scholarly journals Transcriptome Changes in the Mink Uterus during Blastocyst Dormancy and Reactivation

2019 ◽  
Vol 20 (9) ◽  
pp. 2099
Author(s):  
Xinyan Cao ◽  
Jiaping Zhao ◽  
Yong Liu ◽  
Hengxing Ba ◽  
Haijun Wei ◽  
...  
Keyword(s):  
Gene Expression ◽  
Extracellular Matrix ◽  
Regulatory Networks ◽  
Molecular Mechanisms ◽  
Embryo Implantation ◽  
Global Gene Expression ◽  
Gene Clusters ◽  
Protein Protein Interaction ◽  
Kegg Pathways ◽  
Maternal Regulation

Embryo implantation in the mink follows the pattern of many carnivores, in that preimplantation embryo diapause occurs in every gestation. Details of the gene expression and regulatory networks that terminate embryo diapause remain poorly understood. Illumina RNA-Seq was used to analyze global gene expression changes in the mink uterus during embryo diapause and activation leading to implantation. More than 50 million high quality reads were generated, and assembled into 170,984 unigenes. A total of 1684 differential expressed genes (DEGs) in uteri with blastocysts in diapause were compared to the activated embryo group (p < 0.05). Among these transcripts, 1527 were annotated as known genes, including 963 up-regulated and 564 down-regulated genes. The gene ontology terms for the observed DEGs, included cellular communication, phosphatase activity, extracellular matrix and G-protein couple receptor activity. The KEGG pathways, including PI3K-Akt signaling pathway, focal adhesion and extracellular matrix (ECM)-receptor interactions were the most enriched. A protein-protein interaction (PPI) network was constructed, and hub nodes such as VEGFA, EGF, AKT, IGF1, PIK3C and CCND1 with high degrees of connectivity represent gene clusters expected to play an important role in embryo activation. These results provide novel information for understanding the molecular mechanisms of maternal regulation of embryo activation in mink.

scholarly journals Integrative Modelling of Gene Expression and Digital Phenotypes to Describe Senescence in Wheat

Genes ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 909
Author(s):  
Anyela Valentina Camargo Rodriguez
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Molecular Mechanisms ◽  
Developmental Stages ◽  
Computational Modelling ◽  
Plant Morphology ◽  
Reproductive Organs ◽  
Biological Trait ◽  
Cereal Grain ◽  
Plant Level

Senescence is the final stage of leaf development and is critical for plants’ fitness as nutrient relocation from leaves to reproductive organs takes place. Although senescence is key in nutrient relocation and yield determination in cereal grain production, there is limited understanding of the genetic and molecular mechanisms that control it in major staple crops such as wheat. Senescence is a highly orchestrated continuum of interacting pathways throughout the lifecycle of a plant. Levels of gene expression, morphogenesis, and phenotypic development all play key roles. Yet, most studies focus on a short window immediately after anthesis. This approach clearly leaves out key components controlling the activation, development, and modulation of the senescence pathway before anthesis, as well as during the later developmental stages, during which grain development continues. Here, a computational multiscale modelling approach integrates multi-omics developmental data to attempt to simulate senescence at the molecular and plant level. To recreate the senescence process in wheat, core principles were borrowed from Arabidopsis Thaliana, a more widely researched plant model. The resulted model describes temporal gene regulatory networks and their effect on plant morphology leading to senescence. Digital phenotypes generated from images using a phenomics platform were used to capture the dynamics of plant development. This work provides the basis for the application of computational modelling to advance understanding of the complex biological trait senescence. This supports the development of a predictive framework enabling its prediction in changing or extreme environmental conditions, with a view to targeted selection for optimal lifecycle duration for improving resilience to climate change.

scholarly journals Convergent evolution of venom gland transcriptomes across Metazoa

2021 ◽  
Author(s):  
Giulia Zancolli ◽  
Maarten Reijnders ◽  
Robert Waterhouse ◽  
Marc Robinson-Rechavi
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Venom Gland ◽  
Bioactive Molecules ◽  
Specific Gene ◽  
Animal Kingdom ◽  
Venom Glands

Animals have repeatedly evolved specialized organs and anatomical structures to produce and deliver a cocktail of potent bioactive molecules to subdue prey or predators: venom. This makes it one of the most widespread convergent functions in the animal kingdom. Whether animals have adopted the same genetic toolkit to evolved venom systems is a fascinating question that still eludes us. Here, we performed the first comparative analysis of venom gland transcriptomes from 20 venomous species spanning the main Metazoan lineages, to test whether different animals have independently adopted similar molecular mechanisms to perform the same function. We found a strong convergence in gene expression profiles, with venom glands being more similar to each other than to any other tissue from the same species, and their differences closely mirroring the species phylogeny. Although venom glands secrete some of the fastest evolving molecules (toxins), their gene expression does not evolve faster than evolutionarily older tissues. We found 15 venom gland specific gene modules enriched in endoplasmic reticulum stress and unfolded protein response pathways, indicating that animals have independently adopted stress response mechanisms to cope with mass production of toxins. This, in turns, activates regulatory networks for epithelial development, cell turnover and maintenance which seem composed of both convergent and lineage-specific factors, possibly reflecting the different developmental origins of venom glands. This study represents the first step towards an understanding of the molecular mechanisms underlying the repeated evolution of one of the most successful adaptive traits in the animal kingdom.

The expanding world of metabolic enzymes moonlighting as RNA binding proteins

2021 ◽  
Author(s):  
Nicole J. Curtis ◽  
Constance J. Jeffery
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Binding Proteins ◽  
Molecular Mechanisms ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Molecular Structures ◽  
Intermediary Metabolism ◽  
The Many ◽  
And Function

RNA binding proteins play key roles in many aspects of RNA metabolism and function, including splicing, transport, translation, localization, stability and degradation. Within the past few years, proteomics studies have identified dozens of enzymes in intermediary metabolism that bind to RNA. The wide occurrence and conservation of RNA binding ability across distant branches of the evolutionary tree suggest that these moonlighting enzymes are involved in connections between intermediary metabolism and gene expression that comprise far more extensive regulatory networks than previously thought. There are many outstanding questions about the molecular structures and mechanisms involved, the effects of these interactions on enzyme and RNA functions, and the factors that regulate the interactions. The effects on RNA function are likely to be wider than regulation of translation, and some enzyme–RNA interactions have been found to regulate the enzyme's catalytic activity. Several enzyme–RNA interactions have been shown to be affected by cellular factors that change under different intracellular and environmental conditions, including concentrations of substrates and cofactors. Understanding the molecular mechanisms involved in the interactions between the enzymes and RNA, the factors involved in regulation, and the effects of the enzyme–RNA interactions on both the enzyme and RNA functions will lead to a better understanding of the role of the many newly identified enzyme–RNA interactions in connecting intermediary metabolism and gene expression.

scholarly journals NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis

2019 ◽  
Vol 47 (W1) ◽  
pp. W234-W241 ◽  
Author(s):  
Guangyan Zhou ◽  
Othman Soufan ◽  
Jessica Ewald ◽  
Robert E W Hancock ◽  
Niladri Basu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Visual Analytics ◽  
Regulatory Networks ◽  
Meta Analysis ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Perform Gene Expression ◽  
And Performance

Abstract The growing application of gene expression profiling demands powerful yet user-friendly bioinformatics tools to support systems-level data understanding. NetworkAnalyst was first released in 2014 to address the key need for interpreting gene expression data within the context of protein-protein interaction (PPI) networks. It was soon updated for gene expression meta-analysis with improved workflow and performance. Over the years, NetworkAnalyst has been continuously updated based on community feedback and technology progresses. Users can now perform gene expression profiling for 17 different species. In addition to generic PPI networks, users can now create cell-type or tissue specific PPI networks, gene regulatory networks, gene co-expression networks as well as networks for toxicogenomics and pharmacogenomics studies. The resulting networks can be customized and explored in 2D, 3D as well as Virtual Reality (VR) space. For meta-analysis, users can now visually compare multiple gene lists through interactive heatmaps, enrichment networks, Venn diagrams or chord diagrams. In addition, users have the option to create their own data analysis projects, which can be saved and resumed at a later time. These new features are released together as NetworkAnalyst 3.0, freely available at https://www.networkanalyst.ca.

scholarly journals Global transcriptome analysis of rat hypothalamic arcuate nucleus demonstrates reversal of hypothalamic gliosis following surgically and diet induced weight loss

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Pernille Barkholt ◽  
Kristoffer T. G. Rigbolt ◽  
Mechthilde Falkenhahn ◽  
Thomas Hübschle ◽  
Uwe Schwahn ◽  
...  
Keyword(s):  
Gene Expression ◽  
Weight Loss ◽  
Molecular Mechanisms ◽  
Arcuate Nucleus ◽  
Gene Expression Signature ◽  
Global Gene Expression ◽  
Metabolic Effects ◽  
Expression Of Genes ◽  
Hypothalamic Arcuate Nucleus ◽  
Laser Capture

Abstract The central mechanisms underlying the marked beneficial metabolic effects of bariatric surgery are unclear. Here, we characterized global gene expression in the hypothalamic arcuate nucleus (Arc) in diet-induced obese (DIO) rats following Roux-en-Y gastric bypass (RYGB). 60 days post-RYGB, the Arc was isolated by laser-capture microdissection and global gene expression was assessed by RNA sequencing. RYGB lowered body weight and adiposity as compared to sham-operated DIO rats. Discrete transcriptome changes were observed in the Arc following RYGB, including differential expression of genes associated with inflammation and neuropeptide signaling. RYGB reduced gene expression of glial cell markers, including Gfap, Aif1 and Timp1, confirmed by a lower number of GFAP immunopositive astrocyte profiles in the Arc. Sham-operated weight-matched rats demonstrated a similar glial gene expression signature, suggesting that RYGB and dietary restriction have common effects on hypothalamic gliosis. Considering that RYGB surgery also led to increased orexigenic and decreased anorexigenic gene expression, this may signify increased hunger-associated signaling at the level of the Arc. Hence, induction of counterregulatory molecular mechanisms downstream from the Arc may play an important role in RYGB-induced weight loss.

R432 – Gene Expression of the Remodeling Rat Vocal Fold Wound

2008 ◽  
Vol 139 (2_suppl) ◽  
pp. P189-P189
Author(s):  
Tsunehisa Ohno ◽  
Lesley C. French ◽  
Bernard Rousseau
Keyword(s):  
Gene Expression ◽  
Extracellular Matrix ◽  
Messenger Rna ◽  
Vocal Fold ◽  
Molecular Mechanisms ◽  
Type I ◽  
Post Injury ◽  
Time Points ◽  
Procollagen Type ◽  
Procollagen Type I

Problem The authors investigated the expression of key extracellular matrix genes after vocal fold wounding in a rat model to better understand the reparative mechanisms of tissue repair during the remodeling phase of vocal fold injury. Methods Bilateral vocal fold wounds were created in 30 rats. Injured vocal fold specimens were harvested 1, 3, 7, 14, 28, and 56 days after wounding. 5 unwounded rats were used to establish baseline for polymerase chain reaction (PCR). The authors used real-time PCR to quantify messenger RNA expression of procollagen type I, III, interleukin-1 beta (IL-1 beta), decorin, and hyaluronan synthase (HAS) −1, −2, and −3. Analysis of variance was used to detect main effects for gene expression. Post-hoc tests were used to make comparisons between time points. Results Procollagen type I expression was decreased from baseline on post-injury day 1, 28, and 56. Procollagen type III was decreased on post-injury day 1 and 56, and increased from baseline on post-injury day 14. IL-1 beta expression was increased from baseline on post-injury day 1, 3, and 7. Decorin expression was decreased from baseline on post-injury day 1, 3, 7, and 56. HAS-1 expression was decreased from baseline at all post-injury time points. HAS-2 expression was increased from baseline on post-injury day 3, and decreased from baseline on post-injury day 14, 28, and 56. HAS-3 expression was decreased from baseline on post-injury day 1, 28, and 56. Conclusion Findings provide temporal changes in the expression of key extracellular matrix genes during a remodeling phase of vocal fold injury in a rat wound model. Significance Vocal fold wound models provide a means for investigating tissue reparative processes and molecular mechanisms controlling synthesis and degradation of the vocal fold extracellular matrix. Support Vanderbilt University Medical Center.

scholarly journals Trisomy 21 and Down syndrome: a short review

2008 ◽  
Vol 68 (2) ◽  
pp. 447-452 ◽  
Author(s):  
CA. Sommer ◽  
F. Henrique-Silva
Keyword(s):  
Gene Expression ◽  
Down Syndrome ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Global Gene Expression ◽  
Short Review ◽  
Complex Disorder ◽  
Expression Studies ◽  
Gene Expression Studies ◽  
Chromosome Segments

Even though the molecular mechanisms underlying the Down syndrome (DS) phenotypes remain obscure, the characterization of the genes and conserved non-genic sequences of HSA21 together with large-scale gene expression studies in DS tissues are enhancing our understanding of this complex disorder. Also, mouse models of DS provide invaluable tools to correlate genes or chromosome segments to specific phenotypes. Here we discuss the possible contribution of HSA21 genes to DS and data from global gene expression studies of trisomic samples.

scholarly journals Chip-seq and gene expression data for the identification of functional sub-pathways: a proof of concept in lung cancer

2020 ◽  
Author(s):  
Xanthoula Atsalaki ◽  
Lefteris Koumakis ◽  
George Potamias ◽  
Manolis Tsiknakis
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Systems Biology ◽  
Gene Expression Data ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Molecular Mechanisms ◽  
Integrative Approach ◽  
Expression Data ◽  
Gene Regulatory

AbstractHigh-throughput technologies, such as chromatin immunoprecipitation (ChIP) with massively parallel sequencing (ChIP-seq) have enabled cost and time efficient generation of immense amount of genome data. The advent of advanced sequencing techniques allowed biologists and bioinformaticians to investigate biological aspects of cell function and understand or reveal unexplored disease etiologies. Systems biology attempts to formulate the molecular mechanisms in mathematical models and one of the most important areas is the gene regulatory networks (GRNs), a collection of DNA segments that somehow interact with each other. GRNs incorporate valuable information about molecular targets that can be corellated to specific phenotype.In our study we highlight the need to develop new explorative tools and approaches for the integration of different types of -omics data such as ChIP-seq and GRNs using pathway analysis methodologies. We present an integrative approach for ChIP-seq and gene expression data on GRNs. Using public microarray expression samples for lung cancer and healthy subjects along with the KEGG human gene regulatory networks, we identified ways to disrupt functional sub-pathways on lung cancer with the aid of CTCF ChIP-seq data, as a proof of concept.We expect that such a systems biology pipeline could assist researchers to identify corellations and causality of transcription factors over functional or disrupted biological sub-pathways.

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