scholarly journals Identifying core biological processes distinguishing human eye tissues with precise systems-level gene expression analyses and weighted correlation networks

2017 ◽  
Author(s):  
John M Bryan ◽  
Temesgen D Fufa ◽  
Kapil Bharti ◽  
Brian P Brooks ◽  
Robert B Hufnagel ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Mouse Retina ◽  
Human Tissues ◽  
Biological Processes ◽  
Rna Seq ◽  
Human Eye ◽  
Correlation Networks ◽  
Eye Tissues ◽  
Study Gene Expression

AbstractThe human eye is built from several specialized tissues which direct, capture, and pre-process information to provide vision. The gene expression of the different eye tissues has been extensively profiled with RNA-seq across numerous studies. Large consortium projects have also used RNA-seq to study gene expression patterning across many different human tissues, minus the eye. There has not been an integrated study of expression patterns from multiple eye tissues compared to other human body tissues. We have collated all publicly available healthy human eye RNA-seq datasets as well as dozens of other tissues. We use this fully integrated dataset to probe the biological processes and pan expression relationships between the cornea, retina, RPE-choroid complex, and the rest of the human tissues with differential expression, clustering, and GO term enrichment tools. We also leverage our large collection of retina and RPE-choroid tissues to build the first human weighted gene correlation networks and use them to highlight known biological pathways and eye gene disease enrichment. We also have integrated publicly available single cell RNA-seq data from mouse retina into our framework for validation and discovery. Finally, we make all these data, analyses, and visualizations available via a powerful interactive web application (https://eyeintegration.nei.nih.gov/).

scholarly journals Identifying core biological processes distinguishing human eye tissues with precise systems-level gene expression analyses and weighted correlation networks

2018 ◽  
Vol 27 (19) ◽  
pp. 3325-3339 ◽  
Author(s):  
John M Bryan ◽  
Temesgen D Fufa ◽  
Kapil Bharti ◽  
Brian P Brooks ◽  
Robert B Hufnagel ◽  
...  
Keyword(s):  
Gene Expression ◽  
Biological Processes ◽  
Human Eye ◽  
Correlation Networks ◽  
Eye Tissues ◽  
Gene Expression Analyses ◽  
Weighted Correlation

scholarly journals EXPath 2.0: An Updated Database for Integrating High-Throughput Gene Expression Data with Biological Pathways

2020 ◽  
Vol 61 (10) ◽  
pp. 1818-1827
Author(s):  
Kuan-Chieh Tseng ◽  
Guan-Zhen Li ◽  
Yu-Cheng Hung ◽  
Chi-Nga Chow ◽  
Nai-Yun Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Metabolic Pathways ◽  
Expression Profiles ◽  
Regulatory Mechanisms ◽  
Biological Processes ◽  
Expression Data ◽  
Rna Seq ◽  
Correlation Networks ◽  
Transcriptional Regulatory Mechanisms

Abstract Co-expressed genes tend to have regulatory relationships and participate in similar biological processes. Construction of gene correlation networks from microarray or RNA-seq expression data has been widely applied to study transcriptional regulatory mechanisms and metabolic pathways under specific conditions. Furthermore, since transcription factors (TFs) are critical regulators of gene expression, it is worth investigating TFs on the promoters of co-expressed genes. Although co-expressed genes and their related metabolic pathways can be easily identified from previous resources, such as EXPath and EXPath Tool, this information is not simultaneously available to identify their regulatory TFs. EXPath 2.0 is an updated database for the investigation of regulatory mechanisms in various plant metabolic pathways with 1,881 microarray and 978 RNA-seq samples. There are six significant improvements in EXPath 2.0: (i) the number of species has been extended from three to six to include Arabidopsis, rice, maize, Medicago, soybean and tomato; (ii) gene expression at various developmental stages have been added; (iii) construction of correlation networks according to a group of genes is available; (iv) hierarchical figures of the enriched Gene Ontology (GO) terms are accessible; (v) promoter analysis of genes in a metabolic pathway or correlation network is provided; and (vi) user’s gene expression data can be uploaded and analyzed. Thus, EXPath 2.0 is an updated platform for investigating gene expression profiles and metabolic pathways under specific conditions. It facilitates users to access the regulatory mechanisms of plant biological processes. The new version is available at http://EXPath.itps.ncku.edu.tw.

scholarly journals Spatially Resolved Expression of Transposable Elements in Disease and Somatic Tissue with SpatialTE

2021 ◽  
Vol 22 (24) ◽  
pp. 13623
Author(s):  
Braulio Valdebenito-Maturana ◽  
Cristina Guatimosim ◽  
Mónica Alejandra Carrasco ◽  
Juan Carlos Tapia
Keyword(s):  
Gene Expression ◽  
Transposable Elements ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Somatic Tissue ◽  
Rna Seq ◽  
Spatially Resolved ◽  
Repetitive Nature ◽  
Study Gene Expression ◽  
Regulatory Functions

Spatial transcriptomics (ST) is transforming the way we can study gene expression and its regulation through position-specific resolution within tissues. However, as in bulk RNA-Seq, transposable elements (TEs) are not being studied due to their highly repetitive nature. In recent years, TEs have been recognized as important regulators of gene expression, and thus, TE expression analysis in a spatially resolved manner could further help to understand their role in gene regulation within tissues. We present SpatialTE, a tool to analyze TE expression from ST datasets and show its application in somatic and diseased tissues. The results indicate that TEs have spatially regulated expression patterns and that their expression profiles are spatially altered in ALS disease, indicating that TEs might perform differential regulatory functions within tissue organs. We have made SpatialTE publicly available as open-source software under an MIT license.

scholarly journals Co-Expression Network-based Analysis associated with potato initial resistance

2018 ◽  
Author(s):  
Lang Yan ◽  
Xianjun Lai ◽  
Yan Wu ◽  
Xuemei Tan ◽  
Yizheng Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Evolutionary Divergence ◽  
Regulation Mechanism ◽  
Rna Seq ◽  
Functional Studies ◽  
Study Gene Expression ◽  
Gene Modules ◽  
Dramatic Shift

RNA sequencing (RNA-seq) providing genome-wide expression datasets has been successfully used to study gene expression patterns and regulation mechanism among multiple samples. Gene co-expression networks (GCNs) studies within or across species showed that coordinated genes in expression patterns are often functionally related. For potatoes, a large amount of publicly available transcriptome datasets have been generated but an optimal GCN detecting expression patterns in different genotypes, tissues and environmental conditions, is lacking. We constructed a potato GCN using 16 published RNA-Seq datasets covering 11 cultivars from native habitat worldwide. The correlations of gene expression were assessed pair-wisely and biologically meaningful gene modules which are highly connected in GCN were identified. One of the primitively native-farmer-selected cultivars in the Andes, ssp.Andigena, had relative far distance in gene expression patterns with other modern varieties. GCN in further enriched 134 highly and specifically co-expressed genes in ssp.Andigena associated with potato disease and stress resistance, which underlying the dramatic shift in evolutionary pressures during potato artificial domestication. In total, the network was consisted of into 14 gene models that involves in a variety of plant processes, which sheds light on how gene modules organized intra- and inter-varieties in the context of evolutionary divergence and provides a basis of information resource for potato gene functional studies.

Identification of Differentially Expressed Genes Using cDNA-AFLP During Six Days In Vitro Tuberization of Potato

Zuriat ◽  
2015 ◽  
Vol 14 (1) ◽  
Author(s):  
Nono Carsono ◽  
Christian Bachem
Keyword(s):  
Gene Expression ◽  
Developmental Process ◽  
Expression Patterns ◽  
Induction Medium ◽  
In Vitro Tuberization ◽  
Storage Organ ◽  
Developmentally Regulated ◽  
Study Gene Expression ◽  
Differential Pattern

Tuberization in potato is a complex developmental process resulting in the differentiation of stolon into the storage organ, tuber. During tuberization, change in gene expression has been known to occur. To study gene expression during tuberization over the time, in vitro tuberization system provides a suitable tool, due to its synchronous in tuber formation. An early six days axillary bud growing on tuber induction medium is a crucial development since a large number of genes change in their expression patterns during this period. In order to identify, isolate and sequencing the genes which displaying differential pattern between tuberizing and non-tuberizing potato explants during six days in vitro tuberization, cDNA-AFLP fingerprint, method for the visualization of gene expression using cDNA as template which is amplified to generate an RNA-fingerprinting, was used in this experiment. Seventeen primer combinations were chosen based on their expression profile from cDNA-AFLP fingerprint. Forty five TDFs (transcript derived fragment), which displayed differential expressions, were obtained. Tuberizing explants had much more TDFs, which developmentally regulated, than those from non tuberizing explants. Seven TDFs were isolated, cloned and then sequenced. One TDF did not find similarity in the current databases. The nucleotide sequence of TDF F showed best similarity to invertase ezymes from the databases. The homology of six TDFs with known sequences is discussed in this paper.

scholarly journals Cocaine induces paradigm-specific changes to the transcriptome within the ventral tegmental area

2021 ◽  
Author(s):  
Rianne R. Campbell ◽  
Siwei Chen ◽  
Joy H. Beardwood ◽  
Alberto J. López ◽  
Lilyana V. Pham ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ventral Tegmental Area ◽  
Home Cage ◽  
Expression Patterns ◽  
Energy Regulation ◽  
Biological Processes ◽  
Cocaine Exposure ◽  
Self Administration ◽  
Drug Reinforcement ◽  
Ventral Tegmental

AbstractDuring the initial stages of drug use, cocaine-induced neuroadaptations within the ventral tegmental area (VTA) are critical for drug-associated cue learning and drug reinforcement processes. These neuroadaptations occur, in part, from alterations to the transcriptome. Although cocaine-induced transcriptional mechanisms within the VTA have been examined, various regimens and paradigms have been employed to examine candidate target genes. In order to identify key genes and biological processes regulating cocaine-induced processes, we employed genome-wide RNA-sequencing to analyze transcriptional profiles within the VTA from male mice that underwent one of four commonly used paradigms: acute home cage injections of cocaine, chronic home cage injections of cocaine, cocaine-conditioning, or intravenous-self administration of cocaine. We found that cocaine alters distinct sets of VTA genes within each exposure paradigm. Using behavioral measures from cocaine self-administering mice, we also found several genes whose expression patterns corelate with cocaine intake. In addition to overall gene expression levels, we identified several predicted upstream regulators of cocaine-induced transcription shared across all paradigms. Although distinct gene sets were altered across cocaine exposure paradigms, we found, from Gene Ontology (GO) term analysis, that biological processes important for energy regulation and synaptic plasticity were affected across all cocaine paradigms. Coexpression analysis also identified gene networks that are altered by cocaine. These data indicate that cocaine alters networks enriched with glial cell markers of the VTA that are involved in gene regulation and synaptic processes. Our analyses demonstrate that transcriptional changes within the VTA depend on the route, dose and context of cocaine exposure, and highlight several biological processes affected by cocaine. Overall, these findings provide a unique resource of gene expression data for future studies examining novel cocaine gene targets that regulate drug-associated behaviors.

scholarly journals Combined Metabolome and Transcriptome Profiling Reveal Optimal Harvest Strategy Model Based on Different Production Purposes in Olive

Foods ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 360
Author(s):  
Guodong Rao ◽  
Jianguo Zhang ◽  
Xiaoxia Liu ◽  
Xue Li ◽  
Chenhe Wang
Keyword(s):  
Gene Expression ◽  
Olive Oil ◽  
Expression Patterns ◽  
Transcriptome Profiling ◽  
Minor Components ◽  
Harvest Time ◽  
Rna Seq ◽  
Optimal Harvest ◽  
Harvest Strategy ◽  
Different Color

Olive oil has been favored as high-quality edible oil because it contains balanced fatty acids (FAs) and high levels of minor components. The contents of FAs and minor components are variable in olive fruits of different color at harvest time, which render it difficult to determine the optimal harvest strategy for olive oil producing. Here, we combined metabolome, Pacbio Iso-seq, and Illumina RNA-seq transcriptome to investigate the association between metabolites and gene expression of olive fruits at harvest time. A total of 34 FAs, 12 minor components, and 181 other metabolites (including organic acids, polyols, amino acids, and sugars) were identified in this study. Moreover, we proposed optimal olive harvesting strategy models based on different production purposes. In addition, we used the combined Pacbio Iso-seq and Illumina RNA-seq gene expression data to identify genes related to the biosynthetic pathways of hydroxytyrosol and oleuropein. These data lay the foundation for future investigations of olive fruit metabolism and gene expression patterns, and provide a method to obtain olive harvesting strategies for different production purposes.

scholarly journals Bayesian Framework for Detecting Gene Expression Outliers in Individual Samples

2020 ◽  
pp. 160-170
Author(s):  
John Vivian ◽  
Jordan M. Eizenga ◽  
Holly C. Beale ◽  
Olena M. Vaske ◽  
Benedict Paten
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Rna Seq ◽  
Single Patient ◽  
Tissue Samples ◽  
Composite Tissue ◽  
Patient Sample ◽  
Statistical Framework ◽  
Therapeutic Leads ◽  
Upregulated Genes

PURPOSE Many antineoplastics are designed to target upregulated genes, but quantifying upregulation in a single patient sample requires an appropriate set of samples for comparison. In cancer, the most natural comparison set is unaffected samples from the matching tissue, but there are often too few available unaffected samples to overcome high intersample variance. Moreover, some cancer samples have misidentified tissues of origin or even composite-tissue phenotypes. Even if an appropriate comparison set can be identified, most differential expression tools are not designed to accommodate comparisons to a single patient sample. METHODS We propose a Bayesian statistical framework for gene expression outlier detection in single samples. Our method uses all available data to produce a consensus background distribution for each gene of interest without requiring the researcher to manually select a comparison set. The consensus distribution can then be used to quantify over- and underexpression. RESULTS We demonstrate this method on both simulated and real gene expression data. We show that it can robustly quantify overexpression, even when the set of comparison samples lacks ideally matched tissue samples. Furthermore, our results show that the method can identify appropriate comparison sets from samples of mixed lineage and rediscover numerous known gene-cancer expression patterns. CONCLUSION This exploratory method is suitable for identifying expression outliers from comparative RNA sequencing (RNA-seq) analysis for individual samples, and Treehouse, a pediatric precision medicine group that leverages RNA-seq to identify potential therapeutic leads for patients, plans to explore this method for processing its pediatric cohort.

scholarly journals A novel missense variant c.G644A (p.G215E) of the RPGR gene in a Chinese family causes X-linked retinitis pigmentosa

2019 ◽  
Vol 39 (10) ◽  
Author(s):  
Jiewen Fu ◽  
Jingliang Cheng ◽  
Qi Zhou ◽  
Chunli Wei ◽  
Hanchun Chen ◽  
...  
Keyword(s):  
Retinitis Pigmentosa ◽  
Expression Patterns ◽  
Missense Variant ◽  
Vital Role ◽  
Chinese Family ◽  
The Novel ◽  
Rna Seq ◽  
Targeted Next Generation Sequencing ◽  
Retinal Tissue ◽  
Eye Tissues

Abstract The mutations in patients with X-linked retinitis pigmentosa (xlRP) have not been well described in the Chinese population. In the present study, a five-generation Chinese retinitis pigmentosa (RP) family was recruited; targeted next-generation sequencing (TGS) was used to identify causative genes and Sanger sequencing for co-segregation. RNA-seq data analysis and revere transcriptional-polymerase chain reaction (RT-PCR) were applied to investigate gene expression patterns of RP GTPase regulator (RPGR) in human and Rpgr in mouse. A novel, hemizygous, deleterious and missense variant: c.G644A (p.G215E) in the RPGR gene (NM_000328.2) exon 7 of X-chromosome was identified in the proband, which was co-segregated with the clinical phenotypes in this family. RNA-seq data showed that RPGR is ubiquitously expressed in 27 human tissues with testis in highest, but no eye tissues data. Then the expressions for Rpgr mRNA in mice including eye tissues were conducted and showed that Rpgr transcript is ubiquitously expressed very highly in retina and testis, and highly in other eye tissues including lens, sclera, and cornea; and expressed highly in the six different developmental times of retinal tissue. Ubiquitous expression in different tissues from eye and very high expression in the retina indicated that RPGR plays a vital role in eye functions, particularly in retina. In conclusion, our study is the first to indicate that the novel missense variant c.G644A (p.G215E) in the RPGR gene might be the disease-causing mutation in this xlRP family, expanding mutation spectrum. These findings facilitate better understanding of the molecular pathogenesis of this disease; provide new insights for genetic counseling and healthcare.

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