This study is to investigate at the molecular level how a transgenic version of Nipponbare obtained drought resistance phenotype. Using multi-omics sequencing data, we compared a wild-type version of rice (WT) and a transgenic version of rice (erf71) that obtains drought-resistance phenotype by overexpressing OsERF71, one of the AP2/ERF transcription factor families. A comprehensive bioinformatics analysis pipeline, including transcription factor networks and cascade tree, was developed for the analysis of multi-omics data. The analysis results showed that the presence of OsERF71 at the source of the network controlled global gene expression levels in a specific manner to make erf71 survive longer than WT. Our analysis of the time series transcriptome data suggests that erf71 used more energy to survival-critical mechanisms related to translation, oxidative response, and DNA replication, while further suppressing energy consuming mechanisms such as photosynthesis. To support this hypothesis further, we measured the net photosynthesis level in a physiological condition, which confirmed the further suppression of photosynthesis in erf71. In summary, our work presents a comprehensive snapshot of transcriptional modification of a transgenic version of rice and shows how it induced the rice to acquire drought resistance phenotype.

Human pluripotent stem cells (hPSCs) have promising therapeutic applications due to their infinite capacity for self-renewal and pluripotency. Genomic stability is imperative for the clinical use of hPSCs; however, copy number variation (CNV), especially recurrent CNV at 20q11.21, may contribute genomic instability of hPSCs. Furthermore, the effects of CNVs in hPSCs at the whole-transcriptome scale are poorly understood. This study aimed to examine the functional in vivo and in vitro effects of frequently detected CNVs at 20q11.21 during early-stage differentiation of hPSCs. Comprehensive transcriptome profiling of abnormal hPSCs revealed that the differential gene expression patterns had a negative effect on differentiation potential. Transcriptional heterogeneity identified by single-cell RNA sequencing (scRNA-seq) of embryoid bodies from two different isogenic lines of hPSCs revealed alterations in differentiated cell distributions compared with that of normal cells. RNA-seq analysis of 22 teratomas identified several differentially expressed lineage-specific markers in hPSCs with CNVs, consistent with the histological results of the altered ecto/meso/endodermal ratio due to CNVs. Our results suggest that CNV amplification contributes to cell proliferation, apoptosis, and cell fate specification. This work shows the functional consequences of recurrent genetic abnormalities and thereby provides evidence to support the development of cell-based applications.

EWS (Ewing’s Sarcoma) gene encodes an RNA/DNA-binding protein that is ubiquitously expressed and involved in various cellular processes. EWS deficiency leads to impaired development and early senescence through unknown mechanisms. We found that EWS regulates the expression of Drosha and microRNAs (miRNAs). EWS deficiency resulted in increased expression of Drosha, a well-known microprocessor, and increased levels of miR-29b and miR-18b. Importantly, miR-29b and miR-18b were directly involved in the post-transcriptional regulation of collagen IV alpha 1 (Col4a1) and connective tissue growth factor (CTGF) in EWS knock-out (KO) mouse embryonic fibroblast cells. The upregulation of Drosha, miR-29b and miR-18b and the sequential downregulation of Col4a1 and CTGF contributed to the deregulation of dermal development in EWS KO mice. Otherwise, knockdown of Drosha rescued miRNA-dependent downregulation of Col4a1 and CTGF proteins. Taken together, our data indicate that EWS is involved in post-transcriptional regulation of Col4a1 and CTGF via a Drosha–miRNA-dependent pathway. This finding suggests that EWS has a novel role in dermal morphogenesis through the modulation of miRNA biogenesis.

Motivation: Identifying biologically meaningful gene expression patterns from time series gene expression data is important to understand the underlying biological mechanisms. To identify significantly perturbed gene sets between different phenotypes, analysis of time series transcriptome data requires consideration of time and sample dimensions. Thus, the analysis of such time series data seeks to search gene sets that exhibit similar or different expression patterns between two or more sample conditions, constituting the three-dimensional data, i.e. gene-time-condition. Computational complexity for analyzing such data is very high, compared to the already difficult NP-hard two dimensional biclustering algorithms. Because of this challenge, traditional time series clustering algorithms are designed to capture co-expressed genes with similar expression pattern in two sample conditions. Results: We present a triclustering algorithm, TimesVector, specifically designed for clustering three-dimensional time series data to capture distinctively similar or different gene expression patterns between two or more sample conditions. TimesVector identifies clusters with distinctive expression patterns in three steps: (i) dimension reduction and clustering of time-condition concatenated vectors, (ii) post-processing clusters for detecting similar and distinct expression patterns and (iii) rescuing genes from unclassified clusters. Using four sets of time series gene expression data, generated by both microarray and high throughput sequencing platforms, we demonstrated that TimesVector successfully detected biologically meaningful clusters of high quality. TimesVector improved the clustering quality compared to existing triclustering tools and only TimesVector detected clusters with differential expression patterns across conditions successfully. Availability and Implementation: The TimesVector software is available at http://biohealth.snu.ac.kr/software/TimesVector/. Contact:sunkim.bioinfo@snu.ac.kr Supplementary information:

Background: In cancer, mutations of DNA methylation modification genes have crucial roles for epigenetic modifications genome-wide, which lead to the activation or suppression of important genes including tumor suppressor genes. Mutations on the epigenetic modifiers could affect the enzyme activity, which would result in the difference in genome-wide methylation profiles and, activation of downstream genes. Therefore, we investigated the effect of mutations on DNA methylation modification genes such as DNMT1, DNMT3A, MBD1, MBD4, TET1, TET2 and TET3 through a pan-cancer analysis. Methods: First, we investigated the effect of mutations in DNA methylation modification genes on genome-wide methylation profiles. We collected 3,644 samples that have both of mRNA and methylation data from 12 major cancer types in The Cancer Genome Atlas (TCGA). The samples were divided into two groups according to the mutational signature. Differentially methylated regions (DMR) that overlapped with the promoter region were selected using minfi and differentially expressed genes (DEG) were identified using EBSeq. By integrating the DMR and DEG results, we constructed a comprehensive DNA methylome profiles on a pan-cancer scale. Second, we investigated the effect of DNA methylations in the promoter regions on downstream genes by comparing the two groups of samples in 11 cancer types. To investigate the effects of promoter methylation on downstream gene activations, we performed clustering analysis of DEGs. Among the DEGs, we selected highly correlated gene set that had differentially methylated promoter regions using graph based sub-network clustering methods. Results: We chose an up-regulated DEGs cluster where had hypomethylated promoter in acute myeloid leukemia (LAML) and another down-regulated DEGs cluster where had hypermethylated promoter in colon adenocarcinoma (COAD). To rule out effects of gene regulation by transcription factor (TF), if differentially expressed TFs bound to the promoter of DEGs, that DEGs did not included to the gene set that effected by DNA methylation modifiers. Consequently, we identified 54 hypomethylated promoter DMR up-regulated DEGs in LAML and 45 hypermethylated promoter DMR down-regulated DEGs in COAD. Conclusions: Our study on DNA methylation modification genes in mutated vs. non-mutated groups could provide useful insight into the epigenetic regulation of DEGs in cancer.