of salt-responsive genes facilitates understanding of the underlying mechanisms that confer salt tolerance in rice. However, it is still largely unknown how epigenetic mechanisms are associated with the expression of salt-responsive genes in rice and other crops. In this study, we reported tissue-specific gene expression and tissue-specific changes in chromatin modifications or signatures between seedlings and roots in response to salt treatment. Our study indicated that among six of individual mark examined (H3K4me3, H3K27me3, H4K12ac, H3K9ac, H3K27ac and H3K36me3), a positive association between salt-related changes in histone marks and the expression of differentially expressed genes (DEGs) was observed only for H3K9ac and H4K12ac in seedlings and H3K36me3 in roots. In contrast, chromatin states (CSs) with combinations of six histone modification marks played crucial roles in the differential expression of salt-responsive genes between seedlings and roots. Most importantly, CS7 containing the bivalent marks H3K4me3 and H3K27me3, with a mutual exclusion of functions with each other, displayed distinct functions in the expression of DEGs in both tissues. Specifically, H3K27me3 in CS7 mainly suppressed the expression of DEGs in roots, while H3K4me3 affected the expression of down- and up-regulated genes, possibly by antagonizing the repressive role of H3K27me3 in seedlings. Our findings indicate distinct impacts of the CSs on the differential expression of salt-responsive genes between seedlings and roots in rice, which provides an important background for understanding chromatin-based epigenetic mechanisms that might confer salt tolerance in plants.

Conclusions:The dynamic change of CEA, CA125, CYFRA21-1, and SCC-Ag from baseline have prognostic value for late-stage NSCLC patients treated with PD-1/PD-L1 inhibitors. Decrease of associated biomarkers serum levels were associated with favorable clinical outcomes.

Background Determination of genome-wide DNA methylation is significant for both basic research and drug development. As a key epigenetic modification, this biochemical process can modulate gene expression to influence the cell differentiation which can possibly lead to cancer. Due to the involuted biochemical mechanism of DNA methylation, obtaining a precise prediction is a considerably tough challenge. Existing approaches have yielded good predictions, but the methods either need to combine plenty of features and prerequisites or deal with only hypermethylation and hypomethylation. Results In this paper, we propose a deep learning method for prediction of the genome-wide DNA methylation, in which the Methylation Regression is implemented by Convolutional Neural Networks (MRCNN). Through minimizing the continuous loss function, experiments show that our model is convergent and more precise than the state-of-art method (DeepCpG) according to results of the evaluation. MRCNN also achieves the discovery of de novo motifs by analysis of features from the training process. Conclusions Genome-wide DNA methylation could be evaluated based on the corresponding local DNA sequences of target CpG loci. With the autonomous learning pattern of deep learning, MRCNN enables accurate predictions of genome-wide DNA methylation status without predefined features and discovers some de novo methylation-related motifs that match known motifs by extracting sequence patterns. Electronic supplementary material The online version of this article (10.1186/s12864-019-5488-5) contains supplementary material, which is available to authorized users.