Multi-Stage Image-Language Cross-Generative Fusion Network for Video-Based Referring Expression Comprehension.

Video-based referring expression comprehension is a challenging task that requires locating the referred object in each video frame of a given video. While many existing approaches treat this task as an object-tracking problem, their performance is heavily reliant on the quality of the tracking templates. Furthermore, when there is not enough annotation data to assist in template selection, the tracking may fail. Other approaches are based on object detection, but they often use only one adjacent frame of the key frame for feature learning, which limits their ability to establish the relationship between different frames. In addition, improving the fusion of features from multiple frames and referring expressions to effectively locate the referents remains an open problem. To address these issues, we propose a novel approach called the Multi-Stage Image-Language Cross-Generative Fusion Network (MILCGF-Net), which is based on one-stage object detection. Our approach includes a Frame Dense Feature Aggregation module for dense feature learning of adjacent time sequences. Additionally, we propose an Image-Language Cross-Generative Fusion module as the main body of multi-stage learning to generate cross-modal features by calculating the similarity between video and expression, and then refining and fusing the generated features. To further enhance the cross-modal feature generation capability of our model, we introduce a consistency loss that constrains the image-language similarity and language-image similarity matrices during feature generation. We evaluate our proposed approach on three public datasets and demonstrate its effectiveness through comprehensive experimental results.

Unlocking the potential: A novel prognostic index signature for acute myeloid leukemia.

Acute myeloid leukemia (AML) is an aggressive malignancy characterized by challenges in treatment, including drug resistance and frequent relapse. Recent research highlights the crucial roles of tumor microenvironment (TME) in assisting tumor cell immune escape and promoting tumor aggressiveness. This study delves into the interplay between AML and TME. Through the exploration of potential driver genes, we constructed an AML prognostic index (AMLPI). Cross-platform data and multi-dimensional internal and external validations confirmed that the AMLPI outperforms existing models in terms of areas under the receiver operating characteristic curves, concordance index values, and net benefits. High AMLPIs in AML patients were indicative of unfavorable prognostic outcomes. Immune analyses revealed that the high-AMLPI samples exhibit higher expression of HLA-family genes and immune checkpoint genes (including PD1 and CTLA4), along with lower T cell infiltration and higher macrophage infiltration. Genetic variation analyses revealed that the high-AMLPI samples associate with adverse variation events, including TP53 mutations, secondary NPM1 co-mutations, and copy number deletions. Biological interpretation indicated that ALDH2 and SPATS2L contribute significantly to AML patient survival, and their abnormal expression correlates with DNA methylation at cg12142865 and cg11912272. Drug response analyses revealed that different AMLPI samples tend to have different clinical selections, with low-AMLPI samples being more likely to benefit from immunotherapy. Finally, to facilitate broader access to our findings, a user-friendly and publicly accessible webserver was established and available at http://bioinfor.imu.edu.cn/amlpi. This server provides tools including TME-related AML driver genes mining, AMLPI construction, multi-dimensional validations, AML patients risk assessment, and figures drawing.

CancerMHL: the database of integrating key DNA methylation, histone modifications and lncRNAs in cancer.

The discovery of key epigenetic modifications in cancer is of great significance for the study of disease biomarkers. Through the mining of epigenetic modification data relevant to cancer, some researches on epigenetic modifications are accumulating. In order to make it easier to integrate the effects of key epigenetic modifications on the related cancers, we established CancerMHL (http://www.positionprediction.cn/), which provide key DNA methylation, histone modifications and lncRNAs as well as the effect of these key epigenetic modifications on gene expression in several cancers. To facilitate data retrieval, CancerMHL offers flexible query options and filters, allowing users to access specific key epigenetic modifications according to their own needs. In addition, based on the epigenetic modification data, three online prediction tools had been offered in CancerMHL for users. CancerMHL will be a useful resource platform for further exploring novel and potential biomarkers and therapeutic targets in cancer. Database URL: http://www.positionprediction.cn/.

Coregulatory effects of multiple histone modifications in key ferroptosis-related genes for lung adenocarcinoma.

Aim: The present study was designed to investigate the coregulatory effects of multiple histone modifications (HMs) on gene expression in lung adenocarcinoma (LUAD). Materials & methods: Ten histones for LUAD were analyzed using ChIP-seq and RNA-seq data. An innovative computational method is proposed to quantify the coregulatory effects of multiple HMs on gene expression to identify strong coregulatory genes and regions. This method was applied to explore the coregulatory mechanisms of key ferroptosis-related genes in LUAD. Results: Nine strong coregulatory regions were identified for six ferroptosis-related genes with diverse coregulatory patterns (CA9, PGD, CDKN2A, PML, OTUB1 and NFE2L2). Conclusion: This quantitative method could be used to identify important HM coregulatory genes and regions that may be epigenetic regulatory targets in cancers.

Potential biomarkers: The hypomethylation of cg18949415 and cg22193385 sites in colon adenocarcinoma.

Overall cancer hypomethylation had been identified in the past, but it is not clear exactly which hypomethylation site is the more important for the occurrence of cancer. To identify key hypomethylation sites, we studied the effect of hypomethylation in twelve regions on gene expression in colon adenocarcinoma (COAD). The key DNA methylation sites of cg18949415, cg22193385 and important genes of C6orf223, KRT7 were found by constructing a prognostic model, survival analysis and random combination prediction a series of in-depth systematic calculations and analyses, and the results were validated by GEO database, immune microenvironment, drug and functional enrichment analysis. Based on the expression values of C6orf223, KRT7 genes and the DNA methylation values of cg18949415, cg22193385 sites, the least diversity increment algorithm were used to predict COAD and normal sample. The 100 % reliability and 97.12 % correctness of predicting tumor samples were obtained in jackknife test. Moreover, we found that C6orf223 gene, cg18949415 site play a more important role than KRT7 gene, cg22193385 site in COAD. In addition, we investigate the impact of key methylation sites on three-dimensional chromatin structure. Our results will be help for experimental studies and may be an epigenetic biomarker for COAD.

M6A RNA methylation modification and tumor immune microenvironment in lung adenocarcinoma.

Lung adenocarcinoma is one of the deadliest tumors. Studies have shown that N6-methyladenosine RNA methylation regulators, as a dynamic chemical modification, affect the occurrence and development of lung adenocarcinoma. To investigate the relationship between mutations and expression levels of m6A regulators in lung adenocarcinoma, we investigated the mutations and expression levels of 38 m6A regulators. We found that mutations in m6A regulatory factors did not affect the changes in expression levels, and 19 differentially expressed genes were identified. All tumor samples were classified into two subtypes based on the expression levels of 19 differentially expressed m6A-regulated genes. Survival analysis showed significant differences in survival between the two subtypes. To explore the relationship between immune cell infiltration and survival in both subtypes, we calculated the infiltration of 23 immune cells in both subtypes, and we found that the subtype with high immune cell infiltration had better survival. We found that subtypes with low tumor purity and high stromal and immune scores had better survival. The m6A-related immune genes were identified by taking the intersection of differentially expressed genes and immune genes in the two isoforms and calculating the Pearson correlation coefficients between the intersecting immune genes and the differentially expressed m6A-regulated genes. Finally, a prognostic model associated with m6A and associated with immunity was developed using prognostic genes screened from m6A-associated immune genes. The predictive power of the model was evaluated and our model was able to achieve good prediction.

The risk model construction of the genes regulated by H3K36me3 and H3K79me2 in breast cancer.

Abnormal histone modifications (HMs) can promote the occurrence of breast cancer. To elucidate the relationship between HMs and gene expression, we analyzed HM binding patterns and calculated their signal changes between breast tumor cells and normal cells. On this basis, the influences of HM signal changes on the expression changes of breast cancer-related genes were estimated by three different methods. The results showed that H3K79me2 and H3K36me3 may contribute more to gene expression changes. Subsequently, 2109 genes with differential H3K79me2 or H3K36me3 levels during cancerogenesis were identified by the Shannon entropy and submitted to perform functional enrichment analyses. Enrichment analyses displayed that these genes were involved in pathways in cancer, human papillomavirus infection, and viral carcinogenesis. Univariate Cox, LASSO, and multivariate Cox regression analyses were then adopted, and nine potential breast cancer-related driver genes were extracted from the genes with differential H3K79me2/H3K36me3 levels in the TCGA cohort. To facilitate the application, the expression levels of nine driver genes were transformed into a risk score model, and its robustness was tested via time-dependent receiver operating characteristic curves in the TCGA dataset and an independent GEO dataset. At last, the distribution levels of H3K79me2 and H3K36me3 in the nine driver genes were reanalyzed in the two cell lines and the regions with significant signal changes were located.

Ribosome footprint profiling enables elucidating the systemic regulation of fatty acid accumulation in Acer truncatum.

BACKGROUND: The accumulation of fatty acids in plants covers a wide range of functions in plant physiology and thereby affects adaptations and characteristics of species. As the famous woody oilseed crop, Acer truncatum accumulates unsaturated fatty acids and could serve as the model to understand the regulation and trait formation in oil-accumulation crops. Here, we performed Ribosome footprint profiling combing with a multi-omics strategy towards vital time points during seed development, and finally constructed systematic profiling from transcription to proteomes. Additionally, we characterized the small open reading frames (ORFs) and revealed that the translational efficiencies of focused genes were highly influenced by their sequence features. RESULTS: The comprehensive multi-omics analysis of lipid metabolism was conducted in A. truncatum. We applied the Ribo-seq and RNA-seq techniques, and the analyses of transcriptional and translational profiles of seeds collected at 85 and 115 DAF were compared. Key members of biosynthesis-related structural genes (LACS, FAD2, FAD3, and KCS) were characterized fully. More meaningfully, the regulators (MYB, ABI, bZIP, and Dof) were identified and revealed to affect lipid biosynthesis via post-translational regulations. The translational features results showed that translation efficiency tended to be lower for the genes with a translated uORF than for the genes with a non-translated uORF. They provide new insights into the global mechanisms underlying the developmental regulation of lipid metabolism. CONCLUSIONS: We performed Ribosome footprint profiling combing with a multi-omics strategy in A. truncatum seed development, which provides an example of the use of Ribosome footprint profiling in deciphering the complex regulation network and will be useful for elucidating the metabolism of A. truncatum seed oil and the regulatory mechanisms.

Chromosomal-level genome and multi-omics dataset provides new insights into leaf pigmentation in Acer palmatum.

Acer palmatum (A. palmatum), a deciduous shrub or small arbour which belongs to Acer of Aceraceae, is an excellent greening species as well as a beautiful ornamental plant. In this study, a high-quality chromosome-level reference genome for A. palmatum was constructed using Oxford Nanopore sequencing and Hi-C technology. The assembly genome was ∼745.78 Mb long with a contig N50 length of 3.20 Mb, and 95.30 % (710.71 Mb) of the assembly was anchored into 13 pseudochromosomes. A total of 28,559 protein-coding genes were obtained, ∼90.02 % (25,710) of which could be functionally annotated. The genomic evolutionary analysis revealed that A. palmatum is most closely related to A. yangbiense and A. truncatum, and underwent only an ancient gamma whole-genome duplication event. Despite lacking a recent independent WGD, 25,795 (90.32 %) genes of A. palmatum were duplicated, and the unique/expanded gene families were linked with genes involved in plant-pathogen interaction and several metabolic pathways, which might underpin adaptability. A combined genomic, transcriptomic, and metabolomic analysis related to the biosynthesis of anthocyanin in leaves during the different season were characterized. The results indicate that the dark-purple colouration of the leaves in spring was caused by a high amount of anthocyanins, especially delphinidin and its derivatives; and the red colouration of the leaves in autumn by a high amount of cyanidin 3-O-glucoside. In conclusion, these valuable multi-omic resources offer important foundations to explore the molecular regulation mechanism in leaf colouration and also provide a platform for the scientific and efficient utilization of A. palmatum.

Transcriptome profiling provides insights into leaf color changes in two Acer palmatum genotypes.

BACKGROUND: Ornamental trees with seasonally-dependent leaf color, such as Acer palmatum, have gained worldwide popularity. Leaf color is a main determinant of the ornamental and economic value of A. palmatum. However, the molecular mechanisms responsible for leaf color changes remain unclear. RESULTS: We chose A. palmatum cultivars with yellow ('Jinling Huangfeng') and red ('Jinling Danfeng') leaves as the ideal material for studying the complex metabolic networks responsible for variations in leaf coloration. The 24 libraries obtained from four different time points in the growth of 'Jinling Huangfeng' and 'Jinling Danfeng' was subjected to Illumina high-throughput sequencing. We observed that the difference in cyanidin and delphinidin content is the primary reason behind the varying coloration of the leaves. Transcriptomic analyses revealed 225,684 unigenes, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of differentially expressed genes (DEGs) confirmed that they were involved in 'anthocyanin biosynthesis.' Eighteen structural genes involved in anthocyanin biosynthesis were thought to be related to anthocyanin accumulation, whereas 46 MYBs, 33 basic helix-loop-helixs (bHLHs), and 29 WD40s were presumed to be involved in regulating anthocyanin biosynthesis. Based on weighted gene co-expression network analysis (WGCNA), three candidate genes (ApRHOMBOID, ApMAPK, and ApUNE10) were screened in the significant association module with a correlation coefficient (r2) of 0.86. CONCLUSION: In this study, the leaf color changes of two A. palmatum genotypes were analyzed. These findings provide novel insights into variations in leaf coloration and suggest pathways for targeted genetic improvements in A. palmatum.

The chromosome-scale genome provides insights into pigmentation in Acer rubrum.

Acer rubrum L. is one of the most prevalent ornamental species of the genus Acer, due to its straight and tall stems and beautiful leaf colors. For this study, the Oxford Nanopore platform and Hi-C technology were employed to obtain a chromosome-scale genome for A. rubrum. The genome size of A. rubrum was 1.69 Gb with an N50 of 549.44 Kb, and a total of 39 pseudochromosomes were generated with a 99.61% genome. The A. rubrum genome was predicted to have 64644 genes, of which 97.34% were functionally annotated. Genome annotation identified 67.14% as the transposable element (TE) repeat sequence, with long terminal repeats (LTR) being the richest (55.68%). Genome evolution analysis indicated that A. rubrum diverged from A. yangbiense ∼6.34 million years ago. We identified 13 genes related to pigment synthesis in A. rubrum leaves, where the expressions of four ArF3'H genes were consistent with the synthesis of cyanidin (a key pigment) in red leaves. Correlation analysis verified that the pigmentation of A. rubrum leaves was under the coordinated regulation of non-structural carbohydrates and hormones. The genomic sequence of A. rubrum will facilitate genomic breeding research for this species, while providing the valuable utilization of Aceraceae resources.

The prediction of tumor and normal tissues based on the DNA methylation values of ten key sites.

Abnormal DNA methylation can alter the gene expression to promote or inhibit tumorigenesis in colon adenocarcinoma (COAD). However, the finding important genes and key sites of abnormal DNA methylation which result in the occurrence of COAD is still an eventful task. Here, we studied the effects of DNA methylation in the 12 types of genomic features on the changes of gene expression in COAD, the 10 important COAD-related genes and the key abnormal DNA methylation sites were identified. The effects of important genes on the prognosis were verified by survival analysis. Moreover, it was shown that the important genes were participated in cancer pathways and were hub genes in a co-expression network. Based on the DNA methylation levels in the ten sites, the least diversity increment algorithm for predicting tumor tissues and normal tissues in seventeen cancer types are proposed. The better results are obtained in jackknife test. For example, the predictive accuracies are 94.17 %, 91.28 %, 89.04 % and 88.89 %, respectively, for COAD, rectum adenocarcinoma, pancreatic adenocarcinoma and cholangiocarcinoma. Finally, by computing enrichment score of infiltrating immunocytes and the activity of immune pathways, we found that the genes are highly correlated with immune microenvironment.

Discovered Key CpG Sites by Analyzing DNA Methylation and Gene Expression in Breast Cancer Samples.

Breast cancer is the most common cancer in the world, and DNA methylation plays a key role in the occurrence and development of breast cancer. However, the effect of DNA methylation in different gene functional regions on gene expression and the effect of gene expression on breast cancer is not completely clear. In our study, we computed and analyzed DNA methylation, gene expression, and clinical data in the TCGA database. Firstly, we calculated the distribution of abnormal DNA methylated probes in 12 regions, found the abnormal DNA methylated probes in down-regulated genes were highly enriched, and the number of hypermethylated probes in the promoter region was 6.5 times than that of hypomethylated probes. Secondly, the correlation coefficients between abnormal DNA methylated values in each functional region of differentially expressed genes and gene expression values were calculated. Then, co-expression analysis of differentially expressed genes was performed, 34 hub genes in cancer-related pathways were obtained, of which 11 genes were regulated by abnormal DNA methylation. Finally, a multivariate Cox regression analysis was performed on 27 probes of 11 genes. Three DNA methylation probes (cg13569051 and cg14399183 of GSN, and cg25274503 of CAV2) related to survival were used to construct a prognostic model, which has a good prognostic ability. Furthermore, we found that the cg25274503 hypermethylation in the promoter region inhibited the expression of the CAV2, and the hypermethylation of cg13569051 and cg14399183 in the 5'UTR region inhibited the expression of GSN. These results may provide possible molecular targets for breast cancer.

Discovering the key genes and important DNA methylation regions in breast cancer.

BACKGROUND: Breast cancer is the malignant tumor with the highest incidence in women. DNA methylation has an important effect on breast cancer, but the effect of abnormal DNA methylation on gene expression in breast cancer is still unclear. Therefore, it is very important to find therapeutic targets related to DNA methylation. RESULTS: In this work, we calculated the DNA methylation distribution and gene expression level in cancer and para-cancerous tissues for breast cancer samples. We found that DNA methylation in key regions is closely related to gene expression by analyzing the relationship between the distribution characteristics of DNA methylation in different regions and the change of gene expression level. Finally, the 18 key genes (17 tumor suppressor genes and 1 oncogene) related to prognosis were confirmed by the survival analysis of clinical data. Some important DNA methylation regions in these genes that result in breast cancer were found. CONCLUSIONS: We believe that 17 TSGs and 1 oncogene may be breast cancer biomarkers regulated by DNA methylation in key regions. These results will help to explore DNA methylation biomarkers as potential therapeutic targets for breast cancer.

Assembly and comparative analysis of the first complete mitochondrial genome of Acer truncatum Bunge: a woody oil-tree species producing nervonic acid.

BACKGROUND: Acer truncatum (purpleblow maple) is a woody tree species that produces seeds with high levels of valuable fatty acids (especially nervonic acid). The species is admired as a landscape plant with high developmental prospects and scientific research value. The A. truncatum chloroplast genome has recently been reported; however, the mitochondrial genome (mitogenome) is still unexplored. RESULTS: We characterized the A. truncatum mitogenome, which was assembled using reads from PacBio and Illumina sequencing platforms, performed a comparative analysis against different species of Acer. The circular mitogenome of A. truncatum has a length of 791,052 bp, with a base composition of 27.11% A, 27.21% T, 22.79% G, and 22.89% C. The A. truncatum mitogenome contains 62 genes, including 35 protein-coding genes, 23 tRNA genes and 4 rRNA genes. We also examined codon usage, sequence repeats, RNA editing and selective pressure in the A. truncatum mitogenome. To determine the evolutionary and taxonomic status of A. truncatum, we conducted a phylogenetic analysis based on the mitogenomes of A. truncatum and 25 other taxa. In addition, the gene migration from chloroplast and nuclear genomes to the mitogenome were analyzed. Finally, we developed a novel NAD1 intron indel marker for distinguishing several Acer species. CONCLUSIONS: In this study, we assembled and annotated the mitogenome of A. truncatum, a woody oil-tree species producing nervonic acid. The results of our analyses provide comprehensive information on the A. truncatum mitogenome, which would facilitate evolutionary research and molecular barcoding in Acer.

A deep learning model to identify gene expression level using cobinding transcription factor signals.

Gene expression is directly controlled by transcription factors (TFs) in a complex combination manner. It remains a challenging task to systematically infer how the cooperative binding of TFs drives gene activity. Here, we quantitatively analyzed the correlation between TFs and surveyed the TF interaction networks associated with gene expression in GM12878 and K562 cell lines. We identified six TF modules associated with gene expression in each cell line. Furthermore, according to the enrichment characteristics of TFs in these TF modules around a target gene, a convolutional neural network model, called TFCNN, was constructed to identify gene expression level. Results showed that the TFCNN model achieved a good prediction performance for gene expression. The average of the area under receiver operating characteristics curve (AUC) can reach up to 0.975 and 0.976, respectively in GM12878 and K562 cell lines. By comparison, we found that the TFCNN model outperformed the prediction models based on SVM and LDA. This is due to the TFCNN model could better extract the combinatorial interaction among TFs. Further analysis indicated that the abundant binding of regulatory TFs dominates expression of target genes, while the cooperative interaction between TFs has a subtle regulatory effects. And gene expression could be regulated by different TF combinations in a nonlinear way. These results are helpful for deciphering the mechanism of TF combination regulating gene expression.

The effect of key DNA methylation in different regions on gene expression in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a common cancer with high morbidity and mortality. As we all know, the alteration of DNA methylation has a crucial impact on the occurrence of HCC. However, the mechanism of the effect of DNA methylation in different regions on gene expression is still unclear. Here, by computing and analyzing the distribution of differential methylation in 12 different regions in HCC tissues and adjacent normal tissues, not only the hypermethylation of CpG islands and global hypomethylation were found, but also a stable distribution pattern of differential methylation in HCC was found. Then the correlations between DNA methylations in different regions and gene expressions were calculated, and the diversity of correlations in different regions was determined. The key genes of differential methylation and differential expression related to the survival of HCC patients were obtained by using Cox regression analysis, a four-gene prognostic risk scoring model was constructed, and the prognostic performance was well verified. The regions of the differentially methylated CpG sites corresponding to the four key genes were located and their influences on the expression were analyzed. The results indicate that the promoter, first exon, 5'UTR, sixth exon, N_Shore, and S_Shore hypomethylation promotes the expression of key oncogenes, which together lead to the occurrence of HCC. These results might help to study the role of DNA methylation in HCC and provide potential biomarkers for the diagnosis of HCC.

The impact of gene-body H3K36me3 patterns on gene expression level changes in chronic myelogenous leukemia.

Chronic myelogenous leukemia (CML) is a malignant clonal disease of hematopoietic stem cells. Researches have exhibited that the progression of CML is related to histone modifications. Here, we perform the systematic analyses of H3K36me3 patterns and gene expression level changes. We observe that the genes with higher gene-body H3K36me3 levels in normal cells show fewer expression changes during leukemogenesis, while the genes with lower gene-body H3K36me3 levels in normal cells yield obvious expression changes during leukemogenesis (ρ = -0.98, P = 9.30 × 10-8). These findings are conserved in human lung/breast cancers and mouse CML, regardless of gene expression levels and gene lengths. Regulatory element analysis and Random Forest regression display that Hoxd13, Rara, Scl, Smad3, Smad4 and Tgif1 induce the up-regulation of genes with lower H3K36me3 levels (ρ = 0.97, P = 2.35 × 10-56). Enrichment analysis shows that the differentially expressed genes with lower H3K36me3 levels are involved in leukemia-related pathways, such as leukocyte migration and regulation of leukocyte activation. Finally, six driver genes (Tp53, Wt1, Dnmt3a, Cacna1b, Phactr1 and Gbp4) with lower H3K36me3 levels are identified. Our analyses indicate that lower gene-body H3K36me3 levels may serve as a biomarker for the progression of CML.

Systematic analysis of MYB gene family in Acer rubrum and functional characterization of ArMYB89 in regulating anthocyanin biosynthesis.

The v-myb avian myeloblastosis viral oncogene homolog (MYB) family of transcription factors is extensively distributed across the plant kingdom. However, the functional significance of red maple (Acer rubrum) MYB transcription factors remains unclear. Our research identified 393 MYB transcription factors in the Acer rubrum genome, and these ArMYB members were unevenly distributed across 34 chromosomes. Among them, R2R3 was the primary MYB sub-class, which was further divided into 21 sub-groups with their Arabidopsis homologs. The evolution of the ArMYB family was also investigated, with the results revealing several R2R3-MYB sub-groups with expanded membership in woody species. Here, we report on the isolation and characterization of ArMYB89 in red maple. Quantitative real-time PCR analysis revealed that ArMYB89 expression was significantly up-regulated in red leaves in contrast to green leaves. Sub-cellular localization experiments indicated that ArMYB89 was localized in the nucleus. Further experiments revealed that ArMYB89 could interact with ArSGT1 in vitro and in vivo. Overexpression of ArMYB89 in tobacco enhances the anthocyanin content of transgenic plants. In conclusion, our results contribute to the elucidation of a theoretical basis for the ArMYB gene family, and provide a foundation for further characterization of the biological roles of MYB genes in the regulation of Acer rubrum leaf color.

Study on the spatial distribution patterns of histone modifications in Hippo pathway genes.

Hippo pathway can regulate cell division, differentiation and apoptosis, and control the shape and size of organs. To study the distribution patterns of histone modifications of Hippo pathway genes in embryonic stem cells is helpful to understand the molecular regulation mechanism of histone modification and Hippo pathway on stem cell self-renewal. In this study, 19 genes of Hippo pathway including YAP, TAZ, LATS1/2, MST1 and SAV1, and eight histone modifications in embryonic stem cells were chosen to study the spatial distribution patterns of histone modifications. It was found that there were obvious type specificity and the location preference of target regions in the distributions of histone modifications, and H3K4me3 and H3K36me3 played the most important regulatory roles. Through the correlation analysis of histone modifications, a histone modification functional cluster composed of H3K4ac, H3K4me3, H3K9ac and H3K27ac was detected in YAP. In addition, the spatial distribution patterns of histone modifications in Hippo pathway genes were obtained, which provided a new theoretical reference for elucidating the mechanism of histone modifications regulating the gene expression of Hippo pathway, and for revealing the molecular regulatory mechanism of histone modifications affecting the self-renewal of embryonic stem cells by regulating the Hippo pathway.