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 Transcriptome Profiling across Five Tissues of Giant Panda

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Feng Li ◽  
Chengdong Wang ◽  
Zhongxian Xu ◽  
Mingzhou Li ◽  
Linhua Deng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Giant Panda ◽  
Expression Patterns ◽  
Genetic Research ◽  
Gene Expression Pattern ◽  
Transcriptome Profiling ◽  
Ailuropoda Melanoleuca ◽  
Ppi Network ◽  
Illumina Hiseq ◽  
Lung And Kidney

Gene differential expression studies can serve to explore and understand the laws and characteristics of animal life activities, and the difference in gene expression between different animal tissues has been well demonstrated and studied. However, for the world-famous rare and protected species giant panda (Ailuropoda melanoleuca), only the transcriptome of the blood and spleen has been reported separately. Here, in order to explore the transcriptome differences between the different tissues of the giant panda, transcriptome profiles of the heart, liver, spleen, lung, and kidney from five captive giant pandas were constructed with Illumina HiSeq 2500 platform. The comparative analysis of the intertissue gene expression patterns was carried out based on the generated RNA sequencing datasets. Analyses of Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, and protein-protein interaction (PPI) network were performed according to the identified differentially expressed genes (DEGs). We generated 194.52 GB clean base data from twenty-five sequencing libraries and identified 18,701 genes, including 3492 novel genes. With corrected p value <0.05 and |log2FoldChange| >2, we finally obtained 921, 553, 574, 457, and 638 tissue-specific DEGs in the heart, liver, spleen, lung, and kidney, respectively. In addition, we identified TTN, CAV3, LDB3, TRDN, and ACTN2 in the heart; FGA, AHSG, and SERPINC1 in the liver; CD19, CD79B, and IL21R in the spleen; NKX2-4 and SFTPB in the lung; GC and HRG in the kidney as hub genes in the PPI network. The results of the analyses showed a similar gene expression pattern between the spleen and lung. This study provided for the first time the heart, liver, lung, and kidney’s transcriptome resources of the giant panda, and it provided a valuable resource for further genetic research or other potential research.

scholarly journals RNA-seq: primary cells, cell lines and heat stress

2015 ◽  
Author(s):  
Carl J Schmdt ◽  
Elizabeth M Pritchett ◽  
Liang Sun ◽  
Richard V.N. Davis ◽  
Allen Hubbard ◽  
...  
Keyword(s):  
Gene Expression ◽  
Heat Stress ◽  
Sequence Data ◽  
Expression Patterns ◽  
Effective Means ◽  
Cost Effective ◽  
Rna Seq ◽  
Individual Gene ◽  
Examine Gene Expression ◽  
A Cell

Transcriptome analysis by RNA-seq has emerged as a high-throughput, cost-effective means to evaluate the expression pattern of genes in organisms. Unlike other methods, such as microarrays or quantitative PCR, RNA-seq is a target free method that permits analysis of essentially any RNA that can be amplified from a cell or tissue. At its most basic, RNA-seq can determine individual gene expression levels by counting the number of times a particular transcript was found in the sequence data. Transcript levels can be compared across multiple samples to identify differentially expressed genes and infer differences in biological states between the samples. We have used this approach to examine gene expression patterns in chicken and human cells, with particular interest in determining response to heat stress.

scholarly journals Transcriptome analysis of sevoflurane exposure effects at the different brain regions

2020 ◽  
Author(s):  
Hiroto Yamamoto ◽  
Yutaro Uchida ◽  
Tomoki Chiba ◽  
Ryota Kurimoto ◽  
Takahide Matsushima ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Ontology ◽  
Neural Development ◽  
Molecular Mechanisms ◽  
Expression Patterns ◽  
Transcriptional Analysis ◽  
Rna Seq ◽  
Ontology Term ◽  
Whole Brain ◽  
Differently Expressed Genes

AbstractBackgroundsSevoflurane is a most frequently used volatile anaesthetics, but its molecular mechanisms of action remain unclear. We hypothesized that specific genes play regulatory roles in whole brain exposed to sevoflurane. Thus, we aimed to evaluate the effects of sevoflurane inhalation and identify potential regulatory genes by RNA-seq analysis.MethodsEight-week old mice were exposed to sevoflurane. RNA from four medial prefrontal cortex, striatum, hypothalamus, and hippocampus were analysed using RNA-seq. Differently expressed genes were extracted. Their gene ontology terms and the transcriptome array data of the cerebral cortex of sleeping mice were analysed using Metascape, and the gene expression patterns were compared. Finally, the activities of transcription factors were evaluated using a weighted parametric gene set analysis (wPGSA). JASPAR was used to confirm the existence of binding motifs in the upstream sequences of the differently expressed genes.ResultsThe gene ontology term enrichment analysis result suggests that sevoflurane inhalation upregulated angiogenesis and downregulated neural differentiation in the whole brain. The comparison with the brains of sleeping mice showed that the gene expression changes were specific to anaesthetized mice. Sevoflurane induced Klf4 upregulation in the whole brain. The transcriptional analysis result suggests that KLF4 is a potential transcriptional regulator of angiogenesis and neural development.ConclusionsKlf4 was upregulated by sevoflurane inhalation in whole brain. KLF4 might promote angiogenesis and cause the appearance of undifferentiated neural cells by transcriptional regulation. The roles of KLF4 might be key to elucidating the mechanisms of sevoflurane induced functional modification in the brain.

scholarly journals Interferon-regulated genetic programs and JAK/STAT pathway activate the intronic promoter of the short ACE2 isoform in renal proximal tubules

2021 ◽  
Author(s):  
Jakub Jankowski ◽  
Hye Kyung Lee ◽  
Julia Wilflingseder ◽  
Lothar Hennighausen
Keyword(s):  
Gene Expression ◽  
Proximal Tubule ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Regulatory Elements ◽  
Rna Seq ◽  
Angiotensin Converting Enzyme 2 ◽  
Genome Wide ◽  
Cytokine Stimulation

SummaryRecently, a short, interferon-inducible isoform of Angiotensin-Converting Enzyme 2 (ACE2), dACE2 was identified. ACE2 is a SARS-Cov-2 receptor and changes in its renal expression have been linked to several human nephropathies. These changes were never analyzed in context of dACE2, as its expression was not investigated in the kidney. We used Human Primary Proximal Tubule (HPPT) cells to show genome-wide gene expression patterns after cytokine stimulation, with emphasis on the ACE2/dACE2 locus. Putative regulatory elements controlling dACE2 expression were identified using ChIP-seq and RNA-seq. qRT-PCR differentiating between ACE2 and dACE2 revealed 300- and 600-fold upregulation of dACE2 by IFNα and IFNβ, respectively, while full length ACE2 expression was almost unchanged. JAK inhibitor ruxolitinib ablated STAT1 and dACE2 expression after interferon treatment. Finally, with RNA-seq, we identified a set of genes, largely immune-related, induced by cytokine treatment. These gene expression profiles provide new insights into cytokine response of proximal tubule cells.

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 Aberrant Whole Blood Gene Expression in the Lumen of Human Intracranial Aneurysms

Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1442
Author(s):  
Vincent M. Tutino ◽  
Yongjun Lu ◽  
Daizo Ishii ◽  
Kerry E. Poppenberg ◽  
Hamidreza Rajabzadeh-Oghaz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Whole Blood ◽  
Quantitative Polymerase Chain Reaction ◽  
Expression Patterns ◽  
Transcriptome Profiling ◽  
Circulating Biomarkers ◽  
Post Contrast ◽  
Blood Gene Expression ◽  
Transcriptional Changes ◽  
Polymerase Chain

The rupture of an intracranial aneurysm (IA) causes devastating hemorrhagic strokes. Yet, most IAs remain asymptomatic and undetected until they rupture. In the search for circulating biomarkers of unruptured IAs, we previously performed transcriptome profiling on whole blood and identified an IA-associated panel of 18 genes. In this study, we seek to determine if these genes are also differentially expressed within the IA lumen, which could provide a mechanistic link between the disease and the observed circulating gene expression patterns. To this end, we collected blood from the lumen of 37 IAs and their proximal parent vessels in 31 patients. The expression levels of 18 genes in the lumen and proximal vessel were then measured by quantitative polymerase chain reaction. This analysis revealed that the expression of 6/18 genes (CBWD6, MT2A, MZT2B, PIM3, SLC37A3, and TNFRSF4) was significantly higher in intraluminal blood, while the expression of 3/18 genes (ST6GALNAC1, TCN2, and UFSP1) was significantly lower. There was a significant, positive correlation between intraluminal and proximal expression of CXCL10, MT2A, and MZT2B, suggesting local increases of these genes is reflected in the periphery. Expression of ST6GALNAC1 and TIFAB was significantly positively correlated with IA size, while expression of CCDC85B was significantly positively correlated with IA enhancement on post-contrast MRI, a metric of IA instability and risk. In conclusion, intraluminal expression differences in half of the IA-associated genes observed in this study provide evidence for IA tissue-mediated transcriptional changes in whole blood. Additionally, some genes may be informative in assessing IA risk, as their intraluminal expression was correlated to IA size and aneurysmal wall enhancement.

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