RNA-Seq Analysis of Developing Pecan (Carya illinoinensis) Embryos Reveals Parallel Expression Patterns among Allergen and Lipid Metabolism Genes

Abstract Background Sponges contain an astounding diversity of lipids which serve in several biological functions, including yolk formation in their oocytes and the embryos. On animal reproduction, lipids constitute one of the main energy storage forms for the adult and the offspring. The study of lipid metabolism during reproduction can provide information on food-web dynamics and energetic needs of the populations in their habitats, however, there are no studies focusing on the lipid metabolism of sponges during seasonal reproduction. The deep-sea sponge Phakellia ventilabrum (Demospongiae, Bubarida) is a key species of North-Atlantic sponge grounds, but its reproductive biology is not known. In this study, we used histological sections, lipidome profiling (UHPLC-MS), and transcriptomic analysis (RNA-seq) with goal to i. assess the reproductive strategy and seasonality of this species, ii. examine the relative changes in the lipidome signal, and the gene expression patterns (RNA-seq) of enzymes participating in lipid metabolism in female specimens during gametogenesis.Results P. ventilabrum is an oviparous and most certainly gonochoristic species, reproducing in May and September in the different studied areas. Half of specimens were reproducing, generating two to five oocytes per mm2. Oocytes accumulated both protein and lipid droplets. As oogenesis progressed, the signal of most of the unsaturated and monounsaturated triacylglycerides increased, as well as of few other phospholipids. Most of the other lipids and especially those with > 3 unsaturations showed a decrease in signal during the oocyte maturation. In parallel, we detected upregulated genes in female tissues related to triacylglyceride biosynthesis and others related to fatty acid beta-oxidation.Conclusions Triacylglycerides are probably the main type of lipid forming the yolk since this lipid category has the most marked changes, while some other phospholipids may also have a role in oogenesis. In parallel, other lipid categories were oxidized, leading to fatty acid beta-oxidation to cover the energy requirements of female individuals during oogenesis. Variations in the signal of most lipids between the different locations and months suggest that sponges, apart from their own mechanisms of lipid biosynthesis, exploit the food availability in their surroundings to cover the energetic demands in their physiological processes.

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Altered expression patterns of lipid metabolism genes in an animal model of HCV core-related, nonobese, modest hepatic steatosis

BMC Genomics ◽

10.1186/1471-2164-9-109 ◽

2008 ◽

Vol 9 (1) ◽

pp. 109 ◽

Cited By ~ 30

Author(s):

Ming-Ling Chang ◽

Chau-Ting Yeh ◽

Jeng-Chang Chen ◽

Chau-Chun Huang ◽

Shi-Ming Lin ◽

...

Keyword(s):

Animal Model ◽

Lipid Metabolism ◽

Hepatic Steatosis ◽

Expression Patterns ◽

Altered Expression ◽

Lipid Metabolism Genes

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Transcriptome Analysis Reveals Skin Lipid Metabolism Related to Wool Diameter in Sheep

10.1101/051359 ◽

2016 ◽

Cited By ~ 1

Author(s):

Shaoyin Fu ◽

YunXia Qi ◽

Xiaolong He ◽

Lai Da ◽

biao Wang ◽

...

Keyword(s):

Lipid Metabolism ◽

Differentially Expressed Genes ◽

Fiber Diameter ◽

Expression Patterns ◽

Wool Fiber ◽

Differentially Expressed ◽

Rna Seq ◽

Skin Lipid ◽

Skin Development ◽

Qrt Pcr

AbstractWool is one of the most important animal fibers in the textile industry and the diameter directly affects its economic value. However, the molecular mechanisms underlying the wool diameter have not been fully elucidated. In the present study, high-throughput RNA-Seq technology was employed to explore the skin transcriptome using 3 sheep with fine wool (fiber diameter, FD<21.0μm) and 3 sheep with coarse wool (fiber diameter, FD>27.0μm). In total, 28,607,228 bp clean reads were obtained, and 78.88%+/-3.84% was uniquely aligned to the reference genome across the six samples. In total, 19,914 mRNA transcripts were expressed (FPKM>0) in the six skin samples, among which there were certain well-known genes affecting the skin hair cycle, such as KRTAP7-1, KRT14, Wnt10b, Wnt2b, β-catenin, and FGF5. Furthermore, 467 expressed genes were significantly differentially expressed between the two groups, including 21 genes up-regulated and 446 genes down-regulated in the sheep with the smaller fiber diameter. To verify the results, 13 differentially expressed genes were randomly selected to validate the expression patterns using qRT-PCR, and the correlation between the mRNA expression level from qRT-PCR and RNA-Seq data was 0.999 ( P<0.05). These differentially expressed genes were particularly enriched in GO processes related to lipid metabolism, skin development, differentiation, and immune function (P<0.05). The biological processes were involved in collagen catabolism, negative regulation of macromolecule metabolism, steroid hormone stimulation and lipid metabolism. A significant KEGG pathway involving the “metabolism of lipids and lipoproteins” was also enriched. This study revealed that the lipid metabolism might constitute one of the major factors related to wool diameter.

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Dysregulated liver lipid metabolism and innate immunity associated with hepatic steatosis in neonatal BBdp rats and NOD mice

Scientific Reports ◽

10.1038/s41598-019-51143-7 ◽

2019 ◽

Vol 9 (1) ◽

Author(s):

D. Serrano ◽

J. A. Crookshank ◽

B. S. Morgan ◽

R. W. Mueller ◽

M.-F. Paré ◽

...

Keyword(s):

Innate Immunity ◽

Lipid Metabolism ◽

Hepatic Steatosis ◽

Liver Lipid ◽

Nod Mice ◽

Gene Signature ◽

Enhanced Expression ◽

Liver Lipid Metabolism ◽

Hepatic Innate Immunity ◽

Lipid Metabolism Genes

Abstract In a previous study we reported that prediabetic rats have a unique gene signature that was apparent even in neonates. Several of the changes we observed, including enhanced expression of pro-inflammatory genes and dysregulated UPR and metabolism genes were first observed in the liver followed by the pancreas. In the present study we investigated further early changes in hepatic innate immunity and metabolism in two models of type 1 diabetes (T1D), the BBdp rat and NOD mouse. There was a striking increase in lipid deposits in liver, particularly in neonatal BBdp rats, with a less striking but significant increase in neonatal NOD mice in association with dysregulated expression of lipid metabolism genes. This was associated with a decreased number of extramedullary hematopoietic clusters as well as CD68+ macrophages in the liver of both models. In addition, PPARɣ and phosphorylated AMPKα protein were decreased in neonatal BBdp rats. BBdp rats displayed decreased expression of antimicrobial genes in neonates and decreased M2 genes at 30 days. This suggests hepatic steatosis could be a common early feature in development of T1D that impacts metabolic homeostasis and tolerogenic phenotype in the prediabetic liver.

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Integration of transcriptomic data identifies key hallmark genes in hypertrophic cardiomyopathy

BMC Cardiovascular Disorders ◽

10.1186/s12872-021-02147-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Jing Xu ◽

Xiangdong Liu ◽

Qiming Dai

Keyword(s):

Machine Learning ◽

Hypertrophic Cardiomyopathy ◽

Heart Diseases ◽

Expression Patterns ◽

Support Vector ◽

Rna Seq ◽

Ppi Network ◽

Learning Methods ◽

Transcriptomic Data ◽

Machine Learning Methods

Abstract Background Hypertrophic cardiomyopathy (HCM) represents one of the most common inherited heart diseases. To identify key molecules involved in the development of HCM, gene expression patterns of the heart tissue samples in HCM patients from multiple microarray and RNA-seq platforms were investigated. Methods The significant genes were obtained through the intersection of two gene sets, corresponding to the identified differentially expressed genes (DEGs) within the microarray data and within the RNA-Seq data. Those genes were further ranked using minimum-Redundancy Maximum-Relevance feature selection algorithm. Moreover, the genes were assessed by three different machine learning methods for classification, including support vector machines, random forest and k-Nearest Neighbor. Results Outstanding results were achieved by taking exclusively the top eight genes of the ranking into consideration. Since the eight genes were identified as candidate HCM hallmark genes, the interactions between them and known HCM disease genes were explored through the protein–protein interaction (PPI) network. Most candidate HCM hallmark genes were found to have direct or indirect interactions with known HCM diseases genes in the PPI network, particularly the hub genes JAK2 and GADD45A. Conclusions This study highlights the transcriptomic data integration, in combination with machine learning methods, in providing insight into the key hallmark genes in the genetic etiology of HCM.

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Clustering Single-Cell RNA-Seq Data with Regularized Gaussian Graphical Model

Genes ◽

10.3390/genes12020311 ◽

2021 ◽

Vol 12 (2) ◽

pp. 311

Author(s):

Zhenqiu Liu

Keyword(s):

Single Cell ◽

Free Parameter ◽

Graphical Model ◽

Expression Patterns ◽

Information Criterion ◽

Log P ◽

Rna Seq ◽

Clustering Methods ◽

Wide Range ◽

Free Parameters

Single-cell RNA-seq (scRNA-seq) is a powerful tool to measure the expression patterns of individual cells and discover heterogeneity and functional diversity among cell populations. Due to variability, it is challenging to analyze such data efficiently. Many clustering methods have been developed using at least one free parameter. Different choices for free parameters may lead to substantially different visualizations and clusters. Tuning free parameters is also time consuming. Thus there is need for a simple, robust, and efficient clustering method. In this paper, we propose a new regularized Gaussian graphical clustering (RGGC) method for scRNA-seq data. RGGC is based on high-order (partial) correlations and subspace learning, and is robust over a wide-range of a regularized parameter λ. Therefore, we can simply set λ=2 or λ=log(p) for AIC (Akaike information criterion) or BIC (Bayesian information criterion) without cross-validation. Cell subpopulations are discovered by the Louvain community detection algorithm that determines the number of clusters automatically. There is no free parameter to be tuned with RGGC. When evaluated with simulated and benchmark scRNA-seq data sets against widely used methods, RGGC is computationally efficient and one of the top performers. It can detect inter-sample cell heterogeneity, when applied to glioblastoma scRNA-seq data.

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Transcriptome Analysis Reveals HgCl2 Induces Apoptotic Cell Death in Human Lung Carcinoma H1299 Cells through Caspase-3-Independent Pathway

International Journal of Molecular Sciences ◽

10.3390/ijms22042006 ◽

2021 ◽

Vol 22 (4) ◽

pp. 2006

Author(s):

Mi Jin Kim ◽

Jinhong Park ◽

Jinho Kim ◽

Ji-Young Kim ◽

Mi-Jin An ◽

...

Keyword(s):

Cell Cycle ◽

Cell Death ◽

Transcriptome Analysis ◽

Caspase 3 ◽

Apoptotic Cell ◽

Expression Patterns ◽

Nitrogen Compound ◽

Apoptotic Cell Death ◽

Mercury Chloride ◽

Rna Seq

Mercury is one of the detrimental toxicants that can be found in the environment and exists naturally in different forms; inorganic and organic. Human exposure to inorganic mercury, such as mercury chloride, occurs through air pollution, absorption of food or water, and personal care products. This study aimed to investigate the effect of HgCl2 on cell viability, cell cycle, apoptotic pathway, and alters of the transcriptome profiles in human non-small cell lung cancer cells, H1299. Our data show that HgCl2 treatment causes inhibition of cell growth via cell cycle arrest at G0/G1- and S-phase. In addition, HgCl2 induces apoptotic cell death through the caspase-3-independent pathway. Comprehensive transcriptome analysis using RNA-seq indicated that cellular nitrogen compound metabolic process, cellular metabolism, and translation for biological processes-related gene sets were significantly up- and downregulated by HgCl2 treatment. Interestingly, comparative gene expression patterns by RNA-seq indicated that mitochondrial ribosomal proteins were markedly altered by low-dose of HgCl2 treatment. Altogether, these data show that HgCl2 induces apoptotic cell death through the dysfunction of mitochondria.

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Identification and Expression Analysis of the Genes Involved in the Raffinose Family Oligosaccharides Pathway of Phaseolus vulgaris and Glycine max

Plants ◽

10.3390/plants10071465 ◽

2021 ◽

Vol 10 (7) ◽

pp. 1465

Author(s):

Ramon de Koning ◽

Raphaël Kiekens ◽

Mary Esther Muyoka Toili ◽

Geert Angenon

Keyword(s):

Common Bean ◽

Seed Development ◽

Expression Analysis ◽

De Novo ◽

Expression Patterns ◽

Gene Families ◽

Rna Seq ◽

Raffinose Family Oligosaccharides ◽

Specific Expression ◽

Raffinose Synthase

Raffinose family oligosaccharides (RFO) play an important role in plants but are also considered to be antinutritional factors. A profound understanding of the galactinol and RFO biosynthetic gene families and the expression patterns of the individual genes is a prerequisite for the sustainable reduction of the RFO content in the seeds, without compromising normal plant development and functioning. In this paper, an overview of the annotation and genetic structure of all galactinol- and RFO biosynthesis genes is given for soybean and common bean. In common bean, three galactinol synthase genes, two raffinose synthase genes and one stachyose synthase gene were identified for the first time. To discover the expression patterns of these genes in different tissues, two expression atlases have been created through re-analysis of publicly available RNA-seq data. De novo expression analysis through an RNA-seq study during seed development of three varieties of common bean gave more insight into the expression patterns of these genes during the seed development. The results of the expression analysis suggest that different classes of galactinol- and RFO synthase genes have tissue-specific expression patterns in soybean and common bean. With the obtained knowledge, important galactinol- and RFO synthase genes that specifically play a key role in the accumulation of RFOs in the seeds are identified. These candidate genes may play a pivotal role in reducing the RFO content in the seeds of important legumes which could improve the nutritional quality of these beans and would solve the discomforts associated with their consumption.

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Overexpression of a methyl-CpG-binding protein gene OsMBD707 leads to larger tiller angles and reduced photoperiod sensitivity in rice

BMC Plant Biology ◽

10.1186/s12870-021-02880-3 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Mengyu Qu ◽

Zhujian Zhang ◽

Tingmin Liang ◽

Peipei Niu ◽

Mingji Wu ◽

...

Keyword(s):

Expression Patterns ◽

Photoperiod Sensitivity ◽

Rna Seq ◽

Active Expression ◽

Binding Protein Gene ◽

Flowering Regulator ◽

Regulator Genes ◽

Mbd Protein ◽

Delayed Flowering ◽

Mbd Proteins

Abstract Background Methyl-CpG-binding domain (MBD) proteins play important roles in epigenetic gene regulation, and have diverse molecular, cellular, and biological functions in plants. MBD proteins have been functionally characterized in various plant species, including Arabidopsis, wheat, maize, and tomato. In rice, 17 sequences were bioinformatically predicted as putative MBD proteins. However, very little is known regarding the function of MBD proteins in rice. Results We explored the expression patterns of the rice OsMBD family genes and identified 13 OsMBDs with active expression in various rice tissues. We further characterized the function of a rice class I MBD protein OsMBD707, and demonstrated that OsMBD707 is constitutively expressed and localized in the nucleus. Transgenic rice overexpressing OsMBD707 displayed larger tiller angles and reduced photoperiod sensitivity—delayed flowering under short day (SD) and early flowering under long day (LD). RNA-seq analysis revealed that overexpression of OsMBD707 led to reduced photoperiod sensitivity in rice and to expression changes in flowering regulator genes in the Ehd1-Hd3a/RFT1 pathway. Conclusion The results of this study suggested that OsMBD707 plays important roles in rice growth and development, and should lead to further studies on the functions of OsMBD proteins in growth, development, or other molecular, cellular, and biological processes in rice.

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

Foods ◽

10.3390/foods10020360 ◽

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.

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