scholarly journals Characterization and analysis of the transcriptome in Arapaima gigas using multi-tissue RNA-sequencing

2020 ◽  
Author(s):  
Danilo L. Martins ◽  
Leonardo R. S. Campos ◽  
André M. Ribeiro-dos-Santos ◽  
Ana Carolina M. F. Coelho ◽  
Renata L. Dantas ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Expression Patterns ◽  
Gene Clusters ◽  
Molecular Networks ◽  
Specific Gene ◽  
Protein Coding ◽  
Tissue Specific ◽  
Functional Studies ◽  
A Genome ◽  
Arapaima Gigas

AbstractArapaima gigas is a giant bony tongue air-breathing fish, and a promising species for aquaculture due to its particular features. However, there is still a lack of information on its biology and few transcriptome studies are available. Our aim was to characterize the transcriptome of arapaima in order to shed light on molecular networks contributing to its unique traits. Through RNA-sequencing, we generated a transcriptome from eight tissues (brain, pituitary, heart, muscle, kidney, lung, ovary, and testis) collected from arapaima adults specimens. Using a genome-guided strategy associated with homologous protein evidence, 57,706 transcripts were assembled, which aligned to 23,353 high confidence protein-coding genes. The analysis revealed a global view of expression patterns, as well as it allowed us to identify tissue-specific gene clusters, transcription factors within the clusters, and to compare expression patterns between male and female. These analyses has generated tissue-specific and sex-biased transcriptome profiles, which will be helpful to understand its molecular biology, evolution, and also guide future functional studies of the arapaima.

FC 011KIDNEYNETWORK: USING KIDNEY DERIVED GENE EXPRESSION DATA TO PREDICT AND PRIORITIZE NOVEL GENES INVOLVED IN KIDNEY DISEASE

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
Floranne Boulogne ◽  
Laura Claus ◽  
Henry Wiersma ◽  
Roy Oelen ◽  
Floor Schukking ◽  
...  
Keyword(s):  
Gene Expression ◽  
Kidney Disease ◽  
Candidate Gene ◽  
Exome Sequencing ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Genetic Diagnosis ◽  
Specific Gene ◽  
Sequencing Data ◽  
Exome Sequencing Data

Abstract Background and Aims Genetic testing in patients with suspected hereditary kidney disease does not always reveal the genetic cause for the patient's disorder. Potentially pathogenic variants can reside in genes that are not known to be involved in kidney disease, which makes it difficult to prioritize and interpret the relevance of these variants. As such, there is a clear need for methods that predict the phenotypic consequences of gene expression in a way that is as unbiased as possible. To help identify candidate genes we have developed KidneyNetwork, in which tissue-specific expression is utilized to predict kidney-specific gene functions. Method We combined gene co-expression in 878 publicly available kidney RNA-sequencing samples with the co-expression of a multi-tissue RNA-sequencing dataset of 31,499 samples to build KidneyNetwork. The expression patterns were used to predict which genes have a kidney-related function, and which (disease) phenotypes might be caused when these genes are mutated. By integrating the information from the HPO database, in which known phenotypic consequences of disease genes are annotated, with the gene co-expression network we obtained prediction scores for each gene per HPO term. As proof of principle, we applied KidneyNetwork to prioritize variants in exome-sequencing data from 13 kidney disease patients without a genetic diagnosis. Results We assessed the prediction performance of KidneyNetwork by comparing it to GeneNetwork, a multi-tissue co-expression network we previously developed. In KidneyNetwork, we observe a significantly improved prediction accuracy of kidney-related HPO-terms, as well as an increase in the total number of significantly predicted kidney-related HPO-terms (figure 1). To examine its clinical utility, we applied KidneyNetwork to 13 patients with a suspected hereditary kidney disease without a genetic diagnosis. Based on the HPO terms “Renal cyst” and “Hepatic cysts”, combined with a list of potentially damaging variants in one of the undiagnosed patients with mild ADPKD/PCLD, we identified ALG6 as a new candidate gene. ALG6 bears a high resemblance to other genes implicated in this phenotype in recent years. Through the 100,000 Genomes Project and collaborators we identified three additional patients with kidney and/or liver cysts carrying a suspected deleterious variant in ALG6. Conclusion We present KidneyNetwork, a kidney specific co-expression network that accurately predicts what genes have kidney-specific functions and may result in kidney disease. Gene-phenotype associations of genes unknown for kidney-related phenotypes can be predicted by KidneyNetwork. We show the added value of KidneyNetwork by applying it to exome sequencing data of kidney disease patients without a molecular diagnosis and consequently we propose ALG6 as a promising candidate gene. KidneyNetwork can be applied to clinically unsolved kidney disease cases, but it can also be used by researchers to gain insight into individual genes to better understand kidney physiology and pathophysiology. Acknowledgments This research was made possible through access to the data and findings generated by the 100,000 Genomes Project; http://www.genomicsengland.co.uk.

Abstract 18584: Decoding the Regulatory LncRNAs in Neonatal Heart During Perinatal Circulatory Transition

Circulation ◽  
2015 ◽  
Vol 132 (suppl_3) ◽  
Author(s):  
Marlin Touma ◽  
Ashley Cass ◽  
Xuedong Kang ◽  
Yan Zhao ◽  
Reshma Biniwale ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Regulatory Function ◽  
Newborn Mouse ◽  
Protein Coding ◽  
Congenital Diseases ◽  
Gene Pairs ◽  
Neonatal Heart

Background: Fetal to neonatal transition of heart is an elaborate process, during which, neonatal cardiomyocytes undergo functional maturation and terminal exit from the cell cycle. However, transcriptome programming in neonatal cardiac chambers during perinatal stages is understudied. In particular, the changes in long non-coding RNAs (lncRNAs) in neonatal heart have not been explored. Objective: To achieve transcriptome-wide analysis of lncRNAs in neonatal left ventricle (LV) and right ventricle (RV) during maturation stages using deep RNA-Sequencing Methods: Deep RNA-sequencing was performed on male newborn mouse (C57 BL) LV and RV at 3 time points of perinatal circulatory transition: P0, P3 and P7. Reads were mapped to mouse genome (mm10). The lncRNAs annotated in NONCODE database were identified. Differentially expressed lncRNAs were defined as those with coefficient of variation ≥0.2, at a false discovery rate ≤0.05, and expressed at ≥3 RPKM in at least one sample. Correlated lncRNAs/ gene pairs were identified using Pearson’s (r2≥0.8, P≤0.05). A subset of LncRNAs/gene expression was validated using qRT-PCR. Results: Out of the 70, 983 observed unique lncRNAs, approximately 7000 were identified exhibiting significant variation during maturation windows with highly spatial-temporal dependent expression patterns, including approximately 5000 known and 2000 novel lncRNAs. Notably, 20% of these lncRNAs were located within 50 KB of a protein coding gene. Out of a total of 2400 lncRNAs/gene pairs, 10 % exhibited significantly concordant (lncRNA/gene) expression patterns. These correlated genes were significantly enriched in metabolism, cell cycle and contractility functional ontology. Interestingly, some of these lncRNAs exhibited concordance with their neighboring gene in human tissues with congenital heart defects, suggesting conserved, potentially significant, regulatory function. Conclusions: Transcriptome programming during neonatal heart maturation involves global changes in lncRNAs. Their expression concordance with neighboring protein coding genes implicates potential important regulatory role of lncRNAs in neonatal heart chamber specification and congenital diseases.

scholarly journals Single-Cell RNA Sequencing Unveils Unique Transcriptomic Signatures of Organ-Specific Endothelial Cells

Circulation ◽  
2020 ◽  
Vol 142 (19) ◽  
pp. 1848-1862 ◽  
Author(s):  
David T. Paik ◽  
Lei Tian ◽  
Ian M. Williams ◽  
Siyeon Rhee ◽  
Hao Zhang ◽  
...  
Keyword(s):  
Endothelial Cells ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Receptor Expression ◽  
Transcriptional Networks ◽  
Sequencing Data ◽  
Tissue Specific ◽  
Functional Relationships ◽  
Single Cell Rna Sequencing

Background: Endothelial cells (ECs) display considerable functional heterogeneity depending on the vessel and tissue in which they are located. Whereas these functional differences are presumably imprinted in the transcriptome, the pathways and networks that sustain EC heterogeneity have not been fully delineated. Methods: To investigate the transcriptomic basis of EC specificity, we analyzed single-cell RNA sequencing data from tissue-specific mouse ECs generated by the Tabula Muris consortium. We used a number of bioinformatics tools to uncover markers and sources of EC heterogeneity from single-cell RNA sequencing data. Results: We found a strong correlation between tissue-specific EC transcriptomic measurements generated by either single-cell RNA sequencing or bulk RNA sequencing, thus validating the approach. Using a graph-based clustering algorithm, we found that certain tissue-specific ECs cluster strongly by tissue (eg, liver, brain), whereas others (ie, adipose, heart) have considerable transcriptomic overlap with ECs from other tissues. We identified novel markers of tissue-specific ECs and signaling pathways that may be involved in maintaining their identity. Sex was a considerable source of heterogeneity in the endothelial transcriptome and we discovered Lars2 to be a gene that is highly enriched in ECs from male mice. We found that markers of heart and lung ECs in mice were conserved in human fetal heart and lung ECs. We identified potential angiocrine interactions between tissue-specific ECs and other cell types by analyzing ligand and receptor expression patterns. Conclusions: We used single-cell RNA sequencing data generated by the Tabula Muris consortium to uncover transcriptional networks that maintain tissue-specific EC identity and to identify novel angiocrine and functional relationships between tissue-specific ECs.

scholarly journals Limit cycle dynamics can guide the evolution of gene regulatory networks towards point attractors

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Stuart P. Wilson ◽  
Sebastian S. James ◽  
Daniel J. Whiteley ◽  
Leah A. Krubitzer
Keyword(s):  
Gene Expression ◽  
Limit Cycles ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Expression Patterns ◽  
Specific Gene ◽  
Boolean Models ◽  
A Genome ◽  
Order Of Magnitude ◽  
Accelerate Evolution

AbstractDevelopmental dynamics in Boolean models of gene networks self-organize, either into point attractors (stable repeating patterns of gene expression) or limit cycles (stable repeating sequences of patterns), depending on the network interactions specified by a genome of evolvable bits. Genome specifications for dynamics that can map specific gene expression patterns in early development onto specific point attractor patterns in later development are essentially impossible to discover by chance mutation alone, even for small networks. We show that selection for approximate mappings, dynamically maintained in the states comprising limit cycles, can accelerate evolution by at least an order of magnitude. These results suggest that self-organizing dynamics that occur within lifetimes can, in principle, guide natural selection across lifetimes.

scholarly journals Evaluation of Plant-Derived Promoters for Constitutive and Tissue-Specific Gene Expression in Potato

Plants ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1520
Author(s):  
Dmitry Miroshnichenko ◽  
Aleksey Firsov ◽  
Vadim Timerbaev ◽  
Oleg Kozlov ◽  
Anna Klementyeva ◽  
...  
Keyword(s):  
Gene Expression ◽  
Specific Activity ◽  
Expression Patterns ◽  
Camv 35S Promoter ◽  
35S Promoter ◽  
Specific Gene ◽  
Transcriptional Level ◽  
Promoter Strength ◽  
Tissue Specific ◽  
Camv 35S

Various plant-derived promoters can be used to regulate ectopic gene expression in potato. In the present study, four promoters derived from the potato genome have been characterized by the expression of identical cassettes carrying the fusion with the reporter β-glucuronidase (gusA) gene. The strengths of StUbi, StGBSS, StPat, and StLhca3 promoters were compared with the conventional constitutive CaMV 35S promoter in various organs (leaves, stems, roots, and tubers) of greenhouse-grown plants. The final amount of gene product was determined at the post-transcriptional level using histochemical analysis, fluorometric measurements, and Western blot analysis. The promoter strength comparison demonstrated that the StUbi promoter generally provided a higher level of constitutive β-glucuronidase accumulation than the viral CaMV 35S promoter. Although the StLhca3 promoter was predominantly expressed in a green tissue-specific manner (leaves and stems) while StGBSS and StPat mainly provided tuber-specific activity, a “promoter leakage” was also found. However, the degree of unspecific activity depended on the particular transgenic line and tissue. According to fluorometric data, the functional activity of promoters in leaves could be arranged as follows: StLhca3 > StUbi > CaMV 35S > StPat > StGBSS (from highest to lowest). In tubers, the higher expression was detected in transgenic plants expressing StPat-gusA fusion construct, and the strength order was as follows: StPat > StGBSS > StUbi > CaMV 35S > StLhca3. The observed differences between expression patterns are discussed considering the benefits and limitations for the usage of each promoter to regulate the expression of genes in a particular potato tissue.

scholarly journals The Soybean Laccase Gene Family: Evolution and Possible Roles in Plant Defense and Stem Strength Selection

Genes ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 701 ◽  
Author(s):  
Wang ◽  
Li ◽  
Zheng ◽  
Zhu ◽  
Ma ◽  
...  
Keyword(s):  
Gene Family ◽  
Expression Patterns ◽  
Wild Soybean ◽  
Constitutive Expression ◽  
Gene Family Evolution ◽  
Laccase Gene ◽  
Functional Studies ◽  
Stem Strength ◽  
A Genome ◽  
Laccase Genes

Laccase is a widely used industrial oxidase for food processing, dye synthesis, paper making, and pollution remediation. At present, laccases used by industries come mainly from fungi. Plants contain numerous genes encoding laccase enzymes that show properties which are distinct from that of the fungal laccases. These plant-specific laccases may have better potential for industrial purposes. The aim of this work was to conduct a genome-wide search for the soybean laccase genes and analyze their characteristics and specific functions. A total of 93 putative laccase genes (GmLac) were identified from the soybean genome. All 93 GmLac enzymes contain three typical Cu-oxidase domains, and they were classified into five groups based on phylogenetic analysis. Although adjacent members on the tree showed highly similar exon/intron organization and motif composition, there were differences among the members within a class for both conserved and differentiated functions. Based on the expression patterns, some members of laccase were expressed in specific tissues/organs, while some exhibited a constitutive expression pattern. Analysis of the transcriptome revealed that some laccase genes might be involved in providing resistance to oomycetes. Analysis of the selective pressures acting on the laccase gene family in the process of soybean domestication revealed that 10 genes could have been under artificial selection during the domestication process. Four of these genes may have contributed to the transition of the soft and thin stem of wild soybean species into strong, thick, and erect stems of the cultivated soybean species. Our study provides a foundation for future functional studies of the soybean laccase gene family.

scholarly journals Emerging Roles of Estrogen-Regulated Enhancer and Long Non-Coding RNAs

2020 ◽  
Vol 21 (10) ◽  
pp. 3711
Author(s):  
Melina J. Sedano ◽  
Alana L. Harrison ◽  
Mina Zilaie ◽  
Chandrima Das ◽  
Ramesh Choudhari ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Expression Patterns ◽  
Biological Significance ◽  
Rna Seq ◽  
Biological Functions ◽  
Protein Coding ◽  
Rna Molecules ◽  
Non Coding Rna ◽  
Genome Wide ◽  
Non Coding Rnas

Genome-wide RNA sequencing has shown that only a small fraction of the human genome is transcribed into protein-coding mRNAs. While once thought to be “junk” DNA, recent findings indicate that the rest of the genome encodes many types of non-coding RNA molecules with a myriad of functions still being determined. Among the non-coding RNAs, long non-coding RNAs (lncRNA) and enhancer RNAs (eRNA) are found to be most copious. While their exact biological functions and mechanisms of action are currently unknown, technologies such as next-generation RNA sequencing (RNA-seq) and global nuclear run-on sequencing (GRO-seq) have begun deciphering their expression patterns and biological significance. In addition to their identification, it has been shown that the expression of long non-coding RNAs and enhancer RNAs can vary due to spatial, temporal, developmental, or hormonal variations. In this review, we explore newly reported information on estrogen-regulated eRNAs and lncRNAs and their associated biological functions to help outline their markedly prominent roles in estrogen-dependent signaling.

scholarly journals Computational analysis of tissue-specific gene networks: application to murine retinal functional studies

2010 ◽  
Vol 26 (18) ◽  
pp. 2289-2297 ◽  
Author(s):  
Jianfei Hu ◽  
Jun Wan ◽  
Laszlo Hackler ◽  
Donald J. Zack ◽  
Jiang Qian
Keyword(s):  
Gene Networks ◽  
Computational Analysis ◽  
Specific Gene ◽  
Tissue Specific ◽  
Tissue Specific Gene ◽  
Functional Studies

scholarly journals Illuminating a plant’s tissue-specific metabolic diversity using computational metabolomics and information theory

2016 ◽  
Vol 113 (47) ◽  
pp. E7610-E7618 ◽  
Author(s):  
Dapeng Li ◽  
Sven Heiling ◽  
Ian T. Baldwin ◽  
Emmanuel Gaquerel
Keyword(s):  
Information Theory ◽  
Secondary Metabolism ◽  
Gene Function ◽  
Tissue Level ◽  
Two Dimensions ◽  
Molecular Networks ◽  
Specific Gene ◽  
Theory Analysis ◽  
Tissue Specific ◽  
Metabolite Diversity

Secondary metabolite diversity is considered an important fitness determinant for plants’ biotic and abiotic interactions in nature. This diversity can be examined in two dimensions. The first one considers metabolite diversity across plant species. A second way of looking at this diversity is by considering the tissue-specific localization of pathways underlying secondary metabolism within a plant. Although these cross-tissue metabolite variations are increasingly regarded as important readouts of tissue-level gene function and regulatory processes, they have rarely been comprehensively explored by nontargeted metabolomics. As such, important questions have remained superficially addressed. For instance, which tissues exhibit prevalent signatures of metabolic specialization? Reciprocally, which metabolites contribute most to this tissue specialization in contrast to those metabolites exhibiting housekeeping characteristics? Here, we explore tissue-level metabolic specialization in Nicotiana attenuata, an ecological model with rich secondary metabolism, by combining tissue-wide nontargeted mass spectral data acquisition, information theory analysis, and tandem MS (MS/MS) molecular networks. This analysis was conducted for two different methanolic extracts of 14 tissues and deconvoluted 895 nonredundant MS/MS spectra. Using information theory analysis, anthers were found to harbor the most specialized metabolome, and most unique metabolites of anthers and other tissues were annotated through MS/MS molecular networks. Tissue–metabolite association maps were used to predict tissue-specific gene functions. Predictions for the function of two UDP-glycosyltransferases in flavonoid metabolism were confirmed by virus-induced gene silencing. The present workflow allows biologists to amortize the vast amount of data produced by modern MS instrumentation in their quest to understand gene function.

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