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1. Integrative Analysis of Gene Expression and miRNAs Reveal Biological Pathways Associated with Bud Paradormancy and Endodormancy in Grapevine

   https://doi.org/10.3390/plants10040669  

   Smita, Shuchi; Robben, Michael; Deuja, Anup; Accerbi, Monica; Green, Pamela J.; Subramanian, Senthil; Fennell, Anne (April 2021, Plants)

   null (Ed.)

   Transition of grapevine buds from paradormancy to endodormancy is coordinated by changes in gene expression, phytohormones, transcription factors, and other molecular regulators, but the mechanisms involved in transcriptional and post-transcriptional regulation of dormancy stages are not well delineated. To identify potential regulatory targets, an integrative analysis of differential gene expression profiles and their inverse relationships with miRNA abundance was performed in paradormant (long day (LD) 15 h) or endodormant (short day (SD), 13 h) Vitis riparia buds. There were 400 up- and 936 downregulated differentially expressed genes in SD relative to LD buds. Gene set and gene ontology enrichment analysis indicated that hormone signaling and cell cycling genes were downregulated in SD relative to LD buds. miRNA abundance and inverse expression analyses of miRNA target genes indicated increased abundance of miRNAs that negatively regulate genes involved with cell cycle and meristem development in endodormant buds and miRNAs targeting starch metabolism related genes in paradormant buds. Analysis of interactions between abundant miRNAs and transcription factors identified a network with coinciding regulation of cell cycle and epigenetic regulation related genes in SD buds. This network provides evidence for cross regulation occurring between miRNA and transcription factors both upstream and downstream of MYB3R1. 

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2. The S-phase-induced lncRNA SUNO1 promotes cell proliferation by controlling YAP1/Hippo signaling pathway

   https://doi.org/10.7554/eLife.55102  

   Hao, Qinyu; Zong, Xinying; Sun, Qinyu; Lin, Yo-Chuen; Song, You Jin; Hashemikhabir, Seyedsasan; Hsu, Rosaline YC; Kamran, Mohammad; Chaudhary, Ritu; Tripathi, Vidisha; et al (October 2020, eLife)

   null (Ed.)

   Cell cycle is a cellular process that is subject to stringent control. In contrast to the wealth of knowledge of proteins controlling the cell cycle, very little is known about the molecular role of lncRNAs (long noncoding RNAs) in cell-cycle progression. By performing genome-wide transcriptome analyses in cell-cycle-synchronized cells, we observed cell-cycle phase-specific induction of >2000 lncRNAs. Further, we demonstrate that an S-phase-upregulated lncRNA, SUNO1 , facilitates cell-cycle progression by promoting YAP1-mediated gene expression. SUNO1 facilitates the cell-cycle-specific transcription of WTIP , a positive regulator of YAP1, by promoting the co-activator, DDX5-mediated stabilization of RNA polymerase II on chromatin. Finally, elevated SUNO1 levels are associated with poor cancer prognosis and tumorigenicity, implying its pro-survival role. Thus, we demonstrate the role of a S-phase up-regulated lncRNA in cell-cycle progression via modulating the expression of genes controlling cell proliferation. 

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3. LCM and RNA-seq analyses revealed roles of cell cycle and translational regulation and homoeolog expression bias in cotton fiber cell initiation

   https://doi.org/10.1186/s12864-021-07579-1  

   Ando, Atsumi; Kirkbride, Ryan C.; Jones, Don C.; Grimwood, Jane; Chen, Z. Jeffrey (December 2021, BMC Genomics)

   null (Ed.)

   Abstract Background Cotton fibers provide a powerful model for studying cell differentiation and elongation. Each cotton fiber is a singular and elongated cell derived from epidermal-layer cells of a cotton seed. Efforts to understand this dramatic developmental shift have been impeded by the difficulty of separation between fiber and epidermal cells. Results Here we employed laser-capture microdissection (LCM) to separate these cell types. RNA-seq analysis revealed transitional differences between fiber and epidermal-layer cells at 0 or 2 days post anthesis. Specifically, down-regulation of putative cell cycle genes was coupled with upregulation of ribosome biosynthesis and translation-related genes, which may suggest their respective roles in fiber cell initiation. Indeed, the amount of fibers in cultured ovules was increased by cell cycle progression inhibitor, Roscovitine, and decreased by ribosome biosynthesis inhibitor, Rbin-1. Moreover, subfunctionalization of homoeologs was pervasive in fiber and epidermal cells, with expression bias towards 10% more D than A homoeologs of cell cycle related genes and 40–50% more D than A homoeologs of ribosomal protein subunit genes. Key cell cycle regulators were predicted to be epialleles in allotetraploid cotton. MYB-transcription factor genes displayed expression divergence between fibers and ovules. Notably, many phytohormone-related genes were upregulated in ovules and down-regulated in fibers, suggesting spatial-temporal effects on fiber cell development. Conclusions Fiber cell initiation is accompanied by cell cycle arrest coupled with active ribosome biosynthesis, spatial-temporal regulation of phytohormones and MYB transcription factors, and homoeolog expression bias of cell cycle and ribosome biosynthesis genes. These valuable genomic resources and molecular insights will help develop breeding and biotechnological tools to improve cotton fiber production. 

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4. Gene Co-Expression Analysis of Human RNASEH2A Reveals Functional Networks Associated with DNA Replication, DNA Damage Response, and Cell Cycle Regulation

   https://doi.org/10.3390/biology10030221  

   Marsili, Stefania; Tichon, Ailone; Kundnani, Deepali; Storici, Francesca (March 2021, Biology)

   null (Ed.)

   Ribonuclease (RNase) H2 is a key enzyme for the removal of RNA found in DNA-RNA hybrids, playing a fundamental role in biological processes such as DNA replication, telomere maintenance, and DNA damage repair. RNase H2 is a trimer composed of three subunits, RNASEH2A being the catalytic subunit. RNASEH2A expression levels have been shown to be upregulated in transformed and cancer cells. In this study, we used a bioinformatics approach to identify RNASEH2A co-expressed genes in different human tissues to underscore biological processes associated with RNASEH2A expression. Our analysis shows functional networks for RNASEH2A involvement such as DNA replication and DNA damage response and a novel putative functional network of cell cycle regulation. Further bioinformatics investigation showed increased gene expression in different types of actively cycling cells and tissues, particularly in several cancers, supporting a biological role for RNASEH2A but not for the other two subunits of RNase H2 in cell proliferation. Mass spectrometry analysis of RNASEH2A-bound proteins identified players functioning in cell cycle regulation. Additional bioinformatic analysis showed that RNASEH2A correlates with cancer progression and cell cycle related genes in Cancer Cell Line Encyclopedia (CCLE) and The Cancer Genome Atlas (TCGA) Pan Cancer datasets and supported our mass spectrometry findings. 

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5. Network-based multi-task learning models for biomarker selection and cancer outcome prediction

   https://doi.org/10.1093/bioinformatics/btz809  

   Wang, Zhibo; He, Zhezhi; Shah, Milan; Zhang, Teng; Fan, Deliang; Zhang, Wei; Cowen, Lenore (November 2019, Bioinformatics)

   Abstract Motivation Detecting cancer gene expression and transcriptome changes with mRNA-sequencing (RNA-Seq) or array-based data are important for understanding the molecular mechanisms underlying carcinogenesis and cellular events during cancer progression. In previous studies, the differentially expressed genes were detected across patients in one cancer type. These studies ignored the role of mRNA expression changes in driving tumorigenic mechanisms that are either universal or specific in different tumor types. To address the problem, we introduce two network-based multi-task learning frameworks, NetML and NetSML, to discover common differentially expressed genes shared across different cancer types as well as differentially expressed genes specific to each cancer type. The proposed frameworks consider the common latent gene co-expression modules and gene-sample biclusters underlying the multiple cancer datasets to learn the knowledge crossing different tumor types. Results Large-scale experiments on simulations and real cancer high-throughput datasets validate that the proposed network-based multi-task learning frameworks perform better sample classification compared with the models without the knowledge sharing across different cancer types. The common and cancer specific molecular signatures detected by multi-task learning frameworks on TCGA ovarian cancer, breast cancer, and prostate cancer datasets are correlated with the known marker genes and enriched in cancer relevant KEGG pathways and Gene Ontology terms. Availability and Implementation Source code is available at: https://github.com/compbiolabucf/NetML Supplementary information Supplementary data are available at Bioinformatics 

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