scholarly journals Integrated SMRT Technology with UMI RNA-Seq Reveals the Hub Genes in Stamen Petalody in Camellia oleifera

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 749
Author(s):  
Huie Li ◽  
Yang Hu ◽  
Chao Gao ◽  
Qiqiang Guo ◽  
Quanen Deng ◽  
...  
Keyword(s):  
Single Molecule ◽  
Expression Patterns ◽  
Regulation Mechanism ◽  
Camellia Oleifera ◽  
Rna Seq ◽  
Hub Genes ◽  
Similar Expression Pattern ◽  
Transcription Factor Genes ◽  
Key Factor ◽  
Solid Foundation

Male sterility caused by stamen petalody is a key factor for a low fruit set rate and a low yield of Camellia oleifera but can serve as a useful genetic tool because it eliminates the need for artificial emasculation. However, its molecular regulation mechanism still remains unclear. In this study, transcriptome was sequenced and analyzed on two types of bud materials, stamen petalody mutants and normal materials, at six stages of stamen development based on integrated single-molecule real-time (SMRT) technology with unique molecular identifiers (UMI) and RNA-seq technology to identify the hub genes responsible for stamen petalody in C. oleifera. The results show that a large number of alternative splicing events were identified in the transcriptome. A co-expression network analysis of MADSs and all the differentially expressed genes between the mutant stamens and the normal materials showed that four MADS transcription factor genes, CoSEP3.1, CoAGL6, CoSEP3.2, and CoAP3, were predicted to be the hub genes responsible for stamen petalody. Among these four, the expression patterns of CoAGL6 and CoSEP3.2 were consistently high in the mutant samples, but relatively low in the normal samples at six stages, while the patterns of CoSEP3.1 and CoAP3 were initially low in mutants and then were upregulated during development but remained relatively high in the normal materials. Furthermore, the genes with high connectivity to the hub genes showed significantly different expression patterns between the mutant stamens and the normal materials at different stages. qRT-PCR results showed a similar expression pattern of the hub genes in the RNA-seq. These results lay a solid foundation for the directive breeding of C. oleifera varieties and provide references for the genetic breeding of ornamental Camellia varieties.

scholarly journals Time Series RNA-seq in Pigeonpea Revealed the Core Genes in Metabolic Pathways under Aluminum Stress

Genes ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 380 ◽  
Author(s):  
Zhaoxu Gao ◽  
Biying Dong ◽  
Hongyan Cao ◽  
Hang He ◽  
Qing Yang ◽  
...  
Keyword(s):  
Time Series ◽  
Metabolic Pathways ◽  
Expression Patterns ◽  
Plant Stress ◽  
Regulation Mechanism ◽  
Rna Seq ◽  
Aluminum Stress ◽  
Biological Regulation ◽  
Al Stress ◽  
The Impact

Pigeonpea is an important economic crop in the world and is mainly distributed in tropical and subtropical regions. In order to further expand the scope of planting, one of the problems that must be solved is the impact of soil acidity on plants in these areas. Based on our previous work, we constructed a time series RNA sequencing (RNA-seq) analysis under aluminum (Al) stress in pigeonpea. Through a comparison analysis, 11,425 genes were found to be differentially expressed among all the time points. After clustering these genes by their expression patterns, 12 clusters were generated. Many important functional pathways were identified by gene ontology (GO) analysis, such as biological regulation, localization, response to stimulus, metabolic process, detoxification, and so on. Further analysis showed that metabolic pathways played an important role in the response of Al stress. Thirteen out of the 23 selected genes related to flavonoids and phenols were downregulated in response to Al stress. In addition, we verified these key genes of flavonoid- and phenol-related metabolism pathways by qRT-PCR. Collectively, our findings not only revealed the regulation mechanism of pigeonpea under Al stress but also provided methodological support for further exploration of plant stress regulation mechanisms.

scholarly journals Subgenome Bias and Temporal Postponement of Gene Expression Contributes to the Distinctions of Fiber Quality in Gossypium Species

2021 ◽  
Vol 12 ◽  
Author(s):  
Huan Mei ◽  
Bowen Qi ◽  
Zegang Han ◽  
Ting Zhao ◽  
Menglan Guo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Fiber Quality ◽  
Expression Patterns ◽  
Rna Seq ◽  
Hub Genes ◽  
Hub Gene ◽  
Expression Of Genes ◽  
Multiple Dimensions ◽  
Cotton Species ◽  
Expression Bias

As two cultivated widely allotetraploid cotton species, although Gossypium hirsutum and Gossypium barbadense evolved from the same ancestor, they differ in fiber quality; the molecular mechanism of that difference should be deeply studied. Here, we performed RNA-seq of fiber samples from four G. hirsutum and three G. barbadense cultivars to compare their gene expression patterns on multiple dimensions. We found that 15.90–37.96% of differentially expressed genes showed biased expression toward the A or D subgenome. In particular, interspecific biased expression was exhibited by a total of 330 and 486 gene pairs at 10 days post-anthesis (DPA) and 20 DPA, respectively. Moreover, 6791 genes demonstrated temporal differences in expression, including 346 genes predominantly expressed at 10 DPA in G. hirsutum (TM-1) but postponed to 20 DPA in G. barbadense (Hai7124), and 367 genes predominantly expressed at 20 DPA in TM-1 but postponed to 25 DPA in Hai7124. These postponed genes mainly participated in carbohydrate metabolism, lipid metabolism, plant hormone signal transduction, and starch and sucrose metabolism. In addition, most of the co-expression network and hub genes involved in fiber development showed asymmetric expression between TM-1 and Hai7124, like three hub genes detected at 10 DPA in TM-1 but not until 25 DPA in Hai7124. Our study provides new insights into interspecific expression bias and postponed expression of genes associated with fiber quality, which are mainly tied to asymmetric hub gene network. This work will facilitate further research aimed at understanding the mechanisms underlying cotton fiber improvement.

scholarly journals Homoeologue expression insights into the basis of growth heterosis at the intersection of ploidy and hybridity in Cyprinidae (Carassius auratus red var. × Cyprinus carpio)

2015 ◽  
Author(s):  
Li Ren ◽  
Wuhui Li ◽  
Chenchen Tang ◽  
Jun Xiao ◽  
Xiaojun Tan ◽  
...  
Keyword(s):  
Cyprinus Carpio ◽  
Carassius Auratus ◽  
Driving Forces ◽  
Expression Patterns ◽  
Global Gene Expression ◽  
Regulation Mechanism ◽  
Rna Seq ◽  
Allopolyploid Speciation ◽  
Expression Bias ◽  
Changing Patterns

Hybridization and polyploidization are considered important driving forces that form new epigenetic regulations. To study the changing patterns of expression accompanying hybridization and polyploidization, we used RNA-seq and qPCR to investigate global expression and homoeologue expression in diploid and allotetraploid hybrids of Carassius auratus red var. (♀) (R) and Cyprinus carpio (♂) (C). By comparing the relative expression levels between the hybrids and their parents, we defined the expression level dominance (ELD) and homoeologue expression bias (HEB) in liver tissue. The results showed that polyploidization contributed to the conversion of homoeologue ELD. In addition, hybridization had more effect on the change in HEB than polyploidization, while polyploidization has been considered to have more effect on the change of global gene expression than hybridization. Meanwhile, similar expression patterns were found in growth-related genes. The results suggested that hybridization and polyploidization result in differential degrees of maternal HEB in the three tissues tested. The results of this study will increase our understanding of the underlying regulation mechanism of rapid growth in diploid hybrids and allotetraploids. The differential degrees of global expression and homoeologue expression contribute to growth heterosis in newly formed hybrids and allotetraploids, ensuring the on-going success of allopolyploid speciation.

scholarly journals RNA-sequencing Analysis Identifies Genes Associated with Chilling-mediated Endodormancy Release in Apple

2018 ◽  
Vol 143 (3) ◽  
pp. 194-206 ◽  
Author(s):  
Takanori Takeuchi ◽  
Miwako Cecile Matsushita ◽  
Soichiro Nishiyama ◽  
Hisayo Yamane ◽  
Kiyoshi Banno ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Molecular Mechanisms ◽  
Expression Patterns ◽  
Differentially Expressed ◽  
Selection Strategy ◽  
Sequencing Analysis ◽  
Rna Seq ◽  
Screening Criteria ◽  
Physiological Processes ◽  
Transcription Factor Genes

Endodormancy release and the fulfillment of the chilling requirement (CR) are critical physiological processes that enable uniform blooming in fruit tree species, including apple (Malus ×domestica). However, the molecular mechanisms underlying these traits have not been fully characterized. The objective of this study was to identify potential master regulators of endodormancy release and the CR in apple. We conducted RNA-Sequencing (RNA-seq) analyses and narrowed down the number of candidates among the differentially expressed genes (DEGs) based on the following two strict screening criteria: 1) the gene must be differentially expressed between endodormant and ecodormant buds under different environmental conditions and 2) the gene must exhibit chill unit (CU)–correlated expression. The results of our cluster analysis suggested that global expression patterns varied between field-grown buds and continuously chilled buds, even though they were exposed to similar amounts of chilling and were expected to have a similar dormancy status. Consequently, our strict selection strategy resulted in narrowing down the number of possible candidates and identified the DEGs strongly associated with the transition between dormancy stages. The genes included four transcription factor genes, PHYTOCHROME-INTERACTING FACTOR 4 (PIF4), FLOWERING LOCUS C (FLC)-LIKE, APETALLA2 (AP2)/ETHYLENE-RESPONSIVE 113 (ERF113), and MYC2. Their expressions were upregulated during endodormancy release, and were correlated with the CU, suggesting that these transcription factors are closely associated with chilling-mediated endodormancy release in apple.

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.

scholarly journals Rewired Pathways and Disrupted Pathway Crosstalk in Schizophrenia Transcriptomes by Multiple Differential Coexpression Methods

Genes ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 665
Author(s):  
Hui Yu ◽  
Yan Guo ◽  
Jingchun Chen ◽  
Xiangning Chen ◽  
Peilin Jia ◽  
...  
Keyword(s):  
Gene Expression ◽  
Focal Adhesion ◽  
Biological Pathways ◽  
Postmortem Brain ◽  
Rna Seq ◽  
Hub Genes ◽  
Differential Coexpression ◽  
Pathway Crosstalk ◽  
Microarray Datasets ◽  
Transcriptomic Studies

Transcriptomic studies of mental disorders using the human brain tissues have been limited, and gene expression signatures in schizophrenia (SCZ) remain elusive. In this study, we applied three differential co-expression methods to analyze five transcriptomic datasets (three RNA-Seq and two microarray datasets) derived from SCZ and matched normal postmortem brain samples. We aimed to uncover biological pathways where internal correlation structure was rewired or inter-coordination was disrupted in SCZ. In total, we identified 60 rewired pathways, many of which were related to neurotransmitter, synapse, immune, and cell adhesion. We found the hub genes, which were on the center of rewired pathways, were highly mutually consistent among the five datasets. The combinatory list of 92 hub genes was generally multi-functional, suggesting their complex and dynamic roles in SCZ pathophysiology. In our constructed pathway crosstalk network, we found “Clostridium neurotoxicity” and “signaling events mediated by focal adhesion kinase” had the highest interactions. We further identified disconnected gene links underlying the disrupted pathway crosstalk. Among them, four gene pairs (PAK1:SYT1, PAK1:RFC5, DCTN1:STX1A, and GRIA1:MAP2K4) were normally correlated in universal contexts. In summary, we systematically identified rewired pathways, disrupted pathway crosstalk circuits, and critical genes and gene links in schizophrenia transcriptomes.

scholarly journals Integration of transcriptomic data identifies key hallmark genes in hypertrophic cardiomyopathy

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.

scholarly journals Transcriptome analysis and molecular mechanism of linseed (Linum usitatissimum L.) drought tolerance under repeated drought using single-molecule long-read sequencing

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Wei Wang ◽  
Lei Wang ◽  
Ling Wang ◽  
Meilian Tan ◽  
Collins O. Ogutu ◽  
...  
Keyword(s):  
Dna Repair ◽  
Drought Tolerance ◽  
Single Molecule ◽  
Linum Usitatissimum ◽  
Expression Patterns ◽  
Transcriptional Response ◽  
Enrichment Analysis ◽  
Resistant Variety ◽  
Proline Biosynthesis ◽  
Interacting Protein

Abstract Background Oil flax (linseed, Linum usitatissimum L.) is one of the most important oil crops., However, the increases in drought resulting from climate change have dramatically reduces linseed yield and quality, but very little is known about how linseed coordinates the expression of drought resistance gene in response to different level of drought stress (DS) on the genome-wide level. Results To explore the linseed transcriptional response of DS and repeated drought (RD) stress, we determined the drought tolerance of different linseed varieties. Then we performed full-length transcriptome sequencing of drought-resistant variety (Z141) and drought-sensitive variety (NY-17) under DS and RD stress at the seedling stage using single-molecule real-time sequencing and RNA-sequencing. Gene Ontology (GO) and reduce and visualize GO (REVIGO) enrichment analysis showed that upregulated genes of Z141 were enriched in more functional pathways related to plant drought tolerance than those of NY-17 were under DS. In addition, 4436 linseed transcription factors were identified, and 1190 were responsive to stress treatments. Moreover, protein-protein interaction (PPI) network analysis showed that the proline biosynthesis pathway interacts with stress response genes through RAD50 (DNA repair protein 50) interacting protein 1 (RIN-1). Finally, proline biosynthesis and DNA repair structural gene expression patterns were verified by RT- PCR. Conclusions The drought tolerance of Z141 may be related to its upregulation of drought tolerance genes under DS. Proline may play an important role in linseed drought tolerance by maintaining cell osmotic and protecting DNA from ROS damage. In summary, this study provides a new perspective to understand the drought adaptability of linseed.

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

Genes ◽  
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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